Amid concern over the heightening social and economic costs of climate change, policymakers and the public alike are demanding cost-effective climate solutions. 1 The electrification of the light-duty vehicle (LDV) fleet offers one promising pathway to reducing emissions in the transportation sector -the source of around 29% of the US' carbon emissions (World Resources Institute, 2019). 2 Recognizing this potential, federal and state policymakers have set ambitious long-term goals to replace internal combustion engines (ICEs) with electric vehicles (EVs), in conjunction with programs meant to stimulate charging station supply. 3 But even as the importance of charging stations in promoting EV adoption comes to the fore, research on how to stimulate charging station production remains limited. Economic theory indicates that producer subsidies, which lower the out-of-pocket cost of constructing new charging stations, should increase the supply of stations, especially when supply is elastic. But with EV share of the total vehicle market at 5%, private actors are inadequately incentivized to construct charging stations. 4 Range anxiety due to the lack of charging station infrastructure leads consumers not to purchase EVs. 5 In turn, charging station providers facing limited EV demand may find it unprofitable to invest in a large charging station network. This interdependence between charging stations and EV adoption generates a market equilibrium where the quantity of charging stations and EVs is less than socially optimal. Figure 1 highlights this interdependence; DC Stations and EV sales trend relatively similarly throughout the observed period. Growth in DC stations lags growth in EV sales, consistent with the theory that station providers consider market conditions when deciding to build charging stations; it is possible, for example, that station producers take time to respond to growing EV sales by constructing new charging stations. 6 In the most 1 The US, for example, has already suffered more than two trillion dollars in climate change related damages (NCEI, 2022). 2 Different from ICEs, which consume petroleum, EVs may indirectly produce emissions from electricity stored in rechargeable batteries and when operated in all-electric mode, produce zero tailpipe greenhouse gas (GHG) emissions. In addition to emitting 60% fewer GHG emissions, EVs emit, on average, an estimated 61% less volatile organic compounds, 93% less carbon monoxide, 28% less nitrogen oxides, and 32% less black carbon than ICEs (Lattanzio and Clark, 2020; Hall and Lutsey, 2018); Holland  et al. (2016) argue that EV subsidies should be negative in some parts of the US to account for regional variations in sources of electricity generation. An EV in West Virginia, for example, can indirectly produce more carbon emissions than ICEs due to the region's high reliance on coal as a source of electricity. Nonetheless, the net carbon footprint of electricity is steadily declining thanks to the growing shift to clean sources of electricity such as natural gas, solar, and nuclear power (EIA, 2021). 3 In 2021, President Biden issued an executive order targeting 50% nationwide EV sales share in 2030 (The White House,  2021). California leads the way on the state level, requiring that, by 2035, all new cars and passenger trucks sold in California be zero-emission vehicles (Office of Governor Newsom, 2020). In regard to charging station programs, the 2021 Infrastructure Investment and Jobs Act dedicates $7.5 billion to the construction of a comprehensive nationwide network of public EV chargers, and the Alternative Fuel Infrastructure Tax Credit (Infrastructure and Investment and Jobs Act, 2021; The White House, 2021). The Alternative Fuel Infrastructure Tax Credit provides a tax credit of 30% of the total cost, not to exceed $30,000, for fueling equipment for natural gas, propane, liquefied hydrogen, electricity, E85, or diesel fuel blends containing a minimum of 20% biodiesel installed through December 31, 2021. Presently, the program has expired (AFDC, 2022). 4 See Table 2. 5 As a relatively recent technology, EVs contend with several obstacles to widespread adoption, including an inaccessible purchase price, limited range, and long charging times (Bakker and Trip, 2013; Egbue and Long, 2012; She et al., 2017; Zhang  et al 2018). The latter two factors can inspire range anxiety, which in turn may lead to reluctance to purchase EVs among prospective consumers, particularly in "charging deserts" where public stations are scarce (Chen, Kockelman, and Hanna, 2019;  Desai et al., 2021; Hardman et al. 2021; Meunier and Ponssard, 2020; Greene et al., 2020). 6 Agents in counties that have greater EV sales may also have a more positive social attitude towards climate change action and clean transportation in particular. extreme case, the EV market can unravel as a lack of charging stations dissuades consumers from purchasing EVs, which further disincentives suppliers from producing charging stations. This theoretical motivation, together with the lack of empirical evidence on the effects of charging station subsidies, provide impetus for the following question: to what extent do government charging station subsidies increase charging station supply? 7

In this paper, I employ a staggered difference-in-differences approach to evaluate how charging station subsidies affect charging station supply at the county level. Using a rich data set of charging station counts obtained from the National Renewable Energy Laboratory and manually compiled data sets of purchase price and charging station subsidy counts, I quantify the elasticity of charging station supply with respect to charging station subsidies. I leverage the fact that 26 California counties adopted charging station subsidies between 2010 and 2022. I then compare changes in charging station supply, EV sales measures, and county demographic information in these "treated" counties to changes in "untreated" counties -those that did not adopt charging station subsidies between 2010 and 2022. 8 The primary motivation for the analysis is to determine if charging station subsidies are successful at expanding charging station supply, and, if so, where they are most effective. If charging station supply is highly elastic with respect to charging station subsidies, policymakers may consider increasing public funds for such subsidies worthwhile. Understanding this elasticity can also enable policymakers to forecast the amount of subsidies needed to meet a particular charging station deployment objective.
To identify the causal effects of the subsidy, I examine how county-level outcomes of all treated counties evolve in the treatment period from 2010 to 2022. Comparing treated counties to untreated counties, I verify that the conditional independence assumption holds. 9 I employ an event study specification, where the subsidy's effect varies by year, and a staggered difference in difference model where the effect is assumed to be immediate and constant. Since βt may be contaminated by heterogeneous cohort-specific treatment effects, I reproduce the event study with an alternative interaction weighted estimator proposed by Sun and   Abraham (2021) as a robustness check. While I verify the conditional independence assumption is likely to hold for charging stations in the Sun and Abraham estimation, it is not likely to hold for EV sales; sales tend to increase in treated counties even before subsidy adoption. Thus, I assign causal interpretation on the impact of subsidies on charging station supply but not on EV sales. Across various specifications, the staggered difference in differences analysis finds statistically significant effects of charging station subsidies on charging station supply: a 100% increase in charging station subsidies increases charging station supply by 2.5%. Elasticity interpretations from the staggered difference in differences model, otherwise known as a constant elasticity model, are independent of initial subsidy value. However, the constant elasticity model may be skewed in the presence of many zeros (Boulton and Williford, 2018; Green, 2021). Given this consideration, I study the impact of subsidy adoption using the model proposed by Sun and Abraham (2021). This paper contributes to several literatures. First, the literature on charging stations has focused mainly on the direct causal effect of charging stations and charging station subsidies on EV sales (Springel, 2021;   Li et al., 2017; Remmy, 2022; van Dijk, Delacrétaz, and Lanz, 2022; Yu, Li, and Tong, 2016; Fournel, 2022;   Cole et al., 2021; Ma and Fan, 2020; Ledna et al., 2022; Gnann, Plötz, and Wietschel, 2019). My study offers a first analysis to quantify the elasticity of charging station supply with respect to charging station subsidies. More broadly, I contribute to the literature on the role of transportation sector government policy in mitigating climate change (for an overview, see Rapson and Muehlegger, 2021). 10 Graff Zivin et al (2014), Archsmith et al. (2015), and Holland et al. (2016) find that EVs can emit substantially less lifetime carbon emissions and local pollutants than ICEs (depending on regional electricity sources). On a geopolitical level, Ajanovic and Haas (2018) and Fernández et al. (2011) find that replacing ICEs with EVs has important benefits for energy security by reducing reliance on gasoline imports. Increasing charging station supply for EV consumers can be an important factor in realizing these external benefits. Second, I contribute to the emerging literature on socioeconomic dimensions of charging station supply by exploring heterogeneity in the effect of charging station subsidies across various socioeconomic dimensions. Hsu and Fingerman (2021) document disparities in public charging station access across income and race, motivating study of how the effects of charging station subsidies vary among different treatment groups. Disadvantaged communities persistently have less access to charging compared to their wealthier counterparts because of their lower capacity to afford EVs and inability to utilize home charging solutions (Nicholas, Hall,   and Lutsey, 2019; Pierce, McOmber, and DeShazo, 2020; Axsen and Kurani, 2012; Lopez-Behar et al,   2019; Khan et al., 2022). 11 For these regions, ensuring adequate public charging access is especially crucial (Dunckley and Tal, 2016;Funke et al., 2019;Axsen and Kurani, 2012;C2ES, 2017;Canepa, Hardman, and 10 Within the literature on clean energy vehicle diffusion, empirical studies primarily focus on the effect of purchase price subsidies on EV sales (Gallagher and Muehlegger, 2011; Gong, Ardeshiri, and Rashidi, 2020; Li et al., 2019; Diamond, 2009;  Hardman et al., 2017). 11 Charging at home is a popular alternative to public chargers because it is the cheapest and most convenient method of charging. But such an option is typically only accessible for wealthier residents who often live in single or multi-family homes that are spatially conducive to such residential chargers. Low-income residents, meanwhile, rely more on public charging because they tend to live in large multi-unit dwellings (MUDs) which often lack adequate residential charging (Wei et al, 2021;  Muehlegger and Rapson, 2018). Installing residential chargers in rental residences poses a greater challenge as renters have less incentive to bear the cost of upgrading a home they themselves do not own and owners have less incentive to bear the cost of a charger they may not use. Third, recent studies document indirect network effects -there is interdependence between charging station supply and EV adoption (Springel, 2021; Li et al., 2017). Accounting for these indirect network effects, studies estimating the causal relationship between charging stations and EV sales use an instrumental variable methodology (Springel, 2021; Remmy, 2022; Li et al., 2017; van Dijk, Delacrétaz, and Lanz, 2022;   Fournel, 2022; Pavan, 2017). A similar relationship could exist between charging station subsidies and EV sales. While several studies have examined endogeneity in the relationship between charging infrastructure supply and EV adoption, there is scant investigation on how sales measures evolve prior to the adoption of charging station subsidies (Egnér and Trosvik, 2018; Sierzchula et al., 2014). I find that counties that adopt charging station subsidies tend to have faster EV sales growth trajectories than non-adopting counties prior to subsidy adoption. Uncovering this endogeneity is important because it could bias typical differencein-differences estimates; if one incorrectly assumed the parallel trends assumption holds, estimates would be overestimated by 11.5%.13 Further, understanding the presence of significant pre trends suggests future studies that examine the causal relationship between charging station subsidies and EV sales should utilize an instrumental variable design. The paper proceeds as follows. Section 2 describes the background on charging stations and subsidy policy. Section 3 details data, descriptive statistics, and bivariate relationships. Section 4 presents the econometric specifications. Section 5 discusses the results and robustness checks. Section 6 concludes.
Consumers may consider adequate access to charging stations an important factor in deciding whether to purchase an EV. Charging stations are split into three levels -Level 1, Level 2, and DC -and may be employed either in a residential or public setting. Levels 1 and 2 charging use a universal connector that can be plugged into any EV.14 DC stations, meanwhile, have three types of plugs: CHAdeMO, SAE Combo (CCS), and Tesla. 15 Although DC stations are not homogeneous, the growing market of charging plug adapters is increasing charging flexibility among EV owners. Charging speed is the main difference between these three types of stations. DC chargers are the fastest type of charging station, outputting typically between 200 -600 V, and can fully charge an EV in less than an hour (California Energy Commission, n.d). In addition to their fast charging, DC stations are almost exclusively employed in either public or private commercial settings due to the significant cost of investment required. 16 Level 2 chargers are faster, outputting 240 V, and can fully charge an EV overnight; they are the most ubiquitous type of charging station and are commonly used in both commercial and residential settings. Due to their superior charging speed and importance in alleviating range anxiety, I focus on the effect of DC subsidies on DC station supply. Results and discussion on the effect of Level 2 charging subsidies on Level 2 stations can be found in the Appendix. 17 Although the majority of charging station investors are private firms specializing in charging station networks, such as ChargePoint and Blink, governments from city and county to the federal level, as well as utilities corporations, may also directly invest in charging station construction. 18 There are two major types of EVs: battery electric vehicles (BEVs) which are fully electric and derive their power entirely from batteries (e.g.Tesla Model 3), and plug-in hybrid vehicles (PHEVs) which obtain power from batteries and another fuel source, typically gasoline (e.g. Toyota Prius). I group EV sales data by PHEV sales, BEV sales, and EV sales -an aggregate measure of PHEV and BEV sales. Although growing EV sales may reflect an expanding EV fleet, it does not necessarily indicate that consumers are replacing ICEs with EVs. 19 To address this nuance while examining cross sectional differences between treated and untreated counties (see Table 2), I supplement EV sales with ICE and EV share data obtained from the California Energy Commission since vehicles included in ICE and EV share must be "on the road." Charging station subsidies are among the most recent EV incentives to gain prominence. There are three different types of charging station incentives, each targeting a particular charging station type (i.e Level 1, Level 2, DC). Charging station subsidies are predominantly implemented at the state and county level 16 According to a study from the International Council on Clean Transportation, DC chargers typically cost from $28,000 to $140,000 to install, excluding long term maintenance costs (Nicholas, 2019). 17 Level 1 is the slowest, which outputs 120 V of electricity, are almost exclusively used by households, and are considered "trickle chargers" because it takes around 24 hours to fully charge an empty battery. Level 1 chargers are the least costly of the three types because they are often included in the purchase of an EV and can plug directly into a standard 120 V outlet. I exclude Level 1 chargers from the analysis because the population of public Level 1 chargers is extremely limited. 18 San Diego Gas and Electric, and Pacific Gas and Electric, for instance, have programs that directly invest in charging stations (Armenia & Johnson, 2022; EV Charge Program, (n.d.)). 19 For example, a family that owns an ICE may purchase another EV to add to the household fleet. While the new EV appears as an additional "EV sale," it does not necessarily take an ICE off the road -the hypothetical family could continue to drive their existing ICE. Since the average EV owner has a relatively high income, I expect that there are a substantial number of these cases where EV purchases do not actually serve to fully replace an ICE. rather than the federal level. Eligibility requirements vary by state and county, but most California counties require applicants to be registered as a California business or eligible organization. While few subsidy programs restrict access to manufacturers with a charging network size below a certain threshold, programs typically have limits on the amount of incentives available per station, which varies widely by station type. DC subsidies, for instance, vary around $25,000 to $80,000. 20 Some subsidy programs, such as the statewide California Electric Vehicle Infrastructure Project (CALeVIP), offer increased rebate amounts for charging stations built in disadvantaged or low income communities (see data dictionary for more details on disadvantaged and low income classification). Subsidies reimburse businesses through a tax rebate or credit after the point of sale. EV sales data is obtained from the California Energy Commission (California Energy Commission, 2022). Within the county year level, sales are organized by make, model, and fuel type, enabling me to separate sales into PHEV and BEV categories. (3) I manually conduct a legal analysis of charging station subsidy amounts by type. I obtain subsidy amounts for the legal analysis from a variety of subsidy program web pages (see legal appendix). In addition to charging station subsidies, I compile county-year level data on purchase price subsidies to be used as a control variable in later empirical analyses. 22 I link observations at the county and year level, permitting me to study the relationship between charging station subsidies and 20 Level 2 subsidies may range from around $500 to $7,500. 21 For the 2021 cohort, I do not identify event study results in relative years greater than one because they extend beyond the observation period. This limitation further motivates the staggered difference-in-differences estimation, which can be used to extract effects for late adopters.
Original data sources on charging station and charging station subsidy counts can be found in the legal appendix. 22 Although some subsidies may have changed in magnitude prior to their current incentive amount, very few provide information on the size of subsidy magnitude changes and when they occurred, rendering it impossible to incorporate all subsidy magnitude changes on a per subsidy basis in the legal analysis. charging stations, and between charging station subsidies and EV sales. In all three datasets, I restrict attention to the sample period starting in 2010 and ending in 2022. Under this sample restriction, I observe the county profile, charging station counts, charging subsidy amounts, and EV sales measures of 57 California counties, which represents 98.3% of the universe of all California counties. I expand data on charging station subsidies by county-year slightly by contacting subsidy program staff in counties where the necessary information is not publicly available. The legal analysis and charging station counts data are divided into two primary groups -Level 2 and DC -for which I assess subsidy effects separately. The DC group includes the number of DC stations and DC station subsidies in a given county in a given year. 23 I explore heterogeneity across various dimensions. Economic theory suggests that the subsidy should have a greater effect on areas with high population density and high aggregate GDP; subsidies may have a less significant effect in regions with low population density because charging station producers may find it unprofitable to construct stations if there is a limited demand nearby. Equity considerations motivate investigating the subsidy's impact in disadvantaged communities. 24 To enable event study analysis, I group counties according to a dummy variable indicating treatment status: treated counties adopt charging station subsidies within the period from 2010 -2022 while untreated counties do not adopt any such subsidies in the same period. 25 A unique strength of this paper is that it utilizes county level data from California, the leading state in EV adoption and charging infrastructure in the US. 26 There are three pertinent advantages of using California as the observational setting: (1) California is leading state in terms of the amount of charging subsidies and stations, making it a fertile research ground to study the role of subsidies in promoting development of stations; (2) California has the largest EV market in the US, suggesting that the results provide guidance on the effect of subsidies in the event other states adopt similar subsidies down the road; (3) many counties within California do not adopt subsidies, and these counties plausibly provide a valid counterfactual. I collect data on county-level population from the California Department of Finance. From the US Bureau of Economic Analysis, I retrieve GDP figures for each county from 2010 -2020. Rural and disadvantaged county designations are obtained from the California State Association of Counties and the California Office 23 Analogously, the Level 2 group includes the supply of Level 2 stations and Level 2 subsidies in a given county in a given year. Results of the analysis with Level 2 stations can be found in the appendix. 24 Notes on Table 4 describe the criteria for each designation in detail. 25 I assess differences in responsiveness to the subsidy by further separating between aggregate Level 2 stations and Tesla Destination Stations, and between aggregate DC stations and Tesla Superchargers. Tesla operates a substantial of number of Destination (Level 2) and Supercharger (DC) stations, but faces significantly different incentives compared to other private investors: since the business models of dedicated charging station suppliers such as Chargepoint, Blink, and Electrify America revolve around collecting fees from operating charging stations, I expect that they are relatively more sensitive to changes in the cost of constructing charging stations. Meanwhile, since Tesla vehicles can only charge at Tesla's network of proprietary stations at present, Tesla has a significant incentive to ensure adequate charging access for areas of high demand, whether subsidies exist in that region. I am the first in the literature to examine how the effect of the subsidy differs between Tesla and non-Tesla stations. 26 Approximately 39% of EVs nationwide are registered in the state of California. See afdc.energy.gov/data/10962

of Environmental Health Hazard Assessment, respectively. Treated counties are defined to be counties that adopt charging station subsidies between 2010 and 2022. Consistent with this definition, Column 3 in Table 2 shows that treated counties, on average, implement subsidies valued at $43,044 while untreated counties do not receive any subsidies over the sample period. As seen in Column 3, there are also cross sectional differences in charging stations, sales measures, market characteristics, and demographic characteristics between treated and untreated counties. Interestingly, the fact that treated counties have substantially more purchase price subsidies than untreated counties suggests that counties that implement charging station subsidies tend to enact purchase price subsidies as well, perhaps as part of a broader favorable view of government support for EVs. Nonetheless, these cross sectional differences do not pose a problem for my identification strategy, which relies on the assumption that treatment and control counties trend similarly before subsidy adoption, not necessarily that treatment and control counties have similar profiles on average (i.e, parallel trends). Figure 3 identifies potential sources of omitted variable bias (OVB): to be considered a source of omitted variable bias, the variable in question must be extraneous to the model and correlated with the independent variable and dependent variable. Recall the two primary relationships discussed in this paper are those between charging station subsidies and charging stations and between charging station subsidies and EV sales. Thus, Figure 3 plots two potential sources of OVB -purchase price subsidies and GDP -against the three primary independent variables of interest in my analysis: charging station subsidies, charging stations, and EV sales.
The first row of Figure 3 (Panels A, B, and C) displays a positive, statistically significant relationship between GDP and each of the three independent variables, demonstrating that GDP is correlated with charging station subsidies, charging stations, and EV sales. Wealthier consumers have a greater ability to pay for EVs, and as the EV fleet size increases in turn, so too should the private incentive to construct additional charging stations. Meanwhile the second row (Panels D, E, and F) display a positive, statistically significant, albeit more scattered relationship between purchase price subsidies and the three independent variables. While purchase price subsidies are strongly correlated with charging station subsidies, stations, and sales, I expect they have a greater influence on sales compared to stations because they directly reduce the price of purchasing a new EV, directly resulting in greater EV sales. On the other hand, purchase price subsidies do not lower the out of pocket costs of constructing a new charging station. 27 To address these concerns, I control for GDP and purchase price subsidies in later OLS estimates. My empirical framework aims to accomplish the following: (1) I provide initial estimates from a standard staggered differences-in-differences regression;

(2) I conduct an event study robustness check to verify the parallel trends assumption; and (3) I implement an interaction weighted estimator proposed by Sun and   Abraham (2021) to determine if treatment effects are robust to heterogeneous cohort specific effects, where cohorts are defined by calendar year (e.g 2014). Notably, the independent variable in the Sun and Abraham estimation is binary. In my empirical framework, I estimate a generalized staggered difference-in-differences equation that can be applied to examine the effect of charging station subsidies on charging infrastructure supply and EV sales (Beck et al., 2010; Hoynes and Schanzenbach, 2009; Baker, Larcker, and Wang, 2022; Hoynes et al., 2016;   Fauver et al., 2017; Wang et al., 2021):

Where log(Y ct ) is a general outcome variable that is indexed by county c and year t. log(S ct ) represents charging the log transformed amount of station subsidies indexed by county, c, and year, t. β 1 indicates the effect of log(S ct ) on log(Y ct ).28

Given counties adopted charging station subsidies at different times, I allow for year fixed effects, λ t , and county fixed effects ψ c . I include λ t to non parametrically control for national trends in log(Y ct ), and ψ c control for any permanent, unobserved differences across counties. These fixed effects incorporate the conditional independence assumption (i.e parallel trends) which states that in the absence of treatment, unobserved shocks affect all units in the same way.29

For the β 1 estimates to identify the causal effects of log(S ct ) on log(Y ct ), it is necessary that the variables excluded from Equation 1 trend similarly between treatment and control counties ("Parallel Trends Assumption"). 30 In this log-log model, otherwise known as a constant elasticity model, β 1 reveals that a 1% change in log(S ct ) causes a β 1 % change in log(Y ct ), independent of the initial subsidy amount. This is an important consideration because it allows generalization of β 1 to counties that may have vastly different initial subsidy amounts. Using Equation ( 1), I present estimates of treatment subgroups that have high population, high GDP, and are designated as rural or disadvantaged, respectively.31

In the difference-in-differences estimation technique, I include GDP to control for shocks and trends that affect the economic activity of counties, such as business cycles, changes in regulations and laws, and longterm trends in income distribution. I further include population to control for population characteristics that may differentially shape EV and charging station attitudes across counties. I allow county level clustering of errors in estimating equation (1), i.e. allowing for correlation in the error terms over time within counties, because subsidy adoption is a county level decision. While the parallel trends assumption is commonly used by applied economics researchers, it is strong and frequently violated (Abadie, 2005). For example, Ashenfelter (1978)

The event study analysis allows me to assess the timing of the effects and to check whether trends are parallel before subsidy adoption. I follow Schmidheiny and Siegloch (2019) in proposing to bin effect window endpoints. The endpoints -4+ and 3+ sum up all maximum lag and lead events beyond the window. I denote the year of switching from zero subsidies to positive subsidies (i.e subsidy adoption) by event time τ = 0. The generalized event study estimation equation takes the following specification:

Where log(Y ct ) is a general outcome variable that is indexed by county c and year t. λ t are year fixed effects and ψ c are county fixed effects. ct is idiosyncratic noise. The dummy variable 1(t = E c + τ ) indicates whether a county has subsidies in a given year for each event year τ relative to the year of charging station subsidy adoption, where τ is normalized to equal zero in the year that a county adopts subsidies; τ ranges from -4 through 3, which covers the full range of τ values. The estimate at τ = -4 represents the binned value from τ = -4 to τ = -12 and the estimate at τ = 3 indicates the binned value from τ = 3 to τ = 12. The omitted reference year is τ = -1, for which all estimates are set equal to zero. For counties that never adopt charging station subsidies, all 1(t = E c + τ ) are set equal to zero. The γ τ 's and δ τ 's are the parameters of interest because they report the annual mean of log(Y ct ) in event time, after adjusting for year and county fixed effects, and the set of controls.
An appealing feature of this event study design is that because counties adopted charging station subsidies in different calendar years, it is possible to separately identify the γ τ 's, δ τ 's and the year fixed effects λ t . Although it is common practice to report event study coefficients for the full range of τ values à la Equation (2), it is possible that non null coefficients in pre-treatment years are not indicative of anticipatory treatment effects, but rather contamination from significant post treatment period effects; recent literature demonstrates that when the effect found from Equation ( 2) is heterogeneous, it does not represent the weighted average of cohort specific effects and is biased if these effects vary over time (e.g., Borusyak and   Jaravel, 2017; Abraham and Sun, 2021; Athey and Imbens, 2018; de Chaisemartin and D'Haultfoeuille,   2018; Goodman-Bacon, 2021; Brzezinski et al., 2020; Chan and Kwok, 2022; Greenstone and Nath, 2020;   Strezhnev, 2018; Imai and Kim, 2020). To address this concern, I conduct two additional robustness checks:

(1), I re-estimate the event study restricted to the pre-treatment period, and (2), I reproduce the event-study using the interaction-weighted estimator recommended by Abraham and Sun (2021). One important feature of the Sun and Abraham design is that it uses a binary independent variable as opposed to the continuous variable used in the staggered difference-in-differences and event study models. I conduct the robustness check in (1) through an event study design illustrated by the following equation:

Equation ( 3) is a modified version of Equation ( 2) that restricts to pre-treatment periods since non null pre-trends may be driven by heterogeneous cohort-specific effects. The only differences between Equation

(3) and Equation ( 2) are that in Equation ( 3), τ ranges from -4 to -3, with the value at τ = -4 representing the binned value from τ = -4 to τ = -12, and the omitted reference year is τ = -2, for which all coefficients are set equal to zero. δ -1 represents the event study coefficient for τ = -1. Recent literature on using traditional two-way fixed effect models of panel data with staggered treatment timing in difference-in-differences estimations caution against interpreting the results as an average treatment effect (Callaway & Sant'Anna, 2021; Goodman-Bacon, 2021; Sun & Abraham, 2021). Sun and Abraham   (2021) propose an alternative interaction weighted event study estimation that addresses heterogeneous cohort specific treatment effects. The reasoning for using the Sun and Abraham correction is twofold. First, the general event study specification in Equation ( 2) does not weight treatment effects proportional to cohort size. Second, in a typical event study approach, anticipatory effects in a pre-treatment relative year, may not suggest violation of parallel trends but rather reflect the true effect of the policy in the post treatment period. Sun and Abraham (2021), with their three part approach, resolve both of these concerns. Under their approach, I estimate the following:

Where E c denotes the year county c adopts charging station subsidies and E denotes the set of all years in which at least one county adopted charging station subsidies. e represents the year of the event (i.e when a county adopts charging station subsidies). The E c indicator is used in addressing contamination of pre-trends from heterogeneous cohort-specific treatment effects. The variables D cτ are distinct indicators for each event year τ relative to charging station subsidy adoption, where τ is normalized to equal zero in the year that a county adopts subsidies; τ ranges from -12 through 12, which spans the full range of τ values. log(Y ct ) is a general outcome variable indexed by county c, and year t. τ represents event time. ψ c indicate county fixed effects, and λ t denote year fixed effects. β 0 is a constant and ct represents idiosyncratic noise. I take a weighted average across cohort-years to aggregate the ρ τ,e 's to ρ τ . For example, given τ = 1, suppose I have a total of 5 observations in our data set, of which 3 are for states who adopted charging station subsidies in 2012 and 2 are for states who adopted charging station subsidies in 2016. Then

. The accuracy of the predicted treatment effect can be increased if the ρ τ estimate is converted from log points to percentage points. Under the central assumption that the error term, ct , is not correlated with

To investigate the effect of charging station subsidies on charging station supply, I estimate causal effects from two models: a staggered difference-in-differences equation (Equation 1), and the alternative interactionweighted estimator proposed by Sun and Abraham (2021) (Equation 4). I further conduct an event study restricted to the pre-treatment period as a robustness check to probe the validity of the parallel trends identifying assumption. In this section, I examine the effects of the subsidy on charging station supply, considering various differences in county profile. I use the staggered difference-in-differences model in Equation (1). Table 3 displays point estimates for the baseline difference-in-differences specification with county and year fixed effects and GDP and purchase price subsidy controls. As seen in Table 3, a 100% increase in the magnitude of subsidies increases supply by 2.5% (std. err. 0.008). GDP has a positive effect on all the dependent variables of interest, while purchase price subsidies have a positive effect on DC stations and BEV sales. Purchase price subsidies, which directly reduce the price of EVs, do not have a direct effect on charging stations. Meanwhile, GDP has a positive effect on stations and sales -regions with greater wealth tend to have greater EV adoption and charging station availability. These findings are consistent 32 The percentage change in log(Yct) approximated by ρτ is always a lower bound of the actual percentage change in log(Yct). with Figure 3 which shows that purchase price subsidies and GDP are positively correlated with charging station subsidies, charging station supply, and EV sales.
The negative coefficient of purchase price subsidies on PHEV sales could be due to the possibility that purchase price subsidies target BEVs more than PHEVs. Table 4 reports difference-in-differences estimation results for six different specifications from the staggered difference-in-differences design where the independent variable is DC station subsidies. Given the log-log specification, the reported coefficients can be interpreted as elasticities, and standard errors are robust against heteroskedasticity and autocorrelation due to clustering at the county level. Row 1 is the baseline estimation with county and year-fixed effects. Row 2 adds two additional variables -GDP and purchase price subsidies -to control for observed differences in economic activity and alternative EV incentives, respectively, that may affect charging station supply and sales outcomes. As shown in the baseline specification in Column 1, a 100% increase in the magnitude of DC subsidies increases the supply of DC stations by 2.6% (std. err. 0.009). In the baseline + controls specification, the effect is slightly smaller: a 100% increase in the magnitude of subsidies increases supply by 2.5% (std. err. 0.008). These positive coefficients are consistent with Figure 2 which documents a strong correlation between subsidies and station supply and between subsidies and sales. The similar coefficients between the baseline and control specification suggests that substantial omitted variable bias is unlikely. This central finding demonstrates that charging station subsidy supply is elastic in respect to charging station subsidies. This finding affirms the theoretical expectation that charging station subsidies are successful in increasing supply, and suggests that policymakers in other states should consider implementing charging station subsidies if they aim to stimulate their charging station market. 33

In the baseline + controls specification, I find the elasticity of PHEV sales (BEV sales) in respect to subsidies to be 0.021 (0.016), suggesting that a 100% increase in subsidies is associated with a 2.1%

(1.6%) increase in PHEV sales (BEV Sales). Given that these elasticities are overestimates, this finding reinforces the conclusion of Luo ( 2021): a 100% increase in the Clean Vehicle Credit (i.e $7,500 federal EV tax credit) is associated with a 0.432% increase in EV sales, which is 79.4% less than than the estimate on BEV sales (2.1%) and 73% less than the estimate on PHEV sales (1.6%). 34 Even though the elasticities are overestimates, they are still substantively larger than that found in Luo (2021) to suggest charging station subsidies may be more effective than purchase price subsidies at increasing EV sales, which is consistent with findings from Li et al. (2017) and Cole et al. (2021). 33 Although I demonstrate in Section 5.3 that the relationship between EV sales and charging station subsidies lacks causal interpretation, it is possible that such subsidies indirectly increase EV sales through expanding charging access -a topic that future research should investigate. 34 The elasticity in Luo ( 2021) is calculated by dividing the subsidy's effect on EV sales, 43.2%, by 100. I implement the specification in Row 2 which includes the control variables on various subsets of the data to determine if subsidy effects vary based on county characteristics. In Table 4, Rows 3-6 include the same controls and county and year fixed effects in Row 2 but differ in the following ways: Row 3 subsets to counties whose population is greater than the median California county population; Row 4 only considers counties classified as disadvantaged in 2020; Row 5 subsets to counties classified as rural in 2020 in accordance with the guidelines from the California State Association of Counties; and Row 6 subsets to counties with GDP higher than the median GDP across all California counties in 2020. 35 In Rows 3-6, I find that the coefficients on DC stations -0.021 (std. err. 0.009), 0.019 (std. err. 0.010), 0.009 (std. err. 0.014), 0.021 (std. err. 0.008) -respectively, are smaller than the coefficient of 0.026 in the baseline + controls specification; in a pattern common to all the Column 1 Row 3-6 coefficients, the value of β 1 is either smaller in magnitude or less significant than the control specification. 36 Although I expect that the β 1 coefficients on the subsetted groups are greater than that on the controls or baseline specification, my results show the opposite. One possible explanation is that consumers in areas with high population density may express a more unfavorable attitude towards electric vehicles generally, making constructing charging stations particularly unprofitable. Another possibility is that consumers in these regions may be so impoverished that consumers may not be able to purchase EVs whether they have access to charging or not. However one caution is that the results on the heterogeneous subgroups are subject to statistical uncertainty due to larger standard errors. All of the subgroups by definition rely on a smaller sample size. Accordingly, the specifications in Rows 3-6 have a smaller degree of statistical significance compared to the baseline + controls specification. Indeed, the Column 1 coefficient among Rows 3-6 with the least statistical significance -the urban specification -also has the least number of observations (111). Given this consideration, I rely more on the control specification for a generalized elasticity result than the subsetted groups due to its relative statistical strength. As seen in the baseline + controls specifications, I document positive effects of DC subsidies on PHEV and BEV sales. Among the specifications in Rows 3-6, however, the only significant coefficient is on PHEV sales in the high population specification. 35 See data dictionary for more detailed descriptions on selection criteria. 36 In a pattern common to all specifications in Tables 3 andA1, I find null coefficients on Tesla Destination (Level 2) and Supercharger (DC) stations. This finding is consistent with my expectation that Tesla is less responsive to subsidies due to the different incentives it faces compared to conventional charging station suppliers. Tesla could free ride in that they may claim government subsidies even when the subsidies did not induce them to build new stations.
To address this shortcoming, policymakers should consider excluding Tesla stations from subsidy programs to increase cost efficiency. Table 5 reports coefficients on the interaction term between treatment status and relative years in the pretreatment period and the relative post-treatment year τ = -1. A county is defined to be treated if it adopts DC subsidies in the period 2010 -2022 and untreated if it does not. The omitted reference year is τ = -2. As shown in Columns 1 and 2, I document null pre-trends in DC stations and Tesla Supercharger stations. This observed lack of anticipatory effects supports the parallel trends assumption in the staggered difference-in-differences specification (see Equation 1). 37 Perhaps more interestingly, however, I document anticipatory effects of Log(Sales), Log(PHEV Sales), and Log(BEV Sales) for the binned value τ = -4 to τ = -12 and the relative event year τ = -3. The pre-treatment period coefficients in Columns 3-5 are all significant and positively trending: in Log(Sales) the binned value coefficient of -0.23 (std. err. 0.10) increases by 0.10 to -0.13 (std. err. 0.04); in Log(PHEV Sales) the binned value coefficient of -0.14 (std. err. 0.08) increases by 0.03 to -0.11 (std. err. 0.05); and in Log(BEV Sales) the binned value coefficient of -0.43 (std. err. 0.18) increases by 0.26 to -0.17 (std. err. 0.010). 38 The existence of significant pre-trends demonstrates that failure to properly model EV sales dynamics, or the propensity to adopt charging station subsidies based on past EV adoption trends, will lead to biased OLS estimates of charging station subsidies on EV sales. In particular, the positive trend in sales measures reveals that counties that adopt charging subsidies tend to have growing EV sales even prior to subsidy adoption, and further suggests that sales measures may be correlated with unobserved covariates. Thus, the regression results in Table 5 are consistent with Table 2, which reveals that treated counties on average have significantly more EV sales than untreated counties at time of subsidy adoption. This violation in pre-trends is analogous to the one popularized in "Ashenfelter's dip" (Ashenfelter, 1978). Future research that examines the effect of charging station subsidies on EV sales should account for endogeneity in EV sales, for example through an instrumental variable approach. If policymakers act rationally in incorporating knowledge of under investment in charging station infrastructure in their policy decisions, one would expect counties to implement subsidies when EV sales are low or negatively trending so as to rectify the market failure. However, I observe counties that adopt charging subsidies already experience rising EV sales trends even before subsidy adoption. 37 I drop the elasticity interpretation in the event study and follow up Sun and Abraham correction since these models define a binary rather than continuous treatment variable. 38 For example, the coefficient of -0.23 (std. err. 0.10) on the binned value in Log(Sales) means that EV sales decreased by 23% in the period 4 to 12 years before the enactment of the subsidy. Although the general event study specification in Equation ( 3) restricts to the pre-treatment period, significant observed pre-trends could actually reflect the true post-treatment effect of the subsidy. In Table 6, I reproduce the event-study using the interaction-weighted estimator recommended by Abraham and Sun   (2021) to address concerns of heterogeneous cohort specific effects. Consistent with Table 5, I document null pre trends in DC stations and Tesla superchargers, further supporting the parallel trends identifying assumption in the staggered difference-in-differences model. Interestingly, the effect of DC subsidies on DC stations is positive and statistically significant after τ = 1 and increases by 0.14 from 0.36 (std. err. 0.15) in τ = 2 to 0.50 (std. err. 0.17) in the binned value τ = 3 to τ = 12; using the conversion from log points to percentage points in Equation ( 5), I find the enactment of DC subsidies increased DC station supply by 36% two years after enactment and by 50% between 3 and 12 years after enactment. One reason for this lagged effect is that public charging stations take a considerable amount of time to plan, construct, and become fully operational: theoretically, a charging station whose construction process begins at τ = 0 may not open to the public until τ = 2, reflecting the lagged effect. Another reason could be that investors do not immediately respond to the charging station subsidies. Consistent with theoretical expectation, there are null coefficients throughout the full range of τ values for Tesla Supercharger stations. Similar to Table 5, I find significant positive pre-trends for the binned values from τ = -3 to τ = -12 in aggregate sales, PHEV sales, and BEV sales --0.21 (std. err. 0.06), -0.20 (std. err. 0.07), and -0.37 (std. err. 0.09) -respectively. I do not find significant post-treatment effects on sales measures. Moreover, the positive coefficient in the post-treatment period on Log(PHEV Sales) suggests that growing sales is a continuation of faster sales growth in treated counties. Both the event study and Sun and Abraham estimation exhibit pre-trends in EV sales (see Tables 4 and5, respectively), suggesting that any significant documented effects of charging station subsidies on EV sales are likely driven by OVB. Figure 4 provides a graphical depiction of how the point estimates on the event study model restricted to the pre-treatment period differ from those on the Sun and Abraham correction. In Panel A (Panel B), I observe a significant negative (null) pattern in pre-trends in both the generalized event study and the Sun and Abraham correction. However, the significant negative pre-trend in Panel A does not necessarily invalidate causal interpretation because the negative trend in reverses immediately to a positive trend following subsidy adoption, which seems to suggest a meaningful causal effect of the subsidy on station supply. Consistent with my prior findings that treated counties have a greater EV presence prior to subsidy adoption, the observed negative pre-trends may still suggest rising nominal charging station counts.
Since trends in treated counties are relative to control counties, it is possible that negative pre-trends indicate charging station supply grows at a slower rate in counties that adopt subsidies than in counties that do not. In Panel A, although the event study does not yield a significant post-treatment effect, the Sun and Abraham correction does reveal a significant effect in τ = 2 -the subsidy increases Level 2 station supply by 25% two years following subsidy adoption. In contrast to Panel A, Panel B displays null pre-trends for both the event study and Sun and Abraham specifications and demonstrates that they move more in tandem throughout the full range of τ values. This finding validates the parallel trends condition, which allows me to assign elasticity interpretations to the difference-in-differences coefficients in the post-treatment period. Moreover, the post-treatment period coefficients are significant and positive beginning at τ = 2 in the event study and Sun and Abraham correction. Although the central role of charging stations in EV adoption has received growing policy attention in recent years, it is unclear to what extent charging station subsidies stimulate charging station supply. The study provides to my knowledge the first empirical analysis of the effect of charging station subsidies on charging station supply using the approach recommended by Sun and Abraham (2021). Using this novel methodological approach, I estimate the effect of DC charging station subsidy adoption on DC station supply to be 0.36 (std. err. 0.15) and the effect of Level 2 charging station subsidy adoption on Level 2 station supply to be 0.22 (std. err. 0.11), two years following subsidy adoption. Thus, adoption of DC (Level 2) charging subsidies increases DC (Level 2) increases charging station supply by 36% (24.6%) two years after subsidy adoption. This finding suggests that charging station subsidies, at least on the county level in California, are highly effective at increasing charging station supply. If Level 1 stations become more commonplace in the future, researchers should likewise examine the effect of Level 1 subsidies on Level 1 stations. In light of recent legislation, studies that examine the impact of federal charging station grants on charging station supply could provide insight into the effectiveness of alternative methods of government intervention in the charging station market. 39 The effects of the subsidy on charging station supply are not substantially different across various subgroups. In particular, the effects of the subsidy in denser regions do not appear stronger than those in less dense regions. I also do not find that disadvantaged counties benefit more from these subsidies than non-disadvantaged counties. 40 With respect to the event study and Sun and Abraham models, one limitation is that most treated 39 See, for example, the 2021 Infrastructure Investment and Jobs Act, which "invest[s] $7.5 billion to build out a national network of EV chargers in the United States" (The White House, 2021). 40 While these results seem to reject economic theory, due not because of their larger standard errors; by definition, these subgroups rely on a smaller sample size. One method to increase the sample size in the future is to draw from a longer observation period. Another method is to use county level data from states beyond California once they establish a greater charging station presence. counties adopted subsidies late in the observation period. For these 'late adopters,' the availability of post treatment effects may be limited. 41 As the observation period naturally increases over time, follow up studies should use data from more relative years. Such studies should also seek to determine if, consistent with my findings, charging station subsidies generally have greater effects around the first or second relative year. On the same token, it may be insightful to examine if the effect becomes insignificant some years after the introduction of the subsidy. Beyond the causal effect of charging subsidies on charging station supply, my results offer some insights for future research and policy design. Most significantly, the high elasticity of charging stations in respect to charging station subsidies suggests policymakers can consider increasing charging station subsidies to promote charging station supply. In particular, they may enact more subsidies if they wish to address under-investment in charging station infrastructure. Considering significant pre-trends in sales, the question arises of what portion of the observed effect is due to the subsidy and what portion is due to existing investment being directed into counties with the subsidy. Theoretically, investors could have aimed to invest a given amount in charging stations and choose to invest in counties where they can gain additional profits from the subsidy -in this situation, the subsidy does not induce these agents to invest. Although this study addresses this issue through the use of yearfixed effects, future researchers should run additional tests for private charging stations owned by businesses without geographical flexibility to investigate further. The finding that subsidy adoption is responsive to EV sales suggests future research investigating the direct effect of charging station subsidies on EV sales can consider using an instrumental variable approach. More specifically, my discovery of rising EV sales prior to subsidy adoption motivates several fruitful paths for further investigation. First, researchers should examine if rising EV sales pre-trends among treated counties is permanent or transitory. Second, given my findings are limited to the scope of California counties, future studies should investigate if states outside California or other countries similarly experience positive and growing EV sales pre-trends. The results from such an investigation could present significance for evaluating the effectiveness of national level charging subsidy policy. Third, I found that policymakers implement charging station subsidies when EV sales are growing. However, it is unclear if this is also the case for purchase price subsidies; future research should build on my findings by examining if there are significant pre-trends in the relationships between purchase price subsidies and EV sales.
While it is expected that government entities will intervene when the market fails to produce charging stations at the socially optimal quantity, I find that counties that adopt charging subsidies tend to have rising EV sales trends prior to subsidy adoption compared to never adopters. This implies that the government in 41 For instance, a county that adopted subsidies in 2021 observes only the one year relative effect (calendar year 2022). counties with stable or declining EV sales trends -those most affected by the under-investment in charging station infrastructure -don't adopt the subsidies they need to stimulate charging station supply and EV adoption to their socially optimal quantity. In other words, these governments tend to invest in regions that already have a developed EV presence as opposed to those without. Together with the strong effect of charging station subsidies on station supply, the responsiveness of subsidy adoption to prior sales trends argues for alternative subsidy designs to ensure an equitable distribution of charging station subsidies. Notes: The red lines display difference-in-differences coefficients and corresponding 95% confidence intervals for the alternative specification with staggered treatment timing using an "interaction-weighted" estimator proposed by Abraham and Sun (2021), while the black line displays coefficients and their corresponding 95% confidence intervals from the event study specification in Equation ( 2). The red lines corresponds to 95% confidence intervals of ρτ for τ = -4 to τ = 3. For each treatment cohort (defined by whether the county adopts charging subsidies) in the event study (Sun and Abraham) specification I estimate Equation (4) against the control group of never treated counties. The value at τ = -4 represents the binned value from τ = -4 to τ = -12 and the value at τ = 3 indicates the binned value from τ = 3 to τ = 12 for the event study specification in Equation ( 4) that allows for cohort-year interactions with the ρτ 's. Standard errors are clustered at the county level. A county X is defined to adopt in year Y if it has a positive amount of charging subsidies in year Y and zero subsidies in year Y -1. This treatment definition avoids duplicate observations since treated counties may increase their subsidy amount multiple times throughout the observation period. There are no recorded counties that adopted subsidies in 2022 because complete data was unavailable at the time of retrieval. Data on cumulative county-year subsidy counts by station type is sourced from manual compilation of subsidy amounts from various county and state web pages. See legal appendix for original sources. Model:

(1)

(3)   Model:

(1)

(3)

(5)

Even while rising nationwide EV sales and charging station supply may appear to signal promising trends, they mask the diverging paths of EV ownership and charging stations access along socioeconomic dimensions; wealthy whites who can easily access charging are the main impetus behind accelerating EV ownership, while low income Blacks and Hispanics living in charging deserts are left in the dust. To obtain this value, I find the absolute value of the average of the estimate at the binned value from τ = -4 to τ = -12 and the estimate at τ = -2 (-0.21 and -0.02, respectively) in Table6

Tesla is an exception in that Tesla Level 2 stations, like their DC stations, use a proprietary Tesla connector. However, the recent increase in available aftermarket adapters have made Tesla chargers more compatible with other car models. The CHAdeMO plug is compatible with Nissan, Mitsubishi, Kia, Fuji, and Toyota brand models; SAE Combo (CSS) is compatible with U.S. and European EVs, BMW, Volkswagon, Chevy, and some Asian electric vehicles; Tesla plugs are compatible with Tesla vehicles. See https://blinkcharging.com/understanding-ev-charging-plugs/?locale=en

It should be noted that purchase price subsidies may indirectly affect charging station supply by stimulating EV sales. A positive and significant β 1 suggests that increasing the amount of charging station subsidies exerts a positive effect on the charging station supply, while a negative and significant β 1 indicates that increasing the amount of charging stations decreases charging station supply. It is further assumed that E[ ct = 0]

This assumption would be violated, for example, if public attitudes about EVs and charging station shift differentially in treated counties relative to control counties. See notes in Table4for further details on these data subsets.