### Forthcoming in Economic Inquiry

Information Disclosure Policies: Evid ence from the Electricity Industry Magali Delmas, Maria J. Montes-Sancho,

& Jay P. Shimshack* RHR: Delmas, Montes, & Shimshack: Information PoliciesJEL D83, Q58, D21

Information Disclosure Policies: Ev idence from the Electricity Industry

While theory suggests that information programs may correct market failures and improve welfare, the empirical impacts of these programs remain undetermined. We show that mandatory disclosure programs in the electricity industry achieve stated policy goals. We find that the proportion of fossil fu els decreases and the proportion of clean fuels increases in response to disclosure programs. However, th e programs may produce unintended consequences. For example, programs may make “clean” firms cl eaner while leaving “dirty” firms relatively unchanged. If the marginal benefits of pollution abat ement are larger at dirty firms than at clean firms, disclosure programs may indu ce inefficient abatement allocations.

Delmas: Associate Professor, Institute of the Environm ent, University of California, Los Angeles CA 90095. Phone 1-310-825-9310, E-mail delmas@ioe.ucla.edu Montes-Sancho: Assistant Professor, Department of Business Administration, Carlos III University, C/ Madrid 126, Getafe, 28903 Madr id, Spain. Phone +34-91-624-8639, E-mail mmontes@emp.uc3m.es Shimshack: Assistant Professor, Department of Economic s, Tulane University, New Orleans, LA 70118. Phone 1-504-865-5321, Fax 1-504-865-58 69, E-mail jshimsha@tulane.eduI. INTRODUCTION
Developed nations’ environmental policies have evolved substantially in the last several decades. Early pollution control programs involved command and control approaches. Policies then frequently included pollution charges, tradab le permits, and other market based instruments. Most recently, a “third wave” of environmental policy has emerged that emphasizes information provision as an integral part of the risk m itigation strategy. Here, government regulation is replaced or augmented by publicly provided informa tion presumed to assist more cost effective private market and legal forces. Common examples include the toxics re lease inventory, lead paint disclosures, drinking water quality notices, and eco-labels. The empirical effects of such programs, however, remain largely undetermin ed. This paper examines the impact of a prominent mandatory disclosure program on the fu el mix percentages of large electric utility corporations. The prominence of mandatory information polic ies is not restricted to environmental arenas. For example, developed countries’ equity markets generally require firm-level financial information provision. In many countries, agricultu ral goods require country of origin and other health labels. Domestic colleges and universitie s are required by law to inform current and prospective students of crime st atistics, equity data, and perfo rmance metrics. Even significant medical errors must now be disclosed to the community. There are several potential advantages of information provision po licies, and theory suggests that disclosure program s may effectively achieve their goals. Healy and Palepu (2001) provided a survey of the evidence in capital ma rkets. Brouhle and Khanna (2007) demonstrated that information provision can improve product qua lity. In the environmental area, Kennedy et al. (1994), Arora and Gangopadhyay (1999), Maxwe ll et al. (2000), Kircho ff (2000), and Khanna(2001) showed that the provision of information about pollution may correct a market failure, improve performance, and enhance welfare. Despite the literature’s theoretical findings, th e empirical effects of disclosure programs remain inconclusive. Early studies of securiti es regulation found mixed results. See Stigler (1964), Robbins and Werner (1964), and Benston (1973). A more rece nt literature suggested that disclosure programs in financial markets can achieve their desired effects; La Porta et al. (2006) and Greenstone et al. (2006) found that both mark et size and market re turns were positively influenced by mandatory disclosure programs. In product quality settings, Chipty and Witte (1998) established that resource and referral agenci es significantly influenced child care prices, but had no impact on the quality of care. In contrast, Jin and Lesl ie (2003) found that mandatory hygiene grade cards positively affected rest aurant quality and health outcomes. Studies of environmental performance yiel ded similarly mixed results. Desvousges, Smith, and Rink (1992) found that information-base d programs influenced at titudes favorable to radon testing, but testing itself onl y increased when mass media di ssemination was coupled with community-based implementation programs. Kona r and Cohen (1997) and Khanna et al. (1998) found that stock movements associated with To xic Release Inventory (TRI) announcements led to increased abatement and reduc ed emissions. However, Bui (2005 ) found that the declines in emissions after TRI reporting events may have b een attributable to regulation rather than investor pressure. Bennear and Olmstead (2006) found that drinking water quality notices lowered violations for some systems, but not others. This paper is the first empirical economic study of the impacts of mandatory information provision in the electric util ity industry. The disclosure pr ograms considered here differ significantly from the TRI inform ation programs examined previously. Notably, TRI informationis not directly provided to stakeh olders. Further, TRI information frequently requires expertise to process and interpret. These dis tinctions are important, as the br oader literature on information policies suggests that transparency programs ar e most likely to achieve issuing agency goals when disseminated information is simple, understa ndable, standardized, actionable, and designed to directly benefit at least some of the disclosers themselves (Weil et al. 2006). Environmental disclosure programs in elec tricity markets are a promising area of exploration for the efficacy of information policie s for several reasons. First, electricity is a homogeneous commodity. From a consumption point of view, there are no differences in the characteristics of green or brown electricity. Therefore, this setting may allow us to more directly attribute program-induced changes to agent preferen ces. This is not true in much of the broader literature. For example, if eco- or organic- labe led products gain market share, it is difficult to establish whether consumers are expressing pr eferences for environm ental improvement or whether consumers perceive other differences in product quality (like health, safety, and taste). Second, electric utilities are among the leading pollu ters in the United States. For example, about 40 percent of domestic CO 2 and 67 percent of domestic SO 2 emissions are attributable to electricity generation. Third, electr ic disclosure programs exhibit a number of features desirable for econometric identification. For example, the programs were adopted at the state-level and progressively introduced over time, so a ll firms were not impacted uniformly. To what extent did mandatory disclosure la ws affect fuel mix outcomes in the electric utility industry? We address this question by examining monthly firm-level fuel mix and program data from 145 of the largest investor-o wned electric utility co mpanies for the period 1995-2003. We first analyze how firms’ fuel mix percentages respond to mandatory disclosure programs. We prevent bias fr om potential statistical endoge neity using fixed effects andinstrumental variables. We then explore the det ected response in more detail. We use Ordinary Least Squares (OLS) and Instrument Variable (IV) interaction models to explore the effect of customer composition on disclosure responses a nd standard and IV quantile regressions to examine how the entire fuel mix distribution shifts. We find three main results. First, mandatory disclosure programs affect fuel-mix

outcomes . We find that the average proportion of fu el usage attributable to fossil fuels significantly decreases and the average proportion of fuel usage attributable to clean fuels significantly increases in response to disclosure programs in the electric utility industry. Second, customer composition significantly impacts disclosure response. We find that firms’ clean fuel program responses become considerably stronge r (more positive) as the firm proportionately serves more residential customer s. Firms’ fossil fuel program responses become weaker (less negative) as they propor tionately serve more residential customers. Third, pre-existing fuel mix significantly impacts disclosure program response. Our results suggest that firms that already use substantial amounts of clean fuels most signi ficantly increase clean fuel percentages in response to disclosure programs. Similarly, firm s that already use relatively small amounts of fossil fuels most significantly decrease fossil fu el usage in response to disclosure programs. 

### II. THE ELECTRIC UTILITY INDUSTRY

Fuel Mix In 2004, domestic electricity generati on totaled 3,953,407 gigawatt hours. Of total generation, 50 percent was attributab le to coal, 18 percent was attrib utable to gas, and 3 percent was attributable to oil. Nuclear sources generate d nearly 20 percent of electricity. Other energy sources, like hydropower, biomass, geothermal, solar and wind, generated approximately 9 percent (Edison Electric Institute 2005). Cleaner energy sources such as photovoltaic and windplants represent higher operating expenses than nuclear or fossil fuel, but they are growing rapidly. For example, wind power us age increased 27 percent in 2004. Mandatory Information Disclosure Programs In the U.S. electricity industry, information disclosure refers to the mandatory provision of fuel mix percentages and pollu tion discharge statistics to u tility consumers. For example, Minnesota’s Public Utilities Commission decreed: “The Commission recognizes that there is a need for the consumer to be informed and educated on environmental issues and t hat all Minnesota utilities’ customers ... should have similar access to information.” (Minnesota PUC, 2002) The state issued an order requiring regulated utili ties to disclose information on fuel mix and air emissions to customers. Twice annually, utilities mu st include a bill insert that contains a pie chart depicting the mix of fuel sources, a bar ch art of air pollutant emissions, a chart of costs associated with different generating sources, a nd a discussion of energy efficiency measures. Further, the utility must list a phone number and web address on all bills so that consumers can access environmental information. Other states’ disclosure programs are similarly motivated and implemented, although specific details may var y. For example, several states’ disclosure programs require quarterly (rather than biannual) inserts. Figure 1 indicates which states had disclosu re programs in 2005. By that year, 25 states had adopted generation disclosure rules, and these states repr esented over 65 percent of the United States population. Since consumer preferen ces may factor into disclosure impacts, programs may be particularly meaningful in dere gulated states. Indeed, 23 of the 25 state-level disclosure programs were enacted in deregulated states, including NY, IL, TX, MI, AZ, NM, andmuch of the mid-Atlantic and northeastern regi ons. Colorado and Florida instituted mandatory disclosure programs without de regulating their industries. Other Programs in the Electric Utility Industry In addition to the mandatory state-level disc losure programs that are the focus of this study, many electric utilities must comply with other information requirements. Most notably, “major” firms are required to file Federal En ergy Regulatory Commission Form Number 1, the Annual Report for Major Electric Utilities, each and every year. 1 These reports average 140 pages and contain general corporate informati on, financial statements, supporting schedules, and information on environmental investments. In addi tion, electric utilities are required to provide information about their environmental perfor mance to the U.S. Environmental Protection Agency (EPA) and the Energy Information Administration (EIA). Although all of the aforementioned data is publicly accessible through govern ment databases, users typically must have environmental and database expertise to interp ret the information. In marked contrast, disclosure programs are designed explicitly to produce easily accessible and readily interpretable information. Several other policies may also impact firm s’ fuel mix percentages. Examples include Renewable Portfolio Standards, mandatory Green Power Initiatives, and tax incentives. Renewable Portfolio Standards (RPS) typically mandate tradable credit programs with fixed quotas for renewable generation. Mandatory green power policies require utilities operating in the state to offer and publicize green power options to consumer s. State and local sales and corporate tax credits and exemptions provide financial incentives for green energy generation.
III. THEORIES LINKING DISCLO SURE AND FUEL MIX OUTCOMESSeveral theories allow for a link between ma ndatory information disclosure programs and fuel mix outcomes. 2 Perhaps the simplest theoretical explanations entail increased community coercion or investor or employee pressure. In the presence of information on the relative environmental performance of a given firm, community activists may lobby for future regulation or attempt to harm the firm’s reputation with the consuming public (indi rectly reducing demand). Employee turnover and dissatisfaction may re sult from disclosed poor environmental performance (Tietenberg (1998)). Investors may express environmen tal preferences or concerns over future environmental regulation by decreasing demand for shares (Khanna et al. (1998)). However, the information provided by disclosure programs in the electricit y industry is typically already available to highly motivated and trained experts like lawyers, in vestors, and community activists. A more compelling theory, then, for the link between information and environmental performance in the electricity industry might invol ve the threat of future regulation or legal action. In a dynamic political economy context, disclosure programs may simply signal the state’s willingness to impose fu ture regulations on the industr y unless firms self-regulate. Similarly, disclosure programs ma y increase a reporting firm’s susceptibility to liability under legal statutes. Segerson and Miceli (1998) an d Maxwell, Lyon, and Hackett (2000) explore firms’ incentives to preempt future regulation or legal liability. Another persuasive theory for the link between disclosure and environmental performance is a direct demand effect. In the presence of simple, easily interpretable, and directly provided information, consumers may increase demand for fuels perceived as environmentally favorable and decrease dema nd for fuels perceived as environmentally unfavorable. Of course, this mechanism requires: (1) that information affects consumerawareness, (2) that consumer awareness can tran slate to changes in de mand, and (3) current or future consumer choice among electricity product s. However, the mechanism does not require choice among electricity providers

. An emerging literature suggests that cons umer awareness changes in response to environmental information [Desvousges, Smith, a nd Rink (1992), Blamey et al. (2000), Loureiro (2003), Loureiro and Lotade (2005 ), and Leire and Thidell (2005)] . In our context, disclosed information may remind consumers of the conse quences of their own actions, notify customers that alternative fuels exist and are widely used, and demonstrate the variability in utilities’ fuel mix percentages and emissions. Further, shifts in awareness can translate into new consumption outcomes [Teisl, Roe, and Hicks (2002) a nd Shimshack, Ward, and Beatty (2007)]. Consumers increasingly have th e option to purchase greener energy at a price premium, and therefore increasingly have choice among consumer products. Lamarre (1997) and Delmas et al. (2007) found a distinct market niche for re newable energy even at a price premium. Thirysix states and over 600 utilities currently offer green power pric ing programs where consumers can support cleaner energy usage in excha nge for an electricity price increase. 3 Further, there are dozens of certificate programs (many at the nati onal level) that allow consumers to purchase green certificates or green tags that require th e replacement of traditiona l types of energy with greener alternatives. These certificates are avai lable whether or not th e consumer has direct access to green power options from their own provi der. Many utilities were very interested in disclosure programs when they were being considered since su ch policies allowed firms to “distinguish their price structure, fuel mix, or e nvironmental profile in the eyes of the consumer and found mandatory standard labe ls to be a credible way to do that.” (NCCEI 2002) In otherwords, disclosure programs were perceived as an effective and particularly convincing way to price discriminate immedi ately or in the future. Of course, all theories linki ng disclosure programs and fuel mix percentages in the electric utility industry require that supply of a given fuel type category is not completely inelastic. In other words, firms must be able to realistically alter their fuel mix portfolios in the short- to medium- run. On the margin, at least, they can. Wh ile purchasing or building new facilities may be required to dramatically alter fuel mix portfolios, relatively small portfolio shifts are easily obtainable. Fi rst, utilities can alter their capacity ut ilization. Second, major electric utilities can buy and sell power generation in response to changing market conditions. Several theories can explain the link between information disclosure and environmental performance. Empirically, we will follow the br oader information literature and estimate the general impact of mandatory information progr ams. Regressions of quantity on information variables (and other covariates) are identified under any of the mechanisms discussed above, and an identified response represents the impact of disclosure programs on the equilibrium quantity of electricity generated from the specifically analyzed fuel source. 

### IV. DATA

Data sources and content Our research assesses the impact of envir onmental disclosure programs on the fuel mix percentages of major electric utility firms. We focus on fuel mix indicators from the electric power industry for two reasons. First, fuel mix is the most readily identif iable and interpretable measure of environmental performance on disclosure program bill inserts and web postings. Second, information disclosure programs are he terogeneous, yet all require generation mix information.We analyze data from the Energy Informa tion Administration (EIA )’s Annual Electric Power Industry Database and the Interstate Re newable Energy Council (IREC)’s Database of State Incentives for Renewable Energy. Monthly fuel mix data come from forms EIA-906 (and its predecessor EIA-759). We focus on production-ba sed fuel mix rather than sales-based fuel mix to minimize program induced sales shifts across states. Disclosure program information comes directly from IREC’s Database. Since it is possible that other state-level programs like Renewable Portfolio Standards and Green Power initiatives may impact utilities’ fuel mix percentages, we also analyze other program data from the IREC database. The Sample Our sample includes monthly information from 145 major investor-owned electric utility companies. We focus on large investor-owned firms because these companies represent the majority of industry electricit y and pollution generation. Further, EIA data (EIA-906 and EIA759) is imputed for smaller companies based upon in formation from these larger firms. All firms with at least one plant with a capacity of 50 megawatts or more (25 megawatts or more prior to 1999), all firms with nuclear generation, and all fi rms with significant renewable capacity file reports with the EIA for each and every month of operation. Since our data represent the big incumbents in the electric utility industry, the resu lts of our analysis shou ld be extrapolated to smaller firms with a degree of caution. Our sample data are observed at the firm le vel. Management decisions are centralized and disclosure program requirements operate at the firm/product level. Disclosure program requirements and generation produc t definitions cross plant bounda ries, but not firm boundaries. We observe fuel mix percentages for our 145 firms for the 108 months spanning 1995-2003. Our sample begins in 1995 in order to obtain pre-pr ogram information for all impacted states; thefirst disclosure program was enacted in mid- 1997. The sample concludes in 2003 due to data availability. Summary Statistics Conditional on positive generation, aggregate fossil fuels represent approximately 74 percent of generation for all sample firms over all sample periods. We define fossil fuels as coal, oil, and gas. Aggregate clean fuels represent ap proximately 9 percent of generation. We define “clean” fuels as renewables and hydroelectric. 4 Nuclear represents approximately 17 percent of generation. Nuclear is categorized as neither fossil fuel nor clean fuel, and it is analyzed separately in all analyses. Note that our genera tion sample statistics closely correspond to total national proportions for the sample period. Additional summary statistics, broken down by disclosure stat us, are presented in Table 1. Standard errors appear in pa rentheses. The summary statistics in Table 1 indicate that firms never subject to disclosure decreased clean fuel usage and increased fossil fuel usage over the sample period. In contrast, firms subject to disc losure increased clean fuel usage and decreased fossil fuel usage over the sample period. Results in Table 1 are suggestive of disclosu re program effects on fuel mix outcomes, but the differences between the last period and the fi rst period are only statisti cally significant at the five percent level for the clean fuel response of firms never subj ect to disclosure. These simple summary statistics also do not co ntrol for confounding factors that may impact changes in fuel mix over time. Consequently, a more comp lete empirical analysis is necessary. 

### V. PRIMARY METHODS Our overall empirical strategy is to use panel data techniques to exploit within-firm temporal variation in program status to analyze the effect of mandatory disclosure programs onfuel mix percentages. We first use OLS and IV regression methods to demonstrate that disclosure programs significantly reduce the pr oportion of fossil fuel usage and significantly increase the percentage of clean fuel usage. S econd, we examine the disclosure response in more detail. The basic regression model is y it = D it δ + X it β + α i + ε it , where i indexes the unit of observation (a firm) and t indexes time (months). y it represents the perc entage of firm i’s generation in period t attributable to the fuel source being analyzed. D it represents the proportion of firm i’s sales that are subject to an effective disclosure program in period t. The elements of
the vector X it include all of the non-program expl anatory variables discussed below. α i is an
unobserved time invariant individual effect and ε it is the usual time variant idiosyncratic shock. Primary Variables Our key dependent variables represent fuel mix percentages. These include the percentage of firm i’s generation in period t attributable to foss il fuels (including coal, oil, and natural gas), to clean fuels (including hydroelect ricity and renewables like wind, solar, and biomass), or to nuclear power. Our key expl anatory measure is a continuous variable representing the proportion of firm i’s sales that are subject to an operational or effective mandatory disclosure program in period t. If a ll of a firm’s sales are subject to disclosure requirements in a given month, this explanatory variable takes a va lue of 1. If only 80 percent of a firm’s electricity sales are subject to disclosu re in a given month (such that 20 percent of company sales go to states without operational disc losure programs), this variable takes a value of 0.80. Joskow (1998) noted that restructuring of electricity supply ha s the potential to significantly impact industry fuel mix outcomes. On one hand, competitive retail power marketsmay provide market incentives for electric utilitie s to offer green power to consumers (Delmas et al (2007). On the other hand, comp etition in restructuring market s might favor low-cost power and induce utilities to minimize generation costs and prices to consumers by focusing on low cost fuel. As noted in the background section, th e vast majority of disclosure programs were enacted in deregulated states at some point after restructuring. Consequently, we include a variable representing the percent of firm’s sales in deregulated states. 5 We use firm-specific fixed effects to captu re unobservable fuel mix determinants and several nearly constant fuel mix determinants that have been identified in the previous literature. These latter covariates include fi rm size, age, community political and environmental attributes (as often proxied by League of Conservation Vote r (LCV) scores), management profiles, average regulatory stringency, and owners hip type (Delmas et al., 2007). The literat ure indicates that fossil fuel generation percentages decrease with higher LCV scores, decrease with size, increase with merger processes, and in crease with private ownership. 6 Fixed effects also capture differences in input and output prices due to fact ors like distance to fossil fuel markets and statelevel variation in taxation. Since plant production varies seasonally, we in clude quarterly dummy variables. A priori, the impact of seasonality on fuel mix is ambiguous . One might expect that fossil fuel percentages are higher and clean fuel percen tages are lower in the late summe r months as ge neration of hydro and wind is lower during these time periods. Fi nally, we include annual dummies to account for broad trends in prices, technologi cal change, and other factors. Consistency Considerations A potential concern with our key program D it variable is that it may be statistically endogenous. For example, consider the possibility that the likelihood of program adoption is afunction of the average environmental performance of the large electric utilities operating within the state. In terms of the basic regression model, the concern is that the time invariant individual
effect α i is correlated with the program variable D it . However, fixed effects prevent bias from this type of correlation. In our context, the inclusion of fixed effects prohibits th e possibility of bias introduced when program adoption is a function of the temporal average fuel mix of the firm. It is also possible that the program variable D it is correlated with the time variant error

term ε it . For example, consider the possibility that states choose to adopt disclosure programs in periods in which large electric utilities operating within that state are utilizing more fossil fuels than usual . A standard correction for this type of statistica l endogeneity is instru mental variables. Our chosen instrument is the weighted average of program status in states near those states in which the particular firm operates. However, si nce it is possible that emissions from upwind states influence program a doption in the state of interest, our in strument eliminates states directly upwind and directly downwind from the states in which the partic ular firm operates. 7 The validity of this inst rument requires that: ( A1 ) state policymakers’ disclosure program decisions are influenced by disclosure program status in nearby states, and ( A2 ) policy choices in states where firm i does not opera te do not depend direc tly on the environmental performance of firm i in period t. A1 is supported by empirical evidence th at consistently reveals important spillover effects of policy choices in one st ate for decisions in neighboring states. 8 A1 is also testable. The coefficient on the instrument in the first stage regressions is significant in both an economic and a statistical sense (t -statistics above 7). Requirement A2 is simply a maintained assumption, but it seems unlikely firms make pr oduction choices based upon laws that have no influence on them or that states where firm i does not operate decide to adopt a program basedupon the environmental performance of firm i in period t. We later examine the sensitivity of our instrumental variable. 

### VI. EMPIRICAL ANALYSIS

Fixed Effects and Instrume ntal Variables Regressions Do disclosure programs affect fuel mix pe rcentages on average? Our goal here is to investigate the relationship betw een disclosure programs and firm’s fossil fuel and clean fuel usage. Thus, we regressed fuel mix proportion meas ures on the percent of a firm’s sales subject to disclosure requirements and other covariates. Simultaneous estimation of the multiple fuel mix equations through a SUR regression would yield no ef ficiency gain, since the covariates in each equation are identical. We ran both fixed effects linear regressions and fixed effects instrumental variables regressions. Results are presented in Table 2. All computed standard errors are heteroskedastic-consistent. T-stat istics appear in parentheses. 9
Results in Table 2 indicate that the estimated impact of an operational disclosure program is negative and sign ificant at the 1 percent level for fossil fuel production. The results are also economically significant. As th e proportion of the average firm’s sales subject to disclosure increases 1 percent, the average proportion of ge neration attributable to fossil fuels drops between 0.06 percentage points (OLS point es timate) and 0.23 percentage points (IV point estimate). Similarly, results in Table 2 indicate that the estimated impact of an operational disclosure program is positive and significant at the 1 percent level for clean sources like hydroelectric and renewables. As the proportion of th e average firm’s sales subject to disclosure increases 1 percent, the average proportion of ge neration attributable to clean fuels increasesbetween 0.02 percentage points (OLS point es timate) and 0.27 percentage points (IV point estimate). We find that deregulation negatively affected the proportion of fuel mix attributable to clean fuels like renewable and hydro and positiv ely affected the proportion of fuel mix attributable to fossil fuels. Re sults are consistent with the hypothesis that deregulation could potentially trigger investments in lower cost fuel. Seasonality also appears to play a role in fuel mix percentages. The proportion of fossil fuel us age is higher and the proportion of clean fuel usage is lower in the late summer and fall months. This may reflect the fact that generation of hydro and wind is lower during these time periods. We also find that fuel mix decisions appear to trend over time, although non-linearly. Disaggregated results for specific fuel sources mimic these aggregate results. 10 There is a statistically significant negativ e relationship between disclosu re programs and the proportional use of coal. Program effects on oi l and gas are economically small a nd statistically insignificant. There is a statistically significant positive rela tionship between disclosure programs and the proportional use of both renewable and hydroele ctric generation. Results also suggest no statistically significant relations hip between disclosure progra ms and the proportional use of nuclear electricity generation (cat egorized as neither fossil fuel nor clean fuel and therefore omitted from Table 2). Sensitivity Analysis: Renewable Portfolio Standards and Other Programs One concern with the preceding results is omitted variables. Other state and local regulations like Renewable Port folio Standards, mandatory Green Power Initiatives, and tax incentives may also impact firms’ fuel mix percen tages. Of course, the instrumental variable approach should prevent bias unle ss the instrument is itself corre lated with the omitted variable.Further, the adoption of RPS, green power, a nd tax incentive progra ms does not generally closely coincide with the adoption of disclosu re across time and omitted time variant factors only bias results if they are corre lated with disclosure program introductions across both time and space. To be complete, however, we tested wh ether other prominent state-level programs targeting utilities’ fuel mixes impacted our key results. Including variable s indicating the percent of the firm’s sales subject to RPS, mandatory green power, and state and local tax incentives, either individually or simultaneously, yields re gression results (signs, significance, and point estimates) that are extremely similar to the results presented in Table 2. Sensitivity Analysis: Other Assumptions One possible worry is the robustness of our re sults to the chosen in strument. If program implementation is more likely when other states’ firms use more fossil fuels and less clean fuels than normal, our results understate program imp acts. If program implementation is more likely when other states’ firms use less fossil fuels and more clean fuels than normal, our results overstate program impacts. Consequently, we expe rimented with an instrument that contains program information from all states adjacent to those in which the firm operates (not just states that are neither upwind nor dow nwind). Further, while we ca nnot come up with a convincing reason that a state where firm i does not operate (or influence due to drifting pollution) might decide to adopt disclosure based upon firm i’s period t environmental performance, we supposed this was possible. Under this supposition, the st ate where firm i does not operate (or influence due to drifting pollution) should at least not deci de to adopt a program based upon firm i’s past environmental performance. We therefore cons idered additional possible instruments that include lagged program status in neighboring stat es. All tested instruments yield similar (signs, significance, and magnitude) coeffi cient estimates in the first st age and a consistent economicstory (signs, statistical signi ficance, and economic importance) in the second stage. Our presented instrument yields consisten tly conservative empirical magnitudes. Another possible concern is th e sharpness of our study’s program va riables. Perhaps utilities were broadly aware of the disclosure programs prior to their effective date and changed their behavior ahead of time. Of course, if utilities had al ready completely responded to disclosure programs before the effective dates, it would be difficult to reconcile the observed responses in our analyses. However, as a sensitivity test, we repe ated all analyses with program variables that reflect the dates the programs we re enacted. In general, we find qualitatively similar results (in signs and signi ficance) to those reported here , but magnitudes are frequently smaller. Our program variable is constructed by weighti ng each firm’s state-level disclosure status by the percentage of sales that occur in each stat e. A possible apprehension is that the percentage of a firm’s sales attributable to each state may change in response to the program itself. This would introduce bias. However, if we replace ou r program variable with a 0/1 dummy indicating whether any of a firm’s sales are subject to disclo sure, we find qualitatively similar results. Finally, in our analysis we control for the possi bility of persistence with fixed effects and the possibility of systematic changes in technol ogy with time dummies. However, as a sensitivity check, we include an auto-regressive term la gged one year to help control for unobserved technology shifts. Includ ing this lagged discharg e variable did not substantively change the results; signs, significance, and approximate magnitude are similar to those reported.
VII. FURTHER EXPLORATION The regressions in Table 2 dem onstrate that disclosure prog rams reduce fossil fuel usage and increase clean fuel usage on average. Howe ver, it may be informative to explore theseeffects in more detail. Consequently, in this sect ion, we first use regressions with interactions to explore whether the impact of information programs on fuel mix depends upon customer composition. We then explore the impact of di sclosure policies beyond the mean; we utilize conditional quantile regressions to investigate program effects on the entire range of the fuel mix distribution. Regression Models with Interactions Are disclosure program impacts conditional on customer composition? Our goal here is to examine whether the effect of disclosure programs depends upon a firm’s proportion of sales to residential consumers. Residential consumers may be more likely to respond to disclosure programs since the simplified, ea sily interpretable information may be more novel to them. Commercial organizations may si mply have sufficient incentives and resources to obtain fuel mix information without disclosure programs. Fu rther, groups with different preferences may simply respond differently to disclosed information. 11 Consequently, we regress fuel mix proportion measures on the percent of the firm’s sales subject to disclosure, the proportion of the firm’s sales to residentia l consumers, an interaction of the policy variable with the residential variab le, fixed effects, and other covariates. More formally, we consider the regression model y it = D it δ + R it γ + D it R it η + X it β + α i + ε it . D it still represents the proportion of firm i’s sales that ar e subject to an effectiv e disclosure program in period t, R it represents the proportion of firm i’s sale s going to residential customers, and the elements of the row vector X it include all of the non-pr ogram explanatory variables. Since both the program variable D it and its interaction D it R it may be statistically endogenous, we again employ instru mental variables regressions. One instrument remains the same. Our second instrument is the interaction of th e first with the residen tial variable. Since thisinteraction in not a linear combination of the fi rst instrument, it is as valid as the primary instrument itself. Results for aggregate categori es are presented in Table 3. Computed standard errors are heteroskedastic-consistent. T-statistics appear in parentheses. Table 3 coefficients on the un-interacted reside ntial variable indicate that, in the absence of any disclosure program, an increase in sale s to residential customers increases the proportion of fuel mix attributable to nucle ar energy and decreases the proporti on attributable to clean fuels. Coefficients on the un-interacted disclosure progra m variable indicate that when firms sell to no residential customers, programs reduce the propor tion of usage attributable to fossil fuels and increase the proportion of usage attributable to nuclear energy. As always, however, some care should be exercised interpreting coeffici ents conditioned on zeroed variables. The interaction results in Table 3 indicate th at the impact of disc losure programs on both clean fuel usage and fossil fuel usage becomes more positive as the percentage of residential customers rises. In other words, as a firm pr oportionately serves more residential customers, clean fuel program responses become stronge r (more positive). Alternatively, as a firm proportionately serves more residential customers, fossil fuel program responses become weaker (less negative). These results are not inconsistent . Examining the last column of Table 3, we see that the interaction coefficient for nuclear energy is negative and statistically significant. As a firm proportionately serves more residential cu stomers, any nuclear program responses become weaker (less positive). Marginal disclosure program impacts clarify the interpretation of the results in Table 3. At the first quartile of the resi dential customer variable, the ma rginal impact of disclosure on clean fuel usage is +0.061. 12 At the median of this variable, th e marginal impact of disclosure is +0.121, at the third quartile, the marginal imp act of disclosure is +0.175, and at the 90 thpercentile, the marginal impact of disclosure is +0.244. Thus , clean fuel program response becomes stronger (more positive) as a firm proportionately serves more residential customers. The marginal impact of disclosure on fossil fu el usage is -0.487 at the first quartile of the residential variable, -0.435 at the median, -0.388 at the thir d quartile, and -0.328 at the 90 th percentile. Therefore, fossil fuel program res ponse becomes weaker (less negative) as a firm proportionately serves more residential customers. Finally, the marginal impact of disclosure on nuclear fuel usage is +0.457 at the first quartile of the residential variable, +0.379 at the median, +0.309 at the third quartil e, and +0.220 at the 90 th percentile. Nuclear fuel program response becomes weaker (less positive) as a firm propor tionately serves more residential customers. Conditional Quantile Regressions Do disclosure program impacts vary across the fuel-mix distribution? Our goal here is to examine whether the effect of disclosure prog rams depends upon firms’ pre-existing fuel mix portfolios. For example, do disclosure programs im pact high fossil fuel firms the same way they impact low fossil fuel firms? To explore this question, we use Koe nker and Bassett (1978)’s conditional quantile regressions (QREGs). Quantile regressions estimate functional relationships between variables for various points on the probability distri bution of the dependent variab le. Estimated parameters are interpreted as they would be in any ordinary regression model, but the parameter estimates are allowed to vary with the quantile of the di stribution. Typically, linea r models estimate the response of the mean or expected value of some dependent variable to one or more independent variables. Quantile models estimat e the response of the median (or th e 90th percentile or the 20th percentile, etc.) to independent variables. In contexts with heterogeneous responses across thedistribution of the dependent variable, focusing on the mean may overstate, understate, or fail to reveal true changes in distributions (Cade and Noon 2003). 13 In our context, quantile regressions decompos e the mean response re vealed by the linear regression results in Table 2 into changes acro ss the entire probability di stribution of fuel mix levels. In particular, conditional quantile regressions allow us to estimate different slope coefficients for different fuel mix quant iles. For example, a regression on the 50 th percentile of the fossil fuel distribution estimates the effect of disclosure on the sample median of the fossil fuel distribution. A regression on the 20 th percentile of the fossil fuel distribution estimates the effect of disclosure on the 20 th percentile of the dist ribution. If the disclosu re program coefficient is larger for the 20 th percentile than the 50 th percentile of the fossil fuel distribution, results suggest that firms that already use relatively limited amounts of fossil fuels reduce fossil fuels the most in response to disclosure programs. An additional advantage of conditional quantil e regressions in our context relates to censoring. In our data, some observations have proportional clean fuel usage at or near 0 and proportional fossil fuel usage at or near 1. Less commonly, some observations have proportional clean fuel usage at or near 1 and proportional foss il fuel usage at or near 0. Such censoring may bias least squares regres sions, but the weighted l east absolute deviation estimation underlying the quantile regression method minimizes or elimin ates the impact of censoring on the uncensored quantiles. More formally, we consider the linear model for the conditional quantile function, ( | , ) () () () it y it it it it QD D τ ατ δτ τ =+ + X X β for τ between 0 and 1. D it still represents the proportion of firm i’s sales that are subject to an effective disclosure program in period t and the elements of the row vector X it include all of the non-program expl anatory variables. Note that weomit firm-level fixed effects. Including firm-lev el fixed effects in qu antile regressions would yield coefficients that indicate an average firm ’s program responses across the distribution of departures from that individual firm’s typical fuel mix levels. So, the 75 th percentile coefficient in the fossil fuel quantile regression would re present the disclosure response when firms are using a particularly large amount of fossil fuels, re lative to their own typical fossil fuel levels. In contrast, our purpose is to investigate what happens to the overall fuel mix distribution. In other words, we wish to explore if the fuel mix distri bution shifts more strongly for firms that typically use high proportions of fossil fuels. Of course, it is still possible that the program variable D it is statistically endogenous. Therefore, in addition to sta ndard conditional quantile regressi ons, we perform instrumental variable quantile regression. For basic quantile regressions, estimation, variance estimation, and inference follows Koenker and Bassett (1982) a nd Rogers (1993). For instrumental variable quantile regression, we use the implemen tation by Chernozhukov and Hansen (2004) for estimation and inference. Table 4 presents quantile regression results for the impact of disclosure programs on proportional fossil fuel usage. Here, we conduct quantile regressions at the 20 th , 30 th , 40 th , 50 th , and 60 th percentiles because these represent the relevant range for this distri bution. There is little variation below the 20 th percentile, as 15 percent of observa tions reflect proportional fossil fuel usage at or near 0. Similarly, th ere is little variation above the 60 th percentile, as nearly 40 percent of observations reflect fossil fuel usage at or near 1 (100%). Results in Table 4 demonstrate that the di sclosure program point estimates tend to decrease as one moves up the dist ribution of fossil fuel usage. These differences are frequently both statistically and econo mically significant. For example, all matched pair differences except(Q20, Q30) are statistically significant for the standard quantile regressions. Further, the fossil fuel program response at the 20 th percentile is 1.6 (IV QREG poi nt estimates) to 2.2 (standard QREG point estimates) times great er than the response at the 50 th percentile. Results in Table 2 indicated that disclosure program s induce reductions in fossil fuel usage on average. The quantile regression results in Table 4 suggest that it is firms th at already use relati vely limited amounts of fossil fuels that reduce these fuels the mo st in response to disclosure programs. Results for clean fuel respons es to disclosure programs mi rror the fossil fuel results previously presented in Table 4. Point esti mates generally increase as one moves up the distribution of clean fuel usage, suggesting that disclosure pr ograms induce firms that already use substantial amounts of clean fuel to increase clean fuel usage the most. For both fossil fuel and clean fuel responses, care s hould be taken interpreting these results. Technically, the quantile regressions compare differences in the ab sent-disclosure and present-disclosure distributions

. Rank preservation is not gua ranteed. While it is practically quite likely that firms that are at high quantiles of the absent-disclosure distribution also appear at similarly high quantiles of the corresponding present-disclosure distribution, this is not requi red. Thus, quantile regression results strongly suggest, but do not prove, that firms that already use substantial amounts of clean fuels increase clean fuel usage the most in response to disclosure programs. 

### VIII. DISCUSSION & CONCLUSION On the margin, we find a statistically and ec onomically significant im pact of information disclosure programs in the elec tricity industry. We find that mandatory disclosure programs decrease firms’ percentage of generation attributable to fo ssil fuels and increase firms’ percentage of generation attributable to clean fuels like hy droelectric and renewables. As the proportion of the average firm’s sales subject to disclosure requirements in creases 1 percent, theaverage proportion of generation at tributable to fossil fuels drops between 0.06 percentage points and 0.23 percentage points. Further, as the pro portion of the average firm’s sales subject to disclosure increases 1 percent, the average proportion of generati on attributable to clean fuels rises between 0.02 percentage poi nts and 0.27 percentage points. We also find that disclosure program respons es are sensitive to customer composition and pre-exiting fuel mix levels. Firms’ clean fuel program responses become considerably stronger (more positive) as the firm sells to more reside ntial consumers. Fossil fuel program responses become considerably weaker (less negative) as the proportion of sales to residential consumers increases. Further, disclosure program responses differ across the fuel mix distribution. Results suggest that firms that already us e relatively low levels of fossil fuels decrease their fossil fuel percentages the most in response to information disclosure policies. For example, the programinduced decrease in fossil fuel usage is appr oximately 2 times greater for firms generating approximately 38 percent of their energy fr om fossil fuels than for firms generating approximately 83 percent of their energy usage from fossil fuels. The key implication arising from our results is that information disclosure programs that regularly provide easily interpretable informati on can achieve policy goal s. This result holds even when the provided informa tion already exists in the public domain. Other significant policy implications follow from our results. Most notably, information policies may generate unintended consequences. For example, attention should be paid to customer composition when introducing disclosure programs in the electricity industry. When utilities serve high proportions of residential consumers, mandatory informa tion programs may spur pa rticularly significant increases in clean fuel usage. However, in these circumstances, these increases come at the relative expense of nuclear fuel usage and not fo ssil fuel usage. This may be consistent withstakeholder preferences, but it is unlikely to be consistent with air pollution-oriented policy goals. Second, the pre-existing fuel mix results suggest that disclosure programs induce firms that use significant amounts of cl ean fuels to use more clean fu els. In contrast, firms that predominantly use fossil fuels respond relatively weakly to disclosure programs. If the marginal benefits of pollution abatement are larger at high fossil fuel firms than at low fossil fuel firms, disclosure programs may induce in efficient abatement allocations. 14 This paper suggests promising avenues for fu ture research that are beyond the present scope. First, our results indicate that mandato ry information disclosure programs affect the economic incentives and the behavior of utilities. However, assessing the full welfare effects of information-based policies requ ires a differentiation between new generation and ownership changes. Second, our results and anecdotal evid ence suggest that the direct demand theory provides a credible link between mandatory in formation disclosure programs and fuel mix outcomes. For example, sensitivity of program re sponse to customer composition supports direct demand effects. 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Shimshack would like to thank Tufts University’s FRAC for generous fina ncial assistance and the Donald Bren School of Environmental Science and Manage ment for space and support. 1. Major electric utilities are classified as those with annual sales or transmission service that exceeds one of the following: (1) one millio n megawatt hours of tota l annual sales, (2) 100 megawatt hours of annual sales for resale, (3) 500 megawatt hours of gross interchange out, or (4) 500 megawatt hours of wheeling for others (deliveries plus losses). 2. See Khanna (2001) for an excellent ove rview of the literature on non-mandatory environmental policies, including in formation disclosure programs. 3. U.S. Department of Energy, Energy Efficiency and Renewable Energy pages. Retrieved October 2006 from http://www. eere.energy.gov/greenpower/markets/. 4. Here and throughout the paper, we refer to fuels as “clean” if they generate low levels of common air pollutants relative to fossil fuels and are not nuclear . This definition is debatable but consistent with issu ing agencies’ goals that emphasize ai r quality over other environmental or social objectives.5. Ideally, one might also interact disclo sure program variables with deregulation indicators to see if pr ogram responses become stronger in re structured markets. However, since only 2 states implemented disclosure without rest ructuring, such results are quite sensitive to model specification and are not reported. 6. These factors are not literally fixed over the sample period, but practical empirical measures vary little over our sample. For exampl e, data on LCV, ownership, and size exhibit no statistically meaningful variation. Reassuringly, the incl usion or omission of these variables with fixed effects does not affect the signs, significan ce, or approximate magnitudes of any estimated coefficient. 7. Upwind and downwind states are dete rmined by following prevailing westerly, southwesterly, and southerly winds . Pollution transport follows thes e winds fairly closely. See, for example, the Ozone Transport Assessm ent Group (OTAG)’s Map of Ozone Pollution Transport, available online as the Air Qual ity Analysis Workgrou p Results Summary at http://capita.wustl.edu/OTAG/ . 8. For example, Fredriksson and Millimet (2002) find a positive association between states’ environmental policies, and that asso ciation is strongest among neighboring states. Baicker (2005) finds strong spi llover effects for neighboring stat es’ public spending as well. Busch and Jorgens (2005) even find a number of policy convergence mechanisms operating across international political boundaries. 9. Later quantile regressions suggest that slop e parameters are sensitive the distribution of the dependent variables, suggesting the presence of heteroskedasticity. 10. Disaggregated results are available from the authors.11. Indeed, one might expect residential a nd commercial customers to have different preferences even in the absence of disclosure programs. For exam ple, residential customers may be more likely to express green preferences or commercial custom ers may have greater incentives to keep costs down and therefore demand more fossil fuels than residential consumers. Alternatively, commercial consumers may be mo re willing to purchase green power products to appease employees, investors, or their own customers. 12. For the percent of firm’s sales subject to residential consumers, Q25=0.281, Q50=.330, Q75=.374, and Q90=.430. The marginal impacts for the program variable can be calculated as: ∂ y/ ∂ D = δ + R η . For the clean fuel regressions in Table 3, δ = -.285 and η = 1.23. Therefore, the marginal program effect for the clean fuel regression, evaluated at the first quartile of the residential customer vari able, is -.285+1.23(.281) = 0.061. 13. Quantile regressions are increasingly used in economics, especially in areas such as labor, education, health, and environment, wher e heterogeneous responses to covariates have important implications (Cade et al. 1999, Koenker 2005). An es pecially common econometric application is censoring (Koenker 2005). Methodol ogically, quantile regr ession estimates are obtained by minimizing least absolute deviations (errors) with conventional linear programming models. Neat, closed-form solutions are not obtained, but the intu ition is very similar to OLS models that minimize the sum of squared deviations. 14. In a recent meta-analysis of the air pollu tion epidemiology literature, Dominici et al. (2003) found consistent support for upward sloping dose-respons e curves. Ceteris paribus, marginal air pollution damages are greater in th e neighborhood of higher po lluting facilities than in the neighborhood of lower pollu ting facilities. Emissions per KWh are known to increase with fossil fuel usage, ceteris paribus.Abbreviations EPA: Environmental Protection Agency EIA: Energy Information Administration

IREC: Interstate Re newable Energy Council

IV: Instrumental Variables LCV: League of Conservation Voters OLS: Ordinary Least Squares QREG: Quantile Regression

RPS: Renewable Portfolio Standards TRI: Toxic Release InventoryTABLE 1 Mean Fuel Mix Percentage Statistics: Firms s ubject to disclosure vs. Firms not subject to disclosure

Group First Period (Month1) Last Period (Month 108) Difference
Clean Fuels Firms Not Subject to Disclosure During the Sample Period .121 (.049) .113 (.050) -.008 (.004) Firms Subject to Disclosure During the Sample Period .104 (.029) .109 (.031) +.005 (.022) Fossil Fuels Firms Not Subject to Disclosure During the Sample Period .754 (.052) .774 (.053) +.020 (.018) Firms Subject to Disclosure During the Sample Period .701 (.035) .698 (.042) -.003 (.034)TABLE 2 Firm-Level Regression Results: Aggregate

Dependent Variable: Percentage of Fuel Mix Attributable to Fossil Fuels Dependent Variable: Percentage of Fuel Mix Attributable to Clean Fuels Variable Description Linear Regression Instrumental Variables Regression Linear Regression Instrumental Variables Regression Disclosure Program -.057*** (-8.39) -.227** (-2.45) .022*** (4.86) .268*** (3.43) Deregulation .016*** (3.14) .077** (2.33) -.005 (-1.09) -.094*** (-3.21) Season 2 Dummy .004 (1.15) .005 (1.25) .002 (0.51) .001 (0.24) Season3 Dummy .016*** (4.49) .017*** (4.48) -.013*** (-4.10) -.014*** (-3.99) Season4 Dummy -.001 (-0.23) .002 (0.38) -.002 (-0.72) -.006 (-1.56) Year Dummies 8 Year FEs 8 Year FEs 8 Year FEs 8 Year Fes Fixed Effects 144Firm-Level FEs 144Firm-Level Fes 144Firm-Level FEs 144Firm-Level Fes

Notes: The dependent variables are the percentage of fuel mix attr ibutable to the source indicated in the column heading. The key independent variable, the disclosure program variable, ra nges from 0 to 1. It indicates the percentage of firm sales subject to operational disclosure programs. Superscripts *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level. All analyses consist of 14,168 observations from 145 firms over the 108 sample months.TABLE 3 Disclosure & Customer Composition Inst rumental Variable Regression Results

Dependent variables Percent Fossil Fuels Percent Clean Fuels Percent Nuclear
Percent of Sales to Residential -.009 (-0.05) -.672*** (-3.85) .430** (2.53) Disclosure Program -.788*** (-3.15) -.285 (-1.27) .907*** (3.58) Disclosure/Residential Interaction 1.07*** (3.16) 1.23*** (2.90) -1.60*** (-4.34) Deregulation .156*** (2.78) -.027 (-0.65) -.150*** (-2.76) Season Dummies 3 Season Dummies Year Dummies 8 Year Dummies Fixed Effects 144 Firm-Level Fixed Effects

Notes: The dependent variables are the percentage of fuel mix attr ibutable to the source indicated in the column heading. The key independent variable, the disclosure program variable, ra nges from 0 to 1. It indicates the percentage of firm sales subject to operational disclosure programs. Superscripts *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level. Analyses use 13,957 observations from 143 firms over the 108 sample months. 2 firms are missing residential data.TABLE 4 Conditional Quantile Regressions: Fossil Fuels Std. Quantile Regression Instrumental Var. Quantile Regression Q20 Q30 Q40 Q50 Q60 Q20 Q30 Q40 Q50 Q60 Disclosure Program -.323* (-19.9) -.294* (-19.2) -.201* (-13.5) -.149* (-21.1) -.017* (-19.2) -.525* (-9.18) -.985 (-0.04) -.467* (-9.34) -.326* (-4.35) .094 (0.24) Season2 Dummy -.001 (-0.02) .024 (1.60) .023 (1.61) .015 (2.16) .001 (0.01) .003 (0.18) .001 (0.01) .018 (1.64) .002 (0.20) -.001 (-0.02) Season3 Dummy .056* (3.51) .049* (3.33) .041* (2.89) .019* (2.79) .001 (0.01) .040 (2.40) .025 (0.30) .033* (3.22) .002 (0.21) .001 (0.02) Season4 Dummy .001 (0.04) -.001 (-0.01) .012 (0.86) -.010 (-1.47) .001 (0.01) .026 (1.58) .003 (0.03) .017 (1.60) .002 (0.20) -.001 (-0.02) Year Dummies 8 Year Dummies 8 Year Dummies

Notes: The dependent variables are the percentage of fuel mix attr ibutable to the source indicated in the column heading. The key independent variable, the disclosure program variable, ra nges from 0 to 1. It indicates the percentage of firm sales subject to operational disclosure programs. A superscript * indicates statisti cal significance at the 1% level. All analyses consist of 14,168 observations from 145 firms over the 108 sample months. Quantile regressions omit the deregulation variable due to the limited sampling variability near the tails.FIGURE 1 State Generation Disc losure Rules: 2005

D.C.

State has a Disclosure RuleFigure 1 Caption

Source: Database of State Incentives for Renewable Energy (DSIRE). Accessible online at http://www.dsireusa.org/.