Forthcoming in Economic Inquiry

Information Disclosure Policies: Evidence from the Electricity Industry 

Magali Delmas, 

Maria J. Montes-Sancho, 

& Jay P. Shimshack*

RHR: Delmas, Montes, & Shimshack: Information Policies 
JEL D83, Q58, D21 

Information Disclosure Policies: Evidence 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 fuels decreases and the proportion of clean fuels increases in 

response to disclosure programs. However, the programs may produce unintended consequences. 

For example, programs may make “clean” firms cleaner while leaving “dirty” firms relatively 

unchanged. If the marginal benefits of pollution abatement are larger at dirty firms than at clean 

firms, disclosure programs may induce inefficient abatement allocations. 

Delmas: Associate Professor, Institute of the Environment, 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 Madrid, Spain. Phone +34-91-624-8639, E-mail 

mmontes@emp.uc3m.es 

Shimshack: Assistant Professor, Department of Economics, Tulane University, New Orleans, LA 

70118. Phone 1-504-865-5321, Fax 1-504-865-5869, E-mail jshimsha@tulane.edu 
I. 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, tradable 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 mitigation strategy. Here, government regulation is 

replaced or augmented by publicly provided information presumed to assist more cost effective 

private market and legal forces. Common examples include the toxics release inventory, lead 

paint disclosures, drinking water quality notices, and eco-labels. The empirical effects of such 

programs, however, remain largely undetermined. This paper examines the impact of a 

prominent mandatory disclosure program on the fuel mix percentages of large electric utility 

corporations. 

The prominence of mandatory information policies is not restricted to environmental 

arenas. For example, developed countries’ equity markets generally require firm-level financial 

information provision. In many countries, agricultural goods require country of origin and other 

health labels. Domestic colleges and universities are required by law to inform current and 

prospective students of crime statistics, equity data, and performance metrics. Even significant 

medical errors must now be disclosed to the community. 

There are several potential advantages of information provision policies, and theory 

suggests that disclosure programs may effectively achieve their goals. Healy and Palepu (2001) 

provided a survey of the evidence in capital markets. Brouhle and Khanna (2007) demonstrated 

that information provision can improve product quality. In the environmental area, Kennedy et 

al. (1994), Arora and Gangopadhyay (1999), Maxwell et al. (2000), Kirchoff (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, the empirical effects of disclosure programs 

remain inconclusive. Early studies of securities regulation found mixed results. See Stigler 

(1964), Robbins and Werner (1964), and Benston (1973). A more recent 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 market size and market returns were positively 

influenced by mandatory disclosure programs. In product quality settings, Chipty and Witte 

(1998) established that resource and referral agencies significantly influenced child care prices, 

but had no impact on the quality of care. In contrast, Jin and Leslie (2003) found that mandatory 

hygiene grade cards positively affected restaurant quality and health outcomes.

Studies of environmental performance yielded similarly mixed results. Desvousges, 

Smith, and Rink (1992) found that information-based programs influenced attitudes favorable to 

radon testing, but testing itself only increased when mass media dissemination was coupled with 

community-based implementation programs. Konar and Cohen (1997) and Khanna et al. (1998) 

found that stock movements associated with Toxic Release Inventory (TRI) announcements led 

to increased abatement and reduced emissions. However, Bui (2005) found that the declines in 

emissions after TRI reporting events may have been 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 utility industry. The disclosure programs considered here differ 

significantly from the TRI information programs examined previously. Notably, TRI information 
is not directly provided to stakeholders. Further, TRI information frequently requires expertise to 

process and interpret. These distinctions are important, as the broader literature on information 

policies suggests that transparency programs are most likely to achieve issuing agency goals 

when disseminated information is simple, understandable, standardized, actionable, and designed 

to directly benefit at least some of the disclosers themselves (Weil et al. 2006). 

Environmental disclosure programs in electricity markets are a promising area of 

exploration for the efficacy of information policies 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 preferences. This is not true in much of the broader 

literature. For example, if eco- or organic- labeled products gain market share, it is difficult to 

establish whether consumers are expressing preferences for environmental improvement or 

whether consumers perceive other differences in product quality (like health, safety, and taste). 

Second, electric utilities are among the leading polluters in the United States. For example, about 

40 percent of domestic CO2 and 67 percent of domestic SO2 emissions are attributable to 

electricity generation. Third, electric 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 all firms were not impacted uniformly. 

To what extent did mandatory disclosure laws 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-owned electric utility companies for the period 

1995-2003. We first analyze how firms’ fuel mix percentages respond to mandatory disclosure 

programs. We prevent bias from potential statistical endogeneity using fixed effects and 
instrumental variables. We then explore the detected 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 and 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 fuel 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 stronger (more positive) as the firm proportionately 

serves more residential customers. Firms’ fossil fuel program responses become weaker (less 

negative) as they proportionately 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 significantly increase clean fuel percentages in 

response to disclosure programs. Similarly, firms that already use relatively small amounts of 

fossil fuels most significantly decrease fossil fuel usage in response to disclosure programs. 

II. THE ELECTRIC UTILITY INDUSTRY 

Fuel Mix

In 2004, domestic electricity generation totaled 3,953,407 gigawatt hours. Of total 

generation, 50 percent was attributable to coal, 18 percent was attributable to gas, and 3 percent 

was attributable to oil. Nuclear sources generated 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 wind 
plants represent higher operating expenses than nuclear or fossil fuel, but they are growing 

rapidly. For example, wind power usage 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 pollution discharge statistics to utility 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 that all Minnesota utilities’ customers 

... should have similar access to information.” (Minnesota PUC, 2002) 

The state issued an order requiring regulated utilities to disclose information on fuel mix and air 

emissions to customers. Twice annually, utilities must include a bill insert that contains a pie 

chart depicting the mix of fuel sources, a bar chart of air pollutant emissions, a chart of costs 

associated with different generating sources, and 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 vary. For example, several states’ disclosure 

programs require quarterly (rather than biannual) inserts. 

Figure 1 indicates which states had disclosure programs in 2005. By that year, 25 states 

had adopted generation disclosure rules, and these states represented over 65 percent of the 

United States population. Since consumer preferences may factor into disclosure impacts, 

programs may be particularly meaningful in deregulated states. Indeed, 23 of the 25 state-level 

disclosure programs were enacted in deregulated states, including NY, IL, TX, MI, AZ, NM, and 
much of the mid-Atlantic and northeastern regions. Colorado and Florida instituted mandatory 

disclosure programs without deregulating their industries. 

Other Programs in the Electric Utility Industry

In addition to the mandatory state-level disclosure 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 Energy 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 information, financial statements, supporting schedules, and 

information on environmental investments. In addition, electric utilities are required to provide 

information about their environmental performance to the U.S. Environmental Protection 

Agency (EPA) and the Energy Information Administration (EIA). Although all of the 

aforementioned data is publicly accessible through government databases, users typically must 

have environmental and database expertise to interpret the information. In marked contrast, 

disclosure programs are designed explicitly to produce easily accessible and readily interpretable 

information. 

 Several other policies may also impact firms’ 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 consumers. State and local sales and 

corporate tax credits and exemptions provide financial incentives for green energy generation. 

III. THEORIES LINKING DISCLOSURE AND FUEL MIX OUTCOMES 
Several theories allow for a link between mandatory 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 (indirectly reducing demand). 

Employee turnover and dissatisfaction may result from disclosed poor environmental 

performance (Tietenberg (1998)). Investors may express environmental 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 electricity industry is typically 

already available to highly motivated and trained experts like lawyers, investors, and community 

activists. 

A more compelling theory, then, for the link between information and environmental 

performance in the electricity industry might involve 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 future regulations on the industry unless firms self-regulate. 

Similarly, disclosure programs may increase a reporting firm’s susceptibility to liability under 

legal statutes. Segerson and Miceli (1998) and 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 demand for fuels perceived as environmentally 

unfavorable. Of course, this mechanism requires: (1) that information affects consumer 
awareness, (2) that consumer awareness can translate to changes in demand, and (3) current or 

future consumer choice among electricity products. However, the mechanism does not require 

choice among electricity providers. 

An emerging literature suggests that consumer awareness changes in response to 

environmental information [Desvousges, Smith, and 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 consequences 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) and Shimshack, Ward, and Beatty (2007)]. 

Consumers increasingly have the 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 renewable energy even at a price premium. Thirysix states and over 600 utilities currently offer green power pricing programs where consumers 

can support cleaner energy usage in exchange for an electricity price increase.3

 Further, there are 

dozens of certificate programs (many at the national level) that allow consumers to purchase 

green certificates or green tags that require the replacement of traditional types of energy with 

greener alternatives. These certificates are available whether or not the consumer has direct 

access to green power options from their own provider. Many utilities were very interested in 

disclosure programs when they were being considered since such policies allowed firms to 

“distinguish their price structure, fuel mix, or environmental profile in the eyes of the consumer 

and found mandatory standard labels to be a credible way to do that.” (NCCEI 2002) In other 
words, disclosure programs were perceived as an effective and particularly convincing way to 

price discriminate immediately or in the future. 

Of course, all theories linking 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. While purchasing or building new 

facilities may be required to dramatically alter fuel mix portfolios, relatively small portfolio 

shifts are easily obtainable. First, utilities can alter their capacity utilization. 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 broader information literature and estimate the 

general impact of mandatory information programs. 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 environmental 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 identifiable and interpretable 

measure of environmental performance on disclosure program bill inserts and web postings. 

Second, information disclosure programs are heterogeneous, yet all require generation mix 

information. 
We analyze data from the Energy Information Administration (EIA)’s Annual Electric 

Power Industry Database and the Interstate Renewable 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-based 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 electricity and pollution generation. Further, EIA data (EIA-906 and EIA759) is imputed for smaller companies based upon information 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 firms 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 results of our analysis should be extrapolated to 

smaller firms with a degree of caution. 

 Our sample data are observed at the firm level. Management decisions are centralized and 

disclosure program requirements operate at the firm/product level. Disclosure program 

requirements and generation product definitions cross plant boundaries, 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-program information for all impacted states; the 
first 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 approximately 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 generation sample statistics closely correspond to total 

national proportions for the sample period. 

Additional summary statistics, broken down by disclosure status, are presented in Table 

1. Standard errors appear in parentheses. 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 disclosure increased clean fuel usage and decreased 

fossil fuel usage over the sample period. 

Results in Table 1 are suggestive of disclosure program effects on fuel mix outcomes, but 

the differences between the last period and the first period are only statistically significant at the 

five percent level for the clean fuel response of firms never subject to disclosure. These simple 

summary statistics also do not control for confounding factors that may impact changes in fuel 

mix over time. Consequently, a more complete 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 on 
fuel mix percentages. We first use OLS and IV regression methods to demonstrate that 

disclosure programs significantly reduce the proportion of fossil fuel usage and significantly 

increase the percentage of clean fuel usage. Second, we examine the disclosure response in more 

detail. 

The basic regression model is yit = Ditδ + Xitβ +αi + εit, where i indexes the unit of 

observation (a firm) and t indexes time (months). yit represents the percentage of firm i’s 

generation in period t attributable to the fuel source being analyzed. Dit represents the proportion 

of firm i’s sales that are subject to an effective disclosure program in period t. The elements of 

the vector Xit include all of the non-program explanatory variables discussed below. αi is an 

unobserved time invariant individual effect and εit 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 fossil fuels (including coal, oil, and 

natural gas), to clean fuels (including hydroelectricity and renewables like wind, solar, and 

biomass), or to nuclear power. Our key explanatory 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 all of a firm’s sales are subject to disclosure 

requirements in a given month, this explanatory variable takes a value of 1. If only 80 percent of 

a firm’s electricity sales are subject to disclosure in a given month (such that 20 percent of 

company sales go to states without operational disclosure programs), this variable takes a value 

of 0.80. 

Joskow (1998) noted that restructuring of electricity supply has the potential to 

significantly impact industry fuel mix outcomes. On one hand, competitive retail power markets 
may provide market incentives for electric utilities to offer green power to consumers (Delmas et 

al (2007). On the other hand, competition in restructuring markets 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, the 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 capture unobservable fuel mix determinants and 

several nearly constant fuel mix determinants that have been identified in the previous literature. 

These latter covariates include firm size, age, community political and environmental attributes 

(as often proxied by League of Conservation Voter (LCV) scores), management profiles, average 

regulatory stringency, and ownership type (Delmas et al., 2007). The literature indicates that 

fossil fuel generation percentages decrease with higher LCV scores, decrease with size, increase 

with merger processes, and increase with private ownership.6 Fixed effects also capture 

differences in input and output prices due to factors like distance to fossil fuel markets and statelevel variation in taxation. 

Since plant production varies seasonally, we include 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 percentages are lower in the late summer months as generation of hydro 

and wind is lower during these time periods. Finally, we include annual dummies to account for 

broad trends in prices, technological change, and other factors. 

Consistency Considerations

A potential concern with our key program Dit variable is that it may be statistically 

endogenous. For example, consider the possibility that the likelihood of program adoption is a 
function 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 αi is correlated with the program variable Dit. However, fixed effects prevent bias from 

this type of correlation. In our context, the inclusion of fixed effects prohibits the 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 Dit is correlated with the time variant error 

term εit. 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 statistical endogeneity is instrumental variables. 

Our chosen instrument is the weighted average of program status in states near those states in 

which the particular firm operates. However, since it is possible that emissions from upwind 

states influence program adoption in the state of interest, our instrument eliminates states directly 

upwind and directly downwind from the states in which the particular firm operates.7 

The validity of this instrument 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 operate do not depend directly on the environmental performance of 

firm i in period t. A1 is supported by empirical evidence that consistently reveals important 

spillover effects of policy choices in one state 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 production choices based upon laws that have no 

influence on them or that states where firm i does not operate decide to adopt a program based 
upon the environmental performance of firm i in period t. We later examine the sensitivity of our 

instrumental variable. 

VI. EMPIRICAL ANALYSIS 

Fixed Effects and Instrumental Variables Regressions

Do disclosure programs affect fuel mix percentages on average? Our goal here is to 

investigate the relationship between disclosure programs and firm’s fossil fuel and clean fuel 

usage. Thus, we regressed fuel mix proportion measures 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 efficiency 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-statistics appear in parentheses.9 

Results in Table 2 indicate that the estimated impact of an operational disclosure program 

is negative and significant at the 1 percent level for fossil fuel production. The results are also 

economically significant. As the proportion of the average firm’s sales subject to disclosure 

increases 1 percent, the average proportion of generation attributable to fossil fuels drops 

between 0.06 percentage points (OLS point estimate) 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 the average firm’s sales subject to disclosure 

increases 1 percent, the average proportion of generation attributable to clean fuels increases 
between 0.02 percentage points (OLS point estimate) 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 positively affected the proportion of fuel mix 

attributable to fossil fuels. Results 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 usage 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 negative relationship between disclosure programs and the proportional 

use of coal. Program effects on oil and gas are economically small and statistically insignificant. 

There is a statistically significant positive relationship between disclosure programs and the 

proportional use of both renewable and hydroelectric generation. Results also suggest no 

statistically significant relationship between disclosure programs and the proportional use of 

nuclear electricity generation (categorized 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 Portfolio Standards, mandatory Green Power Initiatives, and tax 

incentives may also impact firms’ fuel mix percentages. Of course, the instrumental variable 

approach should prevent bias unless the instrument is itself correlated with the omitted variable. 
Further, the adoption of RPS, green power, and tax incentive programs does not generally 

closely coincide with the adoption of disclosure across time and omitted time variant factors only 

bias results if they are correlated with disclosure program introductions across both time and 

space. To be complete, however, we tested whether other prominent state-level programs 

targeting utilities’ fuel mixes impacted our key results. Including variables 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 regression 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 results to the chosen instrument. If program 

implementation is more likely when other states’ firms use more fossil fuels and less clean fuels 

than normal, our results understate program impacts. 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 experimented 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 downwind). Further, while we cannot 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 state where firm i does not operate (or influence 

due to drifting pollution) should at least not decide to adopt a program based upon firm i’s past 

environmental performance. We therefore considered additional possible instruments that 

include lagged program status in neighboring states. All tested instruments yield similar (signs, 

significance, and magnitude) coefficient estimates in the first stage and a consistent economic 
story (signs, statistical significance, and economic importance) in the second stage. Our 

presented instrument yields consistently conservative empirical magnitudes. 

Another possible concern is the sharpness of our study’s program variables. 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 already 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 repeated all analyses with program 

variables that reflect the dates the programs were enacted. In general, we find qualitatively 

similar results (in signs and significance) to those reported here, but magnitudes are frequently 

smaller. 

Our program variable is constructed by weighting each firm’s state-level disclosure status 

by the percentage of sales that occur in each state. 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 our program variable with a 0/1 dummy indicating 

whether any of a firm’s sales are subject to disclosure, we find qualitatively similar results. 

Finally, in our analysis we control for the possibility of persistence with fixed effects and 

the possibility of systematic changes in technology with time dummies. However, as a sensitivity 

check, we include an auto-regressive term lagged one year to help control for unobserved 

technology shifts. Including this lagged discharge 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 demonstrate that disclosure programs reduce fossil fuel usage 

and increase clean fuel usage on average. However, it may be informative to explore these 
effects in more detail. Consequently, in this section, 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 disclosure 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, easily interpretable information may be more novel to them. 

Commercial organizations may simply have sufficient incentives and resources to obtain fuel 

mix information without disclosure programs. Further, 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 residential consumers, an interaction of 

the policy variable with the residential variable, fixed effects, and other covariates. More 

formally, we consider the regression model yit = Ditδ + Ritγ + DitRit η + Xitβ +αi + εit. Dit still 

represents the proportion of firm i’s sales that are subject to an effective disclosure program in 

period t, Rit represents the proportion of firm i’s sales going to residential customers, and the 

elements of the row vector Xit include all of the non-program explanatory variables.

Since both the program variable Dit and its interaction DitRit may be statistically 

endogenous, we again employ instrumental variables regressions. One instrument remains the 

same. Our second instrument is the interaction of the first with the residential variable. Since this 
interaction in not a linear combination of the first instrument, it is as valid as the primary 

instrument itself. Results for aggregate categories are presented in Table 3. Computed standard 

errors are heteroskedastic-consistent. T-statistics appear in parentheses. 

Table 3 coefficients on the un-interacted residential variable indicate that, in the absence 

of any disclosure program, an increase in sales to residential customers increases the proportion 

of fuel mix attributable to nuclear energy and decreases the proportion attributable to clean fuels. 

Coefficients on the un-interacted disclosure program variable indicate that when firms sell to no 

residential customers, programs reduce the proportion 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 coefficients conditioned on zeroed variables. 

The interaction results in Table 3 indicate that the impact of disclosure 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 proportionately serves more residential customers, 

clean fuel program responses become stronger (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 customers, 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 residential customer variable, the marginal impact of disclosure on 

clean fuel usage is +0.061.12 At the median of this variable, the marginal impact of disclosure is 

+0.121, at the third quartile, the marginal impact of disclosure is +0.175, and at the 90th
percentile, 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 fuel usage is -0.487 at the first quartile of the 

residential variable, -0.435 at the median, -0.388 at the third quartile, and -0.328 at the 90th

percentile. Therefore, fossil fuel program response 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 quartile, and +0.220 at the 90th percentile. Nuclear fuel program response 

becomes weaker (less positive) as a firm proportionately 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 programs depends upon firms’ pre-existing fuel mix 

portfolios. For example, do disclosure programs impact high fossil fuel firms the same way they 

impact low fossil fuel firms? To explore this question, we use Koenker and Bassett (1978)’s 

conditional quantile regressions (QREGs). 

Quantile regressions estimate functional relationships between variables for various 

points on the probability distribution of the dependent variable. 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 distribution. Typically, linear models estimate the 

response of the mean or expected value of some dependent variable to one or more independent 

variables. Quantile models estimate the response of the median (or the 90th percentile or the 20th 

percentile, etc.) to independent variables. In contexts with heterogeneous responses across the 
distribution 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 decompose the mean response revealed by the linear 

regression results in Table 2 into changes across the entire probability distribution of fuel mix 

levels. In particular, conditional quantile regressions allow us to estimate different slope 

coefficients for different fuel mix quantiles. For example, a regression on the 50th percentile of 

the fossil fuel distribution estimates the effect of disclosure on the sample median of the fossil 

fuel distribution. A regression on the 20th percentile of the fossil fuel distribution estimates the 

effect of disclosure on the 20th percentile of the distribution. If the disclosure program coefficient 

is larger for the 20th percentile than the 50th 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 quantile 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 fossil fuel usage at or near 0. Such censoring may 

bias least squares regressions, but the weighted least absolute deviation estimation underlying the 

quantile regression method minimizes or eliminates the impact of censoring on the uncensored 

quantiles. 

More formally, we consider the linear model for the conditional quantile function, 

( | , ) () () () it QD D y it it it it τ X X =+ + ατ δτ τ β for τ between 0 and 1. Dit 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 Xit include all of the non-program explanatory variables. Note that we 
omit firm-level fixed effects. Including firm-level fixed effects in quantile 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 75th percentile coefficient 

in the fossil fuel quantile regression would represent the disclosure response when firms are 

using a particularly large amount of fossil fuels, relative 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 distribution shifts more strongly for firms that typically 

use high proportions of fossil fuels. 

Of course, it is still possible that the program variable Dit is statistically endogenous. 

Therefore, in addition to standard conditional quantile regressions, we perform instrumental 

variable quantile regression. For basic quantile regressions, estimation, variance estimation, and 

inference follows Koenker and Bassett (1982) and Rogers (1993). For instrumental variable 

quantile regression, we use the implementation 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 20th, 30th, 40th, 50th, 

and 60th percentiles because these represent the relevant range for this distribution. There is little 

variation below the 20th percentile, as 15 percent of observations reflect proportional fossil fuel 

usage at or near 0. Similarly, there is little variation above the 60th percentile, as nearly 40

percent of observations reflect fossil fuel usage at or near 1 (100%). 

Results in Table 4 demonstrate that the disclosure program point estimates tend to 

decrease as one moves up the distribution of fossil fuel usage. These differences are frequently 

both statistically and economically 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 20th percentile is 1.6 (IV QREG point estimates) to 2.2 (standard 

QREG point estimates) times greater than the response at the 50th percentile. Results in Table 2 

indicated that disclosure programs induce reductions in fossil fuel usage on average. The quantile 

regression results in Table 4 suggest that it is firms that already use relatively limited amounts of 

fossil fuels that reduce these fuels the most in response to disclosure programs. 

Results for clean fuel responses to disclosure programs mirror the fossil fuel results 

previously presented in Table 4. Point estimates generally increase as one moves up the 

distribution of clean fuel usage, suggesting that disclosure programs 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 should be taken interpreting these results. Technically, the quantile 

regressions compare differences in the absent-disclosure and present-disclosure distributions. 

Rank preservation is not guaranteed. 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 required. 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 economically significant impact of information 

disclosure programs in the electricity industry. We find that mandatory disclosure programs 

decrease firms’ percentage of generation attributable to fossil fuels and increase firms’ 

percentage of generation attributable to clean fuels like hydroelectric and renewables. As the 

proportion of the average firm’s sales subject to disclosure requirements increases 1 percent, the 
average proportion of generation attributable to fossil fuels drops between 0.06 percentage points 

and 0.23 percentage points. Further, as the proportion of the average firm’s sales subject to 

disclosure increases 1 percent, the average proportion of generation attributable to clean fuels 

rises between 0.02 percentage points and 0.27 percentage points. 

We also find that disclosure program responses 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 residential 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 use 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 approximately 2 times greater for firms generating 

approximately 38 percent of their energy from 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 information can achieve policy goals. This result holds 

even when the provided information 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 information programs may spur particularly significant 

increases in clean fuel usage. However, in these circumstances, these increases come at the 

relative expense of nuclear fuel usage and not fossil fuel usage. This may be consistent with 
stakeholder 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 clean fuels to use more clean fuels. 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 inefficient abatement allocations.14

This paper suggests promising avenues for future research that are beyond the present 

scope. First, our results indicate that mandatory information disclosure programs affect the 

economic incentives and the behavior of utilities. However, assessing the full welfare effects of 

information-based policies requires a differentiation between new generation and ownership 

changes. Second, our results and anecdotal evidence suggest that the direct demand theory 

provides a credible link between mandatory information disclosure programs and fuel mix 

outcomes. For example, sensitivity of program response to customer composition supports direct 

demand effects. However, a full understanding of the implications of these information-based 

policies requires precise identification of the underlying mechanism or mechanisms. Third, we 

demonstrate that information programs affect fuel mix outcomes. While fuel mix is correlated 

with emissions, gauging the social value of the policies involves an empirical evaluation of 

program-induced impacts on environmental quality. Finally, our results are suggestive for other 

settings. While mandatory provision programs are becoming increasingly common in other 

countries, the extent of policy induced changes may be sensitive to cross-country institutions. 
References 

Arora, S., and S. Gangopadhyay. "Toward a Theoretical Model of Voluntary Overcompliance." 

J. of Economic Behavior and Organization, 28, 1995, 289-309. 

K. Baicker. “The spillover effects of state spending,” J. of Pub. Econ., 89, 2005, 529–544. 

Bennear, L., and S. Olmstead. “The Impacts of the ‘Right to Know’: Information Disclosure and 

the Violation of Drinking Water Standards.” Mimeo, Duke University, 2006. 

Benston, G. “Required disclosure and the Stock Market: An Evaluation of the Securities Market 

Act of 1934.” American Economic Review, 63, 1973, 132-155. 

Blamey, R., Bennett, J., Louviere,J., Morrison, M., and Rolfe, J. “A test of policy labels in 

environmental choice modeling studies.” Ecological Economics, 32(2), 2000, 269-286. 

Brouhle, K. and M. Khanna. “Information and the provision of quality differentiated products.” 

Economic Inquiry, 45(2), 2007, 377-394. 

Bui, L. “Public Disclosure of Private Information As a Tool for Regulating Emissions: Firmlevel Responses by Petroleum Refineries to the Toxics Release Inventory.” Mimeo, 

Brandeis University, 2005. 

Busch, P., and H. Jörgens. “The international sources of policy convergence: explaining the 

spread of environmental policy innovations.” J of Euro Public Policy, 12(5), 2005, 860-

884. 

Cade, B. and B. Noon. “A Gentle Introduction to Quantile Regression for Ecologists.” Frontiers 

in Ecology and the Environment, 1(8), 2003, 412-420. 

Cade, B., J. Terell, and R. Schroeder. “Estimating Effects of Limiting Factors with Regression 

Quantiles.” Ecology, 80, 1999, = 311-323. 
Chernozhukov, V. and C. Hansen. “Instrumental Variable Quantile Regression.” Mimeo, 

Massachusetts Institute of Technology, 2004. 

Chipty, T. and A. Witte. “Effects of Information Provision in a Vertically Differentiated 

Market.” NBER Working Paper, #6493, 1998. 

Delmas, M., Russo, M., and Montes, M. “Deregulation and Environmental Differentiation in the 

Electric Utility Industry.” Strategic Management Journal, 28(2), 2007, 189-209. 

Desvousges, W.H., Smith, V.K., and H.H. Rink. “Communicating Radon Risks Effectively: The 

Maryland Experience.” J. of Public Policy and Marketing, 11(2), 1992, 68-78. 

Dominici, F., L. Sheppard, and M. Clyde. “Health Effects of Air Pollution: A Statistical 

Review.” International Statistical Review, 71(2), 2003, 243-276. 

Edison Electric Institute. EEI's Statistical Yearbook of the Electric Power Industry, August 2005. 

Fredriksson, P. G., and D. L. Millimet. “Strategic interaction and the determinants of 

environmental policy across US states.” Journal of Urban Economics, 51, 2002, 101-22. 

Greenstone, M., P. Oyer, and A. Vissing-Jorensen. “Mandated Disclosure, Stock Returns, and 

the 1964 Securities Act.” Quarterly J. of Economics, 121(2), 2006, 399-460. 

Jin, G. and P. Leslie. "The Effect of Information on Product Quality: Evidence from Restaurant 

Hygiene Grade Cards." Quarterly J. of Economics, 118(2), 2003, 409-51. 

Joskow, P. “Electricity Sectors in Transition.” Energy Journal, 19(2), 1998, 25-52. 

Healy, P. and K. Palepu. “Information Asymmetry, Corporate disclosure, and the capital 

markets: A review of the empirical disclosure literature.” J. of Accounting and Econ. 31, 

2001, 405-440. 

Kennedy, P., B. Laplante, and J. Maxwell. “Pollution Policy: the Role for Publicly Provided 

Information.” J. Environmental Economics and Management, 26, 1994, 31-43. 
Khanna, M. “Non-Mandatory Approaches to Environmental Protection.” J. Economic Surveys,

15(3), 2001, 291-324. 

Khanna, M., W. Quimio, and D. Bojilova. “Toxic Release Information: a policy tool for 

environmental information.” J Enviro. Economics and Management, 36, 1998, 243-266. 

Kirchhoff, S. “Green Business and Blue Angels.” Environ. Resource Econ, 15, 2000, 403-420. 

Koenker, R. Quantile Regression. Cambridge: Cambridge University Press, 2005. 

Koenker, R. and G. Bassett. “Regression Quantiles.” Econometrica, 46, 1978, 33-50. 

Koenker, R. and G. Bassett. “Robust Tests for Heteroskedasticity based on Regression 

Quantiles.” Econometrica, 50, 1982, 43-61. 

Konar, S. and M. Cohen, “Information as Regulation: The Effect of Community Right to Know 

Laws on Toxic Emissions.” J. Environ. Econ. and Management, 32, 1996, 109-124. 

Kotchen, M. “Green Markets and Private Provision of Public Goods.” J. of Political Economy,

114(4), 2006, 816-834. 

Lamarre, L. “Utility customers go for the green.” EPRI Journal, 22(2), 1997, 6–15. 

La Porta, R. and F. Lopez-de-Silanes. “What works in securities laws?” J. of Finance, 61, 2006, 

1-33. 

Leire, C and Thidell, A. “Product-related environmental information to guide consumer 

purchases – a review and analysis of research on perceptions, understanding and use 

among Nordic consumers.” J. of Cleaner Production, 13, 2005, 1061-1070. 

Loureiro. M. “Rethinking new wines: implications of local and environmentally friendly labels.” 

Food Policy, 28, 2003, 547-560. 

Loureiro, M. and Lotade, J. “Do fair trade and eco-labels in coffee wake up the consumer 

conscience?” Ecological Economics, 53(1), 2005, 129-138. 
Maxwell, J., T. Lyon and S. Hackett, "Self-Regulation and Social Welfare: The Political 

Economy of Corporate Environmentalism." J. of Law and Economics, 43, 2000, 583-618. 

Minnesota Public Utilities Commission. Order Clarifying Disclosure Requirements and Setting 

Procedural Schedule. Docket No. E,G-999/CI-00-1343, September 2002. 

National Council on Competition and the Electric Industry. “Electric Product Disclosure: A 

Status Report.” Consumer Information Series, July 2002. 

Robbins, S. and W. Werner. “Professor Stigler Revisited.” J. of Business, 37, 1964, 406-413. 

Rogers, W. “Calculation of Quantile Regression Standard Errors.” Stata Technical Bulletin, 13, 

1993, 18-19. 

Segerson, K. and T. Miceli. “Voluntary Environmental Agreements: Good or Bad News for 

Environmental Protection?” J. of Enviro. Econ. and Manage., 36(2), 1998, 109-130. 

Shimshack, J., M. Ward, and T. Beatty. “Mercury Advisories: Information, Education, and Fish 

Consumption.” J. of Enviro. Econ. and Management, 53, 2007, 158-179. 

Stigler, G. “Public Regulation of the Securities Market.” J. of Business, 37, 1964, 117-142. 

Teisl, M., B. Roe and R. Hicks. “Can Eco-Labels Tune a Market? Evidence from Dolphin-Safe 

Labeling of Canned Tuna.” J. of Enviro. Econ. and Management, 43, 2002, 339 – 59. 

Tietenberg, T. “Disclosure Strategies for Pollution Control.” Environment and Resource 

Economics, 11, 1998, 587-602. 

US Department of Energy, Energy Information Administration. Power Plant Report, 2003. 

Weil, D., A. Fung, M. Graham, and E. Fagotto. “The Effectiveness of Regulatory Disclosure 

Policies.” J. of Policy Analysis and Management, 25(1), 2006, 155-181. 
Footnotes

 * Corresponding author. We wish to thank the journal editor, 2 anonymous referees, and 

seminar participants at the AERE Sessions at the AEA meetings, the University of Maryland, 

Harvard University, Harvard’s Center for Business and Government, Yale University, the Santa 

Barbara Workshop on Environmental Economics, the World Congress of Environmental & 

Resource Economists, the AERE sessions at the AAEA meetings, and the ISNIE conference. 

Special thanks are also due to Timothy Beatty, Trudy Cameron, Meredith Fowlie, Madhu 

Khanna, John Lynham, Erin Mansur, Gib Metcalf, and Kerry Smith. Shimshack would like to 

thank Tufts University’s FRAC for generous financial assistance and the Donald Bren School of 

Environmental Science and Management 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 million megawatt hours of total 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 overview of the literature on non-mandatory 

environmental policies, including information 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 issuing agencies’ goals that emphasize air quality over other environmental 

or social objectives. 
5. Ideally, one might also interact disclosure program variables with deregulation 

indicators to see if program responses become stronger in restructured markets. However, since 

only 2 states implemented disclosure without restructuring, 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 example, data on LCV, ownership, and size exhibit no 

statistically meaningful variation. Reassuringly, the inclusion or omission of these variables with 

fixed effects does not affect the signs, significance, or approximate magnitudes of any estimated 

coefficient. 

7. Upwind and downwind states are determined by following prevailing westerly, 

southwesterly, and southerly winds. Pollution transport follows these winds fairly closely. See, 

for example, the Ozone Transport Assessment Group (OTAG)’s Map of Ozone Pollution 

Transport, available online as the Air Quality Analysis Workgroup 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 association is strongest among neighboring states. 

Baicker (2005) finds strong spillover effects for neighboring states’ 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 slope 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 and commercial customers to have different 

preferences even in the absence of disclosure programs. For example, residential customers may 

be more likely to express green preferences or commercial customers may have greater 

incentives to keep costs down and therefore demand more fossil fuels than residential consumers. 

Alternatively, commercial consumers may be more 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 variable, 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, where heterogeneous responses to covariates have 

important implications (Cade et al. 1999, Koenker 2005). An especially common econometric 

application is censoring (Koenker 2005). Methodologically, quantile regression estimates are 

obtained by minimizing least absolute deviations (errors) with conventional linear programming 

models. Neat, closed-form solutions are not obtained, but the intuition is very similar to OLS 

models that minimize the sum of squared deviations. 

14. In a recent meta-analysis of the air pollution epidemiology literature, Dominici et al. 

(2003) found consistent support for upward sloping dose-response curves. Ceteris paribus, 

marginal air pollution damages are greater in the neighborhood of higher polluting facilities than 

in the neighborhood of lower polluting 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 Renewable Energy Council 

IV: Instrumental Variables 

LCV: League of Conservation Voters 

OLS: Ordinary Least Squares 

QREG: Quantile Regression 

RPS: Renewable Portfolio Standards 

TRI: Toxic Release Inventory 
TABLE 1 

Mean Fuel Mix Percentage Statistics: Firms subject 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 attributable to the source indicated in the column heading. 

The key independent variable, the disclosure program variable, ranges 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 Instrumental 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 attributable to the source indicated in the column heading. 

The key independent variable, the disclosure program variable, ranges 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 attributable to the source indicated in the column heading. 

The key independent variable, the disclosure program variable, ranges from 0 to 1. It indicates the percentage of firm sales 

subject to operational disclosure programs. 

A superscript * indicates statistical 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 Disclosure Rules: 2005 

D.C.

State has a Disclosure Rule
Figure 1 Caption 

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