Week 1 – Assignment 1: Assess Political Decision Making and Week 2 - Assignment: Examine the Impact of Economic Policies on Society

r Academy of Management Journal 2016, Vol. 59, No. 2, 678–704. http://dx.doi.org/10.5465/amj.2013.0661

COMPETITION, REGULATORY POLICY, AND FIRMS’ RESOURCE INVESTMENTS:

THE CASE OF RENEWABLE ENERGY TECHNOLOGIES

CARMEN WEIGELT Tulane University

EKUNDAYO SHITTU The George Washington University

We study the interplay between regulatory mandates and competition on a focal firm’s new resource investments. While prior literature has separately pointed to the influence of competition and regulatory policy on a focal firm’s resource decisions, less is known about how the policy effect interacts with the competitive effect. Studying how regula- tory mandates moderate the effect of competition on a focal firm’s new resource in- vestments, we show that resource redeployment is not simply a function of internal firm decisions but a response to external forces. We find that regulatory mandates dampen the effect of competitors’ new resource investments on a focal firm’s new resource in- vestments. Distinguishing between different clean technology types, we show that this dampening effect is the stronger, the more distant the new resource is from incumbents’ old resource base, and the more established the mandate is. We test our hypotheses in the context of renewable energy investments in waste-to-energy, wind, and solar in the U.S. electricity industry. Our data comprise 1542 utilities and private energy firms and their renewable investments from 1999 to 2010.

INTRODUCTION

Firms adapt to a changing environment by recon- figuring their resource portfolio (Barney, 1991; Lavie, 2006; Penrose, 1959; Sirmon, Hitt, & Ireland, 2007). Prior research highlights resource portfolio reconfiguration as a difficult process because exist- ing capabilities and resource positions often limit a firm’s new resource investment direction (Delmas, Russo, & Montes-Sancho, 2007; Dierickx & Cool, 1989; Kim, 2013). While the effect of existing firm capabilities on resource investments is well docu- mented, the role of external factors, such as compe- tition, market demand, or government policy, is less

highlighted and considered in resource portfolio investment decisions (Amit & Schoemaker, 1993; Black & Boal, 1994).

Research grounded in the resource-based view (RBV) separately points to either policy or competi- tors’ resource strategies as external drivers of a focal firm’s resource investment decisions. On the one hand, prior work shows firms respond to their com- petitors’ actions with their own resource invest- ments to maintain competitive parity, limit rivalry (McGrath, Chen, & MacMillan, 1998; Peteraf, 1993; Wernerfelt & Karnani, 1987), or imitate competi- tors’ actions, especially in uncertain environments (DiMaggio & Powell, 1983; Lieberman & Asaba, 2006). Consistent with these findings, Capron and Chatain (2008) argue firms should take their com- petitors’ resource investments in factor markets into their own resource considerations—what Markman, Gianiodis, and Buchholtz (2009: 423) coin “compe- tition over resource positions”. On the other hand, regulatory policy is less central to the RBV discus- sion. Research, mostly in an international context, points to regulatory policy as a predictor of firms’ new resource investments (e.g., Henisz & Williamson, 1999; Levy & Spiller, 1994). An exception is Berrone, Fosfuri, Gelabert, and Gomez-Mejia (2013) who study

We would like to thank Associate Editor Kyle Mayer and three anonymous reviewers for their invaluable comments and guidance during the review process that helped strengthen this article. We would also like to thank Nandini Lahiri, Haiyang Li, MB Sarkar, Doug Miller, and Jeffrey York for their insightful comments on earlier drafts of this article. Finally, we thank seminar participants at The Ohio State University, at the Atlanta Competitive Advantage Conference, at a 2013 Academy of Management Conference paper session, and at the Sustainability Ethics and Entrepreneurship Conference for their insightful remarks on earlier versions of this article.

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how U.S. state-level regulatory policy impacts envi- ronmental innovation and find that underperforming firmsaremoresensitivetopolicypressures.However, we know little about the interplay between environ- mental policy and competition on a focal firm’s new resource investments.

A better understanding of how external drivers such as competition and policy interact is crucial because resource portfolio reconfiguration entails uncertainty, and thus the risk that a focal firm mis- perceives an environmental change and initiates an inadequate response. Similarly, Capron and Chatain (2008: 100) stress the need to focus on policy aspects more to better understand a firm’s resource position and note that “regulations play a very important role in the process of defining, trading, and allocating resources among firms”.

We posit that in the interplay between regulatory mandates and competition, a regulatory mandate dampens the effect of competitors’ new resource in- vestments on a focal firm’s new resource investments. We capture competitors’ new resource investments as competitors’ efforts to reconfigure their resource portfolio by adding clean technologies, while regu- latory mandates refer to environmental policies that stipulate the adoption of clean technologies. Clean technologies comprise practices, processes, or tools that reduce waste and lower pollution to provide a more sustainable business (Pernick & Wilder, 2007).

We suggest firms pay more attention to the man- date than to competitors’ investments in the pres- ence of regulatory mandates for clean technologies. This may be because as managers of firms scan and interpret their environment to better target their fu- ture value-maximizing resource portfolio, they focus on some external drivers more than on others for direction (Cyert & March, 1963; Daft & Weick, 1984). Our argument is that regulatory mandate and com- petitors’ resource investments both lower uncer- tainty for a focal firm with respect to: (a) the perceived value of a new resource, (b) the function- ing of a new resource, and (c) the expected resource- related response. A better understanding of how regulatory mandates moderate a firm’s response to competitors’ resource investments may help the firm to prioritize its attention to certain external drivers in resource portfolio reconfiguration. Thus, how com- petitors’ entry into new subfields affects a focal firm’s strategic resource choice may depend on the pres- ence of a regulatory mandate.

This study contributes to prior work in the RBV literature that has begun to bring the external envi- ronment into resource allocation and firm resource

investment decisions (e.g., Capron & Chatain, 2008; Delmas et al., 2007; Kim, 2013; Miller & Shamsie, 1999; Sirmon & Hitt, 2009). In this study, we stress that resource redeployment is not simply a func- tion of internal decisions but a response to external forces. Specifically, we focus on the interplay be- tween regulatory mandates and competition, two external forces that have until now been studied separately. By doing so, we emphasize the role of regulatory mandates relative to that of competitors’ resource strategies in new resource investments and show that firms may shift their attention from com- petition to policy in the presence of both external factors. In addition, this study provides a more nu- anced view of how the interplay of competition and regulatory policy varies across different resource types depending on the resource’s technological distance from incumbents’ old resource base. This nuanced view is valuable as it takes into account that the relative influence of policy and competition on firms’ resource investment decisions can vary by resource type, if regulators allow for more than one resource type to satisfy a specific mandate. Thus, our study also has practical implications for policy makers.

The empirical setting focuses on renewable energy investments in the U.S. utility industry. Our sample consists of 1524 U.S. utilities and private energy firms between 1999 and 2010. We find that regula- tory mandates dampen the effect of competitors’ new resource investments on a focal firm’s new resource investments. This dampening effect is the stronger themoredistantthenewresourceisfromincumbents’ old resource base and the more established the man- date is. While we focus on competition and regulatory mandates in this study, we control for other external influencing factors such as market demand (Delmas et al., 2007; Kim, 2013) or social movements (e.g., Pacheco & Dean, 2015; York & Lenox, 2014) in our empirical model.

THEORETICAL FRAMEWORK AND HYPOTHESES

Environmental Change and Incumbent Firm Resource Portfolio Reconfiguration

The RBV holds that a firm’s resource portfolio provides a sustainable competitive advantage if the resources are valuable, rare, inimitable, and non- substitutable (Barney, 1991; Penrose, 1959). To sus- taina competitive advantage, firms needto constantly evaluate and reconfigure their resource portfolio in

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response to market needs, social shifts, or technolog- ical advances (Lavie, 2006; Sirmon et al., 2007). Firms can reconfigure their resource portfolio by adding resources (Barney, 1986; Makadok, 2001), growing resources within the firm (Dierickx & Cool, 1989), combining resources (Helfat & Peteraf, 2003), or substituting old resources with new ones (Sirmon et al., 2007).

Existing resource rigidities and uncertainty as- sociated with new resource investments are two mechanisms that make resource portfolio reconfi- guration challenging (Hill & Rothaermel, 2003; Lavie, 2006). While extant research focuses on re- source rigidities that can result from organizational inertia (Hannan & Freeman, 1984), long-term, irre- versible asset commitments (Dixit & Pindyck, 1994), prior capabilities (Delmas et al., 2007; Kim, 2013), fears to cannibalize rents from existing resources (Gilbert & Newbery, 1982), or outdated mindsets (Tripsas & Gavetti, 2000), we focus on uncertainty which is the other, less studied, mechanism.

Firms may have difficulties predicting ex ante the future resource portfolio that yields competitive ad- vantage because of uncertainty about the future state of the environment (Milliken, 1987) or atechnology’s trajectory (Black & Boal, 1994; Hill & Rothaermel, 2003; Lavie, 2006). Uncertainty about which tech- nology standard and product features will win in the market may result in firms investing in resources whose value they erroneously perceive as high (Hill & Rothaermel, 2003; Lavie, 2006). More generally, Milliken (1987) suggests that managers face un- certainty about the state of the environment, about how an environmental change affects the firm’s op- erations, and about their available response options and their consequences. Milliken (1987: 136) defines environmental uncertainty as the perceived inability to “understand how components of the environment might be changing”, such as, for example, an in- ability to predict future competitive behavior or regulatory policy. Similarly, Miller and Friesen (1983: 222) view environmental uncertainty as “the rate of change of innovation in the industry as well as the uncertainty or unpredictability of the actions of competitor or customer”. To cope with environ- mental uncertainty managers scan their environ- ment for information about a change, interpret its meaning for their organization, and decide on how to respond to the change (Daft & Weick, 1984). For ex- ample, a firm may face uncertainty about a competi- tor’s next move or about whether a piece of legislation will pass. Interpreting environmental change, such as a new technology’s future, is highly

ambiguous and prone to errors and misperceptions (Milliken, 1990).

As a way to reduce perceived environmental un- certainty, a focal firm may use competitors’ new re- source investments as a guide for its own new resource investments. Prior research shows that competitors’ entry in a new technological subfield may prompt a focal firm to also invest (Aragon- Correa & Sharma, 2003; Wernerfelt & Karnani, 1987). Firms may copy competitors’ strategic actions as- suming that their competitors have better knowledge about the appropriate response (DiMaggio & Powell, 1983; Lieberman & Asaba, 2006).

In addition to competitors’ actions, we posit that a regulatory mandate may reduce uncertainty by guiding afirm on the desired resources in its resource portfolio and on the resource reconfiguration re- sponse that the firm should initiate. Regulatory mandates are command-and-control policies that stipulate the use of certain technologies, fuels, pro- cesses, or forbid the use of specific inputs (Whitesell, 2011). A regulatory mandate affects the value of different resources in the firm’s portfolio by elevat- ing the value a desirable new resource can realize and indirectly designating certain existing resources as undesirable (e.g., manufacturing processes that pollute the air)1 (McWilliams, Van Fleet, & Cory, 2002). A regulatory mandate may elevate the value of a desirable resource by creating a factor market for a new resource (Maijoor & van Witteloostuijn, 1996) where firms can trade or acquire desirable resources to meet the mandate. For example, U.S. utilities can satisfy renewable portfolio standards (RPSs), a regu- latory mandate, by either investing in renewable power generation resources or by purchasing re- newable power from firms producing it. Compared to market-based instruments such as tax incentives, rebates, or subsidies that adjust market forces (price) and thus allow for managerial discretion in whether and how much a firm responds to a policy, command- and-control policies exercise pressure on firms to comply, or face penalties (Whitesell, 2011).

Inthefollowing sections, we present arguments for the effect of competitors’ new resource investments on a focal firm’s subsequent new resource invest- ments (Hypothesis 1). We posit that the presence of a regulatory mandate dampens the positive effect of competitors’ new resource investments (Hypothesis

1 Similarly, the absence of policy mandating invest- ments in a new resource may encourage future investments in the existing technology and indirectly devalue new re- source investments.

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2) because the managers of a focal firm tend to shift their attention more toward the regulatory mandate. We discuss how the moderating effect of a regulatory mandate may vary by resource type (Hypothesis 3) and policy strength (Hypothesis 4). We thus consider that resource portfolio reconfiguration may give firms several options. Peteraf and Bergen (2003) consider resources in terms of their functional sim- ilarity (their use) rather than only their type because resources with different underlying knowledge- bases may perform the same functionality—for ex- ample, utilities generating electricity (functionality) using different resource types (e.g., coal, gas, or wind). In this line of reasoning, Peteraf and Bergen (2003) stress the importance of resource sub- stitutability in understanding how competitors’ re- source investments affect the value of a focal firm’s resource portfolio (Barney, 1991; Peteraf, 1993). Peteraf and Bergen’s (2003) argument is also relevant for regulatory mandates that prescribe the use of cer- tain technologies, but may allow for more than one resource type to satisfy the mandate.

The Role of Competitors’ New Resource Investments

We posit that competitors’ new resource invest- ments lower uncertainty associated with new re- source investments for a focal firm. First, competitors’ new resource investments may reduce uncertainty about the market potential of a new resource. The value of a new resource is determined by the market’s willingness to pay for it (Barney, 1991). Prior research suggests that the presence of competitors in a new market segment can indicate the viability of the new segment and that it is safe for a focal firm to join (Kocak & Ozcan, 2013). Competitors’ investments may also indicate that customers deem a resource legitimate (Suchman, 1995).

Second, as competitors invest in new resources, knowledge about the resource accumulates and a focal firm can learn from its competitors’ experi- ence (Lieberman & Asaba, 2006; Terlaak & Gong, 2008). Competitors’ investments thus reduce un- certainty about how a new resource functions as part of a firm’s resource portfolio. For example, wind power is a new resource in a utility’s electricity generation mix. It is an intermittent power source as the turbines only generate electricity when the wind blows. By observing competitors invest in wind resources, a focal firm gains valuable infor- mation on how competitors have integrated wind in their electricity generation mix. This learning

may prompt the focal firm to also make the new resource investment.

Third, competitors’ new resource investments may reduce uncertainty regarding their resource- related strategies. On the one hand, competitors may proactively invest in new resources to establish a differentiation advantage by distinguishing their resource portfolio from that of other firms. For ex- ample, by making green technology investments an electric utility may increase an environmentally- friendly customer base’s willingness to pay (Delmas et al., 2007) or enhance the firm’s corporate reputa- tion (Sharma & Vredenburg, 1998).

On the other hand, competitors may use their proactive new resource investments to raise a focal firm’s costs. Prior work shows that a firm can raise its rivals’ costs and make its own resources more valu- able by reducing its competitors’ resource effec- tiveness or the quantity of resources available to competitors (Capron & Chatain, 2008; Markman et al., 2009; McWilliams et al., 2002). One such resource-related strategy is continuously improving skills in a new resource type that has the same func- tionality as an existing resource that is more widely owned by competitors or that renders a competitor- owned resource obsolete (Markman et al., 2009). If competitors gain a substantial lead in anew resource, the value of a focal firm’s “old resources” may de- cline (Lavie, 2006) and the opportunity cost of waiting to invest increases (Pacheco-de-Almeida, Henderson, & Cool, 2008).

Firms may also engage in non-market strategies such as lobbying regulators to raise rivals’ resource costs (Bonardi, Holburn, & Vanden Bergh, 2006; Capron & Chatain, 2008; McWilliams et al., 2002). Armed with new competencies, competitors may seek to cement their resource advantage by lobbying for the passage of regulatory policies that they can meet but their rivals cannot (Fremeth & Shaver, 2014). Competitors’ new resource investments may thus lead to outcomes that increase the value of new resources and raise the cost of operating old resources.

Therefore, to avoid a resource disadvantage at a future point in time, a focal firm may invest in new resources to maintain competitive parity. Prior work observes that under conditions of uncertainty a firm benefits from keeping its resource investments at levels similar to those of its competitors (Henderson & Cool, 2003; Lieberman & Asaba, 2006; Sirmon & Hitt, 2009). This is because investing less than com- petitors in, for example, physical plants can cause a firm to fall behind in terms of plant efficiency or

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emissions reduction. Or, investing more than com- petitors in a new resource can lead to negative out- comes as the focal firm may invest in the “wrong resources” (Sirmon & Hitt, 2009). Furthermore, Fremeth (2009) suggests that as a new technology or environmental practice becomes widespread among firms in the industry, regulatory policy regarding the new practice or technology may follow as regula- tors are more inclined to adopt stringent policies if many of their constituent firms can comply. There- fore, competitors’ new resource investments prompt a focal firm to also invest and reconfigure its resource portfolio accordingly.

Hypothesis 1. Competitors’ new resource in- vestments have a positive effect on a focal firm’s subsequent new resource investment.

The Role of Regulatory Mandates in Dampening the Competitive Effect

Regulatory mandates in our study reflect envi- ronmental regulation which “is one of the major sets of social regulations enacted by governments on businesses” (Nehrt, 1998: 78). They play an impor- tant role in the context of environmental sustain- ability by promoting clean technologies in a firm’s resource portfolio. For example, RPSs set the target quantity of resources in a firm’s portfolio that need to be clean electricity generation. However, RPS man- dates do not specify whether utilities invest in their own renewable energy generation assets or purchase renewable power from others to fulfill the mandate (Fabrizio, 2012). Therefore firms have a choice as to how they satisfy the mandate: by investing in re- newables or by purchasing renewable power. RPS mandates2 thus create a factor market and demand for renewable resources.

A regulatory mandate lowers environmental un- certainty as to the government’s position3 on a new resource and creates legitimacy for that resource (Scott, 1995). Knowing the conditions of a regulatory mandate may facilitate a focal firm’s cost/benefit analysis for a new technology investment. Further- more, a regulatory mandate lowers a focal firm’s

response uncertainty as to whether it should invest in the new technology. Prior work on environmental sustainability explains how regulatory mandates exercise pressure on firms to adopt sustainable practices by institutionalizing socially acceptable behavior in the form of rules, standards, and controls (e.g., Bansal, 2005; Delmas & Toffel, 2008; Delmas & Montes-Sancho, 2010). Thus, the presence of a regu- latory mandate may lower environmental uncer- tainty and guide a firm’s response to environmental uncertainty in ways seemingly similar to observing and copying competitors’ actions: should the firm wait or invest in the new technology (response un- certainty) or is the new technology viable in the in- dustry (state uncertainty)?

Since managers face a variety of issues that require their attention, they are likely to focus on some issues more than others, and may even ignore some (Cyert & March, 1963; Daft & Weick, 1984; Dutton & Webster, 1988). Prior work points to a substitution effect be- tween two environmental factors that provide firms with similar information. For example, Pacheco and Dean (2015), studying wind power adoption among utilities, hypothesize that competitors’ wind power adoptions substitute for social movement effects on a focal utility’s response. Similarly, Peng (2003) sug- gests that the strength of formal institutions weakens the effect of informal links on firm strategies.

We propose that regulatory mandates lower the positive effect of competitors’ new resource invest- ments on a focal firm’s subsequent new resource investments. That is, incumbent firms seeking to reduce environmental uncertainty about resource investment decisions pay more attention to regula- tory mandates than their competitors. First, since a specific regulatory mandate is likely to apply not only to the focal firm but also to its competitors, a focal firm’s managers may economize on environ- mental scanning and analysis costs by focusing more on the regulatory mandate. The presence of a regu- latory mandate shifts the focal firm’s environment from one of more managerial discretion to one of more command and control policy that often advo- cates specific technologies, processes, and standards (Aragon-Correa & Sharma, 2003). The regulator’s ability to enforce compliance and impose penalties, fines, and sanctions on firms that do not comply (Short & Toffel, 2010) may direct a focal firm’s new resource investments to a greater extent than com- petitors’ new resource investments do. A focal firm may focus on appeasing regulators to avoid govern- ment scrutiny for non-compliance or negative pub- licity (Short & Toffel, 2008).

2 As of 2010 a total of 29 U.S. states had RPS mandates postulating that utilities have a certain minimum per- centage of renewable resources in their energy generation portfolio.

3 However, the passing of a regulatory mandate does not fully eliminate uncertainty as political processes in gov- ernment or regulatory bodies can lead to the repeal of or amendments to a regulatory policy (Fabrizio, 2012).

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Second, although a focal firm benefits from the in- formation that competitors’ actions contain in un- certain environments, it is possible that competitors misperceive a change which can result in the focal firm investing in the “wrong” resources (Lieberman & Asaba, 2006). In addition, the focal firm’s managers may erroneously analyze and interpret competitors’ resource-based strategies. Prior work in the RBV stresses that copying another firm’s resource-based strategies properly is difficult due to ambiguity and interdependencies among resources in a firm’s port- folio (Dierickx & Cool, 1989; Lavie, 2006) and the stickiness of resources (Szulanski, 1996). Therefore, focal firm managers may focus less on competitors’ resource investments and more on regulatory man- dates that espouse codified rules and commands, and thus specify appropriate action and behavior.

Hypothesis 2. The presence of a regulatory man- datelowerstheeffect ofcompetitors’ newresource investments on a focal firm’s subsequent new re- source investment.

The Policy Effect and the Distance of the New Resource Investment to the Old Resource Base

We suggest that regulatory mandates lower the competitive effect even more for new, more distant, resources. More distant resources are defined as re- sources whose underlying scientific knowledge is further away from the industry’s established resource base. That is, we suggest that a focal firm pays more attention to regulatory mandates than to its competi- tors as guidance for more distant new resource in- vestments. This proposition is important because different resource types are substitutes and can often fulfill the same functionality (Peteraf & Bergen, 2003), thereby creating equifinality in how a de- sired end state can be reached (Katz & Kahn, 1978). For example, several resource types qualify as clean technologies under the regulatory mandate for re- newables in the U.S. electricity industry. This permits utilities to satisfy the mandate and achieve a more environmentally sustainable electricity generation mix in different ways by adding different resource typessuchaswindorsolar.Thus,regulatorymandates may allow for equifinality in how firms satisfy a pol- icy. While we do not hypothesize about firms’ choices among resources, we suggest that new resources vary in distance of their knowledge base from that of in- cumbents’ established resources. The new resource types may vary in underlying operational processes (e.g., use of combustion and burning processes to

generate electricity),theirresourcemanagement (e.g., forecasting resource use and output), and technology (e.g., a new skill base to operate). As a result, un- certainty increases as the knowledge base of a new resource becomes more distant from a focal firm’s established resource base.

A greater distance in knowledge base between new resources and old resources may entail greater un- certainty regarding a resource’s underlying opera- tional knowledge. Although a focal firm can learn from its competitors (Lieberman & Asaba, 2006; Terlaak & Gong, 2008), it may face greater difficulty to correctly scan, interpret, and absorb knowledge that is more distant by observing competitors’ new resource investments. Prior research in the context of new ventures shows that the more distant a new in- vestment is from a firm’s current operations, the greater the uncertainty for success (Henisz & Delios, 2001) and thus the higherthe discount rate applied to the investment (Hill & Rothaermel, 2003; Pindyck, 1991). Fuentelsaz, Gomez, and Polo (2002) show that greater uncertainty associated with more distant markets may delay investment in these markets as firms have difficulty assessing and understanding the potential economic value that can be realized.

Since it is difficult for managers to pay equal at- tention to all environmental factors, they tend to economize on environmental scanning activities (Daft & Weick, 1984). Firms may look more for guidance from regulatory mandates because learning from competitors is challenging when new resource investments are distant from “what is known”. Reg- ulatory mandates may thus push firms to leapfrog into new resource investments.

Hypothesis 3. The more distant a new resource is from incumbent firms’ established resource base, the stronger is the dampening effect of regulatory mandates on the effect of competi- tors’ new resource investments on a focal firm’s subsequent new resource investment.

Strength of Regulatory Mandate Effect

Prior work shows that uncertainty about the du- ration or potential future repeal of a regulatory mandate causes firms to underinvest in resources that are specific to the policy in order to avoid being stranded with long-lived, irreversible investments that outlast the policy (Fabrizio, 2012; Henisz & Zelner, 2001; Levy & Spiller, 1994). Underinvest- ment is likely if the regulatory mandate permits firms to procure new resources from others that have made

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the investment. A resource is called policy-specific when the value of the resource is greater in the presence of the regulatory policy than in its absence. Resource specificity makes resource redeployment to uses other than its intended use costly (Dierickx & Cool, 1989). Hence, the repeal of a regulatory man- date may cause a firm’s resources to be significantly less valuable post policy repeal than ex ante (Henisz & Zelner, 2001).

As policies become more institutionalized, they become more enduring, socially accepted, and more difficult to change (Oliver, 1992). Similarly, Bonardi, Hillman, and Keim (2005) state that it is more diffi- cult for firms to shape their policy environment as norms become institutionalized and competitor in- terests become entrenched. For example, regulatory mandates for renewable electricity generation vary across states in the U.S.: California’s RPS target is 33% of electricity generated from renewables by 2020, Washington and Kansas have a target of 15% and 20%, respectively, and some states currently have no RPS policies (www.dsireusa.org). In this regard Fremeth (2009) observes in the context of environmental sustainability that as firms begin to meet policy requirements, policy makers are more likely to enact more stringent regulations and less likely to repeal a policy that has become institu- tionalized (Khanna & Anton, 2002). This process is likely to raise demand for the new desirable resource while rendering operation of “old established re- sources”, the undesirable resources, more costly. As a consequence, firms may invest more in the new resource not only to increase regulators’ goodwill toward them for demonstrating the desired behavior but also to reap economic benefits from the new re- source (Delmas et al., 2007; Kim, 2013).

In sum, a regulatory mandate becomes a more re- liable guide for new resource investments as it be- comes more established and uncertainty about its persistence declines. We suggest a strengthening of the policy effect proposed in our second hypothesis. That is, the persistence of the regulatory mandate fortifies the value that a firm can reap from its new resource investments while, at the same time, rein- forcing the substitution effect on the firm’s old re- source base. Therefore:

Hypothesis 4. The more established the pres- ence of a regulatory mandate, the more damp- ened is the competitive effect on a focal firm’s new resource investment.

Overall, we study the interplay between com- petition and regulatory mandate on a focal firm’s

subsequent new resource investments. We posit that a regulatory mandate dampens the effect of competi- tors’ new resource investments (Hypothesis 2), and that this dampening effect is the stronger, the more distant the new resource is from incumbents’ old re- source base (Hypothesis 3) and the more established the mandate is (Hypothesis 4).

METHODS

Industry Context

Our empirical context is the electricity generation segment of the U.S. electricity industry and the per- centage of clean technologies in a firm’s electricity generation mix. “Clean technologies” are fuel sources such as wind and solar where cleanliness is defined relative to coal-fired plants without carbon capture (Linn & Richardson, 2013). The electricity generation segmentfeedsintoelectricitytransmissionoverpower lines followed by distribution where the transmitted electricity is transformed to voltage levels for end use via a vast network of power lines and substations.

This empirical context is suitable for studying the influence of external drivers on a firm’s resource portfolio: First, the electricity generation mix is a stra- tegic choice for utilities (Kim, 2013) and comprises different resources such as fossil fuels (oil, natural gas, or coal), nuclear, conventional hydroelectric capac- ities, and renewables (wind, solar, geothermal, bio- mass, or waste-to-energy). Although fossil fuel sources still make up a large part of many utilities’ electricity generation mix, renewables, other than conventional hydroelectric capacity, have accounted for the largest capacity additions in recent years. Waste-to-energy was the main renewable investment for many utilities prior to 2000. Waste-to-energy plants rely on combus- tion to generate heat and steam from municipal solid waste that is fed into large furnaces and burned to generate heat. By relying on a combustion process, waste-to-energy is, from a technological viewpoint, more similar to electricity generation from fossils than are wind and solar because the latter do not burn a fuel source to generate electricity. Wind energy invest- ments have increased since 1998 (Kim, 2013) and solar investments have become more widespread recently.

Second, the electricity generation segment of the U.S. utilities industry has been deregulated for de- cades and thus has an active competitive landscape. Prior to the Public Utilities Regulatory Policies Act (PURPA) of 1978 that allowed independent power production by private energy firms for the sale of electricity to utilities (Joskow, 1988; Russo, 2001),

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major utilities operated as state monopolies that produced, transmitted, and distributed electricity. Following PURPA, the U.S. electricity industry has seen much deregulation in the past 30-plus years such as, for example, the 1992 Energy Policy Act that allowed utilities and non-utilities to own in- dependent power producers. Thus, deregulation has transformed the U.S. electricity generation segment from a regional monopoly to an industry in which firms have become more heterogeneous, have stra- tegic choices, and can differentiate (Delmas & Tokat, 2005; Delmas et al., 2007). While competition in the distribution segment may still be constrained in some markets, the electricity generation segment faces intense competition nationwide (Fremeth & Shaver, 2014; Galbraith & Wald, 2008).

Third, U.S. electric utilities face variance in envi- ronmental policy across states. An RPS stipulates that utilities procure a certain percentage of their elec- tricity from renewable sources. Firms can satisfy RPS mandates by either owning or purchasing re- newable power4 because the mandates do not re- quire utilities to own renewable generation assets but allow utilities to purchase electricity that other firms generated from renewable sources. As of 2010, only 29 U.S. states had RPS mandates, enabling us to test the condition of presence versus absence of the mandate. RPS mandates vary across states in terms of their minimum percentage of renewables in a utility’s electricity portfolio, the target date for reaching that percentage, margins of annual in- creases toward the target year, the starting year, and resource eligibilities, i.e., whether renewable energy certificates (RECs) from other states are allowed to satisfy requirements (Levin, Thomas, & Lee, 2011; Vachon & Menz, 2006) (see Figure 1). The fine for noncompliance usually equals or exceeds the cost of

compliance, e.g., the monetary fine may equal the cost of building renewable generation units or pro- curing renewable energy at market rates.

Data Description

We collected data from the Platts Database of World Electric Power Producers, the U.S. Depart- ment of Energy (DOE) website for the Database of State Incentives for Renewables & Efficiency (DSIRE), and the U.S. Energy Information Administration (EIA) website for the State Energy Data System (SEDS), and the Environmental Protection Agency (EPA) website for the Acid Rain Program (ARP) for state level emis- sions. The Platts Database on North America contains information on all power producers in the U.S., their total megawatt (MW) operational, fuel type, and gen- eratingunits.Weobtaineddatafortheyears1999–2010 from this database for all private energy firms, investor- ownedutilities(IOUs), andgovernment utilities.5 Since the Platts Database has information at the generating unit level for each firm, we summed the information across plants containing multiple generating units and then across a firm’s plants at the state level. As a result we obtained information on each firm’s total MW op- erational at the state level for different fuel types: coal, coke, gas, coal gas (cgas), water, uranium, sun, wind, geothermal, biomass, landfill gas, and wood.

Our unit of analysis is the firm at the state level over 11years.Sincewelaggedtheindependentvariablesby one year, all firms in our sample required at least two data points. We only included firms with operational facilities in the sample providing us with 1542 firms and 10518 firm-year observations. Our sample con- tains 188 IOUs, 848 private energy firms, and 517 government utilities. We use the firm at the state level asunitofanalysisbecauseregulatorymandatessuchas RPSsareenactedat thestatelevel.RPSpoliciesvaryby stateand somestatesdonot have RPSpoliciesin place. These latter states provide a natural control group.

Dependent Variables

We calculate the dependent variable using the Platts Database of World Electric Power Producers.

4 A regulatory mandate (here RPS) may implicitly sub- sidize renewables, while implicitly taxing the operation of polluting plants. This is because a firm investing in re- newables can sell its proceeds (RECs) in the factor market where firms focused on fossil fuel sources may have to buy RECs. Thus, firms operating coal-fired or gas-fired plants not only incur operating costs for their plants, but also need to buy RECs to fulfill the RPS mandate. RECs are certifi- cates generated along with each MW of renewable power and salable in the factor market to firms with existing resource endowments lacking their own renewables in- vestment to satisfy the mandate. Since mandates set the quantity of a desired resource investment, not price, some uncertainty remains about the exact price a firm may gar- ner in the factor market. In contrast, subsidies and taxes set price not quantity (Linn & Richardson, 2013).

5 We coded the following firms as private energy firms: private power developer/producers and energy companies such as ESCO, Energy Operating Services (unreg) and en- ergy trading. Government utilities include government utilities, cooperatives and municipals, and government- owned utility projects at regional, state, and national levels.

2016 685Weigelt and Shittu

A firm’s new resource investment is the percentage of its MW operational from renewable resources relative to its total MW operational at time t11. Renewables comprise electricity generation from wind, sun, bio- mass, geothermal, and landfill gas (Delmas & Montes- Sancho, 2011; Delmas et al., 2007; Kim, 2013). We exclude hydroelectric power from our dependent variable because hydroelectric power does not qualify toward meeting RPS targets in many states (Yin & Powers, 2010). This is because RPS mandates give priority to more recent and new technologies thus excluding most hydro facilities that were built de- cades ago from counting toward the RPS requirement. Hydroelectric power has been in operation in the U.S. since 1882 (Kim, 2013).

%RENEWABLES–MWi;s;t 1 1

5 RENEWABLES–MWi;s;t 1 1

TOT–MWi;s;t 1 1

where i is the firm, s the state, and t the time in years. Since renewables such as wind, solar, biomass,

geothermal, and landfill gas (waste-to-energy) all qualify toward a utility’s RPS requirement, prior re- search often operationalizes renewable investments

in aggregate rather than by renewable type (e.g., Delmas & Montes-Sancho, 2011; Delmas et al., 2007; Fabrizio, 2012; Kim, 2013). Waste-to-energy and wind power are the most widely adopted renewables by firms in the U.S. electricity industry; solar power is a distant third. We found biomass and geothermal power activities sparse with less than one percent of firms in the sample being active in these two re- newables, respectively, and only five states had utilities using biomass and/or geothermal in 2010. We therefore focus on analyses that break out waste- to-energy (present in 45 states in 2010), wind (pres- ent in 36 states in 2010), and solar (present in 34 states in 2010) separately. We create three additional dependent variables to test our hypotheses by re- newable resource type where the value is 1 if a focal firm has invested in the respective renewable (waste- to-energy, wind, or solar) and zero otherwise.

Independent Variables

All independent variables are lagged by one time period and the data source is the Platts Database of World Electric Power Producers unless otherwise stated.

FIGURE 1 States with Renewable Portfolio Standards

WA: 15% x 2020*

OR: 25% x 2025* (large utilities)* 5% – 10% x 2025* (smaller utilities)

NV: 25% x 2025*

MT: 15% x 2015 ND: 10% x 2015

SD: 10% x 2015

CA: 33% x 2020 UT: 20% by 2025*

CO: 30% by 2020 (IOUs) 10% by 2020 (co-ops & large munis)*

AZ: 15% x 2025

NM: 20% x 2020 (IOUs)

KS: 20% x 2020

10% x 2020 (co-ops)

Renewable portfolio standard

Renewable portfolio goal

Solar water heating eligible

HI: 40% x 2030

TX: 5880 MW x 2015*

NMI: 80% by 2015 Guam: 25% x 2035 USVI: 30% x 2025

U.S. Territories

OK: 15% x 2015

IN: 10% x 2025†

WV: 25% x 2025*†

NY: 29% x 2015

OH: 12.5% x 2024

Minimum solar or customer-sited requirement

Extra credit for solar or customer-sited renewables

Includes non-renewable alternative resources

Twenty-nine states + Washington DC and two territories have Renewable Portfolio Standards (eight states and two territories have renewable portfolio goals).

ME: 30% x 2000 New RE: 10 x 2017

New RE: 15% x 2020

NH: 24.8% x 2025

RI: 16% x 2020

CT: 27% x 2020

MD: 20% x 2022

NC: 12.5% x 2021 (IOUs) 10% x 2018 (co-ops & munis)

+ 4.1% solar x 2028†

VT: (1) RE meets any increase in retail sales x 2012;

(2) 20% RE & CHP x 2017

NJ: 20.38% RE x 2021†

DE: 25% x 2026*

DC: 20% x 2020

(+1% annually thereafter)

PA: ~18% x 2021†

MA: 22.1% x 2020

VA: 15% x 2025*

IA: 105 MW

IL: 25% x 2025

MN: 25% x 2025

MI: 10% & 1,100 MW x 2015*

WI: Varies by utility; ~10% x 2015 statewide

(Xcel: 30% x 2020)

MO: 15% x 2021

PR: 20% x 2035

Source: Department of Energy DSIRE Website (http://www.dsireusa.org/summarymaps), March 2013. Used courtesy of North Carolina State University, Raleigh, North Carolina. x 5 by; munis 5 municipalities.

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Competitors’ new resource investments. Com- peting firms in a focal firm’s state are private energy firms, IOUs, and publicly-owned utilities (i.e., gov- ernment utilities). For these we sum the total elec- tricity generation from renewable resources (wind, sun, biomass, geothermal, and landfill gas) at the state level. We measure competitors’ new resource investments as the percentage of MW electricity generated from renewables to total MW operational at the state level in the following way.

%BRENEWABLES–MWbi;s;t

5

+ b2fIOU;GOV;IPPg

BRENEWABLES–MWbi;s;t

+ b2fIOU;GOV;IPPg

BTOT–MWbs;t

where b is the type, i the firm, s the state, and t the year. The variable captures the extent to which competing firms in the state have already invested in renewables. We also calculate the measure of com- petitors’ new resource investments for wind only, for solar only, and for waste-to-energy (landfill gas) only in the same way. For example, the formula for wind energy investments is

%BWIND–MWbi;s;t 5

+ b2fIOU;GOV;IPPg

BWIND–MWbi;s;t

+ b2fIOU;GOV;IPPg

BTOT–MWbs;t

Regulatory mandate. RPSs assess whether a firm operates in a state with or without an RPS mandate. We use data from the DSIRE that maintains in- formation on state-level RPS mandates. The DSIRE database gives the enactment date of the policy and the year in which the RPS took effect. Consistent with prior research in the management literature (e.g., Delmas et al., 2007; Fabrizio, 2012; Pacheco & Dean, 2015) and the energy policy literature6

(e.g., Fischer & Newell, 2008; Lyon & Yin, 2010; Menz & Vachon, 2006), we measure RPS as a dummy

variable that is 1 for states that have an RPS mandate in a respective year and zero otherwise. We control for the number of years that a RPS mandate has been in place by using a count variable with a zero for states without an RPS mandate in a respective year. RPS yearly goals capture the minimum targeted percentage of renewables in a utility’s electricity portfolio mix in a respective state with an RPS mandate (see Figure 1). RPS yearly goals thus capture the extent to which a mandate has become estab- lished in a state where higher yearly goals reflect a greater commitment to renewables. States raise their RPS yearly goals over time as utilities as a group meet prior RPS targets.

Control Variables

We include firm- and state-level controls. Firm characteristics affect a firm’s susceptibility to regu- latory and competitive pressures (Bansal, 2005). We control for firm size with the log of total MW opera- tional (Welch, Mazur, & Bretschneider, 2000). We control for a firm’s existing resource stock. A firm’s endowment in existing technology is measured as the percentage of electricity generation from fossil units to total units operational (Delmas & Montes-Sancho, 2010). Fossils comprise coal, gas, cgas, coke, and oil. Greater electricity generation from fossils is likely associated with stronger capabilities, irreversible commitment, and sunk costs in operating fossil plants, and thus proxies for a firm’s potential to ex- hibit inertia.

%FOSSIL–unitsi;s;t 5 FOSSIL–unitsi;s;t TOT–unitsi;s;t

where i is the firm, s the state, and t the time in years. We control for a firm’s electricity generation mix besides fossils: hydroelectric power is a dummy variable that is 1 for firms that have hydroelectric power in their electricity generation mix and zero otherwise. Nuclear power is a dummy variable that is 1 for firms that have nuclear power in their electricity generation mix and zero otherwise. Since both hy- droelectric and nuclear power sources do not emit, firms with one or both of these power sources may face less pressure to invest in renewable resources. We control for whether a firm had renewable re- sources in its electricity portfolio in the prior year. The variable prior renewables is a dummy variable (Delmas et al., 2007; Kim, 2013).

We control for the average age of a firm’s opera- tional units which may affect the firm’s willingness to invest in new technology. Each plant has several

6 The energy policy literature is inconclusive as to the effectiveness of RPS mandates in promoting renewable investments (Delmas & Montes-Sancho, 2011; Fabrizio, 2012; Menz & Vachon, 2006). For example, Fischer and Newell (2008) find that RPSs as a mandate to promote the reduction of carbon emissions through investment in re- newable technologies are sub-optimal and rather symbolic whereas Yin and Powers (2010) suggest that these policies have a positive effect on renewable energy investment. A reason for these inconclusive findings may be that the effect of an RPS mandate on renewable investments varies by renewable resource type. We test this.

2016 687Weigelt and Shittu

generating units. Older units may be closer to the end of their lifetime and fully depreciated thus making it easier for management to justify new resource in- vestments. Older units may also reflect a firm’s ex- perience with a certain type of electricity generation. We measure average plant age by the average num- ber of years since each generating unit became op- erational (Delmas et al., 2007). We control for the geographic diversification of a firm’s parent corpo- ration by measuring the number of states in which the firm’s parent corporation has operations. A value of 1 indicates that the firm does not have a parent corporation that operates in multiple states. The variable is a count variable.

Prior work suggests that investor-owned utilities are more responsive to renewable policies than publicly-owned utilities (Bonardi et al., 2006; Delmas & Montes-Sancho, 2011). We control for ownership structure by creating two dummy variables for private energy firms and IOUs, respectively, with publicly- owned utilities (i.e., government utilities) as the ref- erence category. Publicly-owned utilities are run by locally elected or appointed officials.

We obtain state-level controls from the EIA website. Electricity demand is the log of electricity consumption at the state level in million kWh. Greater electricity demand may entice firms to grow their generating capacity by adding renewable elec- tricity generating units. Net interstate sales of elec- tricity where 1 represents sales to other states and zero otherwise. The variable captures whether elec- tricity supply exceeds electricity demand in a state. We control at the state-level for the percentage of MW operational by government-owned utilities to total MW operational (government utilities’ share in electricity generation).

We control for Sierra Club membership by calcu- lating per capita Sierra club membership in a state: Sierra Club members in a state divided by the total state population to control for the effect of a green social movement in each state. The variable captures the environmental lobby’s influence on a firm’s renewable investments (Fremeth & Shaver, 2014; Russo, 2003). Prior work shows that the presence of environmental groups has a positive impact on re- newable energy generation (Lyon & Yin, 2010; Sine & Lee, 2009; Vachon & Menz, 2006). Environmental groups mobilize resources and provide information about renewables, thereby helping firms to bridge the knowledge gap regarding new resource (renewables) investments (Berrone et al., 2013; Sine & Lee, 2009). Further, environmental groups exercise normative pressures on firms to invest in renewables because

interest groups can mobilize the media and dissemi- nate information to an otherwise uninformed public on a focal firm’s policy compliance level (Berrone et al., 2013; Bonardi et al., 2006). Doing so likely raises a focal firm’s cost of operation, and hence may prompt the firm to invest in renewables to avoid these costs.

We control for the environmental disposition and political leaning of a state’s population (Fremeth & Shaver, 2014) with Democrats in the state legisla- ture and the presence of a Democratic governor. We obtain information from the U.S. Census Bureau on the number of Democratic and Republican senators and representatives elected to the state legislature and calculate the percentage of Democrats in the legislature. Democratic governor is a dummy with 1 for a Democratic governor and zero otherwise. Re- publicans and Democrats tend to differ in their ideological preferences and voting patterns. Dem- ocrats, on average, are said to be more favorable toward environmental legislation than Republi- cans (Lyon & Yin, 2010; Poole & Rosenthal, 1984). Thus, the political leaning may affect the pressure that utilities face to invest in renewable energy sources.

We control for a state’s natural capital since renewable energy relies on natural resources that are geography specific (Russo, 2003). Sun natural resources are measured as the sun index which captures the amount of direct sunlight received in a state (http://www.neo.ne.gov/statshtml/201. htm). Wind natural resources are measured as the wind energy potential based on the windy land area in a state (National Renewable Energy Lab [NREL], 2010). We logged the variable. Also, RPS mandates may be more likely in states with greater natural capital (Lyon & Yin, 2010; Vachon & Menz, 2006).

To control for corporate tax incentives, rebates, sales tax incentives, and property tax incentives we include state-level financial incentives for renew- ables. We code states for whether or not they offered these incentives on a year-by-year basis where a 1 denotes the offering of the respective financial in- centive and zero otherwise. We add up the number of different financial incentives (rebates, sales tax incentives, corporate tax incentives, and property tax incentives) offered in a state on an annual basis. Financial incentives range from 0 to 4 where a 2, for example, denotes that two financial incentives are offered in a respective state in a respective year (Delmas & Montes-Sancho, 2011). Since the pro- duction tax credit is offered at the federal level

688 AprilAcademy of Management Journal

we add a separate control variable which is a 1 for years in which a production tax credit existed and zero otherwise.

Monopoly power proxies for a utility’s policy influence at the state-level rather than local mu- nicipal level. This variable is the relative size in total MW operational of a focal firm to total MW operational in the state. Monopoly power may also exist if a utility is the only provider of electricity in a market and thus end customers do not have a choice regarding electricity provider. We control for retail market deregulation to capture whether customers in a respective downstream market have electricity provider choice (5 1) or are subject to a regulated end user market (5 0). In a regulated retail market utilities have exclusive rights to provide a designated geographical area with electricity and therefore can more easily predict end user demand. In contrast, in a deregulated retail market utili- ties need to apply more sophisticated demand forecasting tools (Delmas & Tokat, 2005). We follow Kim (2013) in calculating this variable using in- formation from the EIA website.

Model Specification and Estimation

We estimate a random-effects Tobit using annual panel data on 1542 firms from 1999 to 2010. Out of 10518 firm-year observations, 8019 (76%) have values of zero for the dependent variable, implying that a firm did not invest in renewable resources in a specific year. A Tobit is appropriate when the de- pendent variable is continuous and truncated at a lower level (0) and upper level (1). A Tobit esti- mates both the likelihood that the dependent vari- able exceeds the threshold value (here zero for no renewable resource investments in a year) and its value if it does (Greene, 2000). We estimate a random- effects Tobit using STATA to control for unobserved firm and time period effects, as there is not sufficient statistic to condition fixed-effects out of the likeli- hood in a Tobit function.

EðyjX; y > 0Þ 5 Fðb9XsÞb9X 1 swðb9XsÞ Where y is the dependent variable of renewable electricity generation investments and b9X captures the interaction X1X2 between competitors’ new re- source investments (X1) and RPS (X2) for Hypothesis 2 or RPS yearly goals (X3) for Hypothesis 4, re- spectively. The value s is the standard error and es- timated with the model coefficients. The unbalanced panel has 10518 observations for 1542 firms over 11 years and the average number of years for a firm in

the sample is 6.8 years. Our sample does not have a survival bias. We also split the sample by presence/ absence of RPS mandate in a respective year to fur- ther assess the hypotheses.

To test Hypothesis 3 and other hypotheses by each renewable resource type separately, we estimate three sets of random-effect Probit models where 1 equals in- vestments in wind power, solar power, or waste-to- energy,respectively,andzeroequalslackofinvestment.

To ensure robustness of our results we also esti- mated fixed-effects Generalized Least Squares (GLS) regressions and the results were consistent. All in- dependent variables are lagged by one year.

RESULTS

Table 1 presents means, standard deviations, and correlations. A firm’s old resource base negatively correlates with subsequent renewable investments (r 5 2 0.43) in waste-to-energy (r 5 2 0.35), in wind (r 5 2 0.22), and in solar (r 5 2 0.17). Competitors’ new investments (r 5 0.22) in waste-to-energy (r 5 0.31), in wind (r 5 0.10), and in solar (r 5 0.10) positively correlate with a focal firm’s subsequent new resource investments in the respective category. RPS positively correlates with renewable investments (r 5 0.14) in waste-to-energy (r 5 0.11), wind (r 5 0.04), and solar (r 5 0.09). We calculate the variance inflation factors (VIFs) for the variables in our models. A VIF greater than 10 signals harmful multicollinearity (Greene, 2000; Marquardt, 1970). Our VIFs have values ranging from 1.01 to 2.83 with a mean VIF of 1.85 reflecting absence of sizable multicollinearity.

Table 2 shows the random-effects Tobit. The de- pendent variable is the percentage of renewable electricity generation in a firm’s resource portfolio. Tables 3–5 present separate random-effects Probit models for the likelihood that a focal firm invests in waste-to-energy, wind, or solar, respectively. Since both Tobit and Probit are nonlinear, we cannot di- rectly assess the magnitude of the effect, but only the sign and statistical significance from the vari- ables’ coefficient (Bowen, 2012; Hoetker, 2007; Wiersema & Bowen, 2009).

Control Variables Effects

We discuss the control variables in Table 2, Model 1 and compare their effects to those in Tables 3–5, Model 1 that present results by renewable type: waste-to-energy, wind, and solar, respectively. In Table 2 RPS is positive, insignificant, while the years that a state has had an RPS mandate is

2016 689Weigelt and Shittu

positive, significant (b 5 0.004; p , 0.05). This finding implies that how long an RPS mandate has been in place may affect a focal firm’s renewables investments besides only the presence of the man- date. Similarly, the years that RPS has been in place is significant (b 5 0.27; p , 0.05) for wind in- vestments (Table 4, Model 1). Both presence of RPS (b 5 1.09; p , 0.01) and years that RPS has been present (b 5 0.34; p , 0.01) are positive significant for solar investments (Table 5, Model 1), indicating the importance of RPS mandates in promoting solar investments. The RPS effect is not significant for waste-to-energy investments (Table 3, Model 1), maybe because waste-to-energy was the main re- newable for utilities prior to 2000 (Kim, 2013), al- beit waste-to-energy is eligible toward fulfilling the RPS mandate requirements during the time of our study.

We find that firms with hydroelectric power (b 5 2 0.13; p , 0.01), nuclear power (b 5 2 0.05;

p, 0.05),and/oralargepercentageoffossils(b 5 20.32; p , 0.01) tend to invest less in renewables. A reason for this may be that fossils reflect established skills that exhibit inertia, while neither nuclear power nor hydroelectricpowerhasCO2 emissions. Thesefindings aremirroredforwaste-to-energy(Table3). Only fossil is significant for wind (Table 4) and solar (Table 5).

In Table 2, firm size (b 5 0.03; p , 0.01) indicates that firms with more MW power generation are more likely to invest in renewables. Firms with older plants tend to invest less in renewables (b 5 2 0.002; p , 0.01). Regarding utility type and ownership, private energy firms (b 5 0.12; p , 0.01) and IOUs (b 5 0.04; p , 0.10) differ from government utilities in their renewables investment.

At the state level, the percentage of Democrats in the legislature (b 5 0.07; p , 0.05) and a Democratic governor (b 5 2 0.02; p , 0.01) affect overall re- newables investments, while Sierra Club member- ship (b 5 0.01) is insignificant in Model 1, Table 2.

TABLE 1 Measure Characteristics and Correlations

Mean SD Min Max 1 2 3 4 5 6 7 9 10 11 12

1 DV: Investment in renewables 0.05 0.13 0.00 1.00 1.00

2 DV: Investment in waste-to-energy 0.20 0.40 0.00 1.00 0.64 1.00

3 DV: Investment in wind energy 0.04 0.19 0.00 1.00 0.24 0.01 1.00

4 DV: Investment in solar energy 0.03 0.16 0.00 1.00 0.10 0.12 0.18 1.00

5 Old resource endowment (fossil) 0.85 0.25 0.00 1.00 20.43 20.35 20.22 20.17 1.00

6 Hydroelectric power 0.14 0.35 0.00 1.00 20.09 0.00 0.09 0.12 20.63 1.00

7 Nuclear power 0.02 0.15 0.00 1.00 20.04 0.06 0.09 0.23 20.15 0.20 1.00

8 Prior renewables 0.23 0.42 0.00 1.00 0.67 0.84 0.31 0.27 20.49 0.06 0.11

9 Firm size (MW operational) 3.94 2.84 25.30 10.06 0.20 0.41 0.10 0.17 20.26 0.21 0.27 1.00

10 Average plant age 24.15 15.66 0.33 79.33 20.30 20.24 20.01 0.01 20.14 0.39 0.13 0.11 1.00

11 Private energy firm dummy 0.40 0.49 0.00 1.00 0.33 0.27 20.06 20.09 20.08 20.23 20.08 20.01 20.46 1.00

12 IOU dummy 0.14 0.34 0.00 1.00 20.08 0.01 0.11 0.11 20.17 0.31 0.23 0.35 0.30 20.33 1.00

13 Geographic diversification 2.99 4.83 1.00 31.00 0.32 0.32 0.02 20.03 20.15 20.08 20.01 0.29 20.24 0.44 20.11

14 Gov. utilities market share in state 0.23 0.25 0.00 1.00 20.08 20.08 0.08 0.00 0.00 0.08 0.02 20.14 20.01 20.22 0.02

15 Electricity demand in state 11.10 1.08 8.57 12.76 0.06 0.17 20.09 0.08 0.06 20.10 0.06 0.19 20.08 0.23 20.10

16 Net interstate sales of electricity 0.52 0.50 0.00 1.00 0.10 0.17 20.03 0.05 20.06 20.05 0.01 0.07 20.10 0.25 20.05

17 Presence of RPS 0.34 0.47 0.00 1.00 0.14 0.11 0.04 0.09 20.06 20.05 20.03 20.03 20.06 0.22 20.10

18 Years RPS present 0.43 1.18 0.00 9.00 0.13 0.13 20.02 0.07 20.10 20.02 20.03 20.01 20.02 0.24 20.07

19 RPS yearly goals 0.05 0.07 0.00 0.30 0.07 0.11 20.03 0.06 20.09 0.05 20.02 20.05 0.01 0.16 20.06

20 Sierra Club membership 2.39 1.33 0.36 6.73 0.09 0.02 0.04 0.06 20.17 0.08 20.05 20.19 20.09 0.23 20.06

21 Democrats in legislature 0.51 0.13 0.11 0.91 0.18 0.10 0.02 0.03 20.15 20.01 20.02 0.02 20.07 0.31 20.06

22 Democratic governor 0.45 0.50 0.00 1.00 20.06 20.07 20.01 20.03 0.04 20.01 20.01 20.03 0.01 20.02 20.01

23 Retail market deregulation 0.45 0.50 0.00 1.00 0.08 0.11 20.09 0.04 20.03 20.06 20.01 0.01 20.13 0.37 20.12

24 Federal production tax credit 0.54 0.50 0.00 1.00 20.01 20.01 20.01 20.01 0.01 0.00 0.01 0.00 0.00 20.01 0.00

25 Financial incentives (state level) 0.93 0.96 0.00 4.00 0.01 0.03 20.01 20.03 0.05 20.08 20.03 0.02 0.08 20.04 20.06

26 Monopoly power 0.04 0.10 0.00 1.00 0.00 0.16 0.17 0.18 20.18 0.29 0.45 0.52 0.19 20.08 0.40

27 Sun index 0.81 0.26 0.00 1.19 0.00 0.12 20.09 0.04 0.12 20.11 0.05 0.21 0.03 0.07 20.04

28 Wind energy potential 10.18 3.60 20.92 14.46 20.12 20.19 0.08 20.07 0.12 20.02 20.08 20.13 0.12 20.27 0.00

29 Competitors’ renewable investment 0.06 0.05 0.00 0.34 0.22 0.23 0.02 0.13 20.09 20.07 20.05 20.01 20.16 0.24 20.09

30 Competitors’ waste-to-energy investment 0.04 0.04 0.00 0.34 0.26 0.31 20.04 0.11 20.12 20.05 20.03 0.09 20.18 0.27 20.05

31 Competitors’ wind energy investment 0.01 0.03 0.00 0.22 0.03 20.01 0.10 0.05 0.03 20.06 20.05 20.08 0.00 20.04 20.05

32 Competitors’ solar energy investment 0.00 0.00 0.00 0.01 0.06 0.04 20.01 0.10 20.05 0.00 0.00 20.10 20.10 0.22 20.07

690 AprilAcademy of Management Journal

Since Democrats are associated with friendlier vot- ing patterns and ideologies toward renewables than Republicans (Lyon & Yin, 2010; Poole & Rosenthal, 1984), the negative significant effect for Democratic governor is surprising. Tables 3–5 may shed light on this surprising finding. Democratic governor is neg- ative, significant (b 5 2 0.23; p , 0.05) in its effect on waste-to-energy investments (Table 3), but positive, insignificant for wind and solar investments (Tables 4 and 5). Hence, Democratic governors seem to only oppose waste-to-energy plants that emit and hence air pollute, but not wind and solar investments. Sierra Club membership predicts both wind (b 5 0.37; p , 0.05) and solar investments (b 5 0.62; p , 0.01), reflecting the positive effect of environmental interest groups on renewable investments (Lyon & Yin, 2010; Sine & Lee, 2009; Vachon & Menz, 2006). However, Sierra Club is insignificant in predicting waste-to- energyinvestments(Table3),afindingconsistentwith

the Sierra Club’s negative stand toward emitting and polluting new waste-to-energy plants.

Regarding other policy tools, retail market de- regulation (b 5 0.31; p , 0.10) is only significant for waste-to-energy investments. The federal produc- tion tax credit is not significant in Model 1 and fi- nancial incentives at the state level only predict solar investments (b 5 0.36; p , 0.05) (Table 5).

Regarding natural resources, we find that a state’s wind energy potential affects investments in wind (b 5 0.74; p , 0.01) (Table 4), but not investments in waste-to-energy (Table 3) and solar (Table 5). Thus a state’s natural resources affect renewable invest- ments (Russo, 2003).

Hypotheses Tests

Hypothesis 1 predicts a positive competitive effect on a focal firm’s new resource investments. We find

TABLE 1 Continued

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

1.00

20.12 1.00

0.06 20.45 1.00

0.10 20.30 0.47 1.00

0.08 20.28 0.19 0.28 1.00

0.10 20.11 0.20 0.29 0.49 1.00

0.06 0.15 0.21 0.24 0.63 0.78 1.00

0.02 0.04 0.02 0.27 0.23 0.18 0.22 1.00

0.14 20.35 0.11 0.26 0.28 0.33 0.12 0.35 1.00

0.00 20.15 20.01 20.18 0.03 0.01 20.09 0.01 0.02 1.00

0.12 20.38 0.47 0.31 0.29 0.33 0.22 0.29 0.32 0.07 1.00

20.01 0.00 0.00 0.01 20.02 20.03 20.06 0.01 0.01 0.02 0.00 1.00

0.00 20.26 0.07 0.08 0.29 0.27 0.07 20.12 0.26 0.14 0.04 20.04 1.00

0.10 20.01 20.12 20.02 20.07 20.05 20.06 20.06 0.02 20.01 20.06 0.00 20.05 1.00

0.07 20.40 0.63 0.31 0.15 0.13 0.22 20.08 0.01 0.02 0.22 0.00 0.18 0.03 1.00

20.16 0.32 20.14 20.32 0.03 20.06 0.00 20.03 20.25 0.06 20.10 0.00 0.00 20.11 20.10 1.00

0.10 20.08 0.22 0.34 0.48 0.43 0.53 0.35 0.29 20.04 0.23 20.03 0.11 20.07 0.19 20.06 1.00

0.18 20.11 0.09 0.31 0.32 0.38 0.33 0.05 0.22 20.12 0.22 20.01 0.08 0.00 0.07 20.31 0.76 1.00

20.06 20.02 0.11 0.06 0.39 0.15 0.46 0.24 0.11 0.11 20.04 20.05 0.25 20.08 0.17 0.31 0.61 0.03 1.00

20.01 0.02 0.35 0.27 0.23 0.39 0.49 0.56 0.21 20.09 0.30 0.03 20.24 20.09 0.21 0.01 0.50 0.15 0.29 1.00

Note: Correlations greater than |0.02| are significant at p , 0.05.

2016 691Weigelt and Shittu

T A B L E 2

R a n d o m -E ff ec

ts T o b it :D

V 5

F ir m

% o f R en

ew a b le

E le ct ri ci ty

G en

er a ti o n In v es tm

en ts

M o d el

1 M o d el

2 M o d el

3 M o d el

4 M o d el

5 M o d el

6

F u ll m o d el

F u ll m o d el

F u ll m o d el

N o R P S

R P S

R P S

C o n st an

t 2 0 .1 0 3 (0 .0 7 8 )

2 0 .0 8 6 (0 .0 7 7 )

2 0 .3 3 9 * * * (0 .0 7 6 )

2 0 .1 5 0 * (0 .0 8 9 )

0 .3 6 9 * * (0 .1 4 2 )

0 .5 7 5 * * * (0 .1 9 1 )

C o m p et it o rs ’ n ew

re so u rc e

in v es tm

en ts

(a ll re n ew

ab le s)

(H y p o th es is

1 )

0 .2 7 4 * * * (0 .0 5 9 )

0 .4 4 9 * * * (0 .0 7 9 )

0 .5 7 9 * * * (0 .0 8 1 )

0 .1 2 0 (0 .0 9 3 )

0 .1 6 7 (0 .1 8 4 )

P re se n ce

of R P S (r eg u la to ry

p ol ic y)

0 .0 0 6 (0 .0 0 6 )

2 0 .0 0 1 (0 .0 0 6 )

0 .0 0 3 (0 .0 0 6 )

Y ea rs

R P S p re se n t

0 .0 0 4 * * (0 .0 0 2 )

0 .0 0 2 (0 .0 0 2 )

0 .0 0 3 * (0 .0 0 2 )

0 .0 0 1 (0 .0 0 3 )

2 0 .0 0 6 (0 .0 0 6 )

R P S y ea rl y go

al s (i f R P S )

0 .2 0 1 (0 .1 7 8 )

P re se n ce

o f R P S X C o m p et it o rs ’

n ew

re so u rc e in v es tm

en ts

(H y p o th es is

2 )

2 0 .2 9 9 * * * (0 .0 8 9 )

R P S y ea rl y go

al s X

0 .5 6 1 (1 .8 7 2 )

C o m p et it o rs ’ n ew

re so u rc e

in v es tm

en ts

(H y p o th es is

4 )

O ld

re so u rc e en

d o w m en

t (f o ss il )

2 0 .3 1 8 * * * (0 .0 1 9 )

2 0 .3 1 2 * * * (0 .0 1 9 )

2 0 .3 1 3 * * * (0 .0 1 9 )

2 0 .2 7 9 * * * (0 .0 2 3 )

2 0 .3 9 4 * * * (0 .0 3 0 )

2 0 .3 5 9 * * * (0 .0 4 1 )

H y d ro el ec tr ic

p o w er

2 0 .1 3 2 * * * (0 .0 1 5 )

2 0 .1 2 3 * * * (0 .0 1 5 )

2 0 .1 2 6 * * * (0 .0 1 5 )

2 0 .1 1 7 * * * (0 .0 1 9 )

2 0 .1 3 3 * * * (0 .0 2 1 )

2 0 .1 5 8 * * * (0 .0 3 0 )

N u cl ea r p o w er

2 0 .0 4 6 * * (0 .0 2 3 )

2 0 .0 4 6 * * (0 .0 2 2 )

2 0 .0 4 5 * * (0 .0 2 2 )

2 0 .0 2 2 (0 .0 2 8 )

2 0 .0 7 6 * * (0 .0 3 5 )

2 0 .1 4 4 * (0 .0 7 9 )

P ri o r re n ew

ab le s

0 .1 7 8 * * * (0 .0 0 8 )

0 .1 7 1 * * * (0 .0 0 8 )

0 .1 6 9 * * * (0 .0 0 8 )

0 .1 4 5 * * * (0 .0 1 0 )

0 .1 6 2 * * * (0 .0 1 5 )

0 .2 5 5 * * * (0 .0 2 3 )

F ir m

si ze

(M W

o p er at io n al )

0 .0 3 1 * * * (0 .0 0 3 )

0 .0 3 1 * * * (0 .0 0 3 )

0 .0 3 1 * * * (0 .0 0 3 )

0 .0 2 9 * * * (0 .0 0 4 )

0 .0 2 3 * * * (0 .0 0 4 )

0 .0 2 1 * * * (0 .0 0 5 )

A v er ag e p la n t ag e

2 0 .0 0 2 * * * (0 .0 0 0 )

2 0 .0 0 2 * * * (0 .0 0 0 )

2 0 .0 0 2 * * * (0 .0 0 0 )

2 0 .0 0 3 * * * (0 .0 0 0 )

2 0 .0 0 2 * * * (0 .0 0 1 )

2 0 .0 0 2 * * (0 .0 0 1 )

P ri v at e en

er gy

fi rm

d u m m y

0 .1 2 1 * * * (0 .0 1 6 )

0 .1 2 7 * * * (0 .0 1 6 )

0 .1 2 5 * * * (0 .0 1 6 )

0 .1 2 5 * * * (0 .0 1 9 )

0 .0 9 9 * * * (0 .0 2 3 )

0 .0 4 7 * (0 .0 2 9 )

IO U

d u m m y

0 .0 3 8 * (0 .0 1 9 )

0 .0 3 9 * * (0 .0 1 9 )

0 .0 4 0 * * (0 .0 1 9 )

0 .0 7 1 * * * (0 .0 2 1 )

0 .0 0 6 (0 .0 3 4 )

2 0 .0 0 6 (0 .0 4 3 )

G eo

gr ap

h ic

d iv er si fi ca ti o n

0 .0 0 1 * (0 .0 0 1 )

0 .0 0 1 (0 .0 0 1 )

0 .0 0 1 (0 .0 0 1 )

0 .0 0 1 (0 .0 0 1 )

0 .0 0 2 * (0 .0 0 1 )

0 .0 0 2 (0 .0 0 2 )

G o v .u

ti li ti es

m ar k et

sh ar e in

st at e

0 .0 2 7 (0 .0 3 0 )

0 .0 3 6 (0 .0 3 0 )

0 .0 4 1 (0 .0 3 0 )

0 .0 3 6 (0 .0 3 3 )

0 .2 5 4 * * * (0 .0 8 6 )

0 .4 0 5 * * * (0 .1 1 6 )

E le ct ri ci ty

d em

an d in

st at e

0 .0 0 1 (0 .0 0 7 )

2 0 .0 0 0 (0 .0 0 7 )

2 0 .0 0 0 (0 .0 0 7 )

0 .0 0 6 (0 .0 0 8 )

2 0 .0 1 9 (0 .0 1 2 )

2 0 .0 2 9 * (0 .0 1 5 )

N et

in te rs ta te

sa le s o f el ec tr ic it y

0 .0 1 0 (0 .0 0 9 )

0 .0 1 0 (0 .0 0 9 )

0 .0 0 9 (0 .0 0 9 )

0 .0 0 8 (0 .0 1 1 )

0 .0 0 3 (0 .0 1 3 )

0 .0 0 4 (0 .0 2 1 )

S ie rr a C lu b m em

b er sh

ip 0 .0 0 6 (0 .0 0 3 )

0 .0 0 5 (0 .0 0 3 )

0 .0 0 4 (0 .0 0 6 )

0 .0 0 8 * (0 .0 0 4 )

2 0 .0 1 1 * (0 .0 0 7 )

2 0 .0 0 3 (0 .0 1 2 )

D em

o cr at s in

le gi sl at u re

0 .0 7 3 * * (0 .0 3 4 )

0 .0 5 3 (0 .0 3 4 )

0 .0 6 9 * * (0 .0 3 4 )

0 .0 9 7 * * (0 .0 3 9 )

0 .0 2 6 (0 .0 8 1 )

0 .0 2 9 (0 .1 2 7 )

D em

o cr at ic

go v er n o r

2 0 .0 2 1 * * * (0 .0 0 5 )

2 0 .0 1 9 * * * (0 .0 0 4 )

2 0 .0 2 4 * * * (0 .0 0 5 )

2 0 .0 2 3 * * * (0 .0 0 6 )

2 0 .0 2 6 * * * (0 .0 0 9 )

0 .0 0 1 (0 .0 1 5 )

R et ai l m ar k et

d er eg u la ti o n

2 0 .0 0 6 (0 .0 0 9 )

2 0 .0 0 3 (0 .0 0 9 )

2 0 .0 0 4 (0 .0 0 9 )

2 0 .0 2 7 * * (0 .0 1 2 )

0 .0 0 3 (0 .0 1 7 )

2 0 .0 2 2 (0 .0 2 2 )

F ed

er al

p ro d u ct io n ta x cr ed

it 2 0 .0 0 1 (0 .0 0 3 )

2 0 .0 0 1 (0 .0 0 3 )

2 0 .0 0 1 (0 .0 0 3 )

0 .0 0 1 (0 .0 0 4 )

0 .0 0 0 (0 .0 0 5 )

2 0 .0 0 9 (0 .0 0 7 )

F in an

ci al

in ce n ti v es

0 .0 0 1 (0 .0 0 3 )

2 0 .0 0 1 (0 .0 0 3 )

2 0 .0 0 2 (0 .0 0 4 )

0 .0 0 1 (0 .0 0 5 )

2 0 .0 0 3 (0 .0 0 5 )

2 0 .0 0 3 (0 .0 0 8 )

M o n o p o ly

p o w er

2 0 .1 2 9 * * (0 .0 5 8 )

2 0 .1 1 6 * * (0 .0 5 8 )

2 0 .1 0 7 * (0 .0 5 8 )

2 0 .0 4 4 (0 .0 6 1 )

2 0 .1 2 7 (0 .1 0 7 )

2 0 .2 7 6 * * (0 .1 3 5 )

S u n in d ex

2 0 .0 4 8 (0 .0 3 2 )

2 0 .0 6 0 * (0 .0 3 2 )

2 0 .0 5 7 * (0 .0 3 2 )

2 0 .0 9 8 * * * (0 .0 3 5 )

2 0 .1 3 7 * * (0 .0 5 7 )

2 0 .1 6 9 * * * (0 .0 6 4 )

W in d en

er gy

p o te n ti al

2 0 .0 0 1 (0 .0 0 1 )

2 0 .0 0 1 (0 .0 0 1 )

2 0 .0 0 1 (0 .0 0 2 )

2 0 .0 0 2 (0 .0 0 2 )

0 .0 0 1 (0 .0 0 4 )

2 0 .0 0 9 * (0 .0 0 6 )

x 2

2 2 0 3 .5 6 * * *

2 2 0 8 .5 4 * * *

2 2 3 0 .1 8 * * *

1 1 9 0 .1 7 * * *

1 0 4 2 .2 3 * * *

7 5 5 .5 2 * * *

N u m b er

o f o b se rv at io n s

1 0 5 1 8

1 0 5 1 8

1 0 5 1 8

6 8 4 5

3 6 7 3

2 0 8 5

* * * p ,

0 .0 1

* * p ,

0 .0 5

* p ,

0 .1 0 (t w o -t ai le d si gn

if ic an

ce le v el s)

N o te :1

0 5 1 8 o b se rv at io n s, 1 5 4 2 fi rm

s, 1 1 y ea

rs .

692 AprilAcademy of Management Journal

T A B L E 3

R a n d o m -E ff ec

ts P ro b it :D

V 5

F ir m

W a st e- to -E n er gy

R en

ew a b le

In v es tm

en ts

(0 – 1 )

M o d el

1 M o d el

2 M o d el

3 M o d el

4 M o d el

5 M o d el

6

F u ll m o d el

F u ll m o d el

F u ll m o d el

N o R P S

R P S

R P S

C o n st an

t 2 6 .1 5 4 * * * (1 .1 7 8 )

2 7 .3 7 3 * * * (1 .2 3 3 )

2 7 .7 7 8 * * * (1 .2 0 2 )

2 8 .0 3 7 * * * (1 .5 1 8 )

2 8 .8 0 7 * * * (3 .0 8 9 )

8 .7 5 8 * * (4 .3 8 1 )

C o m p et it o rs ’ w as te -t o -e n er gy

in v es tm

en ts

(H y p o th es is

1 )

7 .8 4 0 * * * (1 .7 9 1 )

9 .0 2 2 * * * (2 .2 5 5 )

9 .3 6 4 * * * (2 .5 3 3 )

5 .0 7 5 (3 .3 5 7 )

2 .5 2 3 (5 .5 3 5 )

P re se n ce

o f R P S (r eg u la to ry

p o li cy

) 0 .1 1 2 (0 .1 3 2 )

2 0 .0 4 6 (0 .1 3 8 )

2 0 .0 2 1 (0 .1 4 1 )

Y ea rs

R P S p re se n t

0 .0 3 9 (0 .0 4 7 )

2 0 .0 0 8 (0 .0 4 8 )

0 .0 0 0 (0 .0 4 9 )

0 .0 3 9 (0 .0 6 9 )

0 .0 0 2 (0 .1 3 0 )

R P S y ea rl y go

al s (i f R P S )

7 .7 8 0 * (4 .0 8 9 )

P re se n ce

o fR

P S X C o m p et it o rs ’

w as te -t o -e n er gy

in v es tm

en ts

(H y p o th es is

3 )

2 2 .5 8 9 (2 .9 6 9 )

R P S y ea rl y go

al s X

2 1 1 0 .5 6 * (6 0 .6 5 )

C o m p et it o rs ’ w as te -t o -e n er gy

in v es tm

en ts

(H y p o th es is

4 )

O ld

re so u rc e en

d o w m en

t (f o ss il )

2 0 .8 4 1 * * (0 .3 8 0 )

2 0 .8 3 3 * * (0 .3 8 4 )

2 0 .8 3 8 * * (0 .3 8 4 )

2 0 .7 5 9 (0 .4 8 4 )

2 0 .6 5 8 (0 .6 8 6 )

1 .1 2 7 (0 .8 4 2 )

H y d ro el ec tr ic

p o w er

2 0 .6 0 4 * * (0 .2 6 7 )

2 0 .5 1 9 * (0 .2 7 0 )

2 0 .5 2 3 * (0 .2 6 9 )

2 0 .3 6 6 (0 .3 4 4 )

2 0 .6 2 1 (0 .4 5 7 )

0 .0 6 1 (0 .6 0 6 )

N u cl ea r p o w er

2 0 .8 6 8 * * (0 .3 8 0 )

2 0 .8 4 9 * * (0 .3 8 9 )

2 0 .8 4 1 * * (0 .3 8 9 )

2 0 .9 7 7 * * (0 .4 7 7 )

2 1 .5 3 5 * (0 .7 8 4 )

2 1 .1 8 3 (1 .1 5 7 )

P ri o r re n ew

ab le s

3 .8 6 6 * * * (0 .1 6 6 )

3 .7 9 5 * * * (0 .1 6 8 )

3 .7 9 1 * * * (0 .1 6 8 )

4 .0 8 2 * * * (0 .2 2 8 )

4 .4 3 9 * * * (0 .3 3 4 )

6 .1 5 1 * * * (0 .5 7 9 )

F ir m

si ze

(M W

o p er at io n al )

0 .4 9 3 * * * (0 .0 5 2 )

0 .4 8 2 * * * (0 .0 5 2 )

0 .4 8 3 * * * (0 .0 5 2 )

0 .4 6 4 * * * (0 .0 6 6 )

0 .5 5 6 * * * (0 .0 9 2 )

0 .5 6 9 * * * (0 .1 2 5 )

A v er ag e p la n t ag e

2 0 .0 2 0 * * * (0 .0 0 6 )

2 0 .0 2 1 * * * (0 .0 0 6 )

2 0 .0 2 1 * * * (0 .0 0 6 )

2 0 .0 1 9 * * (0 .0 0 8 )

2 0 .0 2 8 * * * (0 .0 1 1 )

2 0 .0 1 5 (0 .0 1 4 )

P ri v at e en

er gy

fi rm

d u m m y

0 .8 6 8 * * * (0 .2 1 1 )

0 .8 6 1 * * * (0 .2 1 4 )

0 .8 5 9 * * * (0 .2 1 4 )

1 .1 6 7 * * * (0 .2 7 1 )

0 .5 2 4 (0 .3 6 3 )

0 .5 3 9 (0 .4 9 2 )

IO U

d u m m y

0 .1 9 3 (0 .2 5 9 )

0 .2 1 6 (0 .2 6 3 )

0 .2 1 8 (0 .2 6 3 )

0 .4 5 1 (0 .3 0 3 )

2 0 .7 9 3 (0 .5 3 4 )

2 1 .3 6 0 * (0 .7 5 3 )

G eo

gr ap

h ic

d iv er si fi ca ti o n

2 0 .0 0 1 (0 .0 1 3 )

2 0 .0 0 2 (0 .0 1 3 )

2 0 .0 0 3 (0 .0 1 3 )

2 0 .0 2 2 (0 .0 1 7 )

0 .0 0 6 (0 .0 2 2 )

2 0 .0 1 3 (0 .0 2 9 )

G o v .u

ti li ti es

m ar k et

sh ar e in

st at e

0 .7 1 1 (0 .4 6 1 )

0 .8 0 5 * (0 .4 7 2 )

0 .8 3 6 * (0 .4 7 4 )

0 .8 1 6 (0 .5 2 5 )

1 .2 8 4 (1 .7 7 9 )

0 .1 4 1 (2 .3 6 6 )

E le ct ri ci ty

d em

an d in

st at e

0 .1 3 4 (0 .1 0 3 )

0 .2 3 7 * * (0 .1 0 8 )

0 .2 3 1 * * (0 .1 0 9 )

0 .2 5 2 * (0 .1 3 6 )

0 .2 0 4 (0 .2 6 2 )

2 0 .1 0 1 (0 .3 4 2 )

N et in te rs ta te sa le s o fe le ct ri ci ty

0 .0 9 4 (0 .1 6 3 )

2 0 .0 0 4 (0 .1 6 6 )

2 0 .0 0 7 (0 .1 6 6 )

0 .1 0 3 (0 .2 1 3 )

2 0 .2 8 6 (0 .3 0 2 )

2 0 .2 7 5 (0 .4 8 4 )

S ie rr a C lu b m em

b er sh

ip 2 0 .0 5 3 (0 .0 6 3 )

2 0 .0 4 3 (0 .0 6 4 )

2 0 .0 4 9 (0 .0 6 4 )

2 0 .0 1 1 (0 .0 7 9 )

2 0 .2 0 9 (0 .1 5 3 )

2 0 .1 6 7 (0 .2 4 9 )

D em

o cr at s in

le gi sl at u re

2 0 .1 5 4 (0 .6 3 7 )

2 0 .0 3 5 (0 .6 5 3 )

0 .0 4 4 (0 .6 5 9 )

0 .2 2 6 (0 .7 7 9 )

1 .7 1 9 (1 .9 9 7 )

3 .3 8 6 (3 .2 5 3 )

D em

o cr at ic

go v er n o r

2 0 .2 3 2 * * (0 .1 0 7 )

2 0 .2 0 5 * (0 .1 0 9 )

2 0 .1 9 7 * (0 .1 0 9 )

2 0 .2 8 7 * (0 .1 5 9 )

0 .0 2 2 (0 .2 4 7 )

0 .3 0 6 (0 .4 1 8 )

R et ai l m ar k et

d er eg u la ti o n

0 .3 0 6 * (0 .1 6 3 )

0 .3 0 1 * (0 .1 6 6 )

0 .3 0 8 * (0 .1 6 6 )

0 .1 8 2 (0 .2 1 9 )

0 .5 0 7 (0 .4 2 7 )

0 .6 4 2 (0 .6 2 4 )

F ed

er al

p ro d u ct io n ta x cr ed

it 0 .0 1 2 (0 .0 8 0 )

0 .0 0 9 (0 .0 8 1 )

0 .0 1 0 (0 .0 8 1 )

2 0 .0 1 8 (0 .1 1 1 )

0 .0 3 4 (0 .1 4 0 )

0 .0 8 4 (0 .2 0 1 )

F in an

ci al

in ce n ti v es

0 .0 6 2 (0 .0 7 2 )

0 .0 4 9 (0 .0 7 3 )

0 .0 4 2 (0 .0 7 3 )

0 .0 7 7 (0 .1 0 7 )

2 0 .0 9 6 (0 .1 4 1 )

2 0 .2 0 6 (0 .2 1 0 )

M o n o p o ly

p o w er

2 2 .1 4 7 * * (0 .8 6 6 )

2 1 .8 7 2 * * (0 .8 8 4 )

2 1 .8 8 4 * * (0 .8 8 5 )

2 2 .0 4 5 * * (1 .0 0 8 )

2 0 .7 1 9 (1 .9 7 5 )

2 4 .2 2 5 (2 .6 3 7 )

S u n in d ex

0 .5 7 0 (0 .4 5 2 )

0 .1 6 6 (0 .4 6 2 )

0 .2 2 2 (0 .4 6 6 )

0 .1 2 8 (0 .5 4 5 )

1 .1 2 4 (0 .9 7 1 )

1 .3 0 5 (1 .1 8 6 )

W in d en

er gy

p o te n ti al

2 0 .0 9 5 * * * (0 .0 2 1 )

2 0 .0 8 2 * * * (0 .0 2 1 )

2 0 .0 8 0 * * * (0 .0 2 1 )

2 0 .0 7 7 * * * (0 .0 2 6 )

2 0 .0 8 1 (0 .0 6 9 )

2 0 .0 8 2 (0 .1 1 9 )

x 2

8 3 5 .3 4 * * *

8 0 9 .4 4 * * *

8 0 9 .9 3 * * *

4 5 1 .6 1 * * *

2 5 1 .2 6 * * *

1 2 9 .2 8 * * *

N u m b er

o f o b se rv at io n s

1 0 5 1 8

1 0 5 1 8

1 0 5 1 8

6 8 4 5

3 6 7 3

2 0 8 5

* * * p ,

0 .0 1

* * p ,

0 .0 5

* p ,

0 .1 0 (t w o -t ai le d si gn

if ic an

ce le v el s)

N o te :1

0 5 1 8 o b se rv at io n s, 1 5 4 2 fi rm

s, 1 1 y ea

rs .

2016 693Weigelt and Shittu

T A B L E 4

R a n d o m -E ff ec

ts P ro b it :D

V 5

F ir m

W in d R en

ew a b le

In v es tm

en ts

(0 – 1 )

M o d el

1 M o d el

2 M o d el

3 M o d el

4 M o d el

5 M o d el

6

F u ll m o d el

F u ll m o d el

F u ll m o d el

N o R P S

R P S

R P S

C o n st an

t 2 1 6 .4 4 * * * (3 .5 0 5 )

2 1 5 .5 7 * * * (3 .8 2 5 )

2 1 5 .6 6 * * * (3 .8 8 2 )

2 1 4 .9 8 4 * * * (4 .7 1 4 )

2 5 2 .8 2 7 * * * (1 4 .0 3 3 )

2 3 1 .1 0 1 (3 2 .0 8 3 )

C o m p et it o rs ’ w in d

in v es tm

en ts (H

y p o th es is 1 )

1 8 .5 7 9 * * * (5 .4 4 3 )

3 8 .3 4 4 * * * (8 .1 2 5 )

3 9 .2 3 5 * * * (9 .6 8 8 )

4 .0 9 9 (8 .6 3 9 )

2 2 .7 7 2 (4 9 .8 3 )

P re se n ce

o f R P S (r eg u la to ry

p o li cy

) 0 .1 6 1 (0 .3 1 3 )

2 0 .2 9 9 (0 .3 6 9 )

2 0 .1 5 5 (0 .3 5 6 )

Y ea

rs R P S p re se n t

0 .2 6 6 * * (0 .1 2 3 )

0 .3 1 9 * * (0 .1 3 8 )

0 .3 1 0 * * (0 .1 2 4 )

0 .6 3 3 * * * (0 .2 2 7 )

1 .1 3 6 * (0 .6 0 9 )

R P S y ea rl y go

al s (i f R P S )

2 1 5 .5 4 0 (2 4 .7 6 4 )

P re se n ce

o f R P S X

C o m p et it o rs ’ w in d

in v es tm

en ts (H

y p o th es is 3 )

2 2 8 .4 5 1 * * * (8 .5 0 6 )

R P S y ea rl y go

al s X

2 2 3 4 .9 3 (5 3 0 .3 7 )

C o m p et it o rs ’ w in d

in v es tm

en ts (H

y p o th es is 4 )

O ld

re so u rc e en

d o w m en

t (f o ss il )

2 1 .9 1 1 * * (0 .8 9 2 )

2 1 .9 0 2 * * (0 .8 5 9 )

2 1 .8 6 2 * * (0 .9 3 0 )

2 0 .8 3 5 (1 .0 9 1 )

2 9 .6 1 3 * * * (2 .2 0 5 )

2 9 .0 0 4 * * (3 .6 2 4 )

H y d ro el ec

tr ic

p o w er

2 0 .5 7 4 (0 .5 1 9 )

2 0 .4 3 6 (0 .5 4 6 )

2 0 .5 1 7 (0 .5 5 9 )

0 .2 9 5 (0 .7 1 8 )

2 3 .4 1 1 * * (1 .4 3 7 )

2 0 .3 4 7 (2 .1 7 8 )

N u cl ea r p o w er

2 0 .1 9 1 (0 .9 9 7 )

2 0 .3 7 3 (1 .0 1 5 )

2 0 .3 4 6 (0 .9 9 7 )

0 .5 0 1 (1 .4 1 1 )

2 0 .9 1 2 (2 .0 4 6 )

2 1 .7 4 7 (2 .4 8 5 )

P ri o r re n ew

ab le s

3 .2 6 9 * * * (0 .4 3 6 )

3 .3 8 7 * * * (0 .4 3 9 )

3 .2 4 1 * * * (0 .4 7 4 )

3 .5 5 6 * * * (0 .5 3 9 )

3 .7 3 0 * * * (0 .9 5 9 )

6 .3 7 8 * * * (2 .3 0 4 )

F ir m

si ze

(M W

o p er at io n al )

0 .1 2 9 (0 .1 1 7 )

0 .1 3 7 (0 .1 2 8 )

0 .0 9 9 (0 .1 1 9 )

2 0 .0 5 1 (0 .1 3 6 )

0 .1 5 4 (0 .2 3 4 )

2 0 .1 3 7 (0 .3 7 1 )

A v er ag

e p la n t ag

e 2 0 .0 1 4 (0 .0 1 3 )

2 0 .0 1 2 (0 .0 1 4 )

2 0 .0 0 7 (0 .0 1 4 )

2 0 .0 4 7 * * (0 .0 2 3 )

0 .0 5 6 * (0 .0 2 9 )

2 0 .0 7 3 (0 .0 4 5 )

P ri v at e en

er gy

fi rm

d u m m y

2 2 .7 8 1 * * * (0 .7 1 4 )

2 2 .8 3 0 * * * (0 .7 4 5 )

2 2 .6 3 4 * * * (0 .7 6 3 )

2 2 .1 3 4 * * (1 .0 6 2 )

2 3 .5 6 7 * * (1 .4 1 9 )

2 4 .7 3 2 * * (2 .2 2 1 )

IO U

d u m m y

1 .2 2 9 * (0 .6 4 3 )

1 .2 1 3 * * (0 .6 0 5 )

1 .0 8 1 (0 .7 4 7 )

1 .4 0 8 * * (0 .6 7 5 )

2 .7 7 4 * (1 .6 5 2 )

2 .7 4 3 (2 .3 1 3 )

G eo

gr ap

h ic

d iv er si fi ca

ti o n

0 .1 1 2 * * * (0 .0 3 5 )

0 .1 2 9 * * * (0 .0 3 8 )

0 .1 1 6 * * * (0 .0 3 6 )

0 .0 4 8 (0 .0 8 0 )

0 .2 5 1 * * * (0 .0 7 0 )

0 .2 3 9 * * (0 .1 1 4 )

G o v .u

ti li ti es

m ar k et

sh ar e in

st at e

2 0 .8 9 1 (1 .0 3 8 )

2 1 .3 0 9 (1 .1 0 0 )

2 1 .3 5 8 (1 .0 8 2 )

0 .4 7 6 (1 .5 0 6 )

2 5 .1 5 9 (5 .5 6 5 )

2 8 .3 4 6 (9 .5 8 8 )

E le ct ri ci ty

d em

an d in

st at e

2 0 .0 1 3 (0 .3 1 6 )

0 .1 3 8 (0 .3 4 9 )

0 .1 3 8 (0 .3 5 3 )

2 0 .1 6 6 (0 .4 2 4 )

3 .4 4 2 * * * (1 .2 5 2 )

2 .3 7 8 (2 .9 1 9 )

N et

in te rs ta te

sa le s o f

el ec tr ic it y

2 0 .2 1 9 (0 .3 7 9 )

2 0 .1 3 2 (0 .4 1 8 )

0 .0 1 9 (0 .4 1 0 )

0 .7 1 4 (0 .5 9 6 )

2 1 .9 8 6 * (1 .1 6 7 )

2 2 .0 3 9 (3 .5 3 3 )

S ie rr a C lu b m em

b er sh

ip 0 .3 6 5 * * (0 .1 5 3 )

0 .4 7 2 * * * (0 .1 6 6 )

0 .3 4 5 * * (0 .1 6 5 )

0 .5 0 2 * * (0 .2 5 2 )

2 0 .0 0 9 (0 .4 9 3 )

1 .0 8 2 (1 .2 5 9 )

D em

o cr at s in

le gi sl at u re

6 .1 5 5 * * * (1 .6 2 9 )

2 .9 4 9 (1 .8 5 0 )

3 .3 8 9 * (1 .8 7 2 )

4 .5 5 4 * (2 .4 5 3 )

9 .5 8 3 * (5 .2 9 6 )

2 1 6 .5 4 7 (1 1 .5 8 0 )

D em

o cr at ic

go v er n o r

0 .3 8 4 (0 .2 4 9 )

0 .1 7 1 (0 .2 6 1 )

0 .0 4 2 (0 .2 6 2 )

2 0 .2 0 5 (0 .3 6 5 )

1 .5 1 8 * (0 .9 0 9 )

2 0 .0 9 1 (2 .3 1 0 )

R et ai l m ar k et

d er eg

u la ti o n

2 0 .7 0 7 (0 .4 8 3 )

2 0 .6 3 9 (0 .5 1 3 )

2 0 .7 1 9 (0 .5 3 0 )

2 0 .6 3 4 (0 .7 4 7 )

2 6 .2 9 0 * * * (2 .0 8 8 )

2 2 .6 1 4 (4 .9 8 4 )

F ed

er al

p ro d u ct io n ta x cr ed

it 2 0 .2 1 9 (0 .1 6 7 )

2 0 .1 1 6 (0 .1 7 9 )

2 0 .1 0 4 (0 .1 7 6 )

2 0 .0 0 5 (0 .2 3 8 )

2 0 .6 8 2 * (0 .4 0 6 )

2 0 .1 1 4 (0 .6 4 8 )

F in an

ci al

in ce

n ti v es

0 .0 5 2 (0 .1 6 4 )

0 .0 5 9 (0 .1 8 3 )

0 .0 0 5 (0 .1 7 6 )

0 .8 1 5 * * (0 .3 4 8 )

2 0 .8 2 6 * * (0 .3 5 5 )

0 .1 4 6 (0 .6 7 5 )

M o n o p o ly

p o w er

3 .9 5 1 * (2 .2 5 1 )

5 .2 1 6 * * (2 .1 1 5 )

4 .8 3 5 * * (2 .1 2 1 )

5 .9 6 5 * * (2 .7 0 3 )

8 .0 5 5 (5 .4 1 4 )

3 .5 7 3 (7 .8 6 1 )

S u n in d ex

2 4 .6 2 3 * * * (1 .3 4 3 )

2 6 .1 2 2 * * * (1 .2 8 7 )

2 5 .7 8 4 * * * (1 .4 5 4 )

2 3 .9 7 8 * * (1 .7 0 6 )

2 1 3 .1 9 2 * * * (3 .2 6 8 )

2 1 6 .2 0 3 * * (6 .7 1 2 )

W in d en

er gy

p o te n ti al

0 .7 3 9 * * * (0 .1 4 3 )

0 .6 3 1 * * * (0 .1 4 1 )

0 .6 0 7 * * * (0 .1 4 4 )

0 .6 0 7 * * * (0 .1 8 9 )

1 .3 7 3 * * * (0 .4 5 2 )

1 .8 4 5 * * (0 .8 2 8 )

x 2

2 1 1 .0 0 * * *

2 2 2 .1 0 * * *

1 9 9 .7 8 * * *

1 2 1 .3 2 * * *

1 3 4 .4 5 * * *

5 6 .9 5 * * *

N u m b er

o f o b se rv at io n s

1 0 5 1 8

1 0 5 1 8

1 0 5 1 8

6 8 4 5

3 6 7 3

2 0 8 5

* * * p ,

0 .0 1

* * p ,

0 .0 5

* p ,

0 .1 0 (t w o -t ai le d si gn

if ic an

ce le v el s)

N o te :1

0 5 1 8 o b se rv at io n s, 1 5 4 2 fi rm

s, 1 1 y ea

rs .

694 AprilAcademy of Management Journal

T A B L E 5

R a n d o m -E ff ec

ts P ro b it :D

V 5

F ir m

S o la r R en

ew a b le

In v es tm

en ts

(0 – 1 )

M o d el

1 M o d el

2 M o d el

3 M o d el

4 M o d el

5 M o d el

6

F u ll m o d el

F u ll m o d el

F u ll m o d el

N o R P S

R P S

R P S

C o n st an

t 2 2 0 .5 4 9 * * * (4 .5 6 2 )

2 2 0 .5 8 8 * * * (4 .7 2 7 )

2 2 2 .1 4 1 * * * (4 .3 4 4 )

2 2 2 .1 2 5 * * * (6 .9 6 7 )

2 1 8 .0 3 9 * (1 0 .2 6 4 )

2 0 .4 4 7 (1 2 .9 9 )

C o m p et it o rs ’ so la r in v es tm

en ts

(H y p o th es is

1 )

3 .3 1 0 (7 7 .3 5 )

3 3 5 .4 9 * * (1 6 2 .7 3 )

5 6 1 .2 1 * * (2 6 2 .0 7 )

1 6 .0 1 5 (1 2 0 .5 3 )

9 0 .7 7 2 * * (4 1 .7 2 2 )

P re se n ce

o f R P S (r eg u la to ry

p o li cy

) 1 .0 9 3 * * * (0 .3 2 7 )

1 .0 9 2 * * * (0 .3 2 9 )

1 .2 9 2 * * * (0 .3 6 3 )

Y ea

rs R P S p re se n t

0 .3 4 4 * * * (0 .1 0 3 )

0 .3 4 3 * * * (0 .1 1 3 )

0 .1 4 9 * * * (0 .1 1 6 )

0 .3 6 8 (0 .2 2 7 )

1 .0 2 5 * * (0 .4 2 7 )

R P S y ea rl y go

al s (i f R P S )

2 1 .9 5 2 4 (1 .2 8 0 5 )

P re se n ce

o f R P S X C o m p et it o rs ’

so la r in v es tm

en ts

(H y p o th es is

3 )

2 3 3 9 .7 4 * * (1 4 7 .0 9 )

R P S y ea

rl y go

al s X C o m p et it o rs ’

so la r in v es tm

en ts

(H y p o th es is

4 )

2 1 3 3 .5 5 5 * * (5 8 .9 3 7 )

O ld

re so u rc e en

d o w m en

t (f o ss il )

2 2 .8 3 2 * * * (0 .8 8 1 )

2 2 .8 3 2 * * * (0 .8 8 2 )

2 2 .7 9 4 * * * (0 .8 7 9 )

2 4 .8 4 0 * * * (1 .4 3 8 )

2 7 .9 8 9 * * * (1 .7 5 8 )

2 6 .0 3 1 * * (2 .3 8 3 )

H y d ro el ec

tr ic

p o w er

2 0 .1 8 9 (0 .6 7 5 )

2 0 .1 8 9 (0 .6 7 3 )

2 0 .1 7 5 (0 .6 7 6 )

2 0 .1 4 6 (0 .9 7 4 )

2 2 .2 1 7 * * (1 .0 9 9 )

2 0 .5 7 5 (1 .4 8 9 )

N u cl ea r p o w er

2 0 .5 7 4 (0 .7 2 3 )

2 0 .5 7 6 (0 .7 2 3 )

2 0 .6 8 0 (0 .7 1 6 )

2 0 .6 7 6 (0 .8 8 1 )

2 0 .2 5 7 (1 .1 2 7 )

7 .1 7 6 * * * (1 .9 2 4 )

P ri o r re n ew

ab le s

1 .8 1 7 * * * (0 .2 8 8 )

1 .8 1 6 * * * (0 .2 8 9 )

1 .7 9 2 * * * (0 .2 8 9 )

1 .3 2 9 * * * (0 .3 9 6 )

3 .3 4 1 * * * (0 .6 7 6 )

4 .7 0 1 * * * (1 .2 6 4 )

F ir m

si ze

(M W

o p er at io n al )

0 .2 1 9 * (0 .1 2 7 )

0 .2 2 0 * (0 .1 2 8 )

0 .2 0 7 (0 .1 2 8 )

0 .5 2 1 * * (0 .2 3 7 )

2 0 .1 5 6 (0 .1 8 7 )

2 0 .5 2 8 * (0 .2 7 4 )

A v er ag

e p la n t ag

e 2 0 .0 4 2 * * * (0 .0 1 5 )

2 0 .0 4 2 * * * (0 .0 1 5 )

2 0 .0 4 1 * * * (0 .0 1 5 )

2 0 .0 7 9 * * * (0 .0 2 3 )

2 0 .0 2 6 (0 .0 2 1 )

2 0 .0 6 9 * * (0 .0 3 4 )

P ri v at e en

er gy

fi rm

d u m m y

2 5 .4 2 4 * * * (0 .8 2 8 )

2 5 .4 2 7 * * * (0 .8 2 9 )

2 5 .6 1 9 * * * (0 .8 5 4 )

2 7 .2 9 7 * * * (1 .7 1 2 )

2 6 .8 6 3 * * * (1 .4 5 8 )

2 8 .5 7 1 * * * (1 .4 2 9 )

IO U

d u m m y

2 0 .0 9 8 (0 .7 0 0 )

2 0 .1 0 2 (0 .6 9 8 )

2 0 .0 2 1 (0 .6 9 7 )

2 0 .6 4 9 (0 .8 9 2 )

0 .5 6 4 (1 .2 0 6 )

1 .7 7 9 (1 .3 7 1 )

G eo

gr ap

h ic

d iv er si fi ca

ti o n

0 .0 3 1 (0 .0 4 9 )

0 .0 3 1 (0 .0 4 9 )

0 .0 3 9 (0 .0 5 1 )

0 .0 4 9 (0 .0 9 1 )

2 0 .1 1 2 (0 .1 0 2 )

2 0 .0 0 6 (0 .1 0 4 )

G o v .u

ti li ti es

m ar k et

sh ar e in

st at e

2 1 .4 8 5 (1 .4 5 8 )

2 1 .5 0 0 (1 .4 6 2 )

2 1 .2 4 0 (1 .4 0 9 )

2 2 .7 6 9 (1 .8 0 7 )

1 5 .2 3 6 * * * (5 .0 9 4 )

1 0 .8 2 1 * (5 .8 2 9 )

E le ct ri ci ty

d em

an d in

st at e

1 .4 3 7 * * * (0 .4 0 9 )

1 .4 3 9 * * * (0 .4 1 9 )

1 .4 5 9 * * * (0 .4 0 1 )

2 .0 2 8 * * * (0 .6 4 8 )

2 .0 8 3 * (1 .2 2 9 )

1 .2 1 2 (1 .1 8 1 )

N et

in te rs ta te

sa le s o fe

le ct ri ci ty

2 0 .6 5 1 (0 .4 7 4 )

2 0 .6 5 2 (0 .4 7 5 )

2 0 .8 0 3 * (0 .4 7 7 )

2 2 .4 3 3 * * * (0 .8 7 8 )

2 1 .5 6 1 * * (0 .7 0 2 )

2 1 .8 3 9 (1 .3 4 1 )

S ie rr a C lu b m em

b er sh

ip 0 .6 2 2 * * * (0 .1 7 9 )

0 .6 2 2 * * * (0 .1 8 4 )

0 .5 7 8 * * * (0 .1 8 3 )

1 .2 6 6 * * * (0 .3 0 3 )

2 0 .7 3 7 (0 .5 0 5 )

2 0 .4 9 9 (0 .7 1 9 )

D em

o cr at s in

le gi sl at u re

0 .5 7 3 (1 .6 0 3 )

0 .5 8 3 (1 .6 1 1 )

2 0 .1 7 8 (1 .6 3 1 )

1 .2 6 8 (2 .1 8 3 )

2 .8 2 2 (4 .6 9 4 )

2 1 0 .7 9 4 (7 .8 8 6 )

D em

o cr at ic

go v er n o r

0 .4 3 3 (0 .2 6 8 )

0 .4 3 5 (0 .2 6 9 )

0 .1 8 6 (0 .2 8 6 )

2 0 .6 7 5 (0 .4 4 9 )

0 .6 9 1 (0 .6 3 9 )

2 1 .5 2 3 (1 .1 1 2 )

R et ai l m ar k et

d er eg

u la ti o n

0 .1 6 7 (0 .4 7 1 )

0 .1 6 6 (0 .4 7 1 )

0 .1 5 9 (0 .4 7 7 )

0 .4 8 2 (0 .9 1 2 )

2 0 .5 7 4 (1 .5 4 7 )

0 .8 1 5 (1 .8 5 5 )

F ed

er al

p ro d u ct io n ta x cr ed

it 2 0 .1 7 2 (0 .1 5 2 )

2 0 .1 7 3 (0 .1 5 2 )

2 0 .1 8 6 (0 .1 5 3 )

2 0 .3 3 5 (0 .2 1 0 )

2 0 .1 2 7 (0 .3 1 6 )

0 .2 5 1 (0 .4 6 5 )

F in an

ci al

in ce

n ti v es

0 .3 6 4 * * (0 .1 6 7 )

0 .3 6 6 * * (0 .1 6 9 )

0 .3 3 9 * * (0 .1 6 9 )

0 .8 6 2 * * (0 .3 6 1 )

2 0 .0 0 3 (0 .3 2 1 )

0 .2 8 1 (0 .4 9 0 )

M o n o p o ly

p o w er

6 .5 5 6 * * * (1 .8 9 8 )

6 .5 5 7 * * * (1 .9 0 4 )

7 .1 0 7 * * * (1 .8 7 9 )

8 .5 8 5 * * * (2 .4 7 2 )

1 9 .5 0 1 * * * (3 .9 9 6 )

1 9 .4 7 3 * * * (5 .7 6 9 )

S u n in d ex

2 2 .2 4 9 (1 .6 9 7 )

2 2 .2 5 4 (1 .7 0 2 )

2 2 .6 5 6 * (1 .6 0 1 )

2 6 .7 8 2 * * * (2 .2 9 2 )

2 3 .8 0 7 (2 .6 4 3 )

2 6 .1 0 7 * * (3 .0 7 7 )

W in d en

er gy

p o te n ti al

2 0 .2 1 8 * * * (0 .0 6 6 )

2 0 .2 1 8 * * * (0 .0 6 5 )

2 0 .2 3 9 * * * (0 .0 6 7 )

2 0 .4 1 5 * * * (0 .1 1 5 )

2 0 .5 2 7 * (0 .3 0 6 )

2 0 .4 5 2 (0 .3 3 6 )

x 2

2 1 4 .8 8 * * *

2 1 5 .1 9 * * *

2 1 4 .5 3 * * *

7 7 .2 4 * * *

1 7 7 .9 9 * * *

1 5 4 .5 8 * * *

N u m b er

o f o b se rv at io n s

1 0 5 1 8

1 0 5 1 8

1 0 5 1 8

6 8 4 5

3 6 7 3

2 0 8 5

* * * p ,

0 .0 1

* * p ,

0 .0 5

* p ,

0 .1 0 (t w o -t ai le d si gn

if ic an

ce le v el s)

N o te :1

0 5 1 8 o b se rv at io n s, 1 5 4 2 fi rm

s, 1 1 y ea

rs .

2016 695Weigelt and Shittu

that competitors’ new resource investments (all re- newables) are significant (b 5 0.27; p , 0.01) (Table 2, Model 2) supporting Hypothesis 1. This result is also supported in separate resource analyses for competi- tors’ wind investments (b 5 18.58; p , 0.01) on the likelihood that a focal firm has subsequent wind in- vestments (Table 4, Model 2) and competitors’ waste- to-energy investments (b 5 7.84; p , 0.01) on the likelihood that a focal firm has subsequent waste-to- energy investments (Table 3, Model 2). However, competitors’solarinvestmentsareinsignificant(Model 2, Table 5). We further investigate this insignificant effect for competitors’ solar investments and find that the variable turns significant in the absence of the two variables capturing the RPS effect (RPS and years RPS present). The findings thus support Hypothesis 1.

Hypothesis 2 states that the presence of a regulatory mandate dampens the positive effect of competitors’ new resource investments on a focalfirm’s subsequent new resource investments. We mean-centered the di- recteffectspriortointeractingthem.Sinceweestimate a Tobit, we calculate the marginal effect of the in- teractionandexamineitssignandsignificanceoverall sample values of the other variables in the model (Bowen, 2012; Wiersema & Bowen, 2009). A marginal effect is a unit change in the explanatory variable on the dependent variable (Hoetker, 2007). That is, we assess the significance of the interactioncoefficients in Model 3. If significant, we estimate the moderator’s marginal effect on the relationship between explana- tory variable and dependent variable—the true in- teraction effect (Ai & Norton, 2003). Model 3, Table 2 shows the interaction of competitors’ new resource investments with regulatory mandate (b 5 2 0.30; p , 0.01), supporting Hypothesis 2.

We further test Hypothesis 2 by splitting the sample into states with RPS mandates and states without RPS mandates in a respective year to assess the effect of competitors’ new resource investments in the presence of RPS mandates (Table 2, Model 4) and the absence of RPSmandates(Table2,Model5).WhileModel4shows a significant, positive competitive effect (b 5 0.58; p , 0.01), the same effect is insignificant in Model 5 (b 5 0.12). These findings show that RPS mandates dampen the competitive effect, supporting Hypothesis 2.7

To further assess the policy effect we conducted two-sample t-tests between states with RPS mandates and states without RPS mandates to see whether re- newables investments, on average, varied signifi- cantly between both conditions. We find significant t-tests for all renewables (no RPS: mean 5 0.04, SD 5 0.001 versus presence of RPS: mean 5 0.08, SD 5 0.003), for waste-to-energy (no RPS: mean 5 0.03, SD 5 0.001 versus presence of RPS: mean 5 0.06, SD 5 0.002), for wind (no RPS: mean 5 0.005, SD 5 0.001 versus presence of RPS: mean 5 0.01, SD 5 0.001), and for solar (no RPS: mean 5 0.0001, SD 5 0.0001 versus presence of RPS: mean 5 0.003, SD 5 0.001). We conclude that RPS mandates significantly affect firms’ renewable investments.8

Hypothesis 3 predicts that regulatory mandates dampen the competitive effect the more distant the new resource investments are from the established resource base. In our context, waste-to-energy, wind, and solar all qualify as renewables toward RPS re- quirements. Among these three renewable types, waste-to-energy is the most established one. Waste-to- energy is also most similar to fossil technologies in its energygenerationprocessbyburningmunicipal waste to generate electricity. Similar to fossil technologies, electricity is generated through a combustion process in waste-to-energy plants. We test Hypothesis 3 by comparing the interactions between regulatory man- date and competitors’ new resource investments in Model 3 across the three different renewable types (Tables 3–5). We find that while the interaction is negative, significant for wind (b 5 2 28.45; p , 0.01) (Table4) and solar(b 5 2 339.74; p , 0.05) (Table5), it is not significant for waste-to-energy (b 5 2 2.59) (Table 3), thus supporting Hypothesis 3.

Hypothesis 4 states that the more established a reg- ulatory mandate is within a state, the more dampened is the competitive effect, thus suggesting a strengthen- ing of the effect tested in Hypothesis 2. Table 2, Model 6 tests Hypothesis 4 by interacting yearly goals of RPS mandates and competitors’ new resource investments (both mean-centered prior to the interaction) using the subsample of states with RPS mandates. RPS yearly goals only exist in states with RPS mandates. The interaction is insignificant for renewable investments (Table 2), negative, significant for waste-to-energy

7 Similarly, in Table 3 competitors’ waste-to-energy in- vestments (b 5 9.36; p , 0.01) are significant in Model 4 (ab- sence of RPS mandates), but not in Model 5 (presence of RPS mandates). We find the same effects for competitors’ wind in- vestments(b 5 39.24;p, 0.01)(Table4)andcompetitors’solar investments (b 5 561.21; p , 0.05) (Table 5) in the absence of RPS mandates, but not in the presence of RPS mandates.

8 Since U.S. states adopt RPS mandates in different years rather than in one single year for all states, we cannot test the impact of RPS mandates on renewable adoption using the natural experiment approach by Marx, Strumsky, and Fleming (2009) who test the effect of a non-compete en- forcement policy on employee mobility in Michigan.

696 AprilAcademy of Management Journal

(b 5 2 110.56; p , 0.10) (Table 3) and solar (b 5 2133.56; p , 0.05) (Table 5), but negative, insig- nificant for wind (Table 4). Hypothesis 4 is only supported for waste-to-energy and solar investments.

Post-hoc Analyses

Although not explicitly hypothesized, one of our arguments leading up to Hypothesis 1 suggests that competitors’ new resource investments may prompt a focal firm to also invest because competitors lobby regulators to establish standards favorable to their resource-base (Bonardi et al., 2006; Capron & Chatain, 2008; Fremeth, 2009; Fremeth & Shaver, 2014). Hence, the question arises whether com- petitors’ new resource investments promote RPS adoption at the state-level. Following Delmas and Montes-Sancho (2011), we apply a two-stage mod- eling technique to determine RPS at the state level (stage 1) and firm-level renewable investments (stage 2). We create a dataset at the state level (1999–2010). We estimate random-effects Probit models to ac- count for the year effects and obtain the likelihood that a state has an RPS mandate. We include fuel prices (coal prices, natural gas prices, and oil prices at the state level) as instruments in the Probit model. We choose fuel prices as they are exogenously de- termined by the market and visible to the public and political constituencies and thus likely to affect the political process toward passing green policies.9 We obtain the fuel price information from EIA’s data- base. Table 6 shows these models. We then save the predicted values from Table 6, Model 1 and use them to test whether the RPS predicted values explain

focal firm renewables investments (dependent vari- able). We re-estimate Model 2 and Model 3 in Tables 2–5 using the predicted RPS values and show the results in Table 7. The predicted RPS values yield the same results, as those discussed above, and support our hypotheses.

Table 6, Model 1 shows that the more firms in the state have fossils (b 5 2 17.02; p , 0.01), hydro- electric power (b 5 2 32.67; p , 0.01), and/or nu- clear power (b 5 2 7.20; p , 0.10) in their portfolio, the less likely are states to have RPS mandates. Re- garding environmental activists and the political leaning of a state, Sierra Club membership (b 5 1.65; p , 0.05) and Democrats in the legislature (b 5 15.38; p , 0.01) are significant. We find that higher coal, natural gas, and oil prices at the state level raise the likelihood that RPS mandates are present. Model 2 shows that competitors’ new resource investments (b 5 46.73; p , 0.01) affect the likelihood of RPS mandates. While competitors’ waste-to-energy in- vestments (b 5 25.77; p , 0.05) and wind in- vestments (b 5 86.06; p , 0.05) are significant in Model 3, competitors’ solar investments (b 5 1049.9) are not, maybe because solar as a renewable is still early in its diffusion at a utility-scale.

Further, we conduct two robustness checks on RPS mandates: First, we test whether RPS mandates mediate the competitive effect on a focal firm’s new resource investment. We do not find support for a mediating effect. Second, given that policies are discussed publicly and take time to pass, we created a control variable where zero denotes states without discussion on RPS mandates and 1 denotes states where discussion on RPS mandates occurred or RPS mandates had been passed. Our results stayed the same when we included this control variable.

Table 8 presents a summary of our findings.

DISCUSSION AND CONCLUSION

We are interested in how competition and regu- latory policy interact in their effect on a focal firm’s new resource investments. While prior work in the RBV separately points to the influence of competi- tors’ resource decisions on a focal firm’s resource investments (e.g., Capron & Chatain, 2008; Peteraf, 1993), or to the influence of policy in international resource allocation decisions (e.g., Henisz & Delios, 2001; Levy & Spiller, 1994), less is known about whether the policy effect substitutes or complements the competitive effect. Overall, how regulatory policy relates to resource portfolio decisions has been little explored (Nehrt, 1998). While both competitors’

9 While coal and natural gas prices reflect fuel costs for possible substitute resources to renewables in a firm’s elec- tricity generation mix, oil prices affect mostly transportation fuel costs as oil makes up only 1% of utilities’ generation capacity in the U.S., down from around 25% in the 1970s. Therefore, we do not expect oil prices to affect renewable investments in the second stage model. Since coal is used as baseload electricity in many utilities’ electricity generation portfolio, it plays a different role from renewables that rep- resent intermittent power sources. We therefore do not ex- pectcoalpricestoaffectrenewableinvestmentsinthesecond stage. Furthermore, fuel price changes are less likely to affect utility’s asset investments that are long-term commitments andmorelikelytoaffectpolicymakerswhoaremoreficklein the short-term given their limited elected terms. Finally, natural gas is most closely related to renewables: flexible natural gas plants can quickly be scaled up and down to meet additional demand or balance intermittent renewable elec- tricity sources such as wind or solar.

2016 697Weigelt and Shittu

actions and policy reduce uncertainty with respect to the perceived value of a resource, we propose that a focal firm pays more attention to a regulatory mandate than competition, if the former is present. We posit that a regulatory mandate dampens the competitive effect in the context of clean technol- ogies, and that this effect is the stronger the more distant the new resource and the more established the policy is.

We summarize our findings next. We control for heterogeneity in a firm’s resource portfolio consist- ing of old resource endowments (fossils), hydro- electric power, nuclear power, and prior renewable generation assets. Consistent with prior work we find a negative effect of prior resource endowments on new resource investments (Dierickx & Cool, 1989; Leonard-Barton, 1992).

First, we find that competitors’ new resource in- vestments increase a focal firm’s subsequent new resource investments in waste-to-energy, wind, and solar. A reason for this finding may be that com- petitors’ investments reduce uncertainty regarding a new resource’s value and market feasibility. Additionally, prior work on non-market strategies

discusses how firms lobby regulatory bodies to pass policies in their favor (e.g., Bonardi et al., 2006). We build on this work to advance our second reason for why firms may follow their competitors’ lead: that is, competitors’ new resource investments may fore- shadow future policy. We find support for the argu- ment that competitors’ new resource investments increase the likelihood that a state has an RPS mandate.

Second, the interaction between competitors’ new resource investments and regulatory mandate is negative significant. Our finding implies that in the absence of a regulatory mandate that encourages in- vestment, firms use competitors as guidance for their own new resource investments. However, if a regulatory mandate exists, the competitive effect is dampened because firms pay more attention to the mandate than competitors’ actions. This may be be- cause competitors are subject to the same mandate. Firms may also ensure their own compliance with a mandate regardless of what competitors do. Or, the regulatory mandate may reflect the validation of the entire sector of clean energy causing firms to be less sensitive to a competitor’s foray into one particular

TABLE 6 Random-Effects Probit: DV 5 Likelihood of Having an RPS Policy at the State Level

Model 1 Model 2 Model 3

Constant 228.087** (13.545) 240.573*** (13.195) 237.459** (16.859) Competitors’ renewable investment 46.729*** (12.783) Competitors’ waste-to-energy investment 25.769** (12.961) Competitors’ wind energy investment 86.063** (34.760) Competitors’ solar energy investment 1049.918 (1025.782) Old resource endowments (fossil) 217.017*** (4.579) 214.427*** (4.129) 214.085*** (4.556) Hydroelectric power 232.665*** (10.152) 222.901*** (8.640) 218.873** (9.214) Nuclear power 27.200* (3.989) 22.737 (4.875) 23.393 (5.169) Gov. utilities market share in state 211.402* (6.834) 211.017** (5.248) 216.438* (8.637) Electricity demand in state 1.004 (1.041) 1.408 (1.049) 1.321 (1.312) Net interstate sales of electricity 2.325** (1.171) 2.822** (1.393) 2.882** (1.326) Sierra Club membership 1.649** (0.753) 1.590** (0.701) 1.644** (0.809) Democrats in legislature 15.377*** (5.110) 16.568*** (5.330) 17.409*** (6.056) Democratic governor 0.007 (0.539) 20.163 (0.666) 20.250 (0.765) Retail market deregulation 1.584* (0.858) 2.541** (1.238) 1.803 (1.157) Federal production tax credit 20.695* (0.381) 20.644 (0.422) 20.752* (0.448) Financial incentives 1.642** (0.816) 1.625** (0.756) 1.596* (0.829) Sun index 17.633** (8.630) 15.352* (8.158) 16.606* (8.891) Wind energy potential 0.890** (0.411) 0.991*** (0.331) 0.800** (0.382) Coal prices at state level 2.179*** (0.708) 1.818** (0.722) 1.753** (0.801) Natural gas prices at state level 0.409*** (0.144) 0.361** (0.156) 0.378** (0.168) Oil prices at state level 0.239* (0.127) 0.288** (0.126) 0.399*** (0.153) x 2 31.54** 52.20*** 44.11***

Number of observations 576 576 576

*** p , 0.01 ** p , 0.05 * p , 0.10 (two-tailed significance levels)

698 AprilAcademy of Management Journal

T A B L E 7

M o d el s w it h P re d ic te d R P S V a lu es

M ir ro ri n g M o d el s 2 a n d 3 in

T a b le s 2 – 5

A ll re n ew

a b le s

in v es tm

en t

A ll re n ew

a b le s

in v es tm

en t

W a st e- to -e n er gy

in v es tm

en t

W a st e- to -e n er gy

in v es tm

en t

W in d in v es tm

en t

W in d in v es tm

en t

S o la r

in v es tm

en t

S o la r in v es tm

en t

M o d el

1 M o d el

2 M o d el

1 a

M o d el

2 a

M o d el

1 b

M o d el

2 b

M o d el

1 c

M o d el

2 c

C o n st an

t 2 0 .0 8 1 (0 .0 7 8 )

2 0 .3 3 1 * * * (0 .0 7 7 )

2 7 .7 0 6 * * * (1 .2 4 8 )

2 7 .9 2 7 * * * (1 .2 1 4 )

2 1 5 .7 1 2 * * * (4 .0 4 8 )

2 1 3 .5 7 * * * (3 .4 0 7 )

2 1 8 .1 7 * * * (4 .4 0 3 )

2 1 9 .1 6 * * * (4 .5 4 8 )

C o m p et it o rs ’ re n ew

ab le

in v es tm

en t

(H y p o th es is

1 )

0 .2 6 1 * * * (0 .0 6 3 )

0 .4 1 7 * * * (0 .0 9 4 )

8 .6 9 9 * * * (1 .8 6 3 )

1 1 .8 4 5 * * * (2 .8 4 7 )

1 8 .4 8 2 * * (7 .3 1 7 )

2 5 .0 9 6 * * * (6 .5 5 9 )

2 2 6 .9 1 2 (7 8 .5 0 9 )

9 1 4 .8 6 * (5 0 4 .3 6 )

P re d ic te d R P S v al u es

(r eg u la to ry

p o li cy

)

0 .0 0 6 (0 .0 1 6 )

0 .0 0 7 (0 .0 1 6 )

2 0 .5 2 2 (0 .3 4 1 )

2 0 .4 5 8 (0 .3 4 3 )

2 0 .1 3 2 (0 .8 2 3 )

0 .1 8 6 (0 .6 8 6 )

5 .2 1 1 * * * (0 .9 9 5 )

5 .0 4 9 * * * (1 .0 4 1 )

Y ea

rs R P S p re se n t

0 .0 0 2 (0 .0 0 2 )

0 .0 0 4 (0 .0 0 2 )

0 .0 1 3 (0 .0 4 8 )

0 .0 3 8 (0 .0 5 2 )

0 .2 9 5 * * (0 .1 4 2 )

0 .1 8 9 * (1 0 8 )

0 .2 9 1 * * (0 .1 1 8 )

0 .4 1 2 * * * (0 .1 3 5 )

P re d ic te d R P S v al u es

X

C o m p et it o rs ’ in v es tm

en ts

(H y p o th es is

2 )

2 0 .3 0 8 * * (0 .1 3 8 )

2 7 .1 7 0 (4 .8 0 6 )

2 2 2 .7 0 * * (1 0 .0 9 )

2 9 7 1 .7 3 * (5 1 4 .0 9 )

O ld

re so u rc e en

d o w m en

t (f o ss il )

2 0 .3 1 2 * * * (0 .0 1 9 )

2 0 .3 1 2 * * * (0 .0 1 9 )

2 0 .8 3 1 * * (0 .3 8 2 )

2 0 .8 2 4 * * (0 .3 8 2 )

2 1 .9 2 7 * * (0 .8 8 9 )

2 1 .6 3 0 * * (0 .6 9 1 )

2 2 .7 2 0 * * * (0 .9 0 8 )

2 2 .4 5 5 * * * (0 .9 2 9 )

H y d ro el ec

tr ic

p o w er

2 0 .1 2 3 * * * (0 .0 1 5 )

2 0 .1 2 4 * * * (0 .0 1 5 )

2 0 .5 1 3 * (0 .2 6 8 )

2 0 .5 0 8 * (0 .2 6 9 )

2 0 .4 5 2 (0 .5 5 6 )

2 0 .3 9 1 (0 .4 6 7 )

2 0 .0 8 8 (0 .7 0 4 )

2 0 .0 3 6 (0 .7 2 3 )

N u cl ea

r p o w er

2 0 .0 4 6 * * (0 .0 2 3 )

2 0 .0 4 4 * (0 .0 2 3 )

2 0 .8 5 7 * * (0 .3 8 6 )

2 0 .8 4 9 * * (0 .3 8 7 )

2 0 .2 4 6 (1 .0 4 2 )

2 0 .3 2 7 (0 .7 4 9 )

2 0 .2 3 1 (0 .7 6 2 )

2 0 .2 5 1 (0 .7 8 6 )

P ri o r re n ew

ab le s

0 .1 7 0 * * * (0 .0 0 8 )

0 .1 6 9 * * * (0 .0 0 8 )

3 .8 0 2 * * * (0 .1 6 7 )

3 .7 9 8 * * * (0 .1 6 7 )

3 .3 5 9 * * * (0 .4 7 7 )

2 .8 1 6 * * * (0 .3 4 6 )

1 .5 7 6 * * * (0 .2 9 9 )

1 .6 0 9 * * * (0 .3 0 2 )

F ir m

si ze

(M W

o p er at io n al )

0 .0 3 1 * * * (0 .0 0 3 )

0 .0 3 1 * * * (0 .0 0 3 )

0 .4 7 9 * * * (0 .0 5 2 )

0 .4 8 4 * * * (0 .0 5 2 )

0 .1 3 7 (0 .1 3 8 )

0 .0 4 3 (0 .1 0 2 )

0 .2 7 9 * * (0 .1 3 6 )

0 .2 6 7 * (0 .1 3 8 )

A v er ag

e p la n t ag

e 2 0 .0 0 2 * * * (0 .0 0 0 )

2 0 .0 0 2 * * * (0 .0 0 0 )

2 0 .0 2 0 * * * (0 .0 0 6 )

2 0 .0 2 0 * * * (0 .0 0 6 )

2 0 .0 1 3 (0 .0 1 5 )

2 0 .0 0 5 (0 .0 1 1 )

2 0 .0 4 9 * * * (0 .0 1 6 )

2 0 .0 4 8 * * * (0 .0 1 6 )

P ri v at e en

er gy

fi rm

d u m m y

0 .1 2 6 * * * (0 .0 1 6 )

0 .1 2 6 * * * (0 .0 1 6 )

0 .8 4 9 * * * (0 .2 1 3 )

0 .8 5 3 * * * (0 .2 1 3 )

2 2 .8 5 9 * * * (0 .7 7 9 )

2 1 .4 1 9 * * (0 .5 6 8 )

2 5 .5 2 1 * * * (0 .7 9 1 )

2 5 .7 6 9 * * * (0 .8 5 4 )

IO U

d u m m y

0 .0 4 0 * * (0 .0 2 0 )

0 .0 3 9 * * (0 .0 1 9 )

0 .2 0 5 (0 .2 6 1 )

0 .2 1 2 (0 .2 6 2 )

1 .2 5 9 * (0 .6 4 3 )

1 .0 3 8 * * (0 .4 9 1 )

2 0 .2 3 5 (0 .7 7 1 )

2 0 .2 0 1 (0 .7 9 6 )

G eo

gr ap

h ic

d iv er si fi ca ti o n

0 .0 0 1 (0 .0 0 1 )

0 .0 0 1 (0 .0 0 1 )

2 0 .0 0 2 (0 .0 1 3 )

2 0 .0 0 3 (0 .0 1 3 )

0 .1 2 9 * * * (0 .0 3 9 )

0 .0 6 1 * (0 .0 3 2 )

0 .0 2 2 (0 .0 4 9 )

0 .0 3 8 (0 .0 5 2 )

G o v .u

ti li ti es

m ar k et

sh ar e in

st at e

0 .0 3 8 (0 .0 3 1 )

0 .0 4 4 (0 .0 3 1 )

0 .6 1 6 (0 .4 8 4 )

0 .6 9 2 (0 .4 8 9 )

2 1 .2 8 5 (1 .2 1 7 )

2 0 .7 9 4 (0 .9 4 6 )

2 0 .2 2 3 (1 .5 2 3 )

2 0 .1 6 0 (1 .6 1 8 )

E le ct ri ci ty

d em

an d in

st at e

2 0 .0 0 1 (0 .0 0 7 )

2 0 .0 0 1 (0 .0 0 7 )

0 .2 4 7 * * (0 .1 0 8 )

0 .2 2 1 * * (0 .1 0 9 )

0 .1 3 2 (0 .3 6 9 )

0 .0 5 3 (0 .3 1 2 )

1 .4 0 7 * * * (0 .4 1 1 )

1 .4 1 3 * * * (0 .4 3 2 )

N et

in te rs ta te

sa le s o f el ec tr ic it y

0 .0 1 0 (0 .0 0 9 )

0 .0 1 1 (0 .0 0 9 )

0 .0 6 9 (0 .1 7 2 )

0 .0 7 5 (0 .1 7 2 )

2 0 .1 3 9 (0 .4 3 8 )

0 .0 8 8 (0 .4 0 2 )

2 1 .5 9 9 * * * (0 .5 3 3 )

2 1 .7 8 5 * * * (0 .5 6 0 )

S ie rr a C lu b m em

b er sh

ip 0 .0 0 4 (0 .0 0 4 )

0 .0 0 3 (0 .0 0 4 )

2 0 .0 1 8 (0 .0 6 6 )

2 0 .0 3 5 (0 .0 6 7 )

0 .4 6 9 * * * (0 .1 7 1 )

0 .2 0 3 (0 .1 4 2 )

0 .6 8 7 * * * (0 .1 9 4 )

0 .6 5 1 * * * (0 .2 0 2 )

D em

o cr at s in

le gi sl at u re

0 .0 5 1 (0 .0 3 5 )

0 .0 6 0 * (0 .0 3 5 )

0 .1 5 6 (0 .6 6 0 )

0 .2 9 6 (0 .0 6 6 )

3 .0 9 5 (1 .9 1 1 )

2 .8 0 9 * (1 .6 8 3 )

2 3 .6 2 6 * (1 .9 0 7 )

2 4 .5 8 6 * * (2 .0 6 3 )

D em

o cr at ic

go v er n o r

2 0 .0 1 9 * * * (0 .0 0 5 )

2 0 .0 2 2 * * * (0 .0 0 5 )

2 0 .1 9 9 * (0 .1 0 8 )

2 0 .2 1 9 * * (0 .1 1 0 )

0 .1 9 9 (0 .2 6 7 )

0 .1 5 6 (0 .2 3 1 )

0 .5 1 6 * (0 .2 8 4 )

0 .4 0 7 (0 .2 9 5 )

R et ai l m ar k et

d er eg

u la ti o n

2 0 .0 0 5 (0 .0 0 9 )

2 0 .0 0 3 (0 .0 0 9 )

0 .3 6 6 * * (0 .1 7 1 )

0 .3 8 1 * * (0 .1 7 2 )

2 0 .6 5 2 (0 .5 4 6 )

2 0 .7 3 6 (0 .4 8 2 )

2 0 .7 2 6 (0 .5 4 1 )

2 0 .8 7 5 (0 .5 7 8 )

F ed

er al

p ro d u ct io n ta x cr ed

it 2 0 .0 0 1 (0 .0 0 3 )

2 0 .0 0 1 (0 .0 0 3 )

0 .0 0 8 (0 .0 8 1 )

0 .0 1 4 (0 .0 8 1 )

2 0 .1 2 0 (0 .1 8 1 )

2 0 .0 9 5 (0 .1 6 1 )

2 0 .2 1 9 (0 .1 5 9 )

2 0 .2 0 2 (0 .1 6 1 )

F in an

ci al

in ce

n ti v es

2 0 .0 0 1 (0 .0 0 4 )

2 0 .0 0 2 (0 .0 0 4 )

0 .0 7 3 (0 .0 7 4 )

0 .0 6 3 (0 .0 7 5 )

0 .0 3 7 (0 .2 0 0 )

2 0 .0 2 2 (0 .1 5 4 )

0 .2 3 0 (0 .1 7 9 )

0 .2 2 1 (0 .1 8 3 )

M o n o p o ly

p o w er

2 0 .1 1 5 * * (0 .0 5 8 )

2 0 .1 0 8 * (0 .0 5 8 )

2 1 .9 1 4 * * (0 .8 7 9 )

2 1 .9 0 7 * * (0 .8 8 1 )

5 .2 7 3 * * (2 .1 7 4 )

3 .6 2 4 * * (1 .6 7 8 )

7 .7 6 0 * * * (2 .1 0 8 )

8 .2 2 6 * * * (2 .2 4 5 )

S u n in d ex

2 0 .0 5 9 * (0 .0 3 2 )

2 0 .0 5 5 * (0 .0 3 2 )

0 .1 6 6 (0 .4 6 1 )

0 .2 4 7 (0 .4 6 4 )

2 6 .0 8 9 * * * (1 .4 1 2 )

2 3 .2 6 3 * * * (1 .1 0 8 )

2 2 .6 7 1 (1 .6 4 3 )

2 3 .1 0 3 * (1 .7 1 8 )

W in d en

er gy

p o te n ti al

2 0 .0 0 1 (0 .0 0 2 )

2 0 .0 0 1 (0 .0 0 2 )

2 0 .0 6 6 * * * (0 .0 2 4 )

2 0 .0 6 2 * * * (0 .0 2 4 )

0 .6 3 8 * * * (0 .1 4 5 )

0 .4 7 9 * * * (0 .1 2 3 )

2 0 .3 5 3 * * * (0 .0 7 8 )

2 0 .3 7 3 * * * (0 .0 8 2 )

x 2

2 2 1 1 .5 * * *

2 2 1 8 .8 * * *

8 1 7 .3 * * *

8 1 3 .5 * * *

2 1 3 .1 * * *

1 3 8 .9 * * *

2 0 4 .6 * * *

1 8 9 .2 6 * * *

N u m b er

o f o b se rv at io n s

1 0 5 1 8

1 0 5 1 8

1 0 5 1 8

1 0 5 1 8

1 0 5 1 8

1 0 5 1 8

1 0 5 1 8

1 0 5 1 8

* * * p ,

0 .0 1

* * p ,

0 .0 5

* p ,

0 .1 0 (t w o -t ai le d si gn

if ic an

ce le v el s)

N o te :1

0 5 1 8 o b se rv at io n s, 1 5 4 2 fi rm

s, 1 1 y ea

rs .

2016 699Weigelt and Shittu

new energy type because the mandate gives them specific direction.

Third, this study is, to the best of our knowledge, the first that breaks out waste-to-energy, wind, and solar investments in the same study.10 We find that the dampening effect of regulatory mandate on the competitive effect is the stronger the more distant the new resource is in knowledge from the old resource base. We compare waste-to-energy (renewable less distant from fossil as it uses a combustion process to generate electricity) to wind and solar. Regulatory mandates seem to play a greater role the more distant the new resource is, maybe because firms face greater uncertainty investing in new resources whose un- derlying knowledge differs greatly from their exist- ing knowledge base (Hill & Rothaermel, 2003; Tushman & Anderson, 1986). The positive, signifi- cant policy effect for solar, but not for waste-to- energy, further supports this conclusion.

Fourth, we further investigate the uncertainty re- ducing impact of regulatory mandate relative to that of competition in a sub-sample of firms in states with an RPS mandate. As yearly RPS goals increase (i.e., the mandate becomes more established) regulators become arguably more committed to renewables which, in turn, lowers the likelihood of RPS repeal. We find a dampening effect of regulatory mandate on the competitive effect for waste-to-energy and solar investments. A reason for this finding may be that

higher RPS yearly goals reduce uncertainty with re- spect to market demand for renewables and the persistence of the mandate.

We contribute to the RBV literature on resource portfolios that has focused on how firms change their resource position over time (e.g., Lavie, 2006; Sirmon & Hitt, 2009; Sirmon et al., 2007). Specifi- cally, we add to work that has extended the RBV beyond how established resource endowments con- strain a firm’s new resource investments (Dierickx & Cool, 1989; Leonard-Barton, 1992) to consider the influence of external drivers on firm resource in- vestments (e.g., Capron & Chatain, 2008; Delmas et al., 2007; Kim, 2013; Miller & Shamsie, 1999). First, our findings stress the role of regulatory man- dates relative to that of competitors’ resource strate- gies in the composition of a firm’s resource portfolio. These findings imply that resource redeployment is not simply a function of internal decisions but a response to external forces, thereby establishing a bridge from the RBV to institutional theory. We thus respond to prior work in the RBV that has im- plored researchers to consider environmental factors when assessing the value of a firm’s resource port- folio (Black & Boal, 1994) or to acknowledge the difficulty of making superior resource investment decisions in uncertain environments (Amit & Schoemaker, 1993).

Second, we show that when a firm faces several external drivers, the firm is likely to economize by paying more attention to some drivers and less to

TABLE 8 Summary of Analyses Results

All Renewable Resources Waste-to-Energy Wind Solar

Presence of RPS 1 not significant 1 not significant 1 not significant 1 significant Years RPS present 1 significant 1 not significant 1 significant 1 significant Hypothesis 1—Competitive effect Competitors’ new resource investment 1 significant 1 significant 1 significant 1 not significant Hypothesis 2—RPS dampens competitive effect RPS X Competitors’ new resource investment – significant – not significant – significant – significant Split-sample test for Hypothesis 2 Competitors’ new resource investment (non-RPS

states) 1 significant 1 significant 1 significant 1 significant

Competitors’ new resource investment (RPS states) 1 not significant 1 not significant 1 not significant 1 not significant Hypothesis 3—RPS dampens competitive effect (by

resource type) (Knowledge distance of new resource investment) RPS X Competitors’ new resource investment – significant – not significant – significant – significant Hypothesis 4—RPS dampens competitive effect (RPS

states only) (RPS is more established in a state) RPS yearly goals X Competitors’ new resource

investment 1 not significant – significant – not significant – significant

10 We thank the reviewers for this suggestion.

700 AprilAcademy of Management Journal

others. The dampening effect of regulatory mandate on the competitive effect focuses our attention on non-market drivers for new resource investments. Our study thereby points to an understudied area in the RBV literature which is the effect of regulatory policy on the value of a resource and firms’ resource portfolio decisions.

Third, we unpack the influence of competitors relative to that of regulatory mandate across three renewable resource types: waste-to-energy, wind, and solar. By doing so, we show that RPS mandates vary in their effect across renewable resource types. Studies that aggregate renewables may cloud the ef- fect of RPS mandates on firms’ renewable invest- ments. The direct competitive effect that we observe may hint at why some states without RPS mandates still have renewable investment levels close to those in states with RPS mandates.

Our study has practical implications for policy makers: Our findings imply that regulatory mandates for the adoption of clean technologies have a greater impact if few competitors have invested in clean technologies. Moreover, our findings show that reg- ulatory mandates have a greater influence the more distant a new resource is from the resources that in- cumbents are familiar with. We find a stronger regu- latory mandate effect for solar than for waste-to-energy. Lastly, the study’s findings show that regulatory man- dates are the more effective the lower the likelihood for their repeal. Overall, our findings imply that regulatory mandates foster innovation in the U.S. electric utilities industry and supersede competition as a focal firm’s guide for new resource investments.

This study’s limitations are directions for future research. First, Christensen and Bower (1996) sug- gest that existing customer needs can have a strong impact on firms’ resource allocation. While we con- trol for state electricity demand and retail market deregulation, we do not explicitly study the role that customer demand plays in deregulated electricity markets where customers can choose among elec- tricity providers. Second, although the extant liter- ature uses a dichotomous measure for RPS (e.g., Delmas & Montes-Sancho, 2011; Fabrizio, 2012; Yin & Powers, 2010), such a measure is crude. We begin to address the variance in policy across states by looking at yearly RPS goals in a sub-sample of firms in states with an RPS mandate, but more research is needed that takes a more fine-grained look at the policy’s structure. Third, this study focuses on the U.S. electricity industry. Future work may test how the interplay between competition and regulatory policy occurs in a different industry context. Fourth,

future work could study the influence of value net- works on the role of resource portfolios following Fabrizio (2012) who examines the influence of firm capabilities and institutions on vertical integration— for example, the influence of transmission line own- ership and transmission agreements (make or buy) on renewable resource presence in a firm’s electricity portfolio. Lastly, more work is needed that explores the role of firms’ nonmarket strategies in the context of firms’ resource positioning and resource portfolio configurations.

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Carmen Weigelt ([email protected]) is an associate professor in strategy at the A.B. Freeman School of Busi- ness at Tulane University. She received her PhD in strate- gic management from the Fuqua School of Business at Duke University. Her research interests are at the in- tersection of innovation and strategy with a focus on firm boundaries, outsourcing strategies, and the adoption of innovation and new technologies.

Ekundayo Shittu ([email protected]) is an assistant pro- fessor in engineering management and systems engineer- ing in the School of Engineering and Applied Sciences at The George Washington University. He received his PhD in Industrial Engineering and Operations Research from the University of Massachusetts Amherst. His research focuses on decision making under uncertainty particularly with respect to the interplay between economics, environmen- tal policy, competition, energy technology investments, and climate change.

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