Deconstructing Solar Photovoltaic Pricing: The Role of Market Structure, Technology, and Policy.

• Author: Gillingham, Kenneth

1. INTRODUCTION

   Installations of solar photovoltaic (PV) systems have expanded rapidly over the past decade, with continued growth anticipated (Baker et al. 2013, IPCC 2014). In 2013 alone, 38 GW of solar PV were installed globally, marking a roughly 25% increase from the prior year (EPIA 2014). The United States has also witnessed this dramatic growth, with an average annual growth of residential and commercial solar PV systems exceeding 40 percent per year over the last decade (SEIA/GTM Research 2014). Along with this growth has been a substantial decline in PV system prices, with a roughly 50 percent decline from 2009 to 2013 (Barbose et al. 2014, Bazilian et al. 2013, Candelise et al. 2013). Amid this decline, however, there remains considerable heterogeneity in PV system pricing. For example, among residential and small commercial systems installed in 2013, roughly 20 percent were sold for less than $3.90/Watt (W), while a similar percentage was priced above $5.60/W (Barbose et al. 2014).

   This paper empirically examines the observed heterogeneity in equilibrium PV system prices in the United States. We explore different sources of variation to provide evidence on the determinants of such price dispersion, including classic hedonic variables such as the characteristics of the systems and demographics, as well as other plausible equilibrium price shifters, including market structure and policy variables. We use a rich dataset of nearly 100,000 individual solar PV systems installed across the United States over the 2010-2012 period, focusing our analysis on residential and commercial PV systems under 10 kW. We focus on consumer-owned PV systems, but also include a large number of "non-appraised value" third-party owned (TPO) systems in our analysis. As expected, we find that PV system prices differ based on characteristics of the systems. However, our results also point to the important roles of market structure, firm-specific characteristics, and government policy.

   Understanding the determinants of the equilibrium price of solar PV systems is useful from both an academic and policy perspective. Economists have developed a deep theoretical and empirical literature that points to a variety of explanations for price variation (e.g., see Baye et al. (2006) for a comprehensive review). The simplest explanation is that products are not homogenous, so we observe a differentiated product equilibrium, such as in the Bertrand oligopoly model. This hypothesis would lead to price variation based on system characteristics as well as market structure. But even when we have a homogenous product, there are theoretical explanations supporting equilibrium price variation due to information or search costs by consumers or firms (Stigler 1961). Over the past several decades, a variety of information acquisition and transmission models have been posited in the theoretical literature (Burdett and Judd 1983, Carlson and McAfee 1983, Salop and Stiglitz 1977, Varian 1980). With frictions from information acquisition and transmission, consumers trade off the costs (e.g., opportunity or other costs) of obtaining a quote from another firm against the expected benefit from this quote. Price variation then follows with heterogeneous consumer or firm costs--or market power.

   An extensive empirical literature examines the extent to which these factors influence equilibrium prices in a variety of settings, with much of the recent work in online internet markets (Baye et al. 2004, Brynjolfsson and Smith 2000, Ellison and Ellison 2009) and gasoline markets (Barron et al. 2004, Chouinard and Perloff 2007, Shepard 1991). Evidence of significant price variation has also been demonstrated in air travel (Borenstein and Rose 1994), pharmaceuticals (Sorensen 2000), books (Clay et al. 2001), and for many other goods and services in the economy (Crucini and Yilmazkuday 2014). A common theme in many of these papers is that variables capturing market structure, firm characteristics, and policy interventions are important for explaining price variation. This relates closely to a body of work in electricity markets, such as Andersson and Bergman (1995), which establishes the importance of market structure and regulation for equilibrium electricity prices.

   By examining market structure and policy variables, this paper also provides policy-relevant insights. Seel et al. (2014) find the striking result that average residential PV system prices in Germany are roughly half of those in the United States. Since solar PV modules are a globally traded commodity, some of the more likely explanations for this difference in PV system price are differences in policy, characteristics of the local installer base, and market structure. By exploring how these factors influence equilibrium prices in the United States, we shed light on sources of price variability that may be amenable to policy interventions aimed at facilitating cost reductions. With the U.S. Department of Energy's SunShot Initiative dedicated to reducing the cost of installing solar PV systems, and with a wide variety of local, state and national incentive programs for solar, these results can provide relevant policy insights. Our findings, for example, suggest not only that the level of installer competition may affect solar PV prices, but also that some combination of installer experience and scale is bringing down costs, consistent with findings in Bollinger and Gillingham (2014). Moreover, we find evidence that policy measures are associated with differences in PV system prices, much as Dong and Wiser (2013) and Burkhardt et al. (2015) find that local permitting processes can influence PV system prices.

   Our approach follows a vein of the rich price dispersion literature, by estimating the reduced-form relationship between equilibrium prices and likely supply and demand shifters of these prices (Barron et al. 2004, Chouinard and Perloff 2007, Clay et al. 2001, Haynes and Thompson 2008, Shepard 1991). This approach draws from the classic hedonic pricing literature, widely used in economics (Rosen 1974). Wiser et al. (2007) explore a similar approach using data from the early years of California's solar PV market, finding evidence suggestive of higher solar PV prices with policy incentives, economies of scale at the system level, and lower prices for systems on new homes. Davidson and Steinberg (2013) use more recent data on solar PV prices in California and focus on differences in prices between third-party-owned systems that are priced based on the appraised value and all other systems, finding quite different results for each. Their findings also suggest the importance of heterogeneity in installers for the equilibrium price of PV systems. The present paper uses a much more comprehensive dataset to more deeply explore the primary factors that influence equilibrium prices across the United States.

   The remainder of the paper is organized as follows. In Section 2, we describe our dataset. Section 3 descriptively demonstrates the extensive heterogeneity in PV system prices and posits several hypotheses for this heterogeneity. Section 4 describes our empirical methodology, while Section 5 presents our primary results and robustness checks. Section 6 concludes and discusses policy implications.

2. DATA

   This paper leverages an extensive dataset of PV system installations, compiled for Lawrence Berkeley National Laboratory's annual Tracking the Sun (TTS) report series. The raw dataset used in this study, from Barbose et al. (2013), includes reported PV system prices for approximately 231,000 PV installations installed from 1998 through 2012, representing 68% of cumulative grid-connected residential and commercial PV capacity in the United States as of year-end 2012. (1) The data were collected from 47 PV incentive programs in 29 states. All installations that receive a government incentive from these programs are included in our raw data.

   The data contain the total transaction price for each PV system installation, whether that transaction is between installer and site host (in the case of customer-owned systems) or between installer and third-party financier (in the case of third-party owned systems). The transaction price is the pre-incentive price the system owner pays prior to any subsidy payments, such as upfront rebates, tax credits, renewable energy certificates, or performance-based incentives based on the production of the system. Additionally, the data contain detailed information about each PV installation: the date of installation; system size; zip code of the installation; whether the system is residential, commercial, or other; whether the system is customer-owned or owned by a third party (where the host consumer leases the PV system or purchases power from the system); whether the system is installed in new construction; installer of the system; direct incentive payments; presence of a battery; whether the system is ground-mounted; module and inverter manufacturer; and module and inverter model. Based on the module and inverter model, we can infer further characteristics, including whether the module is building integrated PV (vs. rack-mounted), thin-film PV (vs. crystalline), Chinese made (vs. non-Chinese made), and whether the PV system uses micro-inverters (vs. central or string inverters).

   We also bring in data on module and inverter price indices from SEIA/GTM Research to capture U.S. module and inverter price trends (SEIA/GTM Research 2014). Since both modules and inverters are globally traded commodities, the trends in these costs will generally be similar across systems installed in the U.S., and thus these indices are useful cost shifters. (2) Furthermore, we use data on the 2011 combined average state and local sales tax at the state level (Tax Foundation 2014), accounting for the existence and timing of sales tax exemptions for solar PV (DSIRE 2014), and a state-level...