LBNL-59282 

NREL/TP-620-39300 

ERNEST ORLANDO LAWRENCE

BERKELEY NATIONAL LABORATORY

Letting the Sun Shine on Solar Costs: 

An Empirical Investigation of Photovoltaic 

Cost Trends in California 

Ryan Wiser, Mark Bolinger, Peter Cappers, 

and Robert Margolis 

Environmental Energy 

Technologies Division 

 

January 2006

Download from http://eetd.lbl.gov/EA/EMP 

The work described in this paper was funded by the Assistant Secretary of Energy 

Efficiency and Renewable Energy (Office of Planning, Budget & Analysis), and by the 

Office of Electricity Delivery and Energy Reliability (Electric Markets Technical 

Assistance Program) of the U.S. Department of Energy under Contract No. DE-AC02-

05CH11231. 
Disclaimer

This document was prepared as an account of work sponsored by the United States 

Government. While this document is believed to contain correct information, neither 

the United States Government nor any agency thereof, nor The Regents of the 

University of California, nor any of their employees, makes any warranty, express or 

implied, or assumes any legal responsibility for the accuracy, completeness, or 

usefulness of any information, apparatus, product, or process disclosed, or represents 

that its use would not infringe privately owned rights. Reference herein to any specific 

commercial product, process, or service by its trade name, trademark, manufacturer, or 

otherwise, does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States Government or any agency thereof, or The Regents of 

the University of California. The views and opinions of authors expressed herein do 

not necessarily state or reflect those of the United States Government or any agency 

thereof, or The Regents of the University of California. 

Ernest Orlando Lawrence Berkeley National Laboratory is an equal opportunity 

employer. 
LBNL-59282 

NREL/TP-620-39300 

Letting the Sun Shine on Solar Costs: 

An Empirical Investigation of Photovoltaic Cost Trends 

in California 

Prepared for the 

Office of Planning, Budget & Analysis 

Assistant Secretary for Energy Efficiency and Renewable Energy 

U.S. Department of Energy 

and the 

Electric Markets Technical Assistance Program 

Office of Electricity Delivery and Energy Reliability 

U.S. Department of Energy 

Principal Authors: 

Ryan Wiser and Mark Bolinger 

Ernest Orlando Lawrence Berkeley National Laboratory 

Peter Cappers 

Neenan Associates 

Robert Margolis 

National Renewable Energy Laboratory 

January 2006 

The work described in this paper was funded by the Assistant Secretary of Energy Efficiency and 

Renewable Energy (Office of Planning, Budget & Analysis), and by the Office of Electricity 

Delivery and Energy Reliability (Electric Markets Technical Assistance Program) of the U.S. 

Department of Energy under Contract No. DE-AC02-05CH11231. 
Acknowledgements 

The work described in this paper was funded by the Assistant Secretary of Energy Efficiency and 

Renewable Energy (Office of Planning, Budget & Analysis), and by the Office of Electricity 

Delivery and Energy Reliability (Electric Markets Technical Assistance Program) of the U.S. 

Department of Energy under Contract No. DE-AC02-05CH11231. We would particularly like to 

thank Scott Hassell and Lawrence Mansueti, both of the U.S. Department of Energy, for their 

support of this work. The support of the Department of Energy’s Solar Energy Technologies 

Program is also acknowledged. For providing and helping decipher the California Energy 

Commission’s database of solar project costs, we thank Todd Lieberg and Tony Brasil 

(California Energy Commission). Finally, for reviewing earlier versions of this manuscript, we 

thank Lewis Milford (Clean Energy Group), JP Ross (Vote Solar), Galen Barbose (LBNL), Paul 

Denholm (NREL), Scott Hassell (US DOE), Lori Schell (Empowered Energy), Jan McFarland 

(Americans for Solar Power), Howard Wenger (PowerLight), Bill Blackburn and Tim Tutt 

(California Energy Commission), members of the SGIP Working Group (from Southern 

California Edison, Southern California Gas, Pacific Gas & Electric, San Diego Gas & Electric, 

the San Diego Regional Energy Office, and the Energy Division of the California Public Utilities 

Commission), and Barry Cinnamon (Akeena Solar). Of course, any remaining errors or 

omissions are the sole responsibility of the authors. 
Table of Contents 

Executive Summary......................................................................................................................... i 

1. Introduction .................................................................................................................................1 

2. The California Solar Market in Context......................................................................................3 

2.1 Market Overview .................................................................................................................3 

2.2 Policy Incentives..................................................................................................................4 

2.3 Market Concerns..................................................................................................................6 

3. Data and Methodology ................................................................................................................9 

3.1 Data......................................................................................................................................9 

3.2 Variables............................................................................................................................10 

3.3 Methodology......................................................................................................................15 

4. Analysis Results: CEC Systems Under 30 kW in Size ............................................................17 

4.1 Summary Statistics.............................................................................................................17 

4.2 Regression Results.............................................................................................................20 

4.2.1 The Impact of Time, Average Module Cost, and System Status...............................20 

4.2.2 Policies and Incentives...............................................................................................22 

4.2.3 System Size................................................................................................................22 

4.2.4 Installation Type ........................................................................................................23 

4.2.5 Module Type..............................................................................................................23 

4.2.6 Installer/Retailer Type and Experience......................................................................23 

4.2.7 System Location and Population Density..................................................................24 

5. Analysis Results: CPUC Systems 30 kW and Above in Size ..................................................25 

5.1 Summary Statistics.............................................................................................................25 

5.2 Regression Results.............................................................................................................27 

5.2.1 The Impact of Time, Average Module Cost, and System Status...............................28 

5.2.2 Policies and Incentives...............................................................................................29 

5.2.3 System Size................................................................................................................30 

5.2.4 Module Type..............................................................................................................30 

5.2.5 Installer Type and Experience ...................................................................................30 

5.2.6 System Location.........................................................................................................31 

6. Comparing the CEC and CPUC Programs................................................................................32 

6.1 Fundamental Cost Variations Between Systems Funded by the Two Programs...............32 

6.2 Consistency of Cost Reductions Between Systems Funded by the Two Programs ..........34 

7. Conclusions and Recommendations..........................................................................................37 

References......................................................................................................................................40 

Appendix A: Data Manipulation and Cleaning ............................................................................44 

Appendix B: Cost Distributions Over Time .................................................................................50 

Appendix C: Additional Regression Analysis Results.................................................................51 
List of Figures and Tables 

Figure 1. Grid-Connected Solar Capacity in California, through November 15, 2005 ............... 3 

Figure 2. Quarterly and Cumulative Rebate Application History (CEC and CPUC).................. 5 

Figure 3. Evolution of the Standard Rebates for the CEC and CPUC Programs......................... 6 

Figure 4. Average Installed Cost, by System Size (CEC).......................................................... 17 

Figure 5. Average Installed Cost Over Time (6-Month Intervals), by System Size (CEC)....... 18 

Figure 6. Average Installed Cost, by System or Installation Type (CEC)................................. 18 

Figure 7. Impact of Standard Rebate Level on Average Installed Cost (CEC).......................... 19 

Figure 8. Average Installed Cost Over Time, by Module and Non-Module Costs (CEC) ........ 19 

Figure 9. Average Installed Cost, by System Size (CPUC)....................................................... 25 

Figure 10. Average Installed Cost Over Time (6-Month Intervals), by System Size (CPUC).... 26

Figure 11. Average Installed Cost, by System or Installation Type (CPUC).............................. 26 

Figure 12. Impact of Rebate Level on Average Installed Cost (CPUC)...................................... 27 

Figure 13. Average Installed Cost Over Time, by Module and Non-Module Costs (CPUC)..... 27

Figure 14. Average Installed Cost, by System Size (CEC and CPUC)....................................... 32 

Figure 15. Average Installed Cost Over Time (CEC and CPUC) ............................................... 34 

Figure 16. Distribution of System Costs Over Time (CEC)........................................................ 50 

Figure 17. Distribution of System Costs Over Time (CPUC) ..................................................... 50 

Table 1. Summary Information on the Two Final Datasets ......................................................... 9 

Table 2. Independent Variables Used in Analysis...................................................................... 13 

Table 3. Summary Statistics of Variables Used in Analysis...................................................... 14 

Table 4. Regression Results for CEC Dataset (PV Systems < 30 kW)...................................... 21 

Table 5. Regression Results for CPUC Dataset (PV Systems 25 - 1,063 kW).......................... 28 

Table 6. Cost Variations Between the CEC and CPUC Programs: Descriptive Statistics........ 33

Table 7. Cost Variations Between the CEC and CPUC Programs: Regression Results........... 33 

Table 8. Regression Results for Pooled CPUC/CEC Dataset .................................................... 35 

Table 9. Data Manipulation and Cleaning: CEC Dataset.......................................................... 44 

Table 10. Data Manipulation and Cleaning: CPUC Dataset....................................................... 48 

Table 11. Summary Statistics of Variables Used in Analysis (Completed Systems Only)......... 51

Table 12. Regression Results for CEC Dataset (Completed Systems Only)............................... 52 

Table 13. Regression Results for CPUC Dataset (Completed Systems Only)............................ 53 
Executive Summary 

Introduction 

Markets for customer-sited photovoltaic (PV) systems are expanding rapidly, albeit from a small 

base. Government incentives aimed at encouraging reductions in the cost of PV over time are 

the principal drivers for the recent worldwide growth in grid-connected PV capacity. 

This report provides an in-depth statistical analysis of PV system costs in California. Through 

mid-November 2005, a total of 130 MWAC of grid-connected solar capacity was installed 

throughout California,i

 making that state the dominant market for PV in the United States, 

though it still stands a distant third on a worldwide basis behind Germany and Japan. 

The results presented here are based on an analysis of 18,942 grid-connected PV systems totaling 

254 MWAC,

ii either installed, approved for installation, or waitlisted (approved but awaiting 

program funding) under what are currently the two largest PV programs in the state. This 

analysis provides insights on California’s PV market by exploring cost trends, and by untangling 

the various factors that affect the cost of PV systems. Results also have important policy 

ramifications, as they address the interaction between incentive levels and installed costs, and the 

relative cost of different PV applications. 

California’s Solar Programs 

California’s PV market is driven by a mixture of state and local incentives. Most prominent are 

capital cost rebates – denominated in $ per Watt – offered to PV system installers or owners to 

“buy down” the installed cost of solar installations. The two most significant current rebate 

programs are overseen by the California Energy Commission (CEC) and the California Public 

Utilities Commission (CPUC), and it is on these two programs that our analysis is based. 

The CEC has administered a PV rebate program since March 1998, focusing more recently on 

grid-connected systems under 30 kW in size. The CPUC’s program began accepting 

applications in July 2001, and provides rebates to PV systems of at least 30 kW in size. Both 

programs primarily target customers served by the state’s investor-owned utilities.iii Over time, 

both programs have altered the structure and size of their incentives for PV installations, as 

shown in Figure ES-1. In particular, the CEC initiated five gradual reductions beginning in 

2003, while the CPUC imposed a single large reduction in late 2004 and a more recent reduction 

in mid-December 2005. On January 12, 2006, the CPUC ordered a dramatic expansion of these 

programs with a $3.2 billion, 11-year program of declining incentives. 

 

i

 This estimate of 130 MWAC includes PV systems funded under municipal utility programs, in addition to the CEC 

and CPUC programs analyzed in this report. 

ii Unless explicitly presented as otherwise, data on PV capacity and costs are expressed throughout this report in 

terms of WAC (e.g., WAC, kWAC, MWAC, $/WAC), which we convert (where necessary) from WDC-STC (DC Watts at 

standard test conditions) using a de-rate factor of 0.84. We acknowledge that many other solar programs and data 

sources use WDC-STC, making comparisons of California data with those in other states and countries more difficult. 

Given, however, that our underlying system cost data is expressed in terms of WAC, this is the standard that we use. iii At various times, customers of publicly-owned utilities have also been eligible to participate. 

i
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* Within the CEC's program, systems installed on affordable housing and schools have, at times, received higher incentives; 

ow ner-installed systems have, at times, received low er incentives; systems >30 kWAC w ere eligible for rebates from program 

inception to February 2003; and systems >10 kWAC received $2.5/WAC (capped at 40%) from March 1999 to February 2001.

50% CPUC Cap No CPUC Cap

50% CEC Cap No CEC Cap

Figure ES-1. Standard Rebates for the CEC and CPUC Programs 

Analysis Results 

The CEC dataset used for our analysis was updated through April 2005, and contains 17,889 data 

records (72.8 MWAC), including 12,856 completed systems (48.5 MWAC) and 5,033 systems that 

had been approved for a rebate, but that were awaiting completion at the time we received the 

dataset (24.3 MWAC). The CPUC program generally covers systems of at least 30 kW, and our 

dataset includes 1,053 data records (180.8 MWAC), including 327 completed systems (35.7 

MWAC), 464 approved systems (73.4 MWAC), and 262 waitlisted systems (71.7 MWAC). 

Analysis of each dataset was conducted using multivariate regression techniques; the dependent 

variable was the pre-rebate installed cost of PV systems, in 2004 $/WAC. Key findings include: 

Solar Costs Have Declined Substantially Over Time, But Less So Under the CPUC’s Program: 

In real dollar terms, average pre-rebate total installed costs under the CEC’s program have 

declined substantially, from more than $12/WAC (2004 $) in 1998 to less then $9/WAC for 2004-

05 (see Figure ES-2, where time is expressed in quarter-year intervals). Regression results show 

annual average cost reductions among the CEC-funded systems of approximately $0.70/WAC, 

representing a 7.3% annual decline. Larger systems (e.g., 10-30 kW) funded by the CEC are 

found to have experienced more modest cost reductions than have smaller systems. 

As suggested by Figure ES-2, some of the overall cost reductions within the CEC program are 

due to decreases in worldwide module costs (notwithstanding the recent increase in those 

costs).iv In fact, regression results confirm that changes in worldwide module costs have largely 

been passed through directly to PV system purchasers on a one-for-one basis. Much of the 

 

iv Although the CEC database does, in some cases, contain disaggregated information on module, inverter, and labor 

costs, this information is only sparsely reported (and the CPUC database does not provide such information at all). 

As a result, we have used an external index of worldwide module costs over time from Strategies Unlimited to proxy 

module costs for each California system. Non-module costs are then simply the total system cost less the relevant 

module cost index value. Though it would be interesting to more narrowly pinpoint specific drivers of cost 

reductions, given current limitations in the data, the best we can do is to crudely split total costs into module and 

non-module costs. Collecting and analyzing more-detailed cost disaggregations data is an area ripe for future work. 

ii
overall cost reduction, however, has come from improvements in non-module costs – e.g., 

installation and balance of system costs. 

This reduction in non-module costs for CEC-funded systems is encouraging. Unlike module 

costs, which are set in a worldwide market and are therefore heavily influenced by factors 

outside of the control of an individual PV program (e.g., demand for PV in Japan and Germany), 

non-module costs are potentially subject to the influence of local PV programs. And given (as 

noted above) that changes in worldwide module costs appear to simply flow through directly to 

total system costs, reducing non-module costs may be the most appropriate goal for local PV 

programs. Though we are unable to prove conclusively that non-module cost reductions in 

California have been caused by the state’s incentive programs, our analysis results do show that 

non-module cost reductions have been significant. 

0

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2004 $/WAC (Mean)

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Figure ES-2. Costs Trends Over Time (CEC and CPUC) 

In contrast to the longer-running CEC program, which exhibits clear downward cost trends over 

time, costs under the CPUC’s program have declined more moderately (though Figure ES-2 does 

show a more substantial decline – in lock-step with the CEC program – since 2003). Compared 

to the $0.70/WAC (7.3%) average cost reduction in the CEC dataset, regression results show that 

systems funded by the CPUC have seen annual average reductions of $0.36/WAC (4.1%).v

 A 

regression of the combined CEC and CPUC datasets over the time period in which the two 

programs overlap shows similar annual reductions: $0.91/WAC (CEC) and $0.36/WAC (CPUC). 

The more-aggressive (and visually apparent) CEC cost reductions may be due to the larger 

proportional labor and installation costs associated with smaller (< 30 kW) systems and the 

greater opportunities in that market segment for distribution and installation efficiency gains.vi 

Alternatively, it could be a result of policy design – whereas the CEC has (since 2003) gradually 

 

v

 Though Figure ES-2 does not provide a clear visual trend of declining system costs over the entire duration of the 

CPUC program, the regression results – by controlling for other factors – are more reliable than the visual evidence 

provided in the Figure. 

vi We find this same effect – smaller systems exhibiting greater cost reductions over time – not only across 

programs, but also within each of the two programs. 

iii
lowered its rebate over time, the CPUC has been slower to follow suit (see Figure ES-1). 

Though Figure ES-2 – which shows the CEC and CPUC costs declining lock-step since 2003 – 

would appear to discount these explanations, it is important to note the bivariate nature of Figure 

ES-2, and its failure to control for other variables (in contrast to multivariate regression analysis, 

which isolates the impact of each individual variable). Nonetheless, the quality of our data does 

not allow us to definitively explain the difference in cost reductions between the two programs, 

or even prove that the programs themselves are responsible for the cost reductions.vii We 

recommend that future work explore these questions in more detail. 

Policy Incentives and Rebate Levels Impact Pre-Rebate Installed Costs: Figure ES-3 shows a 

tight relationship between standard rebate levels and average pre-rebate installed costs among the 

CEC-funded systems since mid-2000. This close relationship is confirmed through regression 

analysis. In particular, we find that each $1/WAC change in the rebate level has, on average, 

yielded a $0.55-0.80/WAC change in pre-rebate installed costs (with the range representing 

results from different regression models). In other words, when the CEC increased its rebate 

level by $1.5/WAC in early 2001, system purchasers may have only realized $0.3-$0.7/WAC of 

that increase on average, with the remaining $0.8-$1.2/WAC being “captured” by system retailers 

or installers through correspondingly higher prices. By the same token, regression results 

suggest that as the CEC has gradually reduced its rebate level since early 2003, system retailers 

have absorbed some of the decrease by reducing prices. 

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Figure ES-3. Impact of Standard Rebate Level on Average Installed Costs (CEC) 

We also find some evidence that the existence of the percentage rebate cap prior to 2003 may 

have increased pre-rebate system costs somewhat under the CEC program. This result is 

 

vii Though it is perhaps logical to assume that California’s PV programs have caused, or at least contributed to, the 

empirical cost reductions, nothing in our analysis enables us to assign causation – i.e., we are unable to definitively 

conclude that the California programs are driving the cost reductions. To be able to assign causation, we would 

need to similarly analyze a “control” market – i.e., one in which no PV incentive programs exist. Identifying such a 

market for PV may be difficult or impossible, given the widespread public support that PV has garnered, but future 

work could at least analyze other markets in which PV is subsidized, but to a different extent or in a different 

manner than in California. 

iv
consistent with widespread speculation that this cap – which limited the size of the rebate to 50% 

of total eligible costs in an attempt to ensure that the program did not over-subsidize lower-cost 

eligible technologies (such as small wind) – has, perversely, encouraged artificial cost inflation

as a way to maximize the dollar amount of the rebate. 

Analysis of the CPUC dataset yields results that are substantially less policy-rich than those from 

the CEC’s dataset. Nonetheless, we find evidence to support the oft-heard claim in California 

that the CPUC’s richer incentives in recent years ($4.5/WAC until December 2004, with a 50% 

cap) have not motivated system cost reductions to the same extent as under the CEC’s program 

(the CEC’s program also offered $4.5/WAC, but reduced that incentive earlier and more rapidly 

than did the CPUC – see Figure ES-1). As supported by Figure ES-4, regression results show 

that, among similar sized systems (20-40 kW), those funded by the CPUC’s program have had 

pre-rebate installed costs that are on average roughly $0.60/WAC higher than those funded by the 

CEC. Furthermore, some of the systems in the CPUC dataset received sizable local incentives 

(of more than $2/WAC), in addition to those offered under the CPUC’s program. These systems 

recorded higher average costs of roughly $0.60/WAC than did equivalent systems that did not 

have access to additional incentives. We also find some evidence (though not through the 

regression analysis) that the percentage rebate cap in place prior to December 2004 affected PV 

pricing during that period. 

California has also offered a state income tax credit for systems under 200 kW in size, ranging 

from 7.5% to 15% of installed costs, depending on the year of installation. Statistical analysis of 

both the CEC and CPUC datasets offers evidence that the existence and size of the state tax 

credit has increased pre-rebate installed costs to some degree. Retail electricity rates, on the 

other hand, are not found to affect pre-rebate total installed costs, though as discussed in the 

body of the report, our retail electricity rate variable is imperfect. 

In aggregate, these results suggest that heavy subsidies dampen, to some degree, the motivation 

of installers to provide, and/or customers to seek, lower installed costs.viii

Economies of Scale Drive Down Costs as System Size Increases: Focusing on the period in 

which both the CPUC and CEC programs were operating simultaneously, Figure ES-4 shows 

that average system costs fall substantially for larger systems (i.e., there are economies of scale) 

in both datasets, though both datasets also show a leveling off of those economies among larger 

system sizes. Regression results confirm these trends. The largest systems in the CEC dataset 

are roughly $2.5/WAC cheaper than 1 kW installations. Meanwhile, the largest CPUC-funded 

systems are roughly $1.5/WAC less expensive than the smaller systems funded by that program.ix

 

viii Though some might be inclined to read into these results an argument for switching from capacity-based to 

performance-based incentives, we note that there is nothing in our dataset or analysis that allows us to comment on 

the relative superiority of one incentive type over another. 

ix The up tick in average installed costs for CPUC systems sized between 60 and 100 kW is somewhat of an 

anomaly, being heavily influenced by 59 identical applications (out of 209 total applications in this size range), all 

submitted on the same day, by the same installer, and at the same estimated installed cost of $9.82/WAC (2004$). 

v
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Figure ES-4. Installed Cost, by System Size (CEC and CPUC) 

Systems Installed in Large New Home Developments and in Affordable Housing Projects 

Experience Much Lower Costs: Regression results show that systems installed (or planned for 

installation) under the CEC’s program in large new residential developments (totaling 1,946 

systems) have lower costs of approximately $1.2/WAC, on average, compared to the general 

retrofit market. Similarly, the 340 systems used in affordable housing applications, which often 

involve new construction and presumably enable bulk system installation, exhibit costs that are 

$1.9/WAC lower than the general retrofit market. Systems installed in single new homes (or 

small clusters of new homes) exhibit modestly higher costs of approximately $0.18/WAC, 

perhaps due to the custom-designed nature of many of these systems, as well as a lack of the 

economies of scale possible in larger new home developments. Systems installed at schools 

(most are retrofits) do not have statistically significant differences in cost compared to the 

general retrofit market. 

Installer Experience and Type Affects Costs: Within the CEC program, more-experienced 

installers and retailers are found to charge slightly more for their services – approximately 

$0.29/WAC and $0.17/WAC, respectively – relative to those with less experience. In contrast, 

more-experienced installers under the CPUC’s program have priced their systems at lower levels 

than less-experienced installers, with a differential of nearly $0.70/WAC on average. The reason 

for this discrepancy between the two programs is unclear. Meanwhile, owner-installed systems 

in the CEC program (n=862) are found to have considerably lower reported costs than 

contractor-installed systems, with a $1.8/WAC savings on average. Similarly, the sixteen CPUCfunded systems installed at fairgrounds by the California Construction Authority have come in at 

a substantially lower cost than other systems, with a cost differential of roughly $4/WAC, on 

average.x These results suggest that the CEC’s current practice of providing reduced incentives 

for owner-installed systems is appropriate. 

 

x

 The California Construction Authority (CCA) is a Joint Powers Authority organized in August 1988 to provide 

financing, design, inspection and construction management services for fairgrounds throughout California. The low 

cost of the CCA systems is perhaps partially attributable to bulk equipment purchases for multiple fairground 

vi
The Impact of Module Type Varies By Program: In the CEC dataset, projects using thin film 

PV technology – of which there are 318 – are found to have had systematically lower costs than 

those relying on traditional crystalline silicon, with a differential of roughly $0.70/WAC on 

average, though this cost differential has narrowed over time. Though only bordering on 

statistical significance, projects using thin film technology in the CPUC dataset – of which there 

are 111 – are found to have slightly higher costs on average over the course of that program 

(~$0.20/WAC). The reason for this discrepancy between the two programs is unclear. 

System Location Has Impacted Costs: The population density of the location of installation 

appears to have some effect on system costs in the CEC dataset, with more densely populated 

areas experiencing higher average costs. This finding is consistent with the idea that population 

density may be a proxy for the cost of living, and therefore labor costs. Meanwhile, CEC-funded 

systems installed outside of PG&E’s service territory report lower average pre-rebate costs than 

those installed within PG&E’s service territory. In contrast, CPUC-funded systems installed 

outside of PG&E’s territory report higher pre-rebate costs on average. Further analysis would be 

required to understand why costs vary by service territory, and why these effects vary between 

the CEC- and CPUC-funded systems. 

Conclusions and Recommendations 

Results presented here reveal a number of expected, and some unexpected, trends. Perhaps of 

most importance, we find substantial reductions in PV system costs over time, especially among 

systems funded by the CEC’s program. Although our analysis cannot, without comparison to a 

control group, definitively conclude that the CEC and CPUC programs caused these cost 

reductions, it is clear that – despite the lack of continuity and stability experienced by both 

programs – pre-rebate installed costs have come down. 

Several policy recommendations derive from our analysis: 

• Reducing non-module costs should be a primary goal of local PV programs. Unlike 

module costs, which are set in a worldwide market and are therefore heavily influenced by 

factors outside of the control of an individual PV program, non-module costs are potentially 

subject to the influence of local programs. State policymakers may wish to undertake 

programmatic activities aimed specifically at reducing non-module costs, which could range 

from targeted approaches to building local supply infrastructure (e.g., providing business 

development funding to installers, supporting standardized PV products, or offering installer 

training and certification), to something as simple as making PV system cost and 

performance data more publicly accessible to further encourage supply competition. 

• Sustained, long-term programs may enable more significant cost reductions. Sustained, 

sizable, and stable markets for PV may be the most direct way of reducing non-module costs 

because such markets will presumably attract suppliers and encourage those suppliers to 

 projects. Some have also speculated that the CCA is able to install systems at apparently lower costs than the PV 

industry at large due to the fact that it has no marketing, sales, or overhead costs, and/or that certain internal costs 

are not reported. In other words, CCA-installed systems in the CPUC program are essentially the equivalent of 

owner-installed systems in the CEC program. 

vii
create an efficient delivery infrastructure. Though PV cost reductions in California are 

significant, at least among CEC-funded systems, experience from Japan suggests that deeper 

cost reductions are possible with a more sustained policy effort. In mid-January 2006, the 

CPUC issued an order that intends to create such a market with an 11-year, ~$3.2 billion 

program of declining incentives. A goal of the adopted program is to reduce rebate levels by 

roughly 10% each year, in nominal terms, far exceeding the recent system cost reductions 

seen under the California and Japanese programs. 

• The structure and size of PV incentives should encourage cost reduction, not cost 

inflation. We find some troubling evidence that policy design has adversely impacted the 

cost of PV systems in California. For example, the 50% cap on the size of the rebate 

employed by both programs at one time or another appears to have, at best, impeded cost 

reductions, and at worst, contributed to artificial cost inflation. As such, the decision by both 

programs to abandon such percentage caps is a positive development; we encourage other PV 

programs to do the same. Furthermore, the total pre-rebate cost of PV installations in 

California has tracked, to some degree, the size of the rebate itself. Whether this link is 

merely representative of the “teething problems” that are typical of new programs,xi

 or 

should instead be of long-term concern is somewhat unclear. As rebates are reduced over 

time, however, we expect that the link between incentive levels and pre-rebate installed costs 

will be severed, as lower rebates require contractors to price systems at cost in order to 

ensure a sale. Hence, while rich incentives may be required initially to jump-start the market, 

over time the incentives should decline to a level that can support a functional market 

infrastructure without providing room for potential price manipulation. 

• Targeted incentives that account for the relative economics of different system sizes and 

application types may be appropriate. Though there is a significant spread in the data, our 

analysis shows that installed costs are heavily dependent upon the size and type of 

installation. We find clear evidence of sizable economies of scale in PV installations. We 

also find that systems installed in large new home developments are, on average, far more 

economical than retrofitted systems. These results suggest that a further targeting of 

incentives to account for the relative economics of different system sizes and application 

types may be appropriate. 

 

xi Such “teething problems” might include initial over-subsidization intended to spur the market, coupled with 

insufficient supply infrastructure to handle the resulting increase in demand, leading to lackluster competition and 

artificial price increases until new supply infrastructure enters the market. 

viii
1. Introduction 

Markets for solar photovoltaic (PV) systems are expanding rapidly, albeit from a small base. In 

2004, more than 955 MWAC of PV capacity was installed worldwide, up from 658 MWAC in 

2003 (Maycock 2005).1 The growth in worldwide annual capacity additions has averaged 

approximately 35% since 1996 (Maycock 2005), dominated by grid-connected applications. The 

global PV market had an estimated $11 billion in revenue in 2004 (Rogel 2005). 

Despite this vigorous growth, the share of worldwide electricity demand met with photovoltaic 

power remains miniscule, well below 0.1%, and the aggregate expected electricity supply from 

the PV capacity added in 2004 equates to just one natural gas-fired baseload generating plant (or 

perhaps a dozen gas-fired peaking plants). The primary constraint to future expansion is 

economics. Simply put, solar PV is not yet cost-competitive in most grid-connected 

applications, and substantial cost reductions will be required for PV to meaningfully contribute 

to worldwide electricity supply (U.S. DOE 2005; Chaudhari et al. 2005; van der Zwaan and Rabl 

2004; Poponi 2003). The good news is that significant cost reductions are possible, and some 

believe that solar power will have a bright future in a world in which environmental, security, 

and supply constraints could dampen interest in conventional generation (Rogel 2005; Makower 

and Pernick 2004; Pacala and Socolow 2004; Payne et al. 2001; U.S. DOE 2005). 

Local, state, and federal government incentives are (and will continue to be) the principal drivers 

for the recent growth in grid-connected PV capacity (see, e.g., Haas 2003; Osborn et al. 2005; 

Bolinger and Wiser 2002; Duke et al. 2005). Common programs include rebates on the capital 

cost of the installations, high fixed tariffs paid for PV supply, tax incentives, and mandates for 

electricity suppliers to use solar power to meet customer demand. These policies are motivated 

by the widespread popular appeal of solar power, and the positive attributes of PV – modest 

environmental impacts, avoidance of fuel price and supply risks, coincidence with peak electrical 

demand, distributed generation located at the point of use, and installation modularity. 

A key goal of these policy efforts is that of market transformation: to drive down the cost of PV 

over time to a level that does not require substantial government stimulation. The cost of PV 

installations is not uniform, however, and can vary based on time, system size, type of 

installation (e.g., retrofit vs. new home), installer experience, and other factors. Solar costs 

might also be affected by the level and design of policy incentives provided to those installations. 

This paper presents the results of a statistical evaluation of cost trends in California’s market for 

residential and commercial grid-connected PV. It is based on an analysis of 18,942 PV systems, 

totaling 254 MW, that as of mid-2005 had either been completed, approved, or waitlisted (i.e., 

approved but awaiting additional program funding) under California’s two largest solar rebate 

programs: those operated by the California Energy Commission and California Public Utilities 

 

1

 Unless explicitly presented as otherwise, data on PV capacity and costs are expressed throughout this report in 

terms of WAC (e.g., WAC, kWAC, MWAC, $/WAC), which we convert (where necessary) from WDC-STC (DC Watts at 

standard test conditions) using a de-rate factor of 0.84. We acknowledge that many other solar programs and data 

sources use WDC-STC, making comparisons of California data with those in other states and countries more difficult. 

Given, however, that our underlying system cost data is expressed in terms of WAC, this is the standard that we use. 

1
Commission.2

 This analysis provides insights on California’s PV market by exploring cost 

trends over time, and by helping to untangle the various factors that affect the installed cost of 

PV systems. Results may also have important policy ramifications in as much as they address 

the interaction between incentive levels and installed costs, and the relative cost of different solar 

applications. 

A sizable literature has developed that explores historical PV cost trends. Much of this literature 

has used learning or experience curve theory to explore how increases in cumulative PV 

production have driven costs down over time. Findings from this literature vary, but most 

studies show that each doubling of cumulative production has historically led to a reduction in 

module prices of approximately 20% (see, e.g., Kobos et al. in press; McDonald and 

Schrattenholzer 2001; Neij 1997; IEA 2000; Schaeffer et al. 2004). Others have extrapolated 

these findings to argue that government deployment support for PV may be warranted to drive 

the industry down its learning curve and ultimately achieve costs that are comparable to or better 

than the cost of conventional electricity sources (see, e.g., van der Zwaan and Rabl 2004; Duke 

and Kammen 1999; Duke 2002).3

 

Our study has a narrower focus in that it seeks to uncover interesting cost trends from a single 

PV market. In so doing, the results presented here are expected to be of use to California’s 

energy policymakers and stakeholders as existing solar incentive programs are revised, and as 

new and expanded programs are implemented. With a mid-January 2006 California Public 

Utilities Commission order to establish an 11-year, ~$3.2 billion program of declining incentives 

for PV, the results of this analysis are timely. Of course, California is not alone in its support for 

photovoltaic markets: a large number of other U.S. states also offer significant incentives for PV 

(Bolinger and Wiser 2002; Bolinger and Wiser 2003; Chen et al. 2005; Gouchoe et al. 2002), and 

new tax policies at the federal level promise to further stimulate the U.S. market in the years to 

come. A detailed exploration of PV cost trends in California may be of use for benchmarking 

purposes in these other markets. Finally, because California is the third largest market for PV 

worldwide, the results presented here are expected to be of broad international interest. 

We begin in Section 2 of this paper by describing California’s photovoltaic market. Section 3 

then discusses the data that we obtained for our analysis, and the statistical methods used to 

evaluate those data. Section 4 presents multivariate regression results from our analysis of 

smaller residential and commercial solar installations (< 30 kW). Section 5 presents similar 

results from our analysis of larger commercial installations (generally ranging from 30 kW to as 

large as 1 MW). Section 6 compares the two datasets. We end the paper in Section 7 by 

highlighting important implications and conclusions. 

 

2

 Future analysis could expand this evaluation to look at cross-state and perhaps cross-country comparisons of 

installed costs, and could use more up-to-date California data. 

3

 Other studies that have explored average costs and (to a lesser degree) cost trends in the U.S. in recent years have 

focused on: Florida (Szaro 2003), LADWP (Honles 2003), Pennsylvania (Celentano 2005), a sample of 278 systems 

throughout the U.S. (Mortensen 2001), and projects funded under the TEAM-UP Program (SEPA 2001a, 2001b). 

2
2. The California Solar Market in Context 

2.1 Market Overview 

California is the dominant market for PV in the United States, though it still stands a distant third 

on a worldwide basis behind Japan and Germany. According to the International Energy Agency 

(IEA), Japan added 228 MWAC of solar PV capacity in 2004 for a cumulative total of 951 

MWAC. The size of Germany’s market is in some dispute, with the IEA reporting that the 

country added 305 MWAC in 2004, for a cumulative 667 MWAC, while Photon International 

reports 647 MWAC of additions in 2004 for a cumulative total of 1146 MWAC.

4

 Japan and 

Germany have used gradually declining capital rebates and high fixed power purchase tariffs, 

respectively, to spur market expansion. In aggregate, these two markets accounted for at least 

60% of worldwide PV demand in 2004. The IEA reports that the U.S. added 76 MWAC

AC AC

 of PV in 

2004 (including 52 MW of grid-connected capacity), for a cumulative total of 307 MW . 

The California Energy Commission reports that California alone added more than 36 MWAC of 

grid-connected PV in 2004. Through mid-November 2005, 130 MWAC of grid-connected PV 

capacity was installed in California; Figure 1 shows the dramatic growth in the market since the 

year 2000 (data for 2005 represents a partial year, through mid-November).5 According to 

SolarBuzz (2005), in 2004, California’s grid-connected PV additions represented approximately 

85% of all grid-connected additions in the United States.6

0

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Figure 1. Grid-Connected Solar Capacity in California, through November 15, 2005 

 

4

 For IEA data, see: http://www.oja-services.nl/iea-pvps/isr/index.htm. For Photon International data, see Photon 

International (2005). Data are converted from DC Watts at standard test conditions (as reported) to AC Watts using 

a de-rate factor of 0.84. 

5

 The data presented in Figure 1, which includes systems installed under municipal utility programs, comes from the 

California Energy Commission’s website, accessed on January 2, 2006: 

http://www.energy.ca.gov/renewables/emerging_renewables/GRID-CONNECTED_PV.XLS. 6

 IEA and CEC data, on the other hand, suggest that California’s grid-connected PV market in 2004 represented 

70% of the U.S. total (36 MWAC out of 52 MWAC). 

3
2.2 Policy Incentives 

California’s grid-connected PV market is driven by a mixture of state and local incentives. Most 

prominent are capital cost rebates – denominated in $ per Watt – offered to PV system installers 

or owners to “buy down” the installed cost of solar installations. By reducing the cost of PV 

installations, these programs aim to increase PV sales, which should encourage manufacturers, 

sellers, and installers to expand their operations and improve distribution channels, thereby 

inducing overall system cost reductions. The two most significant current rebate programs are 

overseen by the California Energy Commission (CEC) and the California Public Utilities 

Commission (CPUC), but significant rebates are also offered by the state’s publicly owned 

utilities (e.g., SMUD, LADWP – see Figure 1). 

California Energy Commission: The CEC has administered a PV rebate program (called the 

“Emerging Renewables Program”7

) since March 1998, funded primarily by ratepayers of the 

state’s investor-owned electric utilities (IOUs). From program inception to February 2003, the 

CEC provided rebates to both small and large PV systems installed by customers taking electric 

service from one of the state’s IOUs. Starting in March 2003, the program has focused primarily 

on residential and small commercial systems under 30 kW in size.8

 As of May 2005, the CEC 

had paid out roughly $196 million of incentive funds to nearly 13,000 completed PV systems 

totaling 51 MWAC of capacity. An additional $80 million was encumbered to approximately 

5,000 (25 MWAC) approved but not yet completed PV systems.9 Historically, approximately 

70% of all applications have resulted in completed systems.10

California Public Utilities Commission (CPUC): The CPUC’s Self-Generation Incentive 

Program (SGIP) was established as a direct result of the state’s energy crisis, and began 

accepting applications in July 2001. The program provides rebates for customer-sited PV 

systems of at least 30 kW in size (systems can exceed 1 MW in size, but the rebate only applies 

to the first 1 MW) and installed by customers taking electric or gas service from one of the 

state’s IOUs. SGIP funds are collected from electric and gas ratepayers. As of June 2005, the 

program had paid out $142 million in incentive funds to 327 completed PV systems totaling 

nearly 36 MWAC of capacity. An additional $300 million was obligated to 465 approved but not 

yet completed PV systems (totaling 74 MWAC). 265 systems, totaling more than $250 million of 

rebate funds, were approved but on a waitlist due to insufficient program funding at current 

 

7

 In addition to PV systems, small wind turbines, fuel cells using renewable fuels, and solar thermal electricity 

systems – all sized to generate less than 200% of the site’s electricity needs on an annual basis – are eligible for the 

program. To date, PV systems have accounted for roughly 99% of all funded systems. For more information, see 

http://www.consumerenergycenter.org/erprebate/index.html. 

8

 For certain periods of time, customers of publicly-owned utilities have also been eligible to participate. 9

 Additional systems have been completed and approved since April 2005. We use dated information here to be 

consistent with the dataset used in the subsequent analysis. 

10 The CEC’s program was evaluated in 2000, and among other things, that evaluation found little reduction in 

installed costs for smaller solar systems over the program’s first two years, but sizable cost reductions for larger 

systems (RER 2000). 

4
incentive levels.11 Compared to the CEC’s program, a larger number of SGIP applications are 

either rejected or withdrawn, with an average drop-out rate of roughly 45%.12

Figure 2 depicts the historical demand for PV within each program through early- to mid-2005, 

shown in terms of the number of megawatts of PV applying for rebates over time. Quarterly 

application activity within the CEC’s program was slow until the electricity crisis hit in 

2000/2001 (the crisis was also the impetus for establishing the CPUC’s program). Starting in 

2003, volatility in quarterly applications within the CEC’s program reflects, in large part, lastminute surges in the number of applications just prior to the regularly scheduled reduction in 

incentive levels every six months, on the first day of January and July.13

0

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 CPUC Cumulative (right scale)

to 78

Figure 2. Quarterly and Cumulative Rebate Application History (CEC and CPUC) 

Over time, both the CPUC and the CEC programs have altered the structure and size of their 

incentives for PV installations (see Figure 3). The CPUC initiated its incentives at $4.5/WAC, 

capped at 50% of pre-rebate installed costs, and dropped the incentive level to $3.5/WAC with no 

percentage cap on December 16, 2004; the CPUC further reduced the incentive to $2.8/WAC for 

applications received after December 15, 2005. The CEC’s standard incentive started at $3/WAC

with a 50% incentive cap, increased during the energy crisis to $4.5/WAC, and has then declined 

progressively in recent years to its current level of $2.8/WAC. The percentage cap was eliminated 

in February 2003. Affordable housing, solar schools, and owner-installed installations, as well 

as larger systems, have – at various times – received different incentive levels and percentage 

caps. 

 

11 In late 2005, the CPUC decided to dramatically increase SGIP program funding for 2006 in part to help fund 

waitlisted projects. 

12 A large number of impact and process evaluation reports have been prepared for the SGIP (see, e.g., Itron 2003, 

2004, 2005a; RER 2003). These reports often discuss pre-rebate installed costs, but do not analyze cost trends in 

any detail. 

13 The volatile pattern exhibited in 2003 and 2004 is not evident in the second quarter of 2005, because in Figure 2, 

the second quarter of 2005 is a partial quarter (running only through April 15). Furthermore, the CEC decided to 

forego the regularly scheduled reduction in incentive levels on July 1, 2005, leaving the rebate at $2.8/WAC for the 

entire year. 

5
0.0

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Standard Rebate* (nominal $/WAC)

 CEC

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* Within the CEC's program, systems installed on affordable housing and schools have, at times, received higher incentives; 

ow ner-installed systems have, at times, received low er incentives; systems >30 kWAC w ere eligible for rebates from program 

inception to February 2003; and systems >10 kWAC received $2.5/WAC (capped at 40%) from March 1999 to February 2001.

50% CPUC Cap No CPUC Cap

50% CEC Cap No CEC Cap

Figure 3. Evolution of the Standard Rebates for the CEC and CPUC Programs 

In addition to these rebate programs, other policies supporting PV in California include: net 

metering requirements, simplified interconnection standards, exemptions of solar systems from 

certain utility fees (specifically, “standby” and “exit” fees), and an exemption of PV systems 

from state property taxes. California has also provided a state income tax credit (ITC) for 

systems 200 kW or smaller, which was as high as 15% from January 2001 through December 

2003, dropping to 7.5% from January 2004 through December 2005. With highly “tiered” rate 

structures, and time-of-use rates available to some customers, PV electricity in some cases 

offsets retail electricity rates of more than $0.20/kWh. 

Finally, the federal government has allowed commercial systems an attractive five-year 

accelerated depreciation schedule and a federal ITC of 10%, which increased to 30% on January 

1, 2006 (and will remain at 30% through 2007, unless extended). Residential systems are also 

eligible for a 30% federal ITC (capped at $2000) in calendar years 2006 and 2007 (unless 

extended). 

2.3 Market Concerns 

Several pertinent concerns have been expressed about the California solar programs, some of 

which will be informed by the analysis presented in this report. 

Some have questioned whether current (or even possibly future) funding commitments are (or 

will be) sufficient to drive PV system costs down to competitive levels in a reasonable period of 

time. Recent increases in the cost of silicon and therefore solar cells, modules, and installations 

have added some weight to these concerns, though most believe that the current silicon shortage 

will be temporary and that PV module costs will continue their march downward within several 

years (Rogel 2005). It is also important to keep in mind that silicon and PV module costs are 

determined in a worldwide market that is largely outside of the influence of California’s PV 

programs, while installation and balance of system (i.e., non-module) costs may be largely 

6
determined at the local level, and are therefore subject to the influence of local PV programs. As 

such, trends in total system installed costs are arguably a less-appropriate measure of program 

success than are trends in non-module (or balance of system) installed costs.14

Others have noted the historical lack of continuity and stability in the existing programs, 

illustrated in Figure 2 by the notable variability and gaps in quarterly incentive applications, and 

driven by changes in the incentive levels and availability of funds. This lack of certainty may 

lower investor confidence in the California market, and limit efficiencies in manufacturing and 

distribution, thereby compromising the ability of the program to achieve cost reductions (Osborn 

et al. 2005; Steigelmann et al. 2005). The state’s energy agencies apparently agree with this 

contention, writing jointly: “California policies are clearly supportive of the on-grid solar market, 

but that support was unevenly distributed and often unavailable.” (CEC and CPUC 2005). 

There have also been some misgivings about the size and structure of the incentive programs 

themselves. For example, some have questioned whether the size of the incentives has (at times) 

been so large as to compromise the willingness of installers to lower total installed prices for 

customers (Osborn et al. 2005; CEC 2005). Others have expressed the view that when the size 

of the CEC and CPUC’s rebates were limited to a certain percentage of pre-rebate installed costs, 

the existence of those percentage caps may have dampened incentives for system cost reductions. 

As an example, under a binding 50% cap, every $1 in cost reduction will reduce the incentive 

payment by $0.5, meaning that the customer captures only 50% of the cost savings. Likewise, 

the customer will pay only 50% of any cost increase. So although in theory a percentage cap can 

help to prevent over-subsidization and can reduce the need to regularly adjust fixed rebate levels 

in response to system cost changes, the specific mechanics of the percentage cap do not provide 

a strong incentive for cost reductions, and – even more vexing – do provide opportunities for 

gaming of the program (Bolinger and Wiser 2003; RER 2003).15 Finally, some have wondered 

whether the state should place more emphasis on certain PV applications where cost 

competitiveness may be more readily achieved; for example, incorporating PV systems into new 

building construction, where design and installation efficiencies may help lower customer costs. 

At least some of these concerns have already been, or are in the process of being, addressed. The 

CEC eliminated the percentage cap on its rebates in February 2003, and the CPUC followed suit 

in December 2004 (see Figure 3, above). Meanwhile, Gov. Schwarzenegger has announced his 

support for the California Million Solar Roofs initiative, with a goal of encouraging 3,000 MW 

of new solar PV systems through a long-term, sustained, declining incentive program. The 

 

14 In Sections 4-6 we present evidence showing that most of the recent cost reductions in California’s PV markets (at 

least for smaller systems) have come from non-module costs, though our analysis is not robust enough to conclude 

that such cost reductions have been caused by California’s PV programs. 15 Gaming opportunities range from the relatively straightforward “gold-plating” of systems with expensive features, 

knowing that the rebate program will pick up half of the incremental cost, to much more nefarious schemes. As an 

example of the latter, there have been anecdotal reports of installers artificially pricing systems (that, for example, 

actually cost $8/W) at $9/W in order to maximize the dollar amount of the rebate ($4.5/W, capped at 50% of eligible 

costs), and then sharing the ill-gotten incremental rebate with the system purchaser, to the financial benefit of both 

purchaser and installer. In this light, it is interesting to note that 30% of all PV systems funded by the CPUC during 

the period in which the 50% cap was in place were priced from $8.75-$9.25/WAC (50% were priced from $8-

$9.25/WAC). The corresponding numbers in the four-month period of our sample after the cap was dropped (in midDecember 2004) are 5% and 11.5%, respectively, with the entire cost distribution having shifted to the left (i.e., 

toward lower costs). 

7
CPUC – in conjunction with the CEC – is developing an implementation plan for this initiative, 

and on January 12, 2006 approved the outlines of such a program. The program will place some 

new emphasis on the residential new construction market (through incentives offered by the 

CEC), though not to the exclusion of retrofit markets, and is envisioned to be sizable (~$3.2 

billion) and stable (~11 years) enough to significantly reduce system costs over time. Incentive 

levels are slated to drop by an average of at least 10% each year. Incentives may ultimately vary 

to some degree by system size or market segment, though the CPUC declined to establish such 

differentiated incentives initially (except for low-income and affordable housing projects, which 

will receive higher incentive levels). A pay-for-performance incentive structure is to be 

explored. Though many of the details remain to be resolved, this order will dramatically expand 

the PV market in the state. 

8
3. Data and Methodology 

3.1 Data 

Data on installed PV costs and other variables come primarily from the program databases 

provided by the CEC (for systems less than 30 kW) and the CPUC (for systems 30 kW and 

above). The CEC dataset was provided to us in May 2005, and the CPUC database was updated 

through June 2005.16 Table 1 summarizes the content of each dataset, after the data were cleaned 

and data records with missing entries were eliminated (see Appendix A for more details on our 

data manipulation and cleaning procedures). 

Table 1. Summary Information on the Two Final Datasets 

CEC CPUC 

System Size Range 0.024 kW – 30 kW* 25 kW** – 1,063 kW 

System Cost Restriction $4/WAC – $30/WAC $4/WAC – $30/WAC

Systems Eliminated Due to Cost Restriction 85 (0.5 MW) 4 (1.3 MW) 

System Status for Those Included in Final Dataset 

Completed 12,856 (48.5 MW) 327 (35.7 MW) 

Approved 5,033 (24.3 MW) 464 (73.4 MW) 

Waitlisted 0 (00.0 MW) 262 (71.7 MW)

TOTAL 17,889 (72.8 MW) 1,053 (180.8 MW) 

Application Date Range 03/20/98 – 04/15/05 07/23/01 – 04/15/05 

Completion Date Range 04/08/98 – 04/07/05 06/18/02 – 05/17/05 

* The CEC program initially funded systems over 30 kW in size, but ceased providing funding to such systems in 

March 2003. We exclude these larger systems from our analysis (a total of 66 systems, and 9.1 MW of capacity) to 

ensure that a limited number of outliers do not unduly affect our analysis results. 

** Although the CPUC program rules state that only systems of at least 30 kW in size are eligible, the CPUC 

database does contain a few systems less than 30 kW (and as low as 25 kW). 

Our final cleaned CEC dataset covers a total of 17,889 data records (72.8 MWAC), including 

12,856 completed systems (48.5 MWAC) and 5,033 systems that had been approved for a rebate, 

but that were awaiting completion at the time we received the database (24.3 MWAC).17 The data 

include rebate applications received from March 1998 to April 2005, and project completion 

dates of April 1998 to April 2005. To minimize the possible influence of outliers, our analysis 

only includes systems under 30 kW in size, and with costs in the range of $4 to $30/WAC.

18 

The CPUC program generally covers systems of at least 30 kW in size (though a small number 

of systems are marked as under this size range in the database that we received), and our final 

 

16 More recent data are now available from both programs, and future analysis should use these updated data. 

17 We do not include systems whose rebate applications were declined, or whose application was approved but 

subsequently cancelled. 

18 Systems with costs outside of this range likely represent data entry errors, or atypically cheap or expensive 

systems. Different reasonable system cost restrictions were tested, with limited effect on regression results. 

9
dataset includes 1,053 data records (180.8 MWAC), including 327 completed systems (35.7 

MWAC), 464 approved systems (73.4 MWAC), and 262 waitlisted systems (71.7 MWAC). 

Applications were received from July 2001 to April 2005, and systems have completion dates 

from June 2002 to May 2005. As with the CEC data, to mitigate the possible influence of 

outliers, our analysis only includes systems with costs in the range of $4-$30/WAC.

19 

3.2 Variables 

In all cases, the dependent variable is the pre-rebate per-unit total installed cost of individual PV 

systems, in $/WAC, calculated by dividing total pre-rebate installed costs by system size.20 For 

completed systems, this cost figure represents actual costs as reported to rebate program 

administrators. For approved or waitlisted systems, the cost represents an approximation of 

actual costs provided upon rebate application; because costs are often established by contract 

prior to the submission of a rebate application, these costs are considered relatively firm. All 

dollar values are translated into real 2004$, using historical monthly CPI data (seasonally 

adjusted). System size is denominated in WAC, which equates to module WDC-PTC (DC Watts at 

PVUSA test conditions) multiplied by the inverter efficiency rating.21 Note that many other 

solar programs and data sources use WDC-STC (DC Watts at standard test conditions), making 

comparisons of California data with those in other states somewhat more difficult. 

A more appropriate dependent variable would be one that accounts for system performance – 

i.e., $/kWh rather than $/W. This metric would enable us to, for example, normalize the 

additional cost of a tracking system used to enhance system performance. Because system 

performance data are not regularly collected by the CEC or CPUC programs, however, we were 

forced to use the “second-best” choice of dependent variable, $/WAC.

22

Table 2 identifies and defines the independent variables used for each dataset, while Table 3 

provides summary statistics; for more information on variable definition, and on our data 

manipulation and data cleaning procedures, see Appendix A. 

• Date of Application: Both datasets include a variable for the date of rebate application, in 

months. One would generally expect that pre-rebate installed costs would decline with time 

due to manufacturing improvements and distribution channel efficiencies. Though this 

variable does allow us to track cost changes over time, it is important to note that we are 

unable to test whether the CEC and CPUC programs caused these cost reductions. To test 

 

19 Different system cost restrictions were tested, with some impact on regression results. With no easy way of 

determining which cost range was objectively “right,” we opted to maintain consistency with the CEC dataset. 20 Though California’s PV programs will support systems with battery back-up, the extra costs of such back-up are 

not eligible for incentive payments. Costs reported here represent “eligible costs,” excluding any costs for battery 

back-up. 

21 The approach used to calculate inverter efficiencies has changed over the course of the rebate programs. The data 

presented here for system size are based on the inverter efficiency ratings used at the time of system rebate 

application. An analysis of inverter efficiency ratings over time shows little change in average ratings (less than 1%, 

on average), so changes in rating methodologies should not be a major source of error in the results presented later. 

22 Future work might also explore using customer demand (e.g., applications received) as the dependent variable to 

evaluate the determinants of demand for solar PV systems. 

10
for such causation would require cost data from a control market in which state incentives are 

not provided (or are provided at lower levels than in California).23 

• System Size: Both datasets also include a variable for system size. With presumed 

economies of scale and installation, larger systems are expected to come in at lower costs. 

• Policy-Based Variables: Though one might hope that the size of policy incentives would 

have no impact on pre-rebate installed costs, it is possible that the positive impact of these 

incentives on the economics of a solar PV system might dampen the motivation for system 

installers to aggressively reduce pre-rebate installed costs. Policy variables incorporated in 

the CEC dataset include the level of the rebate, the existence of a percentage cap, the level of 

the state tax credit, and the utility-specific applicable retail electricity rate.24 Similar 

variables were tested on the CPUC dataset, but for reasons discussed later, our final model 

runs only include as policy variables the level of the state tax credit, the utility-specific retail 

rate, and whether particular installations had access to other sizable financial incentives 

(most prominently, incentives offered by a local utility that are, to some degree, additional to 

the incentives offered by the CPUC; similar data were not provided by the CEC). 

• System Installation Status: Dummy variables for approved (CEC and CPUC) and waitlisted 

(CPUC) systems are included, to test for any systematic variations in system costs between 

completed systems, and approved and waitlisted systems. 

• System Location: Dummy variables for the location of the system (defined by utility service 

territory) are included to test for any systematic differences in installed costs in these 

territories, relative to systems located in PG&E’s service area. We have no hypothesis as to 

why costs might differ by utility service territory, other than the potential impact of 

differences in cost of living or retail rates (each of which is addressed through its own 

separate variable). 

• Installation Type: Four non-overlapping dummy variables for various system installation 

types are included to test for differences in pre-rebate installed costs, relative to systems 

installed in standard residential or commercial retrofit applications. These variables are only 

possible for the CEC dataset, and include systems installed: (1) in bulk in large, new-home 

residential developments; (2) in single new homes or small clustered new-home 

developments; (3) as part of an affordable housing project; or (4) on a school. One would 

expect that systems installed in bulk in new construction (large residential development, and 

affordable housing) would come in at lower costs. 

• Installer and Retailer Experience and Type: A number of dummy variables are included to 

test for the impact of installer and retailer experience, and type. For the CEC dataset, the 

available data allow us to test for the impact of installer experience and retailer experience on 

pre-rebate system prices, and to test for whether owner-installed systems are more or less 

expensive than contractor-installed systems. The CPUC dataset allows us to test for the 

impact of installer experience; we also include a dummy variable for systems installed by the 

California Construction Authority (effectively, an owner-installer) because on visual 

 

23 We recommend that future work evaluate PV costs across multiple markets to test for such effects. 24 Because our retail rate variable is constructed from average retail rates, and therefore only indirectly accounts for 

the highly tiered rate structures facing most Californians, it is far from perfect (for more information on the 

construction of this variable, see Appendix A). Future work that more accurately characterizes and quantifies the 

specific retail rate that will be displaced by each PV system would be beneficial. 

11
inspection of the data it was clear that these systems have been substantially less expensive 

than the more common contractor-installed systems in the CPUC dataset.25 

• Population Density: Population density, by zip code, was included for the CEC dataset 

(missing zip codes did not allow us to replicate this variable for the CPUC dataset). One 

might expect that system costs would be lower in more densely populated areas due to the 

existence of PV supplier competition; on the other hand, these areas may experience higher 

costs because of the typically higher cost-of-living in urban areas and the impact of those 

costs on installation labor expenses. 

• Thin Film: Both datasets have a dummy variable for systems using thin-film PV 

technology, a less common but potentially less-expensive PV technology than crystalline 

silicon. 

• Module Cost Index: Pre-rebate installed costs are also expected to vary based on the 

prevailing cost of PV modules, measured here as an annual average of global module prices, 

and ultimately included only for the CEC dataset.26

Several additional variables were also explored, without success. Of most relevance, we 

constructed and tested a variable for cumulative program-related PV installations, under the 

assumption that non-module system costs might follow a state- or program-specific learning 

curve (due, for example, to reductions in installation costs with installation experience). We 

found this variable to be somewhat collinear with time, however, and a linear representation of 

time fit our underlying data far better than cumulative PV additions, at least for the CEC 

dataset.27 We also explored and tested several different representations of installation supply 

constraints, under the assumption that PV costs could rise at times when the installation 

infrastructure is strained. Despite several attempts, we were unable to construct a successful 

variable to measure these possible effects. 

 

25 The California Construction Authority (CCA) is a Joint Powers Authority organized in August 1988 to provide 

financing, design, inspection and construction management services for fairgrounds throughout California. The low 

cost of the CCA systems is perhaps partially attributable to bulk equipment purchases for multiple fairground 

projects. Some have also speculated that the CCA is able to install systems at lower costs than the PV industry at 

large due to the fact that it has no marketing, sales, or overhead costs (and/or that certain internal costs are not 

reported). In other words, CCA-installed systems in the CPUC program are essentially the equivalent of ownerinstalled systems in the CEC program. 

26 Although the CEC database does, in some cases, contain disaggregated information on module, inverter, and labor 

costs, this information is only sparsely reported (and the CPUC database does not provide such information at all). 

As a result, we have used an external index of worldwide module costs over time from Strategies Unlimited to proxy 

module costs for each California system. 

27 More generally, using global photovoltaic price trends, Papineau (2006) finds that the addition of a time variable 

to a model that also includes cumulative production generally makes the cumulative production variable statistically 

insignificant, leading to questions over the predictive power of experience curves more generally. 

12
Table 2. Independent Variables Used in Analysis 

Datasets Name 

CEC CPUC 

Definition 

TIME_MONTH U U Date of application, in months, numbered consecutively 

from beginning of each program* 

SYSTEM_SIZE U U Size of installation (WAC) 

MAX_STD_REBATE U Tried/

Failed Maximum standard rebate (2004 $/WAC)** 

REBATE_%_CAP U Tried/

Failed 1 if rebate has a percentage cap 

STATE_TAX_CREDIT U U 0, 7.5%, 15% state ITC depends on time of application, and 

system size 

RETAIL_RATES U U

A function of system size (to determine whether residential 

or commercial retail rates are appropriate) and location (to 

determine applicable utility). See Appendix A for details. 

OTHER_INCENTIVES U 1 if other incentives are greater than $2/WAC

APPROVED U U 1 if approved, but not completed 

WAITLISTED U 1 if waitlisted, but not completed 

SCE U U 1 if in SCE’s territory 

SDG&E U U 1 if in SDG&E’s territory 

OTHER_UTILITY U U 1 if in another territory*** 

NEW_CONST_LARGE U 1 if installed with many other systems as part of a large new 

residential development 

NEW_CONST_SINGLE U 1 if installed on a single new home, or as part of a small 

cluster of new homes 

SCHOOLS U 1 if installed at a school 

AFFORDABLE_HOUSING U 1 if installed as part of an affordable housing project 

OWNER_INSTALLED U 1 if installed by owner 

INSTALLER_EXPERIENCE U U 1 for installers in the top 5% of installation companies in 

terms of aggregate installations over course of program 

RETAILER_EXPERIENCE U 1 for retailers in the top 5% of retailers in terms of 

aggregate installations over course of program 

CALIF_CONST_AUTORITY U 1 for systems installed by California Construction Authority 

POPULATION_DENSITY U By zip code, population divided by geographic area 

(person/miles2

) 

THIN_FILM U U 1 if system includes thin-firm PV modules 

MODULE_COST_INDEX U Tried/

Failed 

Annual index of global PV module prices in the power 

sector (2004 $/WDC-STC) 

* We use the date of application (rather than completion) under the presumption that system costs are largely set, by 

contract, prior to the submission of a rebate application. 

** This variable accounts for changes in standard rebates over time, but not non-standard rebates that have been 

provided for affordable housing, schools, and owner-installed systems 

*** For the CEC data, other utility territories include: Sacramento (SMUD), Bear Valley Electric Service, Palo 

Alto, and 13 smaller publicly-owned utilities with just a few systems each. For the CPUC data, they include Los 

Angeles (LADWP), Sacramento (SMUD), and 14 smaller publicly-owned utilities with just a few systems each. 

13
Table 3. Summary Statistics of Variables Used in Analysis 

CEC (< 30 kW) CPUC (>= 30 kW) Continuous Variables 

Mean Max Min Std. Dev. Mean Max Min Std. Dev.

INSTALLED_COST 9.6 29.3 4.1 2.2 8.8 23.7 4.4 1.6 

TIME_MONTH 62.4 85.0 0.0 14.6 32.9 45.0 0.0 11.1 

SYSTEM_SIZE 4.1 30.0 0.0 3.7 171.7 1,063 24.6 223.6 

MAX_STD_REBATE 3.9 4.8 2.7 0.7 4.2 4.8 3.4 0.6 

STATE_TAX_CREDIT 0.09 0.15 0.00 0.05 0.08 0.15 0.00 0.05 

RETAIL_RATES 0.13 0.19 0.08 0.01 0.13 0.16 0.05 0.02 

MODULE_COST_INDEX 3.5 4.5 3.3 0.2 3.5 3.9 3.3 0.1 

POPULATION_DENSITY 2,629 52,959 0.25 4,147 n/a n/a n/a n/a 

Dummy Variables Observations 

(Dummy = 1) 

Percent 

of Total 

Observations 

(Dummy = 1) 

Percent 

of Total 

REBATE_%_CAP 5,193 29% 678 64% 

OTHER_INCENTIVES n/a n/a 56 5% 

APPROVED* 5,033 28% 464 44% 

WAITLISTED* n/a n/a 262 25% 

SCE** 3,706 21% 294 28% 

SDG&E** 3,247 18% 151 14% 

OTHER_UTILITY** 139 1% 113 11% 

NEW_CONST_LARGE† 1,946 11% n/a n/a 

NEW_CONST_SMALL† 771 4% n/a n/a 

SCHOOLS† 60 0% n/a n/a 

AFFORDABLE_HOUSING† 340 2% n/a n/a 

OWNER_INSTALLED 862 5% n/a n/a 

INSTALLER_EXPERIENCE 9,454 53% 257 24% 

RETAILER_EXPERIENCE 6,152 34% n/a n/a 

CALIF_CONST_AUTHORITY n/a n/a 16 2% 

THIN_FILM 318 2% 111 11% 

* In addition to approved and waitlisted systems, the CEC and CPUC datasets include 12,856 and 327 completed 

systems, respectively. 

** PG&E, which represents the “base” utility territory to which the others are compared, contains the majority of 

systems: 10,797 (60%) systems in the CEC dataset, and 495 (47%) systems in the CPUC dataset.28

† Standard retrofits represent the “base” type of system to which others are compared in the CEC dataset, and 

include 14,772 total systems (83%). 

 

28 The datasets do not easily allow us to hypothesize and test why PG&E appears to host a disproportional (at least 

relative to load) share of PV systems. 

14
3.3 Methodology 

Analysis was conducted using multivariate regression techniques, with the dependent variable 

the per-unit, pre-rebate installed cost ($/WAC).29 White’s Test showed the presence of 

heteroscedasticity (non-constant variance in the error structure) in a number of our ordinary least 

squares (OLS) regression results; accordingly, we consistently present corrected standard errors 

using the Generalized Method of Moments (GMM) in the results that follow. 

We also explored the possibility of colinearity among independent variables, both by reviewing 

correlation coefficients and by sequentially adding or subtracting variables to test for correlated 

effects. In some cases, variables with significant colinearity were eliminated. As discussed 

below, we also developed a separate regression model that includes a subset of these collinear 

variables where complete variable elimination did not seem prudent. 

Various functional forms were tested for the continuous independent variables. For many, we 

ultimately use a simple linear form. We use a logarithmic form for SYSTEM_SIZE because, 

upon visual inspection and after statistical tests, that functional form appeared to fit our 

underlying data better than the alternatives that we tested. The square root of 

POPULATION_DENSITY is used because it fits the data well, and because it is assumed that 

changes in density in sparsely populated areas will have a greater affect than similar changes in 

density in more-densely populated urban areas. We also tested alternative functional forms for 

the TIME_MONTH month, but none outperformed a simple linear model. 

After testing a considerable number of alternative regression models, we developed four final 

models for both the CEC and the CPUC datasets: 

• Model 1 includes many of the independent variables listed earlier, but excludes the 

MODULE_COST_INDEX and the MAX_STD_REBATE variables. By excluding these 

variables, both of which are correlated with time, this model provides the best representation 

of aggregate cost reductions with time. (This model, as well as models 2 and 4, also excludes 

REBATE_%_CAP, STATE_TAX_CREDIT, and RETAIL_RATES). 

• Model 2 is equivalent to Model 1, but includes the MODULE_COST_INDEX and 

MAX_STD_REBATE variables, allowing an evaluation of the impact of the rebate on prerebate system costs, as well as an assessment of non-module cost reductions over time. 

• Model 3 is equivalent to Model 2, but adds three additional variables that experience some 

colinearity among themselves and with other independent variables, but that have policy 

interest: REBATE_%_CAP, STATE_TAX_CREDIT, and RETAIL_RATES. 

• Model 4 contains the same independent variables as Model 2, but includes a large number of 

crossed terms to determine whether the coefficients of certain variables included in other 

models are affected by time or by system size. The crossed variables include 

DATE_MONTH, NEW_CONST_LARGE, NEW_CONST_SMALL, 

 

29 We also explored using pre-rebate total installed cost as the dependent variable (2004 $), as opposed to per-unit 

pre-rebate installed costs (2004 $/WAC). The resulting total cost regression models explained a far greater fraction 

of total cost variation (i.e., had a much higher R2

, of around 0.9), but we chose to focus on the lower-R2 per-unit cost 

results because they do not require a transformation of regression results to present the more intuitive and policyrelevant per-unit cost results. 

15
OWNER_INSTALLED, INSTALLER_EXPERIENCE, RETAILER_EXPERIENCE, 

POPULATION_DENSITY, and THIN_FILM (the CPUC dataset does not contain many of 

these variables, and so includes fewer crossed terms). 

Each of these models was applied to: (1) just the completed PV systems, (2) to “pooled” data 

including both completed and approved systems, and (3) in the case of the CPUC dataset, to 

pooled data including completed, approved, and waitlisted systems. Though some differences in 

regression results exist depending on which dataset was used, they are not substantial. The 

results that follow therefore emphasize the fully pooled datasets (i.e., completed and approved 

systems for the CEC; completed, approved, and waitlisted systems for the CPUC). For reference 

purposes, Appendix C presents regression results for just the completed systems. 

Though we present results from all four models for both the CPUC and CEC pooled datasets, 

Model 2 was unsuccessful when applied to the CPUC SGIP data: coefficients for 

MODULE_COST_INDEX and the MAX_STD_REBATE were sometimes of opposite sign 

from what we expected, and colinearity was a clear problem. After numerous attempts, we were 

unsuccessful in rectifying this problem. We therefore place little emphasis on Model 2 results 

for the CPUC dataset. For a similar reason, we excluded the REBATE_%_CAP variable from 

the CPUC’s Model 3 results. The unfortunate result of these exclusions is that the CPUC models 

provide substantially less policy richness than the CEC’s models. 

16
4. Analysis Results: CEC Systems Under 30 kW in Size 

4.1 Summary Statistics 

Figures 4 through 8 show in graphical format some of the potential relationships between prerebate installed cost (in 2004 $/WAC) and a subset of the potential explanatory variables 

described in Section 3. The figures use the pooled CEC dataset of PV systems under 30 kW in 

size, including both completed and approved systems. 

The overall average pre-rebate installed cost of the CEC systems is $9.6/WAC (2004 $), which 

equates to approximately $8.0/WDC-STC (assuming a 0.84 de-rating). A review of the bivariate 

relationships portrayed in Figures 4 and 5 suggest that average installed costs exhibit some 

economies of scale, most pronounced in systems less than 10 kW in size, and that average 

installed costs – over all system sizes – have generally declined over time. 

In particular, Figure 4 shows that over the course of the program, average system costs for 

installations under 2 kW have been more than $10.5/WAC (2004 $), while systems over 10 kW 

have had average costs of roughly $8.5/WAC (2004 $). Meanwhile, Figure 5 shows that average 

pre-rebate installed costs (in 2004 $) have declined substantially over time (where time is 

expressed in half-year intervals), from more than $12/WAC in the initial year of the program to 

less than $9/WAC for the more recent installations. 

Some of this cost decline is due to a reduction in high-cost outliers. For example, from 1998 

through 2000, 14% of systems had costs greater than $4/W above the average system cost at that 

time. In 2004 and 2005, just 1% of systems had costs more than $4/W above the average system 

cost. This reduction in outliers is not surprising, and likely reflects a maturing PV market in 

which price competition is increasing over time. However, as shown by the cost distributions 

presented in Appendix B, the overall cost reductions are due not just to a narrowing of the cost 

range, but also to a shift of the distribution towards lower-cost systems. 

6

7

8

9

10

11

12

13

14

< 2 kW

(n=3720)

2-5 kW

(n=10,233)

5-10 kW

(n=3,068)

10-15 kW

(n=495)

15-20 kW

(n=165)

20-25 kW

(n=77)

>25 kW

(n=131)

System Size (kW)

Installed Cost (2004 $/WAC)

 Mean

 +/- 1 Std. Dev.

Figure 4. Average Installed Cost, by System Size (CEC)

17
8

9

10

11

12

13

14

121212121212121

1998 1999 2000 2001 2002 2003 2004 2005

Mean Installed Cost* (2004 $/WAC)

 0-2 kW

 2-5 kW

 5-10 kW

 10-30 kW

 All Systems

* Within each system size bin, we excluded any 6-month period that contained fewer than 5 applications. This impacted only the 2 largest bins (>5 kW).

Figure 5. Average Installed Cost Over Time (6-Month Intervals), by System Size (CEC)

Figure 6 depicts many of the cost relationships revealed by our use of dummy variables. Though 

these bivariate results do not control for the impact of other variables, many of the relationships 

shown in Figure 6 (e.g., systems installed in new home construction and affordable housing 

costing less than retrofitted systems, owner-installed systems costing less than contractorinstalled systems, etc.) are generally consistent with those revealed through the multivariate 

regression results, as presented in the next section. 

5

6

7

8

9

10

11

12

13

Retrofit

(n=14,772)

New Homes (Lg)

(n=1,946)

Affordable

(n=340)

Owner-Installed

(n=862)

Contr.-Installed

(n=17,027)

Exp. Cont.

(n=9,454)

Inexp. Cont.

(n=8,435)

Exp. Ret.

(n=6,152)

Inexp. Ret.

(n=8,435)

Thin Film

(n=318)

Crystalline

(n=17,571)

SCE

(n=3,706)

SDG&E

(n=3,247)

PG&E

(n=10,797)

Other

(n=139)

System / Installation Type

Installed Cost (2004 $/WAC)

 Mean

 +/- 1 Std. Dev.

Figure 6. Average Installed Cost, by System or Installation Type (CEC)

Finally, Figures 7 and 8 illustrate the influence of module costs, non-module costs, and rebates 

on average pre-rebate total installed costs over time (with time expressed in half-year intervals). 

Specifically, Figure 7 shows a tight relationship between standard rebate levels and average prerebate installed costs, particularly since mid-2000, with pre-rebate installed costs following 

rebate levels both higher (in 2001) and then gradually lower (starting in 2003). The average cost 

18
to the PV customer has remained more or less constant since 2001, with some modest increase in 

customer costs since the end of 2003. 

0

2

4

6

8

10

12

14

1

(n = 34)

2

(76)

1

(106)

2

(92)

1

(70)

2

(122)

1

(989)

2

(1,251)

1

(1,123)

2

(1,330)

1

(3,359)

2

(3,594)

1

(3,333)

2

(2,181)

1

(229)

2004 $/WAC (Mean)

 Total Cost

 Standard Rebate

 Customer Cost

1998 1999 2000 2001 2002 2003 2004 2005

Figure 7. Impact of Standard Rebate Level on Average Installed Cost (CEC) 

As shown in Figure 8, reductions in average total pre-rebate installed costs over time have been 

largely driven by reductions in non-module costs. Module costs (represented here by a generic 

module cost index – not by actual application data on module costs) have declined somewhat 

over the course of the CEC’s program, but non-module costs have dropped significantly, from 

roughly $8/WAC (2004 $) during the initial years of the program to just over $5/WAC (2004 $) 

more recently. This trend in non-module costs is encouraging, and is perhaps the most that an 

individual PV program can hope to achieve given that module costs are set in a worldwide 

market and are therefore heavily influenced by factors outside of the control of the local program 

(e.g., demand for PV in Japan and Germany). As noted earlier, however, we are unable to prove 

conclusively that the reductions in non-module costs in California have been caused by the 

state’s incentive programs absent analysis of non-module cost trends in other control markets. 

0

2

4

6

8

10

12

14

1

(n = 34)

2

(76)

1

(106)

2

(92)

1

(70)

2

(122)

1

(989)

2

(1,251)

1

(1,123)

2

(1,330)

1

(3,359)

2

(3,594)

1

(3,333)

2

(2,181)

1

(229)

2004 $/WAC (Mean)

 Module Cost Index

 Non-Module Cost

 Total Cost

1998 1999 2000 2001 2002 2003 2004 2005

Figure 8. Average Installed Cost Over Time, by Module and Non-Module Costs (CEC) 

19
4.2 Regression Results 

The bivariate relationships shown above are intriguing, but regression analysis is required to 

control for the influence of other factors. Table 4 summarizes regression results for the four 

basic models specified earlier, as applied to the pooled CEC dataset for systems under 30 kW in 

size. Although these regressions only explain a fraction of the overall variance in the data (R2

 of 

0.29 to 0.32), a large proportion of the independent variables have statistically significant 

(defined here as at least 90% significance, or p-values of less than 0.10) impacts on pre-rebate 

installed costs. Most of these relationships support the hypotheses reported earlier, and are 

consistent with corresponding regressions applied to just the CEC’s completed PV systems (see 

Appendix C). There remains a significant amount of unexplained variation in the underlying 

data, however, suggesting that PV costs are impacted by a number of variables not included in 

our analysis30 and/or that there is simply a considerable amount of unexplainable noise in PV 

system costs that may reflect an immature market in which price competition is not robust. 

4.2.1 The Impact of Time, Average Module Cost, and System Status 

Model 1 results (see Table 4) show that, with each successive monthly period, an average cost 

reduction of $0.058/WAC (all data reported here will be in 2004 $) has been experienced, 

equating to an annual reduction of approximately $0.70/WAC. At the mean system cost of 

$9.6/WAC, this represents a 7.3% annual reduction in pre-rebate installed costs, significantly 

outpacing the 2.6% average annual inflation rate over the approximately seven year period 

covered by the data (implying that prices have fallen in nominal terms as well). 

Some of this decline is due to a decrease in the average cost of PV modules over the course of 

the program (notwithstanding recent increases in those costs – see Figure 8). Pre-rebate system 

costs may also be affected by changing rebate levels over time (see Figure 7). Model 2 includes 

independent variables that control for these influences, and the regression results show that even 

considering these effects, pre-rebate non-module system costs have dropped by an average of 

approximately $0.025/WAC each month, or $0.30/WAC each year. At the mean system cost, this 

non-module cost reduction is 3.1% per year, still exceeding (to a limited extent) the average 

impact of inflation on nominal costs over the seven-year time period. 

Model 4, by including crossed terms, helps to illuminate how system size has influenced cost 

reductions over time. In particular, the coefficient for the variable DATE_MONTH x LN_SIZE 

indicates that larger systems have – on average – experienced a smaller drop in costs over time 

than have smaller systems. Applying this coefficient, a 10 kW PV system would experience an 

annual reduction in costs that is roughly $0.20/WAC less than that of a 2 kW system. That costs 

are dropping more rapidly for smaller systems should perhaps come as little surprise: with a 

greater fraction of costs used for labor and balance-of-systems, smaller PV systems may have the 

most to gain from distribution and installation channel efficiencies. 

 

30 For example, the low resolution of the data prohibited us from distinguishing between different product types that 

can have widely varying costs – e.g., roof-mounted systems vs. tracking systems vs. systems structured as carports. 

20
Table 4. Regression Results for CEC Dataset (PV Systems < 30 kW) 

Model 1 Model 2 Model 3 Model 4 Variable 

coef. p Coef. p coef. p coef. p 

INTERCEPT 19.921 <0.01 11.562 <0.01 12.334 <0.01 14.202 <0.01

TIME_MONTH -0.058 <0.01 -0.025 <0.01 -0.026 <0.01 -0.100 <0.01

LN_SYSTEM_SIZE -0.846 <0.01 -0.853 <0.01 -0.856 <0.01 -1.411 <0.01

APPROVED 0.258 <0.01 0.377 <0.01 0.357 <0.01 0.257 <0.01

SCE -0.098 <0.01 -0.101 <0.01 -0.109 <0.01 -0.081 0.02 

SDG&E -0.291 <0.01 -0.336 <0.01 -0.308 <0.01 -0.366 <0.01

OTHER_UTILITY -2.126 <0.01 -2.436 <0.01 -2.518 <0.01 -2.432 <0.01

NEW_CONST_LARGE -1.188 <0.01 -1.228 <0.01 -1.206 <0.01 -1.353 0.02 

NEW_CONST_SMALL 0.179 <0.01 0.180 <0.01 0.175 <0.01 2.118 0.07 

SCHOOLS 0.285 0.25 0.365 0.14 0.351 0.16 0.216 0.39 

AFFORDABLE_HOUSING -1.925 <0.01 -1.898 <0.01 -1.845 <0.01 -1.791 <0.01

OWNER_INSTALLED -1.755 <0.01 -1.800 <0.01 -1.803 <0.01 -0.686 0.58 

INSTALLER_EXPERIENCE 0.282 <0.01 0.288 <0.01 0.288 <0.01 1.925 <0.01

RETAILER_EXPERIENCE 0.169 <0.01 0.171 <0.01 0.170 <0.01 0.325 0.51 

SQRT_POP_DENSITY 4.2E-03 <0.01 4.2E-03 <0.01 4.2E-03 <0.01 1.9E-02 <0.01

THIN_FILM -0.699 <0.01 -0.732 <0.01 -0.733 <0.01 -5.389 <0.01

MAX_STD_REBATE 0.730 <0.01 0.556 <0.01 0.794 <0.01

MODULE_COST_INDEX 1.008 <0.01 0.906 <0.01 1.218 <0.01

REBATE_%_CAP 0.219 0.08 

STATE_TAX_CREDIT 1.647 <0.01 

RETAIL_RATES 1.151 0.59 

DATE_MONTH x LN_SIZE 0.011 <0.01

NEW_CONST_LARGE x LN_SIZE -0.685 <0.01

NEW_CONST_SMALL x LN_SIZE -0.019 0.86 

OWNER_INSTALLED x LN_SIZE 7.2E-03 0.96 

INSTALLER_EXP x LN_SIZE -0.079 0.16 

RETAILER_EXP x LN_SIZE 0.075 0.18 

SQRT_POP_DENSITY x LN_SIZE -2.2E-03 <0.01

THIN_FILM x LN_SIZE 0.295 0.26 

NEW_CONST_LARGE x TIME 0.083 <0.01

NEW_CONST_SMALL x TIME -0.024 <0.01

OWNER_INSTALLED x TIME -0.020 <0.01

INSTALLER_EXP x TIME -0.015 <0.01

RETAILER_EXP x TIME -0.012 <0.01

SQRT_POP_DENSITY x TIME 4.8E-05 0.21 

THIN_FILM x TIME 0.042 <0.01

R-SQUARED 0.29 0.30 0.30 0.32 

OBSERVATIONS (n) 17,889 17,889 17,889 17,889 

The module cost index variable is included in Models 2, 3 and 4, and averages near unity in all 

cases (1.01, 0.91, and 1.22). This is to be expected, and shows that aggregate changes in average 

module costs translate directly into similar-sized changes in pre-rebate system installed costs. 

Approved but not yet completed systems are found to have costs that are on average $0.25-

$0.38/WAC higher than completed systems (depending on the regression run). The reason for 

this differential is not altogether clear, but may be due to the fact that most of the approved 

21
systems in the CEC’s dataset have been recently submitted, during a time in which silicon 

shortages have yielded higher module costs. 

4.2.2 Policies and Incentives 

The level and design of the CEC’s rebate also appears to have had a significant impact on prerebate installed costs. Though the coefficient is somewhat sensitive to model specification (due 

in part to colinearity with other variables), the regression results for MAX_STD_REBATE 

suggest – as does Figure 7 – that pre-rebate installed costs have tracked (to some degree) the 

level of the rebate itself, and that system purchasers have therefore not benefited from the full

amount of the rebate (with some of it “captured” by system retailers or installers through higher 

prices).31 

In particular, we find that each $1/WAC change in the rebate level has, on average, yielded a 

$0.55-0.80/WAC change in pre-rebate installed costs (the range reflects the results of the different 

regression runs for Models 2, 3, and 4). In other words, when the CEC increased its rebate level 

by $1.5/WAC (from $3/WAC to $4.5/WAC) in early 2001, system purchasers only realized $0.3-

$0.7/WAC of that increase, with the remaining $0.8-$1.2/WAC being “captured” by system 

retailers or installers through correspondingly higher prices. By the same token, our regression 

results (which do not distinguish between an increase or decrease in rebate levels32) suggest that 

as the CEC has gradually reduced its rebate level since early 2003, system retailers have 

absorbed some of the decrease by reducing prices, thereby leaving the net cost to the system 

purchaser essentially unchanged. These results are consistent with the bivariate relationships 

presented earlier in Figures 7 and 8. 

Though colinearity problems exist for the variables added in Model 3 (REBATE_%_CAP, 

STATE_TAX_CREDIT, and RETAIL_RATES ), regression results provide weak evidence that 

the existence of the percentage rebate cap may also have had some impact on pre-rebate system 

costs. Model 3 shows that average system costs were roughly $0.20/WAC higher during the 

period in which the percentage cap was in place, than when it was removed. There is also some 

evidence that the size of the state tax credit may have impacted system costs, with the coefficient 

for STATE_TAX_CREDIT implying that an increase in the credit from 0% to 15% was matched 

with an increase in system installed costs of approximately $0.25/WAC. We find no statistical 

evidence that retail rates have had an impact on pre-rebate installed costs.33 

4.2.3 System Size 

Larger systems are found to be systematically less expensive than smaller systems, with 

LN_SYSTEM_SIZE showing statistically significant and sizable effects. Compared to a 1 kW 

system, regression results for Model 2 suggest that larger installations have lower costs as 

 

31 Though some might be inclined to read into these results an argument for switching from capacity-based to 

performance-based incentives, we note that there is nothing in our dataset or analysis that allows us to comment on 

the relative superiority of one incentive type over another. 

32 We did test for differential regression results depending on whether MAX_STD_REBATE increased or 

decreased, but were unable to find any significant results. 

33 Though again, our retail rate variable only indirectly accounts for California’s highly tiered rate structure, and 

therefore may not be adequately specified. 

22
follows: 3 kW ($0.90/WAC lower); 5 kW ($1.4/WAC lower); 10 kW ($2/WAC lower); and 30 kW 

($2.9/WAC lower). These results are consistent with the bivariate relationships presented earlier 

in Figure 4, and show that economies of scale are particularly dramatic among smaller systems, 

but diminish somewhat among larger systems (i.e., costs drop by $2/WAC for 10 kW systems 

relative to 1 kW systems, but only drop by an additional $0.90 WAC for 30 kW systems). 

4.2.4 Installation Type 

The nature of the installation clearly has had a substantial impact on average pre-rebate installed 

costs. Systems installed or planned for installation in large new residential developments 

(totaling 1,946 systems) show cost reductions of approximately $1.2/WAC, on average, compared 

to the general retrofit market. Similarly, the 340 systems used in affordable housing 

applications, which often involve new construction and presumably enable bulk system 

installation, exhibit costs that are $1.9/WAC lower than the general retrofit market, on average. 

These results are directionally consistent with the bivariate relationships shown earlier in Figure 

6. Systems installed in single new homes (or small clusters) exhibit modestly higher costs of 

approximately $0.18/WAC on average, perhaps due to the custom-designed nature of many of 

these systems, as well as a lack of the economies of scale possible in larger new home 

developments. Systems installed at schools (of which there are only 60, and most are retrofits) 

do not have statistically significant differences in cost compared to the general retrofit market. 

Some of these cost differentials have varied with time and, to a lesser extent, with system size, as 

revealed by the results of Model 4. The cost savings associated with systems installed in large 

new home developments, for example, are shown to be higher for larger systems, but also to be 

narrowing over time. This latter finding is consistent with results presented in Appendix C, 

which show that completed large residential development projects have experienced cost savings 

that that have averaged $1.7/WAC, larger than the $1.2/WAC average cost advantage show when 

completed and approved systems are considered together. The cost disadvantage of systems 

installed individually or in small clusters on new homes has similarly been narrowing with time. 

4.2.5 Module Type 

Projects using thin film PV technology – of which there are 318 – are found to have had 

systematically lower costs than those relying on traditional crystalline silicon, with a differential 

of roughly $0.70/WAC on average. These results are generally consistent with the mean costs 

shown earlier in Figure 6. Model 4, however, shows that this cost differential has narrowed over 

time: the coefficient for the THIN_FILM x TIME variable is positive and statistically 

significant. This trend is also consistent with the regression results presented in Appendix C for 

only completed systems, which reveal that completed systems using thin film technology have 

had costs that are approximately $0.90/WAC lower than those employing crystalline silicon. 

4.2.6 Installer/Retailer Type and Experience 

Owner-installed systems (totaling 862 systems, or 5% of our dataset) are found to have 

considerably lower reported costs than contractor-installed systems, with a $1.8/WAC savings on 

average over the course of the CEC’s rebate program. More-experienced installers and retailers 

are found to charge slightly more for their services, relative to installers and retailers with less 

23
experience: approximately $0.29/WAC and $0.17/WAC, respectively. These results are generally 

consistent with the bivariate relationships depicted earlier in Figure 6. 

These differentials have, in some cases, changed with time and system size (see Model 4 results). 

The reported cost savings from owner-installed systems, for example, have increased over time. 

The cost disadvantage associated with more experienced installers and retailers, on the other 

hand, has decreased with time. There is also some statistical evidence (which is stronger in the 

completed-only regression than in the pooled regression) that installer experience imposes a less 

significant cost disadvantage on larger systems than on smaller ones. 

4.2.7 System Location and Population Density 

In comparison to the “base” service territory of PG&E (where 60% of the systems have been 

located), systems installed in other areas report lower pre-rebate costs. For example, systems 

located in SCE’s service territory are found to be approximately $0.10/WAC cheaper, while 

systems located in SDG&E’s territory have reported roughly $0.30/WAC lower costs, on average. 

Systems sited in other utility territories have reported substantially lower costs still, $2.1-

2.5/WAC below those in PG&E.34 These other territories primarily include public power utilities, 

and the lower costs reported for systems may reflect the presence of other local utility incentives 

that are not tracked by the CEC, or may otherwise reflect local bulk purchase and installation 

programs. These potential explanations are pure suppositions, however – nothing in our dataset 

or analysis allows us to definitively explain the inter-utility cost differences described above (we 

did try an imperfect measure of utility-specific retail rates, but with little success). 

The population density of the location of installation also appears to have some effect on system 

costs, with more densely populated areas experiencing higher average costs. As an example, the 

regression results suggest that moving from an area with a population density of 500 per square 

mile to one of 7,000 per square mile (the mean population density in our data is 2,600) results in 

an average system cost increase of roughly $0.25/WAC. Model 4 results further show that this 

cost disadvantage appears to be greater for smaller systems. This finding is consistent with the 

idea that population density may be a proxy for the cost of living, and therefore labor costs. One 

would expect smaller systems to generally be more sensitive to labor costs than larger systems, 

because small systems likely require a greater proportionate amount of installation labor. 

 

34 If one considered just completed systems, as reported in Appendix B, these cost differentials are somewhat larger. 

24
5. Analysis Results: CPUC Systems 30 kW and Above in Size 

5.1 Summary Statistics 

Focusing now on the larger systems (30 kW and above) under the CPUC’s SGIP program, 

Figures 9 through 13 depict some of the potential relationships between pre-rebate installed cost 

and some of the pertinent explanatory variables described in Section 3. The figures use the 

pooled CPUC dataset including completed, approved, and waitlisted systems. 

The overall average pre-rebate installed cost of the CPUC systems is $8.8/WAC (2004 $), which 

equates to approximately $7.4/WDC-STC (assuming a 0.84 de-rating). A review of the data 

presented in Figure 9 suggests that, as was the case for CEC-funded systems, the average 

installed cost of CPUC-funded systems exhibits economies of scale, most pronounced for 

systems less than 450 kW. The up tick in average installed costs for systems sized between 60 

and 100 kW is somewhat of an anomaly, being heavily influenced by 59 identical applications 

(out of 209 total applications in this size range), all submitted on the same day, by the same 

installer, and at the same estimated installed cost of $9.82/WAC (2004$). Overall, the largest 

systems funded by the CPUC’s program have costs that are on average roughly $1/WAC lower 

than the smaller systems funded by this program. 

6

7

8

9

10

11

12

<40 kW

(n=234)

40-60 kW

(n=169)

60-100 kW

(n=209)

100-200 kW

(n=192)

200-300 kW

(n=70)

300-450 kW

(n=65)

450-600 kW

(n=47)

600-800 kW

(n=23)

>800 kW

(n=44)

System Size (kW)

Installed Cost (2004 $/WAC)

 Mean

 +/- 1 Std. Dev.

Figure 9. Average Installed Cost, by System Size (CPUC)

In contrast to the longer-running CEC program, which exhibited clear downward cost trends over 

time, costs under the CPUC’s program have been far more flat (or even increasing, and then 

decreasing), as shown in Figure 10, though costs do appear to have declined since 2002. 

Appendix B presents the distribution of these costs, in modified histogram form, and shows that 

the distribution appears to have shifted toward lower-cost systems over time. 

25
6.5

7.0

7.5

8.0

8.5

9.0

9.5

10.0

21212121

2001 2002 2003 2004 2005

Mean Installed Cost (2004 $/WAC)

 <100 kW

 100-300 kW

 >300 kW

 All Systems

* Within each system size bin, we excluded any 6-month period that contained fewer than 5 applications. This impacted only the largest bin (>300 kW).

Figure 10. Average Installed Cost Over Time (6-Month Intervals), by System Size (CPUC)

Figure 11 depicts many of the cost relationships revealed by our use of dummy variables. 

Though not able to control for other factors, many of the relationships shown in Figure 11 are 

consistent with those revealed through multivariate regression results, as presented in the next 

section. 

4

5

6

7

8

9

10

11

12

13

CACA

(n=16)

Other

(n=1,037)

Exp. Inst.

(n=257)

Inexp. Inst.

(n=796)

Thin Film

(n=111)

Crystalline

(n=942)

SCE

(n=294)

SDG&E

(n=151)

PG&E

(n=495)

Other

(n=113)

Other Incentive

(n=56)

None

(n=997)

System / Installation Type

Installed Cost (2004 $/WAC)

 Mean

 +/- 1 Std. Dev.

Figure 11. Average Installed Cost, by System or Installation Type (CPUC)

Finally, Figures 12 and 13 illustrate the influence of module costs, non-module costs, and rebates 

on average pre-rebate total installed costs over time (with time expressed in half-year intervals). 

The relationships depicted here are not as strong as they were under the CEC’s program, perhaps 

26
in part because of the relatively short history of the program, coupled with an unwavering rebate 

level over most of that time period.35

0

1

2

3

4

5

6

7

8

9

10

2 (n=21) 1 (n=45) 2 (n=75) 1 (n=55) 2 (n=115) 1 (n=238) 2 (n=140) 1 (n=364)

2001 2002 2003 2004 2005

2004 $/WAC (Mean)

 Total Cost

 Standard Rebate

 Customer Cost

Figure 12. Impact of Rebate Level on Average Installed Cost (CPUC)

0

1

2

3

4

5

6

7

8

9

10

2 (n=21) 1 (n=45) 2 (n=75) 1 (n=55) 2 (n=115) 1 (n=238) 2 (n=140) 1 (n=364)

2001 2002 2003 2004 2005

2004 $/WAC (Mean)

 Module Cost Index

 Non-Module Cost

 Total Cost

Figure 13. Average Installed Cost Over Time, by Module and Non-Module Costs (CPUC)

5.2 Regression Results 

As with the earlier CEC analysis, Table 5 summarizes our regression results for the fully pooled 

CPUC dataset for the four basic models specified earlier (regression results for just the 

completed systems are reported in Appendix C). The regressions only explain a fraction of the 

overall variance in the data (R2

 of 0.25), and a higher proportion of the independent variables are 

 

35 Our data and analysis are not sufficient to allow us to conclude with certainty the reasons for the differences 

between the CPUC and CEC programs. 

27
statistically insignificant (i.e., p-value >0.10) than in the earlier CEC results. This may be caused 

by the much smaller sample size of the CPUC dataset, or more fundamentally from greater 

“noise” in that dataset. Nonetheless, a number of the independent variables are significant, and 

many of the relationships support the hypotheses reported earlier. 

Table 5. Regression Results for CPUC Dataset (PV Systems 25 - 1,063 kW) 

Model 1 Model 2 Model 3 Model 4 Variable 

coef. P coef. p coef. p coef. p 

INTERCEPT 11.258 <0.01 18.565 <0.01 10.753 <0.01 12.022 <0.01 

TIME_MONTH -0.030 <0.01 -0.053 <0.01 -0.026 <0.01 -0.049 0.03 

LN_SYSTEM_SIZE -0.322 <0.01 -0.335 <0.01 -0.232 <0.01 -0.510 <0.01 

APPROVED 0.067 0.69 0.209 0.32 0.077 0.66 0.065 0.71 

WAITLISTED -0.222 0.32 -0.354 0.19 -0.265 0.23 -0.270 0.23 

SCE 0.309 <0.01 0.292 0.01 0.283 0.02 0.282 0.01 

SDG&E 0.486 <0.01 0.501 <0.01 0.400 0.07 0.430 <0.01 

OTHER_UTILITY 0.072 0.69 0.054 0.76 0.009 0.97 0.059 0.74 

OTHER_INCENTIVES 0.626 0.10 0.626 0.09 0.567 0.13 0.676 0.08 

INSTALLER_EXPERIENCE -0.686 <0.01 -0.671 <0.01 -0.673 <0.01 -0.134 0.76 

CALIF_CONST_AUTHORITY -4.028 <0.01 -4.096 <0.01 -4.042 <0.01 -3.649 <0.01 

THIN_FILM 0.183 0.18 0.172 0.23 0.206 0.14 -2.434 <0.01 

MODULE_COST_INDEX -1.116 0.13 

MAX_STD_REBATE -0.635 <0.01 

STATE_TAX_CREDIT 3.011 0.06 

RETAIL_RATES -1.695 0.70 

DATE_MONTH x LN_SIZE 0.005 0.34 

INSTALLER_EXP x LN_SIZE -0.075 0.37 

THIN_FILM x LN_SIZE 0.450 <0.01 

INSTALLER_EXP x TIME -0.005 0.61 

THIN_FILM x TIME 0.016 0.26 

R-SQUARED 0.25 0.25 0.25 0.25 

OBSERVATIONS (n) 1,053 1,053 1,053 1,053 

5.2.1 The Impact of Time, Average Module Cost, and System Status 

Though Figure 10 does not provide a clear visual trend of declining system costs over time, our 

Model 1 regression results (which control for the impact of other variables) reveals an average 

cost reduction of $0.03/WAC per month (all data reported here will be in 2004 $), equating to an 

annual reduction that has averaged $0.36/WAC. At the mean system cost of $8.8/WAC, this 

represents a 4.1% annual reduction in pre-rebate installed costs, outpacing the 2.4% average 

inflation rate over the nearly four-year period covered by the data. Appendix C shows that for 

completed systems, the average cost reductions have been slightly larger at $0.44/WAC per year. 

These yearly cost declines are substantially smaller than those for the CEC dataset (which had 

annual average reductions of approximately $0.70/WAC), though it deserves note that the larger 

systems (i.e., 10-30 kW) in the CEC dataset were found to be on a more moderate cost trajectory 

over time, relative to the smaller systems supported by the CEC. Similarly, though not 

statistically significant (p = 0.34), the coefficient on the crossed DATE_MONTH x LN_SIZE in 

28
our Model 4 results for the CPUC suggests that larger CPUC systems may also have witnessed 

less pronounced reductions over time than smaller systems. It is not entirely clear whether the 

more modest average cost declines in the CPUC dataset are due to: (1) fundamental differences 

in the design of the CEC and CPUC programs; (2) differences in the size of the underlying PV 

systems; or (3) differences in the time periods covered (with the CEC’s program operating over a 

seven-year period, compared to the CPUC’s four-year period). Some of these issues are 

explored further in Section 6. 

As reported in Section 4.2.1, our analysis of the CEC data revealed that some of the annual cost 

reductions were due to changes in PV module costs. We were unable to replicate this finding 

with the CPUC dataset. As mentioned earlier, we were unsuccessful in developing a reasonable 

set of Model 2 results for the CPUC dataset, as both the MODULE_COST_INDEX and the 

MAX_STD_REBATE variables showed coefficients of the opposite sign than expected, with 

colinearity being a clear problem. We therefore heavily discount our Model 2 results here, and 

are unable to say anything definitive about the impact of module cost changes on system 

installed costs. These results suggest that the CPUC’s dataset is more “noisy” and that cost 

trends are less-discernable than was the case with the CEC dataset (the bivariate figures in 

Section 5.1 also support this contention). 

Though not statistically significant, waitlisted systems appear to have been priced somewhat 

below (by ~$0.25/WAC) completed systems on average, perhaps suggesting that costs have a 

tendency to rise between the date of application and completion. On the other hand, similar 

results are not found for approved systems. 

5.2.2 Policies and Incentives 

Our inability to formulate a useful Model 2 for the CPUC dataset yields overall results that are 

substantially less policy-rich than those based on the CEC’s dataset. The coefficient for 

MAX_STD_REBATE, for example, is of the opposite sign that one would expect, suggesting 

that it may be picking up some unknown trend in the data (for the completed-only dataset in 

Appendix C, the coefficient is positive, but not statistically significant). Similarly, due to 

colinearity with the MAX_STD_REBATE variable, we were unable to include a 

REBATE_%_CAP variable in Model 3. We again find no statistical evidence that retail rates 

have had an impact on pre-rebate installed costs, though as noted earlier, our retail rate variable 

is imperfect. 

Despite these limitations, at least two interesting policy-related results derive from our regression 

analysis of the CPUC dataset: 

• First, as with the CEC data, we find some weak evidence that the size of the state tax credit 

has affected pre-rebate installed costs (though colinearity is again of some concern here). 

Specifically, the coefficient for STATE_TAX_CREDIT implies that an increase in the state 

credit from 0% to 15% was matched with an increase in installed costs of approximately 

$0.45/WAC. The same result, however, is not supported by the completed-only regressions in 

Appendix C, which show a statistically insignificant coefficient for this variable. 

29
• Second, we find that the existence of other sizable local incentives for PV installations 

(which are, to some degree, additive to the CPUC’s incentives) have affected system 

installed costs. Specifically, the 56 PV systems that have received (or will receive) local 

incentives of more than $2/WAC (typically offered by the local publicly owned utility) have 

recorded higher costs of ~$0.60/WAC, on average (these results are largely consistent with the 

bivariate relationship shown in Figure 11). For the 38 completed PV installations receiving 

such incentives, the average differential is higher, at approximately ~$0.80/WAC (see 

Appendix C). This suggests that heavy subsidies dampen, to some degree, the motivation of 

installers to provide, and/or customers to seek, lower installed costs. 

In addition, though we were unable to evaluate the impact of the percentage rebate cap through 

regression analysis, it is interesting to note that 30% of all PV systems funded by the CPUC 

during the period in which the 50% cap was in place were priced from $8.75-$9.25/WAC (50% 

were priced from $8-$9.25/WAC). The corresponding numbers in the four-month period of our 

sample after the cap was dropped (in mid-December 2004) are 5% and 11.5%, respectively, with 

the entire cost distribution having shifted to the left (i.e., toward lower costs). These summary 

statistics support the contention that the percentage cap had the effect of inflating costs. 

5.2.3 System Size 

As with the CEC-funded systems, larger installations are found to be systematically less 

expensive than smaller systems on average. Based on Model 1 results, and compared to a 30 kW 

system, we find that larger installations have lower average costs as follows: 100 kW 

($0.40/WAC lower); 250 kW ($0.70/WAC lower); 500 kW ($0.90/WAC lower); and 1,000 kW 

($1.1/WAC lower). These regression results are largely consistent with the bivariate relationships 

depicted in Figure 9 (again, discounting the 60-100 kW size range due to potential data 

anomalies). 

5.2.4 Module Type 

Though only bordering on statistical significance (and far from significance in the completedonly regressions reported in Appendix C), projects using thin film technology – of which there 

are 111 – are found to have had slightly higher costs on average over the course of the CPUC 

program (~$0.20/WAC). This differs from the CEC results, which showed systematically lower 

costs of roughly $0.70/WAC on average. The reasons for this difference are unclear. Model 4 

demonstrates that the CPUC thin film cost differential has varied with system size, with larger 

thin film systems being relatively more expensive (compared to crystalline silicon) than smaller 

thin film installations. 

5.2.5 Installer Type and Experience 

Unlike the CEC results, more-experienced installers under the CPUC’s program have priced 

their systems at lower levels than less-experienced installers, with a differential of nearly 

$0.70/WAC on average (experienced installers of completed systems have had prices ~$0.45/WAC

lower than their less-experienced counterparts, on average). Model 4 shows that this differential 

has not changed markedly with time, or with system size (though Model 4 results in Appendix C 

suggest that the advantages of installer experience for completed systems have narrowed with 

30
time). Why the impact of installer experience varies between the CEC and CPUC datasets is 

unclear. 

Relative to the other systems in the dataset, the 16 systems managed and installed by the 

California Construction Authority have come in at a substantially and surprisingly lower cost of 

~$4/WAC, on average. This may be due to the bulk purchase of PV equipment made possible by 

these multiple installations, and by the fact that the Construction Authority sought to bid out 

different elements of their projects separately, eliminating the need for a value-added, fullservice installer. Some have also speculated that the CCA is able to install systems at apparently 

lower costs than the PV industry at large due to the fact that it has no marketing, sales, or 

overhead costs, and/or that certain internal costs are not reported. 

5.2.6 System Location 

In comparison to the majority of systems installed in PG&E’s territory (where 47% of the 

CPUC-funded systems have been located), systems installed in other areas report higher prerebate costs. Systems located in SCE’s service territory are found to be approximately 

$0.30/WAC more expensive, while systems located in SDG&E’s territory have reported roughly 

$0.45/WAC higher costs, on average (the OTHER_UTILITY variable is not statistically 

significant). The reasons for these inter-utility cost differences (which differ from the CEC 

results) are unclear, and would require further analysis. Furthermore, similar results are not 

found in the regressions for completed systems shown in Appendix C, where none of the utility 

dummy variables reach statistical significance. 

31
6. Comparing the CEC and CPUC Programs 

Sections 4 and 5 analyzed the CEC and CPUC datasets separately, not allowing for ready 

comparison between the two programs. In this section we are interested in two specific 

questions that can be explored through a combined, side-by-side analysis of the two programs: 

• Are there significant pre-rebate installed cost differences between the two programs? 

• Have costs declined with time in a comparable fashion across the two programs? 

6.1 Fundamental Cost Variations Between Systems Funded by the Two Programs 

The CEC and CPUC programs have operated over different time periods, and have targeted 

different system sizes, complicating direct comparisons between the two. Figure 14 shows 

average pre-rebate system installed costs, by size range, for both the CEC and CPUC programs. 

The figure only includes rebate applications received during the period of time (within our 

sample) in which both programs were operating contemporaneously: July 1, 2001 to April 15, 

2005. Clearly, economies of scale exist, with larger installations coming in at significantly lower 

costs than smaller ones. The smaller graph inset in the upper right-hand corner of Figure 14 

zooms in on the 0-55 kW size range, to more clearly depict the system size range in which the 

two programs overlap.36

7.5

8.0

8.5

9.0

9.5

10.0

10.5

0 100 200 300 400 500 600 700 800 900 1000

System Size (kWAC)

Mean Installed Cost (2004 $WAC)

 CEC

 CPUC

8.0

8.5

9.0

9.5

10.0

10.5

0 5 10 15 20 25 30 35 40 45 50 55

System Size (kWAC)

2004 $/WAC

Figure 14. Average Installed Cost, by System Size (CEC and CPUC)

Though systems funded by the CPUC’s program have, on average, exhibited somewhat lower 

costs than those funded by the CEC’s program, the opposite appears to be the case when 

considering only similar-sized systems. This can be seen not only in Figure 14, but also Table 6, 

which presents summary statistics for systems in the 20-40 kW range from both programs. 

These data suggest that for similar-sized systems installed over the same time period, CPUC-

 

36 Although the CPUC program nominally funds only systems that are at least 30 kW in size, it has funded two 

systems below that threshold, with reported sizes as low as 25 kW. 

32
funded installations have been more expensive than CEC-funded systems (by $0.77/WAC for the 

pooled dataset that includes completed, approved, and waitlisted systems, and by $1.21/WAC for 

just the completed systems). 

Table 6. Cost Variations Between the CEC and CPUC Programs: Descriptive Statistics 

(20-40 kW systems, approved between 07/01/01 and 04/15/05) 

Fully Pooled Data 

CEC CPUC 

Observations (n) 202 237 

Average System Size (kW) 26.0 kW 32.7 kW 

Mean System Cost ($/WAC) $8.20/WAC $8.97/WAC

A simple regression of the same data included in Table 6 (focusing on the fully pooled dataset, 

but now including both CPUC and CEC data) yields the same basic conclusion, while controlling 

for variations in system size and application date. The coefficient for CPUC_DUMMY in Table 

7 shows that among similar-sized systems, those funded by the CPUC’s program exhibit prerebate installed costs that have been $0.60/WAC higher than those funded by the CEC. 

Table 7. Cost Variations Between the CEC and CPUC Programs: Regression Results 

(20-40 kW systems, approved between 07/01/01 and 04/15/05) 

Variable Coefficient p-value 

INTERCEPT 8.887 <0.01 

TIME_MONTH -0.030 <0.01 

LN_SYSTEM_SIZE 0.101 0.86 

CPUC_DUMMY 0.612 <0.01 

R-SQUARED 0.10 

OBSERVATIONS (n) 439 

These findings support the oft-heard claim in California that the CPUC’s richer incentives in 

recent years ($4.5/WAC until December 2004, with a 50% cap – see Figure 3 from Section 2.2) 

have not motivated system cost reductions to the same extent as under the CEC’s program (the 

CEC’s program also offered $4.5/WAC at one time, but reduced that incentive more rapidly than 

the CPUC). This finding is also consistent with results presented earlier that showed that rebate 

levels (for the CEC dataset) and the availability of other incentives (for the CPUC dataset) have 

influenced pre-rebate installed costs among both datasets.37

 

37 Some might speculate that unaccounted-for differences in the two programs, rather than (or in addition to) policy 

design, could explain the higher installed costs among CPUC-funded systems. For example, Section 4 found that 

within the CEC program, owner-installed systems, as well as systems installed on new home and affordable housing 

developments, cost significantly less on average than retrofit systems. Since these three applications (i.e., ownerinstalled, new home construction, and affordable housing) are unique to the CEC program (due to the 30 kW size 

threshold, which more or less precludes them from the CPUC program), some might speculate that these types of 

programmatic differences have driven the cost differential shown in Tables 6 and 7. Though our analysis does not 

enable us to lay such contentions entirely to rest, we reiterate that Tables 6 and 7 pertain only to systems of 20-40 kW 

in size – i.e., a size range in which few, if any, CEC-funded owner-installed, new home construction, or affordable 

housing systems reside. As such, we find it likely that the cost differences are caused by differences in policy 

design. 

33
6.2 Consistency of Cost Reductions Between Systems Funded by the Two Programs 

Analysis results presented in Sections 4 and 5 suggest that the CEC’s program for smaller (< 30 

kW) PV systems has yielded greater cost reductions over time than has the CPUC’s program for 

larger (30 kW and above) systems. This is somewhat supported by the summary information 

provided in Figure 15, which shows mean installed costs over time (expressed in monthly 

intervals), by date of rebate application. Although average installed costs in both programs have 

been on a similar trajectory (and in a very similar range) since mid-2003, earlier CPUC costs 

exhibit significant variability with little apparent trend. The bivariate nature (i.e., cost vs. time, 

with no attempt to control for other variables) of the graph, however, makes it difficult to draw 

conclusions from Figure 15 alone. 

7

8

9

10

11

12

13

14

Mar-98

Jul-98

Nov-98

Mar-99

Jul-99

Nov-99

Mar-00

Jul-00

Nov-00

Mar-01

Jul-01

Nov-01

Mar-02

Jul-02

Nov-02

Mar-03

Jul-03

Nov-03

Mar-04

Jul-04

Nov-04

Mar-05

Mean Installed Cost (2004 $/WAC)

 CEC

 CPUC

Figure 15. Average Installed Cost Over Time (CEC and CPUC)

To investigate this question statistically, we combine the CPUC and CEC datasets (including 

completed, approved, and waitlisted systems), again truncating the date range from July 1, 2001 

to April 15, 2005. We include only those independent variables that were included in the earlier 

Model 1 results from Sections 4 and 5, crossed with either a CPUC or CEC dummy.38 

Table 8 reports the results of this combined CPUC/CEC regression. 

 

38 We acknowledge that this combination of the two datasets is not perfect, and potentially excludes variables that 

could hold some explanatory power over differences in installed costs among the programs. For example, a few of 

the CPUC-funded systems may have been installed as shade-providing carports, which one would expect to have 

higher installed costs. Similarly, one of the installers active in the CPUC program often adheres PV modules to 

insulating roofing panels, which again could result in higher costs (while presumably delivering higher value as 

well). For the most part, the datasets were not robust enough to enable us to tease out and examine these potentially 

important variables. 

34
Table 8. Regression Results for Pooled CPUC/CEC Dataset 

(systems approved between 07/01/01 and 04/15/05) 

Variable Coefficient p-value 

INTERCEPT 12.208 <0.01 

TIME_MONTH -0.076 <0.01 

CPUC_TIME_MONTH 0.046 <0.01 

LN_SYSTEM_SIZE -0.814 <0.01 

CPUC_LN_SYSTEM_SIZE 0.492 <0.01 

CEC_APPROVED 0.457 <0.01 

CPUC_COMPLETED -0.950 <0.01 

CPUC_APPROVED -0.883 0.02 

CPUC_WAITLISTED -1.172 <0.01 

CEC_SCE -0.089 <0.01 

CEC_SDG&E -0.266 <0.01 

CEC_OTHER_UTILITY -2.333 <0.01 

CEC_NEW_CONST_LARGE -1.316 <0.01 

CEC_NEW_CONST_SMALL 0.163 0.02 

CEC_SCHOOLS 0.250 0.18 

CEC_AFFORDABLE_HOUSING -2.030 <0.01 

CEC_OWNER_INSTALLED -1.864 <0.01 

CEC_INSTALLER_EXPERIENCE 0.228 <0.01 

CEC_RETAILER_EXPERIENCE 0.144 <0.01 

CEC_SQRT_POP_DENSITY 4.9E-3 <0.01 

CEC_THIN_FILM -0.594 <0.01 

CPUC_SCE 0.309 <0.01 

CPUC_SDG&E 0.486 <0.01 

CPUC_OTHER_UTILITY 0.072 0.69 

CPUC_OTHER_INCENTIVES 0.626 0.10 

CPUC_INSTALLER_EXPERIENCE -0.686 <0.01 

CPUC_CALIF_CONST_AUTHORITY -4.028 <0.01 

CPUC_THIN_FILM 0.183 0.18 

R-SQUARED 0.31 

OBSERVATIONS (n) 17,453 

Most of the results presented in this combined regression are consistent with our earlier findings. 

Economies of scale are found to exist, with greater economies among the smaller CEC-funded 

systems than the larger CPUC-funded systems. Experienced installers are found to charge more 

in the CEC dataset, but charge less in the CPUC dataset. The impact of the various utility 

service territory dummies also remains significant, with differences between the two datasets. 

Further work would be required to understand the reasons for these discrepancies. The results 

for the other variables included in the combined regression are also generally consistent with the 

results presented earlier. 

Of most interest here, the TIME_MONTH coefficient shows that systems funded by the CEC 

over this time period experienced an average monthly decrease in pre-rebate installed cost of 

$0.076/WAC ($0.91/WAC each year). The CPUC_TIME_MONTH variable shows that the 

systems funded by the CPUC have experienced far more modest average cost declines of 

$0.03/WAC ($0.36/WAC each year). Annual average cost reductions for CEC-funded systems (< 

30 kW) outpaced those of CPUC-funded systems (30 kW and above) by a multiplier of 2.5. As 

hypothesized earlier, this may be due to the more substantial (proportionally) labor and 

35
installation costs associated with smaller systems and the greater opportunities in that market 

segment for distribution and installation efficiency gains.39 Alternatively, it could be a result of 

policy design – whereas the CEC has (since 2003) progressively lowered its rebate over time, the 

CPUC has been slower to follow suit. The fact that CEC- and CPUC-funded average system 

costs have apparently tracked each other since mid-2003 (see Figure 15), however, makes both 

of these explanations somewhat less likely. Ultimately, the quality of our data does not allow us 

to definitively explain the difference in cost reductions between the two programs, or even prove 

that the programs themselves are responsible for the cost reductions.40 We recommend that 

future work explore these questions in more detail. 

 

39 As shown in Sections 4 and 5, we find this same effect – smaller systems exhibiting greater cost reductions over 

time – not only across programs, but also within each of the two programs. 

40 Though it is perhaps logical to assume that California’s PV programs have caused, or at least contributed to, the 

empirical cost reductions, as discussed earlier, nothing in our analysis enables us to assign causation – i.e., we are 

unable to definitively conclude that the California programs are driving the cost reductions. To be able to assign 

causation, we would need to similarly analyze a “control” market – i.e., one in which no PV incentive programs 

exist. Identifying such a market for PV may be difficult or impossible, given the widespread public support that PV 

has garnered, but future work could at least analyze other markets in which PV is subsidized, but to a different 

extent or in a different manner than in California. 

36
7. Conclusions and Recommendations 

The promise of electricity generated from photovoltaics is alluring: PV is renewable, clean, 

distributed, modular, and fuel-free. In an era of concern about energy security, energy price 

volatility, and the global climate, solar energy offers one potential technological solution. As a 

result, adoption of solar power is accelerating, driven primarily by government incentives. But 

cost reductions remain vital for the long-run success of the solar market. A recent report 

prepared for the CPUC, for example, suggests that the CPUC’s SGIP is not yet cost-effective 

from a societal or non-participant perspective (Itron 2005b). 

Historical experience, engineering studies, and learning curve theory tell us that solar costs will 

drop over time, though the degree of future price reduction remains uncertain. A primary goal of 

government incentive programs is to encourage these cost reductions. In fact, some have argued 

that long-term, sustained government deployment programs will be necessary to drive 

investments in manufacturing and system delivery infrastructure, and thereby reduce costs. 

Japan’s success in creating a sizable, stable, and well-functioning residential solar market 

through a sustained long-term commitment to progressively lower rebates is often cited as 

evidence of this effect (see, e.g., Osborn et al. 2005). The Japanese program supported more 

than 220,000 residential PV systems from its inception in 1994 through 2004, including over 

60,000 in 2004 alone. Costs for standard 3 kW residential systems averaged roughly $6.2/WDC

in 2004, or $7.4/WAC. These costs have declined over time as a well-functioning market 

infrastructure has developed, with residential PV costs declining by an average of 8.3% annually 

from 1998 to 2004 (in real Yen/kW for standard 3 kW systems) (RTS 2004; RTS 2005). 

The analysis presented in this paper finds clear evidence that the California market has also 

experienced reductions in PV costs over time. Cost reductions are particularly significant for 

CEC-funded systems, where regression results show an average annual decline of approximately 

$0.70/WAC – nearly twice that achieved under the CPUC program.41 At the means of the data, 

this represents annual average percentage declines of 7.3% and 4.1% for CEC- and CPUCfunded systems, respectively. Although our analysis cannot, without comparison to a control 

group, definitively conclude that the CEC and CPUC programs caused these cost reductions, it is 

clear that – despite the lack of continuity and stability experienced by both programs – pre-rebate 

installed costs have come down, especially among the CEC-funded systems. 

Some of these overall cost reductions are due to decreases in worldwide module costs 

(notwithstanding the recent increase in those costs). Much of the cost reductions, however, 

appear to have come from improvements in non-module costs – e.g., installation and balance of 

system costs. These reductions in non-module costs are encouraging because they demonstrate 

improvements in the delivery infrastructure for PV in California. 

Several policy recommendations fall out of our analysis: 

 

41 This cost decline is generally consistent with that found by Mortensen (2001), who looked at 278 systems 

installed between 1997 and 2000, and found annual cost reductions over time of $0.60/ WDC (in real, 1997 dollars). 

37
Reducing non-module costs should be a primary goal of local PV programs. Unlike module 

costs, which are set in a worldwide market and are therefore heavily influenced by factors 

outside of the control of an individual PV program (e.g., demand for PV in Japan and Germany), 

non-module costs are potentially subject to the influence of local PV programs. Our analysis of 

the CEC dataset, for example, shows that module costs appear to be directly passed through onefor-one to the system purchaser, leaving non-module costs – e.g., installation and balance of 

system costs – as the remaining cost items potentially influenced by a local PV program. 

Though we are unable to prove conclusively that non-module cost reductions in California have 

been caused by the state’s incentive programs, our analysis results do show that non-module cost 

reductions have been significant. State incentive programs may wish to undertake programmatic 

activities aimed specifically at reducing non-module costs, which could range from targeted 

approaches to building local supply infrastructure (e.g., providing business development funding 

to installers, supporting standardized PV products, or offering installer training and certification), 

to something as simple as making PV system cost and performance data more publicly accessible 

and transparent to further encourage supply competition. 

Sustained, long-term programs may enable more significant cost reductions. Sustained, 

sizable, and stable markets for PV may be the most direct way of reducing non-module costs 

because such markets will presumably attract suppliers and encourage those suppliers to create 

an efficient delivery infrastructure. Though California’s cost reductions are significant, 

experience from Japan suggests that deeper cost reductions are possible. Japan’s average 

residential system cost of $7.4/WAC in 2004 (for standard 3 kW installations) compares to an 

average of roughly $8.8/WAC in California (for 2-5 kW systems with application dates in 2004). 

Moreover, just focusing on CEC-funded installations in the 2-5 kW size range with application 

dates of 1999-2004 (see Figure 742), the average annual cost reduction has been 5.2%, 

substantially below the 8.9% annual average reduction for 3 kW systems installed in Japan over 

the same period. Since 2001, however, average annual residential system cost reductions in 

Japan and California have been approximately equal, at 7.3% for California and 6.7% for Japan. 

These data perhaps suggest that as the CEC’s solar program has scaled up it has begun to 

experience a similar cost-reduction dynamic as evidenced in Japan, but that further cost declines 

are possible. In mid-January 2006, the CPUC issued an order that intends to create a much more 

sizable and stable market for PV in California; a key goal of that program is to replicate and 

even exceed the cost reductions seen under the Japanese program (the adopted program is to 

reduce rebate levels by roughly 10% each year, in nominal terms, far exceeding the recent 

system cost reductions seen under the California and Japanese programs). 

The structure and size of PV incentives should encourage cost reduction, not cost inflation. 

We find some troubling evidence that policy design has adversely impacted the cost of PV 

systems in California. For example, the 50% cap on the size of the rebate employed by both 

programs at one time or another appears to have, at best, impeded cost reductions, and at worst, 

contributed to artificial cost inflation. As such, the decision by both programs to abandon such 

percentage caps is a positive development; we recommend that other state PV incentive 

programs do the same. Furthermore, the total pre-rebate cost of PV installations in California 

has tracked, to some degree, the size of the rebate itself. In particular, there appears to be a tight 

relationship between rebate levels and average pre-rebate installed costs among CEC-funded 

 

42 Data for 1998 are not included here due to the limited number of systems in that year. 

38
systems. The CPUC’s relatively richer rebate also appears to have resulted in some cost inflation 

for systems installed under that program. This is particularly true for systems that also received 

sizable local incentives, in addition to those offered under the CPUC’s program. These results 

suggest that heavy subsidies can dampen, at least to some degree, the motivation of installers to 

provide, and/or customers to seek, lower installed costs. Whether this link is merely 

representative of the “teething problems” that are typical of new programs,43 or should instead be 

of long-term concern is somewhat unclear. As rebates are reduced over time, however, we might 

expect that the link between incentive levels and total pre-rebate installed costs will be severed, 

as lower rebates require contractors to price systems at cost (with a reasonable margin for profit) 

in order to ensure a sale. Hence, while rich incentives may be required initially to jump-start the 

market, over time the incentives should decline to a level that can support a functional market 

infrastructure without providing room for potential price manipulation.44

Targeted incentives that account for the relative economics of different system sizes and 

application types may be appropriate. Analysis presented in this paper suggests that installed 

costs are heavily dependent upon the size and type of installation. Though there is a significant 

spread in the data, we find clear evidence of sizable economies of scale in PV installations, with 

larger PV systems coming in (on average) at significantly lower installed costs than smaller 

systems. We also find that systems installed in large new home developments are, on average, 

far more economical than retrofitted systems, or even systems installed in smaller new home 

clusters. Systems installed on affordable housing, which also often involve new construction and 

presumably enable bulk system installation, also show significant savings on an installed cost 

basis. Finally, the data reveal that owner-installed systems in the CEC program, and systems 

installed by the California Construction Authority (which might also be considered ownerinstalled systems, of a sort) in the CPUC program, have come in at substantially lower costs than 

contractor-installed systems.45 In aggregate, these results suggest that a further targeting of 

incentives to account for the relative economics of different system sizes and application types 

may be appropriate.46

 

43 Such “teething problems” might include initial over-subsidization intended to spur the market, coupled with 

insufficient supply infrastructure to handle the resulting increase in demand, leading to lackluster competition and 

artificial price increases until new supply infrastructure enters the market. 44 Though some might be inclined to draw from this discussion of capacity-based incentives an argument in favor of 

performance-based incentives, we note that nothing in our dataset or analysis permits a comparison of these different 

types of incentives. 

45 These results suggest that the CEC’s current practice of providing reduced incentives for owner-installed systems 

is appropriate. 

46 Targeting PV incentives may become even more critical under the Energy Policy Act of 2005’s new or expanded 

federal tax credits for PV, which went into effect in January 2006. Specifically, certain entities (e.g., non-taxable 

entities or taxable entities subject to the alternative minimum tax) will not be able to benefit from the new or 

expanded federal tax credits. Furthermore, the new 30% residential credit is capped at $2,000, which means that it 

is worth relatively more to smaller than to larger residential PV systems. These differential impacts suggest that 

unless states fine-tune their incentives, they may end up disadvantaging certain market segments, while oversubsidizing others. 

39
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43
Appendix A: Data Manipulation and Cleaning 

A considerable amount of data cleaning and manipulation was required for the CEC and, to a 

much lesser extent, for the CPUC datasets. Tables 9 and 10 summarize these details. 

Table 9. Data Manipulation and Cleaning: CEC Dataset 

CEC Variable Data Manipulation and Cleaning 

SYSTEM COST 

Dependent variable, expressed in terms of $/WAC. Only systems with costs 

ranging from $4-30/WAC were included in the analysis. Systems with costs 

outside of this range likely represent data entry errors, or atypically cheap or 

expensive systems. Different reasonable system cost restrictions were tested, with 

limited effect on regression results. These cost restrictions eliminated 85 systems, 

totaling 0.5 MW, from the analysis. Costs represent “eligible costs,” excluding 

any costs for battery back-up. The data used in our analysis are based on the 

inverter efficiency ratings used at the time of system rebate application. An 

analysis of inverter efficiency ratings over time shows little change in average 

ratings (less than 1%, on average), so changes in rating methodologies should not 

be a major source of error in the results. In the database provided by the CEC, we 

used the “system cost” field over the “total system cost” field, where available. 

System costs were converted to real 2004 dollars. 

TIME_MONTH 

Date of application, in months, numbered consecutively from beginning of each 

program. We tested alternative functional forms, as well as different time periods 

(e.g., quarterly, semi-annual), but none outperformed a simple monthly linear 

model. In the dataset provided by the CEC, we used the “@30date” field 

(representing the date the application was received by the CEC) where available 

and when that date preceded the “date received” field; we used the “date received” 

field otherwise (representing the date on which the system application was entered 

into the CEC dataset – sometimes up to a month after the application was actually 

received). Where neither field was filled in, we were in some cases able to 

estimate a date based on other information provided in the CEC dataset. 

SYSTEM_SIZE 

Limited to systems less than 30 kW in size. The CEC program initially funded 

systems over 30 kW in size, but ceased providing funding to such systems in 

March 2003. We exclude these larger systems from our analysis (a total of 66 

systems, and 9.1 MW of capacity) to ensure that a limited number of outliers do 

not unduly affect our analysis results. We use a logarithmic functional form 

because, upon visual inspection and after statistical tests, that functional form 

appeared to fit our underlying data better than the alternatives that we tested. The 

AC size data used in our analysis are based on the inverter efficiency ratings used 

at the time of system rebate application. 

MAX_STD_REBATE 

Equals the CEC’s published rebate for contractor-installed systems (not on 

schools or affordable housing) in $/WAC (2004 $). In nominal dollars, this 

variable equals $3/WAC from inception to Feb 8, 2001, when it increases to 

$4.5/WAC. Thereafter, it decreases to $4/WAC on February 19, 2003, $3.8/WAC on 

July 1, 2003, $3.2/WAC on January 1, 2004, $3/WAC on July 1, 2004, and 

$2.8/WAC on January 1, 2005. For systems above 10 kW, the standard rebate 

dropped from $3/WAC to $2.5 WAC prior to February 8, 2001 – the date was 

determined through a review of incentive applications. We maintained an 

application-date-based determination of standard incentive levels, even where 

actual incentive payments seemed to disagree with those value (such 

discrepancies were notable during the transition from $3 to $4.5/WAC incentive 

levels, when customers were able to update earlier applications at the higher 

incentive level). 

44
CEC Variable Data Manipulation and Cleaning 

REBATE_%_CAP 

Dummy variable equal to 1 when the rebate is capped at 50% (or 40% for systems 

above 10 kW for a short duration) of eligible installed costs (from program 

inception through February 18, 2003). We attempted to develop a variable to 

reflect the degree to which the percentage cap was binding, but that variable did 

not out-perform the simple one used here. Variable only used only in Model 3. 

STATE_TAX_CREDIT 

State tax credit (15% for systems installed in 2001-2003, 7.5% for systems 

installed in 2004-2005; 0% otherwise) was signed by the Governor on October 8, 

2001. Given that our dates are based on application receipt, rather than system 

completion, for the purposes of this variable we assume that there is an 

“expected” 6-month lag between application receipt and project completion. We 

therefore set this variable equal to 15% for applications received from October 8, 

2001 through June 30, 2003; 7.5% for applications received from July 1, 2003 

through June 30, 2005, and 0% for all other dates. Only applies to systems under 

200 kW in size. Used only in Model 3. 

RETAIL_RATES 

We assumed that systems less than or equal to 6 kW are residential systems (and 

so pay residential retail rates), and systems larger than 6 kW are commercial 

systems (and so pay commercial retail rates). For each reservation, we first 

determine in what utility service territory the system is located. If PGE, SCE, 

SDGE, SMUD, or LADWP, this variable equals the average residential or 

commercial retail rate (from Energy Information Administration, or EIA, 

databases) from the current and previous 11 months (12-month average). EIA 

average retail rate data for all other utilities is annual (i.e., not monthly), so if 

system is installed in any utility service territory other than those mentioned 

above, this variable equals the applicable annual average residential or 

commercial retail rate at the time of application. So, for each reservation, the 

retail rate variable is a function of system size (to determine whether residential or 

commercial retail rates are appropriate) and location (to determine applicable 

utility). Used only in Model 3. 

APPROVED Dummy variable equal to 1 if the rebate application has been approved, but not 

yet completed. We included the following database categories of systems as 

“approved:” A (approved), CR (claim received), ET (extended), H (hold), and W 

(warning letter). We did not include systems that had been cancelled (C), 

declined (D), expired (EX), or received but not yet approved (R). The default is a 

completed system (PC, for “project complete”). 

SCE 

Dummy variable equal to 1 if the system is or will be installed in SCE’s service 

territory. Where field is not filled in, used zip code or city to estimate appropriate 

territory. Fixed small number of clearly discrepant entries. Default location is 

PG&E service territory. 

SDG&E 

Dummy variable equal to 1 if the system is or will be installed in SDG&E’s 

service territory. Where field is not filled in, used zip code or city to estimate 

appropriate territory. Fixed small number of clearly discrepant entries. Default 

location is PG&E service territory. 

OTHER_UTILITY 

Dummy variable equal to 1 if the system is or will be installed in a utility service 

territory other than PG&E’s, SCE’s, or SDG&E’s. Where field is not filled in, 

used zip code or city to estimate appropriate territory. Fixed small number of 

clearly discrepant entries. Default location is PG&E service territory. 

45
CEC Variable Data Manipulation and Cleaning 

NEW_CONST_LARGE 

Dummy variable equal to 1 if the system is or will be installed on a new home 

(during construction phase) as part of an effort to install PV on multiple homes 

within a new subdivision. Generally must include at least three homes to qualify. 

Determinations made both according to CEC database “Category” and “Utility” 

fields, as well as a judgment based on “Contractor” field in some cases (where it 

is clear that the system is part of a larger development). Default is a residential 

retrofit system. 

NEW_CONST_SINGLE 

Dummy variable equal to 1 if the system is or will be installed on a new home 

(during construction phase), but not as part of an effort to install PV on multiple 

homes within a new subdivision. Generally must include less than three homes to 

qualify. Determinations made both according to CEC database “Category” and 

“Utility” fields, as well as a judgment based on “Contractor” field in some cases 

(where it is clear that the system is part of a larger development). Default is a 

residential retrofit system. 

SCHOOLS Dummy variable equal to 1 if the system is or will be installed on a school, based 

on CEC database “Category” field. 

AFFORDABLE_HOUSING Dummy variable equal to 1 if the system is or will be installed on affordable 

housing, based on CEC database “Category” field. 

OWNER_INSTALLED 

Dummy variable equal to 1 if the system clearly is or will be installed by the 

system owner, rather than a hired contractor, based on CEC database “C-Install” 

and “C_field.” If not clearly indicated as owner installed, we assumed that 

systems were contractor installed (though we acknowledge likely errors in making 

this assumption). 

INSTALLER_EXPERIENCE 

Installers are identified as “experienced” installers (variable = 1) if they rank 

within the top 5% of all installers in terms of the number of systems completed 

over the entire data period. Used contractor ID numbers in CEC database. Fixed 

clear ID number errors; filled in blanks with most likely values in numerous 

instances based on contractor or retailer name; consolidated IDs for firms that 

appeared to be the same; created new IDs where missing values (over 1000 

records); where no data provided, assumed that inexperienced installer. 

RETAILER_EXPERIENCE 

Retailers are identified as “experienced” retailers (variable = 1) if they rank within 

the top 5% of all retailers in terms of the number of systems completed over the 

entire data period. Cleaned up data entry errors, and made names consistent 

where possible and where a single firm appears to be entered under different 

names. Filled in blanks with best guesses, where necessary, based on historical 

contractor-retailer relationships. 

POPULATION_DENSITY 

Population density (population per square mile) was derived from zip code and/or 

address listings in the database, in combination with US Census Bureau data by 

zip code tabulation area. We used a square root functional form because it fits the 

data well, and because it is assumed that changes in density in sparsely populated 

areas will have a greater affect than similar changes in density in more densely 

populated urban areas. Used city average population density, where zip code is 

blank but city is identified. Filled in with statewide average values where data is 

lacking. 

THIN_FILM 

Dummy variable equal to 1 if the system involves thin-film (e.g., amorphous 

silicon, copper indium selenide, or cadmium telluride) rather than crystalline 

technology. This information was discernible from the module manufacturer 

and/or model number. 

46
CEC Variable Data Manipulation and Cleaning 

MODULE_COST_INDEX 

Annual variable that, from 1998-2004, equals the average cost (in 2004 $/WDCSTC) of power (rather than “commodity”) modules from Strategies Unlimited 

(2005). 2005 value derived from the percentage change in Solar Buzz’s “All Solar 

Module Index” from 2004 (average of monthly values) to 2005 (through October), 

applied to 2004 Strategies Unlimited value. 

47
Table 10. Data Manipulation and Cleaning: CPUC Dataset 

CPUC Variable Data Manipulation and Cleaning 

SYSTEM COST 

Dependent variable, expressed in terms of $/WAC. Only systems with costs 

ranging from $4-30/WAC were included in the analysis. Systems with costs 

outside of this range likely represent data entry errors, or atypically cheap or 

expensive systems. Different system cost restrictions were tested, with some 

impact on regression results. With no easy way of determining which cost 

range was objectively “right,” we opted to maintain consistency with the CEC 

dataset (i.e., $4-30/WAC). These cost restrictions eliminated 4 systems, totaling 

1.3 MW, from the analysis. Data on eligible system costs should be broadly 

consistent with that reported for the CEC. Eliminated two systems labeled as 

PV systems that appeared to be wind systems. Eliminated records for which 

cost data were not provided (~220 projects on PG&E’s waiting list). System 

costs were converted to real 2004 dollars. 

TIME_MONTH 

Date of application, in months, numbered consecutively from beginning of each 

program. We tested alternative functional forms, as well as different time 

periods (e.g., quarterly, semi-annual), but none clearly outperformed a simple 

monthly linear model. 

SYSTEM_SIZE 

Theoretically limited to systems of at least 30 kW in size, though the CPUC 

database does contain a few systems less than 30 kW (and as low as 25 kW). 

We use a logarithmic functional form because, upon visual inspection and after 

statistical tests, that functional form appeared to fit our underlying data no 

worse than the alternatives that we tested. The AC size data used in our analysis 

are based on the inverter efficiency ratings used at the time of system rebate 

application. 

MAX_STD_REBATE 

Equals the CPUC’s published rebate for PV systems in $/WAC (2004 $) at the 

time of first application. In nominal dollars, this variable equals $4.5/WAC

from inception through December 15, 2004, after which it decreased to 

$3.5/WAC. Used in Model 2 only. 

REBATE_%_CAP 

Dummy variable equal to 1 when the rebate is capped at 50% of eligible 

installed costs (from program inception through December 15, 2004). Not 

used, due to colinearity problems. 

STATE_TAX_CREDIT 

State tax credit (15% for systems installed in 2001-2003, 7.5% for systems 

installed in 2004-2005; 0% otherwise) was signed by the Governor on October 

8, 2001. Given that our dates are based on application receipt, rather than 

system completion, for the purposes of this variable we assume that there is an 

“expected” 6-month lag between application receipt and project completion. 

We therefore set this variable equal to 15% for applications received from 

October 8, 2001 through June 30, 2003; 7.5% for applications received from 

July 1, 2003 through June 30, 2005, and 0% for all other dates. Actual lag is 

closer to one year, but used 6-months to remain consistency with approach used 

for CEC dataset. Only applies to systems under 200 kW in size. Used only in 

Model 3. 

48
CPUC Variable Data Manipulation and Cleaning 

RETAIL_RATES 

We assumed that all systems funded by the CPUC program are commercial 

systems (and so pay commercial retail rates). For each reservation, we first 

determine in what utility service territory the system is located. If PGE, SCE, 

SDGE, SMUD, or LADWP, this variable equals the average commercial retail 

rate (from Energy Information Administration, or EIA, databases) from the 

current and previous 11 months (12-month average). EIA average retail rate 

data for all other utilities is annual (i.e., not monthly), so if system is installed in 

any utility service territory other than those mentioned above, this variable 

equals the applicable annual average commercial retail rate at the time of 

application. So, for each reservation, the retail rate variable is a function of 

system size (to determine whether residential or commercial retail rates are 

appropriate) and location (to determine applicable utility). Used only in Model 

3. 

OTHER_INCENTIVES 

Dummy variable equal to 1 if the application indicates that the system will also 

receive other (i.e., non-CPUC) incentives of more than $2/WAC. Note that 

systems have been able to only capture a portion of these other incentives, as 

the CPUC incentive is reduced to some degree when other incentives are 

offered (though the rules for this reduction have changed over time). 

APPROVED Dummy variable equal to 1 if the rebate application has been approved, but not 

yet completed. The default is a completed system. 

WAITLISTED Dummy variable equal to 1 if the rebate application has been waitlisted, due to 

insufficient program funding. The default is a completed system. 

SCE Dummy variable equal to 1 if the system is or will be installed in SCE’s service 

territory. Default location is PG&E service territory. 

SDG&E Dummy variable equal to 1 if the system is or will be installed in SDG&E’s 

service territory. Default location is PG&E service territory. 

OTHER_UTILITY 

Dummy variable equal to 1 if the system is or will be installed in a utility 

service territory other than PG&E’s, SCE’s, or SDG&E’s. Default location is 

PG&E service territory. 

INSTALLER_EXPERIENCE Installers are identified as “experienced” installers (variable = 1) if they rank 

within the top 5% of all installers in terms of the number of systems completed 

over the entire data period. Filled in blank entries where possible; where not 

possible, assumed that installer was inexperienced. 

CALIF_CONST_AUTHORITY Dummy variable equal to 1 if the California Construction Authority is listed as 

the installer. 

THIN_FILM Dummy variable equal to 1 if the system involves thin-film (e.g., amorphous 

silicon, copper indium selenide, or cadmium telluride) rather than crystalline 

technology. This information was discernible from the module manufacturer 

and/or model number. 

MODULE_COST_INDEX Annual variable that, from 1998-2004, equals the average cost (in 2004 $/WDCSTC) of power (rather than “commodity”) modules from Strategies Unlimited 

(2005). 2005 value derived from the percentage change in Solar Buzz’s “All 

Solar Module Index” from 2004 (average of monthly values) to 2005 (through 

October), applied to 2004 Strategies Unlimited value. 

49
Appendix B: Cost Distributions Over Time 

The distribution of PV system costs under the CEC and CPUC programs have changed over 

time. Figures 16 and 17 illustrate these trends for the CEC and CPUC, respectively. In the case 

of the CEC-funded systems, the distribution of system costs has both narrowed and shifted 

towards lower costs over time. For CPUC-funded systems, the distribution has shifted to the left 

with little noticeable narrowing of the distribution itself. 

0%

5%

10%

15%

20%

25%

30%

35%

40%

4-55-66-77-88-9

9-10

10-1111-1212-1313-1414-1515-1616-1717-1818-1919-2020-2121-2222-2323-2424-2525-2626-2727-2828-2929-30

Installed Cost (2004 $/WAC)

Frequency

 1998-2000 (n=500)

 2001-2003 (n=11,646)

 2004-2005 (n=5,743)

Figure 16. Distribution of System Costs Over Time (CEC) 

0%

5%

10%

15%

20%

25%

30%

35%

40%

4-55-66-77-88-9

9-10

10-1111-1212-1313-1414-1515-1616-1717-1818-1919-2020-2121-2222-2323-2424-2525-2626-2727-2828-2929-30

Installed Cost (2004 $/WAC)

Frequency

 2001-2003 (n=311)

 2004-2005 (n=742)

Figure 17. Distribution of System Costs Over Time (CPUC) 

50
Appendix C: Additional Regression Analysis Results 

Tables 11 through 13 provide summary statistics and regressions results for just the completed 

CEC- and CPUC-funded PV systems. Results presented here are reasonably consistent with 

those using the fully pooled data, presented in the body of the report. 

Table 11. Summary Statistics of Variables Used in Analysis (Completed Systems Only) 

CEC (< 30 kW) CPUC (>= 30 kW) Continuous Variables 

Mean Max Min Std Dev Mean Max Min Std Dev 

INSTALLED_COST 9.9 29.3 4.1 2.3 9.0 17.2 4.8 1.7 

TIME_MONTH 57.7 83.0 0.0 14.1 20.2 39.0 0.0 9.3 

SYSTEM_SIZE 3.8 30.0 0.1 3.0 109.1 1,050 30.0 147.5 

MAX_STD_REBATE 4.1 4.8 2.7 0.6 4.7 4.8 4.5 0.1 

STATE_TAX_CREDIT 0.10 0.15 0.00 0.06 0.10 0.15 0.00 0.05 

RETAIL RATES 0.13 0.19 0.08 0.01 0.14 0.16 0.07 0.02 

MODULE_COST_INDEX 3.5 4.5 3.3 0.3 3.4 3.9 3.3 0.2 

POPULATION_DENSITY 2,690 52,959 0.41 4,138 n/a n/a n/a n/a 

Dummy Variables Observations 

(Dummy = 1) 

Percent 

of Total 

Observations 

(Dummy = 1) 

Percent 

of Total 

REBATE_%_CAP 5,149 40% 327 100% 

OTHER_INCENTIVES n/a n/a 38 12% 

SCE** 2,961 23% 81 25% 

SDG&E** 1,945 15% 36 11% 

OTHER_UTILITY** 132 1% 50 15% 

NEW_CONST_LARGE† 710 6% n/a n/a 

NEW_CONST_SMALL† 242 2% n/a n/a 

SCHOOLS† 15 0% n/a n/a 

AFFORDABLE_HOUSING† 75 1% n/a n/a 

OWNER_INSTALLED 733 6% n/a n/a 

INSTALLER_EXPERIENCE 7,147 56% 140 43% 

RETAILER_EXPERIENCE 4,766 37% n/a n/a 

CALIF_CONSTR_AUTHORITY n/a n/a 16 5% 

THIN-FILM 265 2% 35 11% 

** PG&E, which represents the “base” utility territory to which the others are compared, contains the majority of 

systems: 7,818 (61%) systems in the CEC dataset, and 160 (49%) systems in the CPUC dataset 

† Standard retrofits represent the “base” type of system to which others are compared in the CEC dataset, and 

include 11,814 total systems (92%) 

51
Table 12. Regression Results for CEC Dataset (Completed Systems Only) 

Model 1 Model 2 Model 3 Model 4 Variable 

coef. p coef. p coef. p coef. p 

INTERCEPT 20.488 <0.01 11.850 <0.01 11.794 <0.01 13.536 <0.01

TIME_MONTH -0.058 <0.01 -0.024 <0.01 -0.024 <0.01 -0.090 <0.01

LN_SYSTEM_SIZE -0.914 <0.01 -0.923 <0.01 -0.927 <0.01 -1.342 <0.01

SCE -0.129 <0.01 -0.132 <0.01 -0.145 <0.01 -0.115 <0.01

SDG&E -0.465 <0.01 -0.510 <0.01 -0.494 <0.01 -0.522 <0.01

OTHER_UTILITY -2.244 <0.01 -2.576 <0.01 -2.683 <0.01 -2.581 <0.01

NEW_CONST_LARGE -1.714 <0.01 -1.744 <0.01 -1.718 <0.01 -3.383 0.03 

NEW_CONST_SMALL 0.331 <0.01 0.316 0.01 0.280 0.02 4.378 0.07 

SCHOOLS -0.219 0.78 0.049 0.95 -0.043 0.96 0.193 0.82 

AFFORDABLE_HOUSING -2.034 <0.01 -1.873 <0.01 -1.775 <0.01 -1.827 <0.01

OWNER_INSTALLED -1.622 <0.01 -1.679 <0.01 -1.685 <0.01 -0.251 0.86 

INSTALLER_EXPERIENCE 0.457 <0.01 0.466 <0.01 0.467 <0.01 2.314 <0.01

RETAILER_EXPERIENCE 0.187 <0.01 0.191 <0.01 0.189 <0.01 0.546 0.41 

SQRT_POP_DENSITY 3.4E-03 <0.01 3.3E-03 <0.01 3.3E-03 <0.01 8.9E-03 0.38 

THIN_FILM -0.880 <0.01 -0.916 <0.01 -0.919 <0.01 -4.654 0.03 

MAX_STD_REBATE 0.732 <0.01 0.560 <0.01 0.783 <0.01

MODULE_COST_INDEX 1.075 <0.01 1.173 <0.01 1.291 <0.01

REBATE_%_CAP 0.191 0.14 

STATE_TAX_CREDIT 2.325 <0.01 

RETAIL_RATES 0.929 0.69 

DATE_MONTH x LN_SIZE 0.010 <0.01

NEW_CONST_LARGE x LN_SIZE -0.332 0.11 

NEW_CONST_SMALL x LN_SIZE -0.172 0.45 

OWNER_INSTALLED x LN_SIZE -0.079 0.65 

INSTALLER_EXP x LN_SIZE -0.168 0.05 

RETAILER_EXP x LN_SIZE 0.060 0.43 

SQRT_POP_DENSITY x LN_SIZE -9.2E-04 0.44 

THIN_FILM x LN_SIZE 0.199 0.52 

NEW_CONST_LARGE x TIME 0.071 <0.01

NEW_CONST_SMALL x TIME -0.041 <0.01

OWNER_INSTALLED x TIME -0.016 0.04 

INSTALLER_EXP x TIME -8.7E-03 0.02 

RETAILER_EXP x TIME -0.015 <0.01

SQRT_POP_DENSITY x TIME 3.7E-05 0.43 

THIN_FILM x TIME 0.042 <0.01

R-SQUARED 0.27 0.28 0.28 0.29 

OBSERVATIONS (n) 12,856 12,856 12,856 12,856 

52
Table 13. Regression Results for CPUC Dataset (Completed Systems Only) 

Model 1 Model 2 Model 3 Model 4 Variable 

coef. p coef. p coef. p coef. p 

INTERCEPT 11.989 <0.01 9.926 0.76 11.286 <0.01 12.938 <0.01 

TIME_MONTH -0.037 <0.01 -0.037 0.52 -0.037 <0.01 -0.085 0.08 

LN_SYSTEM_SIZE -0.431 <0.01 -0.441 <0.01 -0.453 <0.01 -0.527 0.11 

SCE 0.035 0.87 0.039 0.85 0.050 0.82 -0.032 0.88 

SDG&E -0.273 0.28 -0.227 0.39 -0.065 0.88 -0.398 0.12 

OTHER_UTILITY -0.384 0.20 -0.381 0.21 -0.151 0.73 -0.380 0.27 

OTHER_INCENTIVES 0.827 0.01 0.836 0.01 0.869 <0.01 0.728 0.05 

INSTALLER_EXPERIENCE -0.470 0.01 -0.452 0.02 -0.451 0.02 -0.693 0.45 

CALIF_CONST_AUTHORITY -4.210 <0.01 -4.230 <0.01 -4.181 <0.01 -3.766 <0.01 

THIN_FILM 0.081 0.75 0.084 0.74 0.077 0.75 -2.929 0.05 

MODULE_COST_INDEX -0.842 0.16 

MAX_STD_REBATE 1.072 0.87 

STATE_TAX_CREDIT -0.408 0.82 

RETAIL_RATES 5.461 0.46 

DATE_MONTH x LN_SIZE 0.006 0.62 

INSTALLER_EXP x LN_SIZE -0.206 0.30 

THIN_FILM x LN_SIZE 0.683 0.02 

INSTALLER_EXP x TIME 0.056 <0.01 

THIN_FILM x TIME 0.003 0.90 

R-SQUARED 0.35 0.35 0.35 0.37 

OBSERVATIONS (n) 327 327 327 327 

53