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“Here Come the Sun” with Its Quality-Adjusted Price Index
Tat UnbundlingTarek M. Harchaoui1 and Niek Scholze2
1Department of Economics & ManagementGroningen Growth & Development Centre
University of Groningen
2Rotterdam School of ManagementErasmus University
February 2017
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Motivation2
• Major churning in this business:
Entry of new and highly dynamic firms Industry highly competitive
• Food for thought for industrial organization economists
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Motivation3
• Rapid decline in prices related to technical change
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Motivation• Merit of Krugman’s article is to bring to the fore some
ideas that have been reasonably well-established amongst engineers and scientists in solar energy Bowden et al. (2010); Naam (2011); Hutchby (2014)
• Moore’s law equivalent seems to be happening in the solar panel manufacturing business
• Somewhat similar to the semiconductors/micro processor chips
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Motivation• The evidence from the “ground” seems to be
supportive:
• Crude numbers point to fundamental changes which potentially huge impact on the economy
5
Price ($/watt)
Power (watts)
Size (inches)
Conversion factor (%)
1953≥ 2015
1,7850.70
230230
230×13041×25
4.523.5
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Scope of the Paper• Construct a quality-adjusted price index for solar
panels Quantify whether green energy has the potential of
generating new sources of economic growth—bring numerical estimates where before there had been none
─ Better understanding of the dynamics of pricing in the business of manufacturing of solar panels
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Related Literature Jerry Hausman (1999, JBES)
- New product and consumer’s welfare
Dale Jorgenson (2000, AER)- New sources of economic growth resulting from
the IT revolution
Unni Pillai (2015, EE)- The industrial organization of solar panels
manufacturing
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Remainder of the Presentation Background information on the industry Framework Source data and broad trends on the industry Empirical results Concluding remarks
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Milestones in the Development of the Solar Panel Business• 1950s: Development of the blueprints for photovoltaic
cells at Bell Labs• 1960s: Non commercial applications propelled forward
by the space race• 1970s: Commercial applications started with the first
oil shock:• First manufacturers were conglomerates:
• IT sector: Texas instruments, RCA and Sharp• Oil sector: Exxon → Solar Power
ARCO → Solar Technology
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Milestones in the Development of the Solar Panel Business• The landscape of the business has been completely
revamped in the early years of the new millennium:• Business model increasingly less relying on market
concentration• Emergence of pure-play companies
• Underlying factors:• Government policy favourable to renewable energy• Decline in the price of silicon—key ingredient used to
build crystalline silicon solar cells
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Rank Company Country of Origin
Production (in MG)
Global Market
Share (%)1 Yingli Green Energy China 2.622 6.62 Trina Solar China 2.560 6.43 Canadian Solar Canada 2.020 5.14 First Solar U.S. 1.628 4.15 JA Solar China 1.252 3.16 Jinko Solar China 1.215 3.07 Kyocera Japan 1.200 3.08 Flextronics Singapore 1.058 2.69 Hanwha-SolarOne China 1.050 2.6
10 Solar Frontier Japan 0.995 2.5World Total 40.0
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Framework 12
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Framework 13
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Framework14
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Framework 15
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Framework 16
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Source Data• Built from scratch with a combination of financial
statements and information on solar panels
• Financial statements:• Data on cost of goods sold, mark-ups, volume of solar
panels produced• This has made possible to construct unit cost, which
combined with the mark-up, led to the direct estimate of the average price
• These data have been compiled for 16 manufacturers over the 2004-13 period
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Source Data• Photon database:
• Technical performance (efficiency of the panel)• Reliability (years of product warranty) • Size• Vintage
+ Silicon prices
• Of the 2,723 considered only 831 offered a full suite of characteristics. The rest has been dropped
• The next step was to map this information on characteristics with that on prices.
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Source Data• The two sets of information were matched:
• The information on characteristics had to be aggregated using a geometric mean, an approach widely used by official statistics in the absence of adequate weights
• The aggregation of characteristics were brought to the level where they match the calculated price
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Year N Cost Price Efficiency Warranty Lifespan Weight Mark-up2004 4 5.4 5.4 12.1 2.4 0.79 13.4 1.32005 4 4.5 5.0 12.2 2.7 0.80 14.1 10.42006 10 3.9 4.3 12.1 3.9 0.80 15.4 7.52007 13 3.9 4.4 13.1 5.2 0.80 16.7 11.92008 13 3.7 4.1 13.3 5.5 0.80 17.0 11.62009 14 2.4 2.8 13.4 5.9 0.80 17.6 14.22010 16 1.7 2.0 13.8 6.8 0.80 18.0 14.32011 15 1.6 1.6 14.4 8.0 0.80 18.4 0.62012 14 1.2 1.1 15.1 9.2 0.80 19.1 -7.42013 9 0.9 0.9 15.6 10.9 0.81 18.9 -5.7
AAGR (%) -17.8 -18.4 2.9 18.4 0.3 3.9 -218.1
Mean Values of Relevant Variables
Note: N = number firms; AAGR = Average annual growth rate.
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Years 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 TotalFirms 4 4 10 13 13 14 16 15 14 9 112
China 1 1 2 3 3 4 6 6 6 3 35Others 3 3 8 10 10 10 10 9 8 6 77
Efficiency< 10 0 0 1 1 0 0 0 0 0 0 2
10-15 4 4 9 11 12 13 15 13 8 0 8915-20 0 0 0 1 1 1 1 2 6 9 21
Warranty< 5 4 4 5 3 2 2 1 0 0 0 21
5-10 0 0 5 10 11 12 14 12 5 1 70> 10 0 0 0 0 0 0 1 3 9 8 21
Weight10-15 3 2 3 4 3 1 1 1 1 1 2015-20 1 2 7 8 8 10 14 12 9 4 75
> 20 0 0 0 1 2 3 1 2 4 4 17
Structure of the Dataset
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Variable Pooled Model Adjacent Period RegressionsFull Model Restricted Model 2004-2008 2009-2013
InterceptEfficiency10Efficiency10-15Efficiency 15-20LifespanWarranty5Warranty5-10Warranty10Weight10-15Weight15-20Weight20ChinaDistance-withinDistance-between
1.548 (0.542)***
0.241 (0.124) *
0.568 (0.235) **
0.921 (0.384) **
0.054 (0.048)0.106 (0.074)0.125 (0.062) *
0.183 (0.078) **
0.069 (0.076)0.097 (0.086)0.109 (0.095)
-0.216 (0.074) ***
0.105 (0.055) *
-0.063 (0.041)
1.567 (0.545) ***
0.235 (0.128) *
0.572 (0.217) ***
0.932 (0.375) **
0.111 (0.058) *
0.132 (0.062) *
0.185 (0.079) *
-0.235 (0.077) ***
0.112 (0.052) *
-0.092 (0.054) *
1.467 (0.933)0.112 (0.101)0.453 (0.224) *
0.697 (0.399) *
0.038 (0.441)0.112 (0.065) *
0.103 (0.046) **
0.151 (0.141)0.102 (0.134)0.127 (0.912)0.021 (0.085)-0.109 (0.073)0.092 (0.056)-0.011 (0.045)
1.675 (0.705) ***
0.241 (0.122) *
0.564 (0.247) **
0.921 (0.253) ***
0.121 (0.541)0.901 (0.443) *
0.146 (0.081) *
0.195 (0.123)0.081 (0.097)0.763 (0.518)0.139 (0.468)
-0.306 (0.094) ***
0.121 (0.062) *
-0.141 (0.070) *
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Variable Pooled Model Adjacent Period RegressionsFull Model Restricted Model 2004-2008 2009-2013
Year2005Year2006Year2007Year2008Year2009Year2010Year2011Year2012Year2013Years2004-2008Years2009-2013
-0.114 (0.043) ***
-0.392 (0.172) **
-0.498 (0.167) ***
-0.521 (0.173) ***
-0.987 (0.455) *
-1.258 (0.409) ***
-1.541 (0.581) **
-2.318 (1.146) *
-2.641 (1.187) *
-0.121 (0.041) ***
-0.412 (0.181) *
-0.519 (0.169) ***
-0.522 (0.162) ***
-0.986 (0.441) *
-1.266 (0.385) ***
-1.548 (0.569) ***
-2.354 (1.102) *
-2.669 (1.172) ***
-0.757 (0.342) *
-1.871 (0.784) **
NR2
1120.862
1120.857
440.654
680.752
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Restricted ModelYear Time-dummy Parameter Estimates
200520062007200820092010201120122013
-0.121-0.412-0.519-0.522-0.986-1.266-1.548-2.354-2.669
Year Cumulative Index Annual Variation2004200520062007200820092010201120122013
1.0000.8860.6620.5950.5930.3730.2820.2130.0950.069
0.890.750.901.000.630.760.750.450.73
Quality-Adjusted Price Indexes Based on Fixed-Effect Estimation
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Restricted ModelYear Time-dummy Parameter Estimates
200520062007200820092010201120122013
-0.131 -0.404 -0.671 -0.761 -1.117 -1.469 -1.667 -2.501 -2.901
Year Cumulative Index Annual Variation2004200520062007200820092010201120122013
1.0000.8770.6680.5110.4670.3270.2300.1890.0820.055
0.880.760.770.910.700.700.820.430.67
Quality-Adjusted Price Indexes Based on a Random-Effect Estimation
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Variable EstimatesIntercept 2.116 (0.616)***
Efficiency10 0.721 (0.233) ***
Efficiency10-15 0.745 (0.290) **
Efficiency 15-20 0.906 (0.422) *
LifeSpan 0.120 (0.055) *
Warranty5 0.130 (0.122)Warranty5-10 0.141 (0.091)Warranty10 0.197 (0.106) *
China -0.284 (0.103) ***
Distance-within 0.134 (0.072) *
Distance-between -0.109 (0.048) *
Entrant -0.015 (0.006) ***
Vintage2004 -0.025 (0.011) *
Vintage2005 -0.037 (0.016) *
Vintage2006 -0.089 (0.046) *
Vintage2007 -0.110 (0.052) *
Vintage2008 -0.154 (0.076) *
Variable EstimatesVintage2009 -0.224 (0.092) **
Vintage2010 -0.234 (0.121) *
Vintage2011 -0.251 (0.149)Vintage2012 -0.267 (0.195)Vintage2013 -0.246 (0.219)Year2005 -0.164 (0.069) **
Year2006 -0.455 (0.185) **
Year2007 -0.705 (0.205) ***
Year2008 -0.808 (0.292) ***
Year2009 -1.187 (0.457) ***
Year2010 -1.556 (0.506) ***
Year2011 -1.788 (0.673) ***
Year2012 -2.621 (1.002) ***
Year2013 -2.966 (1.369) *
N 112R2 0.892
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Concluding Remarks
› Is there a Moore’s law equivalent for solar? Yes! Quality-adjusted prices declined at a
staggering rate› By how much green energy can potentially lift global
economic growth? 1/10 of a percentage point within reasonable tight
bounds› Price discounts were an important vehicle of the
churning in this business.
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