sunspots that matter: behavioral biases in solar technology adoption … · 2 days ago · 3...

Introduction Institutional setting Data Results Mechanisms Conclusion

Sunspots that matter: behavioral biases insolar technology adoption

Stefan Lamp1

1Toulouse School of Economics

Economic Theories and Low-carbon Transformation Policies,Cambridge

June 22-23, 2017

Sunspots that matter: behavioral biases in solar technology adoption. Stefan Lamp


Motivation

I Climate change mitigation requires large investment inrenewables (COP 21, Paris)

I Policies supporting the adoption of renewable energiesguaranteeing positive returns on investment, e.g. feed-in tari�s

I Even with positive return expectations, adoption rates remainlow in many contexts

I ‘Energy-e�ciency gap’: importance of behavioral channels



Research question

I Empirically test for the presence of behavioral biases in thehousehold investment decision to install rooftop solar

I Provide evidence on the underlying mechanisms and consumerheterogeneity

I Can behavioral biases be important for aggregate marketoutcomes? (green product di�usion)

IFindings:

I Investment choices are over-influenced by the current stateof sunshine in line with projection bias and salience

I A month of exceptional sunshine leads to approximately 7%increase in the growth rate of solar installations



Behavioral biases in technology adoption

IDurable goods - consumers must forecast how much utilitythey will derive from future consumption, including consumptionin di�erent states of the world

I Behavioral biases can lead to misguided consumer decisions(impulse purchases; non-investment).

IIntertemporal evaluations may expose consumers to a varietyof behavioral biases, mainly:

I Present bias (myopia)I SalienceI Projection bias



Behavioral biases in technology adoption

Testable implications

1 Behavioral consumers respond to idiosyncratic variations in

sunshine

2 Timing of installations: main e�ect 2 months after purchasedecision (supply side)

3 Precipitation and other weather variables should onlya�ect uptake decisions to a degree in which they are correlatedwith sunshine or supply-side restrictions

4 Both positive and negative deviations from the long-termsunshine means should impact uptake decisions



Residential solar in Germany

020

040

060

0C

umul

ativ

e in

stal

latio

ns (t

hd)

050

100

150

New

inst

alla

tions

(thd

)

2000 2005 2010Date

New installations Cumulative uptake

Figure: Residential solar adoption inGermany (2000-11)

IDemand:

I Feed-in tari� policy (since2000)

I Limited financialuncertainty and abovemarket return: ~ 6-9% p.a.

I Financially motivatedinvestment decisions



Feed-In Tari�s and Cost of Solar

I Annual FIT adjustments mimic price movement: constantaverage return consideration

Feed-In Tari�



Solar supply

05

1015

Freq

uenc

y

0 10 20 30Time gap (weeks) btw first customer contact and completion of installation

Note: Online survey with German solar installers, August 2015. Author's calculation. N = 48.

I Residential solar mostly sold bylocal installers (electricians, gas &water, etc.)

I Average timing: installation 2months after contract signature

I Main marketing channel: word-ofmouth

Installer survey



Data

ISolar installations [Electricity Network Operators]

I Universe of residential installations: address, size, connection date

IWeather [German Weather Service]

I Gridded weather data: sunshine hours, temperature (min,max, mean), and rainfall. 1971-2011; 1x1km grid, monthlyfrequency.

I Weather station data: sunshine hours, temperature (min,max, mean), rainfall, snow, and cloud cover. ≥ 1971-2011; 51stations, daily frequency.

IAdditional covariates

I Demographics (Destatis), Online search (Google), news(LexisNexis),

I Price data (EuPD research), installer survey (author).

County-month aggregation, main sample period: 2000-2011Sunspots that matter: behavioral biases in solar technology adoption. Stefan Lamp


Cumulative solar adoption (Dec 2011)



The e�ects of exceptional weather

Analyze the e�ect of weather levels and periods of exceptional

weather (shocks):

I Recover the long-term weather distribution for eachcounty-month (1990-2011).

I Define positive (negative) weather shocks as a weatherrealization above (below) one standard deviation of the long-termmean in a given county-month

I Consider NL e�ects: county-month demeaned sunshine hoursShock distribution (time) Shock distribution (test)



Solar installations and sunshine shocks

All > Median sunshine shock Æ Median sunshine shock

New solar installations 10.02 11.53 8.71(18.27) (20.02) (16.51)

Sunshine hours 139.66 139.60 139.72(77.38) (76.32) (78.28)

Mean temperature (C) 9.45 9.32 9.56(6.62) (6.62) (6.62)

Population 204019 199232 208142(228264) (271895) (182422)

Hh income per capita (Eur2010) 18823 18964 18703(2304) (2259) (2335)

University degree (%) 8.43 8.35 8.50(3.93) (4.01) (3.86)

Unemployment rate (%) 9.77 8.83 10.57(4.62) (4.16) (4.85)

Green voters (%) 7.69 7.79 7.60(3.49) (3.39) (3.56)

Observations 57888 26784 31104



Solar installations and sunshine shocks

510

15In

stal

latio

ns (m

ean)

-2 0 2 4 6Months relative to sunshine shock

Normal sunshine Sunshine shockNote: Unconditional mean of small scale solar installations relative to sunshine shock.



Empirical strategy

Ln1 Instc,t + 1

Buildingsc,1999

2= – +

6ÿ

i=0—iweatherc,t≠i +

3ÿ

i=1”c,mi + ◊y + ‘c,t

Identification

I Randomness of local weatherI Time gap: purchase decision to completion of installation

I Ln1

Instc,t+1Buildingsc,1999

2: share of residential solar installation in county c at

time t of potential total market (1999)

DV

I weatherc,t≠i current and lagged weather variables: levels and ‘shocks’

I q3i=1 ”c,mi county-month fixed e�ects that vary with the three main FIT

phases

I ◊y Year fixed-e�ects

ICounty clustered standard errors



Main empirical results



Sunshine shock

(1) (2) (3)

Sunshine shock -0.003 -0.018** 0.003(0.007) (0.008) (0.007)

L.Sunshine shock -0.004 -0.015* 0.002(0.008) (0.009) (0.008)

L2.Sunshine shock 0.107*** 0.092*** 0.119***(0.007) (0.009) (0.008)

L3.Sunshine shock 0.014* -0.002 0.025***(0.007) (0.009) (0.007)

L4.Sunshine shock -0.015** 0.017* -0.002(0.007) (0.009) (0.007)

L5.Sunshine shock -0.030*** -0.039*** -0.029***(0.007) (0.009) (0.007)

L6.Sunshine shock -0.007 -0.035*** -0.005(0.007) (0.008) (0.007)

Observations 55476 55476 55476R2 0.642 0.834 0.859Year FE Y N NCounty-Month FE Y Y YQuarter FE N Y NCounty-Year FE N N Y



Weather shocks

-.002

0.0

02Lo

g (S

olar

/ R

esid

. Bui

ldin

gs)

-1 1 3 5Lags

Sunshine PrecipitationNote: Total sunshine hours and cumulative amount of precipitation at county-month aggregation. 2000-2011.

Note: Regression specification (1). Point estimates and 95% CI for Sunshine andPrecipitation shocks. Regression controls for mean temperature shocks and includescounty-month FE and year FE. Standard errors clustered at county level.



Non-linear e�ects: sunshine distribution

0.0

05.0

1.0

15D

ensi

ty

-200 -100 0 100 200Demeaned sunshine hours

Note: Use variations from the long-term mean in each county-month to generate 7 bins.All regression results relative to zero bin (long-term mean omitted from regression)



Non-linear e�ects: Bins

-.3-.1

50

.15

.3Lo

g (S

olar

inst

alla

tions

/ R

esid

. bui

ldin

gs)

-4 -2 0 2 4Sunshine bins

NL effects: combined Lags 2 and 3Note: Bins relative to long-term sunshine mean in a given county (zero category omitted from regression).

Note: Point estimates for deviations from long-term sunshine mean. Sum of Lags 2and 3. Zero category omitted from regression.



HeterogeneityHH Inc High Educ Vote Green

Sunshine shock -0.009 -0.015 -0.036***(0.010) (0.010) (0.011)

L.Sunshine shock -0.002 -0.003 0.016(0.010) (0.011) (0.011)

L2.Sunshine shock 0.078*** 0.091*** 0.031***(0.011) (0.011) (0.011)

L3.Sunshine shock 0.005 0.006 0.005(0.011) (0.011) (0.011)

L4.Sunshine shock 0.001 -0.005 0.017(0.011) (0.010) (0.010)

L5.Sunshine shock -0.026** -0.005 -0.010(0.010) (0.011) (0.010)

P(50) ◊ Sunshock 0.012 0.025* 0.061***(0.014) (0.014) (0.015)

L.P(50) ◊ Sunshock -0.006 -0.002 -0.047***(0.015) (0.015) (0.016)

L2.P(50) ◊ Sunshock 0.058*** 0.031** 0.147***(0.015) (0.015) (0.017)

L3.P(50) ◊ Sunshock 0.015 0.014 0.015(0.015) (0.015) (0.015)

L4.P(50) ◊ Sunshock -0.031** -0.021 -0.063***(0.015) (0.015) (0.016)

L5.P(50) ◊ Sunshock -0.007 -0.050*** -0.038**(0.016) (0.016) (0.016)

Observations 55476 55476 55476R2 0.794 0.794 0.794Year FE Y Y YCounty-Month FE Y Y Y



Robustness

I Weather station dataI

Placebo tests: Snow shocks Snow ; Wind installationsWind investment

I Sample split & subsamples Sample split

I Outlier: Exclude state-by-state observations.I Control for past installation base and sunshine levels.I Fixed-e�ect structure and standard error correlation (Conley,

1999; Discroll and Kraay, 1998)



Mechanisms

INeoclassical mechanisms

I HarvestingI Rainy-day Cloud cover

I Supply-side response Price adjustment

I News News and Online information

I Consumer learning Climate & technology

IBehavioral mechanisms

I Projection biasI SalienceI Present biasI Climate change beliefs CC & weather beliefs

I Biased weather forecast



Conclusions

IRobust evidence for behavioral bias in a financiallyimportant investment decision, in line with projection bias andsalience

I An exceptional month of sunshine leads to a 7% increase insolar growth at county level

IBehavioral bias matters for aggregate uptake - broaderimplications for e�ective policy design



Histogram: county-month installations

0.0

5.1

.15

.2D

ensi

ty

0 20 40 60 80New installations (≤ 10 kWp)

Note: Small scale solar installations (county-month). For exposition cut at percentile 99.

Regression



Supply-side response(1) (2) (3) (4)

L.Installation -0.0001* -0.0001* -0.0001*** -0.0001***(0.0000) (0.0000) (0.0000) (0.0000)

L2.Installation 0.0000 0.0000 -0.0000 -0.0000(0.0001) (0.0001) (0.0000) (0.0000)

Sunshine shock 0.0011 -0.0041 -0.0041(0.0130) (0.0079) (0.0079)

L.Sunshine shock 0.0146 0.0130 0.0130(0.0125) (0.0089) (0.0089)

L2.Sunshine shock 0.0099 -0.0028 -0.0028(0.0193) (0.0110) (0.0110)

Observations 1835 1835 3104 3104R2 0.609 0.609 0.639 0.639Quarter-State FE Y Y Y Y

Note: Installer bid price data for residential solar plants at county-quarter (2010-11).Columns 1 and 3 refer to original data, while columns 2 and 4 use an interpolateddataset. Sun1: sunshock in at least two months of quarter. Sun2: sunshock in at leastone month of quarter. Regression includes county-year FE and quarter-of-yeardummies. *p <.1, ** p < .05, *** p < .01. Standard errors clustered at county level.

Survey Alternative Mechanisms



Robustness: wind investors

050

0010

000

1500

0C

umul

ativ

e in

stal

latio

ns

0.5

11.

52

Cou

nty-

mon

th in

stal

latio

ns (m

ean)

2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1Date

New wind installations Cumulative installations

(1)

F.Sunshine shock 0.004(0.004)

Sunshine shock 0.008(0.005)

L.Sunshine shock 0.002(0.004)

L2.Sunshine shock 0.003(0.004)

L3.Sunshine shock 0.008*(0.004)

L4.Sunshine shock -0.004(0.004)

L5.Sunshine shock -0.005(0.004)

L6.Sunshine shock -0.013***(0.004)

Observations 42470R2 0.320Year FE YCounty-month FE Y

Dependent variable: number of new wind installations. Point estimates and 95% CI forsunshine shock. Clustered standard errors at county level. Sample limited to 310counties with at least one Wind installation. Robustness



Robustness: Snow shocks

-.002

0.0

02Lo

g (S

olar

/ R

esid

. Bui

ldin

gs)

0 2 4 6Lags

Snowfall [mm]

Note: Reduced sample: counties with weather stations. Robustness



Robustness: Sunshine shock

(1) (2) (3) (4) (5)

Sunshine shock -0.003 0.002 -0.008 -0.003 0.007(0.007) (0.007) (0.007) (0.007) (0.008)

L.Sunshine shock -0.004 0.016** 0.055*** -0.005 -0.011(0.008) (0.007) (0.009) (0.008) (0.010)

L2.Sunshine shock 0.107*** 0.113*** 0.108*** 0.107*** 0.114***(0.007) (0.007) (0.007) (0.007) (0.008)

L3.Sunshine shock 0.013* 0.015** 0.009 0.013* 0.017**(0.007) (0.007) (0.007) (0.007) (0.007)

L4.Sunshine shock -0.015** 0.008 -0.012* -0.015** 0.013*(0.007) (0.007) (0.007) (0.007) (0.007)

L5.Sunshine shock -0.030*** -0.010 -0.021*** -0.030*** -0.010(0.007) (0.007) (0.007) (0.007) (0.007)

L6.Sunshine shock -0.007 -0.005 -0.011 -0.006 -0.001(0.007) (0.007) (0.007) (0.007) (0.007)

Observations 55476 55476 55476 55476 55476R2 0.794 0.796 0.795 0.794 0.796Year FE Y Y Y Y YCounty-month FE Y Y Y Y YLagged temperature N Y N N YLagged sunshine N N Y N YLagged installations N N N Y Y



Solar adoption and news

010

2030

40N

ews

010

000

2000

030

000

New

Hh

sola

r ins

talla

tions

2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1Date

Solar installations News solar (rhs)News climate change (rhs)

News



020

4060

80C

ount

y-m

onth

inst

alla

tions

(mea

n)

2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1Date

Note: Monthly mean of newly added solar installations. Vertical lines indicate three main incentive (FIT) periods.

Regression model



Sunny-day hypothesis

-.05

0.0

5Lo

g (S

olar

/ R

esid

. Bui

ldin

gs)

0 2 4 6Lags

Cloudcover

-.001

0.0

01Lo

g (S

olar

/ R

esid

. Bui

ldin

gs)

0 2 4 6Lags

Precipitation

Note: Weather station data. Cloudcover measures degree of clear sky (1-8). Precipitation: Tot. rainfall in mm.Regression controlling for mean temperature.

Note: Main regression specification with reduced sample of weather station counties.Point estimates and 95% CI for cloud-cover and precipitation. Regression controls fortemperature. Clustered standard errors at county. Robustness



<=2005 >2005 >2005 >2005

Sunshine shock -0.004 -0.008 0.007 0.004(0.013) (0.010) (0.010) (0.009)

L.Sunshine shock 0.079*** -0.001 0.001 0.014(0.013) (0.011) (0.011) (0.012)

L2.Sunshine shock 0.060*** 0.076*** 0.087*** 0.088***(0.012) (0.010) (0.010) (0.009)

L3.Sunshine shock 0.044*** 0.015 0.023** 0.049***(0.012) (0.010) (0.011) (0.010)

L4.Sunshine shock -0.034*** 0.061*** 0.079*** 0.045***(0.012) (0.010) (0.010) (0.011)

L5.Sunshine shock 0.017 -0.108*** -0.084*** -0.146***(0.012) (0.009) (0.009) (0.010)

L6.Sunshine shock -0.070*** -0.039*** -0.027*** -0.102***(0.014) (0.009) (0.009) (0.009)

L.Internet Search 0.001***(0.000)

L2.Internet Search 0.001***(0.000)

L3.Internet Search 0.001***(0.000)

L.News solar 0.014***(0.001)

L2.News solar 0.016***(0.001)

L3.News solar -0.005***(0.001)

Observations 26532 28944 28944 28944R2 0.718 0.744 0.749 0.756Year FE Y Y Y YCounty-Month FE Y Y Y Y

News time series Robustness



Learning

Can exceptional sunshine induce learning?

IWeather and Climate

I Weather shocks do not carry information on future climateLong-term climate

I No evidence for spatial correlation and autocorrelation of weathershocks Shock correlation

I Return-on-invest calculations based on long-term sunshine

ISolar technology

I Learning about product existenceI Behavioral response (salience & projection bias)

Alternative Mechanisms



Competing behavioral mechanisms

1 Present bias & myopia (Laibson, 1997)I Hyperbolic discounting.I All weather-related utility in period of purchase.

2 Salience (Bordalo, Gennaioli, and Shleifer, 2012; Bordalo,Gennaioli, and Shleifer, 2013)

I All deviations from the long-term sunshine mean impact uptake.I Asymmetric e�ects for positive and negative shocks.




Competing behavioral mechanisms

3 Beliefs about climate change

(Li, Johnson, and Zaval, 2011; Deryugina, 2013)I Literature points to link between temperature and climate change.I Evidence for both weekly and monthly outliers.

4 Biased weather forecast

(Krueger and Clement, 1994; Burger-Scheidlin, 2014)I Important for wider relevance of topic.I Not directly testable in data.I Evidence suggests that people are aware of long-term climatic

conditions in their region.I Eight-week time lag from decision-making to installation.




Installer survey

0 .2 .4 .6 .8Percent

Radio & TVOthers

BannersNo advertisement

Social networksDirect mailing

Print mediaOnline ads

EventsWord-of-mouth

Note: Solar PV installer survey, August 2015.Author's calculation. N = 56.

Percent of answers: yesMain advertisement channels

I Online survey: Solar installer survey(August 2015).

I 56 completed answers (3217 contacts)I 35% of installers adopt Marketing strategies

according to season.I 13% (7 out of 56) mention that the weather

impacts their sales strategies.I Main argument: good weather helps for

site-visits. Only two installers mentionsunshine as sales argument.

Supply response



Installer survey: motivation

0 .2 .4 .6 .8 1Percent

Others

Regulation

Environment

Financial investment

Lower electr. bill

Increasing electr. prices


Percent of answers: important or very important Motivation to buy solar

Market



Installer survey: decision variables

0 .2 .4 .6 .8 1Percent

Current weatherFinancing informationExp. future FIT policyExp. future PV prices

Climatic conditionsLocal policies

Current FIT policyCurrent PV prices

Social networkEconomic information


Percent of answers: important or very important Main variables affecting customer choice

Market



020

4060

80C

ount

y-m

onth

inst

alla

tions

(mea

n)

2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 2012m1Date

Note: Monthly mean of newly added solar installations. Vertical lines indicate three main incentive (FIT) periods.

Empirical specification



Sunshineshock:WhiteNoisetest-autocorrelationwithincounty[inanycaseallowforcluster-robustSE!]

TemperatureShock12lags 24lags 40lags 40lags

ShareofcountieswithQ-statisticat1% 0.007 0.074 0.102 0.037

ShareofcountieswithQ-statisticat5% 0.077 0.209 0.274 0.239

Icalculatedforeachofthe402countiesseparatelytheQ(Portmanteau)testforwhitenoise.Thetabledisplaysthepercentofcountiesthatshowaautocorrelationat1%and5%significancelevel.Lagsspecifiesthenumberofautocorrelationstocalculate.

Thetabledisplaysthepercentofcountiesthatshowaautocorrelationat1%and5%significancelevel.Lagsspecifiesthenumberofautocorrelationstocalculate.

Note:Tempshock-morespatiallycorrelatedaswell-inoneregion…that'swhynoresponse;NO…Ialsorunontemperaturelevels!!

Keymessage:sunshineshock:welldefined

Portmanteau:http://www.stata.com/manuals13/tswntestq.pdf

Sunshineshock:MoranI'songlobalspatialcorrelation-sunshineshockandsunshinelevels;usingdistinctspatialweightingmatrices

SunshineShock

Q-(Portmanteau) test for white noise. Percent of counties that reject the null of noautocorrelation at 1% and 5% at di�erent lag structure.

Icalculatedforeachofthe402countiesseparatelytheQ(Portmanteau)testforwhitenoise.Thetabledisplaysthepercentofcountiesthatshowaautocorrelationat1%and5%significancelevel.Lagsspecifiesthenumberofautocorrelationstocalculate.

Thetabledisplaysthepercentofcountiesthatshowaautocorrelationat1%and5%significancelevel.Lagsspecifiesthenumberofautocorrelationstocalculate.

Note:Tempshock-morespatiallycorrelatedaswell-inoneregion…that'swhynoresponse;NO…Ialsorunontemperaturelevels!!

Sunshineshock:MoranI'songlobalspatialcorrelation-sunshineshockandsunshinelevels;usingdistinctspatialweightingmatrices

full

1stquartile

distance

1stquartile

distance

(binary) full

1stquartile

distance

1stquartile

distance

(binary)

ShareofcountieswithMoran'sIat1% 0.021 0.035 0.062 0.048 0.061 0.061

ShareofcountieswithMoran'sIat5% 0.083 0.076 0.09 0.067 0.067 0.133

IcalculateMoran'sIstatisticofglobalspatialcorrelationforeachofthe144timeperiodsseparately.ThetabledisplaysthepercentofperiodswheretheNullofnospatialcorrelationcanberejectedatthe1%and5%significancelevel.Iassumedifferentweightingmatrices:fullweightallowsforall402countiesattimetcanbecorrelated,whileweight5takesonlyintoaccountcloserneighbors(3rdquartiledistance=4.6)andbinaryassumes0/1weights.

Firstquartiledistance:2.1,Iuse2…

Note:Sunshockswiththisdefinitiondonothappeneverymonth:therearemonth,e.g.2000m2withzerosunshineshocksinallcounties

MoranIonglobalspatialcorrelation:https://en.wikipedia.org/wiki/Moran%27s_I

Sunshine Sunshineshocks

Moran’s I statistic of global spatial correlation for each of the 144 time periods. Thetables displays the percent of periods for which the null of no spatial correlation can be

rejected at the 1% and 5% level respectively.

Weather shock



Market trends: residential solar

-1999200020042009 2012

RenewableEnergyAct(2000,EEG)

-  Feed-inTariffs(FIT)-  20yearhorizon-  5%annualdegression

EEG1stAmendment(2004)-  Upwardadjustmentof

householdFITrates-  Overallcapsremoved

EEG2ndAmendment(2009)-  Corridordegression-  On-siteconsumpIon

(voluntary)

EEGincen=vesforsolarchangedimportantlyfrom

2012onwardstowardson-siteconsump=onand

lowerFIT.

Demand



Long-term weather distribution0

100

200

300

400

Suns

hine

hou

rs (G

erm

any)

1970m1 1980m1 1990m1 2000m1 2010m1Date

Monthly sunshine hours Annual mean sunshine hours

-50

510

1520

Mea

n te

mpe

ratu

re (G

erm

any)

1970m1 1980m1 1990m1 2000m1 2010m1Date

Monthly average temperature Annual mean temperature

Weather shock



Distribution of sunshine shocks0

.1.2

.3.4

.5Su

nshi

ne s

hock

(cou

nty

mea

n)

1 2 3 4 5 6 7 8 9 10 11 12Note: Distribution of sunshine shock by month.

0.2

.4.6

.8Su

nshi

ne s

hock

(cou

nty

mea

n)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011Note: Distribution of sunshine shock by year.

Weather shock



Weather station vs. gridded data0

.002

.004

.006

Den

sity

0 100 200 300 400Kernel density: Sunshine hours

Station dataGrid data

kernel = epanechnikov, bandwidth = 7.8319

Kernel density estimate

0.0

2.0

4.0

6D

ensi

ty

-10 0 10 20 30Kernel density: Mean temperature

Station dataGrid data

kernel = epanechnikov, bandwidth = 0.6828

Kernel density estimate

Weather shock


sunspots that matter: behavioral biases in solar technology adoption … · 2 days ago · 3...

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