enduring effects of changes in billing frequency: evidence ...casey j. wichman resources for the...
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![Page 1: Enduring effects of changes in billing frequency: Evidence ...Casey J. Wichman Resources for the Future Camp Resources XXIII Wilmington, NC August 9, 2016 Casey J. WichmanEnduring](https://reader033.vdocuments.us/reader033/viewer/2022061002/60b0b3c67dcac15812249d45/html5/thumbnails/1.jpg)
Enduring effects of changes in billing frequency:Evidence from urban water use
Casey J. WichmanResources for the Future
Camp Resources XXIIIWilmington, NC
August 9, 2016
Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Motivation
For environmental policy, how robust is information provisionas a regulatory instrument?• Increasing reliance on non-pecuniary mechanisms for conservation
• Varying degrees of persistence
Previous work:• One-time social norm treatments can have lasting implications
(Bernedo, Ferraro, & Price, 2014)
• The impact of sustained treatments decline (slowly) over time (Allcottand Rogers, 2014)
• Moral suasion and economic incentives have diverging long-runimplications (Ito et al., 2015)
• More frequent billing (pre-paid metering) may relax credit constraints(Jack and Smith, 2015)
Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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This talk
I exploit a natural experiment in water demand in whichresidential customers were transitioned from bi-monthly tomonthly billing
• Water customers increase consumption 3-5% in response to morefrequent billing
• Can be reconciled in model of misperceived consumption costs, wheremore frequent information allows consumers to optimize “better”
• Dynamic treatment effects suggest that responses persist, but declineslowly over time
• Lowest users increase by the largest percentage
• No evidence of heterogeneous responses by income class
Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Data & empirical setting
Experimental setting in Durham, NC:
• Starting in Dec. 2011, water billing districts wereindividually transitioned from bi-monthly to monthlybilling• Primary motivation: Delinquent payments, leak detection• Meter replacement
• By Sep. 2014, all of Durham’s 17 billing districts weretransitioned to monthly billing.
Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Tiered rate schedule under (bi-)monthly billing
12
34
56
Mar
gina
l pric
e ($
/ccf
)
0 5 10 15 20 25 30 35 40Water usage (ccf)
Bi-monthly rate schedule Monthly rate schedule
Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Bunching at kink points?
Con
sum
ptio
n de
nsity
0 1000 2000 3000 40002011 Water usage (cf)
Consumption density Block rate cut-offs
Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Data & empirical setting
Residential water billing data:
• ∼5.5 years of billing records in Durham, North Carolina• Feb 2009 - June 2015• ∼59,000 households
• Matched with tax assessor data at parcel level• Assessed value of home, lot size, age, square footage
• 2010 Census block group demographics• Percent renters, racial composition, household size
• Weather variables from the NC State Climate Office• Daily maximum temperature, total rainfall
Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Event study
(Conditional on weather, and time and route fixed effects)
Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Empirical frameworkSpatial regression discontinuity, exploiting district boundaries,over time,
wijt = αi + βBFjt + γDf(Disti) + X′γ1 + W′γ2 + τt + εijt
• wijt is daily water use at the household level
=⇒ β is a local average treatment effect (LATE) of increases inbilling frequency on daily water consumption
• Avoids concerns of non-random selection into treatment
• Controls for unobserved changes in “neighborhood” characteristicsover time
• Bandwidth of 500 feet from district boundary
Re-estimate to obtain “dynamic” LATE,
30∑S=0
βS · 1(BFjt−S)
Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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RD design (1 year before treatment)
Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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RD design (year after treatment)
Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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RD design (2 & 3 years after treatment)
Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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RD design (observables)
Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Main regression results
Spatial RDFull sample <2000 ft <1000 ft <500 ft
BFijt 0.685** 0.581** 0.555** 0.541**(0.239) (0.237) (0.223) (0.213)
Household fixed effects? Y Y Y YTime fixed effects? Y Y Y YWeather covariates? Y Y Y Y
Observations 2,086,488 1,642,061 1,003,800 539,601R-squared 0.030 0.030 0.029 0.028Number of households 58,780 46,496 28,535 15,412
Dependent variable is daily water consumption in cubic feet. Robust standard errors inparentheses clustered at billing district level. *** p<0.01, ** p<0.05, * p<0.1.
Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Casey J. Wichman Enduring effects of billing frequency August 9, 2016
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Summary and implications
Main results:
• The average treatment effect masks important dynamicbehavior.
• Dynamic treatment effects decrease over ∼2 years
• Consumers in the lowest quartile of consumption increaseusage by the highest percentage.
=⇒ consumers responding to something other than price?
• No evidence of heterogeneity among proxies for wealth.• Little support that more frequent billing helps smooth
consumption month to month.
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Questions?
Thank you
Casey J. Wichman Enduring effects of billing frequency August 9, 2016