Energy Efficiency
Intro
Lecture draws on:
Allcott and Greenstone (JEP 2012) Allcott (Annual Review 2014) Gerarden, Newell and Stavins (JEL 2018)
Energy services require capital
Source: Allcott and Greenstone (2012)
Long standing assertion that energy efficient capital suboptimally deployed

Serious interest began during oil crisis in 1970s

Engineers concluded that many energy saving technologies were slow to diffuse
 for example Amory Lovins and RMI
Over time interest has centered on apparent “winwin” from environmental perspective
 Energy consumption associated with many externalities
40 years later this view still has a large audience
There are many “rational” reasons for EE gap
 agency issues
 ie landlord tenant
 resale issues
 home improvements
 engineering models wrong
 Fowlie, Greenstone and Wolfram (QJE 2018)
Research focussed on “behavioral” explanations
 [present bias]
 tradeoff is over cash flows not consumption
 car or mortgage payments also flows
 firm mispricing problem less clear
 car companies don’t sell you gasoline
 tradeoff is over cash flows not consumption
 inattention/ salience
 Chetty et al (2009); Hossain \& Morgan (2006)
 cognitive issues /confusion
 Abaluck \& Gruber (2011); Allcott (2013)
Setup
Consumers have indirect utility \(u_{j} = \eta (y  p_j  \gamma g_j) + \nu_j\)
 $p_j$ is upfront cost
 $\nu_j$ is the usage utility
 $g_j$ is the lifetime energy cost
Assuming $g$ is calculated and discounted appropriately, a natural test is to estimate $\gamma$ and test if its equal to 1.
Lifetime energy costs
\[g_{j} = \sum\limits_{t=0}^{T} \delta^t m(r_j,e_t)r_j e_t\] $m$ is utilization
 $r_j$ is energy requirement for capital $j$
 $e$ is the energy price
Note
 $\delta, m, T, e$ could all have $i$ subscripts
 if $m$ is endogenous, can lead to a “rebound effect”
Early literature tried to estimate $\delta_i$
Hausman (1979) estimates a discrete choice model for AC’s
\[u_{ij} = \eta (y_i  p_j  \delta_i \bar m_i r_j e_t) + \alpha X_j + \epsilon_{ij}\] Has a small survey of households with submetered ACs
Literature then turned to within product variation
 In the cross section, concern that $E[r_j \epsilon_j] \ne 0$
However, $g_{ij}$ varies for many other reasons within product
 energy prices vary
 across regions, time
 lifetime varies at time of sale
 Sallee et al. look at mileage
Allcott and Wozny
 What’s the research question?
 What’s the empirical strategy?
 What data do they have?
AW cost to own
\(p_{j} + \sum\limits_{t=0}^{T} \delta^t m(r_j,e_t)r_j e_t\)
 used car auction vehicle prices from Manhiem
 what are the assumptions using this?
 assumed real discount rate of 6%
 vehicle loan rate 6.9%
 S\&P avg return 5.9%
 (exogenous) VMT ($m$) and $T$ from NHTSA
 fuel economy from EPA
 national avg gasoline prices / oil futures
Controlling for product quality clearly matters
Identifying variation
AW use hedonic regression
Typical logit share identity (Berry, 1994): \(\ln s_{jat}  \ln s_{ot} = \eta (p_{jat}  \gamma g_{jat}) + \psi_{ja} + \tilde \xi_{jat}\)
AW actually estimate: \(p_{jat} =  \gamma g_{jat} + \tau_t + \psi_{ja} + \epsilon_{jat}\)
Why do they do this?
Berry equation rearranged is \(p_{jat} =  \gamma g_{jat}  \frac{1}{\eta}(\ln s_{jat}  \ln s_{ot} ) + \psi_{ja} + \tilde \xi_{jat}\)
 The don’t have shares.
 Absorb $s_{ot}$ with time FEs.
 Put product FEs in $\psi_{ja}$
 $\xi$ and the remaining share difference in the $\epsilon$
\(p_{jat} =  \gamma g_{jat} + \tau_t + \psi_{ja} + \epsilon_{jat}\)
 So this tests it tests whether relative vehicle prices move oneforone with changes in the relative gasoline costs.
 Coeff of interest $\gamma$ recovered with OLS.
AW also using a grouping estimator

group all cars by month, above / below median
 $Z_{jat}^u = 1(f_{ja} > f^{50}) \times 1(t=u$)

instrument for $G$ with $Z$

why do they do this?
Jerry Hausman’s (2001) “iron law of econometrics”: due to measurement error, the magnitude of a parameter estimate is usually smaller in absolute value than expected
What is the null hypothesis that AW want to test?
\[p_{jat} =  \gamma g_{jat} + \tau_t + \psi_{ja} + \epsilon_{jat}\]Visual results
AW Results  Sensitivity
What do people takeaway from this paper?
 tradeoff implies discount rate of about 15%
 for new cars, close to 1
 old cars, very low

results sensitive to how G is constructed

are people making mistakes?
 what questions does this leave open?
Allcott and Knittel
Recent emphasis has been on experiments

Many studies similar to AW

Although panel data helps, interpretation still requires econometrician to construct $g$

If hypothesis is that $\gamma < 1$ due to inattention, imperfect information or bounded rationality, an alternative is to experimentally vary exactly those margins
Alternative: AK 2019 conduct two large field experiments
Experiment #1:
 Hang out at car dealers and intercept potential buyers
 Randomly explain fuel economy savings to some
Experiment #2:
 Conduct and online survey of people how say they are in the market for a new car
 In addition to following up on what people actually bought, AK elicit WTP for fuel economy.
Some advantages of surveys

can ask people about consideration sets

can ask about beliefs

can elicit WTP
Consideration sets reveal important differences
Beliefs about fuel costs noisy, but not biased
Results
Policy Implications
Some conclusions from core EE gap lit

assertion that consumers making large mistakes on average appears incorrect

once you account for product unobservables and use exogenous fuel cost variation, tradeoffs seem close to rational

experimental studies directly educating or directing consumer attention to energy costs have found very small impacts
Future directions
 Heterogeneity in both values and bias
 policy implications
 targeting
 What role do firms play here?
 Are they offering the “right” set of products?
 How do consumer’s form beliefs?
 we know the true calculation is hard
 rational inattention (Sallee JLE 2014)
 we know the true calculation is hard
Allcott and Taubinsky
Can policymakers improve welfare in this market?

develop a theoretical framework in presence of heterogenous bias and tastes

implement two experiments informing consumers about CFLs

evaluate welfare effects:
 optimal subsidy
 [ban on incandescents]
Corrective taxation with damage heterogeneity
Pigou (1920):
\(\tau = D'(e)\)
If damages are heterogenous, first best achieved with polluter specific tax
\[\tau_i = D_i'(e_i)\]Diamond (BJE 1973): If damages are heterogeneous, but you can only set one tax rate, it should be equal to the average damages at the margin when the tax is implemented.
\[\tau_h = E_i[ D'_i(e_i(\tau_h))]\]AT show this also applies to correcting biases
Setup
 unit demand: $j \in {E,I}$
 no outside option
 utility: $u_j = v_j + z  P_j$
 where $z$ is the consumer’s budget
 choose $E$ if $v  b > p$
 $v = v_E  v_I$
 $b$ is bias
Demand with and without bias
Welfare effect of subsidy
\[W(s) = Z(s) + v_I  p_I + \int_{vb \ge p}(v  p) dF dG\] perfect competition: $p = c  s$
Proposition 1:
\[W'(s) = (s  B(p)) D'_B(p)\]where $B = E(b  v  b = p)$ is the average marginal bias – ie the average bias for consumers on the margin the (subsidized) price 
Optimal subsidy: \(s^* = B(c  s^*)\)
Average Marginal Bias
AMB is the key object of policy interest
Need not be constant
 Average marginal bias $\ne$ average bias
How can we estimate the average marginal bias?
Option 1:
 use within consumer variation in informedness
Option 2:
 estimate demand elasticity wrt tax and information
 calibrate difference
TESS experiment

Artefactual field experiment

Give consumers $10

Elicit WTP ($v$) for CFLs

Inform some consumers about cost to own to recover $b$
Many papers have used TESS
Informed vs uninformed demand
Average marginal bias not constant
Welfare effects
Welfare effects
Allcott, Knittel and Taubinsky
Extend AT model to allow correlation in bias and valuation
 consumers of type $j$ have distortions $d$ (nests bias from AT)
 value $v  d$

population average $\bar d = \sum \alpha_j d_j$
 define targeting as:
\(\tau (s) \equiv cov ( d_j, Q'_j(cs) )\)
 so a high $\tau(s)$ is well “targetted”
Welfare and optimal subsidy
Result 1: Poor targeting reduces welfare gains
\[W'(s) = (s  \bar d) \cdot D'(cs) + \tau(s)\]Result 2: Optimal poorly targeted subsidy could be small, even if average bias is large
\[s^* = \bar d  \frac{\tau(s)}{D'(c  s)}\][ Optimal subsidy increasing in $\tau(s)$ ]
Bias is correlated with observables
As is policy takeup
Where does this leave us?

Optimal subsidy depends on average marginal bias

Not sufficient to know bias and responsiveness separately: need to show biased consumers are actually the onces affected by the policy.
Allcott and Sweeney
What we did
 partner with major appliance retailer
 field experiment at call center
 customer information and rebates
 sales agent incentives
 audit phone calls to check compliance
 survey consumers
Some thoughts on running a field experiment
 getting institutional buy in was tough
 in end, partner lost interest
 compliance a big issue
 has implications for feasible policy too
Some agents much better than others
Survey reveals widespread confusion, but small bias
Main Results
Agents Target Scripts
What role do firms play in this?

Allcott & Sweeney: Sales agents can target. Can we leverage this somehow?

Houde (2014): firms bunch product characteristics around subsidy / label cutoffs

Houde (2018): labeling may further reduce average purchased quality