Strategic Energy Price Pass-Through and the Value of Loss Load

Last registered on May 30, 2024


Trial Information

General Information

Strategic Energy Price Pass-Through and the Value of Loss Load
Initial registration date
May 23, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
May 30, 2024, 3:16 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.


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Primary Investigator

HEC Lausanne

Other Primary Investigator(s)

PI Affiliation
HEC Lausanne/Romande Energie
PI Affiliation
HEC Lausanne

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
The goal of this project is to study the pass of energy price shock using survey data and information treatments, and the relationship between pass-through rate and the value of loss load. Using a novel firm-level survey, we survey a large sample of firms (N=200-600) about their pass-through rate for past and future energy price shocks. We create information treatments, between firms, where we vary the nature of the energy price shock with the goal to identify the strategic component of energy price pass-through. In particular, we distinguish between country-level, sectoral, and firm-specific energy price shocks. For a small subset of surveyed firms, we plan to link the survey data with administrative data with electricity consumption and energy contract details.
External Link(s)

Registration Citation

Benhima, Kenza, Julia Chappuis and Sebastien Houde. 2024. "Strategic Energy Price Pass-Through and the Value of Loss Load." AEA RCT Registry. May 30.
Sponsors & Partners

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Experimental Details


The main intervention consists of introducing information treatments inside an online survey for firms. The information treatments create variations between surveyed firms regarding the nature of the energy price shock they are facing. We created three scenarios where we have national, sectoral, or firm-specific energy price shocks. All the randomization is done via the online survey for the sample population of firms that decide to participate in the survey.

The survey is designed to elicit each firm's pass-through rate for a given type of energy price shock, as well as firms' beliefs about the pass-through for the rest of the economy.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
This study has two main outcome variables: the pass-through rate of an energy price shock and the value of loss load.
Primary Outcomes (explanation)
For the pass-through rate of an energy price shock, we directly ask survey participants (firm managers) about it in the survey. We are using a slider from -100% to +100%. For each respondent, we are planning to take the data as is from those questions.

Our goal is to estimate the average pass-through for each firm and their beliefs about the pass-through in the rest of the economy. To compute the bias in beliefs about the pass-through, we will compare the average of the firms' specific pass-through for the whole sample, i.e., E[p_i], with the average of the firm-specific beliefs of the pass-through rate for the whole economy E[p].

For the value of loss load (VOLL), we are asking three questions to construct this variable. First, we have a value of loss load that corresponds to a willingness to pay to avoid a temporary cut in electricity. We specifically asked for the percentage increase in their current energy price that firms will be willing to pay to avoid such a cut. Using information about the electricity price a firm currently play, we can then compute the marginal WTP: percentage increase X current electricity price. This is our first WTP estimate of the VOLL. We call it the short-term VOLL

The second and third questions we ask allow us to compute a risk premium on medium/long-term energy contracts, which is our second measure of VOLL. For this measure, we divide the marginal WTP: which is the percentage increase respondents declare X current electricity price divided by the change in risk to avoid a power cut. Note the vary the change in risk, we are thus planning to estimate a quadratic function to match the VOLL calculated between the VOLL obtained for each change in risk to account for risk aversion.

Secondary Outcomes

Secondary Outcomes (end points)
We will collect several variables to look for secondary outcomes.
First, for a small sample of firms, we will collect electricity data and we will aim to estimate a price of elasticity using the historical variation in energy prices. We want to estimate the price elasticity using the staggered expiration of the energy contracts of firms active on the market. Note that we have administrative electricity data for 400+ firms, which we will be able to use to compute an elasticity. However, only a subset of these firms will participate in the survey. If the number of firms is large enough, we will look at the correlation between the elasticity (if we manage to get enough variation across firm characteristics) and the main survey outcomes.

We plan to investigate if the pass-through rate, biased beliefs, and VOLL vary with firms characteristics.

Note that participants are rewarded with a lottery where they can win a small gift. They can choose to donate their compensation to a charity. We plan to analyze this decision and use this as a dimension of heterogeneity to classify managers into altruistic types.

We also plan to use contract information to investigate if firms that were exposed to the energy crisis because their contracts expired during the crisis have different reported pass-through (past and expected).

Secondary Outcomes (explanation)
We plan to investigate if the pass-through rate, biased beliefs, and VOLL vary with firm characteristics: firm size, sector, donation to charity, exposure risk due to energy contract type.

Experimental Design

Experimental Design
The survey elicits a pass-through of energy price shock for each survey participant. We introduce a between-subject variation in the nature of the energy price shock using a 1/3 randomization in Qualtrics, where some firms have a firm-specific shock, others have a sector-specific shock, and others have a nationwide shock.

Beliefs were not incentivized but we incentivized participation with a lottery.
Experimental Design Details
Not available
Randomization Method
The randomization is done via Qualtrics.
Randomization Unit
Survey respondant.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
No clustering
Sample size: planned number of observations
200-600 firms managers.
Sample size (or number of clusters) by treatment arms
1/3 in each treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We have not conducted power calculation as we didn't have good prior on the pass-through rate: our main outcome variable.

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number