Information Nudges, Subsidies, and Crowding Out of Attention: Field Evidence from Energy Efficiency Investments

Last registered on June 21, 2024


Trial Information

General Information

Information Nudges, Subsidies, and Crowding Out of Attention: Field Evidence from Energy Efficiency Investments
Initial registration date
March 28, 2018

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
March 31, 2018, 5:14 PM EDT

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

Last updated
June 21, 2024, 6:05 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator

Bocconi University

Other Primary Investigator(s)

PI Affiliation
University of Bochum

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
How can information substitute or complement financial incentives such as Pigouvian subsidies? We answer this question in a large-scale field experiment that cross-randomizes energy efficiency subsidies with information about the financial savings of LED lighting. Information has two effects: It shifts and rotates demand curves. The direction of the shift is ambiguous and highly dependent on the information design. Informing consumers that an LED saves 90% in annual energy costs increases LED demand, but showing them that 90% corresponds to an average of 11 euros raises demand for less efficient technologies. The rotation of the demand curve is unambiguous: information dramatically reduces both own-price and cross-price elasticities, which makes subsidies less effective. The uniform decrease in price elasticities suggests that consumers pay less attention to subsidies when information is provided. We structurally estimate that welfare-maximizing subsidies can be 200% larger than the Pigouvian benchmark when combined with information.
External Link(s)

Registration Citation

Rodemeier, Matthias and Andreas Löschel. 2024. "Information Nudges, Subsidies, and Crowding Out of Attention: Field Evidence from Energy Efficiency Investments." AEA RCT Registry. June 21.
Former Citation
Rodemeier, Matthias and Andreas Löschel. 2024. "Information Nudges, Subsidies, and Crowding Out of Attention: Field Evidence from Energy Efficiency Investments." AEA RCT Registry. June 21.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The number of LED, CFL, halogen and incandescent light bulbs purchased.

The price elasticity for all four light bulb technologies (incandescent, halogen, CFL, LED).

We also estimate whether our interventions affected the extensive margin, i.e. the probability of purchasing a light bulb.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Visitors of an online shop are randomly assigned to different groups in a 3x5 between-subject design.

We vary both the prices of different lighting technologies and the information provided on energy efficiency. This experimental design facilitates the estimation of price elasticities for different lighting technologies under different sets of information provision.

After customers went through the purchasing decision they receive an invitation to participate in a survey. The survey elicits variables such as energy and financial literacy, income, patience, environmental attitudes and other consumer-specific characteristics.
Experimental Design Details
The experiment will be implemented in the online shop of a German appliance retailer.

Potential customers who visit the website of the appliance retailer are randomly assigned to one of 15 groups with equal probability. We use a 3x5 design in which customers get randomized into 3 different informational groups (groups 1 to 3) and 5 different price discount groups (groups A to E). Visitors receive either 1) no information 2) coarse information or 3) specific information on the financial benefits of energy efficiency. In addition, every visitor is randomly assigned to a price discount on A) incandescent bulbs B) halogen bulbs C) CFL bulbs D) LED bulbs or E) does not receive a price discount.

Group 1
Visitors in group 1 receive no additional information on the financial benefits of energy efficiency, other than the information already provided by the online retailer (e.g. wattage, energy efficiency label, etc.).

Group 2
For subjects in group 2, a banner is shown at the top of the browser and contains information on the annual electricity savings of three lighting technologies (halogen, CFL, LED) in comparison to a traditional 40W incandescent light bulb. We visualize these savings by using a bar chart.
In particular, subjects in group 2 are only informed about the savings of these light bulbs in percentage (e.g. a 4 LED saves 90% compared to the 40W incandescent). We do not inform subjects explicitly about how these relative savings in electricity costs translate into monetary savings. This information screen serves as our coarse-information nudge as it provides subjects with potentially useful information but leaves room for interpretation (e.g. it may be unclear to the consumer whether 90% savings translate into 2 or 200 Euros per year).

Group 3
Subjects in group 3 receive almost the same banner as subject in group 2 but are also informed about the annual savings of the different lighting technologies in Euro. That is, besides receiving information on relative savings (in percent) we also tell them the absolute savings (in Euro). We explicitly tell the subject which electricity price and utilization of the light bulb we have assumed for calculating the monetary savings.
The informational intervention in group 3 serves as our nudge with more specific information as it informs the consumer about both relative and absolute savings of different lighting technologies.

Groups A-E
Each of the three groups is divided into 5 subgroups (A-E) in which we offer subjects a 20% price discount on one of the four lighting technologies or no discount. Thus, we have 15 in groups in total: 1.A, 1.B, 1.C, 2.A, …, 3.E. The price discount is shown directly next to the informational intervention so that every subject wo has seen the information should also have seen the individual discount. The randomly assigned price discounts facilitate the estimation of price elasticities which are needed for our welfare analysis.

After subjects made their purchase, they are invited to participate in a survey. Participation is incentivized through a lottery. The survey includes questions on the level of energy literacy, financial literacy, environmental attitudes, patience, education, age, gender and the subject’s zip-code.

We use this survey to deal with the challenge of bias heterogeneity. The recent literature has used within-subject designs to elicit the bias (under-/ overvaluation of energy efficiency) for each subject individually. Since this is generally not possible in a natural field experiment with a between-subject design, we collect information on variables that have predictive power about the individual-specific treatment effect but are not affected by the treatment. This should allow us to identify the “bias type” of the consumer (e.g. high or low bias). We then interact our treatments with these variables and estimate heterogeneous treatment effects (of both the informational and the price interventions). To identify the bias type, we intent to use questions on the level of financial literacy, income, patience and some additional questions on energy literacy that are unlikely to be influenced by our intervention.

As an alternative approach to using answers from the survey, we match the customers zip-code with available data on income and other relevant variables that potentially identify the bias-type of the consumer (e.g. green party vote share).

Our sufficient statistic approach allows us to estimate the effect of our intervention on consumer surplus under a set of assumptions. For a more complete welfare analysis, we also take into account how our intervention could have affected environmental externalities (CO2 emissions) if subjects bought more or less efficient lighting technologies. We plan to take estimates on the social value of a ton of CO2 from the established literature.
Randomization Method
This experiment is between-subject. The retailer will use a professional A/B-testing tool from a third-party provider to randomly assign each visitor into one of 15 experimental groups with equal probability.
Randomization Unit
The randomization unit is the HTTP-Cookie of the visitor.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
The treatments are not clustered.
Sample size: planned number of observations
600,000 website visitors
Sample size (or number of clusters) by treatment arms
40,000 website visitors per treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
University of Münster
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Collection Completion Date
Final Sample Size: Number of Clusters (Unit of Randomization)
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
Final Sample Size (or Number of Clusters) by Treatment Arms
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

How much information should governments reveal to consumers if consumption choices have uninternalized consequences to society? How does an alternative tax policy compare to information disclosure? We develop a price theoretic model of information design that allows empiricists to identify the welfare effects of any arbitrary information policy. Based on this model, we run a natural field experiment in cooperation with a large European appliance retailer and randomize information regarding the financial benefits of energy-efficient household lighting among more than 640,000 subjects. We find that more informative signals strongly decrease demand for energy efficiency, while less informative signals increase demand. More information reduces social welfare because the increase in consumer surplus is outweighed by the rise in environmental externalities. By randomizing product prices, we identify the optimal tax vector as an alternative policy and show that sizable taxes on energy-inefficient products yield larger welfare gains than any information policy.
Rodemeier, Matthias, and Andreas L¨oschel. 2020. “The Welfare Effects of Persuasion and Taxation: Theory and Evidence from the Field.” CESifo Working Paper No. 8259.

Reports & Other Materials