Economics of a Light Bulb: Experimental Evidence on CFLs and End-User Behavior

Last registered on August 14, 2014

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Pre-Trial

Trial Information

General Information

Title

Economics of a Light Bulb: Experimental Evidence on CFLs and End-User Behavior

RCT ID

AEARCTR-0000461

Initial registration date

August 14, 2014

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

August 14, 2014, 2:31 PM EDT

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

Locations

Country

Kyrgyzstan

Region

Central Asia

Primary Investigator

Name

Eliana Carranza

Affiliation

World Bank

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Other Primary Investigator(s)

PI Name

Sendhil Mullainathan

PI Affiliation

Harvard University

Contact Investigator

PI Name

Robyn Meeks

PI Affiliation

University of Michigan

Contact Investigator

Additional Trial Information

Status

On going

Start date

2010-10-01

End date

2014-10-10

Keywords

Environment & Energy

Additional Keywords

electricity, development, energy, CFLs, WTP, rebound effects, peer effects

JEL code(s)

O130

Secondary IDs

Abstract

In developing countries, residential access to modern energy and lighting improves living standards and productivity (World Bank, 2006). Yet due to electricity constraints, blackouts and high prices are not uncommon. Because lighting is a major component of electricity consumption, energy efficient technologies such as compact fluorescent lamps (CFLs) can reduce electricity demand and help energy-constrained countries to meet conservation goals. Using a randomized study design, we examine the adoption, utilization, and diffusion of CFLs in Kyrgyzstan. We find that energy efficient light bulbs have a significant impact on electricity use, that there is evidence of a rebound in electricity use, and that the behavioral response differs between households that received CFL bulbs for free vs. for a positive price. Moving forward, we continue to evaluate reasons for these differential responses.

External Link(s)

Registration Citation

Citation

Carranza, Eliana, Robyn Meeks and Sendhil Mullainathan. 2014. "Economics of a Light Bulb: Experimental Evidence on CFLs and End-User Behavior." AEA RCT Registry. August 14. https://doi.org/10.1257/rct.461-1.0

Former Citation

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Our intervention is the deployment of compact fluorescent lamps (CFLs) at a free or reduced price in Kyrgyzstan. We vary the intensity of the intervention at a regional level, in order to elicit information on network effects related to CFL usage.

Intervention Start Date

2013-03-01

Intervention End Date

2014-04-30

Primary Outcomes

Primary Outcomes (end points)

Key outcomes variables include household-reported willingness to pay (WTP) measures, rebound effects, and electricity usage reports from the meter logs.

Primary Outcomes (explanation)

Our narrow definition of "rebound effect" excludes indirect cross price and income effects. The energy use of lighting (E), measured in kW, is defined to be the product of the energy intensity of such service (I) in kw/hour, and the household consumption of hours of lighting (A): E = I*A. Households determine their consumption A(T) based on the price per hour of lighting (T) rather than the price per kW used to produce lighting (P). The relationship between the prices is T = EP/A.

We are interested in improving energy efficiency, i.e., what happens when "I" falls. We differentiate E with respect to energy efficiency (y = 1/I) to determine the rebound (the electricity consumed for services, taking into account the electricity elasticity of consumption costs).

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

To better understand the adoption, use, and diffusion process of CFL technology, we use a randomized trial in Kyrgzstan to inspect: (1) the "rebound" effects, i.e., behavioral responses that boost consumption and offset potential electricity savings of CFLs and (2) the role of peer networks in diffusing information. We elicit measures of willingness to pay (WTP) to study these effects, as this metric will affect treatment adoption.

The two-stage randomization works as follows: first, villages in Kyrgyzstan are selected based on accessibility. Next, we randomize transformer groups (TGs) within these villages into groups of treatment intensity: control (0% treated), low-intensity (60% treated), and high-intensity (80% treated). Finally, we randomly select 20 percent of households in each TG for survey. These households are randomized into three groups: control (no CFL offer), T1 (free CFLs), and T2 (reduced-price CFLs).

In terms of site selection, all regions in the study must be low-income, have high rates of electrification, and have relatively substantial household expenditure on energy. Participating households must have formal connections to electrical grid, have household-specific meters for electricity consumption, and pay for electricity based on actual electricity consumption rather than a fixed charge.

Experimental Design Details

Randomization Method

We run a computer-based randomization.

Randomization Unit

We have two levels of randomization. First, we randomize transformer groups into different treatment intensities. Next, we randomize at the household level into free CFLs, reduced-price CFLs, and no CFLs (i.e., control).

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

We selected 124 transformer groups (TGs).

Sample size: planned number of observations

1,000 households

Sample size (or number of clusters) by treatment arms

39 TGs control, 45 low-intensity, 40 high intensity.
Within these groups:
Control TGs:
Control: 200 households

Low-intensity TGs:
Control: 186 households
Free: 143 households
Buy: 134 households

High intensity TGs:
Control: 71 households
Free: 136 households
Buy: 134 households

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

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