Myopic Consumption
Last registered on December 17, 2018


Trial Information
General Information
Myopic Consumption
Initial registration date
May 24, 2014
Last updated
December 17, 2018 3:45 AM EST

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Primary Investigator
Graduate Institute of International and Development Studies
Other Primary Investigator(s)
PI Affiliation
Chr. Michelsen Institute
Additional Trial Information
Start date
End date
Secondary IDs
Recent theoretical and empirical work on consumers’ behavior emphasizes the importance of self-control problems. While different theories can explain self-control problems, an important part of the literature focuses on “βδ” models of time-inconsistent preferences. In those models, individuals are assumed to be either (i) sophisticated, aware of their preferences, and taking commitment measures to protect their present-self from the behavior of their future-selves; or (ii) partially or fully naïve and ignoring the inconsistency in their preferences when deciding on their present consumption.

Both theoretical and empirical studies alike have so far focused on the “forward looking” part of the consumers: how do they choose current and future consumption, given how much knowledge they have about their own preferences and the available commitment technologies. We on the other hand want to focus on another assumption that we believe is fundamental, but yet not researched. Naïve consumers are typically assumed to ignore not only their true future behavior, but also all their past decisions. The naïve consumer that makes time-inconsistent choices is assumed unable to look back at his past choices and infer from them the true shape of his preferences.
External Link(s)
Registration Citation
Somville, Vincent and Lore Vandewalle. 2018. "Myopic Consumption." AEA RCT Registry. December 17.
Former Citation
Somville, Vincent, Lore Vandewalle and Lore Vandewalle. 2018. "Myopic Consumption." AEA RCT Registry. December 17.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
All our outcome variables are based on the expenditures information from our surveys. We have this information on a weekly basis for the duration of the experiment. We use both weekly values and averages over the whole course of the experiment.
The hypothesis that we want to test is whether the treatment changes the weekly expenditures, and its composition. The outcome variables are related to the first four categories described in the intervention: (i) tobacco and alcohol, (ii) unnecessary cosmetics, (iii) beverages and snacks outside the household, and (iv) necessary goods. We look at:
- (Y1) The expenditures made on each of the four categories separately (4 outcome variables)
- (Y2) The total expenditures over all the goods in the four categories (1 outcome variable)
- (Y3) The share of each of the four categories in the total over the four categories (4 outcome variables)
- (Y4) Whether or not the household consumed goods from the first three categories (3 outcome variables)
- (Y5) The quantities bought within each of the first three categories.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We provide information about past expenditures to households. There are control households and two treatments: some households receive a short overview of their past expenditures, others a more detailed overview.
Experimental Design Details
Not available
Randomization Method
The randomizations are done with the software Stata. We followed David McKenzie and Miriam Bruhn’s recommendations in dealing with the uneven numbers in some strata and we followed the Stata code that they shared on the World Bank’s “Development Impact” blog on the 11th of June 2011.
Randomization Unit
The unit of observation is the head, or spouse of the head of the household.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
18 villages
Sample size: planned number of observations
576 households (32 per village)
Sample size (or number of clusters) by treatment arms
190 control households, 194 households received the short budget sheet, and 192 households received the long budget sheet.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We have 32 households per village, or 576 households in total
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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