Whose brighter future? Parent-bias and investments in children.
Last registered on November 07, 2018

Pre-Trial

Trial Information
General Information
Title
Whose brighter future? Parent-bias and investments in children.
RCT ID
AEARCTR-0003535
Initial registration date
November 06, 2018
Last updated
November 07, 2018 12:07 AM EST
Location(s)

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Primary Investigator
Affiliation
University of zurich
Other Primary Investigator(s)
PI Affiliation
University of Zurich
Additional Trial Information
Status
On going
Start date
2018-11-05
End date
2019-03-31
Secondary IDs
This study has also been known as "Boosting patience for poverty reduction: a field experiment in Malawi"
Abstract
This document presents the pre-analysis plan of a lab-in-the-field experiment that will be conducted in Salima district of Malawi. This experiment aims at documenting a new form of time-inconsistency: parent-bias. While present-bias characterizes individuals who are over-optimistic about their willingness to allocate resources to delayed consumption in the future, parent-bias characterizes individuals who are over-optimistic about their willingness to allocate resources to their children in the future.

We document the extent to which individuals are sophisticated, or cognizant about their preference reversals and their demand for commitment. We investigate the extent to which framing interventions -- such as labeling consumption -- are able to mitigate parent-bias.
External Link(s)
Registration Citation
Citation
Lichand, Guilherme and Juliette Thibaud. 2018. "Whose brighter future? Parent-bias and investments in children.." AEA RCT Registry. November 07. https://www.socialscienceregistry.org/trials/3535/history/36883
Experimental Details
Interventions
Intervention(s)
This study will document whether parents make plans to invest in their children’s in the future, but are tempted to reverse them at the moment the investment needs to be done, favoring their own consumption at the expense of investments in their children. Additionally, we will measure whether parents, at the time of making investment decisions, are aware of the risk that they could change their mind in the future, and demand commitment devices to help them stick to their plans. Answering these questions could contribute to explain why investments in children’s health and education are so low. Moreover, it would produce important insights to design cost-effective interventions, such as commitment devices, to reduce the temptation to divert resources away from children.
We present here the design of a lab-in-the-field experiment designed to test the following hypotheses: 1) Do parents discount their own future consumption and that of their children differently?
2) Does this differential discounting give rise to within-household inconsistencies (parent-bias)? 2
3) Is there demand for commitment devices to help mitigate parent-bias, above and beyond demand for commitment devices that help mitigate classical present-bias?
4) Do parents demand to involve their children in future decisions as a commitment device?
5) Is the demand for commitment explained by parents’ beliefs that they might be tempted to change their plans in the future?
6) Can labeling mitigate parent-bias?
7) Can encouraging children to participate in household decisions increase investments in children and mitigate parent-bias?
Intervention Start Date
2018-11-05
Intervention End Date
2019-01-31
Primary Outcomes
Primary Outcomes (end points)
-Share of peanuts to be consumed by the child at t = j (j ∈{2,3}), when making the decision at t = 1;
-Share of peanuts to be consumed by the child at t =2, when making the decision at t =k, (k ∈{1,2});
-Difference between the share of peanuts allocated to be consumed by the child at t = 2 while making the decision at t = 1 and t = 2;
- Difference between the share of peanuts allocated to be consumed by the child at t = 3 and t = 2 while making the decision at t = 1 and t = 2;
-Take-up of the probabilistic commitment devices,
-Willingness to let the child participate in round 2 decision.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
We will define the following dummy variables:
-Parent-bias;
-Child-bias;
-Present-bias;
-Present-bias.
Secondary Outcomes (explanation)
We will define the following dummy variables:
-Parent-bias: Equal to 1 if parents reallocate more towards their own consumption than they had originally planned;
-Child-bias: Equal to 1 if parents reallocate more towards their child's consumption than they had originally planned;
-Present-bias: Equal to 1 if parents choose to receive more, on average across interest rates, in the earlier time period when making the decision at t=2 than t=1 in part Blue.
-Present-bias: Equal to 1 if parents choose to reallocate more peanuts
Experimental Design
Experimental Design
This experiment will be conducted in 80 villages of Salima district in Malawi, with 2,400 participants.
Experimental Design Details
Not available
Randomization Method
The randomization will happen alongside the first visit of data collection, and will be done with the tablet.
Randomization Unit
Individual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
2400
Sample size: planned number of observations
2400
Sample size (or number of clusters) by treatment arms
Type of Commitment :
Probabilistic (2000 respondents) // Child’s participation (chosen) (400 respondents)

Framing
Baseline (800 respondents)// Labeling (400 respondents) // Random Anchoring (400 respondents)// Child's participation (imposed) (400 respondents)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
1. Do parents discount their own future consumption and that of their children differently? Hypothesis 1a: Parents discount their own future consumption more than that of their children. We are testing whether parents allocate a larger share of peanuts for their child to consume at t=2 than at t=3, when making the decision at t=1. We will pool the observations from all our subsamples except “Child’s participation (imposed)” to conduct this analysis. This would allow us to detect a 0.0886 s.d. difference in the share of peanuts allocated to the child at t = 2 and t = 3, which is 2.55% of the mean of the outcome variable, as measured in the pilot. For consistency with the rest of our analyses, we will also conduct the same regression in the “Probabilistic commitment device × Baseline” sample, which would allow us to detect a 0.1402 s.d. difference, which represents 4.21% of the pilot mean of the outcome variable 2. Does this differential discounting give rise to within-household inconsistencies (parent-bias)? Hypothesis 2a: Parents exhibit parent-bias. We are testing whether parents allocate a smaller share of peanuts for their children to consume at t=2 when making the decision at t=2 rather than t=1. We will conduct this regression in the “Probabilistic commitment device × Baseline” sample, which would allow us to detect a 0.1402 s.d. difference, which is 3.46% of the pilot value of the mean outcome variable. Hypothesis 2b: Parents exhibit Within-household Present-Bias. We are testing whether the gap between the planned t=2 and t=3 allocations is larger when parents make the decision at t=2 than t=1. We will conduct this regression in the “Probabilistic commitment device × Baseline” sample, which would allow us to detect a 0.1402 s.d. difference between both gaps (90% of the mean outcome variable, as measured in the pilot). 3. Is there demand for commitment devices to help mitigate parent-bias, above and beyond demand for commitment devices that help mitigate present-bias? Hypothesis 3a: Parents demand commitment devices to help them stick to their within-household allocation plans. 2,000 respondents in our sample are offered a probabilistic commitment device to help them stick to their planned within-household allocation. They are offered this probabilistic commitment device at 3 different prices: 0.5/1/1.5 packets of peanuts. We will plot the demand curve for this commitment device, at different level of prices. Hypothesis 3b: The demand for commitment devices to help parents stick to their within-household allocation plans is smaller than the demand for commitment devices to help them stick to their inter-temporal allocations. The respondents are also offered a probabilistic commitment device to help them stick to their inter-temporal allocation. We will pool the observations from all our “Probabilistic commitment devices” subsamples except “Child’s participation (chosen)” to conduct this analysis. This would allow us to detect a 0.0991 s.d. difference between the take-up of both types of commitment devices (2.4% of the mean value of the outcome variable, as measured in the pilot). For consistency with the rest of our analyses, we will also conduct the same regression in the “Probabilistic commitment device×Baseline” sample, which would allow us to detect a 0.1402 s.d. difference (3.4% of the mean outcome variable). 6. Can labeling mitigate time-inconsistencies? Hypothesis 6: Reminding parents of their past choices will decrease time inconsistencies. -Pooling samples from the “Baseline” and “Labeling” treatment arms, we will measure whether labeling help mitigate time-inconsistencies. This sample size enables us to detect a 0.1717 standard deviation decrease in the change of the share of peanuts allocated to be consumed by the child at t=2 following the introduction of labeling (160% of the mean of the outcome variable in the Baseline treatment arm, as measured in the pilot). Distinguishing between the role of labeling and anchoring We will distinguish between the role played by labeling and anchoring, by polling the “Labeling” and “Anchoring” samples. This sample size enables us to detect a 0.1983 standard deviation difference in the distance between the amount of peanuts allocated to the child by the parents at t = 2 and in the allocation presented to them. 7. Can encouraging children to participate in household decisions increase investments in children and mitigate parent-bias? Hypothesis 7a: Making children participate in household decisions increases investments in children We will test this hypothesis by pooling the “Baseline” and “Child’s decision (imposed)” samples. This sample size enables us to detect a 0.1717 standard deviation increase in the share of peanuts allocated to the child following the increase in child{'}s bargaining power (3.55% of the mean outcome variable in the baseline treatment arm, as measured in the pilot). Hypothesis 7b: Making children participate in household decisions decreases reallocation towards parents We will test this hypothesis by pooling the “Control” and “Child’s decision (imposed)” samples. This sample size enables us to detect a 0.1717 standard deviation decrease in the change in the share of peanuts allocated to the child following the increase in child{'}s bargaining power. 8. Heterogeneity analysis: do mothers and fathers discount the future differently? We will look at whether mothers and fathers differ in terms of investments in children on different dimensions: 8a. Do mothers plan to invest more in their children in the future? We will test this hypothesis in our baseline sample. Our sample size allows us to detect a 0.2949 s.d. difference in the share of peanuts mothers and fathers plan to allocate for their child’s t=2 consumption, when making the plan at t=1 (7.70% of the mean outcome variable in the pilot) 8b. Do mothers invest more in the children when the time comes? We will test this hypothesis in our baseline sample. Our sample size allows us to detect a 0.2949 s.d. difference in the share of peanuts mothers and fathers plan to allocate for their child’s t=2 consumption, when making the decisionplan at t=2 (6.1% of the mean outcome variable in the pilot) 8c. Are fathers more time-inconsistent than mothers? We will test this hypothesis in our baseline sample. Our sample size allows us to detect a 0.2949 s.d. difference in the change in the share of peanuts mothers and fathers plan to allocate for their child’s t=2 consumption, when making the decision plan at t=2 and t=1. (316% of the mean outcome variable in the pilot) 8d. Do mothers demand more commitment devices to stick to their within- household allocation plans? We will test this hypothesis in our probabilistic commitment device sample. Our sample size allows us to detect a 0.2084 s.d. difference in the take-up of the probabilistic commitment device between mothers and fathers (4.8% of the mean of the outcome variable, as measured in the pilot). 8e. Do mothers demand to let their children participate in the t = 2 decision more? We will test this hypothesis in our “Child’s commitment (chosen)” subsample. Our sample size allows us to detect a 0.3243 s.d. difference in the willingness to let the child participate between mothers and fathers. (37% of the mean of the outcome variable, as measured in the pilot).
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
University of Zurich Human Subjects Committee of the Faculty of Economics, Business Administration, and Information Technology
IRB Approval Date
2018-10-30
IRB Approval Number
OEC_IRB_2018-044