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Whose brighter future? Parent-bias and investments in children.
Last registered on March 27, 2019

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
March 27, 2019 6:14 AM EDT
Location(s)
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
2019-09-30
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. 2019. "Whose brighter future? Parent-bias and investments in children.." AEA RCT Registry. March 27. https://doi.org/10.1257/rct.3535-2.0.
Former Citation
Lichand, Guilherme, Juliette Thibaud and Juliette Thibaud. 2019. "Whose brighter future? Parent-bias and investments in children.." AEA RCT Registry. March 27. http://www.socialscienceregistry.org/trials/3535/history/44219.
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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
This experiment will be conducted in 80 villages of Salima district in Malawi, with 2,400 participants. As this experiment will be conducted alongside another project’s baseline survey, this sample size was based on the power calculations for our other project. Within each village, our sample is built using a random walk approach: the enumerators assess the eligibility of every 5th or 4th house they encounter in the village while following a pre-determined path. The households are considered eligible to participate in the experiment if: 1) There is at least one child aged 3-12 in the household, 2) Both parents live in the household, 3) Nobody is allergic to peanuts in the household. The second criteria was added to guarantee that households in which we interrogate fathers are not on average different from households in which we interrogate mothers. Only mothers are invited to take part in the experiment in 64 villages. In the remaining 16 villages, we randomly select whether the mother or the father, within the eligible household, will be invited to participate in the experiment. In those villages, we over-sample fathers to ensure that we will have a large enough number of fathers in our experiment. We aim to have 360 fathers in our sample. If the household has more than one child aged 3-12, we will randomly select which child will be invited to take part in the experiment. The subjects are randomized across treatment arms in two steps: • They are first allocated to be offered different commitment devices: a “Probabilistic commitment” or “Child’s participation (chosen)”. •Subjects allocated to being offered a probabilistic commitment device are then allocated to different framings of choice at t = 2: "Baseline", "Labeling", "Random", "Child's participation (imposed)". A. Commitment devices Probabilistic commitment devices We offer the respondents in those treatment arms a probabilistic commitment device (following Augenblick, Niederle and Sprenger (2015)), which decreases the likelihood that the t = 2 allocation is chosen over the t = 1 allocation. In other treatment arms or if the respondents do not wish to take up a commitment device, the t = 1 decision will be executed with a 10% probablity. If the respondents take up a commitment device, the probability that the t = 1 decision will be executed increases to 90%. This allows us to observe both t = 1 and t = 2 decisions for all respondents, irrespective of commitment and guarantees the credibility of both decisions because the respondents are aware in each round that their decision can be selected to be executed. The respondents are offered to take up a probabilistic commitment device after making a decision for each scenario during the first visit. We randomly vary the price of the commitment device: to purchase the commitment device, the respondent will have to forego 0.5/1/1.5 packets of peanuts at t = 3. Child’s participation (chosen) We ask respondents in this subsample whether they would like to invite their child to make the t = 2 decision for part Red with them. This could be a way for t = 1 parents to force their t = 2 self to stick to the plan they had made for their child. We randomly vary the price of this commitment device: to purchase the commitment device, the respondent will have to forego 0/0.5/1/1.5 packets of peanuts at t = 3. B. Framing of choices Baseline The enumerators speak to the respondents alone during the second visit. The respondents are invited to taste a small quantity of peanuts at the beginning of the interview, explained the rules of the experiment one more time and asked how they would like to act in each scenario. Labeling treatment The enumerators speak to the respondents alone during the second visit. The respondents are invited to taste a small quantity of peanuts at the beginning of the interview, explained the rules of the experiment one more time, presented with the allocation choice they have made in scenario Red at t = 1 and asked how they would like to act in each scenario. Random Anchoring treatment The enumerators speak to the respondents alone during the second visit. The respondents are invited to taste a small quantity of peanuts at the beginning of the interview, explained the rules of the experiment one more time, presented with a random allocation in scenario Red and asked how they would like to act in each scenario. This treatment arm enables us to distinguish between the effect of labeling itself and of anchoring. Child’s participation (imposed) During the second visit, the children are asked to participate in part Red decision in this treatment arm. The respondents and the children are invited to taste a small quantity of peanuts at the beginning of the interview, explained the rule of the experiment. The respondent makes a decision on part Blue alone and on part Red jointly with the child. This treatment arm enables us to measure the impact of an increase in the child’s bargaining power on parent-bias, without the self-selection inherent to parents having chosen to involve their child as a commitment device. C. Survey instruments Naive and sophisticates Understanding how sophisticated individuals are with regards to their future behavior is key to interpreting the demand for commitment devices. However, incentivizing questions eliciting beliefs about one’s own future behavior can lead to changes in this future behavior (Acland and Levy, 2015) or can encourage individuals to use predictions about their own behavior as a commitment device (Augenblick and Rabin, Forthcoming). To circumvent this problem, we adopt a strategy following closely that of Toussaert (2018). After making a choice for each scenario, respondents are asked an incentivized question eliciting their beliefs about others’ behavior: • Scenario Blue: We are asking many other households to make those decisions. Do you think that two days from now most other people will... – Choose to receive MORE peanuts immediately than they did today? – Choose to receive LESS peanuts immediately than they did today? – Choose to receive the same amount of peanuts immediately as they did today? • Scenario Red: We are asking many other households to make those decisions. Do you think that two days from now most other people will.. – Choose to give LESS peanuts to the child than they did today? – Choose to give MORE peanuts to the child than they did today? – Choose to give the same amount of peanuts to the child as they did today? Correctly predicting the behavior of the majority of the population will earn the respondents one additional packet of peanuts at the end of round three. Those questions are motivated by research that shows that people use information about their own behavior to inform their beliefs about the behavior of others.
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
Analysis Plan

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Is the intervention completed?
No
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Data Publication
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Is public data available?
No
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