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Cognitive droughts
Last registered on July 03, 2017

Pre-Trial

Trial Information
General Information
Title
Cognitive droughts
RCT ID
AEARCTR-0000683
Initial registration date
April 05, 2015
Last updated
July 03, 2017 11:23 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
University of Zurich
Other Primary Investigator(s)
PI Affiliation
University of Warwick
Additional Trial Information
Status
Completed
Start date
2015-03-02
End date
2015-06-30
Secondary IDs
Abstract
This paper tests whether uncertainty about future rainfall affects farmers’ decision-making through cognitive load. Behavioral theories predict that rainfall risk could impose a psychological tax on farmers, leading to material consequences at all times and across all states of nature, even within decisions unrelated to consumption smoothing, and even when negative rainfall shocks do not materialize down the line. Using a novel technology to run lab experiments in the field, we combine survey experiments with recent rainfall shocks to test the effects of rainfall risk on farmers’ cognition to test whether it decreases farmers’ attention, memory and impulse control, and increases their susceptibility to a variety of behavioral biases. Testing whether farmer’s cognitive performance is relatively less impaired in tasks involving scarce resources can provide evidence of whether the effects operate through the mental bandwidth mechanism. Last, exploiting random variation in the monthly payday of Bolsa Família (Brazil's flagship conditional cash transfer program), we will decompose the cognitive effects of uncertainty into those of risk and anticipation.
External Link(s)
Registration Citation
Citation
Lichand, Guilherme and Anandi Mani. 2017. "Cognitive droughts." AEA RCT Registry. July 03. https://doi.org/10.1257/rct.683-3.0.
Former Citation
Lichand, Guilherme, Guilherme Lichand and Anandi Mani. 2017. "Cognitive droughts." AEA RCT Registry. July 03. http://www.socialscienceregistry.org/trials/683/history/19141.
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Experimental Details
Interventions
Intervention(s)
Survey experiments that randomize drought-related messages aimed at priming farmers about future rainfall risk.

We also exploit natural experiments, linked to random variation in rainfall levels with respect to local historical trends, and randomness in paydays of Bolsa Família - since it is based on the last digit of CPF, Brazil's social security number.
Intervention Start Date
2015-03-09
Intervention End Date
2015-06-20
Primary Outcomes
Primary Outcomes (end points)
We document the effects of the treatments on worries about rainfall and 3 groups of outcomes:

(1) Cognitive performance in tasks aimed at assessing working memory, attention and impulse control (executive functions) and sensitivity to anchoring;

(2) Focus, comprising tasks involving scarse resources;

(3) Economic decisions, such as willingness to attend to credit and insurance offers.
Primary Outcomes (explanation)
We have several outcome variables for each of the 3 types of outcomes:

(1) Executive functions are measured through digit span tests in which subjects must remember as many digits from a given sequence of numbers and through stroop tests, in which subjects must answer the number of times they hear a particular digit repeated in a sequence, controlling the impulse of pressing the digit itself. Anchoring is measured by high price bands choices after being primed with a high number for some other product;

(2) Tunneling, through the relative valuation of the scarce resources in simple trade-offs relative to the valuation of a non-scarce resource in the same trade-off and through word search games, in which subjects must correctly identify whether or not they heard specific words in a sequence of words narrated with audio distortion. Sensitivity to framing, defined as as disagreement between subject’s decisions to go to a different location to buy or get resources, in each case, varying the baseline value/quantity from low to high;

(3) Potential demand for insurance and/or credit, measured by subjects' willingness to listen to information about offers of these products (an indicator variable, and an intensity variable that expresses how long subjects were willing to listen to the recorded information).
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Half of our subjects will be randomly assigned to a drought-related message at the begging of each automated voice call (interactive voice response unit, IVR), while the others will listen to neutral message. We randomize at the individual level at each call and wave (for a total of 24 experiments, since we have 6 call per wave, and 4 monthly waves, between March and June).

We expect the drought-related message to prime farmers about water scarcity, making water top-of-mind. The effects of the treatment on outcomes will be benchmarked against the effects of real rainfall shocks, and compared to the effects of distance to payday of Bolsa Família.
Experimental Design Details
Randomization Method
Randomization done in office by a computer
Randomization Unit
Individual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
2,000 to 5,000 farmers
Sample size: planned number of observations
8,000 to 20,000 (2,000 to 5,000 farmers over 4 waves)
Sample size (or number of clusters) by treatment arms
1,000 farmers in the treatment group, and up to 4,000 farmers in the control group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Power Calculations Power calculations for some outcomes of interest are based on the results of the June wave of the 2014 pilot. Calculations of minimum detectable effects assume 10%, 5% or 1% statistical significance, 80% statistical power, a sample size of 5,000 farmers, 50% of non-response rate (also based on the average for the 2014 pilot) and 50% random assignment to priming about rainfall (a crucial element of the experiments). Given these parameters, minimum detectable effects (in absolute value) are given by: |MDE|=(t_(1-κ)+t_alpha ) √(σ^2/p(1-p) x 4/NxW) , Where t_(1-κ) is the t-statistic associated with confidence level 1-κ, t_α is the t-statistic associated with power α, σ is the standard deviation of the effect, p is the proportion of the 5, observations in the treatment group, N is the number of observations, and W is the number of waves. The term 4/NxW incorporates the 50% non-response rate and the 50% random assignment to priming about rainfall, which divide the effective number of observations by a factor of 4. Tables 1 to 3 present computations for the outcomes that most resemble the ones we would like to include the 2015 design, under 10%, 5% and 1% significance levels. Table 4 presents the details of the computations for one of the outcomes as an example. Table 1 – Minimum detectable effects (absolute value) under 10% significance (A) Two waves Minimum Detectable Effect (absolute value) June coefficient June std. dev. Average T = 1250 T = 1125 T = 1000 T = 875 T = 750 T = 625 T = 500 T = 375 T = 250 Cognitive tasks Digit span 0.13 0.09 0.24 0.035 0.036 0.038 0.040 0.042 0.046 0.050 0.057 0.069 Stroop 0.01 0.08 0.19 0.032 0.033 0.034 0.036 0.038 0.041 0.046 0.052 0.063 Garbled words 0.06 0.13 0.18 0.041 0.043 0.045 0.047 0.050 0.054 0.060 0.068 0.082 Price recall Beans (Week 1) -0.11 0.11 0.31 0.038 0.039 0.041 0.043 0.046 0.049 0.054 0.062 0.075 Corn (Week 1) -0.08 0.11 0.29 0.039 0.040 0.042 0.044 0.047 0.050 0.056 0.063 0.077 Goat (Week 1) 0.02 0.08 0.16 0.033 0.034 0.035 0.037 0.040 0.043 0.047 0.054 0.065 (B) Five waves Minimum Detectable Effect (absolute value) June coefficient June std. dev. Average T = 1250 T = 1125 T = 1000 T = 875 T = 750 T = 625 T = 500 T = 375 T = 250 Cognitive tasks Digit span 0.13 0.09 0.24 0.022 0.023 0.024 0.025 0.027 0.029 0.032 0.036 0.044 Stroop 0.01 0.08 0.19 0.020 0.021 0.022 0.023 0.024 0.026 0.029 0.033 0.040 Garbled words 0.06 0.13 0.18 0.026 0.027 0.028 0.030 0.032 0.034 0.038 0.043 0.052 Price recall Beans (Week 1) -0.11 0.11 0.31 0.024 0.025 0.026 0.027 0.029 0.031 0.034 0.039 0.047 Corn (Week 1) -0.08 0.11 0.29 0.024 0.025 0.026 0.028 0.030 0.032 0.035 0.040 0.048 Goat (Week 1) 0.02 0.08 0.16 0.021 0.021 0.022 0.024 0.025 0.027 0.030 0.034 0.041 Table 2 – Minimum detectable effects (absolute value) under 5% significance (A) Two waves Minimum Detectable Effect (absolute value) June coefficient June std. dev. Average T = 1250 T = 1125 T = 1000 T = 875 T = 750 T = 625 T = 500 T = 375 T = 250 Cognitive tasks Digit span 0.13 0.09 0.24 0.039 0.041 0.043 0.045 0.048 0.052 0.057 0.065 0.078 Stroop 0.01 0.08 0.19 0.036 0.037 0.039 0.041 0.043 0.047 0.051 0.059 0.071 Garbled words 0.06 0.13 0.18 0.047 0.048 0.050 0.053 0.057 0.061 0.067 0.077 0.093 Price recall Beans (Week 1) -0.11 0.11 0.31 0.043 0.044 0.046 0.048 0.052 0.056 0.061 0.070 0.084 Corn (Week 1) -0.08 0.11 0.29 0.043 0.045 0.047 0.050 0.053 0.057 0.063 0.071 0.086 Goat (Week 1) 0.02 0.08 0.16 0.037 0.038 0.040 0.042 0.045 0.048 0.053 0.061 0.073 (B) Five waves Minimum Detectable Effect (absolute value) June coefficient June std. dev. Average T = 1250 T = 1125 T = 1000 T = 875 T = 750 T = 625 T = 500 T = 375 T = 250 Cognitive tasks Digit span 0.13 0.09 0.24 0.025 0.026 0.027 0.028 0.030 0.033 0.036 0.041 0.049 Stroop 0.01 0.08 0.19 0.023 0.023 0.024 0.026 0.027 0.030 0.033 0.037 0.045 Garbled words 0.06 0.13 0.18 0.029 0.031 0.032 0.034 0.036 0.039 0.043 0.048 0.059 Price recall Beans (Week 1) -0.11 0.11 0.31 0.027 0.028 0.029 0.031 0.033 0.035 0.039 0.044 0.053 Corn (Week 1) -0.08 0.11 0.29 0.027 0.029 0.030 0.031 0.033 0.036 0.040 0.045 0.055 Goat (Week 1) 0.02 0.08 0.16 0.023 0.024 0.025 0.027 0.028 0.030 0.034 0.038 0.046 Table 3 – Minimum detectable effects (absolute value) under 1% significance (A) Two waves Minimum Detectable Effect (absolute value) June coefficient June std. dev. Average T = 1250 T = 1125 T = 1000 T = 875 T = 750 T = 625 T = 500 T = 375 T = 250 Cognitive tasks Digit span 0.13 0.09 0.24 0.048 0.050 0.052 0.055 0.058 0.063 0.069 0.079 0.096 Stroop 0.01 0.08 0.19 0.044 0.045 0.047 0.050 0.053 0.057 0.063 0.072 0.086 Garbled words 0.06 0.13 0.18 0.057 0.059 0.062 0.065 0.069 0.075 0.082 0.094 0.113 Price recall Beans (Week 1) -0.11 0.11 0.31 0.052 0.054 0.056 0.059 0.063 0.068 0.075 0.085 0.103 Corn (Week 1) -0.08 0.11 0.29 0.053 0.055 0.057 0.061 0.064 0.070 0.077 0.087 0.105 Goat (Week 1) 0.02 0.08 0.16 0.045 0.047 0.049 0.051 0.055 0.059 0.065 0.074 0.089 (B) Five waves Minimum Detectable Effect (absolute value) June coefficient June std. dev. Average T = 1250 T = 1125 T = 1000 T = 875 T = 750 T = 625 T = 500 T = 375 T = 250 Cognitive tasks Digit span 0.13 0.09 0.24 0.030 0.032 0.033 0.035 0.037 0.040 0.044 0.050 0.060 Stroop 0.01 0.08 0.19 0.028 0.029 0.030 0.031 0.033 0.036 0.040 0.045 0.055 Garbled words 0.06 0.13 0.18 0.036 0.037 0.039 0.041 0.044 0.047 0.052 0.059 0.072 Price recall Beans (Week 1) -0.11 0.11 0.31 0.033 0.034 0.036 0.037 0.040 0.043 0.047 0.054 0.065 Corn (Week 1) -0.08 0.11 0.29 0.034 0.035 0.036 0.038 0.041 0.044 0.048 0.055 0.067 Goat (Week 1) 0.02 0.08 0.16 0.028 0.030 0.031 0.032 0.035 0.037 0.041 0.047 0.057 Table 4 – Example for Digit Span (Week 2) under 1% significance Control group (Uninsured) Insured Insured Insured Insured Insured Insured Insured Insured Insured 5000 - Insured 1250 1125 1000 875 750 625 500 375 250 Waves 2 2 2 2 2 2 2 2 2 t_(1-κ) 2.58 2.58 2.58 2.58 2.58 2.58 2.58 2.58 2.58 t_α 0.84 0.84 0.84 0.84 0.84 0.84 0.84 0.84 0.84 p 0.25 0.225 0.2 0.175 0.15 0.125 0.1 0.075 0.05 σ 0.0926 0.0926 0.0926 0.0926 0.0926 0.0926 0.0926 0.0926 0.0926 MDE 0.048 0.050 0.052 0.055 0.058 0.063 0.069 0.079 0.096 Under 10% and 5% significance, both the two- and five-wave designs would be able to detect the effects for Digit span, Garbled words, and price recall of beans and corn. The five-wave design would be very close to detecting the effect of the treatment on recall of goat average prices. Under 1% significance, the two-wave design would no longer be able to detect the effect of the treatment on the score of Garbled words, while the five-wave design would still be able to detect it. None of the two designs would be able to detect the effect of the treatment on Stroop scores in week 1 or on recall of goat prices in week 2. Having said that, the estimated effects for the June wave were very small, so it might as well be that such effects are not statistically significant after the planting stage, when uncertainty about rainfall has been resolved. As an illustration, we estimated a four-fold effect of the treatment on stoop scores in the March wave. It follows that the number of waves is binding in this design in what comes to detecting the effects of interest, particularly with respect to Garbled words, the one task representing the “focus dividend” of tunneling in our 2014 pilot.
Supporting Documents and Materials

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IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Harvard Human Research Protection Program
IRB Approval Date
2015-02-17
IRB Approval Number
14-0448-01
Analysis Plan

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Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
Yes
Intervention Completion Date
June 30, 2015, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
June 30, 2015, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
Was attrition correlated with treatment status?
No
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?
No
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)
REPORTS & OTHER MATERIALS