Scarcity, Risk and Loss Aversion: An inquiry into the effects of cognitive load

Last registered on November 15, 2021

Pre-Trial

Trial Information

General Information

Title
Scarcity, Risk and Loss Aversion: An inquiry into the effects of cognitive load
RCT ID
AEARCTR-0008252
Initial registration date
November 11, 2021
Last updated
November 15, 2021, 11:34 AM EST

Locations

There are documents in this trial unavailable to the public. Use the button below to request access to this information.

Request Information

Primary Investigator

Affiliation
University of East Anglia

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2021-11-16
End date
2022-04-15
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Understanding the decision-making process is the cornerstone of better policy interventions. Broadly, poverty has focussed on systemic factors, individual factors, environmental pressures or a combination of the three. However, another side of the story remains relatively less understood  – the causal effects of poverty that change the decision-making process itself. A rapidly emerging debate between policymakers brings forth this fourth perspective. Within this framework, the shortfall of resources or poverty changes cognitive systems that ultimately affect the decision. At first glance, low pickup rates of preventative health, medications or high-interest borrowing behaviours of the poor seem actively self–sabotaging. Looking closer, they seem natural fallouts of the easily activated, challenging to suppress, interfering monetary thoughts that shape valuations and associations. Scarcity or the feeling of having less than one needs alters the decision-making process in itself. This sensitivity to 'what matters' changes preferences. Poverty triggered mechanisms make economic decisions more difficult by curtailing cognitive control.
The involuntary load presses the processing into redirecting the slower, deliberative system two towards what needs to be taken care of immediately. At the same time, other preferences get overwhelmingly guided by the faster, affective system—such recalibration in preference construction results in the rational-bias split or tunnelling. Our work is an inquiry into this dichotomy of risk preferences. We use the two-period natural harvest cycle combined with the priming of financial worries to study the decision-attribute dependent loss and risk aversion of farmers in 800 people sample from Bwikhonge in Uganda for gains and losses in a between-within subject design. We explain the process that begins involuntarily with scarcity has psychological implications and bifurcates the two aversions. For choices that resolve the scarcity at hand, we find more expected utility consistent decisions. We hypothesise participants to exhibit lower risk aversion and loss aversion for scarcity relevant decisions. In contrast, the two increase for irrelevant choices.
External Link(s)

Registration Citation

Citation
Pande, Suvarna. 2021. "Scarcity, Risk and Loss Aversion: An inquiry into the effects of cognitive load ." AEA RCT Registry. November 15. https://doi.org/10.1257/rct.8252-1.0
Experimental Details

Interventions

Intervention(s)
The design has three core elements - scarcity treatment, a cognitive load test and the risk-choice game. The experiment begins with identifying the sources of scarcity, measuring its impact on the cognitive systems, risk and loss aversion.
Intervention Start Date
2021-11-18
Intervention End Date
2022-04-15

Primary Outcomes

Primary Outcomes (end points)
We compare the differences in choice switches in the matched groups. The participants choose one of the six from a menu arranged in increasing order of riskiness.
Primary Outcomes (explanation)
For the two decision attributes, choice number 1 in the first would indicate greater risk aversion than number 6 in the latter.

Secondary Outcomes

Secondary Outcomes (end points)
1. Effectiveness of priming - the self-reported impact of shock scarcity.

2. Cognitive load test score - time and accuracy score on a two-question task.
Secondary Outcomes (explanation)
1. Effectiveness of priming - We introduce three real-life scenarios as our priming tool to the randomly chosen half of the sample in each session. The situation read-outs get at the individual features of financial scarcity. A Likert scale accompanies the three questions to self-report the hypothesised impact of shock scarcity.

2. Cognitive load test score - We measure the time and accuracy on a two-question task after the first intervention. The tasks are chosen to keep in mind the limitations of literacy and field constraints. Theoretically, cognitive load works through working memory, attention and inhibitory control impairment. Therefore, to quantify the causal mental impact of scarcity on decision-making systems, all our participants complete a numerical Stroop and a Digit span test.

Experimental Design

Experimental Design
After allocation to gains or losses in both the natural periods, we randomly assign the scarcity priming treatment to half of the participants. This is followed up with a cognitive load test and a within-subject decision attribute treatment.
Experimental Design Details
Not available
Randomization Method
Coupon system
Randomization Unit
Individual-level randomisation for domain and scarcity treatment.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Individual-level clustering at experiment nodes. Total participants across the two periods = 400+400.
Sample size: planned number of observations
Same as cluster
Sample size (or number of clusters) by treatment arms
In all, we have the following treatments - domain (gains, losses), scarcity (natural, primed) and decision attribute (relevant, irrelevant).
The first two are assigned between subjects, while the latter is within. Therefore, once the domain and scarcity treatment level is fixed, all participants face the second treatment.
Sample size by treatment arms -
Period 1 (Natural scarcity) = 400
Gains + Losses = 200 +200
Gains in period 1 = 200
Break up of 200 participant sample in the gains domain in period 1 -
full treatment = (natural x primed x decision attribute) = 100
natural x non-primed x decision attribute treatment = 100
Losses in period 1 = 200
Break up of 200 participant sample in the losses domain in period 1 -
full treatment = (natural x primed x decision attribute) = 100
natural x non-primed x decision attribute treatment = 100

Period 2 (No natural scarcity) = 400
Gains + Losses = 200 +200
Gains in period 2 = 200
Break up of 200 participant sample in the gains domain in period 2 -
no natural scarcity x primed x decision attribute = 100
no natural scarcity x non-primed x decision attribute treatment = 100
Losses in period 2 = 200
Break up of 200 participant sample in the losses domain in period 2 -
no natural scarcity x primed x decision attribute = 100
no natural scarcity x non-primed x decision attribute treatment = 100
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
University of East Anglia
IRB Approval Date
2021-07-31
IRB Approval Number
N/A