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

Last registered on June 29, 2022

View Trial History

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

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

November 15, 2021, 11:34 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated

June 29, 2022, 3:05 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

Uganda

Region

Africa

Primary Investigator

Name

Suvarna Pande

Affiliation

University of East Anglia

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

In development

Start date

2021-11-16

End date

2022-07-31

Keywords

Behavior, Lab

Additional Keywords

Scarcity, prospect theory, risk, loss aversion cognition, decision-making, dual thinking models, choice architecture, rational inattention, salience, split

JEL code(s)

D91, C93, D81, D87

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. 2022. "Scarcity, Risk and Loss Aversion: An inquiry into the effects of cognitive load ." AEA RCT Registry. June 29. https://doi.org/10.1257/rct.8252

Sponsors & Partners

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

2022-05-15

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

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

Scarcity treatment - any felt scarcity can be natural, unanticipated or a combination of both. Geo-sensing data from the region shows bimodal harvest seasons. We use this knowledge of agricultural cycles as a starting point. Our prospective subjects have lived through the annual patterns year after year and have experience making decisions under these conditions. This distinct lean-plenty phase gives us a naturally occurring base for anticipated and expected scarcity. Using this annual harvest cycle for the region, we add the priming treatment to account for the unpredictability. For each of the lean-harvest base levels, we introduce real-life scenario readouts to make shock salient. The self-reported Likert scale questions differ in triggering thoughts about financial scarcity in the treatment and control groups. Each precludes one of the four characteristics of scarcity as a force (easily triggered, spontaneous, persistent and value realisation). The readouts make the unanticipated scarcity salient within each of these periods. The control group gets innocuous three scenario readouts with a similar word count. Therefore, we get a 2*2 between-subjects frame for both domains.

Next, the two-question clt has a measure for working memory (Digit span test) and inhibitory control (Stroop task). Finally, to measure the risk and loss aversion, we use a variation of the Eckel Grossman (eg) game. A typical eg design requires participants to choose one out of six pairs arranged in ascending order of riskiness. The payoffs in each of the six prospects are equally likely. The two six-prospect lists differ only in one aspect – decision attribute. That is, the subjects in each state face identical eg list that differ in payoff-use case – for the same period (Decision Card Y) and six months hence (Decision card Z). This second intervention gives the within-subject measure of tunnelling. The comparisons indicate the degree of realignment, thus testing the primary hypotheses for the two domains. Thus, each choice is a function of the expected, unexpected scarcity and relevance/irrelevance status.

Overall, the design has two cross-cutting treatment arms - Scarcity and Decision Attribute. The first varies the nature of scarcity for treatment and control. Once the 2*2 (Natural*Scarcity treatment) states are fixed for all participants, the treatment of decision relevance is introduced, making it a 2*2*2 design. The experiment is a factorial combination of between and within-subject design and measures the mechanism behind the causality – the clt.

Randomization Method

Coupon system

Randomization Unit

Individual-level randomisation for domain and scarcity treatment.

Was the treatment clustered?

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)

Supporting Documents and Materials

IRB