Resilience to climate change: diversification strategies and correlation neglect

Last registered on March 22, 2024


Trial Information

General Information

Resilience to climate change: diversification strategies and correlation neglect
Initial registration date
March 12, 2024

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
March 19, 2024, 4:54 PM EDT

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

Last updated
March 22, 2024, 9:32 AM EDT

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


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Primary Investigator

University of Göttingen

Other Primary Investigator(s)

PI Affiliation
University of Göttingen
PI Affiliation
Universidad del Rosário

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
As the frequency and severity of extreme weather events increase, smallholder farmers face growing vulnerability. Crop diversification can act as insurance against climate variability, but the presence of correlation neglect in farmers' decision making could lead to suboptimal risk management. Our study examines the relationship between income diversification and correlation neglect among smallholder farmers. Firstly, we assess the extent of correlation neglect and its effects on investments and land use decisions. Next, we examine methods for reducing correlation neglect by comparing different ways of presenting historical information on the profitability of two crops. We propose that integrating information, such as presenting the returns of both crops together, may facilitate the comparison of investment options and reduce cognitive limitations that contribute to correlation neglect. To test this hypothesis, we assess investment decisions based on different information-providing treatments in a framed field experiment with farmers in rural Brazil. Moreover, we investigate whether the treatments improve the accuracy of beliefs about the returns of the two crops. We contribute to the empirical literature on correlation neglect by applying the framework to the specific context of agricultural decision-making in a developing country and by investigating a potential mechanism to reduce this bias.
External Link(s)

Registration Citation

Duarte Lisboa Paschoaleto, Rafael, Marcela Ibanez and Ferley Rincón. 2024. "Resilience to climate change: diversification strategies and correlation neglect." AEA RCT Registry. March 22.
Experimental Details


The intervention is situated in Assis, Brazil, a region heavily reliant on agricultural activities. Our study employs a well-structured lab in the field experiment framed with real economic incentives, complemented by survey data. Participants will engage with a questionnaire in loco, individually, electronically, and with an anticipated completion time ranging from 90 to 120 minutes.
Our primary objective is to examine the presence of correlation neglect in the participants decisions, gauging not only its presence, but also the extent to which it manifests. In particular, we aim at understanding how correlation neglect relates with both the degree of correlation across decision problems and the individual degree of risk aversion among participants.
Next, we intend to evaluate the impact of integrated information provision on mitigating correlation neglect and/or fostering increased crop diversification. To discern such effects, participants will be randomly assigned to either the integrated or segregated information treatments. While the informational content remains consistent across both groups, the representation of assets’ returns differs—presented either jointly in a single graph or distinctly in two separate graphs.
As a secondary objective, we investigate whether the participants’ beliefs about the distribution of returns is more accurate when the information is provided jointly. For this reason, we include belief elicitation questions following the allocation decision problems.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The primary outcomes are the measures of correlation neglect. We use two different measures; (i) the share allocated to the dominated asset, and (ii) the distance between the theoretical optimal and the actual allocation to the dominated asset.
Primary Outcomes (explanation)
The two measures of correlation neglect indicate the extent to which individuals deviate from a diversified portfolio selection. The first measure is the share allocated to the dominated asset. When faced with a decision, participants will be presented with two investment options distinguished solely by their expected returns. The one with the lower expected return is considered dominated and should only be chosen if the individual seeks to increase diversification. This is a common measure of correlation neglect in the empirical literature (Laudenbach et al. 2021, Kalil and Sonsino 2009). The second measure is the distance between the theoretical optimal and the actual allocation to the dominated asset. This is an innovation proposed by our study, where the optimal allocation is calculated based on the degree of correlation between the assets and the participants’ individual degree of risk aversion (Markowitz 1952, Sandmo 1969).

Secondary Outcomes

Secondary Outcomes (end points)
The secondary outcome is a measure of belief about the returns of the investment options. We use an instrument that comprises three different belief elicitation questions.
Secondary Outcomes (explanation)
The belief measure will be constructed using the obviously related instrumental variable (ORIV) approach. This method consolidates different questions and formulations to provide an estimator that is more efficient, as outlined by Gillen, Snowberg, and Yariv (2019). For this reason, we ask three types of questions concerning the perceived returns of the assets: qualitative, quantitative, and probabilistic, in order to ensure that the instrument is less prone to measurement errors (Haaland et al. 2023).

Experimental Design

Experimental Design
We use a between-within-subjects experimental design, wherein participants answer a questionnaire delivered in loco via tablets. Each participant is tasked with making a series of investment decisions involving varying degrees of correlation among asset returns, i.e., negative correlation, no correlation and positive correlation. In addition, participants are randomly assigned to one of two treatments:
• Integrated Information: assets returns' distribution data is presented jointly.
• Segregated Information: assets returns' distribution data is presented separately.
Randomization will be made at the individual level upon start of the questionnaire and it is computer-based, i.e., calculated by the experiment programming tool oTree (Holzmeister, 2017). The experiment has six parts.
Experimental Design Details
Not available
Randomization Method
Randomization will be made at the individual level at the beginning of the questionnaire. The randomization is computer-based and will be calculated using the experiment programming tool, oTree.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
200 farmers from the region of Assis, Brazil.
Sample size (or number of clusters) by treatment arms
100 farmers will be assigned to the integrated treatment, and 100 to the segregated treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Based on previous empirical results, we assume as reasonable values for the standard deviation of the main outcome variable 0.25. Considering 80% power, a 5% significance level in a two-tailed test scenario, and at least 100 participants allocated to each treatment independently, the smallest detectable effect size is estimated to be 0.0995, around 0.4 standard deviation of the outcome variable. Such an estimate is conservative, as it does not account for potential reduction in variance resulted with the inclusion of control variables, nor the expected variance reduction in one of the treatment groups.

Institutional Review Boards (IRBs)

IRB Name
Ethics Committee at the Universidad del Rosario, Bogotá, Colombia
IRB Approval Date
IRB Approval Number