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Higher-Order Risk Preferences and Risk-Management Behavior among Italian Winegrowers

Last registered on February 11, 2026

Pre-Trial

Trial Information

General Information

Title
Higher-Order Risk Preferences and Risk-Management Behavior among Italian Winegrowers
RCT ID
AEARCTR-0017606
Initial registration date
January 22, 2026

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
February 09, 2026, 10:56 AM EST

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

Last updated
February 11, 2026, 8:55 AM EST

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

Locations

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Primary Investigator

Affiliation
University of Trento

Other Primary Investigator(s)

PI Affiliation
University of Trento
PI Affiliation
Texas A&M University
PI Affiliation
Agricultural University of Athens

Additional Trial Information

Status
In development
Start date
2026-01-23
End date
2026-04-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study elicits risk and higher-order risk (HOR) preferences of Italian winegrowers. More specifically, we elicit risk aversion, prudence, and temperance, and test their external validity by correlating them to both stated intentions and real-world agricultural risk-management behavior. Previous work suggests a link between HOR and real world behavior (see for example Noussair et al 2014, and Schneider and Sutter 2026). Data are collected through an online survey comprising four components: (i) stated preferences regarding an innovative “green insurance” product that provides economic benefits conditional on the adoption of sustainable practices; (ii) an incentivized experimental elicitation of risk and higher-order risk preferences using 17 lottery choices adapted from Noussair et. al (2014); (iii) subjective beliefs about climate-related production losses affecting grape production; and (iv) a short questionnaire on socio-demographics, farm characteristics, and self-reported risk-management practices; v) at the end of the study, farmers decide whether they want to receive a weather station on loan. Adopting a smart climate technology such as a weather station can be interpreted as a mild preventive measure against climate-related risks and, therefore, as a risk-management strategy. Survey and experimental data will be integrated, where available, with administrative records on observed risk-management behavior, such as insurance participation and insured area and value.The study is implemented using the oTree open-source framework (https://www.otree.org/).

References:
Noussair, C. N., Trautmann, S. T., & van de Kuilen, G. (2014). Higher order risk attitudes, demographics, and financial decisions. The Review of Economic Studies, 81(1), 325–355.
Sutter, Matthias, and Sebastian O. Schneider. "Risk preferences and field behavior: The relevance of higher-order risk preferences." American Economic Review (2026).
External Link(s)

Registration Citation

Citation
Barba, Francesca Romana et al. 2026. "Higher-Order Risk Preferences and Risk-Management Behavior among Italian Winegrowers." AEA RCT Registry. February 11. https://doi.org/10.1257/rct.17606-1.1
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-01-23
Intervention End Date
2026-04-30

Primary Outcomes

Primary Outcomes (end points)
Key independent variables (experimental measures)
- Risk aversion: Number of “safe” choices in decisions 1-5 (range 0-5)
- Prudence: Number of choices classified as prudent in decisions 6-10 (range 0-5)
- Temperance: Number of choices classified as temperate choices in decisions 11-15 (range 0-5)

Primary behavioral outcomes:
Reported behavior:
- Insurance status (current and past 5 years; yes/no)
- Mutual fund participation (current and past 5 years; yes/no)
- Adoption of active defense practices (current; check-list items)
Stated intentions:
- Interest in taking up green insurance (Likert 1-7)
Observed behavior:
- Participation in the weather-station loan draw (yes/no)
- Administrative records that may include data such as insurance participation (yes/no), total insured area and value, premium paid by the farmer, type of insured product, participation to mutual funds, depending on data availability at local consortium level.

Covariates:
Age, gender, education, years of experience in viticulture, farm size (in hectares), and region. Climate-belief measures (Section 3) may be included as controls and/or explored as moderators.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study consists of four sections.
In Section 1, farmers state their preferences for an innovative “green insurance” product and selected attributes. Green insurance is described as an insurance product offering economic benefits conditional on the adoption of selected sustainable practices.
In Section 2, risk aversion, prudence, and temperance are elicited via 17 incentivized binary lottery choices following Noussair et al. (2014). The choices are grouped in four consecutive parts: decisions 1-5 elicit risk aversion, 6-10 elicit prudence, 11-15 elicit temperance, and 16-17 test relative risk aversion and relative prudence under expected utility. Participants are presented with one choice at a time and they always have to choose between two options (no indifference option). The left–right position of lotteries is randomized. Lotteries are presented in compound form and framed as independent coin tosses.
In Section 3, we elicit subjective beliefs about the frequency of extreme climatic events affecting grape production.
In Section 4, participants complete a short questionnaire on socio-demographics, farm characteristics, and self-reported risk-management practices.
In Section 5, participants have the option to enter a draw to receive a weather station, provided free of charge under a two-years loan agreement (for use and return). Adopting this type of smart technology can be viewed as a preventive measure that helps manage climate-related risks and can therefore be considered a risk-management strategy.
Survey data will be integrated, when available, with administrative data on real risk-management behavior (e.g., insurance coverage, insured area, insured value), provided by farmers’ defense consortia.

Incentives: All participants receive a one-year subscription to a professional agricultural magazine as a non-monetary show-up reward. In addition, 10% of participants are randomly selected to receive a monetary payment ranging from €5 to €170 based on their decisions and luck. The payment is determined by one randomly selected lottery choice from Section 2 and the corresponding simulated coin toss outcome. Payments are made via fuel vouchers.
Additionally, at the end of the study (Section 5), participants have the option to enter a draw to receive a weather station on free loan. 35 who enter the draw will be randomly selected to receive the device. Participants are informed about this opportunity only at the end of the study, since the decision to enter the draw is used as an outcome variable in the analysis.
Experimental Design Details
Not available
Randomization Method
Randomization done by the computer software (oTree or R): random selection of the payoff-relevant lottery choice and random selection of participants receiving monetary incentives (oTree) and weather stations (R).
Randomization Unit
Individual participants (winegrowers)
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Not applicable (individual-level randomization)
Sample size: planned number of observations
150 individual winegrowers
Sample size (or number of clusters) by treatment arms
Not applicable (no treatment arms; correlational analysis with experimental measures)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Internal Review Board - Department of Economics and Management, University of Trento
IRB Approval Date
2025-09-29
IRB Approval Number
DEM-IRB-2025/09
Analysis Plan

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