Demand for Income Risk Mitigation

Last registered on December 20, 2023
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Pre-Trial

Trial Information

General Information

Title
Demand for Income Risk Mitigation
RCT ID
AEARCTR-0012678
Initial registration date
December 11, 2023

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
December 20, 2023, 9:45 AM EST

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

Locations

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Primary Investigator

Name
Erina Ytsma
Affiliation
Carnegie Mellon University
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Other Primary Investigator(s)

PI Name
Geoffrey Parker
PI Affiliation
Dartmouth College
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Additional Trial Information

Status
In development
Start date
2023-12-11
End date
2024-12-31
Keywords
Behavior, Finance & Microfinance, Labor, Welfare
Additional Keywords
Income risk, Pay protection, Unemployment Insurance, Contract work
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Independent contract work (including gig work) has increased in the 2000s (e.g. Collins et al. (2019); Katz and Krueger (2019a, 2019b); Abraham et al. (2020)). Such work may enable people to self-insure against income or expenditure shocks, but short-term contracts or gigs are also risky. For instance, there is uncertainty whether a suitable job is available when needed, and risks of (last-minute) job cancellations or hours being cut. Moreover, independent contractors are often excluded from unemployment insurance (UI), (employer-provided) health insurance and retirement programs, as well as occupational health and safety regulations, wage and hour laws. Thus, as regular employment contracts decrease, firms are insuring agents against risk less. This begs the question whether there is demand for (supplemental) risk mitigation, specifically of income risk.

To answer this question we employ an RCT on a nationwide online labor market platform to estimate the demand for insurance against various types of income risk. Workers using the platform will be presented with a series of discrete choices between a job contract that offers less insurance and one that offers more insurance against a specific type of income risk. Workers are randomized into different insurance groups, each of which is presented with a different insurance option against a specific income risk. Workers are also randomized into different information treatment groups and different alternative insurance options groups. Worker choices will allow us to estimate the marginal rate of substitution (MRS) and willingness to pay (MRS) for income risk mitigation, across risk types, insurance levels and pay levels, the sensitivity of the MRS and WTP to changes in beliefs, and workers’ willingness to trade-off flexibility for insurance.
External Link(s)

Registration Citation

Citation
Parker, Geoffrey and Erina Ytsma. 2023. "Demand for Income Risk Mitigation." AEA RCT Registry. December 20. https://doi.org/10.1257/rct.12678-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2023-12-11
Intervention End Date
2023-12-18

Primary Outcomes

Primary Outcomes (end points)
Our primary outcome of interest is workers' demand for income risk mitigation. We measure this as a worker's marginal rate of substitution (MRS) and willingness to pay (WTP) for income risk mitigation. These are derived from workers’ choices in the series of binary choices between job contracts with more and less insurance.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
We study eight secondary outcomes:
1-3. Workers' relative preference (ranking) over income risk insurance, a flexible replacement job option, and an inflexible replacement job option (3 ranks, one for each option).
4. Workers' beliefs of the probability that they will experience a negative income shock.
5. Workers' beliefs of the probability that they will be able to find a job at short notice.
6. Workers' marginal propensity to consume (MPC) out of expected, positive income.
7. Workers’ MPC in response to an unexpected, negative income shock.
8. The odds ratio of the two MPC measures.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Workers are recruited through the platform's app, with a notification. The notification contains a link to an external survey, in which respondents are asked to make a series of binary choices between a job contract that offers more and less insurance. The premium and pay-out for the contract that offers more insurance varies between binary choices. The survey is ex ante incentive compatible because the platform is considering introducing one or more of the proposed insurance offerings, and workers' choices may be implemented.

Respondents are randomized into 6 insurance groups, which differ in the kind of income risk they insure, the level of insurance and/or the pay level. Respondents are also randomized into three information treatment groups: a group that receives information about income risk; a group that receives information about last-minute job finding probabilities on the platform; and a group that receives no information. Respondents are asked about their beliefs about the probability that they would experience a negative income shock, as well as their beliefs about the probability that they would be able to find a job at short notice. These beliefs, as well as the aforementioned insurance preference binary choices, are elicited both before and after the information treatment.

Respondents are also asked for their preferences for an income insurance contract, relative to a flexible replacement job option and an inflexible replacement job option. For this elicitation, respondents are assigned to four flexibility-security groups (2 x 2), that differ in the insurance premium for the insurance contract (high or low) and the time frame for the flexible replacement job option (short or long).
Respondents are cross-randomized across all randomizations (insurance group, info treatment, flexibility-security group). We employ stratified randomization, with eligible workers split into 16 mutually exclusive strata based on their sign-up date and experience on the platform, as well as the size of the market in which they are located.
Experimental Design Details
Not available
Randomization Method
Randomization will be done online, in Qualtrics. We employ stratified randomization, distinguishing 16 strata.
Randomization Unit
Randomization is at the level of the individual worker.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Roughly all workers who accepted at least one job on the platform in the past 6 months will be invited to participate. This is a pool of around 23500 workers. Based on past response rates on the platform, we expect 900 - 3,500 workers to respond.
Sample size: planned number of observations
Same as cluster, since design is not clustered.
Sample size (or number of clusters) by treatment arms
All randomizations will be done evenly, so 1/6th of the sample will be assigned to each insurance group; 1/3rd to each information treatment group; and 1/4th to each flexibility-security group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Carnegie Mellon University Institutional Review Board (CMU IRB)
IRB Approval Date
2023-11-30
IRB Approval Number
MOD202300000758
Analysis Plan

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