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Field
Trial Start Date
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Before
September 16, 2024
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After
September 20, 2024
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Field
Last Published
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Before
September 17, 2024 11:51 AM
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After
September 19, 2024 10:18 PM
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Field
Intervention Start Date
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Before
September 20, 2024
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After
September 26, 2024
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Field
Primary Outcomes (End Points)
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Before
The key outcome of interest is the insurance plan recommendation from an insurance producer (or insurance agent). We are interested in mean differences in recommendation quality across consumer and agent characteristics. We measure recommendation quality by looking at the mean number of Medicare Advantage recommendations, plan prices, and the number of incorrect/misleading statements. We will measure recommendation quality against an expert benchmark, consumer choice, and other agent recommendations. We also consider a random choice benchmark. We are interested in the heterogeneity in recommendations and response rates by insurance agent characteristics based on publicly available data and randomly assigned consumer characteristics. We will also use text analysis from recordings to study variations in the language used by caller/consumer and insurance producer characteristics.
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After
The key outcome of interest is the quality of the insurance plan recommendation from an insurance producer (or insurance agent). Our primary outcome evaluates the quality of an agent's recommendation against what an expert would recommend. We are primarily interested in the heterogeneity in recommendations and response rates by insurance agent characteristics based on publicly available data and randomly assigned consumer characteristics. Insurance agent characteristics we focus on include number of appointments, firm affiliation, number of states licensed in, tenure, and advertising presence. We will also use text analysis from recordings to study variations in the language used by caller/consumer and insurance producer characteristics.
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Field
Primary Outcomes (Explanation)
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Before
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After
We measure recommendation quality as whether or not the agent recommends Medicare Advantage and the average plan price of the recommendation.
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Field
Randomization Unit
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Before
Consumers will be randomly assigned to insurance agents (and therefore insurance agent characteristics) by a computer. Variation in consumer characteristics will also be randomly assigned.
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After
Consumers will be randomly assigned to insurance agents (and therefore insurance agent characteristics) by a computer. Variation in consumer characteristics such as age will also be randomly assigned.
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Field
Planned Number of Clusters
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Before
We will collect approximately 400-800 observations. There is a chance we will have fewer than 400 observations if more observations than expected do not meet our screening criteria. We will exclude incomplete observations, off-topic observations, and responses from individuals who are not licensed insurance producers. (Complete responses from individuals who are not licensed can be reported separately.) The maximum number of observations will also depend on take-up, screening, and whether we are able to obtain funding to look at multiple sources of heterogeneity.
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After
We will collect approximately 400-800 observations, depending on our ability to obtain additional funding and when our current sources of research funding run out. We will exclude incomplete observations, off-topic observations, and responses from individuals who are not licensed insurance producers. (Complete responses from individuals who are not licensed can be reported separately.) We anticipate having funding for a minimum of 400 observations, however there is a chance we will have fewer than 400 observations if response rates are lower than expected or if several observations do not meet our screening criteria.
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Field
Planned Number of Observations
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Before
We will collect approximately 400-800 observations. There is a chance we will have fewer than 400 observations if more observations than expected do not meet our screening criteria. We will exclude incomplete observations, off-topic observations, and responses from individuals who are not licensed (complete responses from individuals who are not licensed can be reported separately). The maximum number of observations will also depend on take-up, screening, and whether we are able to obtain funding to look at multiple sources of heterogeneity.
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After
We will collect approximately 400-800 observations, depending on our ability to obtain additional funding and when our current sources of research funding run out. We will exclude incomplete observations, off-topic observations, and responses from individuals who are not licensed (complete responses from individuals who are not licensed can be reported separately). We anticipate having funding for a minimum of 400 observations, however there is a chance we will have fewer than 400 observations if response rates are lower than expected or if several observations do not meet our screening criteria. The maximum number of observations will also depend on take-up, screening, and whether we are able to obtain additional funding.
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Field
Intervention (Hidden)
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Before
We will conduct a correspondence study that randomly assigns caller/client characteristics to a randomly selected group of insurance agents. We will study variation in recommendations across health insurance producers to determine which agents license, demographic, and geographical characteristics are correlated with better or worse recommendations. We plan to focus on producer characteristics from publicly available data.
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After
We will conduct a correspondence study that randomly assigns caller/client characteristics to a randomly selected group of insurance producers (or insurance agents). We will study variation in recommendation quality across insurance agents to determine which agents licensure, demographic, and geographical characteristics are correlated with better or worse recommendations. We plan to focus on insurance agent characteristics from publicly available data.
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Field
Secondary Outcomes (End Points)
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Before
We will look at heterogeneity in recommendation quality and response rates by insurance agent characteristics based on publicly available data (e.g. firm affiliated with, number of states licensed in, number of appointments, tenure, advertising presence, etc.) We will also look at differences in the regulatory and market environment.
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After
We will also look the the number of incorrect/misleading statements an agent makes as a measure of recommendation quality. Our primary outcome looks at the quality of the agent's recommendation against an expert benchmark, but we also use consumer choice, other agent recommendations, and random choice as benchmarks. We will also look at the impact of state-level differences in the regulatory and market environment.
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