|
Field
Last Published
|
Before
October 09, 2025 04:47 PM
|
After
March 01, 2026 12:36 PM
|
|
Field
Primary Outcomes (End Points)
|
Before
The key outcome of interest is the quality of the Medicare plan recommendations from the insurance producer (or insurance agent). We will examine three primary outcomes: i) whether the agent recommends Medicare Advantage (MA), ii) the difference between the recommended Medicare Supplement (Medigap) plan premium and the lowest Medigap premium available to the consumer, and iii) the difference between the recommended Medigap premium and the lowest Medigap premium offered by insurance companies the agent is currently appointed with. More specifics on this are included in our pre-analysis plan.
|
After
The key outcome of interest is the quality of the Medicare plan recommendations from the insurance producer (or insurance broker or agent). We will examine two main outcomes: i) whether or not the agent recommends Medicare Advantage (MA), ii) the difference between the recommended Medicare Supplement (Medigap) plan premium and the lowest-cost Medigap plan premium available to the consumer. For simplicity, we also create a "bad recommendations" index combining these two outcomes into a single metric. We also measure the Medigap outcome two additional ways: iii) the Medigap Agent Markup (the difference between the recommended Medigap premium and the lowest Medigap premium offered by insurance companies the agent is currently appointed with) and iv) the Medigap percent markup (the Medigap Markup as a percent of the price of the lowest-cost identical plan. More specifics on how we measure the primary outcomes are included in our pre-analysis plan.
|
|
Field
Primary Outcomes (Explanation)
|
Before
These are the primary outcomes because they are the most direct measures of recommendation quality for our scenario.
|
After
These are the primary outcomes because they are the most direct measures of recommendation quality for our scenario. Whether Medicare Advantage or Original Medicare is the correct recommendation depends on the specific scenario and senior characteristics.
|
|
Field
Planned Number of Clusters
|
Before
With our current research funding, we expect to be able to collect between 450-850 observations. For additional detail, please see our pre-analysis plan.
|
After
With our current research funding, we expect to be able to collect between 650-950 observations. For additional detail, please see our pre-analysis plan.
|
|
Field
Planned Number of Observations
|
Before
With our current research funding, we expect to be able to collect between 450-850 observations. For additional detail, please see our pre-analysis plan.
|
After
With our current research funding, we expect to be able to collect between 650-950 observations. For additional detail, please see our pre-analysis plan.
|
|
Field
Intervention (Hidden)
|
Before
We will conduct a correspondence study that randomly assigns Medicare-eligible seniors to call insurance producers (or insurance agents) for plan recommendations. We will study variation in recommendation quality across consumer and agent characteristics (e.g. whether or not the agent works at a large call center). We also test whether perceived competition (i.e. randomly assigning consumers to signal that they will solicit and compare recommendations from multiple agents) improves recommendation quality. Finally, we take advantage of a natural experiment in the Medicare Advantage market where agent commissions change to estimate the extent to which recommendations are driven by incentives.
|
After
We will conduct a correspondence study that randomly assigns Medicare-eligible seniors to call insurance producers (or insurance brokers or agents) for plan recommendations. We will study variation in recommendation quality across consumer and insurance agent characteristics (e.g. whether or not the insurance broker works at a large call center). We also test whether perceived competition (i.e. randomly assigning consumers to signal that they will solicit and compare recommendations from multiple agents) improves recommendation quality and take advantage of a natural experiment in the Medicare Advantage market where broker commissions change to estimate the extent to which recommendations are driven by incentives. Finally, we vary consumer fit for Original Medicare versus Medicare Advantage.
|
|
Field
Pi as first author
|
Before
No
|
After
Yes
|