Frakt, Austin B.Feyman, Yevgeniy2024-01-042024-01-042024https://hdl.handle.net/2144/47896Medicare Advantage (MA), a private alternative to Traditional Medicare (TM), covers over 50 percent of Medicare beneficiaries and accounts for a similar share of spending (in 2023). The government pays private insurers a monthly amount to offer coverage to beneficiaries. The plans covering most MA enrollees – preferred provider organizations (PPOs), health maintenance organizations (HMOs), and point of service (POS) plans – are also required to maintain provider networks that restrict access to certain providers and meet government adequacy requirements. In paper one, we develop a method for measuring the restrictiveness of provider networks in MA without relying on provider directories. This approach relies on prescription drug event (PDE) data for MA enrollees to identify providers seen by enrollees. Focusing on primary care providers (PCPs) as a high-prescribing specialty, we use a prediction model trained on stand-alone prescription drug plans (PDPs) to estimate the number of providers that would have been seen absent network restrictions, allowing estimation of a measure of network restrictiveness for MA plans. Our findings suggest that MA plans reduced access to PCPs to 60.6% of what we would expect it to be absent network restrictions. HMOs tended to have the most restrictive networks, and rural areas were most affected by network restrictions. When developing provider networks, MA insurers seek to maximize profit while meeting regulatory standards. To make networks attractive to patients, insurers might have to include providers that are differentiated by quality, brand-name, or other characteristics. These so-called “star providers” are those that are difficult to exclude from networks due to market power, potentially driven by product differentiation or other behavior. In the second paper, we build on prior work identifying star providers in other markets, and using claims data, we develop a measure of demand for provider groups among TM beneficiaries. Using this measure, we identify star provider groups, of which 81.04% are in-network for at least one MA plan, compared to 26.3% for others (SMD: 1.31). While these groups had a larger share of beneficiaries than others (5.69% vs 1.14%, SMD: 0.57) (indicating market power), they tended to have a similar number of providers. These findings suggest that there exist provider groups that limit the ability of MA insurers to flexibly modify networks, which may affect how regulators view proposed mergers. Insurers participating in MA must offer benefits at least as valuable as TM, but typically expand benefits beyond what TM offers, and they are required to have an out-of-pocket limit on beneficiary costs. Payment changes might affect the value of these benefits. Reductions in payment might lead to narrower networks or less expansive benefits, for instance. In the third and final paper, we use a one-time reduction in government payments in 2015 to identify the extent to which payments change network breadth, benefits, and/or advertising effort. We find that less than 100% of the reduction is passed through to beneficiaries. 40.6% of the reductions are passed through as less generous benefits while 27.6% are passed through as higher premiums. We find a reduction in zero-premium plans but no effect on advertising effort or network restrictiveness. A major contribution of our analyses is the development of a novel method for measuring provider network restrictiveness, allowing regulators and researchers to evaluate the role of provider networks in affecting access without relying on provider director data. Our results are consistent with prior work suggesting that the MA market is generally non-competitive and that a less than competitive provider market may make it difficult for insurers to modify provide networks.en-USAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/EconomicsThesis/Dissertation2024-01-040000-0001-7372-7671