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CHAPTER 3. MEDIGAP 131 levels in this area will reflect this propensity to travel. A more subtle concern is that the marginal Medigap purchaser may be more (or less) likely to travel than the average Medicare beneficiary. If this is the case, then controlling for average local health care utilization is insufficient. Basically, the concern is that although most people in Rural Town, USA don’t travel to Big City, USA for care, the marginal Medigap purchaser might be the type of person who does. A solution to this problem is to define markets broadly enough so that the closest Big City, USA is included in the market. We believe this is exactly what we do by using HRRs to define local markets as these regions are constructed explicitly to include the major hospital where people travel for sophisticated treatment. Therefore, given a measure of costs which accounts for travel and the conservative definition of local areas, we think that the exclusion restriction is unlikely to be violated. Nevertheless, if out-of-HRR costs did somehow directly impact costs, it would downwardly bias the coefficient on Medgap, working against the direction of our main finding. 19 Medicare Advantage A final topic of discussion is our proxy for the price and availability of Medicare Advantage coverage: a second order polynomial in the county-year specific MA penetration rate. Controlling for the determinants of MA enrollment is important for the explanatory power of the choice equation as Medigap and MA are typically viewed as substitutes. For similar reasons, it is important to include MA enrollees in the sample of analysis as MA coverage is the relevant alternative coverage to consider for changes in the Medigap market. Yet for valid estimation, the variables used to predict MA choice must satisfy the exclusion restriction of not directly impacting medical costs. Below we make the case for their exclusion. The main argument for the exogeneity of county-year specific MA penetration rates is that much of the variation in this variable is driven by high-level federal 19 In this case, higher out-of-HRR costs would simultaneously shift individuals out of coverage through the instrument and increase costs directly, creating a negative correlation between coverage and unobserved costs and biasing downwards the coefficient on Medigap.
CHAPTER 3. MEDIGAP 132 policy that could not plausibly be related to the unobserved component in the cost equation. MA penetration decreased over the first part of our sample as policies enacted in the 1997 Balanced Budget Act to reduce Medicare payments indirectly decreased MA payments. MA penetration rates were then buoyed up by increased payments written into the 2003 Medicare Modernization Act (?). In addition, the goal of MA has shifted over time from promoting efficiency to including broader goals such as regional equity meaning there has been great variation over time in federal policy toward regional MA insurers. Thus most of the changes over time were driven by federal policy that could not be related to the unobservable. We also statistically test for the endogeneity of the county-year specific MA penetration variables. Recall that our Meidgap choice equation has two sets of excluded variables: Medigap premiums and the MA penetration variables. Based on the as- sumption that estimates from a specification using only Medigap premiums are con- sistent but inefficient and estimates from a specification with both Medigap premiums and MA penetration are efficient, a Durbin-Wu-Hausman test squarely fails to reject the exogeneity of MA penetration variables. In our preferred specification with the full set of controls, the test returns a Chi-squared value of 0.8, a value vastly below the 5 percent critical value of 37.6. Tests run on other specifications yield similar results. 3.6 Results and Discussion 3.6.1 Premiums and Medigap Choice Below we present baseline estimates of the empirical model, starting with the first stage premium equation, followed by the multinomial logit second stage Medigap choice equations, and finally the third stage cost equation. Each of these equations is estimated with three different sets of control variables. A sparse specification including only key variables and year fixed effects; a specification that includes the demographic controls; and a full specification that, along with the demographic variables,
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ESSAYS IN PUBLIC FINANCE AND INDUST
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I certify that I have read this dis
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insurance choice. Among other thing
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Acknowledgements I am deeply thankf
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Contents Preface iv Acknowledgement
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2.5.3 The Value of Plan Choice ....
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List of Tables 1.1 Asset Exemption
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4.9 First Week Contract Spending fo
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4.3 I.T. Contracting by Week ......
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CHAPTER 1. BANKRUPTCY 2 of the dead
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CHAPTER 1. BANKRUPTCY 4 relevant le
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CHAPTER 1. BANKRUPTCY 6 superficial
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CHAPTER 1. BANKRUPTCY 8 After treat
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CHAPTER 1. BANKRUPTCY 12 newly edit
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CHAPTER 1. BANKRUPTCY 15 bound of t
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CHAPTER 1. BANKRUPTCY 20 or coverag
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CHAPTER 1. BANKRUPTCY 22 homestead
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CHAPTER 1. BANKRUPTCY 24 log seizab
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CHAPTER 1. BANKRUPTCY 26 on the bas
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CHAPTER 1. BANKRUPTCY 28 to seizabl
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CHAPTER 1. BANKRUPTCY 31 CARA utili
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CHAPTER 1. BANKRUPTCY 33 Puzzle 3:
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CHAPTER 1. BANKRUPTCY 35 of income
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CHAPTER 1. BANKRUPTCY 40 TaxSim pro
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Payments CHAPTER 1. BANKRUPTCY 42 $
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OOP payments CHAPTER 1. BANKRUPTCY
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ln(seizable sssets) CHAPTER 1. BANK
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50 60 70 80 90 100 Insurance covera
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Pr(WTP > premium) CHAPTER 1. BANKRU
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CHAPTER 1. BANKRUPTCY 52 Table 1.1:
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CHAPTER 1. BANKRUPTCY 62 Table 1.11
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CHAPTER 1. BANKRUPTCY 64 Table 1.14
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CHAPTER 2. HEALTH PLAN CHOICE 66 pl
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CHAPTER 2. HEALTH PLAN CHOICE 68 ar
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CHAPTER 2. HEALTH PLAN CHOICE 70 pl
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CHAPTER 2. HEALTH PLAN CHOICE 73 va
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CHAPTER 2. HEALTH PLAN CHOICE 75 An
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CHAPTER 2. HEALTH PLAN CHOICE 77 2.
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