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CHAPTER 3. MEDIGAP 135 of selecting into Medigap at current prices. These individuals are probably more price sensitive when it comes to premiums. If they are more sensitive to the price of medical care induced by Medigap coverage as well, then the moral hazard effect for these marginal individuals will be larger than the average Medigap policyholders. We think it is important to emphasize two things about the interpretation of our baseline estimates related to this point. First, the moral hazard of individuals on the margin is the precisely the parameter of interested for policy analysis of a Medigap premium tax or subsidy. If the government levies, for example, a tax on Medigap premiums, then the individuals that select out of Medigap as a result of the tax will reduce their total medical care utilization by our baseline estimate. (In Section 3.7, we demonstrate the effect of tax and subsidy policies using our estimates.) Second, one can view our moral hazard estimate as an upper bound on the moral hazard of all Medigap beneficiaries following the logic that marginal beneficiaries have greater moral hazard than infra-marginal beneficiaries. To compare our effect to other estimates in the literature, we follow standard practices (??) and convert our point estimates to arc elasticities of expenditure (?). Under the approximation that Medigap decreases the “price” of a unit of healthcare from 0.2 to 0.0, the preferred point estimate of 0.57 implies an arc elasticity of 1.13. We think there are good reasons for why our estimate is higher than the “gold standard” estimate of 0.22 from the RAND Health Insurance Experiment (HIE) of the 1970s. The first is that our estimate may be local to particularly price sensitive individuals, as discussed above. A second reason is that elderly people may be more price sensitive than the non-elderly population. While the RAND HIE only included people under age 65, the Medicare population we study includes those over age 65. Other papers have found results consistent with the idea that the elderly have higher price elasticities of medical utilization (??). A third reason is that innovation in medicine over the last 40 years could have affected price elasticities, perhaps by creating more treatment options for a given diagnosis that individuals can substitute among.
CHAPTER 3. MEDIGAP 136 3.6.3 Robustness and External Validity Our first robustness check addresses the log specification for medical costs. It is well known that the skewed distribution of outcomes can create difficulties in estimating models with medical costs as a dependent variable. To show that the log specification we use is not driving the results, Panel A of Table 3.8 shows point estimates from the three-stage model where costs in the cost equation are denoted in levels. For com- parison, we divide these estimates by the average costs in our sample and display the results next to the estimated parameters from the model in logs. With demographic controls, the implied percentage changes in medical spending are similar to the log estimates (0.68 versus 0.57) although less precisely estimated. As discussed earlier, our analysis is restricted to the sample of individuals who turned 65 no sooner than the year 2000, as price data is not available from before this year. This, of course, means that the estimates should not be directly ascribed to the entire Medigap population because of potential age or cohort heterogeneity in the main effect. However, we can shed some light on the external validity of the main results with simple OLS regressions. Panel B of Table 3.8 shows OLS estimates of log medical costs on a Medigap indicator for the entire sample of individuals in the data and the subset we analyze. With demographic and health controls, the OLS estimates from the samples are very similar (0.34 versus 0.37). However, to the extent that health controls may be partly determined by Medigap choice, the specification with only demographic covariates may be more appropriate. The lower OLS estimate for the full sample in this specification (0.11 versus 0.35) suggests some caution when extrapolating the estimates of out sample. Finally, as discussed Section 3.5, there is in fact a small degree of within-state variation in Medigap premiums in a small number of states in the sample (Maestas et al., 2009). To examine the robustness of the main results to this variation, we restrict our sample to states where, according to Maestas et al., the within-state coefficient of variation of Medigap premiums is less than 0.10 for 75 percent of firms issuing plans. 20 Panel C of Table 3.8 shows full model and OLS estimates of the coefficient on Medigap 20 The states we drop are CA, FL, IL, LA, MI, NV, NY, PA, TX, and VA.
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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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