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CHAPTER 3. MEDIGAP 133 includes controls for health status such as self-reported health, indicators of medical diagnoses and treatment, and widely-used measures of functional status known as Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL). Table 3.5 presents the first stage estimates of Medigap premiums on state- and HRR-level costs in logs. Standard errors are clustered at the year-by-state level because premiums vary at this level. For the purposes of our policy counterfactual analysis, the most important parameter in this table is the elasticity of premiums with respect to state-level costs. This coefficient is 0.50 to 0.60 across specifications and precisely estimated (standard error of less than 0.07). As discussed in Section 3.5, most of the identifying variation comes from state-level FFS Medicare costs. With HRR costs and the other controls, the F-statistic on state-level costs is well above the critical level. Marginal effects for the probability of Medigap choice from the multinomial logit choice equation are presented in Table 3.6. Recall that residuals from the premium equation are included as a control variable to overcome the potential endogeneity of premiums. The premium semi-elasticity of demand is estimated at a stable 0.38 to 0.48 across specifications. (The premium elasticity estimates from the linear probability model, shown in Appendix Table 3.11, are similar, ranging from 0.31 to 0.42.) With demographic controls, the point estimates are significantly negative at more than the 5 percent level. In the preferred logit specification with the full set of controls (specification 3), we can reject any premium elasticity outside of 0.13 to 0.82 with a 95 percent confidence interval. Males are 3 percent less likely to purchase Medigap coverage. Medigap partici- pation is 20 percent lower for blacks, increasing in education, but flat with respect to income. Although we do not show it here, it should be noted that there is strong evidence that Medigap choice is increasing in factors the proxy for good health as well as underlying cognitive ability (?).
CHAPTER 3. MEDIGAP 134 3.6.2 Causal Impact on Costs Table 3.7 presents estimates of the third stage cost model. Columns 1 through 3 show simple OLS estimates of log total costs on a Medigap indicator and controls; columns 4 through 6 show estimates with predicted values from the multinomial logit model for Medigap choice used as instruments. The models are estimated with block bootstrap standard errors clustered by individual for consistency (?). In Appendix Table 3.12, we show 3SLS estimates of the cost equation using the linear probability model for Medigap choice. The OLS estimates (columns 1 to 3) suggest that Medigap coverage increases total medical utilization by about 40 percent. With the full set of controls, a 95 percent confidence interval allows us to reject any effect outside of 16 to 65 percent. The instrumental variables estimates are about 40 percent larger than the OLS estimate across the different sets of controls. With demographic controls (columns 5 and 6), the point estimates suggest that Medigap coverage causally increases medical utilization by 57 to 61 percent for individuals local to the instrument. (The 3SLS estimates, shown in Appendix Table 3.12 indicate effects of 59 to 63 percent.) In the logit specification with the full set of controls, a 95 percent confidence interval allows us to reject any effect outside of 30 to 84 percent. We find it sensible that the instrumental variables estimates are larger than those estimated by OLS (57 versus 41 percent). As we have discussed elsewhere, Fang et al. (2009) find strong evidence of advantageous selection on a set of health variables that increase the R-squared of their baseline model from 7 to 21 percent. Yet even these controls leave 79 percent of the variation unexplained. If there is some selection on information contained in this remaining variation, and this selection continues to be advantageous, then we would expect an approach that overcomes this selection problem to yield larger estimates than a simple OLS approach. A second reason for why our baseline estimates may be larger than the OLS estimates is because we are measuring the moral hazard effect for individuals on the margin of selecting into Medigap. The OLS estimates are best interpreted as an average moral hazard effect that may be biased due to selection. In contrast, our baseline logit estimates capture the moral hazard effect for individuals on the margin
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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 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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