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CHAPTER 1. BANKRUPTCY 21 publicly insured are excluded from the baseline sample, correlation between asset exemption laws and eligibility thresholds for public insurance programs might bias the estimates though sample selection. To assuage this concern, I estimate the regression models on samples excluding and including the publicly insured. I also control for the presence and generosity of Medicaid Medically Needy programs that provide an alternative form of safety net coverage. 30 In the cost equations, I control for hospital characteristics and other state-level factors (e.g., share of private hospitals) that may affect out-of-pocket payments. 31 1.5 The Effect on Insurance Coverage 1.5.1 First Stage Estimates Table 1.2 shows implied first stage regressions of log seizable assets on different instru- mental variables. Column 1 shows estimates with the baseline instrument. Columns 2 and 3 show estimates with instruments that isolate the variation due to seizable homestead equity and seizable non-homestead assets. These instruments are calculated by taking a constant, nationally representative of households and calculating their level of seizable homestead and non-homestead equity as though they lived in each state. Column 4 shows estimates using the instrument that isolates variation due to 1920 homestead exemptions. All the specifications include demographic controls (age group, family structure, race, education, and income), state controls (mean income, percent unemployed, percent covered by Medicaid), and year fixed effects. Standard errors in all specifications here and throughout the paper are clustered at the state level. The instruments have substantial power. The baseline instrument has an F-statistic of nearly 500 (column 1) and even the instrument based on 1920 gracious shared by Amanda Kowalski. 30I thank Jay Bhattacharya for alternating me to the presence of these programs. I use 2003 data on these programs taken from ?. 31In particular, I control for the share of private and nonprofit hospitals, taken from the Hospital Statistics 2005 published by the American Hospital Association. I control for Disproportionate Share Hospital payments per 1,000 residents taken from the Kaiser Family Foundation. I control for the number of Federally Qualified Health Centers per 100,000 residents.
CHAPTER 1. BANKRUPTCY 22 homestead exemptions has an F-statistic over 20 (column 4). 1.5.2 Baseline Coverage Estimates Before turning to the regression estimates, Figure 1.8 presents visual evidence on the crowd-out prediction. Plots on the same row show the exact same data. Datapoints are indicated with state abbreviations in the plots in the left column and with circles proportional to the number of observations in the plots in the right column. Panels A and B plot insurance coverage against seizable assets averaged by state. In the raw data, there is a strong upward-slopping relationship. The relationship is consistent across most states, with the outliers mainly states with very few observations. Panels C and D plot insurance coverage against the baseline instrument averaged by state— the graphical analogue to a bivariate reduced form regressions. The upward-sloping relationship is similar although the figures are more noisy. (I discuss Panels E and F below.) Table 1.3 presents the baseline regression specifications. The first four columns show OLS estimates. Column 1 only has controls for year fixed effects. Column 2 adds the demographic and state controls. Columns 3 and 4 add controls for wealth and premiums. 32 Since wealth and premiums may be directly affected by asset exemption laws, the parameter estimates with these controls are inappropriate for counterfac- tuals involving changes in these exemptions. The coefficient on log seizable assets is 6.13 in column 1 and about 2.5 across the other specifications. The similar estimates with the wealth and premium controls suggest that the effect is not mediated through these channels. 33 Columns 5 to 8 show the 2SLS estimates with the baseline instrument (column 1 of Table 1.2), adding controls in the same manner as columns 1 through 4. The coefficient on log seizable assets is 5.43 in the preferred specification with demographic 32The premium index is state fixed effects from a regression of log premiums on plan and state fixed effects. 33The slight increase in columns 4 and 8 is due to the fact that premium data is only available for a subset of states. When compared to a specification without this control estimated on the same sample, controlling for premiums very slightly reduces the coefficient on log seizable assets. This is consistent with a negative correlation between asset exemption levels and insurance premiums.
Page 1 and 2: ESSAYS IN PUBLIC FINANCE AND INDUST
Page 3 and 4: I certify that I have read this dis
Page 5 and 6: insurance choice. Among other thing
Page 7 and 8: Acknowledgements I am deeply thankf
Page 9 and 10: Contents Preface iv Acknowledgement
Page 11 and 12: 2.5.3 The Value of Plan Choice ....
Page 13 and 14: List of Tables 1.1 Asset Exemption
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Chapter 3 Private Coverage and Publ
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CHAPTER 3. MEDIGAP 119 be of intere
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CHAPTER 3. MEDIGAP 121 hazard for i
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CHAPTER 3. MEDIGAP 123 have benefit
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CHAPTER 3. MEDIGAP 128 We use boots
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CHAPTER 3. MEDIGAP 130 of costs and
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CHAPTER 3. MEDIGAP 132 policy that
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CHAPTER 3. MEDIGAP 134 3.6.2 Causal
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CHAPTER 3. MEDIGAP 136 3.6.3 Robust
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CHAPTER 3. MEDIGAP 138 calculated u
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CHAPTER 3. MEDIGAP 140 (10000,11000
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CHAPTER 3. MEDIGAP 142 Table 3.2: S
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