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. Author manuscript; available in PMC: 2018 Feb 12.
Published in final edited form as: Am Econ Rev. 2018 Feb;102(2):308–352. doi: 10.1257/aer.20161038

Table 6.

Impact of Hospitalization on Other Credit Report Outcomes

Any Bankruptcy to Date Credit Limit Credit Score Credit Card Balances Automobile Loan Balance
(1) (2) (3) (4) (5)
Panel A. Non-Elderly Insured (Ages 25 to 64)
12-month effect 0.0013 −515 −1.6 −293 −102
(0.0003) (154) (0.200) (94) (28)
[<.001] [<.001] [<.001] [0.0018] [<.001]
48-month effect 0.0042 −2,215 −1.8 −1,208 −507
(0.0009) (440) (0.500) (253) (71)
[<.001] [<.001] [<.001] [<.001] [<.001]
Pre-hospitalization mean 0.034 37,664 731 11,942 6,684
Number of Individuals 383,718 383,718 371,715 383,718 383,718
Number of Observations 3,131,534 3,131,534 2,942,253 3,131,534 3,131,534

Panel B. Non-Elderly Uninsured (Ages 25 to 64)
12-month effect 0.0048 −678 −5 −264 −267
(0.000) (131) (0.300) (83) (29)
[<.001] [<.001] [<.001] [0.0014] [<.001]
48-month effect 0.014 −690 6.6 −443 −349
(0.001) (353) (0.600) (214) (73)
[<.001] [0.051] [<.001] [0.038] [<.001]
Pre-hospitalization mean 0.037 15,145 655 5,376 3,981
Number of Individuals 153,617 153,617 137,913 153,617 153,617
Number of Observations 1,256,759 1,256,759 1,017,096 1,256,759 1,256,759

Panel C. Elderly (Ages 65 and Older)
12-month effect −0.00019 370 −1.4 72 69
(0.0002) (138) (0.200) (73) (17)
[0.4] [0.0073] [<.001] [0.32] [<.001]
48-month effect −0.001 −448 −3.3 −30 194
(0.0007) (393) (0.500) (187) (43)
[0.16] [0.25] [<.001] [0.87] [<.001]
Pre-hospitalization mean 0.016 36,967 824 7,016 2,143
Number of Individuals 414,547 414,547 405,389 414,547 414,547
Number of Observations 2,959,802 2,959,802 2,833,027 2,959,802 2,959,802

Notes: Samples are non-elderly insured, uninsured, and the elderly (see Appendix Table 13, columns 3, 6, and 9). All columns report effects based on OLS estimates of equation (5). All variables are observed from 2002 to 2011. Standard errors (clustered on the individual) are in parentheses and p-values are in brackets. All estimates are weighted to adjust for individuals’ sampling probabilities.