Private Payers Serving Individuals with Disabilities and Chronic Conditions

Appendix D: Least-Squares Regressions


TABLE OF CONTENTS

Table D-1. POS versus Indemnity Insurance Choice (Employer A - Full Sample) Probit Regression Results
Table D-2. Number of Admissions (Employer A) Non-Linear Least-Squares Regressions
Table D-3. Number of Hospital Days (Employer A) Non-Linear Least-Squares Regressions
Table D-4. Number of Outpatient Visits (Employer A) Non-Linear Least-Squares Regressions
Table D-5. Any Rehabilitation Services (Employer A) Non-Linear Least-Squares and Probit Regressions
Table D-6. Inpatient Payments (Employer A) Non-Linear Least-Squares Regressions
Table D-7. Outpatient Payments (Employer A) Non-Linear Least-Squares Regressions
Table D-8. Total Payments (Employer A) Non-Linear Least-Squares Regressions
Table D-9. Insurance Choice (Employer A - Prescription Drug Subsample) Probit Regression Results
Table D-10. Number of Prescriptions (Employer A) Non-Linear Least-Squares Regressions
Table D-11. Prescription Payments (Employer A) Non-Linear Least-Squares Regressions
Table D-12. Total Payments Including Prescription Payments (Employer A) Non-Linear Least-Squares Regressions
Table D-13. HMO versus Indemnity Insurance Choice (Employer B) Results from Probit Regressions
Table D-14. Number of Admissions (Employer B) Non-Linear Least-Squares Regressions
Table D-15. Number of Hospital Days (Employer B) Non-Linear Least-Squares Regressions
Table D-16. Number of Outpatient Visits (Employer B) Non-Linear Least-Squares Regressions
Table D-17. Any Rehabilitation Services (Employer B) Non-Linear Least-Squares and Probit Regressions
Table D-18. Inpatient Payments (Employer B) Non-Linear Least-Squares Regressions
Table D-19. Outpatient Payments (Employer B) Non-Linear Least-Squares Regressions
Table D-20. Total Expenditures (Employer B) Non-Linear Least-Squares Regressions
Table D-21. Estimated Incremental Effects of POS Choice on Utilization and Expenditure Measures (Employer A)
Table D-22. Estimated Incremental Effects of HMO Choice on Utilization and Expenditure Measures (Employer A)


TABLE D-1. POS versus Indemnity Insurance Choice (Employer A - Full Sample) Probit Regression Results
Independent Variables Parameter Estimates Standard Errors Incremental Effects1, 2
Constant .0238 (0.058)** 0.095
DEMOGRAPHIC CHARACTERISTICS
Gender 0.164 (0.019)** 0.065
Age -0.016 (0.001)** -0.006
Metropolitan statistical area 0.595 (0.025)** 0.237
EMPLOYMENT STATUS
Early retiree -0.297 (0.022)** -0.118
DISABILITY STATUS
Activity limiting condition -0.030 (0.019) -0.012
Mental per se disability only -0.050 (0.038) -0.020
Physical and mental per se disability -0.238 (0.054)** -0.095
Percent correctly predicted (overall)3 61.124    
POS 62.472    
Indemnity 60.128    
*, ** denote p< = 0.05 and 0.01, respectively. Results for Employer A are based on 1995 data only.
  1. For categorical variables, an incremental effect measures the percentage point change in the probability of the patient choosing the POS plan (as opposed to the indemnity plan) associated with a 1 percentage point change in that characteristic.
  2. For continuous variables, the incremental effect measures the change in this probability associated with a per-unit change in the variable of interest.
  3. The percent correctly predicted is determined by applying the parameter estimates to the sample data and comparing the predicted choices to the actual choices.


TABLE D-2. Number of Admissions (Employer A) Non-Linear
Least-Squares Regressions
Independent Variables Selection Model1 Non-Endogenous Model2
Parameter
Estimates		 Standard
Errors		 Parameter
Estimates		 Standard
Errors
Constant -4.305 (1.051)** -4.058 (0.535)**
INSURANCE CHOICE
POS 1995 0.374 (1.296) 0.001 (0.042)
DEMOGRAPHIC CHARACTERISTICS
Gender 0.332 (0.103)** 0.355 (0.057)**
Age 0.056 (0.024)** 0.053 (0.022)**
Age squared -0.564 (0.227)** -0.561 (0.227)**
Metropolitan statistical area -0.427 (0.282) -0.351 (0.057)**
EMPLOYMENT STATUS
Early retiree 0.186 (0.150) 0.146 (0.055)**
HEALTH CONDITIONS
Number of unique MDCs 1995 0.227 (0.008)** 0.227 (0.008)**
DISABILITY STATUS
Activity limiting condition 0.609 (0.057)** 0.605 (0.055)**
Mental per se disability only -0.117 (0.117) -0.123 (0.114)
Physical and mental per se disability 0.482 (0.154)** 0.452 (0.092)**
SELECTION PARAMETER
Theta1 -0.228 (0.785) — —
Sum of Squared Residuals3 10024.672 10025.089
* significant at the p = .05 level. ** significant at the p = .01 level. Results for Employer A are based on 1995 data only.
  1. The selection model controls for the joint determination of insurance coverage and the outcome variable. The extent of this joint determination is captured by the variable theta.
  2. In the non-endogenous model, insurance choice and outcomes are treated as separate decisions.
  3. The sum of squared residuals is a measure of model fit. A smaller value indicates a better fit.


TABLE D-3. Number of Hospital Days (Employer A) Non-Linear
Least-Squares Regressions
Independent Variables Selection Model1 Non-Endogenous Model2
Parameter
Estimates		 Standard
Errors		 Parameter
Estimates		 Standard
Errors
Constant -5.237 (1.728)** -4.549 (1.689)**
INSURANCE CHOICE
POS 1995 0.998 (1.638) 0.090 (0.115)
DEMOGRAPHIC CHARACTERISTICS
Gender 0.031 (0.223) 0.081 (0.184)
Age 0.113 (0.065)* 0.106 (0.072)
Age squared -1.006 (0.719) -0.986 (0.761)
Metropolitan statistical area -0.258 (0.230) -0.112 (0.123)
EMPLOYMENT STATUS
Early retiree 0.503 (0.223)** 0.411 (0.146)**
HEALTH CONDITIONS
Number of unique MDCs 1995 0.292 (0.022)** 0.292 (0.022)**
DISABILITY STATUS
Activity limiting condition 0.655 (0.108)** 0.643 (0.105)**
Mental per se disability only -0.122 (0.177) -0.134 (0.173)
Physical and mental per se disability 1.004 (0.229)** 0.938 (0.172)**
SELECTION PARAMETER
Theta1 -0.542 (0.958) — ---
Sum of Squared Residuals3 1059482.700   1059571.500  
* significant at the p = .05 level. ** significant at the p = .01 level. Results for Employer A are based on 1995 data only.
  1. The selection model controls for the joint determination of insurance coverage and the outcome variable. The extent of this joint determination is captured by the variable theta.
  2. In the non-endogenous model, insurance choice and outcomes are treated as separate decisions.
  3. The sum of squared residuals is a measure of model fit. A smaller value indicates a better fit.


TABLE D-4. Number of Outpatient Visits (Employer A) Non-Linear
Least-Squares Regressions
Independent Variables Selection Model1 Non-Endogenous Model2
Parameter
Estimates		 Standard
Errors		 Parameter
Estimates		 Standard
Errors
Constant 1.208 (0.332)** 1.174 (0.139)**
INSURANCE CHOICE
POS 1995 0.027 (0.525) 0.083 (0.013)**
DEMOGRAPHIC CHARACTERISTICS
Gender 0.028 (0.035) 0.025 (0.014)*
Age 0.030 (0.006)** 0.030 (0.006)**
Age squared -0.342 (0.062)** -0.342 (0.062)**
Metropolitan statistical area -0.009 (0.119) -0.021 (0.017)
EMPLOYMENT STATUS
Early retiree 0.083 (0.062) 0.090 (0.017)**
HEALTH CONDITIONS
Number of unique MDCs 1995 0.179 (0.003)** 0.179 (0.003)**
DISABILITY STATUS
Activity limiting condition 0.091 (0.015)** 0.092 (0.014)**
Mental per se disability only 0.170 (0.026)** 0.171 (0.024)**
Physical and mental per se disability 0.187 (0.055)** 0.192 (0.030)**
SELECTION PARAMETER
Theta1 0.035 (0.325) — ---
Sum of Squared Residuals3 3085123.200 3085131.000
* significant at the p = .05 level. ** significant at the p = .01 level. Results for Employer A are based on 1995 data only.
  1. The selection model controls for the joint determination of insurance coverage and the outcome variable. The extent of this joint determination is captured by the variable theta.
  2. In the non-endogenous model, insurance choice and outcomes are treated as separate decisions.
  3. The sum of squared residuals is a measure of model fit. A smaller value indicates a better fit.


TABLE D-5. Any Rehabilitation Services (Employer A) Non-Linear Least-Squares and Probit Regressions
Independent Variables Selection Model1 Non-Endogenous Model2
Parameter
Estimates		 Standard
Errors		 Parameter
Estimates		 Standard
Errors
Constant -5.440 (0.689)** -2.475 (0.286)**
INSURANCE CHOICE
POS 1995 3.001 (0.439)** 0.241 (0.026)**
DEMOGRAPHIC CHARACTERISTICS
Gender -0.137 (0.052)** -0.022 (0.029)
Age 0.058 (0.200)** -0.301 (0.012)
Age squared -0.589 (0.200)** -0.301 (0.127)**
Metropolitan statistical area -0.681 (0.105)** -0.114 (0.036)**
EMPLOYMENT STATUS
Early retiree 0.420 (0.078)** 0.083 (0.036)**
HEALTH CONDITIONS
Number of unique MDCs 1995 0.169 (0.014)** 0.133 (0.006)**
DISABILITY STATUS
Activity limiting condition 0.598 (0.065)** 0.361 (0.031)**
Mental per se disability only 0.017 (0.110) -0.200 (0.066)**
Physical and mental per se disability 0.236 (0.127)* -0.038 (0.073)
SELECTION PARAMETER
Theta1 -1.523 (0.221)** — ---
Sum of Squared Residuals3 1638.004  
Likelihood Function4   -6029.884
* significant at the p = .05 level. ** significant at the p = .01 level. Results for Employer A are based on 1995 data only.
  1. The selection model controls for the joint determination of insurance coverage and the outcome variable. The extent of this joint determination is captured by the variable theta.
  2. In the non-endogenous model, insurance choice and outcomes are treated as separate decisions.
  3. The sum of squared residuals is a measure of model fit. A smaller value indicates a better fit.
  4. The value of the likelihood function is a measure of model fit for probit regressions. Note that the sum of squared residuals and the value of the likelihood function cannot be compared.


TABLE D-6. Inpatient Payments (Employer A) Non-Linear
Least-Squares Regressions
Independent Variables Selection Model1 Non-Endogenous Model2
Parameter
Estimates		 Standard
Errors		 Parameter
Estimates		 Standard
Errors
Constant -6.462 (2.830)** -5.775 (2.079)**
INSURANCE CHOICE
POS 1995 0.913 (1.431) -0.007 (0.171)
DEMOGRAPHIC CHARACTERISTICS
Gender 0.505 (0.125)** 0.558 (0.117)**
Age 0.152 (0.091)* 0.144 (0.082)*
Age squared -1.625 (0.990)* -1.597 (0.943)*
Metropolitan statistical area -0.390 (0.227)* -0.217 (0.094)**
EMPLOYMENT STATUS
Early retiree 0.550 (0.389) 0.444 (0.293)
HEALTH CONDITIONS
Number of unique MDCs 1995 0.265 (0.032)** 0.264 (0.031)**
DISABILITY STATUS
Activity limiting condition 0.907 (0.134)** 0.899 (0.130)**
Mental per se disability only -1.068 (0.167)** -1.085 (0.169)**
Physical and mental per se disability 0.295 (0.210) 0.226 (0.215)
SELECTION PARAMETER
Theta1 -0.551 (0.768) — ---
Sum of Squared Residuals3 190562.730 190584.18
* significant at the p = .05 level. ** significant at the p = .01 level. Results for Employer A are based on 1995 data only.
  1. The selection model controls for the joint determination of insurance coverage and the outcome variable. The extent of this joint determination is captured by the variable theta.
  2. In the non-endogenous model, insurance choice and outcomes are treated as separate decisions.
  3. The sum of squared residuals is a measure of model fit. A smaller value indicates a better fit.


TABLE D-7. Outpatient Payments (Employer A) Non-Linear
Least-Squares Regressions
Independent Variables Selection Model1 Non-Endogenous Model2
Parameter
Estimates		 Standard
Errors		 Parameter
Estimates		 Standard
Errors
Constant -3.056 (0.763)** -2.171 (0.440)**
INSURANCE CHOICE
POS 1995 1.017 (0.620)* -0.148 (0.043)**
DEMOGRAPHIC CHARACTERISTICS
Gender 0.171 (0.051)** 0.230 (0.048)**
Age 0.045 (0.021)** 0.035 (0.019)*
Age squared -0.503 (0.219)** -0.470 (0.208)**
Metropolitan statistical area -0.421 (0.126)** -0.204 (0.058)**
EMPLOYMENT STATUS
Early retiree 0.264 (0.085)** 0.148 (0.063)**
HEALTH CONDITIONS
Number of unique MDCs 1995 0.247 (0.010)** 0.246 (0.009)**
DISABILITY STATUS
Activity limiting condition 0.150 (0.066)** 0.140 (0.065)**
Mental per se disability only -0.413 (0.057)** -0.430 (0.056)**
Physical and mental per se disability -0.011 (0.119) -0.096 (0.105)
SELECTION PARAMETER
Theta1 -0.699 (0.349)** — ---
Sum of Squared Residuals3 28345.365 28360.222
* significant at the p = .05 level. ** significant at the p = .01 level. Results for Employer A are based on 1995 data only.
  1. The selection model controls for the joint determination of insurance coverage and the outcome variable. The extent of this joint determination is captured by the variable theta.
  2. In the non-endogenous model, insurance choice and outcomes are treated as separate decisions.
  3. The sum of squared residuals is a measure of model fit. A smaller value indicates a better fit.


TABLE D-8. Total Payments (Employer A) Non-Linear Least-Squares Regressions
Independent Variables Selection Model1 Non-Endogenous Model2
Parameter
Estimates		 Standard
Errors		 Parameter
Estimates		 Standard
Errors
Constant -3.637 (1.337)** -2.948 (1.049)**
INSURANCE CHOICE
POS 1995 0.832 (0.750) -0.098 (0.084)
DEMOGRAPHIC CHARACTERISTICS
Gender 0.358 (0.071)** 0.409 (0.065)**
Age 0.087 (0.045)* 0.079 (0.042)*
Age squared -0.950 (0.487)* -0.925 (0.474)*
Metropolitan statistical area -0.404 (0.140)** -0.227 (0.059)**
EMPLOYMENT STATUS
Early retiree 0.389 (0.177)** 0.290 (0.143)**
HEALTH CONDITIONS
Number of unique MDCs 1995 0.253 (0.016)** 0.253 (0.016)**
DISABILITY STATUS
Activity limiting condition 0.494 (0.071)** 0.486 (0.070)**
Mental per se disability only -0.592 (0.077)** -0.606 (0.075)**
Physical and mental per se disability 0.185 (0.126) 0.115 (0.116)
SELECTION PARAMETER
Theta1 -0.560 (0.419) — ---
Sum of Squared Residuals3 247929.590 247988.620
* significant at the p = .05 level. ** significant at the p = .01 level. Results for Employer A are based on 1995 data only.
  1. The selection model controls for the joint determination of insurance coverage and the outcome variable. The extent of this joint determination is captured by the variable theta.
  2. In the non-endogenous model, insurance choice and outcomes are treated as separate decisions.
  3. The sum of squared residuals is a measure of model fit. A smaller value indicates a better fit.


TABLE D-9. Insurance Choice (Employer A - Prescription Drug Subsample) Probit Regression Results
Independent Variables Parameter
Estimates		 Standard
Errors		 Incremental
Effects1,2
Constant -0.299 (0.071)** -0.114
DEMOGRAPHIC CHARACTERISTICS
Gender 0.193 (0.023)** 0.074
Age -0.008 (0.001)** -0.003
Metropolitan statistical area 0.486 (0.029)** 0.186
EMPLOYMENT STATUS
Early retiree -0.268 (0.025)** -0.102
DISABILITY STATUS
Activity limiting condition -0.029 (0.022) -0.011
Mental per se disability only -0.076 (0.046)* -0.029
Physical and mental per se disability -0.247 (0.064)** -0.095
 
Percent correctly predicted (overall)3 30.637    
POS 12.261    
Indemnity 91.442    
*, ** denote p <= .05 and 0.01, respectively. Results for Employer A are based on 1995 data only. Drug subsample includes prescription users only.
  1. For categorical variables, an incremental effect measures the change in the probability of choosing the POS plan associated with a one-percentage-point change in that characteristic.
  2. For continuous variables, the incremental effect measures the change in this probability of choosing the POS associated with a per-unit change in the variable of interest.
  3. The percent correctly predicted is determined by applying the parameter estimates to the sample data and comparing the predicted choices to the actual choices.


TABLE D-10. Number of Prescriptions (Employer A) Non-Linear
Least-Squares Regressions
Independent Variables Selection Model1 Non-Endogenous Model2
Parameter
Estimates		 Standard
Errors		 Parameter
Estimates		 Standard
Errors
Constant 0.011 (0.388) -0.061 (0.254)
INSURANCE CHOICE
POS 1995 -0.400 (0.789) -0.222 (0.017)**
DEMOGRAPHIC CHARACTERISTICS
Gender -0.080 (0.059) -0.092 (0.019)**
Age 0.099 (0.011)** 0.100 (0.010)**
Age squared -0.959 (0.105)** -0.960 (0.106)**
Metropolitan statistical area -0.101 (0.139) -0.130 (0.022)**
EMPLOYMENT STATUS
Early retiree 0.294 (0.082)** 0.311 (0.023)**
HEALTH CONDITIONS
Number of unique MDCs 1995 0.103 (0.004)** 0.103 (0.004)**
DISABILITY STATUS
Activity limiting condition 0.096 (0.021)** 0.097 (0.019)**
Mental per se disability only 0.006 (0.044) 0.011 (0.037)
Physical and mental per se disability 0.169 (0.090)* 0.184 (0.049)**
SELECTION PARAMETER
Theta1 0.110 (0.488) — ---
Sum of Squared Residuals3 5042909.900 5042984.100
* significant at the p = .05 level. ** significant at the p = .01 level. Results for Employer A are based on 1995 data only. Drug subsample includes prescription users only.
  1. The selection model controls for the joint determination of insurance coverage and the outcome variable. The extent of this joint determination is captured by the variable theta.
  2. In the non-endogenous model, insurance choice and outcomes are treated as separate decisions.
  3. The sum of squared residuals is a measure of model fit. A smaller value indicates a better fit.


TABLE D-11. Prescription Payments (Employer A) Non-Linear
Least-Squares Regressions
Independent Variables Selection Model1 Non-Endogenous Model2
Parameter
Estimates		 Standard
Errors		 Parameter
Estimates		 Standard
Errors
Constant -3.277 (0.517)** -3.095 (0.459)**
INSURANCE CHOICE
POS 1995 0.257 (0.545) -0.145 (0.026)**
DEMOGRAPHIC CHARACTERISTICS
Gender 0.095 (0.044)** 0.119 (0.032)**
Age 0.102 (0.018)** 0.101 (0.018)**
Age squared -1.108 (0.180)** -1.104 (0.180)**
Metropolitan statistical area -0.153 (0.096) -0.091 (0.037)**
EMPLOYMENT STATUS
Early retiree 0.513 (0.068)** 0.476 (0.041)**
HEALTH CONDITIONS
Number of unique MDCs 1995 0.119 (0.007)** 0.119 (0.007)**
DISABILITY STATUS
Activity limiting condition 0.088 (0.031)** 0.084 (0.031)**
Mental per se disability only -0.125 (0.050)** -0.135 (0.048)**
Physical and mental per se disability 0.143 (0.077)* 0.111 (0.062)*
SELECTION PARAMETER
Theta1 -0.243 (0.324) — ---
Sum of Squared Residuals3 22302.240 22303.277
* significant at the p = .05 level. ** significant at the p = .01 level. Results for Employer A are based on 1995 data only. Drug subsample includes prescription users only.
  1. The selection model controls for the joint determination of insurance coverage and the outcome variable. The extent of this joint determination is captured by the variable theta.
  2. In the non-endogenous model, insurance choice and outcomes are treated as separate decisions.
  3. The sum of squared residuals is a measure of model fit. A smaller value indicates a better fit.


TABLE D-12. Total Payments Including Prescription Payments (Employer A) Non-Linear Least-Squares Regressions
Independent Variables Selection Model1 Non-Endogenous Model2
Parameter
Estimates		 Standard
Errors		 Parameter
Estimates		 Standard
Errors
Constant -3.058 (1.278)** -3.308 (1.128)**
INSURANCE CHOICE
POS 1995 -0.911 (1.434) -0.099 (0.102)
DEMOGRAPHIC CHARACTERISTICS
Gender 0.433 (0.094)** 0.377 (0.068)**
Age 0.100 (0.045)** 0.102 (0.045)**
Age squared -1.180 (0.482)** -1.178 (0.495)**
Metropolitan statistical area -0.056 (0.251) -0.202 (0.060)**
EMPLOYMENT STATUS
Early retiree 0.256 (0.257) 0.343 (0.145)**
HEALTH CONDITIONS
Number of unique MDCs 1995 0.242 (0.017)** 0.243 (0.016)**
DISABILITY STATUS
Activity limiting condition 0.487 (0.072)** 0.495 (0.068)**
Mental per se disability only -0.649 (0.076)** -0.632 (0.074)**
Physical and mental per se disability — — — ---
SELECTION PARAMETER
Theta1 0.502 (0.850) — ---
Sum of Squared Residuals3 1904084.230 194096.570
* significant at the p = .05 level. ** significant at the p = .01 level. Results for Employer A are based on 1995 data only. Drug subsample includes prescription users only.
  1. The selection model controls for the joint determination of insurance coverage and the outcome variable. The extent of this joint determination is captured by the variable theta.
  2. In the non-endogenous model, insurance choice and outcomes are treated as separate decisions.
  3. The sum of squared residuals is a measure of model fit. A smaller value indicates a better fit.


TABLE D-13. HMO versus Indemnity Insurance Choice (Employer B) Results from Probit Regressions
Independent Variables Model 1 Model 2
Parameter
Estimates		 Standard
Errors		 Incremental
Effects1,2		 Parameter
Estimates		 Standard
Errors		 Incremental
Effects1,2
Constant 1.089 (0.049)** 0.425 1.000 (0.048)** 0.391
DEMOGRAPHIC CHARACTERISTICS
Gender 0.030 (0.018)* 0.012 0.052 (0.017)** 0.020
Age -0.025 (0.001)** -0.010 -0.025 (0.001)** -0.010
Child 0.035 (0.038) 0.014 -0.015 (0.038) -0.006
INSURANCE STATUS
Spouse 0.031 (0.021) 0.012 0.035 (0.021)* 0.014
Dependent -0.615 (0.046)** -0.240 -0.576 (0.046)** -0.225
EMPLOYMENT STATUS
Early Retiree -0.366 (0.029)** -0.143 -0.373 (0.029)** -0.146
PAST HEALTH CARE USAGE
Number of outpatient claim-days 1994 -0.008 (0.001)** -0.003 — — ---
Number of admissions 1994 0.023 (0.015) 0.009 — — ---
DISABILITY STATUS
Activity limiting condition -0.002 (0.019) -0.001 -0.020 (0.019) -0.008
Mental per se disability only -0.020 (0.029) -0.008 -0.039 (0.029) -0.015
Physical and mental per se disability -0.103 (0.040)** -0.040 -0.164 (0.040)** -0.064
Percent Correctly Predicted3 63.160 62.484
HMO 47.016 44.972
Indemnity 75.082 75.418
* significant at the p = .05 level. ** significant at the p = .01 level. Model 1 (columns 1-3) used 1994 utilization measures as predictors of insurance choice, while Model 2 (columns 4-6) excluded these 1994 variables.
  1. For categorical variables, an incremental effect measures the change in the probability of choosing the HMO plan associated with that characteristic.
  2. For continuous variables, the incremental effect measures the change in the probability of choosing the HMO associated with a one-unit change in the variable of interest.
  3. The percent correctly predicted is determined by applying the parameter estimates to the sample data and comparing the resultant predicted choices to the actual choices.


TABLE D-14. Number of Admissions (Employer B) Non-Linear
Least-Squares Regressions
Independent Variables Selection Model 11 Selection Model 21 Non-Endogenous Model2
Parameter
Estimates		 Standard
Errors		 Parameter
Estimates		 Standard
Errors		 Parameter
Estimates		 Standard
Errors
Constant -2.367 (0.652)** -3.532 (1.850)* -4.358 (0.668)**
INSURANCE CHOICE
HMO 1995 -2.111 (0.717)** -0.906 (2.704) 0.094 (0.071)
DEMOGRAPHIC CHARACTERISTICS
Gender 0.340 (0.069)** 0.351 (0.075)** 0.330 (0.070)**
Age -0.007 (0.024) 0.013 (0.032) 0.023 (0.026)
Age Squared 0.000 (0.000) 0.000 (0.000) 0.000 (0.000)
Child 0.017 (0.368) -0.086 (0.392) -0.086 (0.393)
INSURANCE STATUS
Spouse 0.252 (0.083)** 0.227 (0.100)** 0.215 (0.083)**
Dependent -0.885 (0.580) -0.344 (0.791) -0.109 (0.642)
EMPLOYMENT STATUS
Early Retiree -0.155 (0.158) 0.002 (0.427) 0.135 (0.101)
HEALTH CONDITIONS
Number of unique MDCs 1995 0.237 (0.015)** 0.261 (0.016)** 0.260 (0.016)**
DISABILITY STATUS
Activity limiting condition 0.639 (0.089)** 0.648 (0.087)** 0.655 (0.086)**
Mental per se disability only -0.752 (0.136)** -0.733 (0.138)** -0.718 (0.137)**
Physical and mental per se disability 0.234 (0.108)** 0.267 (0.174) 0.327 (0.107)**
SELECTION PARAMETER
Theta 1.319 (0.395)** 0.616 (1.644) — ---
Sum of Squared Residuals3 11338.523 11375.722 11377.612
* significant at the p = .05 level. ** significant at the p = .001 level. Model 1 (columns 1-3) used 1994 utilization measures as predictors of insurance choice, while Model 2 (columns 4-6) excluded these 1994 variables.
  1. The selection model controls for the joint determination of insurance coverage and the outcome variable. The extent of this joint determination is captured by the variable theta.
  2. In the non-endogenous model, insurance choice and outcomes are treated as separate decisions.
  3. The sum of squared residuals is a measure of model fit. A smaller value indicates a better fit.


TABLE D-15. Number of Hospital Days (Employer B) Non-Linear
Least-Squares Regressions
Independent Variables Selection Model 11 Selection Model 21 Non-Endogenous Model2
Parameter
Estimates		 Standard
Errors		 Parameter
Estimates		 Standard
Errors		 Parameter
Estimates		 Standard
Errors
Constant -5.731 (3.288)* -3.850 (7.109) -4.243 (1.154)**
INSURANCE CHOICE
HMO 1995 1.481 (2.245) -0.102 (8.121) 0.344 (0.167)**
DEMOGRAPHIC CHARACTERISTICS
Gender 0.629 (0.171)** 0.585 (0.206)** 0.575 (0.140)**
Age 0.092 (0.061) 0.069 (0.101) 0.073 (0.042)*
Age Squared -0.001 (0.000) -0.001 (0.000) -0.001 (0.000)
Child -1.011 (0.724) -0.817 (0.710) -0.823 (0.721)
INSURANCE STATUS
Spouse -0.045 (0.116) -0.026 (0.140) -0.030 (0.111)
Dependent 1.475 (1.440) 0.878 (2.482) 0.989 (1.061)
EMPLOYMENT STATUS
Early Retiree 0.443 (0.221)** 0.311 (1.102) 0.369 (0.156)**
HEALTH CONDITIONS

Number of unique MDCs 1995

 0.314 (0.033)** 0.295