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Table 3 Factors associated with IPs prevalence and amount paid (based on Probit and GLM)

From: Informal payments for inpatient health care in post-health transformation plan period: evidence from Iran

CharacteristicsPart 1 Paying informally with money, yes/no Yes = 1 (Probit)Part 2 Amount paid informally GLM
VariablesCoef (SE)p-valueMarginal effects (SE)Coef(SE)p-value
Sex, female−0.14(0.10)0.185−0.01(0.18)− 0.57 (0.29)0.052
Adult, yes−0.88(0.15)0.000−0.14(0)1.25 (1.05)0.236
Residence (ref: country’s capital, Tehran)
 Other city0.26(0.11)0.0150.03(0.01)0.06 (0.29)0.827
 Village−0.29(0.25)0.228−0.03(0.19)0.22(0.63)0.722
 Insured, yes0.75(0.25)0.0030.06(0)4.76(0.40)0.000
 Hospital stay, days−0(0)0.001−0(0.86)− 0(0)0.000
Hospital type (ref: public)
 Private0.38(0.13)0.0030.05(0.01)−1.13(0.48)0.018
 Social−0.7(0.21)0.001−0.06(0)−2.78(1.24)0.025
Hospital service (ref: surgery)
 Medical treatment0.87(0.15)0.0000.09(0)1.11(1.02)0.277
 Diagnostic measures0.80(0.19)0.0000.08(0)1.49(1.11)0.182
 Caesarean Section0.43(0.55)0.4340.04(0.5)−5.290.98)0.000
 Other1.47(0.2)0.0000.20(0)0.50(0.97)0.603
 Household size0.01(0.02)0.6380(0.64)− 0.15(0.05)0.002
 Household income, monthly0(0)0.0000(0)0(0)0.183
 Household head, age−0.03(0)0.000−0(0)0(0)0.735
Household head, an education level (ref: primary)
 High school−0.60(0.14)0.000−0.06(0)0.920.023
 College−0.04(0.13)0.740−0(0.74)0.110.698
N of respondents2027  310 
 Prob>chi2 = 0.0000  AIC = 30.10 
 Pseudo R2 = 0.5318  BIC = -120.06 
  1. Source: Authors’ analysis of data from Informal Patient Payments dataset
  2. Notes: Bolding used to reflect P values < 0.05. 0(0) values represent extremely low coefficient values