# Table 7 OLS regression results

Dependent variable:   Dummy = 1 if paid
X Male = 1 Dirt floor = 1 SD (time) > median
Price − 0.001932 [0.000503]*** − 0.002326 [0.000633]*** − 0.001215 [0.000703]* − 0.002098 [0.000540]***
Waiting time 0.001209 [0.000336]*** 0.001038 [0.000419]** 0.001607 [0.000500]*** 0.002405 [0.000945]**
Price*X = 1   0.000987 [0.001036] − 0.001535 [0.001018] 0.000303 [0.000435]
Waiting time*X = 1   0.000438 [0.000719] − 0.000734 [0.000672] − 0.001218 [0.001005]
Observations 279 279 279 279
WTP − X 0.63 0.45 1.32 1.15
F-statistic for test WTP = 0 7.21*** 4.2** 2.47 6.46**
WTP − X = 1   1.10 0.32 0.66
F-statistic for test WTP = 0   2.16 3.02* 6.51**
Difference in WTP between X = 0 and X = 1   − 0.66 1.01 0.49
F-statistic for test of difference in WTP = 0   0.71 1.36 2.18
Socioeconomic controls Yes Yes Yes Yes
Date fixed effects Yes Yes Yes Yes
1. Robust standard errors in brackets
2. The dependent variables are dummy taking value of one if individuals paid for the non-waiting consult Column 2 presents the results by gender (interacting with an indicator for males). Column 3 shows estimates by type of floor in the individual’s dwelling and column 4 shows interactions of the price and time variables with an indicator of whether the standard deviation in the announced waiting time during the day of the patient’s visit was higher than the median in our sample
3. Table 4 in the main text is the analog of this table. However, in this case we assume a linear probability model and estimate the regression through ordinary least squares (tables in the main text are estimated through a logit regression)
4. * Significant at 10%
5. ** Significant at 5%
6. *** Significant at 1%