# How to interpret logistic regression coefficients with interactions between binary and continuous variables?

I know this has been already asked, but I am quite confused about the interpretation of logit regression estimates if I have interacted variables (continuous and binary ones).

I run the following regression:

model <- glm(elected ~ treat + factor(School) + factor(Race) +
treat*Treat.City, data = subset(df, Year == 2016),


My dependent variable elected is equal to 1 if a political candidate got elected, 0 otherwise. treat equals 1 if the candidate belongs to a treatment group, 0 if belongs to the control group.

After controlling for schooling and race dummy variables, I have put the interaction treat*Treat.City, in which Treat.City is a continuous variables indicating the percentage of treatment candidates in relation to the total number of challengers inside candidate's i city.

Running the regression in R, I have the following results:

Call:
glm(formula = elected ~ treat + factor(School) + factor(Race) +
treat * Treat.City, family = binomial(link = "logit"), data = subset(df,
Year == 2016))

Deviance Residuals:
Min      1Q  Median      3Q     Max
-1.875  -1.321   1.000   1.039   1.262

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)                        0.42387    0.15196   2.789 0.005281 **
treat                             -0.22397    0.03879  -5.775 7.71e-09 ***
factor(School)MÉDIO_INCOMPLETO     0.04055    0.03452   1.174 0.240227
factor(School)SUPERIOR_COMPLETO    0.11976    0.03221   3.718 0.000201 ***
factor(School)SUPERIOR_INCOMPLETO  0.11576    0.02947   3.929 8.55e-05 ***
factor(Race)BRANCA                -0.12757    0.15054  -0.847 0.396742
factor(Race)INDÍGENA              -0.57795    0.26393  -2.190 0.028542 *
factor(Race)Preta_Parda           -0.20933    0.15073  -1.389 0.164897
Treat.City                         1.50083    0.61352   2.446 0.014435 *
treat:Treat.City                   2.80625    0.95484   2.939 0.003293 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 54123  on 39893  degrees of freedom
Residual deviance: 54033  on 39884  degrees of freedom
AIC: 54053

Number of Fisher Scoring iterations: 4


How can I interpret such coefficients? More specifically, how can I numerically make a statement about the effect of the treatment on the probability in getting elected?

Can I make any clear interpretation about this 'Intensity of treatment' variable that Treat.City is?

The odds of being elected when you are not treated increases by a factor $\exp(1.50083)\approx 4.49$ or $(4.49-1)\times100\%=349\%$ if you move from a city with no one treated to a city where everyone is treated.
This effect of Treat.City increases by a factor $\exp(2.80625\approx16.55)$ or $(16.55-1)\times100\%=1555\%$ if one is treated. For more see: http://maartenbuis.nl/publications/interactions.html
Given the large size of the effect I will assume that Treat.City is not a percentage but a proportion. The effects will be more realistic and easier to interpret when you turn Treat.City into percentages.
• Thank you! Is there a way I could evaluate, everything else constant, the difference in the predicted probability of being elected of two treated candidates? The only difference between them would be the Treat.City variable - one being on the 25th percentile, the other on the 75th? Commented Oct 18, 2017 at 0:28
• assuming I want to do something simpler: I calculate predicted values for my dependent variable evaluating each covariate at its mean for two hypothetical units. The only difference between them would by the value of Treat.City. If the difference of my predicted elected variable between these two is 0.49, does this means one is 49 percentage points more likely to get elected than the other? Commented Oct 19, 2017 at 16:07