Interpretation of categorical ordinal outcome variable of Propensity Score Matching

Dear all,
I am investigating the impact of energy access on average daily income using propensity score matching.
My treatment variable is energy access – which is a binary variable (whether or not households connect to electricity). My outcome variables is average daily income which is a categorical ordinal variables.
I used the propensity score matching to estimate the effects of the programme but I am confused on how to interpret the output. Since I could not say, for example, the average daily income increased 0.4268293 1639686572256.png


Less is more. Stay pure. Stay poor.
Does ATET = average treatment effect on the treated?

If you have multiple outcome groups, you will likely have seven estimates. Each level versus the base case - given you are using mutinomial or ordinal logistic reg, right?


Less is more. Stay pure. Stay poor.
Can some one have 1000-3000 and 3000-5000 or 5000-7000, well no. So you have multiple outcome groups. Also of note which group is a person with an income off 5000 in?

Ordinal logistic regression.