[George Tim]

With daily data and 10 stores, I took this to mean N = 10 and T is pretty large. The five states doesn't directly factor in. If so, the Driscoll-Kraay standard errors are entirely appropriate. I think you should include state fixed effects. You're interested in the coefficient on the treatment dummy, which happens to be an interaction. Without controls, this is the usual difference-in-differences estimator using the sample averages of the treated and control, before and after. D-K gives a convenient way of obtaining a standard error.

Thank you! Since I don't have pre-treatment data, I can't do diff-in-diff and only a twoways fixed effect model. In this case, I will have to add control variables. One possible control is fuel prices. The policy dummy affects weekly fuel prices (not the other way around) as well and fuel prices also affects the dependent variable (weekly retail food price). While choosing controls, do they need to be correlated to the main interaction variable or is it okay if it is correlated to only one of dummies in the interaction variable? Can I use fuel price as a control?

[Pintu Batra]

I am using a cross sectional dataset of 1,00,000 individuals with the information of their incomes (in Indian Rupee), education (in years), male(=1 if male and 0 otherwise), current age (in years). I am trying to estimate the relationship between income (dependent variable) and education (independent variable). I am confused between using the two strategies.

1. Include sex as a binary variable and current age as a control. In this case, my regression command is as follows:

 reg income education i.male age

(1)

2. As an alternative to this, I am told to use current age as fixed effects. In a nutshell, I would create age dummies and then include them in my regression model.

areg income education i.male i.age

(2).

Here are my doubts:

1. I am finding it difficult to understand the interpretation of the coefficients of education and male dummy in the two models.
2. How are they different from each other?
3. Which model should be preferred between the two?

I am also attaching the picture of the outputs as well.

Thanks in advance !!!

[Mukesh Punia]

1. If education is a ratio/level variable, then it is an increase in income with an increase in an additional year of education. But, check carefully your education variable, whether it is the year of schooling (i.e. 0-20 ..) or the level of education (i.e. no-schooling, primary, secondary, etc.)

2. In the case of age fixed effect, each age group has its own intercept, and simply using age as a continuous variable means an additional age increase or decrease in income.

3. Someone more experienced may suggest something better.

* while sing areg you use

areg income education i.male, ab(age)

[Clyde Schechter]

Elaborating a bit on what Mukesh Punia says regarding age vs i.age, when you use age your model constrains the relationship between age and income to be linear (after taking into account education and sex). By contrast, when you use i.age the relationship between age and income can be anything: income at any given age is independent of income at any other age, as each year of age gets its own intercept.

Fortunately, you did not use -absorb(age)- in your code,* so we can actually see the age coefficients. Just looking at them casually, while there is plenty of noise involved, they do appear to increase more or less linearly with age up to about age 50 and then seemingly plateau. This suggests that a model constraining the relationship to be linear would be mis-specified. Now, looking at the R² statistics for both models, we see that the change is small. And we also notice that the coefficients for sex and education don't differ much across the two models. So the difference may not be very important. One could quantify that by getting AIC and BIC statistics for each models (-estat ic-).

*I do not mean that it would be wrong to do so. I'm simply pointing out that by not using -absorb(age)- we were able to actually see the single-year age coefficients which made it possible to judge whether a linear age-income relationship seems right. If you used -absorb(age)- you would get the same results, except that the age coefficients would not have been shown and it would be more of a guess.