[Clyde Schechter]

OK, so your situation is much more complicated than it originally seemed.

Given that you have interactions of migration with both age group and farm size, there is no such thing as "the effect of migration for age group 15-25." So, no, you cannot estimate what does not exist. There are different effects of migration for age group 15-25, one for each category of farm size. Now, there are various ways you can calculate some sort of summary of these effects, of which the commonest would be to average them, weighting the average by the proportion of study participants in each farm size category. This would be known as "the average marginal effect." This is also a statistic that -margins- could give you if you were able to use it.

I think the best you can do is to calculate an average of appropriate coefficients here. In terms of your model equation what you want is (beta1 + beta6 + beta7 + beta8)/4. Or better, calculate the proportions of the estimation sample in all size categories, let's call them w1, w2, w3, and w4, where w1+w2+w3+w4 = 1. A better estimator is w1*beta1 + w2*beta6 + w3*beta7 + wr*beta8. You can calculate that with -lincom-. Of course, to use -lincom- you will have to replace the beta's with the actual names of the various _b[] coefficients output by your regression.

[Sapna Goel]

Thank you so much for the clarification. I appreciate it.


However, I wanted to ask what if i include the interactions one by one.
for example:
First, I could run this:
Farm_days= alpha +beta1(migration) +beta2(migration*(25-35)) +beta3(migration*(35-45))+beta4(migration*(45-59)) + beta5(upper_caste)+beta6(household income)

caste: upper caste, lower caste (based)

I have other controls, but won't be relevant for my query here.

So now the effect of migration for (15-25) will simply be: beta
Effect of migration for (25-35) will be: beta1+beta2.


And then I run a separate regression (say):

Farm_days= alpha+beta1(migration) +beta2(small_farm*migration) +beta3(medium_farm*migration)+ beta4(large_farm*migration) + other controls


Now the effect of migration for landless category will be: beta1
Effect of migration for small farm: beta1+beta2


So, basically, what I am saying is that is it conceptually okay if I only include one interaction term at a time.


So, first I run a regression in which I don't take any interaction terms. From that regression, I will figure out the effect of migration on farm_days, both within and in-between effect.

Then i include the interactions one by one, and only one at a time see how the effect of migration may vary depending on particular household and individual level characteristics.
This way, it would be very easy. But I am not sure if it would be the right way to do it or not.
Kindly clarify.
If there is also some textbook reference for these, lemme know.

So far, I have framed my model following Schunck et al.,(2013).

[Sapna Goel]

Hello

I have some doubts in understanding the hybrid model.

The hybrid model divides an effect into two parts: in-between effect and within effect.

I will ask my doubt with an example:

I am working with a panel data set with monthly observations.
My dependent variable is farm days spent by women in a month.

My independent variable is household level monthly migration status. We say a household is migrant household if any male member of the household migrated in a given month.

now, my understanding is that the within effect captures: if in a given household migration happens, then how does it affect the days spent by women compared to if migration didn't happen.

For the in-between effect: we are comparing the difference in farm days spent by women between a migrant and non-migrant family.

However, the household level monthly migration status obviously changes on a month -to-month basis.
So in the in-between effect, what exactly are we getting.
Is it a comparison between families that were migrant families some months and families that didn't have any migrant member during the entire study period ?

Or is it this:
Suppose family A migrates in January. Family B doesn't migrate in January. Family C doesn't migrate in January. So we compare the difference in farm days between family A on one hand and Family B and Family C on other hand.
Then suppose Family A doesn't migrate in February. Family B migrates. Family C migrates. Then are we comparing the difference in farm days between family B and C on one hand and family A on other hand?

And then we sort of average out the effects?

Also, if I add with-in and in-between effect, do I get a meaningful coefficient?

Kindly clarify.
Thank you