How to solve the significant differences in the socioeconomic characteristic of the samples in Multi-data?

[Xue Gao]

Hello!
I want to combine data from three places. However, there are significant differences in the socioeconomic characteristics of the samples in the three places. How can I solve this problem?
Thank you very much!

[Joseph Coveney]

What problem? Locality and poverty (wealth) correlate. That's not new.

What is it that you're trying to do?

[Xue Gao]

I investigated the willingness to pay of protecting a beach resource in A area, a beach resource in B area and a beach resource in C area. I want to combine data to study three beach difference. However, there are significant differences in the socioeconomic characteristics of the samples in the three places. For example, income and education are different in three areas. How can I solve the difference in socioeconomic characterristic?
Thank you very much.

[Joseph Coveney]

I'm not sure what you mean by "solve the difference", but you might be able to form an interaction term of whatever index of socioeconomic status that you're using (continuous or nearly so, I assume) and beach area (three categories). Use Stata's factor variable notation.

Then use margins at some level of interest of the index to explore what kinds of difference in the outcome variable (willingness-to-pay) you get between beach areas.

[Xue Gao]

I'm not sure what you mean by "solve the difference", but you might be able to form an interaction term of whatever index of socioeconomic status that you're using (continuous or nearly so, I assume) and beach area (three categories). Use Stata's factor variable notation.

Then use margins at some level of interest of the index to explore what kinds of difference in the outcome variable (willingness-to-pay) you get between beach areas.

Because there are significant differences in the socioeconomic characteristics of the samples in the three places. I can't merge data from three places and do the regression directly. I have to find a way to solve the sample difference of socioeconomic characteristics. Do the interaction can solve the problem?
Thanks for your help.

[Joseph Coveney]

Why can't you merge data from three places? Are you saying that the index for socioeconomic status for Beach Area A is qualitatively a different type of index from that for Beach Areas B and C, sort of like a Byrom's SES index on a scale of 0 to 100 that weighs education more heavily versus a Wolcot's SES index on a scale of 1, 2 and 3 that weighs income more heavily? What is your problem here?

If there is modest quantitative difference in the three means of a single SES index, then might want to consider multiple regression with the index as a covariate (think: ANCOVA). You can use the index as a covariate ("main effects" alone). Or, as I suggested, you can add an interaction term, as well. If you include an interaction term, even if it is "not significant", for margins you would choose a level of the index that has a fairly good representation between the three beach areas. Either way, if you need to adjust for differences statistically, you'll be seasoning your dish with a hefty amount of assumptions.

You can extend this approach directly if you have multiple indexes of socioeconomic status, say, a variable for educational attainment, and one for net worth, one for annual income and so on.

I have never used this with more than two groups, but you might be able to consider a propensity score approach if you have a lot of data and a decent level of overlap in the values of the score between beach areas.

Are you saying that you have absolutely no overlap in observations of the SES index between beach areas? That there is complete confounding?

Again, what is your problem?

[Morad Bali]

Joseph is right regarding the use of indexes. This is the solution to your "problem" as explained in the first place.

Personally, I wonder why you absolutely want to merge it all ?
If you have similar variables in your areas, then you might want to simply run one model for each. The use of indexes would be indeed the best (particularly to ease interpretations).

It would be easier to have a sample of your data, or at least a clear explanation of what you have (frequency, units, etc.). For example, I wonder what variable you use in order to represent the "the willingness to pay". If it is an index that you created yourself, then you might want to run 3 SVAR. Impulse Response function will help you to isolate the impact of other variables on the "the willingness to pay". It's a possible way to do it. As Joseph said, ANCOVA is also a possibility.

[Xue Gao]

The index for socioeconomic status for three areas is same. There is only quantitative difference in the three means of a single SES index.
WTP (willingness to pay) in the three areas is different. There are two factors that affect WTP level, one is the beach resource themselves and the other is socioeconomic characteristics. I want to study the impact of the beach resource themselves on WTP. But I don't know how to solve the difference in the socioeconomic characteristics of the samples.
Thank you!

[Joseph Coveney]

But I don't know how to solve the difference in the socioeconomic characteristics of the samples.
Thank you!

Well, I count at least four suggestions above. Do you plan to look into any of them or do you intend to continue with the daddy-make-it-go-away line?

[Xue Gao]

Well, I count at least four suggestions above. Do you plan to look into any of them or do you intend to continue with the daddy-make-it-go-away line?

Thank you for your help!
I think i got it. I will use margins ( Adjusted Predictions at Representative values) at some level of interest of the index to find the difference in WTP.
Thank you!