<p>I am trying to find the relationship between one independent variable with two dependent ones. I was doing some reading, and I can use MANOVA to regress this.</p>
<p>However, my original ID strategy was to use RDD because of a natural experiment I'm trying to observe. Hence, I assume I cannot combine these two.</p>
<p>I am trying to observe if there was a change of migration on education quality and quality of life when this reform took place.</p>
<p>Would it be better if I use two separate RDD regressions, or would that cause an issue?</p>
<p>If there exists some other strategy, I would appreciate every suggestion.</p>

CC BY-SA 4.0
2021-11-25T13:04:31.883Z
I think it is common in the literature to use separate RDD regressions analyse the effect of a `treatment' on various different outcomes. So I guess using two separate RDD regressions would not be a problem.
tdm
CC BY-SA 4.0
2021-11-25T14:13:38.010Z
Ok, I see. Thank you for your suggestion!
Maybeline Lee
<regression><identification>
Identification strategy
Maybeline Lee
# Identification strategy

(Asked by: username_0 on 2021-11-25)

I am trying to find the relationship between one independent variable with two dependent ones. I was doing some reading, and I can use MANOVA to regress this.

However, my original ID strategy was to use RDD because of a natural experiment I'm trying to observe. Hence, I assume I cannot combine these two.

I am trying to observe if there was a change of migration on education quality and quality of life when this reform took place.

Would it be better if I use two separate RDD regressions, or would that cause an issue?

If there exists some other strategy, I would appreciate every suggestion.

**Tags:** regression, identification
> **Comments:**
> **username_1**: I think it is common in the literature to use separate RDD regressions analyse the effect of a `treatment' on various different outcomes. So I guess using two separate RDD regressions would not be a problem.  
> **username_0**: Ok, I see. Thank you for your suggestion!  

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## 0 answers

