<p>It is not a critical result at all.</p>
<p>First, the difference is very small. Both round to 0.61 and we don't usually look at further digits.</p>
<p>Most importantly though, the R-squared is largely irrelevant to difference-in-difference analysis.</p>
<p>You should be most worried about whether your identifying assumptions (e.g. parallel trends) hold. Secondly, you can consider whether the dummy may change the interpretation of your results. Lastly, consider if it could be a &quot;bad&quot; control in the theoretical sense, as discussed, e.g. in Angrist and Pischke's book &quot;Mostly harmless econometrics&quot;.</p>
<p>Other considerations will not matter for your attempt to causally identify the coefficient of interest, which is the goal of difference-in-difference.</p>
<p>All your result means (albeit oversimplified) is that the dummy has a bit less predictive power than the other variables on average. But you are not trying to predict/forecast the outcome variable. You are trying to causally identify an effect (of another variable). So even if the drop in R-squared were larger, it would not itself matter.</p>
<p>And if you are trying to forecast, then difference-in-difference is not the right method for you.</p>

CC BY-SA 4.0
2021-09-24T14:00:25.137Z
2021-09-24T14:00:25.137Z
BB King
<p>In my regression using OLS using Difference-in-Differences regression, when I add a dummy variable into my regression, the Adjusted R-squared reduce from 0.6117 to 0.6111. Is it a critical result and what should I talk about the impact of adding this dummy variable.
Many thanks in advance.</p>

CC BY-SA 4.0
2021-09-23T01:39:57.020Z
You can use the [F-test](https://sites.duke.edu/bossbackup/files/2013/02/FTestTutorial.pdfhttps://sites.duke.edu/bossbackup/files/2013/02/FTestTutorial.pdf) to compare the two models.
Herr K.
CC BY-SA 4.0
2021-09-23T05:11:52.313Z
I would not pay too much attention to $R^2_{adj.}$. See e.g. ["Justification for and optimality of $R^2_{adj.}$ as a model selection criterion"](https://stats.stackexchange.com/questions/408267).
Richard Hardy
<econometrics>
What should we conclude when the Adjusted R square decrease when adding a dummy variable into the regression?
Nguyen Lis
# What should we conclude when the Adjusted R square decrease when adding a dummy variable into the regression?

(Asked by: username_0 on 2021-09-22)

In my regression using OLS using Difference-in-Differences regression, when I add a dummy variable into my regression, the Adjusted R-squared reduce from 0.6117 to 0.6111. Is it a critical result and what should I talk about the impact of adding this dummy variable.
Many thanks in advance.

**Tags:** econometrics

> **Comments:**
> **username_1**: You can use the [F-test](https://sites.duke.edu/bossbackup/files/2013/02/FTestTutorial.pdfhttps://sites.duke.edu/bossbackup/files/2013/02/FTestTutorial.pdf) to compare the two models.  
> **username_2**: I would not pay too much attention to $R^2_{adj.}$. See e.g. ["Justification for and optimality of $R^2_{adj.}$ as a model selection criterion"](https://stats.stackexchange.com/questions/408267).  

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## 1 answers

### Answer by username_3 on 2021-09-24. Score: 4 | Accepted answer

It is not a critical result at all.

First, the difference is very small. Both round to 0.61 and we don't usually look at further digits.

Most importantly though, the R-squared is largely irrelevant to difference-in-difference analysis.

You should be most worried about whether your identifying assumptions (e.g. parallel trends) hold. Secondly, you can consider whether the dummy may change the interpretation of your results. Lastly, consider if it could be a "bad" control in the theoretical sense, as discussed, e.g. in Angrist and Pischke's book "Mostly harmless econometrics".

Other considerations will not matter for your attempt to causally identify the coefficient of interest, which is the goal of difference-in-difference.

All your result means (albeit oversimplified) is that the dummy has a bit less predictive power than the other variables on average. But you are not trying to predict/forecast the outcome variable. You are trying to causally identify an effect (of another variable). So even if the drop in R-squared were larger, it would not itself matter.

And if you are trying to forecast, then difference-in-difference is not the right method for you.


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