<p>I think a DiD will not work here as the parallel trend assumption probably doesn't hold in your case.</p>
<p>However, given that the treatment is based on a number of parameters that are (I assume) observed, you could potentially use some type of discontinuity design to estimate the effect of the policy around the threshold values of these parameters.</p>
<p>To be more precise, your treatment neighbourhoods would be the the neighbourhoods that have the sign but are very close to loosing it, while the control neighbourhoods would be the neighbourhoods that are just below the threshold for obtaining the sign. Then if prices increases are a continuous function of all other covariates, the difference in treatment vs control would measure the effect of the signs for neighbourhoods around the threshold.</p>
<p>Of course there are potential endogeneity issues that you'll need to take into account as you can expect neighbourhoods to do some effort to get above the threshold.</p>

CC BY-SA 4.0
2021-05-22T19:47:14.347Z
This has given me some thought - thank you for the feedback. I was struggling with whether it was the correct method or not - I will look into a discontinuity design, thank you!
Sirmimer
CC BY-SA 4.0
2021-05-22T18:18:03.147Z
2021-05-22T18:18:03.147Z
tdm
<p>I have a panel data set of housing prices from different neighborhoods in a city - and wish to test whether or not a policy has had an impact on the housing prices. The policy (treatment) is simply a sign signaling whether it is a good or bad neighborhood (based on parameters which are already known).</p>
<p>Initially I figured I could mirror the paper by Card and Krueger (1994) with a difference-in-differences estimation however what complicates my issue is that the policy enables neighborhoods to go from good-bad and bad-good over a span of 10 years. So they could potentially get a treatment multiple times.</p>
<p>Any suggestions into papers which has studied policies in a similar fashion or at methods which could be applied is greatly appreciated.</p>

<econometrics>
Difference-in-Differences setup for policy evalution
Sirmimer
# Difference-in-Differences setup for policy evalution

(Asked by: username_0 on 2021-05-22)

I have a panel data set of housing prices from different neighborhoods in a city - and wish to test whether or not a policy has had an impact on the housing prices. The policy (treatment) is simply a sign signaling whether it is a good or bad neighborhood (based on parameters which are already known).

Initially I figured I could mirror the paper by Card and Krueger (1994) with a difference-in-differences estimation however what complicates my issue is that the policy enables neighborhoods to go from good-bad and bad-good over a span of 10 years. So they could potentially get a treatment multiple times.

Any suggestions into papers which has studied policies in a similar fashion or at methods which could be applied is greatly appreciated.

**Tags:** econometrics

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## 1 answers

### Answer by username_1 on 2021-05-22. Score: 5 | Accepted answer

I think a DiD will not work here as the parallel trend assumption probably doesn't hold in your case.

However, given that the treatment is based on a number of parameters that are (I assume) observed, you could potentially use some type of discontinuity design to estimate the effect of the policy around the threshold values of these parameters.

To be more precise, your treatment neighbourhoods would be the the neighbourhoods that have the sign but are very close to loosing it, while the control neighbourhoods would be the neighbourhoods that are just below the threshold for obtaining the sign. Then if prices increases are a continuous function of all other covariates, the difference in treatment vs control would measure the effect of the signs for neighbourhoods around the threshold.

Of course there are potential endogeneity issues that you'll need to take into account as you can expect neighbourhoods to do some effort to get above the threshold.

> **Comments:**
> **username_0**: This has given me some thought - thank you for the feedback. I was struggling with whether it was the correct method or not - I will look into a discontinuity design, thank you!  

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