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re: st: Regression Discontinuity (RD) Designs, sharp discontinuity: basic question about implementation with "rd"


From   "Ariel Linden, DrPH" <ariel.linden@gmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   re: st: Regression Discontinuity (RD) Designs, sharp discontinuity: basic question about implementation with "rd"
Date   Tue, 11 Oct 2011 11:09:18 -0400

Hi Stefano,

I am a bit confused by your variables. If I understand correctly, your X
variable is previous job tenure which is ranges from 0-52 months and your
cutoff is 36. However, your "treatment" is whether a person gets severance,
which, I am assuming can be at any point along the X variable continuum?

In the RD design, the cutoff is the treatment assignment, so to make it
work, you'd have to have everyone at or above 36 months receive severance
and everyone below 36 months not receive severance. I am not sure that is
what you have done here? 

I am not an economist (I don?t even play one on television), but I am not
sold on the premise that length of previous tenure is associated the outcome
variable (unless it is mediated vis-à-vis the severance). I also assume that
the size of the severance will be associated with the Y variable, and may or
may not have a strong independent association with the X variable (the
recent CEO of HP just got fired after a year on the job and got a
multi-million dollar severance). Thus, the type of position (or perhaps
salary level of previous job) will moderate the relationship.

Therefore, I am not sure you have the right variables, or the right modeling
approach here. Perhaps you should consider switching to a mediation
(controlling for moderators) approach, or perhaps a time series approach
with two or three variables, (a) length of previous job tenure, (b) length
of time unemployed thereafter, (c) relative size of severance?

I hope this helps

Ariel 




Date: Mon, 10 Oct 2011 21:15:37 +0200
From: Stefano Lombardi <lombardi_stefano@fastwebnet.it>
Subject: st: Regression Discontinuity (RD) Designs, sharp discontinuity:
basic question about implementation with "rd"

Hello everybody,

I have a big problem in computing a sharp regression discontinuity 
design via the "rd" function. I have read a number of papers about the 
underlying theory, but I cannot carry out even a very basic RD design.. 
Unfortunately I found very little information on Statalist and on the 
whole Internet as well.. Could you please give a hand?  Every comment 
would be tremendously helpful. Here is my (labor economics) setting:

"tenure_cat":    discrete forcing variable, Z = last job tenure (in 
months = 13, 14, ..., 52)
"severance":     treatment, X_T = lump-sum severance payment
"nonendur":     outcome, y = non-employment duration (days between the 
layoff and the start of the new job)
The cut-off is at Z_0 = 36 months (after three years of job tenure, a 
person who is laid off is going to receive a severance payment with 
probability 1).
Does the severance payment cause a variation in the job search?

I also have "mean_nonedur" = "nonedur" mean conditioned on "tenure_cat" 
(basically the mean of y for each month between 13 to 52)

My aim is to set a RD design with the mean nonemployment duration in 
days against Z in months. My first best would be to estimate the outcome 
gap through a second or higher order polynomial. All the data "far" from 
the cut-off have already been manually eliminated, hence I simply need 
to run the RD design with all the available data.


As very first step, I simply tried to run the following command:

. rd nonedur sevpay ten_cat, z0(36)
Three variables specified; jump in treatment
at Z=36 will be estimated. Local Wald Estimate
is the ratio of jump in outcome to jump in treatment.

  Assignment variable Z is ten_cat
  Treatment variable X_T is sevpay
  Outcome variable y is nonedur

Estimating for bandwidth 9.826534218815946
A predicted value of treatment at cutoff lies outside feasible range;
switching to local mean smoothing for treatment discontinuity.
score variables for model __00000P contain missing values
r(322);

Probably is nonsense, but I also tried to run the same command with 
"mean_nonedur" instead of "nonedur".. same result from Stata.

Could you give me any suggestion about this issue? Is there something 
related to the bandwidth choice?

Thank you very much,

Stefano Lombardi


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