Suppose you are asked to evaluate a new staff training program to reduce wait times at the Department of Motor Vehicles (DMV) locations. The program is instituted at a randomly chosen 250 of the 500 DMV offices in the state at the beginning of the previous year. You have data for all visits to all the offices for the whole year. This data includes these variables: y: Appointment duration in minutes (including wait time) T: 1 if the office was assigned to the new program (treatment), 0 otherwise urban: 1 if the office is in an urban area, 0 otherwise a) You start by estimating this equation, using data on the entire sample of DMV offices assigned to Treatment and Control: Yi = 30+ 3₁T; +i Assuming the program had no effect on the duration of the non-waiting portions of the appointment, is ₁ an unbiased estimate of the effect of the program on wait times? Why or why not? b) Which parameter does ₁ identify if DMV offices in the Treatment group can choose whether to implement the new program, and only 70% of them decided to implement it (but you still use data on the entire sample of offices assigned to Treatment and Control to estimate this equation)? What about in the case in which there is full compliance with the Treatment, so that all DMV offices assigned to treatment actually implement it? c) Suppose you believe the treatment effect is different in urban and non-urban locations. You estimate the following model: Yi = ẞo + B₁T; + 3₂urban; + ßzurban; × T; +€į. What is the difference in expected wait time between a treated and un-treated location? 1 d) Suppose you estimate the above model and get the following estimates: What is the effect of the program in (i) an urban location? (ii) a non-urban location? Bo=15, 3₁ = -2, 3₂ = 10, 33 = -2, e) Suppose that instead of rolling out this program randomly, the central DMV management had asked local offices to come forward and apply for this program, and the program had been rolled out on a first come, first serve basis. Would B₁ in the above regression still identify the causal effect of the program on wait times? Why or why not?
Suppose you are asked to evaluate a new staff training program to reduce wait times at the Department of Motor Vehicles (DMV) locations. The program is instituted at a randomly chosen 250 of the 500 DMV offices in the state at the beginning of the previous year. You have data for all visits to all the offices for the whole year. This data includes these variables: y: Appointment duration in minutes (including wait time) T: 1 if the office was assigned to the new program (treatment), 0 otherwise urban: 1 if the office is in an urban area, 0 otherwise a) You start by estimating this equation, using data on the entire sample of DMV offices assigned to Treatment and Control: Yi = 30+ 3₁T; +i Assuming the program had no effect on the duration of the non-waiting portions of the appointment, is ₁ an unbiased estimate of the effect of the program on wait times? Why or why not? b) Which parameter does ₁ identify if DMV offices in the Treatment group can choose whether to implement the new program, and only 70% of them decided to implement it (but you still use data on the entire sample of offices assigned to Treatment and Control to estimate this equation)? What about in the case in which there is full compliance with the Treatment, so that all DMV offices assigned to treatment actually implement it? c) Suppose you believe the treatment effect is different in urban and non-urban locations. You estimate the following model: Yi = ẞo + B₁T; + 3₂urban; + ßzurban; × T; +€į. What is the difference in expected wait time between a treated and un-treated location? 1 d) Suppose you estimate the above model and get the following estimates: What is the effect of the program in (i) an urban location? (ii) a non-urban location? Bo=15, 3₁ = -2, 3₂ = 10, 33 = -2, e) Suppose that instead of rolling out this program randomly, the central DMV management had asked local offices to come forward and apply for this program, and the program had been rolled out on a first come, first serve basis. Would B₁ in the above regression still identify the causal effect of the program on wait times? Why or why not?
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:Suppose you are asked to evaluate a new staff training program to reduce wait times at the Department of Motor
Vehicles (DMV) locations. The program is instituted at a randomly chosen 250 of the 500 DMV offices in the state at
the beginning of the previous year. You have data for all visits to all the offices for the whole year. This data includes
these variables:
y: Appointment duration in minutes (including wait time)
T: 1 if the office was assigned to the new program (treatment), 0 otherwise
urban: 1 if the office is in an urban area, 0 otherwise
a) You start by estimating this equation, using data on the entire sample of DMV offices assigned to Treatment and
Control:
Yi = 30+ 3₁T; +i
Assuming the program had no effect on the duration of the non-waiting portions of the appointment, is ₁ an unbiased
estimate of the effect of the program on wait times? Why or why not?
b) Which parameter does ₁ identify if DMV offices in the Treatment group can choose whether to implement the new
program, and only 70% of them decided to implement it (but you still use data on the entire sample of offices assigned
to Treatment and Control to estimate this equation)? What about in the case in which there is full compliance with the
Treatment, so that all DMV offices assigned to treatment actually implement it?
c) Suppose you believe the treatment effect is different in urban and non-urban locations. You estimate the following
model:
Yi = ẞo + B₁T; + 3₂urban; + ßzurban; × T; +€į.
What is the difference in expected wait time between a treated and un-treated location?
1
d) Suppose you estimate the above model and get the following estimates:
What is the effect of the program in
(i) an urban location?
(ii) a non-urban location?
Bo=15, 3₁ = -2, 3₂ = 10, 33 = -2,
e) Suppose that instead of rolling out this program randomly, the central DMV management had asked local offices to
come forward and apply for this program, and the program had been rolled out on a first come, first serve basis. Would
B₁ in the above regression still identify the causal effect of the program on wait times? Why or why not?
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