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# Overlap in the assignment of kids to Gifted and Talented (GT) education

INSTRUCTIONS TO CANDIDATES

Part 0 – Open Stata, and make your own do-file

Using windows explorer, make a new folder called c:/rproject/clab42.

Download the datafile gt_data.dta and dofile progs.do from Cv and put it in this folder.

Start Stata through the start menu button

In de white command window type doedit to start de do-file editor. Place the Stata screen on the left and the do-file editor on the right such that you can easily switch between the two.

In the first two lines of the do-file type

cls //this clears the screen

clear all //this clears the memory cd "c:/rproject/clab42" //this is your path

Save the do-file as clab42.do.

In the following, as always, don’t forget to apply Rules 1&2: paste the command in your clab42.do

file, press

to save and

to run.

Part 1 – Overlap in the assignment of kids to Gifted and Talented (GT) education

In these exercises you are going to replicate results from the lecture and estimate the effect of following a gifted and talented program (gt) on students’ math and language scores in secondary education. The data are drawn from Booij et al. (2016). In Part 1 you will try and figure out if you could apply a Regression Control (RC) design, as you did in previous CLabs 3.1 and 3.2.

1. Open the data and familiarize yourself using desc and sum. Visualize the data using

hist XXX, frac name(hist_XXX)

where XXX is the name of the variable that you are looking at. Check out all variables (except id, of course). What variables do you think are realized prior to, and what are realized post of the GT progam?

2. To estimate the effect of the gt we will ultimately have to compare individuals that get the program (gt=1) to those that don’t (gt=0). Type

tabstat *, by(gt) format(%6.2f)

to see the difference in means between these two groups in terms of all variables in the dataset. Are both groups comparable? Do we have apples to apples or apples to oranges here? If there is selection, is it positive or negative?

3. Observing imbalance (prior differences between the groups) is not a good sign, but not necessarily a problem. Imbalance with respect to gender, for example, only poses a big problem if it predicts math and/or language scores. A simple way to check this is to do a regression using prior variables as predictors. For simplicity we will neglect cohort and only look at math:

reg resmath male age ist cito, r

Is gender predictive? So, is imbalance with respect to gender a problem?

It is not always easy to interpret the coefficients from a regression. The coefficient of age, for example,

π½Μππππ  = −.1639968 and significant (π‘ − π π‘ππ‘ = −3.89). What does it mean? It means that when a student’s age is 1 year higher, her predicted grade is 0.16 lower. That seems a lot, but is it? That depends on how big a 1 year change is.

Recall from q1 that ππ·(πππ) = 0.48. A 1 unit change in age is more than 2 × ππ·(πππ). If ±2 × ππ·(πππ) around the mean contains about 95% of the data, 2 × ππ·(πππ) must contain about 47.5%. So if your age is 1 year higher, you “overtake” about 47.5% of students and go from being one of the youngest to one of the oldest. A big change. Also, you can look at the histogram of age to see if you think a 1 year age difference is large or not given this sample.

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