Empirical Exercise 3
In this exercise, we’re going to analyze data from Ignaz Semmelweis’ handwashing intervention in the maternity hospital in Vienna. The data come from
Semmelweis’ (1861) book, and some helpful person put them on Wikipedia.
We’ll be reading them in from Excel using the import excel command.
In this exercise, we’ll review the different ways to estimate simple difference-in-differences models. We’ll also learn how to export results to word, excel, or latex using Stata’s esttab and putexcel commands. The putexcel command is more customizable, but it also takes more work (i.e. coding). esttab is a very straightforward tool for getting basic regression results out of stata and into pretty much any format.
You can access the in-class activity (below) as a do file or pdf.
You can also access the main empirical exercise (also below) as a do file or pdf.
Getting Started
Data on maternal mortality rates in Vienna are contained in the Excel file E3-Semmelweis1861-data.xlsx. The spreadsheet inlcudes annual data from 1833 (when the Vienna Maternity Hospital opened its second clinic) through 1858. Mortality rates are reported for Division 1 (where expectant mothers were treated by doctors and medical students) and Division 2 (where expectant mothers were treated by midwives and trainee midwives from 1841 on). In Semmelweis’ difference-in-differences analysis, Division 1 was the (ever-)treated group.
Our first task is to import this Excel file into Stata using the import excel command. The
do file E3-questions.do does this: after the usual top-of-the-do-file preliminaries, it includes the
command:
import excel using E3-Semmelweis1861-data.xlsx, ///
sheet("ViennaBothClinics") first
The option sheet tells Stata which worksheet within the Excel file E3-Semmelweis1861-data.xlsx to select. The option
first indicates that the first row of the spreadsheet should be treated as variable names and not as one of the observations.
After importing the data, the do file then adds labels to all the variables using the label variable command (which can be
abbreviated as label var). Use the describe and summarize commands to familiarize yourself with the data set. Which variable
records the maternal mortality rate in Division 1 of the hospital? What is the average maternal mortality rate in Division 1? What is
the average maternal mortality rate in Division 2?
The next lines of the code in the do file save the data in Stata format, and then graph maternal mortality rates in Division 1
and Division 2. If you have important the data correctly, Stata should generate a figure that looks like this:
What patterns do you notice in this figure? How do maternal mortality rates in the two divisions of the hospital compare?
In-Class Activity
Question 1
Use the list command to list the the notes contained in the data set by year. If you only want to list the rows of data
that include a note (i.e. where the Note variable is non-missing), you can add !missing(Note) at the end of the command.
Question 2
In what year did the hospital first move to the system where patients in Division 1 were treated by doctors and patients in Division 2 were treated by midwives? Drop the observations (years) before this happened using the drop command.
Make sure that you record this and all your subsequent commands in your do file, so that you can re-run your code later.
Question 3
Generate a post variable equal to one for years after the handwashing policy was implemented (and zero otherwise).
Question 4
What is the mean postpartum mortality rate in the doctors’ wing (Division 1) prior to the implementation of the handwashing policy?
Question 5
Now let’s put this result in a table. We’re going to use the putexcel command to write our results into an Excel file. putexcel
is a simple command that allows you to write Stata output to a particular cell or set of cells in an Excel file. Before getting started
with putexcel, use the pwd (“print working directory”) command in the Stata command window to make sure that you are writing your
results to an appropriate file. Use the cd command to change your file path if necessary (add this to your do file). Then set up the Excel file that will
receive your results using the commands:
putexcel set E3-DD-table1.xlsx, replace
putexcel B1="Treatment", hcenter bold border(top)
putexcel C1="Control", hcenter bold border(top)
putexcel D1="Difference", hcenter bold border(top)
putexcel A2="Before Handwashing", bold
putexcel A4="After Handwashing", bold
At this point, it is worth opening your Excel file to make sure that you are writing to it successfully. Be sure to close the file after you look at it; Stata won’t write over an open Excel file. The column and row labels should all appear in bold font (the bold option), and the column headings in cells B1, C1, and D1 should be centered (the hcenter option) and have a border above them (the border() option).
Question 6
Now that we know that putexcel is working, we can add the mean of the variable Rate1 (the maternal mortality rate in Division 1) for the years prior to the introduction of handwashing. You can use the return list command after a command like summarize to see what statistics the summarize command stored in Stata’s short-term memory as locals. Any of these statistics can be exported to Excel.
sum Rate1 if post==0
return list
We’re going to put the mean maternal mortality rate in Division 1 prior to the handwashing intervention in cell B2: the Treatment column, in the Before Handwashing row.
local temp_mean = string(r(mean),"%03.2f")
putexcel B2=`temp_mean', hcenter
The string command in the first line creates a local macro containing a string that is the mean calculated the last time you used the summarize command, rounded to 2 decimal places. The second line writes that local macro in our Excel file. Notice that the local macro being exported to Excel appears in single quotes. If you are just writing a local macro to Excel using the putexcel command, it does not need to appear in (double) quotes, but sequences of letters and numbers do need double quotes. The hcenter option tells Excel to center the number within the column.
We can calculate the standard error of the mean by taking the standard deviation (reported by the sum command) and dividing it by the square root of the number of
observartions (also reported by the sum command). What is the standard error of the mean postpartum mortality rate in the doctors’ wing prior to Semmelweis’ handwashing
intervention?
Question 7
We can calculate the standard error of the mean by taking the standard deviation (reported by the sum command) and dividing it by the square root of the number of observations (also reported by the sum command). What is the standard error of the mean postpartum mortality rate in the doctors’ wing prior to Semmelweis’ handwashing intervention? Use the code below to add this to your table.
local temp_se = string(r(sd)/sqrt(r(N)),"%03.2f")
putexcel B3="(`temp_se')", hcenter
Question 8
We can also use the ci means command to calculate the mean and standard error of a variable. Use
return list to see what statistics are saved after ci means. Use ci means and putexcel to add
the mean and standard error of Rate1 in the post-treatment period to your table.
Question 9
Calculate the difference (between the mean of Rate1 in the pre-treatment period and the mean of Rate1 in the post-treatment period) and the associated standard error by hand using the formula (and the display command).
Question 10
Now confirm that you get the same result using the ttest command. How would you export the results of the ttest command into your table using putexcel?
Question 11 (SKIP THIS IN CLASS)
Now complete the table.
Empirical Exercise
Create a do file that reads in your copy of Semmelweis’ data and restricts attention to the period when doctors worked in the first clinic and midwives worked in the second clinic. All your do files should start with the same set of commands at the top.
clear all
set scheme s1mono
set more off
set seed 314159
** change working directory as appropriate to where you want to save
cd "C:\Users\pj\Dropbox\ECON-523\topics\3-DD1\stata"
** load data
use E3-semmelweis-vienna-by-wing.dta
drop if Year<1840
Question 1
Use the following reshape command to convert your data into a panel data set containing a variable Rate and a variable clinic that indicates whether an observation comes from Clinic 1 (doctors) or Clinic 2 (midwives). How many observations are there in the data set now? How many from each clinic?
reshape long Rate, j(clinic) i(Year)
Question 2
Generate a post variable equal to one for years after the handwashing policy was implemented (and zero otherwise) and a treatment variable equal to one for the doctors’ wing (and zero otherwise).
Question 3
Generate the interaction term you need to estimate a difference-in-differences model in a regression framework.
Question 4
Use the label variable command to give your variables short, easy to interpret labels.
Question 5
Implement difference-in-differences in an OLS regression framework. Use the command eststo clear immediately before your regression command, and then use the command eststo (estimates store) immediately after. This will save your results.
Question 6
You can use the esttab command to make a table of your regression results. Try it by typing esttab in the command window. The command esttab using clinic-regs.rtf will save your table as a word document. Look through the esttab options to make your table look more professional. Report standard errors rather than t-statistics in parentheses below your coefficients. Have your variable labels appear in place of variable names, and make sure your first column is wide enough to accommodate the labels you have given your variables. Make the column with your regression coefficients say OLS at the top using esttab’s mtitle option. You can learn more about making tables in esttab here.
Question 7
Take a screenshot of your finished regression table and upload it to gradescope.
Question 8
Which coefficient in the regression table (i.e. the coefficient on which variable) is the difference-in-differences estimate of the treatment effect of handwashing on maternal mortalty?
Question 9
Which regression coefficient is the estimate of the degree of selection bias?
Question 10
Which regression coefficient is the estimate of the time trend in the absence of treatment?
This exercise is part of Module 3: Difference-in-Differences 1.