Iebaltab

iebaltab is a Stata command that produces balance tables, or difference-in-means tables, with multiple groups or treatment arms. It is a useful tool to use while sampling, conducting data analysis and exporting results in a reproducible manner. This article outlines the command's features and provides examples for use.

Read First

• This command is a part of the package ietoolkit. To install all the commands in this package including iebaltab, type ssc install ietoolkit in Stata.
• For detailed instructions on how to implement the command in Stata, type help iebaltab in Stata.

Overview

iebaltab is a Stata command that produces balance tables, or difference-in-means tables, with multiple groups or treatment arms. The command can test for statistically significant differences between either one control group and all other groups or between all groups against each other. The command also allows for fixed effects, covariates and different types of variance estimators.

iebaltab issues helpful error messages if the command is mis-specified or if the nature of the data leaves the potential for the results to be misinterpreted or invalid. For example, if an observation has a missing value in a variable used in an F-test for joint significance, then Stata cannot do anything but drop that observation. The command will issue an error unless the user specifies the option to suppress the error or specifies an option that tells the command how to interpret missing values.

The command attaches notes to the bottom of the table with information on, for example, which significance levels are used for stars, which fixed effects or covariates that were included (if any) etc.

Implementation

This section outlines the basic functionalities of the command -- estimating descriptive stats, t-tests, and F-tests -- with sample code.

Generating Descriptive Stats

reg balancevarname if groupvar = groupcode

where balancevarname refers to the variables (one at a time) listed in balancevarlist, groupvar refers to the variable listed in the option grpvar(varname), and groupcode refers to the value corresponding to the group for which the means and standard errors are estimated. _b[cons] from the returned results is the group mean and _se[cons] is the standard error in the group mean. Fixed effects and covaraiates are never included in this regression.

Running t-tests

reg balancevarname testgroupdummy
test testgroupdummy 

where testgroupdummy is a dummy with the value 0 for one of the groups compared in the t-test and 1 for the other group. r(p), from the returned results, is used when adding stars to the tables according to the thresholds specified in option starlevels().

Running F-tests

reg testgroupdummy balancevarlist
testparm balancevarlist

where r(p), from the returned results, is used when adding stars to the tables according to the thresholds specified in option starlevels().

Including Fixed Effects

xi : reg balancevarname testgroupdummy i.fixed
test testgroupdummy
xi : reg testgroupdummy balancevarlist i.fixed
testparm balancevarlist 

where fixed refers to the variable included as the fixed effects in option fixedeffects().

Including Covariates

reg balancevarname testgroupdummy covariatesvarlist
test testgroupdummy
reg testgroupdummy balancevarlist covariatesvarlist
testparm balancevarlist 

where covariatesvarlist refers to the variables inlcuded as the control variables in option covariates().

Including Non-Default Variance Estimators

reg balancevarname testgroupdummy, vce(vcetype)
test testgroupdummy
reg testgroupdummy balancevarlist, vce(vcetype)
testparm balancevarlist

where vcetype is the variance estimator specified.

Combining Them All

All options described above can be included in the same regression, for example:

xi : reg balancevarname testgroupdummy i.fixed covariatesvarlist, vce(vcetype)
test testgroupdummy 

Examples

Example 1

ebaltab {it:outcome_variable}, grpvar({it:treatment_variable}) browse 

In the example above, let's assume that treatment_variable is a variable that is 0 for observations in the control group, and 1 for observations in the treatment group. Then in this example, the command will show the mean of {it:outcome_variable} and the standard error of that mean for the control group and the treatment group separately, and it will show the difference between the two groups and test if that difference is statistically significant.

Example 2

global project_folder "C:\Users\project\baseline\results"}
iebaltab ''outcome_variable'', grpvar(''treatment_variable'') ///
save("$project_folder\balancetable.xlsx")} 

Here, the table is saved to file instead of being shown in the browser window as in Example 1.

Example 3

iebaltab ''outcome1 outcome2 outcome3'', grpvar(''treatment_variable'') ///
save("$project_folder\balancetable.xlsx") ///
rowlabels("outcome1 Outcome variable 1 @ outcome2 Second outcome variable")

Here, there are now three variables listed as balance variables. In option rowlabels(), two of those balance variables are given a row label to use in lieu of the variable name. Instead of outcome1 and outcome2, the row titles will read "Outcome variable 1" and "Outcome variable 2", respectively. Since outcome3 is not otherwise specified in rowlabels(), the command will use the variable name of outcome3 as the row title.

Back to Parent

This article is part of the topic Stata Coding Practices

Additional Resources

• DIME Analytics' Descriptive Statistics: Creating Tables
• Read more about ietoolkit here on GitHub