26 Feb How Do I Create Dummies In Stata?

Creating dummy variables in Stata is a common task in data analysis. Dummy variables, also known as indicator variables, are binary variables that indicate the presence or absence of a particular characteristic in a dataset. In this way, they can be used to represent categorical variables in regression models. This article will explain how to create dummies in Stata.

To create a dummy variable, you first need to identify the variable that you want to convert to a dummy variable. Let’s say we have a variable called “gender” with two categories: male and female. We want to create a dummy variable for male, where the value of the variable is 1 if the observation is male and 0 if the observation is female.

To create this dummy variable, we can use the “generate” command in Stata. The syntax is as follows:

In this command, we are creating a new variable called “male_dummy” and assigning it the value of 1 if the “gender” variable is equal to “male” and 0 otherwise.

If we have a categorical variable with more than two categories, we can create multiple dummy variables. For example, if we have a variable called “race” with three categories: white, black, and Asian, we can create two dummy variables: one for black and one for Asian. The syntax would be as follows:

In this way, we have created two dummy variables that represent the black and Asian categories of the “race” variable.

It’s important to note that when creating dummy variables, you should always leave one category out as a reference category. This is called the “baseline” category, and it is represented by 0 for all the dummy variables. Leaving one category out as the baseline category prevents perfect multicollinearity, which is when two or more independent variables are perfectly linearly related. In our example with the “gender” variable, we left the “female” category out as the baseline category.

In summary, creating dummy variables in Stata is a straightforward process that involves using the “generate” command to assign binary values to observations based on a categorical variable. Always remember to leave one category out as the reference or baseline category to avoid multicollinearity.