There are two easy ways to create dummy variables in Stata. Let’s begin with a simple
dataset that has three levels of the variable group:

input group
1
1
2
3
2
2
1
3
3
end

We can create dummy variables using the tabulate command and the generate( ) option, as shown
below.

tabulate group, generate(dum)

      group |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          3       33.33       33.33
          2 |          3       33.33       66.67
          3 |          3       33.33      100.00
------------+-----------------------------------
      Total |          9      100.00

list 

         group      dum1      dum2      dum3 
  1.         1         1         0         0  
  2.         1         1         0         0  
  3.         2         0         1         0  
  4.         3         0         0         1  
  5.         2         0         1         0  
  6.         2         0         1         0  
  7.         1         1         0         0  
  8.         3         0         0         1  
  9.         3         0         0         1

The tabulate command with the generate
option created three dummy variables called dum1, dum2 and dum3.

An Example Using the High School and Beyond Dataset

Using High School and Beyond dataset we wish to account for variability in the writing
test scores using information on reading, math and the program type the student is in. The
categorical variable prog has three levels: 1) general program, 2) academic
program, and 3) vocational program. First, we will load the dataset from the Internet,
then we will create dummy variables for prog using the tabulate command.

use https://stats.idre.ucla.edu/stat/stata/notes/hsb2, clear

tabulate prog, generate(prog)

    type of |
    program |      Freq.     Percent        Cum.
------------+-----------------------------------
    general |         45       22.50       22.50
   academic |        105       52.50       75.00
   vocation |         50       25.00      100.00
------------+-----------------------------------
      Total |        200      100.00

The tabulate command with the generate
option created the following variables: prog1, prog2, and prog3. In a
regression analysis we can only use two of the three dummy variables. Since prog
has three levels it uses two degrees of freedom. Here is the regression analysis.

regress write read math prog2 prog3

      Source |       SS       df       MS              Number of obs =     200
-------------+------------------------------           F(  4,   195) =   41.03
       Model |  8170.58624     4  2042.64656           Prob > F      =  0.0000
    Residual |  9708.28876   195  49.7860962           R-squared     =  0.4570
-------------+------------------------------           Adj R-squared =  0.4459
       Total |   17878.875   199   89.843593           Root MSE      =  7.0559

------------------------------------------------------------------------------
       write |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        read |    .289028   .0659478     4.38   0.000     .1589656    .4190905
        math |   .3587215   .0745443     4.81   0.000     .2117048    .5057381
       prog2 |   .6647754    1.32845     0.50   0.617    -1.955198    3.284749
       prog3 |  -2.253484   1.468445    -1.53   0.127    -5.149556    .6425886
       _cons |   19.00854    3.40933     5.58   0.000     12.28465    25.73243
------------------------------------------------------------------------------

In the analysis all of the variables were statistically
significant except for prog2 and prog3. However, it is necessary to remember that it is the
combination of prog2 and prog3 that makes up the variable program type.
Let’s test prog2 and prog3 together.

test prog2 prog3

 ( 1)  prog2 = 0.0
 ( 2)  prog3 = 0.0

       F(  2,   195) =    2.32
            Prob > F =    0.1015

As it turns out, by testing prog2 and prog3
together, we find that the variable program type is not statistically significant.

We can also do this in one step using the i. or factor variable notation, as shown below. Factor variables create indicator variables from categorical variables and are allowed with most estimation and postestimation commands Note how the results below match those above exactly.

regress write read math i.prog

      Source |       SS           df       MS      Number of obs   =       200
-------------+----------------------------------   F(4, 195)       =     41.03
       Model |  8170.58624         4  2042.64656   Prob > F        =    0.0000
    Residual |  9708.28876       195  49.7860962   R-squared       =    0.4570
-------------+----------------------------------   Adj R-squared   =    0.4459
       Total |   17878.875       199   89.843593   Root MSE        =    7.0559

------------------------------------------------------------------------------
       write |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        read |    .289028   .0659478     4.38   0.000     .1589656    .4190905
        math |   .3587215   .0745443     4.81   0.000     .2117048    .5057381
             |
        prog |
   academic  |   .6647754    1.32845     0.50   0.617    -1.955198    3.284749
   vocation  |  -2.253484   1.468445    -1.53   0.127    -5.149556    .6425886
             |
       _cons |   19.00854    3.40933     5.58   0.000     12.28464    25.73243
------------------------------------------------------------------------------

As we did in the prior example, we can test the overall effect of program
type with the test command as shown below.

test 2.prog 3.prog

 ( 1)  2.prog = 0
 ( 2)  3.prog = 0

       F(  2,   195) =    2.32
            Prob > F =    0.1015

The contrast command can be used to get the multi-degree-of-freedom test of the categorical variable.

contrast prog

Contrasts of marginal linear predictions

Margins      : asbalanced

------------------------------------------------
             |         df           F        P>F
-------------+----------------------------------
        prog |          2        2.32     0.1015
             |
 Denominator |        195
------------------------------------------------

For more information

See the Stata manual on tabulate and factor variables.