MODELO MINCERIANO DE DIFERENCIAS EN DIFERENCIAS

\[ ln(wage)= B_0 +B_1(Programa) + B_2(Time) + B_3(Programa*Time) + B_4S + B_5exper \\ + B_6exper^2 + B_7age + B_8size + B_9unemp +B_{10}urban + B_{11}male \]

Modelo log - lvl (OLS)

Construcción de experiencia laboral:

• Proxy: Edad(age) - educación (s) - 5 años de preescolar = exper

Diff n Diff

\[ B_3(Programa*Time) \] Variables de control

• age, size, unemployment, urban, male

Resultados

## 
## model
## ==================================================================================
##                                                           log(wage)               
## ----------------------------------------------------------------------------------
## factor(programa)participo                                   -0.149                
##                                                            (0.330)                
##                                                                                   
## factor(TIME)final                                          -0.699**               
##                                                            (0.316)                
##                                                                                   
## s                                                          0.139***               
##                                                            (0.038)                
##                                                                                   
## age                                                         -0.031                
##                                                            (0.021)                
##                                                                                   
## size                                                        -0.270                
##                                                            (0.215)                
##                                                                                   
## unemp                                                      -0.619**               
##                                                            (0.264)                
##                                                                                   
## urban                                                      0.859***               
##                                                            (0.284)                
##                                                                                   
## factor(male)1                                              0.753***               
##                                                            (0.239)                
##                                                                                   
## exper                                                                             
##                                                                                   
##                                                                                   
## factor(programa)participo:factor(TIME)final                0.921**                
##                                                            (0.450)                
##                                                                                   
## Constant                                                   6.973***               
##                                                            (1.470)                
##                                                                                   
## Observations                                                  86                  
## R2                                                          0.542                 
## Adjusted R2                                                 0.487                 
## Residual Std. Error                                         1.035                 
## F Statistic                                                9.982***               
## ----------------------------------------------------------------------------------
## Notes:                                      ***Significant at the 1 percent level.
##                                             **Significant at the 5 percent level. 
##                                             *Significant at the 10 percent level.

## `summarise()` has grouped output by 'TIME'. You can override using the `.groups` argument.

Visualización