ANALYSIS REGRESSION OF ENOE DATA

INTRODUCTION

• In this model I´m going to study some variables which could have an impact on someone’s monthly income based on the ENOE’s data (Encuesta Nacional de Ocupación y Empleo). *According to the OLS method, I took some steps to carry out this analysis. First and foremost, I declared to variables; some of them were created as “dummy variables” in order to show the impact of each one on monthly income. Then I constructed our econometric model, based on some assumptions. After this, I ran regression interpreting the results at the same time.

I installed some packages some to make my estimations. Then I loaded data base.

library(haven)
library(car)
library(stargazer)
library(lmtest)
SDEMT118 <- read_dta(file.choose())

I have deleted some rows.

SDEMT118 <- SDEMT118[SDEMT118$eda > 18,] # solo mayores de edad
SDEMT118 <- SDEMT118[SDEMT118$ingocup > 0,] # solo ingresos mayores a cero
SDEMT118 <- SDEMT118[SDEMT118$anios_esc != 99,] # años de escolaridad distintos a 99 (informacion no disponible)

Encoded some variables to make regression easier.

SDEMT118$mujer <- recode(SDEMT118$sex, "1=0; 2=1") # mujer=1; 0=hombre
table(SDEMT118$mujer)

    0     1 
67727 44933 

SDEMT118$casado <- recode(SDEMT118$e_con, "5=1; 1=0; 2=0; 3=0; 4=0; 6=0; 9=0") # mujer=1; 0= hombre
table(SDEMT118$casado)

    0     1 
61811 50849 

The regression running focusing on important variables.After I summarized with “stargazer” the regression in order for interpret the results more easily.

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=================================================
                         Dependent variable:     
                    -----------------------------
                               ingocup           
-------------------------------------------------
mujer                       -1,674.128***        
                              (33.637)           
                                                 
eda                          225.596***          
                               (6.501)           
                                                 
I(eda2)                       -1.897***          
                               (0.073)           
                                                 
anios_esc                    532.111***          
                               (3.877)           
                                                 
casado                       400.821***          
                              (33.925)           
                                                 
hrsocup                       38.751***          
                               (0.908)           
                                                 
Constant                    -5,614.665***        
                              (143.866)          
                                                 
-------------------------------------------------
Observations                   112,660           
R2                              0.186            
Adjusted R2                     0.186            
Residual Std. Error    5,289.041 (df = 112653)   
F Statistic         4,285.675*** (df = 6; 112653)
=================================================
Note:                 *p<0.1; **p<0.05; ***p<0.01
                   0.5 %       99.5 %
(Intercept) -5985.245723 -5244.083605
mujer       -1760.773857 -1587.482721
eda           208.851245   242.340426
I(eda^2)       -2.085561    -1.708883
anios_esc     522.125467   542.096777
casado        313.435587   488.207189
hrsocup        36.410549    41.090605

• In the regression, the variable mujer shows us that a woman tends to earn 28.3% less than a man.
• The second variable (eda) tells us that if a person is older by one more year, that person tends to earn 4% more.
• the third variable I(eda2) means that at a certain age, like if for example the person is 61 he will earn -0.8% less than the person who is 60. (The age of 60 is taken as the turning point of the age variable)
• Then the next variable (anios_esc) shows that for one more year of schooling, the person will earn 7.5% more than before.
• The variable (casado) says that a person who is married tends to earn 3.8% more than a person who is not (single). The next variable (hrsocup) tells us that for one more hour worked per week, the person will earn 1.1 more in his monthly income.
  
• The R squared tells us that our model explains only 30.1% on the variation of the monthly income.

Making an Hipothesis with our model

attach(SDEMT118)
hist(log(ingocup), ylim = c(0, 40000), col = 2:16, main = "Monthly income")

I can assume that each variable is very significant at the significance level of 5%.

Then I created a graph (histogram) of the “ingocup” variable.

    studentized Breusch-Pagan test

data:  reg
BP = 697.06, df = 6, p-value < 2.2e-16

Doing a Breusch-Pagan test and a White test and interpreting the result.

Breusch-Pegan test

bptest(reg, ~fitted(reg) + I(fitted(reg)^2))

    studentized Breusch-Pagan test

data:  reg
BP = 1110.1, df = 2, p-value < 2.2e-16

White test

reg3 <- coeftest(reg, hccm) 
reg3

t test of coefficients:

               Estimate  Std. Error t value  Pr(>|t|)    
(Intercept) -5.6147e+03  1.4484e+02 -38.766 < 2.2e-16 ***
mujer       -1.6741e+03  3.2742e+01 -51.130 < 2.2e-16 ***
eda          2.2560e+02  6.0300e+00  37.412 < 2.2e-16 ***
I(eda^2)    -1.8972e+00  7.1811e-02 -26.420 < 2.2e-16 ***
anios_esc    5.3211e+02  6.0771e+00  87.560 < 2.2e-16 ***
casado       4.0082e+02  3.5058e+01  11.433 < 2.2e-16 ***
hrsocup      3.8751e+01  1.0034e+00  38.620 < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

stargazer(reg, reg3, type = "text")

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===============================================================
                                Dependent variable:            
                    -------------------------------------------
                               ingocup                         
                                 OLS               coefficient 
                                                      test     
                                 (1)                   (2)     
---------------------------------------------------------------
mujer                       -1,674.128***         -1,674.128***
                              (33.637)              (32.742)   
                                                               
eda                          225.596***            225.596***  
                               (6.501)               (6.030)   
                                                               
I(eda2)                       -1.897***             -1.897***  
                               (0.073)               (0.072)   
                                                               
anios_esc                    532.111***            532.111***  
                               (3.877)               (6.077)   
                                                               
casado                       400.821***            400.821***  
                              (33.925)              (35.058)   
                                                               
hrsocup                       38.751***             38.751***  
                               (0.908)               (1.003)   
                                                               
Constant                    -5,614.665***         -5,614.665***
                              (143.866)             (144.836)  
                                                               
---------------------------------------------------------------
Observations                   112,660                         
R2                              0.186                          
Adjusted R2                     0.186                          
Residual Std. Error    5,289.041 (df = 112653)                 
F Statistic         4,285.675*** (df = 6; 112653)              
===============================================================
Note:                               *p<0.1; **p<0.05; ***p<0.01

conclusion: There is heteroskedasticity given the fact that 2.2e-16 < 0.05 So I’m going to adjust using-robust estandar errors.

attach(SDEMT118)

The following objects are masked from SDEMT118 (pos = 3):

    ageb, ambito1, ambito2, anios_esc, buscar5c, busqueda, c_inac5c, c_ocu11c, c_res,
    casado, cd_a, clase1, clase2, clase3, con, cp_anoc, cs_ad_des, cs_ad_mot, cs_nr_mot,
    cs_nr_ori, cs_p12, cs_p13_1, cs_p13_2, cs_p14_c, cs_p15, cs_p16, cs_p17, cs_p20_des,
    cs_p22_des, d_ant_lab, d_cexp_est, d_sem, dispo, domestico, dur_des, dur_est, dur9c,
    e_con, eda, eda12c, eda19c, eda5c, eda7c, emp_ppal, emple7c, ent, est, est_d, fac,
    h_mud, hij5c, hrsocup, imssissste, ing_x_hrs, ing7c, ingocup, l_nac_c, loc, ma48me1sm,
    medica5c, mh_col, mh_fil2, mujer, mun, n_ent, n_hij, n_hog, n_pro_viv, n_ren,
    nac_anio, nac_dia, nac_mes, niv_ins, nodispo, p14apoyos, par_c, per, pnea_est,
    pos_ocu, pre_asa, r_def, rama, rama_est1, rama_est2, remune2c, s_clasifi, salario,
    scian, sec_ins, seg_soc, sex, sub_o, t_loc, t_tra, tcco, tip_con, tpg_p8a, trans_ppal,
    tue_ppal, tue1, tue2, tue3, upm, ur, v_sel, zona

marrfem <- as.numeric(mujer==1 & casado ==1) #married female
singmal <- as.numeric(mujer==0 & casado==0) #single male
singfem <- as.numeric(mujer==1 & casado==0) #single female
reg2 <- lm(log(ingocup) ~ marrfem + singmal + singfem + eda + I(eda^2) + anios_esc + hrsocup)
stargazer(reg2, type = "text")

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=================================================
                         Dependent variable:     
                    -----------------------------
                            log(ingocup)         
-------------------------------------------------
marrfem                       -0.352***          
                               (0.006)           
                                                 
singmal                       -0.085***          
                               (0.005)           
                                                 
singfem                       -0.319***          
                               (0.005)           
                                                 
eda                           0.040***           
                               (0.001)           
                                                 
I(eda2)                      -0.0004***          
                              (0.00001)          
                                                 
anios_esc                     0.074***           
                              (0.0005)           
                                                 
hrsocup                       0.011***           
                              (0.0001)           
                                                 
Constant                      6.617***           
                               (0.019)           
                                                 
-------------------------------------------------
Observations                   112,660           
R2                              0.302            
Adjusted R2                     0.302            
Residual Std. Error      0.641 (df = 112652)     
F Statistic         6,971.641*** (df = 7; 112652)
=================================================
Note:                 *p<0.1; **p<0.05; ***p<0.01

coeftest(reg2, vcov = hccm(reg2, type = "hc1"))

t test of coefficients:

               Estimate  Std. Error t value  Pr(>|t|)    
(Intercept)  6.6170e+00  2.0543e-02 322.110 < 2.2e-16 ***
marrfem     -3.5198e-01  6.5972e-03 -53.353 < 2.2e-16 ***
singmal     -8.5322e-02  5.0904e-03 -16.762 < 2.2e-16 ***
singfem     -3.1894e-01  5.3188e-03 -59.964 < 2.2e-16 ***
eda          3.9727e-02  9.0720e-04  43.791 < 2.2e-16 ***
I(eda^2)    -4.1828e-04  1.0797e-05 -38.742 < 2.2e-16 ***
anios_esc    7.4409e-02  5.2588e-04 141.494 < 2.2e-16 ***
hrsocup      1.1132e-02  1.3647e-04  81.573 < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

• In this case, there is no heteroskedasticity anymore, because we have made more robust our estimated errors so we can not reject the assuption of homoskedasticity.

Creating three dummy variables to run the new regression and see what is the effect of being single female/male, and married female/male.

SDEMT118$job <- recode(SDEMT118$emp_ppal, "1=0; 2=1") # empleo informal=1; 0=empleo formal
reg2 <- lm(log(ingocup) ~ job + mujer + eda + I(eda^2) + anios_esc + casado + hrsocup, data=SDEMT118)
stargazer(reg2, type = "text")

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=================================================
                         Dependent variable:     
                    -----------------------------
                            log(ingocup)         
-------------------------------------------------
job                           0.400***           
                               (0.004)           
                                                 
mujer                         -0.280***          
                               (0.004)           
                                                 
eda                           0.037***           
                               (0.001)           
                                                 
I(eda2)                      -0.0004***          
                              (0.00001)          
                                                 
anios_esc                     0.057***           
                              (0.0005)           
                                                 
casado                        0.019***           
                               (0.004)           
                                                 
hrsocup                       0.009***           
                              (0.0001)           
                                                 
Constant                      6.678***           
                               (0.017)           
                                                 
-------------------------------------------------
Observations                   112,660           
R2                              0.357            
Adjusted R2                     0.357            
Residual Std. Error      0.615 (df = 112652)     
F Statistic         8,939.598*** (df = 7; 112652)
=================================================
Note:                 *p<0.1; **p<0.05; ***p<0.01

• I can see in the first variable (marrfem) that a woman that is married tends to earn 35.2% less that a man who is married.
• In the second one (singmal) says that a man who is single tends to earn 8.5% than a man who is married.
• In the last one (singfem) I can see that a woman that is single tends to earn 31.9% less than a man that is married.

CONCLUSION

• Acoording to the economic theory, there are no redundant variables because I have specified to variables correctly, and much less there is perfect collinearity with independent variables.

• I wondered if it’s possible that some other variables exist which might have an influence on monthly income, so according to the ENOE data, we took into account the informal and formal job variable (job). I made the regression, and according to this, the variable shows that for the simple fact of having a formal job, someone could earn 40% more that someone who doesn’t. I use dummy varibles for this regression too.

SDEMT118$job <- recode(SDEMT118$emp_ppal, "1=0; 2=1") # empleo informal=1; 0=empleo formal

reg2 <- lm(log(ingocup) ~ job + mujer + eda + I(eda^2) + anios_esc + casado + hrsocup, data=SDEMT118)
stargazer(reg2, type = "text")