Skip to main content

Table 7 Results of Eqs. (13) and (14)

From: Implicit redistribution within Argentina’s social security system: a micro-simulation exercise

 

Private sector

Public sector

Male

Female

Male

Female

Does not contribute

Contributes

Does not contribute

Contributes

Does not contribute

Contributes

Does not contribute

Contributes

Age

0.2601*** (0.017)

0.1941*** (0.009)

0.0379** (0.015)

0.1432*** (0.013)

−3.0915 (5.191)

0.4562*** (0.173)

0.1169*** (0.041)

0.1642*** (0.017)

Age 2a

−4.4763*** (0.438)

−3.3804*** (0.235)

−0.5632 (0.381)

−2.8788*** (0.341)

53.3424 (90.187)

−8.5124** (3.581)

−2.7686** (1.106)

−2.9035*** (0.407)

Age 3b

2.2647*** (0.362)

1.7768*** (0.193)

0.1830 (0.310)

1.8444*** (0.278)

−27.9807 (48.226)

5.0675** (2.424)

2.1535** (0.936)

1.6586*** (0.318)

Education 2c

0.3346*** (0.017)

0.3142*** (0.007)

0.2279*** (0.017)

0.4319*** (0.019)

−2.7132 (5.109)

0.4311*** (0.088)

0.3298*** (0.031)

0.3584*** (0.013)

Education 3d

0.9304*** (0.047)

0.6985*** (0.015)

0.5319*** (0.031)

0.6139*** (0.026)

−3.0300 (6.660)

0.7707*** (0.107)

0.9950*** (0.043)

0.5269*** (0.013)

Constant

4.2137*** (0.197)

4.7306*** (0.114)

6.5077*** (0.179)

5.3670*** (0.177)

13.7157 (21.958)

3.1601** (1.403)

5.5498*** (0.487)

4.9795*** (0.226)

IMR

    

35.1362 (48.537)

0.1455 (2.863)

  

Observations

38,228

 

24,214

 

19,483

19,483

19,184

 
  1. For men in the public sector the Stata command movestay did not converge to a solution, so in this case we run two Heckman regressions. In the first regression, the dependent variable in the selection equation was a dummy variable equal to 1 if individual i had an informal job, while in the second regression the dummy variable was equal to 1 if individual i had a formal job
  2. Source: own calculations
  3. * Significant at 10 %; **significant at 5 %; ***significant at 1 %
  4. a(Age^2)/1,000
  5. b(Age^3)/100,000
  6. cComplete high school/incomplete tertiary-university
  7. dComplete tertiary-university