Table 4 reports difference-in-differences regressions that absorb the effects of additional covariates, including metro area/age group fixed effects. Motivated by the pension wealth simulation in table 1, we categorize the three younger age groups (16–25, 26–35, 36–45) into a “more treated” group and the two older groups (46–55, 56–65) into a “less treated” or “control” group. We interact an indicator for being in the less-treated group (age 45 or below) with year effects for each year over the 1988–2003 period, omitting the interaction with 1997, the implementation year. We control flexibly for metro area–year effects and age group–metro area effects. The number of observations, 1,280, reflects the number of metro area/age group/year cells ($16×5×16$). We weight each cell by employment in the IMSS administrative records. The key finding is that we see little evidence of a differential pretrend but robust evidence of a relative decrease in evasion for the younger age groups following the passage of the reform. We see robust negative coefficients on the 1(age 45 or below)-year interaction in the later years of the study period for the wage gap (medians) and excess mass measures. The estimates for the wage gap (means) measure are somewhat less robust, but the results for the means measure are still largely consistent with the patterns for the other two measures. Overall, we interpret the results as supportive of our second main theoretical implication: evasion for younger age groups declined relatively more than evasion for older groups. Additional results, reported in appendix D.1, suggest that these results are not driven by discrepancies in the reporting of employment in the two data sources.

Table 4.
Differential Effects of Pension Reform on Evasion
Wage gap (medians) (1)Wage gap (means) (2)Excess Mass (15th percentile) (3)
1(age $<=$ 45) $×$ 1988 0.015 0.034 0.011
(0.033) (0.040) (0.011)
1(age $<=$ 45) $×$ 1989 0.025 0.036 0.018
(0.027) (0.025) (0.016)
1(age $<=$ 45) $×$ 1990 0.033 0.018 0.016
(0.035) (0.031) (0.013)
1(age $<=$ 45) $×$ 1991 −0.011 0.027 0.001
(0.031) (0.026) (0.012)
1(age $<=$ 45) $×$ 1992 −0.011 −0.015 0.010
(0.028) (0.026) (0.012)
1(age $<=$ 45) $×$ 1993 0.027 0.033 0.003
(0.027) (0.023) (0.009)
1(age $<=$ 45) $×$ 1994 −0.005 −0.035 0.011
(0.027) (0.026) (0.009)
1(age $<=$ 45) $×$ 1995 −0.025 0.002 −0.006
(0.031) (0.022) (0.014)
1(age $<=$ 45) $×$ 1996 −0.020 −0.028 −0.007
(0.022) (0.030) (0.009)
1(age $<=$ 45) $×$ 1998 0.001 0.019 −0.023**
(0.034) (0.039) (0.009)
1(age $<=$ 45) $×$ 1999 −0.014 −0.021 −0.023**
(0.028) (0.026) (0.010)
1(age $<=$ 45) $×$ 2000 −0.062** −0.051** −0.027***
(0.028) (0.022) (0.010)
1(age $<=$ 45) $×$ 2001 −0.065** −0.030 −0.023**
(0.025) (0.024) (0.011)
1(age $<=$ 45) $×$ 2002 −0.073*** −0.081*** −0.023**
(0.026) (0.022) (0.010)
1(age $<=$ 45) $×$ 2003 −0.087*** −0.046 −0.025**
(0.025) (0.028) (0.012)
Age group-metro area effects Yes Yes Yes
Metro-year effects Yes Yes Yes
$R$-squared 0.96 0.95 0.99
$N$ 1,280 1,280 1,280
Wage gap (medians) (1)Wage gap (means) (2)Excess Mass (15th percentile) (3)
1(age $<=$ 45) $×$ 1988 0.015 0.034 0.011
(0.033) (0.040) (0.011)
1(age $<=$ 45) $×$ 1989 0.025 0.036 0.018
(0.027) (0.025) (0.016)
1(age $<=$ 45) $×$ 1990 0.033 0.018 0.016
(0.035) (0.031) (0.013)
1(age $<=$ 45) $×$ 1991 −0.011 0.027 0.001
(0.031) (0.026) (0.012)
1(age $<=$ 45) $×$ 1992 −0.011 −0.015 0.010
(0.028) (0.026) (0.012)
1(age $<=$ 45) $×$ 1993 0.027 0.033 0.003
(0.027) (0.023) (0.009)
1(age $<=$ 45) $×$ 1994 −0.005 −0.035 0.011
(0.027) (0.026) (0.009)
1(age $<=$ 45) $×$ 1995 −0.025 0.002 −0.006
(0.031) (0.022) (0.014)
1(age $<=$ 45) $×$ 1996 −0.020 −0.028 −0.007
(0.022) (0.030) (0.009)
1(age $<=$ 45) $×$ 1998 0.001 0.019 −0.023**
(0.034) (0.039) (0.009)
1(age $<=$ 45) $×$ 1999 −0.014 −0.021 −0.023**
(0.028) (0.026) (0.010)
1(age $<=$ 45) $×$ 2000 −0.062** −0.051** −0.027***
(0.028) (0.022) (0.010)
1(age $<=$ 45) $×$ 2001 −0.065** −0.030 −0.023**
(0.025) (0.024) (0.011)
1(age $<=$ 45) $×$ 2002 −0.073*** −0.081*** −0.023**
(0.026) (0.022) (0.010)
1(age $<=$ 45) $×$ 2003 −0.087*** −0.046 −0.025**
(0.025) (0.028) (0.012)
Age group-metro area effects Yes Yes Yes
Metro-year effects Yes Yes Yes
$R$-squared 0.96 0.95 0.99
$N$ 1,280 1,280 1,280

Data from IMSS and ENEU baseline samples of men, collapsed to metro area/age group/year level. Regressions weighted by IMSS employment in each cell. Dependent variables are as explained in notes to table 3. In calculating evasion measures, we pool ENEU data across quarters within year. Age group–metro area effects are defined for five age groups (16–25, 26–35, 36–45, 46–55, 56–65). $***$1%, $**$5%, $*$10% level. See section IV and appendix C for further details of data processing.

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