# **3. EMPIRICAL STRATEGY**

To estimate the effect of the law of 1998, children who turned age 14 after the law change of December 1998 (treatment) were compared with those who turned age 14 before the change in law (comparison). As differences in labor force participation can arise from differences in ages or cohorts, we applied two DD estimates as control for these potential confounding effects. One estimate uses as counterfactual the difference in participation rates between those who turned 14 after December 1997 and those who turned 14 before December 1997. This is the different-cohorts-and-same-ages DD. In the second estimate, the counterfactual is the difference in participation rates between those who turned 13 after December 1997 and those who turned 13 on or before December 1997. This is the different-ages-and-same-cohorts DD. Table 1 shows how the ‘treatment’ and ‘comparison’ groups are defined for these two estimates.

Since we controlled for age (in weeks) linearly, the first difference in the DD exercise can be interpreted as a parametric regression discontinuity design.

<u>Table 1 – Definition of the Eligible and Comparison Groups for the DD Estimates</u>

||PNAD 1998|PNAD 1999|
|---|---|---|
|Treatment Group|_Different coho_<br>Turned 14 between<br>January and June, 1998|_rts and same ages_<br>Turned 14 between January<br>and June, 1999|
|Comparison Group|Turned 14 between July<br>and December,1997|Turned 14 between July and<br>December,1998|
||_Different ages_|_and same cohorts_|
|Treatment Group|Turned 13 between<br>January and June, 1998|Turned 14 between January<br>and June, 1999|
|Comparison Group|Turned 13 between July<br>andDecember,1997|Turned 14 between July and<br>December,1998|

The identification strategy for the DD depends on two assumptions: (1) any difference in labor force participation between the treatment and control groups exists in level but not in difference. That means the groups would show a common trend in the absence of the changes in law; and (2) all unobserved characteristics that can be correlated with the eligibility status of the individual or other covariates are additive and timeinvariant. In our first regression model, we assumed that any differences in participation rates between treated and comparison groups that are due to age effects, is constant over time. Whereas in the second regression model, we assumed a cohort effect that is fixed over time (see Meyer 1995).

The estimation of the impact of the law of 1998 on the outcomes of interest is conducted through the linear probability regression model:

# ��� ��� ����� �� ����� ������ ������� ����

where ��� is the outcome variable of individual _i_ in time _t_ , ��� is the vector of observed characteristics of individual _i_ in time _t_ . The vector includes dummies gender and ethnicity, the years of schooling of the household head, the age of the household head, the gender of the household head, and dummy variables for regions and the metropolitan region. In our first regression, �� is a dummy variable that equals 1 if individual _i_ turned aged 14 in the first semester of 1998 or 1999, and 0 if individual _i_ turned 14 in the second semester of 1997 or 1998, ��� is a year dummy that takes value 1 in 1999 (after the law was passed) 4