Intervención de glaciares de roca

2.4.1 Short-run effects of Reunification on socio-emotional development

We causally estimate changes in SED using the natural experiment of German Reunifi- cation in October 1990, whereby students’ birth cohort and the timing of Reunification jointly determine their exposure to the change in regime. We use this variation to identify the effect of regime change on three dimensions of SED: anger, anxiety and self-confidence. In particular, we analyze the change in the SED of the younger cohort before and after Reunification, using as the counterfactual trend the evolution of the older cohort’s psycho- logical well-being at the same age before Reunification. Importantly, the regime change allows us to isolate a change in SED that is not driven by age effects. In a second step, we study how these changes in SED translate into changes in longer-term behavior and outcomes.

The survey follows two cohorts – one being three years older than the other cohort is – between 1985 and 1995. We exploit the comparability across cohorts at the same age and the structure of the data, which at regular (annual) intervals, surveys the students on the same questions, to identify the effect of regime change on SED. The “treatment” of interest is that of regime change on the SED of the younger cohort. The older cohort serves as the “control” group, capturing how socio-emotional skills would have evolved if there had been no Reunification. For instance, the older cohort at age 14 (in 1988) is in the pre-Reunification period, while the younger cohort at age 14 (in 1991) is in the post-Reunification period.

We estimate the change in SED for the younger cohort from before to after Reunification (i.e., between 1989 and 1991), using the older cohort as a control for the trend across the same ages for the younger cohort. The empirical design is such that we focus closely on the ages directly pre- and post-Reunification for the younger cohort, i.e., when aged 12 to 14, which allows us to identify the short-run effects of Reunification. More generally, we estimate the following equations:

SEDic= β0+ β1Ti+ β2Pic+ β3(TiPic) + β4Fi+ Xicδ + ic (2.1)

SEDic= β0+ β2Pic+ β3(TiPic) + Di+ ic (2.2)

c. Ti is a dummy indicating “treatment” (i.e., taking the value of one if the individual

belongs to the younger cohort and zero otherwise), and Pic indicates the “post” period,

representing the student’s age. Since we restrict the analysis to ages 12 to 14, Pic is a

dummy variable that takes the value of one if the age of the individual is 14 (where age 12 is the excluded category); Fi is a gender dummy taking the value of one if the student

is female. Xic is a vector of pre-determined individual-specific characteristics. In a second

specification, we include individual fixed effects Di (see equation (2)).

To understand the gender differences in impact on SED, we estimate equations (1) and (2) by fully interacting the specification with the female dummy Fi, leading to:

SEDic= β0+ β1Ti+ βF1(TiFi) + β2Pic+ β2F(PicFi) (1.1)

+ β3(TiPic) + β3F(TiPicFi) + β4Fi+ Xicδ + (XicFi)δF + ic

SEDic= β0+ β2Pic+ β2F(PicFi) + β3(TiPic) + β3F(TiPicFi) + Di+ ic (2.1)

The main coefficients of interest are β3 and β3F, which capture the effect of a change in

regime (β3), and whether this effect differs by gender (βF3). The interaction term (TiPic )

takes the value of one if a student is from the younger cohort and is 14 years old, which is in the post-Reunification period for the young cohort, while (TiPicFi ) takes the value of one

if the student is female, in the young cohort and in the post-Reunification. All equations are estimated using ordinary least squares with standard errors that are clustered at the individual level.

One possible way to apply the Difference-in-Differences approach is to compare the young and the old cohorts in the same years before and after Reunification. However, the older cohort is also likely affected by Reunification, such that we might expect a response within the “control” group as well. In our application of the Difference-in-Differences approach, we compare the younger and the older cohorts at the same age. In this way, the older cohort is not affected by Reunification since the relevant ages are all before Reunification, and it allows us to control for age (life-cycle) effects, which are likely to be particularly important during adolescence. More specifically, we control for how the younger cohort’s socio-emotional development would have developed without Reunification by making use of the change in these measures within the control group at the same ages. Under the parallel trend assumption, it is assumed that without the German Reunifi-

cation, the young cohort’s psychological development between ages 12 and 14 would have been the same as the older cohort’s psychological development between ages 12 and 14. We test this parallel trend assumption by conducting a placebo test in which we compare the evolution of the SED for the younger cohort in the pre-period with that of the older cohort.

2.4.2 Linking socio-emotional development to long-run behavior and out- comes

In this section, we discuss how we study the link between SED and long-run behavior and outcomes. We measure whether anger, anxiety, and self-confidence – and changes in these variables – impact students’ longer-run externalizing/internalizing behavior, as well as behavioral control issues, health and well-being, and academic performance, and whether this relationship differs by gender.

We estimate the following equations:

Bic = γ0+ γ1∆SEDic+ γ2SEDic,pre+ γ3Ti+ γ4Fi+ ic (2.3)

Bic= γ0+ γ1∆SEDic+ γ1F(∆SEDicFi) + γ2SEDic,pre (3.1)

+ γ₂F(SEDic,preFi) + γ3Ti+ γ3F(TiFi) + γ4Fi+ ic

where Bic is an indicator for a certain behavior (or a measure of health, well-being or

academic performance) of individual i in cohort c, SEDic,precaptures the level of a certain

socio-emotional skill at age 12 (i.e., before Reunification for both cohorts), and ∆SEDic

captures how a certain SED indicator changed from age 12 to age 14 (i.e. before vs. after Reunification for the young cohort). The coefficient of interest is γ1, which measures how

an exogenous change in SED affects individuals’ later behavior and outcomes. Equation (3.1) repeats the exercise but measures the heterogeneity by gender.