# **1.3 Age, cohort and time effects for secondary school enrollment**

We construct 144 cohort trajectories for the 18 countries included in our data base, and each cohort is observed at different lengths during different years (depending on the surveys available). Our data base includes observations starting in 1980, so the oldest cohorts for whom a full trajectory can be followed from the first year of school age into adulthood are those born in 1974-1976. These individuals can be observed until they are about 34 to 36 years old by 2010. Cohorts born during the 9 previous years are observed first in 1980 when they are already of late primary or LS age and we can trace their education choices from the ages of 15-17 onwards.<sup>14</sup> The youngest cohorts in our analysis are those born between 1994 and 1996 and expected to enter primary during 2000-2002. We can follow them up to the age of attending USE around 9 years later. The cohort trajectories constructed with our data reveal that school enrollment rates decline fastest in a critical period between ages 15 and 20, where attendance rates drop from around 80 to 40 percent (Figure 4). After age 20 cohort trajectories decline more smoothly and reach levels below 10 percent at around age 25.

Figure 4 allows observing several sources of variation. The first is differences across cohorts. Such “cohort effects” reflect different structural conditions for different generations. For the cohorts that start being observed at around age 12, generations show significantly lower attendance levels of around 70 percent compared to the group with the highest rates of about 90 percent. The change in attendance levels between the school attendance trajectories in Cohorts 1, 2, 3 and 4 would belong to these types of effects, which tend to apply to all individuals in one cohort as compared to another (Figures 1 and 3).

The second is variations along the life cycle of each cohort. The declining trend in school attendance that is observed as all cohorts age is normally classified as an “age effect.” The inverted “u” shape patterns in school attendance observed are a good illustration of this.

A third source of variation is “time effects,” which correspond to shocks that can affect school attendance choices irrespective of an individual’s age or cohort. An economic crisis or a natural disaster that alters the expected age pattern of school attendance and that affects all cohorts in a country would be examples. These effects, however, are not immediately apparent in our data.

Several authors have attempted to identify the aforementioned age, cohort, and time effects separately for analysis of the dynamics of saving over the life cycle.<sup>15</sup> Following Székely and Karver (2014) we adapt this approach to identify patterns of time-use choices, and in particular, school attendance trajectories among youth. The logic behind estimating age, cohort, and time effects is simpler than in the case of household savings since we are dealing with indicators that are binary rather than continuous, and that follow a very different (and less complex) underlying

> 14 Cohorts born earlier will start being observed when they are already 18 years of age or more, so the information on their trajectories is beyond the scope of our analysis.

> 15 S ee Attanasio (1993), Attanasio & Banks (1998), Attanasio (1998), and Attanasio and Székely (2001).

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