after dropping out is assumed to be made after all adults in the household have made their choice and it is assumed to not affect that choice. Third, the composition of the household is assumed to be exogenous. Under these assumptions, the dependent variable of the multinomial logit estimation represents the employment status of a young individual _i_ of region _r_ that left school between quarter _q-4_ and _q_ . The variable takes the value of 1 if the individual is not in education or employment ( _nini_ ) in _q_ ; 2 if the individual holds a formal job in q; and 3 if the individual holds an informal job in q. The model includes a similar set of fixed effects and household characteristics as the specifications discussed above.

_Table 5: Multinomial logit model for transition out of school – coefficient estimates_

||Bra<br>|zil<br>|Mex<br>|ico<br>|Arge<br>|ntina<br>|
|---|---|---|---|---|---|---|
||Formal|Informal|Formal|Informal|Formal|Informal|
|Negative income|||||||
|shock (quarter=_q_)|-0.340***|-0.198***|-0.250***|-0.057|-0.337***|-0.024|
||(0.039)|(0.063)|(0.054)|(0.06|(0.08)|(0.071)|
|Male|0.425***|0.721***|0.626***|0.647***|0.655***|0.615***|
||(0.05)|(0.08)|(0.064)|(0.074|(0.138)|(0.126)|
|Age (quarter=_q-4_)|0.197***|0.084***|0.171***|-0.004|0.318***|0.130***|
||(0.008)|(0.013)|(0.011)|(0.013|(0.016)|(0.015)|
|Income share of main|-0.456***|-0.13|-0.092|0.02|0.007|0.089|
|earner (quarter_= q-4_)|(0.091)|(0.147)|(0.139)|(0.16)|(0.194|(0.177)|
|Employed (quarter=|1.432***|1.595***|1.160***|1.331***|1.411***|1.570***|
|_q-4_)|(0.042)|(0.067)|(0.067)|(0.076|(0.095|(0.091)|
|Quintile (quarter=_q-_|-0.006|-0.187***|0.070***|-0.049*|0.155***|-0.129***|
|_4_)|(0.018)|(0.029)|(0.025)|(0.028|(0.037|(0.036)|
|Constant|-4.038***|-3.354***|-3.116***|-0.086|-7.183***|-3.535***|
||(0.262)|(0.419)|(0.272)|(0.314)|(0.488)|(0.443)|
|Observations|19,930|19,930|9,669|9,669|6,906|6,906|
|Pseudo-R2|0.142|0.142|0.090|0.090|0.136|0.136|

_Notes:_ The table reports coefficients of a multinomial logit analysis for the transition out of school. The omitted category is “not in education or employment ( _nini_ )”. Results from each country are estimated using a multinomial logit model which includes the following variables not reported in this table: number of siblings in the household, characteristics of the main earner (age, gender, and educational attainment), secondary and tertiary wage premium by gender, regional, quarterly, and year fixed effects. ***, ** and * denote significance at 10%, 5% and 1% levels, respectively. _Source:_ Authors’ estimates using LABLAC (CEDLAS and the World Bank).

Table 5 reports the coefficients of experiencing an income shock on the relative likelihood of entering into each of the activities conditional on having exited school where the reference category is becoming _nini_ . In all countries, once demographic, socioeconomic and family characteristics are controlled for, a negative income shock has a negative effect of being employed, either in a formal job relative to those who left school and did not experience a negative shock. In Brazil, this effect is also found for informal employment. This suggests that school exits caused by income shocks are associated with a lower probability of finding a higher 23