|Chi-squared|1196|640.1|9415|4082|597.3|99.62|
|---|---|---|---|---|---|---|
|Loglikelihood|-11000000|-4558000|-15100000|-5073000|-13551|-4384|

_Source:_ Authors’ estimates using LABLAC (CEDLAS and the World Bank).

_Note:_ This table presents estimates of the relationship between schooling exit rates and an unemployment shock (the household’s main earner transitioned from employment into unemployment). To assess better the unemployment shock effect, we have excluded the observations that received a negative income shock (except, if the income shock was due to the unemployment shock). The same set of repressors used for the regression reported in Table 3 is included for each regression. Quarter, year, and country were considered as strata. Indicators for whether the individual completed secondary or tertiary between the first and last appearance in the data is included in the specification but not reported in the table above. These two control variables are not included in the Mexico model since they are not possible to construct given the survey structure. ***, ** and * denote significance at 10%, 5% and 1% levels, respectively. Heteroskedasticity-robust standard errors are reported in the second line of each country.

We also consider a logit model for the household income shock and the unemployment shock. For the household income shock, results are qualitatively consistent with the ones found for the Cox proportional hazards model for Brazil and Mexico (Appendix Table A.4). However, the results for the unemployment shock in all three countries and for the income shock in Argentina differ between the hazard model and the logit model. The difference between the logit and hazard results suggests the relationship between the likelihood of exiting school and the time spent in school is not independent, and that, as a consequence, a logit specification would result in biased estimates.

# **6. LIFE AFTER SHOCK: PRELIMINARY EVIDENCE**

Dropping out from secondary and tertiary education has a wide range of consequences. Among those, it affects a youth’s ability to access better economic opportunities, as, on average, higher education generates higher returns in the labor market. The lower future earnings and the lack of skills accumulation can make it more difficult to escape poverty as an adult (OECD/CAF/ECLAC 2016). Therefore, from a policy perspective, it is also important to understand the short-term consequences of leaving school “under duress” from a household income shock. We do this by considering the employment choice undertaken by those who exit school, compared to those who exit school in the absence of a household income shock. That is, what is the activity status in quarter _q_ for youth who were enrolled in school in _q-4_ but were not in school in quarter _q_ and how does this differ between those who experienced an income shock and those who did not?

To consider this question, we use a discrete occupational choice model (multinomial logit model) to estimate the probability of working in either a formal or informal job relative to not working (being a _nini_ ), conditional on having dropped out of school. The goal of this section is modest. It seeks to obtain orders of magnitude for the likely effect of a negative income shock on the household’s main earner on the type of employment status of those who exit school after such shock. To do so, the model makes three assumptions. First, it ignores how the decision of youth’s time allocation is made within the household. Instead, the model of youth employment status is a reduced form of the outcome of such decision process. Second, youth’s choice of employment 22