Fractured lives: understanding urban youth vulnerability in Perú

ABSTRACT

The paper reports on a mixed methods study that sought to analyse determinants of youth labour market and educational disengagement in Peru. It begins by questioning the widespread focus on NEET – youth not in employment, education or training – as a measure of youth vulnerability in countries with extensive informal labour markets where labour precarity can be as problematic as unemployment for young people’s futures. A broader category of ‘urban vulnerable’ youth, including both NEET and precarious workers, is proposed and used as the basis for analysing the factors that influence young people’s trajectories. Key factors and shocks in youth trajectories are identified through qualitative life histories, and are tested using cross-section and panel survey data. Findings from the study have implications for the analysis of youth labour market vulnerability in the Global South, as well as for the policies that seek to address this problem.

KEYWORDS:

• Urban youth
• NEET
• vulnerability
• precarity
• mixed methods research

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

María Balarin http://orcid.org/0000-0003-2043-2209

Notes

1 Different criteria for inclusion were used for independent (self-employed) and dependent workers. Included independent workers were those whose monthly earnings were less than half of the minimum wage, and whose activity or business was either (i) conducted informally, (ii) conducted in an improvised way or (ii) had lasted for less than six months in the past year. For dependent workers, we also considered those earning less than half of the minimum wage and were either (i) informally employed or (ii) worked for less than 15 hours a week. Following findings from the qualitative work which showed that many young people are unwaged family workers whose responsibilities got in the way of their studying or finding paid employment, we also included among precarious workers those in this group who performed the un-waged activity for more than 15 hours per week.

2 We used a non-linear logit method, correcting standard intra-group errors by geographical conglomerates and using the following specification: $Pr(\mathrm{u}\mathrm{r}\mathrm{b}\mathrm{a}\mathrm{n}-\mathrm{v}\mathrm{u}\mathrm{l}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}{)}_i=f({\text{β}}_0+{\text{β}}_1\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{d}\mathrm{u}\mathrm{a}\mathrm{l}+{\text{β}}_2\mathrm{F}\mathrm{a}\mathrm{m}\mathrm{i}\mathrm{l}\mathrm{y}+{\text{β}}_3\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{x}\mathrm{t}\mathrm{u}\mathrm{a}\mathrm{l}+{\mu }_i)$ Where the probability of being urban-vulnerable (NEET or precarious) is taken to be the outcome of factors acting at the individual, family and contextual levels.

3 The analysis was conducted using the following specification $\mathrm{P}\mathrm{r}(\mathrm{u}\mathrm{r}\mathrm{b}\mathrm{a}\mathrm{n}-\mathrm{v}\mathrm{u}\mathrm{l}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}{)}_i=f({\text{β}}_0+{\text{β}}_1\mathrm{S}\mathrm{h}\mathrm{o}\mathrm{c}{\mathrm{k}}_i+{\mu }_i)$. We only specified shocks because we had already controlled for the two groups to be as similar as possible using various individual, family and contextual variables using a propensity score matching technique in order to identify and compare two highly similar groups within the panel, one that was and one that was not affected by a certain shock.

4 Factor and shock analysis data are provided in Tables A1 and A2 (Annex 1).

5 The impact of mental health issues also became evident in some of the interviews but was not explicitly explored in the work.