Quality of Employment: Intergenerational Labor Precariousness from the Perspective of the IMCE in Santa Cruz De La Sierra 2023 – 2025

This study evaluates the Quality of Employment (QoE) and analyzes intergenerational labor precariousness in Santa Cruz de la Sierra, Bolivia, during the 2023–2025 period. The conceptual framework is based on the Theory of Segmented Labor Markets (SLMT), focusing on the unequal insertion of different generational cohorts. A quantitative approach was adopted, using the Multidimensional Quality of Employment Index (IMCE), which is grounded in the Alkire-Foster (AF) double-threshold model. The IMCE assesses deprivation across three main dimensions: Labor Income, Labor Stability, and Employment Conditions. The poverty line for poor quality employment was set at K = 50%. The results are based on a representative sample of 415 employed workers (ages 18–65). Findings confirm that the overall QoE remains considerably low (IMCE2025= 0.45) and below the historical Bolivian national average (0.47). The Headcount Ratio of Poor-Quality Employment (H) reached 68%. The intergenerational analysis confirms labor market segmentation: Generation Z exhibits the highest vulnerability (IMCEZ = 0.37), validating its predominant insertion into the secondary sector. Youth precariousness is highlighted by high contractual instability (27% without a formal contract) and lack of social protection (68% without access to benefits). Gender disparity is acute: Generation Z females show the lowest IMCE (0.36) and an incidence rate of 92% (H), implying that almost all young employed women face poor quality employment. The structural challenge facing the labor market is fundamentally the quality of employment, not merely access. Urgent public policy intervention is required to strengthen contractual stability and social protection mechanisms to promote Decent Work, particularly for highly vulnerable groups like Generation Z women.

Article file ID not found.

The authors declare no conflict of interest.

Not applicable

The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.