Measuring Sustainable Development Progress in Peru Using Multivariate Latent Markov Models

Development is a complex phenomenon that involves economic, social, and environmental transformations. In recent decades, sustainable development (SD) has gained prominence as a policy objective, emphasizing balanced progress in economic growth, social inclusion, and environmental protection. However, measuring SD progress remains challenging, given the need to consider such multiple dimensions, which often show trade‐offs; this is especially true in developing countries such as Peru, where rapid socioeconomic changes coexist with environmental degradation. Traditional metrics, such as GDP or composite indicators such as the Human Development Index, often fail to capture the multidimensional and dynamic nature of SD, especially in terms of the environmental side. This paper employs a multivariate latent Markov model (LMM) to assess Peru's progress toward SD from 2004 to 2017, incorporating economic, social, and environmental indicators. LMMs are advantageous, as they account for unobserved heterogeneity and state transitions between sustainability levels over time, offering a nuanced understanding of SD dynamics. Our findings reveal that while Peru experienced economic and social improvements during the study period, the inclusion of environmental factors in the SD measure curbs overall progress, highlighting potential trade‐offs between poverty reduction and environmental quality. The results underscore the importance of integrating environmental considerations into SD strategies, particularly in the context of rapid economic growth. This study contributes methodologically by applying a dynamic and data‐driven approach to measuring SD and provides valuable information on the interaction among its dimensions.