Explaning Variability in the Global Labour Market Using Principal Component Analysis and Multiple Imputation
Explaning Variability in the Global Labour Market Using Principal Component Analysis and Multiple Imputation

Summary/Abstract: This article investigates the underlying factors contributing to global labour market variability by applying Principal Component Analysis (PCA) and cluster analysis to data from 191 countries. Focusing on a broad set of economic, demographic and institutional indicators, the article seeks to uncover the primary dimensions shaping labour market dynamics worldwide. Key variables include GDP per capita, the Human Development Index (HDI), unemployment and poverty rates, labour freedom and corruption perception indices, average wages, and demographic characteristics such as population structure and migration rates. To ensure data completeness and robustness, multiple imputation was employed to address missing values. PCA was then used to reduce dimensionality and identify latent structures within the data. The resulting principal components were subsequently used in k-means clustering, which revealed four distinct clusters of countries sharing similar labour market profiles. The findings confirm that economic development and institutional quality are the dominant forces behind variations in labour market conditions across countries, while demographic variables, such as age distribution and migration, also play a meaningful role. These results support the hypothesis that clusters of countries with similar labour market profiles can be identified using the economic, demographic and institutional variables as inputs. The identification of country clusters further enables comparative insights and highlights region-specific challenges and opportunities. For policymakers, the study emphasises the importance of promoting economic stability, improving institutional frameworks and designing targeted interventions that consider demographic realities. It also calls for future research to incorporate additional socioeconomic dimensions and longitudinal data to more comprehensively capture the evolving nature of global labour markets.

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