An entropic approach for calculating the Human Development Index (HDI) in rural communities: a case study in Ștefan Vodă, Republic of Moldova

Abstract

The Human Development Index (HDI) is an essential tool for assessing socio-economic progress, but its traditional calculation methods, based on equal weights, can ignore significant variations between indicators. This study proposes an alternative method, using information entropy to objectively determine weights in the HDI calculation, with practical application in the Ștefan Vodă rayon, Republic of Moldova. The analysis focuses on one urban and 22 rural localities, highlighting regional disparities and the necessity for an even more nuanced approach. Data for HDI calculation were analyzed for five key dimensions: education, economy, infrastructure, health and demography. The calculation process followed three stages: normalization (Pij), entropy calculation (Ej) and determination of weights (wj). The entropy revealed the following objective weights: infrastructure (38.7%), economy (27.7%), health (11.6%), education (11.0%) and demography (11.0%). These differ significantly from the equal weights (20% each), underlining the predominant importance of infrastructure and economy in the context of rural development. The comparison between the initial HDI and the corrected HDI shows significant variations: Purcari (+10.87%, due to strong economic and infrastructure performance); underestimated localities – Alava (-25.13%) and Ștefănești (-20.67%), where the initial HDI overestimated the development due to the uniform weighting; and the case of Ștefan Vodă city, where the corrected HDI (0.747) is slightly lower than the initial one (0.756), reflecting a more realistic assessment when priorities are objectively weighted. The application of entropy highlighted two critical aspects: the high weight of the infrastructure indicator (38.7%), which highlights the critical investment deficit in rural areas, and the significant weight of the economic indicator (27.7%), which reflects pronounced inequalities between localities and requires targeted interventions. The results are aligned with similar studies from Eastern and South-Eastern Europe, where entropy was used to correct composite indices in regional development contexts. The study demonstrates that the entropy approach provides a more accurate assessment of the HDI in rural areas, identifying critical dimensions that require interventions.