# Spatial econometric analysis of health workforce distribution and its influencing factors in Inner Mongolia, China

## TL;DR

This study examines how health workers are distributed across Inner Mongolia, China, and identifies factors like income and bed density that influence their spatial patterns.

## Contribution

The study introduces spatial econometric methods to analyze health workforce distribution in Inner Mongolia, capturing both direct and indirect spatial effects.

## Key findings

- Health workforce shows significant spatial clustering, with high concentrations in urban areas and low in rural regions.
- Disposable income and bed density positively influence health workforce distribution, while population density has a negative effect.
- Spatial interactions suggest that factors in one region affect neighboring regions, highlighting the need for collaborative policies.

## Abstract

Although health workforce equity has gained more attention, few studies have explored its spatial distribution and influencing factors in Inner Mongolia, a vast and diverse region of China. Existing researches often use simple geographic adjacency-based models that do not fully consider both location and economic factors. To address this, this study applies spatial econometric methods to examine the direct and indirect effects of influencing factors on health workforce distribution in Inner Mongolia from 2013 to 2022.

Data were obtained from the Inner Mongolia Statistical Yearbook (2013–2022). Health workforce (HW) was measured by the number of health professionals per 1,000 persons. Spatial distribution and clustering patterns were analyzed using Global and Local Moran’s I. Four types of independent variables were selected: socioeconomic factors (per capita GDP and disposable income), demographic factors (population density and population growth), institutional environment (fiscal self-sufficiency rate), and supportive resources (beds density). A spatial panel econometric model was applied to assess both direct and indirect effects.

Significant spatial clustering of HW was found throughout the study period. High-high clusters were concentrated in the Hohhot-Baotou-Erdos region, while low-low clusters appeared in remote rural and pastoral counties. The Spatial Durbin Model (SDM) was chosen to explore the influencing factors. Direct effects showed that disposable income and bed density positively influenced HW within a county, whereas population density exhibited a significant negative impact. Indirect effects revealed that disposable income, fiscal self-sufficiency rate, and bed density also had positive spatial associations on HW in neighboring regions.

Health workforce allocation in Inner Mongolia shows significant spatial disparities, with decreasing clustering over time, indicating reduced regional heterogeneity. Disposable income, bed density, and fiscal self-sufficiency positively affect HW and exhibit notable spatial associations, while population density has a negative impact. To optimize allocation, policies should enhance regional collaboration and resource sharing, increase fiscal support and promote medical alliances.