Modeling the Dynamics and Spillovers of the Health Labor Market: Evidence from China’s Provincial Panel Data

Yang Fu,Ying Mao,Jinlin Liu,Bin Zhu

doi:10.3390/su10020333

Abstract

Health workforce misdistribution is a major challenge faced by almost all countries. A more profound understanding of the dynamics of the health labor market provides evidence for policy makers to balance health workforce distribution with solid evidence. However, one major deficit of existing theoretical and empirical studies is that they often ignore the intra-regional spillovers of the health labor market. This study builds a theoretical “supply–demand–spillover” model that considers both intra-regional supply and demand-side factors, and inter-regional spillovers, hence providing a theoretical reference point for further in-depth studies. Using spatial econometric panel models, the effect of all determinants and spillovers were empirically measured based on a Chinese panel data set, shedding light on health workforce policies in China.

Highlights

In December 2015, the United Nations officially established global Sustainable Development Goals (SDGs), putting health in a central position due to its inalienable contributions to both individuals and societies [1]
Following the procedures specified in the methods, we first did the LR test 1 and concluded that the dynamic spatial Durbin panel model (SDPM) was better than its static form
In the context of this study, the dynamic SDPM with spatial effects was preferentially compared with the time fixed and both fixed models, as the data set belonged to the short panel (n > T)

Summary

Introduction

In December 2015, the United Nations officially established global Sustainable Development Goals (SDGs), putting health in a central position due to its inalienable contributions to both individuals and societies [1]. The availability of a health workforce shapes the conditions and prospects of population health substantially, and plays an important role in sustainable human development. It has been established that health workforce density (i.e., the ratio of the health workforce population to the overall population, expressed as the number of health workers per 1000 people [4]) is significantly associated with health outcomes in certain areas. Wider and denser coverage of the health workforce will most likely bring positive health outcomes by improving overall capacity for disease detection and response, and contribute towards achieving the SDGs [1]. The studies on the determinants of a health workforce in one area remain insufficient

Objectives

Results

Discussion

Conclusion