Dynamics, regional heterogeneity and robustness of fiscal poverty alleviation efficiency in China: dynamic network DEA and Bootstrap resampling methods

¹ School of Business, Xiangtan University, Xiangtan 411105, P.R. China
² School of Business, Hunan Normal University, Changsha 410081, P.R. China
³ School of Mathematics and Statistics, Hunan Normal University, Changsha 410081, P.R. China
⁴ School of Business Administration, Hunan University, Changsha 410082, P.R. China

^* Corresponding author: xiaohelu1986@163.com

Received: 22 May 2023
Accepted: 25 March 2024

Abstract

This paper aims to tackle the issues of evaluating the dynamic performance of fiscal poverty alleviation in 22 Chinese provinces (regions) over 2016–2019. First, we open up the internal structure of the fiscal poverty alleviation system (FPAS) and clarify its input-output process as a two-stage series system consisting of the public investment process and poverty reduction process. On this basis, we construct dynamic network DEA models with and without carryover activities for measuring the period efficiency and overall efficiency of FPASs (i.e., FPAS efficiency) and the period efficiency and overall efficiency of evaluation indicators (i.e., indicator efficiency), and also prove the relationship between the two proposed models. Second, we combine the proposed dynamic network DEA models and Bootstrap resampling method to assess the robustness of FPAS efficiency for exploring the risk of returning to poverty for each FPAS. The results show that: (i) the carryover activities have some impact on the FPAS efficiency and indicator efficiency; (ii) the period efficiency shows an upward trend, and most of FPASs or evaluation indicators have been at high efficiency in the sample period, but there is still regional heterogeneity; (iii) there are some differences between the indicator efficiency and there are some inconsistencies between them and the FPAS efficiency; (iv) bootstrap resampling results indicate that several FPASs have a great risk of returning to poverty.