Performance Analysis of Capital Development Funds in Lorestan Province by COFOG Divisions and Groups Using Data Envelopment Analysis

Authors

    Mohammad Khodabakhshi Department of Applied Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
    Mahtab Mirkooshesh Department of Applied Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran

Keywords:

Data Envelopment Analysis, Capital Asset Acquisition, Budget Allocation, COFOG Classification, Lorestan Province

Abstract

Government investment in development projects is a primary mechanism for creating new production and service capacities. However, these projects often face challenges, including budget deficits and incomplete funding allocation. Given the competition between current expenditures and development credits for public funds, employing efficient management mechanisms for development projects is crucial. Optimization methods and efficiency analysis models, such as Data Envelopment Analysis (DEA), can be valuable tools. DEA is a non-parametric method used to evaluate the efficiency of homogeneous decision-making units (DMUs) based on multiple inputs and outputs. This study examines the performance of capital investment credits in Lorestan Province over six years, categorized by COFOG divisions and groups, using the Fair DEA (KA) model. The results show consistent rankings for 2021, 2020, and 2018, while other years exhibit differences. A comparative analysis with other provinces using the CCR and CSW models reveals that Lorestan’s efficiency was below the national average in 2017 and 2018 but improved from 2019 to 2021, surpassing the national average.

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Published

2025-03-20

Submitted

2025-01-27

Revised

2025-02-27

Accepted

2025-03-11

Issue

Section

Articles

How to Cite

Khodabakhshi, M. ., & Mirkooshesh, M. . (2025). Performance Analysis of Capital Development Funds in Lorestan Province by COFOG Divisions and Groups Using Data Envelopment Analysis. The Decision Science and Intelligent Systems, 1(2), 22-52. https://dsisj.com/index.php/dsisj/article/view/19

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