A Model for the Distribution of Construction Credits Based on Network Data Envelopment Analysis

Public budget allocation refers to the process by which governments determine how to distribute their financial resources among various social needs and priorities. It is a complex and multifaceted process that involves decisions with profound consequences for citizens' welfare and the overall functioning of society. Budget allocation influences the amount and quality of public services such as healthcare, education, and infrastructure and can also affect economic development and the distribution of wealth within a country. In recent years, there has been a growing interest in examining how public budgets are allocated and whether current methods and priorities align with societal needs and values. Continuous fluctuations in budget allocation policies in Iran, especially in critical areas such as education and healthcare, have led to regional inequalities. To address this issue, a planned and sustainable approach to credit distribution is required—one based on the actual needs of each province and aimed at balanced development. In this context, the present study aims to propose a performance-based planning model tailored to the nature of the problem for the distribution of provincial capital credits. The objective is to achieve macroeconomic and social goals, including reducing income inequality, alleviating deprivation, increasing production and employment, lowering unemployment rates, and enhancing social well-being, all based on the performance of the provinces.

The approach used in this research is mathematical modeling based on a conceptual framework that applies data envelopment analysis (DEA) in a novel way. As a case study, the model has been tested in the education and healthcare sectors. In the proposed model, specific values of deviation in variable values (delta) are determined by experts based on national and provincial credit distribution policies. Additionally, the allocation of budgetary resources is determined based on the performance evaluation and ranking of provinces to achieve predefined goals.

The implementation of the model using 1401 data demonstrates its efficiency compared to conventional and previous capital credit distribution methods employed by Iran's Planning and Budget Organization. The results indicate that the proposed method not only enhances accountability for achieving set goals but also facilitates continuous improvement by enabling provinces to learn from past experiences and make informed decisions for future activities.One of the most significant features of this model is its adaptability to budgetary changes. The model’s inputs can always be modified based on new policies, ensuring its relevance and applicability over time.

The proposed two-phase approach begins with the DEA model to align with the nature of the problem and assess provincial performance. In the second phase, a credit change management approach is employed for sensitivity analysis. A key distinguishing feature of this model is the expert-driven determination of delta values based on national and provincial credit distribution policies. Additionally, each province’s optimal share of total allocated credits can be calculated and received, providing policymakers with a crucial tool to enhance the efficiency of provinces and guide them toward an optimal state.