preprints.org > social sciences > decision sciences > doi: 10.20944/preprints202310.0571.v1

Preprint Concept Paper Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 9 October 2023 / Approved: 9 October 2023 / Online: 10 October 2023 (08:23:29 CEST)

This research undertakes a quantitative exploration of the impact of procurement mo-dalities on economic efficiency and inclusive procurement within the Dominican Repub-lic's public sector. Applying data science methodologies and machine learning techniques, this study analyzes a rich dataset of public procurement contracts spanning from 2018 to 2023. A focus is placed on understanding how different procurement modalities affect contract amounts and processing times—two key indicators of economic efficiency. Machine learning models, including Random Forest and Gradient Boosting, are em-ployed to predict these essential metrics across different procurement modalities. The findings indicate a significant positive correlation between certain procurement modali-ties and economic efficiency. The machine learning models reveal that procurement strategies focused on local sourcing and transparent bidding processes tend to yield high-er economic returns and more equitable outcomes. This research underscores the poten-tial of quantitative analysis to inform procurement policies, fostering a more economi-cally efficient and inclusive procurement ecosystem in the Dominican Republic's public sector. The insights garnered from this study could have significant implications for the design of more inclusive procurement strategies, contributing to socio-economic growth and corruption reduction in the Dominican Republic.

Public Procurement; Machine Learning; Socio-Economic Impacts; Inclusive Procurement; Dominican Republic; Random Forest; Gradient Boosting

Social Sciences, Decision Sciences

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