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全球合同级公共采购数据集。

Global Contract-level Public Procurement Dataset.

作者信息

Fazekas Mihály, Tóth Bence, Abdou Aly, Al-Shaibani Ahmed

机构信息

Central European University, Quellenstraße 51, 1100, Wien, Austria.

University College London, Gower Street, London WC1E 6BT, UK.

出版信息

Data Brief. 2024 Apr 15;54:110412. doi: 10.1016/j.dib.2024.110412. eCollection 2024 Jun.

DOI:10.1016/j.dib.2024.110412
PMID:38698797
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11063991/
Abstract

One-third of total government spending across the globe goes to public procurement, amounting to about 10 trillion dollars a year. Despite its vast size and crucial importance for economic and political developments, there is a lack of globally comparable data on contract awards and tenders run. To fill this gap, this article introduces the Global Public Procurement Dataset (GPPD). Using web scraping methods, we collected official public procurement data on over 72 million contracts from 42 countries between 2006 and 2021 (time period covered varies by country due to data availability constraints). To overcome the inconsistency of data publishing formats in each country, we standardized the published information to fit a common data standard. For each country, key information is collected on the buyer(s) and supplier(s), geolocation information, product classification, price information, and details of the contracting process such as contract award date or the procedure type followed. GPPD is a contract-level dataset where specific filters are calculated allowing to reduce the dataset to the successfully awarded contracts if needed. We also add several corruption risk indicators and a composite corruption risk index for each contract which allows for an objective assessment of risks and comparison across time, organizations, or countries. The data can be reused to answer research questions dealing with public procurement spending efficiency among others. Using unique organizational identification numbers or organization names allows connecting the data to company registries to study broader topics such as ownership networks.

摘要

全球政府总支出的三分之一用于公共采购,每年总计约10万亿美元。尽管公共采购规模巨大且对经济和政治发展至关重要,但缺乏关于合同授予和招标的全球可比数据。为填补这一空白,本文引入了全球公共采购数据集(GPPD)。我们使用网络爬虫方法,收集了2006年至2021年期间来自42个国家的7200多万份合同的官方公共采购数据(由于数据可用性限制,每个国家涵盖的时间段有所不同)。为克服各国数据发布格式不一致的问题,我们将发布的信息标准化,以符合通用数据标准。对于每个国家,收集关于买方和供应商、地理位置信息、产品分类、价格信息以及合同签订过程细节(如合同授予日期或所遵循的程序类型)的关键信息。GPPD是一个合同级数据集,其中计算了特定筛选条件,以便在需要时将数据集缩减为成功授予的合同。我们还为每份合同添加了几个腐败风险指标和一个综合腐败风险指数,从而能够对风险进行客观评估,并在不同时间、组织或国家之间进行比较。这些数据可被重新用于回答有关公共采购支出效率等方面的研究问题。使用独特的组织识别号码或组织名称,可以将数据与公司注册信息相连接,以研究更广泛的主题,如所有权网络。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d938/11063991/f39cacfaf915/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d938/11063991/6a7af0ac52ac/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d938/11063991/f6932aa6f8d4/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d938/11063991/5c93c7fe86f3/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d938/11063991/dda13e823086/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d938/11063991/f39cacfaf915/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d938/11063991/6a7af0ac52ac/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d938/11063991/f6932aa6f8d4/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d938/11063991/5c93c7fe86f3/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d938/11063991/dda13e823086/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d938/11063991/f39cacfaf915/gr5.jpg

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Portuguese public procurement data for construction (2015-2022).葡萄牙建筑领域公共采购数据(2015 - 2022年)。
Data Brief. 2023 Mar 16;48:109063. doi: 10.1016/j.dib.2023.109063. eCollection 2023 Jun.
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Development aid contracts database: World Bank, Inter-American Development Bank, and EuropeAid.发展援助合同数据库:世界银行、美洲开发银行和欧洲援助组织。
Data Brief. 2022 Apr 2;42:108121. doi: 10.1016/j.dib.2022.108121. eCollection 2022 Jun.
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United states department of defense (DoD) real property repair, alterations, maintenance, and construction project contract data: 2009-2020.美国国防部(DoD)不动产维修、改建、维护及建设项目合同数据:2009 - 2020年
Data Brief. 2020 Aug 5;32:106128. doi: 10.1016/j.dib.2020.106128. eCollection 2020 Oct.