Colleagues, I apologize if you are receiving this more than one time. It just means we subscribe to the same listservs.
I am writing to let you know that last night AidData published the 3.0 version of its Global Chinese Development Finance (GCDF) dataset, the 3.0 version of its Tracking Underreported Financial Flows (TUFF) methodology, a 415-page Belt and Road Reboot report, as well as an accompanying set of materials/resources. At the conclusion of this email I describe features/improvements to the new dataset and encourage all of you (and your students) to use it in your research and teaching. You can access all of these materials/resources at the links below: Full report (Chapter 3 on ESG of particular relevance to this group): https://docs.aiddata.org/reports/belt-and-road-reboot/Belt_and_Road_Reboot_Full_Report.pdf Dataset: https://www.aiddata.org/data/aiddatas-global-chinese-development-finance-dataset-version-3-0 Methodology: https://www.aiddata.org/publications/aiddata-tuff-methodology-version-3-0 Dashboard: https://china.aiddata.org/ Media release: https://www.aiddata.org/blog/belt-and-road-bounces-back Executive Summary: https://docs.aiddata.org/reports/belt-and-road-reboot/Belt_and_Road_Reboot_Executive_Summary.pdf Many of you are probably familiar with earlier version of AidData's GCDF dataset, which has been widely used in the social sciences (see https://www.aiddata.org/blog/aiddatas-china-data-wins-2023-best-dataset-award-from-ipes). However, the latest (3.0) version of the GCDF dataset includes some new features/variables that could be especially useful to quantitatively -- and qualitatively -- oriented social scientists. Here's a summary of what is new/different: * Comprehensive sector and LIC/MIC coverage: The 3.0 dataset provides comprehensive coverage of all Chinese grant- and loan-financed projects (worth $1.34 trillion) across all sectors and all low-income countries (LICs) and middle-income countries (MICs) over 22 commitment years (2000-2021). All projects are assigned 3-digit OECD sector codes and ODA/OOF designations to enable comparisons with other official sources of international development finance. * Donor and lender coverage: The 3.0 dataset captures projects supported by 791 official sector donors and lenders in China. At the individual project/transaction-level, it also identifies the participation of 1,225 co-financing institutions-including Western commercial banks, multilateral development banks, and OECD-DAC development finance institutions that have chosen to collaborate or coordinate with Beijing. Another new feature is the inclusion of two "flag" variables that allow for easy identification of projects that involve (a) non-Chinese financiers or (b) multilateral institutions. * Borrower and recipient coverage: The 3.0 dataset identifies 5,037 receiving (borrowing) institutions and categorizes each one by type (government agency, state-owned bank, state-owned company, special purpose vehicle/joint venture, intergovernmental organization, private sector, etc.), country of origin (recipient country, China, or a third country), and, when applicable, role (direct borrower or indirect borrower through an on-lending arrangement). It also identifies 422 institutions ("accountable agencies") that have supported Chinese loan-financed projects and activities by providing repayment guarantees, credit insurance policies, and collateral which can be seized in the event of default. * Financial instrument coverage: The 3.0 dataset allows users to easily differentiate between the 10,291 grant-financed projects/activities and 4,776 loan-financed projects/activities. However, given that Beijing relies on an increasingly diverse set of debt instruments to finance its overseas development program in LICs and MICs, AidData has also introduced a new loan categorization scheme that allows users to isolate 23 specific types of loan instruments (project loans, emergency rescue loans, syndicated loans, bilateral loans, etc.), * Borrowing terms and conditions: There is no other publicly available dataset of China's overseas loan commitments with global coverage from 2000-2021 that identifies borrowing terms and conditions. The 3.0 dataset identifies 2,699 interest rates, 3,315 maturity lengths, 1,854 grace periods, 498 commitment fees, 480 management fees, and 2,537 grant elements across 4,776 loans in Africa, Asia, Oceania, Eastern and Central Europe, the Middle East, and Latin America and the Caribbean. It also identifies 668 loans backed by third-party repayment guarantees, 529 loans supported by credit insurance policies, and 1,015 loans underpinned by one or more sources of collateral. Other new features include the addition of a penalty interest variable, a credit insurance fee variable, a first (originally scheduled) loan repayment variable, a last (originally scheduled) loan repayment variable, three different grant element variables (one based on the 'old' OECD method, one based on the 'new' OECD method, and one based on the IMF/World Bank method), and a loan-level financial distress flag variable that identifies whether there is any narrative evidence (from the 'description' field in the dataset) that the borrower had - or is having - difficulty meeting its repayment obligations. * Temporal granularity: The 3.0 dataset provides an unprecedented level of detail on project commencement (implementation start) dates and project completion (implementation end) dates. It identifies precise, calendar day-level commencement dates for 11,286 projects and calendar day-level completion dates for 11,542 projects. The 3.0 dataset also provides data on the originally scheduled project commencement dates and completion dates, which has paved the way for the introduction of two new measures ("Deviation from Planned Implementation Start Date" and "Deviation from Planned Completion Date") of the degree to which projects ran (or are running) ahead of schedule or behind schedule. * Geospatial coverage and precision. Another important value addition is the level of geographical detail regarding where Chinese ODA- and OOF-financed projects take place. For 9,497 projects that have physical footprints or involve specific locations, the 3.0 dataset extracts point, polygon, and line vector data via OpenStreetMap URLs and provides a corresponding set of GeoJSON files and geographic precision codes. 72 72% (6,919) of these projects include "precise" or "approximate" geocodes; the remaining 28% (2,578 projects) are measured at an administrative unit level. * Qualitative detail: The 3.0 dataset provides detailed project narratives that "tell the story" of each project in the "description" field. The average length of each project narrative increased from 144 words in 2.0 dataset to 169 words in the 3.0 dataset. Whereas the project narratives in the 2.0 dataset consisted of 1.93 million words (roughly the same number of words one would find in 19 full-length books), the project narratives in the 3.0 dataset consist of 3.48 million words (roughly the same number of words one would find in 34 full-length books). Also, the 3.0 dataset is underpinned by ~148,000 sources in more than a dozen languages, and AidData released all of these sources (including thousands of unredacted loan and grant agreements, escrow account agreements, rescheduling agreements, deeds of security, share pledge agreements, commercial contracts, and forensic audits) last night. Hope the dataset and the connected products are beneficial to your research and teaching. Very best. Mike Michael J. Tierney Hylton Professor of Government and International Relations Director, Global Research Institute (757) 870-3870 Follow Us: LinkedIn<https://www.linkedin.com/school/globalresearchinstitute/> | Twitter<https://twitter.com/global_wm> [email signature] -- You received this message because you are subscribed to the Google Groups "gep-ed" group. To unsubscribe from this group and stop receiving emails from it, send an email to gep-ed+unsubscr...@googlegroups.com. 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