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Cited by 265

The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.

Aid, China, and Growth: Evidence from a New Global Development Finance Dataset

Open access
Dreher, Axel, Fuchs, Andreas, Parks, Bradley, Strange, Austin, Tierney, Michael J.·2021·American Economic Journal: Economic Policy

Abstract

This article introduces a new dataset of official financing from China to 138 developing countries between 2000 and 2014. It investigates whether Chinese development finance affects economic growth in recipient countries. The results demonstrate that Chinese development finance boosts short-term economic growth. An additional project increases growth by between 0.41 and 1.49 percentage points 2 years after commitment, on average. While this study does not find that significant financial support from China impairs the overall effectiveness of aid from Western donors, aid from the United States tends to be more effective in countries that receive no substantial support from China. (JEL F35, O19, O47, P33, P34)

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Crossref · 10.1257/pol.20180631

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Metadata

Authors
Dreher, A., Fuchs, A., Parks, B., Strange, A., & Tierney, M. J.
Venue
American Economic Journal: Economic Policy
Published
2021-05-01
Volume
13
Issue
2
Pages
135-174
ISSN
  • 1945-7731 (ISSN-L)
  • 1945-774X
OpenAlex
W3161899344
Cited by
265
References
54
FWCI
88.08
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Aid, China, and Growth: Evidence from a New Global Development Finance Dataset. https://app.cochat.ai/papers/10.1257/pol.20180631
The paper
Dreher, A., Fuchs, A., Parks, B., Strange, A., & Tierney, M. J. (2021). Aid, China, and Growth: Evidence from a New Global Development Finance Dataset. American Economic Journal: Economic Policy. https://doi.org/10.1257/pol.20180631

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.