How and why do the policy areas covered in World Bank loan conditions change over time and across borrowers? We hypothesize that shifts in the Bank’s economic research and policy priorities influence Bank loan conditions, even after controlling for country characteristics and international political aspects. To test this claim we apply keyword-assisted topic models to the analysis of over 13,000 World Bank policy loan conditions and close to 35,000 World Bank research papers published between 1985 and 2014. Contrary to the criticism levelled against the Bank that changes in research and policy priorities are mostly rhetorical and have little substantive effect on Bank lending, we find that internal research and policy priority shifts explain the conditions in a Bank loan at least as well as more traditional donor or borrower-specific measures central to IPE models of Bank lending.
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We show the count of loans by country and year in Table C in the Online Appendix.
Indeed, Bank efforts to group or categorize conditions have changed. Compare post-2004 thematic groupings in http://pubdocs.worldbank.org/en/757261462982621141/DPF-database-FY18.xlsx (found at http://projects-beta.worldbank.org/en/projects-operations/products-and-services#DPF) to older themes (World Bank, 2001: 23–24). But such categorization is a different analytical interest than our focus on tracing the textual content of conditions.
While our dataset comprises all loans since 1980, when the Bank started imposing conditions for all loans, coverage of covariates is extremely limited for the first four years. Our data is similar to the Bank’s Adjustment Lending Conditionality and Implementation Database (ALCID), though extended by Bank staff in the years since ALCID stopped and shared for this research.
We correct spelling extensively throughout the documents—without this work the model produces correlations based on consistent misspellings. The list of excluded words is available in the replication data for this study.
Random sampling itself does not guarantee that the covariates in our later analysis have the same means as if we had included the 200 loans in our analysis. We therefore check for covariate balance as defined for matching models and verify that the means of the covariates in the analysis and the training sample are very close. The plot is shown in the Online Appendix.
We scraped the texts from the WB research database at https://openknowledge.worldbank.org/, accessed between May and June 2018.
Both variables could be measured directly, but coverage is extremely limited in the World Bank Development Indicators, which begs questions about how the Bank evaluates country policies in these areas.
We include crises since 2012, the end of this dataset, manually. See the Online Appendix for details.
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The authors thank Merih Angin, José Fernández-Albertos, Paul Cadario, World Bank staff members who gave confidential interviews and data, participants at the 2018 EPSA and 2020 PEIO Conferences, and Shusei Eshima, Kosuke Imai and Tomoya Sasaki for generous advice on the methodology. Haruka Takagi provided excellent research assistance.
This research was partially funded by Insight Grant No. 435140487 of the Social Sciences and Humanities Research Council of Canada (SSHRC).
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Cormier, B., Manger, M.S. Power, ideas, and World Bank conditionality. Rev Int Organ (2021). https://doi.org/10.1007/s11558-021-09427-z
- World Bank
- Sovereign borrowing
- Development finance