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Cited by 665

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.

Growth and Governance: Models, Measures, and Mechanisms

Kurtz, Marcus J., Schrank, Andrew·2007·The Journal of Politics

Abstract

The regnant scholarly consensus linking good governance—the quality of public administration—to economic development has undergone surprisingly little empirical scrutiny. We examine the relationship by asking two questions: How confident are we in our cross-national measures of good governance? How solid are the empirical foundations of the growth-governance causal linkage? Our results suggest that the dominant measures of governance are problematic, suffering from perceptual biases, adverse selection in sampling, and conceptual conflation with economic policy choices. Within the limits of somewhat problematic measures, the evidence suggests that there is far more reason to believe that growth and development spur improvements in governance than vice versa. The policy implications are profound, for international organizations and governments are beginning to condition developmental aid on problematic measures of administrative performance.

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Crossref · 10.1111/j.1468-2508.2007.00549.x

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Metadata

Authors
Kurtz, M. J., & Schrank, A.
Venue
The Journal of Politics
Published
2007-05
Volume
69
Issue
2
Pages
538-554
ISSN
  • 0022-3816 (ISSN-L)
  • 1468-2508
OpenAlex
W2104670548
Cited by
665
References
55
FWCI
76.77
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Growth and Governance: Models, Measures, and Mechanisms. https://app.cochat.ai/papers/10.1111/j.1468-2508.2007.00549.x
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Kurtz, M. J., & Schrank, A. (2007). Growth and Governance: Models, Measures, and Mechanisms. The Journal of Politics. https://doi.org/10.1111/j.1468-2508.2007.00549.x

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.