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Corruption risk and political dynasties: exploring the links using public procurement data in the Philippines

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Abstract

Corruption plays a central role in underdevelopment in the Philippines, yet there is no reliable, non-aggregate, and periodic measurement for corruption in the country. This study demonstrates the use of statistical techniques to synthesize information from public procurement contracts into one indicator to measure corruption risk for each province in the Philippines from 2004 to 2018. The results show corruption risk decreased from the 2004 term to 2013, and increased to an all-time high in 2016. Regression analysis also shows that two measures of political power concentration among clans—a Hirschman–Herfindahl Index applied to the political sphere (Political HHI), and the Size of the Largest Dynasty per Province—is significantly and positively linked to the corruption risk indicator at least at the 5% significance level. This result coheres with emerging literature on political dynasties, suggesting that these debilitate checks and balances and increase the risk of impunity and malgovernance at the local level, particularly in the Philippines. This study highlights the importance of studying corruption vis-à-vis the evolving issue of political dynasties amassing power, and provides further evidence that reforms are required in this area to promote development in democracies.

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Fig. 1

Source: Transparency International (https://www.transparency.org/en/cpi)

Fig. 2

Source: World Bank (https://databank.worldbank.org/databases/control-of-corruption)

Fig. 3

Source: Social Weather Stations (https://www.sws.org.ph/swsmain/artcldisppage/?artcsyscode=ART-20161005151549)

Fig. 4

Source: Authors’ own calculations (color figure online)

Fig. 5

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Notes

  1. See Republic Act 9184. Further information available at: https://www.officialgazette.gov.ph/2016/08/29/implementing-rules-and-regulations-of-republic-act-no-9184/.

  2. Using a survey of randomly selected municipalities, Azfar and Gurgur (2008) found that corruption has a negative effect on health outcomes in the Philippines. Specifically, they constructed a corruption index from survey variables and found it has a significant negative effect on health services like immunization in children, patient waiting times, accessibility of health clinics, among others. In addition, Magtulis and Poquiz (2017) examined the relationship of government size (as measured by government expenditure over GDP) and the CPI over time using vector autoregression, and found a significant positive relationship between government spending and corruption perception.

  3. Mendoza et al. (2015) note that bribery can serve as grease or sand in the wheels of commerce, affecting firm performance (at the micro‐level) and, ultimately, economic growth (at the macro‐level). Analyzing a dataset of over 2000 micro, small and medium scale enterprises in over 30 cities in the Philippines, they find evidence that corruption greases the wheels of commerce for Philippine SMEs, particularly in cities with poor business environments.

  4. Elbahnasawy and Revier (2012), for example, empirically analyzed the potential correlates of Transparency International’s CPI and World Bank’s Worldwide Governance Indicators for 159 countries from 1998 to 2005, and found evidence that perceptions of a strong rule of law predicts a reduced level of corruption. Furthermore, income level of countries was also a significant predictor—poorer countries were associated with more corruption.

  5. Fat dynasty share is the proportion of all elected officials in a province who have relatives simultaneously holding positions within a given election term.

  6. Political HHI contribution of 1 family with 50% market share is (\({50}^{2}=2500\)), while the political HHI contribution of 10 families with 10% market share each is (\({10}^{2}+ {10}^{2}+\dots + {10}^{2}=1000\)).

  7. The data is limited by the fact more contracts are present in regions closer to Manila due to those areas being more economically developed and having better access to technology and information technology needed to ensure the complete and timely upload of contracts to PhilGEPS.

  8. This covers the following positions within a province: Governor, Vice-governor, Congressperson, Provincial Board Member, Mayor, Vice-Mayor, and Councilor.

  9. Similar to other recent studies that link development indicators like poverty to concentration of political power in the Philippines (eg. Mendoza et al 2016), we only claim correlation and not causality through the empirical models. Through the OLS and fixed effects panel regression, we estimate the strength and direction of the relationship between the corruption risk indicator and the measures of political dynasties, but do not imply that political dynasties cause increased or decreased corruption risk. The variables used in the study cannot explain nor control for temporal order, hence causation cannot be claimed.

  10. All provinces in the Philippines hold elections every three years on the exact same day, and three-year electoral terms for all elected positions used in this study are aligned for each province. The 5 three-year terms used in this study are those which began in the following years: 2004, 2007, 2010, 2013, and 2016.

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Acknowledgements

The authors would like to thank the Ateneo de Manila University for supporting our project. We also thank Daniel Gingerich and the University of Virginia Quantitative Collaborative for providing valuable feedback to an earlier draft of the study. We would also like to thank the reviewers for their valuable feedback to the current draft. The views expressed herein are those of the authors and do not necessarily reflect the views of Ateneo de Manila University.

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Authors and Affiliations

  1. Woodrow Wilson Department of Politics, University of Virginia, Charlottesville, VA, USA

    Daniel Bruno Davis

  2. Ateneo Policy Center, School of Government, Ateneo de Manila University, Quezon City, Philippines

    Ronald U. Mendoza & Jurel K. Yap

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  1. Daniel Bruno Davis
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  2. Ronald U. Mendoza
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  3. Jurel K. Yap
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Contributions

DBD: Conceptualization, Writing—Original Draft, Writing—Review and Editing, Methodology, Software, Formal Analysis, Data Curation. RUM: Conceptualization, Writing—Original Draft, Writing—Review and Editing, Supervision. JKY: Methodology, Software, Formal Analysis, Data Curation, Writing—Original Draft, Writing—Review and Editing, Visualization.

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Correspondence to Jurel K. Yap.

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Appendix

Appendix

See Figs. 

Fig. 6
figure 6

Source: Authors’ own calculations

Scree Plot of the IRT Model.

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6,

Fig. 7
figure 7

Source: Authors’ own calculations

IRT Curve for Procurement Mode.

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7 and Table 

Table 6 Correlation table for empirical model 1.
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Table 7 Correlation table for empirical model 2.
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Table 8 Panel Regression with Fixed Effects Estimates of the second empirical model controlling for differences over time (model C)
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Table 9 Rotated Factor Loadings, SS loadings, and Factor Correlations of the IRT Model.
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9.

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Davis, D.B., Mendoza, R.U. & Yap, J.K. Corruption risk and political dynasties: exploring the links using public procurement data in the Philippines. Econ Gov 25, 81–109 (2024). https://doi.org/10.1007/s10101-023-00306-4

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