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Measuring Corruption in Infrastructure: Evidence from Transition and Developing Countries

2009; Taylor & Francis; Volume: 45; Issue: 3 Linguagem: Inglês

10.1080/00220380802265066

1743-9140

Charles Kenny,

Public-Private Partnership Projects

Abstract This paper examines what we can say about the extent and impact of corruption in infrastructure using existing evidence. There is evidence that most perceptions measures appear to be very weak proxies for the actual extent of corruption in the infrastructure sector, largely (but inaccurately) measuring petty rather than grand corruption. Survey evidence is more reliable, but limited as a tool for differentiating countries in terms of access to infrastructure finance or appropriate policy models. The paper suggests that a focus on bribe payments as the indicator of the costs of corruption in infrastructure may be misplaced. Acknowledgements This paper does not necessarily reflect the views of the World Bank, its Executive Directors or of the countries that they represent. Thanks to Antonio Estache, Jonathan Halpern, Laszlo Lovei, Gregory Kisunko, Todd Moss, Oliver Morrissey, Tina Soreide, Richard Messick and an anonymous reviewer of the journal for comments and, in particular, Jim Anderson both for detailed comments and for pointing out a significant error in an earlier draft. Remaining errors and opinions are mine. Notes 1. Based on investment and maintenance estimates from Fay and Yepes (2003). 2. Aggregating perceptions scores from different sources does not necessarily improve accuracy. Variation in sources for country scores within a given year and across years and lack of independence between sources, both increase the magnitude of variation between scores required to declare a 'statistically significant' difference in perceived corruption – a variation which is already quite large. It also makes comparing scores for a country over time problematic (Arndt and Oman, Citation2006; Knack, Citation2006). 3. Exacerbating this problem and helping to explain the continuing decline in Peru's CPI rank as other indicators suggested the corruption situation was improving, is that the CPI often incorporates data that is two to three years old. 4. Accessed at: http://www.enterprisesurveys.org/ 5. Similarly, Davis (2004) suggests that unaccounted for water accounts for 35 per cent of total flows in India. 6. The ongoing World Bank effort to build a database of road construction and rehabilitation costs should help to provide benchmarks against which to estimate excess costs of construction in transport, accessed at: http://www.worldbank.org/transport/roads/rd_tools/rocks_main.htm 7. Accessed at: http://info.worldbank.org/governance/beeps/ 8. This amongst firms which reported a percentage and did not answer 'don't know'. 9. These are unweighted country average responses. 10. The average of within-country standard deviations is 1.2 compared to the standard deviation of country averages which is 0.4. 11. This assumes mid-point values for the data ranges (2.5% for the 0–5% range, for example) and 30 per cent for answers of 'above 20 per cent'. The lowest possible estimate (assuming 0% for the 0–5% range, and so on) is four per cent, the highest (assuming 5% for the 0–5 range and so on, and 100% for the 'above 20%' answer) is 10 per cent. 12. The F-stat. is 1.43. 13. It is also worth noting that this situation may be considerably better with the larger samples of the 2005 dataset. 14. A 21 observation regression produced the following result: (% firms expected to give a gift to get an electrical connection) = 22 −1.56*(TI CPI). The CPI does not enter significantly at 10 per cent. 15. This (and subsequent calculations) view the corrupt payment as a transfer but accounts for a (high) marginal cost of government funds lost to corruption of 1.50 (a 50% deadweight loss). 16. This is approximately the economic impact of poor road construction suggested by Olken (2004). 17. Although there are better dependent variables for water, non-technical losses would be a better dependent variable for electricity (again, this is not available for as many countries). 18. These results, positive and negative alike, are open to all of the usual concerns with econometric exercises regarding questions of causality and the stability of coefficients in the presence of multicolinearity and omitted variables. 19. For example, in the Philippines, physical audits combined with a GIS system are being used to determine if roads and bridges actually exist and what state they are in as part of a drive towards improved transport governance. Furthermore, especially at the level of project selection and measurement of infrastructure quality, there is no need to survey 100 per cent of proposed projects or infrastructure stocks – a random representative sample would suffice to suggest if the sector is performing well. 20. Of course, this will depend on the level of such payments – if they start doubling contract prices, for example, they will become a serious issue but, luckily, also much easier to spot.