Rich and Naïve? Assessing the Effects of Norwegian Aid on Political Corruption, 1980–2018

Abstract

The effectiveness of aid is heatedly debated in academia and policy circles. Annually, billions of dollars are transferred from industrialized countries to developing countries out of moral and practical concerns. Can aid from Norway, a country apparently with little strategic interests, a great deal of political consensus in support of aid, and much wealth, make a difference towards achieving better political governance in the poorer world? Using data on bilateral – and good governance aid per capita as measures of the value of aid to recipients, and novel data on political corruption, we find that aid from Norway associates negatively with political corruption, whereas total aid from all donors associates positively. The substantive impacts of these effects, however, are minimal. More sophisticated analyses accounting for selection effects and endogeneity suggest that Norwegian aid is perhaps following good governance rather than causing it. This finding, while not supporting aid optimism, might somehow comfort Norwegian taxpayers who might rest assured that their money is not unduly benefiting the corrupt. While there is no support for the extremely pessimistic view of Norway’s generosity towards the poorer world, one might still question its instrumental value if it only follows success rather than causes it.

KEYWORDS:

• Norwegian aid
• corruption
• causality
• aid effectiveness

Supplemental data

Supplemental data for this article can be accessed at https://doi.org/10.1080/08039410.2020.1829028

Notes

1 In 2017, roughly a billion people lived on less than $2 per day, the threshold for absolute poverty. Total global aid that year was $127 billion. See https://data.worldbank.org/indicator/dc.dac.totl.cd (accessed 20.01.2020).

2 For a history of the Donor Assistance Committee’s (DAC) foreign aid priorities, see https://www.oecd.org/dac/stats/the07odagnitarget-ahistory.htm (accessed 20.01.2020).

3 We test for serial correlation by running the Woodridge test on the basic model. We could not reject the null hypothesis that our data suffer from first-order serial correlation.

4 These data are available at: https://databank.worldbank.org/reports.aspx?source=world-development-indicators&preview=on#. (last accessed 27 January 2020).

5 The GDP deflator is obtained from the US Bureau of Economic Analysis: https://www.bea.gov (accessed 27 January 2020).

6 The official exchange rates for the Norwegian Kroner and the US$: https://www.norges-bank.no (last accessed 27 January 2020).

7 See https://ucdp.uu.se (last accessed 28 January 2020).

8 The standardized coefficient is computed by multiplying the coefficient with the within standard deviation of the x variable. This product is then divided by the within standard deviation of y and multiplied by 100 to express as percentage.

9 Results not shown out of space consideration but available from authors upon request.

Additional information

Notes on contributors

Elise Støver Toft

Elise Støver Toft has a Master's degree in Political Science from the Norwegian University of Science and Technology in Trondheim. She completed minors in Pedagogy, English, and Religion. Elise currently works for the city council in Bergen and hopes to follow a career in an international organization focusing on questions of equality, justice, education, and human rights.

Indra de Soysa

Indra de Soysa (PhD) is Professor of Political Science at the Norwegian University of Science and Technology. He teaches international political economy, with a special focus on development. His articles appear in Journal of Conflict Resolution, International Organization, European Journal of Criminology, International Studies Quarterly, Energy Policy, Business & Society, Journal of Peace Research among several others. He is a member of the Royal Norwegian Academy.