Donor motives, public preferences and the allocation of UK foreign aid: a discrete choice experiment approach

Abstract

This paper develops a prescriptive model for the inter-country allocation of aid from the UK government. The model incorporates three broad motives for allocating aid: recipient need, donor interests and absorptive capacity (the ability of recipient countries to use aid effectively). To determine each motive’s relative importance, a discrete choice experiment (DCE) involving more than 1600 members of the UK general population was conducted. Absorptive capacity is the most important motive, and recipient need and donor interests are equally but much less important. Current UK aid allocations are compared with those prescribed by the model. Some countries, including China, India and Indonesia, would receive much more if aid were allocated according to the model; other countries, including Afghanistan, Ethiopia and Pakistan, would receive much less. Cluster analysis reveals that the political parties voted for by DCE participants at the 2015 general election are, inter alia, related to their aid preferences.

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Fig. 1
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Notes

  1. 1.

    All dollar amounts reported are US dollars.

  2. 2.

    An exception is Lightfoot et al. (2016) which draws on one specific survey question to examine public preferences for using aid to advance national interest versus allocating it to those most in need: The specific survey question was: “Some people say that Britain’s foreign aid should simply be distributed to the countries which are most in need of help. Others say that we should put our own national interests first when deciding how to distribute foreign aid in the developing world. On a scale from 0 (according to need) to 6 (according to our national interests), which number best represents your view about how aid should be distributed?” Responses from the survey question in both 2010 and 2015 indicate an almost equal balance between the two preferences.

  3. 3.

    Other prescriptive models of aid allocation include Llavador and Roemer (2001), Collier and Dollar (2001, 2002), McGillivray et al. (2002), Cogneau and Naudet (2007), Wood (2008) and McGillivray and Clarke (2018).

  4. 4.

    Not all aid effectiveness studies have detected a relationship between aid and growth (e.g. Roodman 2007; Rajan and Subramanian 2008). However, numerous surveys of the aid literature (Morrissey 2001; McGillivray et al. 2006; Clemens et al. 2012; Glennie and Sumner 2014) as well as recent meta-analyses of foreign aid and growth (Mekasha and Tarp 2011, 2018) have confirmed (on average) a positive relationship between foreign aid and growth.

  5. 5.

    Not all studies test for diminishing returns to aid. Those that do usually include a quadratic (aid squared) term and commonly find an inverted U-shaped relationship between aid and growth, implying there are diminishing and eventually negative returns to aid. Note that modelling a nonlinear relationship between aid and growth by using a quadratic term is restrictive because this does not allow for the ‘big push’ theory which suggests that aid has increasing returns.

  6. 6.

    The DAC updates its list of eligible ODA recipients every three years. The list includes all low- and middle-income countries based on Gross National Income per capita as defined by the World Bank, and all the Least Developed Countries as defined by the United Nations (see OECD 2018).

  7. 7.

    This software and method have been used previously, inter alia, to measure preferences for the types of countries international development NGOs should allocate funds to (Hansen et al. 2014) and for how the New Zealand government should allocate bilateral aid (Cunningham et al. 2017). Both studies used university students as participants, with Cunningham et al. having a relatively small sample of 185.

  8. 8.

    Discrete choice experiments are also sometimes referred to as conjoint analysis.

  9. 9.

    The average part-worth utilities for the full sample were very similar to those for the restricted sample and make very little difference to the modeled aid amounts to individual recipients.

  10. 10.

    The omitted category for political parties is ‘other’ which includes voting for the Democratic Unionist Party, the Ulster Unionist Party, Sinn Fein, the Plaid Cymru Party of Wales, the Scottish Nationalist Party or the Social Democrats.

  11. 11.

    For example, as noted by an anonymous referee, countries rather than territories might be favoured by donors due to their sovereignty. Countries with a larger land mass might present greater opportunities for resource exploration and extraction, and countries with larger GDPs might be favoured due to their potential for access to larger exports markets.

  12. 12.

    Thank you to an anonymous reviewer for this insight.

  13. 13.

    The absorptive capacity index score was adjusted for these 26 countries: Afghanistan, Bangladesh, Burkina Faso, Burundi, Cambodia, Central African Republic, Chad, Cote d’Ivoire, Democratic Republic of Congo, Ethiopia, Guinea, Haiti, Kenya, Liberia, Mali, Mozambique, Myanmar, Niger, Peoples Democratic Republic of Korea, Sierra Leone, Somalia, South Sudan, Sudan, Tanzania, Uganda and Yemen.

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Acknowledgements

The authors are very grateful to Trong Anh Trinh for research assistance and to an anonymous reviewer for very incisive and helpful comments and suggestions. The usual disclaimer applies.

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Appendix

Appendix

See Tables 7, 8, 9, 10 and 11.

Table 7 The composite index of absorptive capacity
Table 8 Descriptive and summary statistics of variables used in the multinominal logit regressions
Table 9 Modelled UK ODA
Table 10 The 10 largest gainers from modelled ODA (β = 0.5, α = 0.68)
Table 11 The 10 largest losers from modelled ODA (β = 0.5, α = 0.68)

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Feeny, S., Hansen, P., Knowles, S. et al. Donor motives, public preferences and the allocation of UK foreign aid: a discrete choice experiment approach. Rev World Econ 155, 511–537 (2019). https://doi.org/10.1007/s10290-019-00351-4

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Keywords

  • Foreign aid
  • Discrete choice experiment (DCE)
  • Cluster analysis
  • UK

JEL Classification

  • F35
  • H50
  • C90