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Locating foreign aid commitments in response to political violence

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An Erratum to this article was published on 01 November 2016

Abstract

Following a recent trend towards disaggregation in studies of foreign aid and political violence, we evaluate the determinants of foreign aid sub-nationally. We focus our attention upon political violence as a key subnational determinant of aid commitments and argue that donors commit aid to areas with recent political violence in the hope of ameliorating need and bolstering stability. This being the case, however, we contend not all areas experiencing violence are equally likely to receive aid commitments. This is because potential donors are faced with a dilemma—balancing risk and reward—that leads them to question whether they can effectively deliver aid to areas under conditions of extreme violence. We test these two hypotheses and provide confirmation for them in the context of bilateral aid commitments to local areas within Sub-Saharan African states experiencing civil war between 1990 and 2007.

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Notes

  1. Our focus in this paper is on bilateral aid flows. With respect to aid from multilateral institutions, Öhler and Nunnenkamp (2014) demonstrate that subnational and regional needs do not factor centrally in decision-making. Rather, recipient governments appear to channel aid towards sectors of society they themselves favor. Their finding aligns with Jablonski’s (2014) evidence of Kenyan officials distributing aid from foreign donors to electorally important communities.

  2. At the equator this amounts to an approximately 55 km by 55 km square.

  3. PRIO-GRID has been utilized in recent years to study political violence and mobile phone service coverage in local areas (Pierskalla and Hollenbach 2013), ethnic constituencies and violence against civilians (Fjelde and Hultman 2013), and drought, rainfall and civil conflict violence (von Uexkull 2014).

  4. This dataset also contains information on the aid allocations from a number of multilateral agencies and non-governmental agencies, but we opt to focus on bilateral commitments since the theoretical link between political violence and aid presumes that committing aid to an area is motivated, in some capacity, by the donor’s own state security interests. Multilateral agencies and non-governmental organizations are not likely to have the same type of security interests.

  5. The OECD recognizes three definitional concepts of aid: pledges, commitments and disbursements. For this project, it is necessary to draw a distinction between commitments and disbursements. Commitments are “a firm obligation, expressed in writing and backed by the necessary funds, undertaken by an official donor to provide specified assistance to a recipient country or a multilateral organisation,” and bilateral commitments are “recorded in the full amount of expected transfer, irrespective of the time required for the completion of disbursements.” Disbursements, on the other hand, are the “release of funds to or the purchase of goods or services for a recipient; by extension, the amount thus spent,” and record “the actual international transfer of financial resources, or of goods or services valued at the cost to the donor.” Admittedly, georeferenced data on disbursements might be preferable to commitments but remains elusive at this time. We confidently utilize these data on georeferenced commitments, however, as a strong proxy measure for disbursements, as recent work has shown that nearly all commitments are met after two years (Hudson 2013, p. 113), including to Sub-Saharan African states (ibid., p. 115).

  6. Findley et al. (2011) label this as precision code four; precision codes one (the most precise), two, and three also are included. We have added a table to Sect. 3 of the appendix that provides a summary of the precision of coding. That table shows that 41 % of observations are coded at locations that are within or nearby a specifically named populated place.

  7. Reviewers noted that an alternative specification would involve coding commitments according to their dollar value. We prefer to use the number of new commitments for our dependent variable, because this better captures the expected outcome of our logic, namely that donors adjust the number of new commitments they make to specific locations. Nonetheless, we have specified models with dollar values as the dependent variable. Those are discussed in the appendix.

  8. Terrorist Violence Severity is taken from the Life and Property Losses data field corresponding to “Total number of individuals killed;” Conflict Violence Severity is taken from the data field corresponding to the “Best estimate” of casualties.

  9. This variable is modified such that cells with no neighbors in conflict are recoded as zero rather than missing.

  10. These data are available only for the years 1990, 1995, 2000 and 2005. In order to capture values for the intermittent years the missing data are generated by an averaging process. For example, if the gross cell product per capita of a cell in 1990 is 100 and grows to 200 in 1995, the values for the intervening years are as follows: 1991 = 120; 1992 = 140; 1993 = 160; 1994 = 180.

  11. Nordhaus (2006) also includes ratings for the quality of data used in this measure’s construction and only instances wherein no major inconsistencies in one of the original GCP data sources are considered.

  12. In order to utilize the HFP in this way, it was modified in several ways. First, because its own cells are smaller than PRIO-GRID’s, they were aggregated to match and subsequently coded with the mean. Second, HFP provides a single score for each cell over the 1995–2004 period. To maintain as many observations as possible, we extended this score earlier to 1990 and later to 2007.

  13. In a small number of instances, e.g., the cells comprising the island nation of Comoros, a cell may not possess the full complement of eight state-based neighboring PRIO-GRID cells.

  14. The 71 and 35 % increases were both, respectively, calculated in Stata with the listcoef command (Long and Freese 2014).

  15. Having aggregated up to the administrative level, the data for our dependent variable no longer have a disproportionate number of zero values. Accordingly, we estimate only regular negative binomial regressions for these robustness checks.

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  1. School of Government and Public Policy, University of Arizona, Tucson, USA

    Paul Bezerra & Alex Braithwaite

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  1. Paul Bezerra
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An erratum to this article is available at http://dx.doi.org/10.1007/s11127-016-0384-x.

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Bezerra, P., Braithwaite, A. Locating foreign aid commitments in response to political violence. Public Choice 169, 333–355 (2016). https://doi.org/10.1007/s11127-016-0377-9

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