Who Gets the Goodies? Overlapping Interests and the Geography of Aid for Trade Allocation in Bangladesh

Abstract

The Sustainable Development Goal principle of “leaving no one behind” has led to increased attention being paid to patterns of intra-country allocation of foreign aid. We contribute to these efforts by considering a particular type of foreign aid, Aid for Trade (AfT), to discern allocation objectives of aid. We match a novel, geo-coded, dataset on over 11,000 Bangladeshi exporting firms to over one thousand AfT project locations in Bangladesh similarly geo-coded by AidData and expanded by ourselves. We use this matched data to employ spatial techniques that evaluate political economy logics of aid allocation, wherein AfT is functionally targeted towards exporting firms, is allocated based on prebendalism, and/or is directed to high poverty areas. Our analysis finds support that AfT is allocated based on functional or prebendalist logics. The results for poverty are more nuanced. When considered in a stand-alone fashion, poverty is associated with a smaller likelihood of allocation. However, some evidence suggests that when the other logics are present, the impact of poverty on allocation becomes positive. These findings suggest that the politics of aid allocation is a nuanced and intricate dance with multiple overlapping or competing logics.

Keywords:

• Foreign aid
• trade
• aid for trade
• aid allocation
• sub-national analysis

Acknowledgements

The authors thank Doris Aja, Patrick Mascott, Tianyang Song and Daniel Noble Stairs for excellent research assistance with this project. The authors also thank Ryan Briggs, Martijn Schoonvelde, and members of the University of California Riverside Department of Political Science and AidData at the College of William and Mary for comments on an earlier draft.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.unido.org/events/aid-trade-global-review-2019-supporting-economic-diversification-and-empowerment.

2 Although as implied by Dreher et al. (2019), in some instances, donors may simply not care how aid is allocated.

3 Indeed, Rahman and Giessen (2017) use quantitative text analysis to determine the (sometimes contrasting) informal interests and motivations behind several donors’ forest development projects in Bangladesh.

4 Household Income and Expenditure Survey (HIES) 2005 and 2010 rounds data show that large number of subdistricts experienced increase in poverty despite the reduction in poverty rates in respective divisions; levels of moderate and extreme poverty have increased in 158(114) sub-districts in 2005(2010) (see GED 2013).

5 https://www.thedailystar.net/opinion/governance/news/bangladesh-descends-corruption-ranking-1694551.

6 https://www.unfpa.org/sites/default/files/pub-pdf/LDC_Fact_Sheet.pdf.

7 Some important exceptions include Dietrich, Mahmud, & Winters, 2018; Amin & Murshed, 2017; Sawada, Mahmud, & Kitano, 2018; Winters, Dietrich, & Mahmud, 2018.

8 A senior official from the Bangladesh Export Promotion Bureau confirmed in an interview that this directory should substantively capture the entire population of exporting firms in Bangladesh. (Interview date 26-02-2019).

9 With shapefiles from https://gadm.org/data.html.

10 http://www.electiondataarchive.org.

11 The 2014 election was not contested by the main opposition party and the 2018 election resulted in the ruling Awami League and allies winning 289 of 300 seats.

12 Available at https://dataforgood.facebook.com/dfg/tools/relative-wealth-index.

13 However, even at the 2.4 km level, some ADM4 units, particularly in urban areas, do not have a RWI reading inside the unit boundary. Accordingly, we impute values for these ADM4 units by taking the average of their 8 nearest neighbours. We include results excluding these areas in the robustness checks.

14 The data also includes error estimates for each observation. We do not expect there to be systematic error, not do we think the error in these estimates is likely to be greater than that of other subnational estimates.

15 https://www.brookings.edu/blog/future-development/2019/09/24/the-geography-of-poverty-hotspots/

16 Contemporary debate identifies pros and cons with both linear and non-linear estimation strategies. However, there are main reasons to prefer linear estimators, especially when using interaction terms as we do here. (Gomila 2021). Models 2 and 4 in Table 1 (OLS) are equivalent to models 3 and 4 in Table A2, respectively.

17 Available at https://designstudio.worldbank.org/maps/2016/3323/res/data/zila_and_upazila_data.zip.

18 http://www.worldbank.org/en/news/feature/2014/09/30/poverty-maps.

19 https://international.ipums.org/international-action/sample_details/country/bd.

20 https://www.wfp.org/content/undernutrition-maps-bangladesh-2012.

Additional information

Funding

This research is supported by Irish Research Council Laureate Programme Grant [No. IRCLA/2017/92] (TRADE ME). Comments welcome at [email protected]. All errors and omissions remain the authors’ own. All data and replication code is available from the authors upon request ([email protected]). All views and conclusions are the authors own and do not represent the views or conclusions of any of the authors’ institutions.