Tracking Climate Aid in Malawi: Results from Pilot Program on Geomapping and Climate Coding

1. Tracking Climate Aid in Malawi: Results from Pilot Program on Geomapping and Climate Coding Justin Baker, University of Texas at Austin (jcbaker@utexas.edu) Christian Peratsakis, Development Gateway (cperatsakis@developmentgateway.org) Kate Weaver, University of Texas at Austin (ceweaver@austin.utexas.edu)

2. Tracking Climate Aid in Malawi Driving Questions: • How is aid addressing climate vulnerability in Malawi? • Is aid going to the places/areas/people most in need? • How does our tracking method compare to other methods (pilot study of tracking methodology)? • Can this methodology extended to other situations?

3. What did we do? • Partnered with Ministry of Finance in Malawi and Development Gateway (Aid Management Platform system) to gain access to available project documents across donors in Malawi. • Geocoded all aid projects in AMP system • Coded all projects for climate adaptation aid, using own methodology • Used project documents where possible, coded at the activity level, rather than the project level

4. Geocoding • Identify multiple activities within aid projects • Subnational location of aid activities (8 levels of proximity) • Sector tags • Donor tags • Layer indicators (e.g. aid activities on top of poverty levels) • Interactive map on CCAPS Dashboard http://strausscenter.org/ccaps/

5. Climate Coding - Process SHOW PORTRAIT FLOWCHART

6. Coding Process (continued) - Rationale Activities coded between four ‘poles’ (definitions available) Weighted scores give continuous numerical score, which can be placed back on spectrum for comparison Numbers deliberately chosen to facilitate comparison to OECD Adaptation Markers Coding at activity level gives greater ‘granularity’ (insight into where money is actually being spent) Most detailed analysis possible (without detailed budget information, etc) Uses established method of AidData activity coding

7. Climate and Geocoding How targeted is climate aid? Overlay on CCAPS Vulnerability Team maps Need refinement for Malawi-specific vulnerability

8. Dataset Description • 754 Projects Climate Coded • Correspond to >2500 coded locations, >2900 activities • ~83% agreement between coders • ~4 activities per project • large variation between donors

9. Project Findings The distribution of climate scores changes according to how it is viewed. Interestingly, there is not a significant difference overall between the number of CD/CO projects and CD/CO dollars committed. Yet there is variation within donors. The distribution is dominated by general development projects, even after projects have been weighted for activity scores. This is likely due to emphasis on health, infrastructure, budget support sectors.

10. Distribution of Climate Aid by Donor Number of Climate Projects across Donors (all Climate Projects) Only climate aid (CO, CD), by US$ commitment amount (Top 20) • By number of projects and commitment amounts, we see USAID, the European Union, the World Bank, and Norway near the top in each case. • Ireland also shows strong climate adaptation emphasis, yet does not have the same resources as the others).

11. Activity/Project level coding Activity Level Project Level • Activity coding gives an extra level compared to coding a project overall. • For example, Norway’s CO share nearly doubles when activities are considered rather than projects. • This is important, as climate adaptation activities within broader projects can be missed as they are crowded out by the broader thrust of the project.

12. Committed Financial Amounts By Project By Activity* (Millions of Dollars) • *Activities were assigned financial amounts by dividing each project amount by the number of activities contained by the project. • *~3% of project commitment amounts are not available on the AMP system currently.

13. Financial Commitments vs Activity Numbers • Relying on numbers of projects alone can be misleading, as financial data gives better insight into the breakdown of projects. Ideally, activity-level budget information would allow the most detailed and rigorous assessment of adaptation funding within development projects. Huge variation in scope and resources of donors, therefore solid financial commitments and disbursements are necessary for an accurate picture of aid.

14. Sector Analysis Top sector is general budget support, activity unspecified Impossible to code well Transparency of activity >$400 million in unspecified aid altogether (~7%) Within specified sectors, focus is largely on health, with some infrastructure and food aid. • Top Activity Codes by Financial Amount Committed

15. Top Activities in AMP Portfolio • Sectors with most climate projects are unsurprising: Water & Irrigation, Agricultural training, Environmental Protection, etc.

16. Comparison to OECD Adaptation Marker 2010 OCED Adaptation Marlers recorded only 42 projects as having “significant” (1) or “primary” (2) adaptation content. Large variation in reporting practice makes it nearly impossible to match up AMP projects (reported in country) with OECD projects (different titles, ID numbers, different languages). Missing Data: Some donors missing from AMP; many donors missing from OECD Adaptation Marker reporting Furthermore, we noted several problems even within the small number of coded projects: Inconsistency – many donors, including the United States, did not report any marked projects at all (unlikely that the US did not have any adaptation-related projects in 2010) Over-coding –e.g. Denmark projects on Democratic Participation and Basic Health Care.

17. Analytical Findings • Breakdown by financial amount, number of activities, or number of projects yield different results • Higher variation would be expected with more detailed and accessible project documents • Climate aid (narrowly defined) makes up just 1-2% of aid to Malawi. This is consistent with our previous analysis of adaptation aid across Africa • Norway, the World Bank, USAID, and the European Union are among the donors most involved in adaptation aid. Japan and Ireland have several adaptation-related projects, yet their financial contributions are much smaller than the others mentioned.

18. Lessons • Variation in quality and quantity of donor reporting constrains coding efforts • Updated, activity-level budget information would be ideal standard for use in tracking climate aid • OECD Rio and Adaptation Marker reporting practices still problematic

19. Geocoding and Aid Transparency:Maps, Gaps, and Traps • Maps • What we cannot tell from looking at maps • Transparency Gaps • Holes in data • Inconsistencies (project IDs, titles, etc) • Lack of consistent and timely disbursement data • Lack of detailed/activity level budget • Transparency Traps • Politicization of reporting (self-reporting creates credibility issues) • Maps are not comprehensive and difficult to keep updated, limiting utility for aid coordination and country level budget management and planning. • Limited to ODA donors. Need NGO, government and CSO tracking to capture full picture of adaptation work in countries. • Tracking channels of delivery/implementation partners needed to follow money and effectively evaluate allocation and impact