1. Measuring Aid: The Issues
2017/1
Measuring Aid: The Issues
Oliver Morrissey & Victor Murinde
Project Workshop, 3-4 March 2017, SOAS University of London
Project ES/N013344/1: Delivering Inclusive Financial Development and Growth

2. Measuring Aid
Outline
Broad context: aid effectiveness based on measures of Aid/GDP ratios
Specific context: fiscal effects of aid requires a measure of the aid recorded in the budget
Measurement: do sources matter?
Illustration I: alternative sources of GDP
Illustration II: recipient vs donor measures of aid

3. Data and Aid Effectiveness
Aid Effectiveness is important
Desire to understand the effect of aid on growth and economic performance [and core mediating variables]
Political implications, both for support on aid and relations with developing countries
Policy implications – conditionality vs selectivity
Aid instruments and design (e.g. budget support)

4. Data and Aid Effectiveness
Two Data Concerns (SSA)
National Income (effect on growth or poverty)
How reliable are GDP data (Jerven, rebasing)
Does source (measurement) matter?
Aid (Recipient perspective)
Donor allocation ≠ what recipient receives [how much money flows into the economy] (growth effect)
Aid to recipient ≠ aid through government [donor projects; civil society; public expenditure] (fiscal effect)

5. What is the trend in GDP?

6. What is the trend in GDP?

7. What is the trend in GDP?

8. How about Investment/GDP?

9. Aid and Recipients
Is Aid Fungible?
Assumes that aid can be allocated to sectors according to donor intentions, but if the aid does not go through the budget it cannot appear as spending [projects]
Simple example: Government spends 20 on health; donors give aid to health of 10 (either sector aid to budget, or 5 to budget and 5 to donor projects]
Health aid all 10 to budget, spending can be 30
Only aid of 5 to budget, spending can only be 25
[less than 25 if fungible; <30 does not mean fungible]

10. Aid and Recipients
Literature suggests that over half of sector aid appears to be fungible, but estimates are imprecise and overstate direct aid-spending link
Allowing for ‘off-budget aid’ significantly reduces estimates and suggests negligible actual fungibility
Even incorporating the high estimates of fungible use, there is no reduction in aid effectiveness (on intended outcomes)
Fiscal effects of aid examines relationship between aid (received in budget), revenue (tax), government spending and borrowing

11. Alternative Donor Aid Measures
Aid and Recipients
Alternative Donor Aid Measures
Standard DAC ODA measure (donor allocation from budget)
subtract elements from the reported donor measure
‘Country Programmable Aid’ (CPA) introduced by the DAC in The CPA subtracts from donor’s gross aid:
items that are expenditures incurred by and in the donor (such as for development education and research, administration and costs of hosting refugees in the donor country)
or that do not provide finance for development purposes in the recipient (such as humanitarian or emergency aid or the book value of debt relief).

12. Recipient Aid Measures
Aid and Recipients
Recipient Aid Measures
Using recipient aid data as reported in budget
For example, Aid Management Platform (AMP)
Derive from IMF transfers to country recorded as from ‘general government’ in BoP data:
comprises government current transfers (grants), capital transfers (loans) and investment income (projects of a duration of more than one year) minus debt relief

13. Aid and Recipients
Anderson & Hanson (2015) compare different measures of aid for Tanzania,
CPA equivalent to % donor aid over (closest in and )
AMP equivalent to % donor aid over (closest in and )
BoP equivalent to 20-60% donor aid before 2000 and to % over (c80% in )

14. Aid and Recipients
Tanzania: Lowest estimates imply aid/GNI about 5% in 1990s, highest falling from 25% to about 10%
Most estimates within 10-15% over
Kenya: aid as recorded by the government rarely exceeded 5% of GDP over ; in WDI data the average over the same period was 8% and often exceeded 10%.

15. Aid with Tanzanian data (Timmis)

16. Aid with Ethiopian data (Timmis)

17. Conclusions Source of data affects GDP measures
Data and Aid Measures
Conclusions
Source of data affects GDP measures
Long run inferences may be robust, but if specific episodes are of interest …
Donor aid measures often twice the value of recipient measures
Recipient behaviour requires recipient perspective
‘data aware’ country studies to understand the effects of aid, and role of aid in financial inflows

18. Data and Aid Measures
Going Forward
Key research question: How does a new measure of aid inflows (from a recipient’s perspective) compare with existing measures, in explaining the impact of aid on domestic financial development and economic growth?
Also, controlling for differences in gender and recovery from fragile state conditions?
Possible sample countries: Ghana, Rwanda, Tanzania, Uganda, Zambia, Ethiopia and Kenya

19. References
Andersen, O. W. & H. Hansen (2015), Can ‘Real Aid’ Be Measured? (mimeo, University of Copenhagen). Bwire, T., T. Lloyd & O. Morrissey (2017), Fiscal Reforms and Fiscal Effects of Aid in Uganda, Journal of Development Studies (forthcoming) Mascagni, G. and E. Timmis (2014) Fiscal Effects of Aid in Ethiopia: Evidence from CVAR applications, CREDIT Research Paper 14/06 Morrissey , O. (2015), Aid and Government Fiscal Behaviour, World Development (69, ) Morrissey , O., T. Lloyd and D. M’Amanja (2007) ‘Aid and Growth in Kenya: A Time Series Approach’, in S. Lahiri (ed), Theory and Practice of Foreign Aid [Kenya] OECD (2011), 2011 OECD Report on Aid Predictability: Survey on Donors’ Forward Spending Plans , Paris: Organization for Economic Co- operation and Development. van de Sijpe, N. (2013). Is foreign aid fungible? Evidence from the Education and Health Sectors. World Bank Economic Review, 27(2), 320–356.