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

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

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)

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)

What is the trend in GDP?

What is the trend in GDP?

What is the trend in GDP?

How about Investment/GDP?

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]

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

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 2007. 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).

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

Aid and Recipients Anderson & Hanson (2015) compare different measures of aid for Tanzania, 1990-2012 CPA equivalent to 60-100% donor aid over 2000-2012 (closest in 2004-06 and 2008-11) AMP equivalent to 60-100% donor aid over 2002-2012 (closest in 2004-06 and 2010-11) BoP equivalent to 20-60% donor aid before 2000 and to 60-80% over 2001-2008 (c80% in 2002-06)

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 2002-2012 Kenya: aid as recorded by the government rarely exceeded 5% of GDP over 1970-2000; in WDI data the average over the same period was 8% and often exceeded 10%.

Aid with Tanzanian data (Timmis)

Aid with Ethiopian data (Timmis)

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

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

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, 98-105) 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 2011-2013, 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.