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Evaluation of the Aid-Growth Relationship Presented by Ghassan Baliki and Emiko Nishii Development Workshop 04.11.2010 Outline • Empirical Framework of Rajan and Subramanian (2005) • Potential drawbacks • How can we understand the Aid-Growth relationship better? • Aid Effectiveness Literature (AEL): A Meta-Study • Main Findings and Concluding Remarks Rajan and Subramanian (2005): What Does the Cross-Country Evidence Really Show? • Endogeneity issues – Aid may depend on level of income (i.e. donors increase aid inflows based on recipients’ needs) >> Aid can’t be exogenous with respect to growth • Constructing instruments for Aid is necessary • Alesina & Dollar (1998) >> Aid is often allocated based on historical & diplomatic reasons Rajan and Subramanian (2005): Cont’d Constructing IV for Aid: 1) Find the share of donor d’s aid allocated to recipient r in year t. 2) Use the predicted share to compute aid to GDP ratio received by country r in year t. Rajan and Subramanian (2005): Cont’d • Dependent variable: average annual growth rate of per capita GDP ( 1960-2000, 1970-2000, 1980-2000, 19902000) • The results suggest that with exclusion of outliers, 3 out of 5 cases, the coefficient of Aid is negative, and significant in none. >> decomposition of Aid is necessary to understand the Aid-Growth relationship better Rajan and Subramanian (2005): Cont’d Disaggregate Aid by: 1) Sectors (social, economic and food) 2) Timing of impact (short, and long impact) e.g. whereas food aid should not be expected to affect long-run growth, social & economic aid should 3) Type of donor (multilateral vs. bilateral) i.e. multilateral aid is less ‘political’ than bilateral aid >> the results show that no sub-categories have any significant impact Rajan and Subramanian (2005): Cont’d • Non-linear & conditional effects of Aid on growth. - Aid effectiveness depends on policy environments? “aid effectiveness depends on the institutions that restrict appropriation of public funds by rent seeking agents” Hodler (2007) >> inclusion of policy measures (e.g. CPIA by the World Bank) - Diminishing return of aid? >> inclusion of aid squared • Results suggest that in no case, the coefficients are significant. >> potentially driven by endogeneity and country-specific characteristics Rajan and Subramanian (2005):Cont’d - the first-difference GMM - the system GMM First-difference GMM system GMM Total aid - and significant - and insignificant Short-impact aid - and significant - and insignificant Economic aid + and significant + and significant >> the results are fragile (e.g. depends on the # of lags or independent variables included, the results change) Rajan and Subramanian (2005): Cont’d • Quantitative Impact of Aid Assumption: Mainly, Aid influences growth through increasing public investment. α=0.35, Y/K=0.45, and β=1 give a suggested coefficient of 0.16. >> the coefficients for many existing literature are overestimated. Rajan and Subramanian (2005): Cont’d • We must pay attention to the potential importance of a previously neglected factors. The importance of understanding ‘Aid influences growth through which channels exactly?’: >> In this context, investigating ‘What’s preventing aid from having a positive impact on growth?’ may be helpful. Related Literature: - Lensink and Morrissey (1999) “Uncertainty of Aid Inflows and the Aid-Growth Relationship” - Rajan and Subramanian (2005) “What Undermines Aid’s Impact on Growth?” Lensink and Morrissey (1999): “Uncertainty of Aid Inflows and the Aid-Growth Relationship” • Aim: The paper seeks to find whether uncertainty associated with (volatility of) the level of aid inflows affects the impact of aid on growth. • Potential impact of Aid on growth with the presence of Uncertainty: - investors may postpone/cancel investment decisions - Aid is an important component of government revenues >>volatility of receipts may impact on fiscal behavior, thus growth Policies/Institutions may be conditional on aid inflows. Financial Resources Inflows from DAC to Developing Countries Lensink and Morrissey (1999) Cont’d • Dependent variable: avg. growth rate of GDP per capita • Aid=level of Aid • Construct proxy for uncertainty 2) calculate the standard deviation of the residuals from the forecasting equation 1) a forecasting equation is estimated (as a first or second-order autoregressive process, extended with a time trend) >> The coefficients on uncertainty are negative and significant. >> When the uncertainty measure is included Aid becomes significant and positive Lensink and Morrissey (1999): Cont’d Still some drawbacks……… • By using the cross-country approach, there are possibilities that exogenous factors leading to a bias estimator. • Almost any explanatory variable could be found to have a significant effect whereas the ‘truth’ is that apparent significance is due to common causalities or spurious regressions >> omitted variable bias still remains. • Is “growth” a good variable to capture the effectiveness of aid? Rajan and Subramanian (2005): “What Undermines Aid’s Impact on Growth?” What’s preventing Aid from having a positive impact on growth? - The Aid-Competitiveness Approach • Best way to check aid-effectiveness is to compare ‘fact’ and ‘counterfact’ >> not possible. • Instead, check whether labor-intensive industries grow relatively slower in countries with high aid-inflow compare to non-laborintensive industries. • This approach allows us to capture 1) within-country differential effects, and 2) country treatment effect to understand the effect of aid. Rajan and Subramanian (2005): “What Undermines Aid’s Impact on Growth?” How Aid can influence growth through ‘competitiveness’ channel? Under the fixed exchange rate: • Aid spent on domestic goods pushes up the price of recourses that are in limited supply domestically (e.g. skilled worker). Under the flexible exchange rate: • Aid inflows increase nominal exchange rate, thus reducing competitiveness. Rajan and Subramanian (2005): “What Undermines Aid’s Impact on Growth?” • Strong evidence consistent with aid undermining the competitiveness of the labor-intensive or exporting sectors. • In countries that receive more aid, labor-intensive and exportable sectors grow slower relative to capital-intensive and non-exportable sectors. • Aid inflows do cause overvaluation Are the results compelling? • Major exports sector for all recipient counties is labor-intensive? • on balance, whether these adverse competitiveness effects offset any beneficial effects of aid is unclear. AEL – Doucouliagos and Paldam (2006, 2007a, 2008) ▫ Do the estimates of the AEL converge to something we might term 'truth'? ▫ Can we identify the main innovations which cause (prevent) convergence? ▫ Do biases exist while uncovering the 'truth' about aid effectiveness ? Three Perennial Problems ▫ 1) Priors ▫ 2) Data Mining ▫ 3)Incentives - Innovation with skepticism - Reliance on independent replication - and the Reluctance Hypothesis Absolute Aid Ineffectiveness Why is it Puzzling? ▫ “Why would they” vs. “If it is, it must be rational” → Aid fatigue ▫ Marginal Project vs. Financed Project ▫ Always via accumulation? ▫ No repay means no crowding out Meta-Analysis • Priors and Biases: ▫ Polishing ▫ Ideology ▫ Goodness • Meta Analysis Methodologies: ▫ Meta-Significance Test (MST) ▫ Precision-Effect Testing (PET) ▫ Funnel Asymmetry Test (FAT) The Three Family Models of AEL Does Aid Cause Increasing Accumulation? Large but probably not full crowding effect A Large Crowding Out Does Aid Cause Increasing Growth? • Neoclassical Model: • Results: ▫ Decline in variation over time and with sample size ▫ More Extreme points ▫ Average decreasing ▫ Non-symmetrical funnel around horizontal axis Funnel Plot, No Economic Significance! Aid Growth Effects: Reluctance Trends Is the Effect of Aid on Growth Conditional? • Good Policy Model (Burnside and Dollar, 2000): • The Medicine Model (Lensink and White, 2001): Conclusion of the Three Meta-Studies