Improving Aid Predictability and Design of Budget Support

1. Dynamics of Aid CPIA BS WB 121607 Improving Aid Predictability and Design of Budget Support December 16, 2007 Alan Gelb, World Bank

2. The Problem • Unpredictable aid conflicts with the need to provide stable funding for long-run programs to assist countries reach the MDGs. • As donors improve coordination coordination gains offset by “herd effect” surges and shortfalls in funding. Fewer donors – better coordination- more volatility • Budget Support should be seen in the context of the overall aid envelope: • Exogenous volatility versus performance-based volatility • Design of budget support instruments for predictability, within the overall aid budget

3. Stylized Facts on Aid Volatility • Total ODA has been relatively stable. But at country level, aid has been volatile and unpredictable. • Program aid more so. • Volatility reflects a mix of “exogenous” shocks and performance-related factors. Studies differ: 30/70? 50/50? • Aid has been mildly pro-cyclical. Data lags, counterpart funds? • Unpredictabilitry is costly: at macro level and, in budgets, for public sector efficiency (misallocation, erosion of performance contracts) Danger of a low level equilibrium: cautious countries, donors seeing less need, lower effectiveness

4. The “Exogenous” Aid Shocks can be Cushioned…. • Simulations suggest that countries can cushion “exogenous” aid shocks at reasonable cost. • Simple reserve buffer target and spending rules: • If reserve is high, rule defends spending ceiling • If reserve is low, rule defends spending floor • Ceiling – floor is 105% - 95% of mean spending • Simulated for random and auto-correlated aid shocks • Questions: • How often does the reserve buffer run out? • How many years’ notice do donors and countries have before buffer is exhausted?

5. By moderate reserves backed up by an error-correction process • In most cases, moderate reserves (2-3 months imports) and slightly accommodative rules can stabilize spending within 5 percent of targets: • Even when reserve depleted, countries and donors have several years’ notice to take remedial action • Need effective consultative process to correct sustained disbursement deviations, backed up by a clear performance framework and donors’ understanding of what the reserves are for.

6. Performance-Based Aid and Multi-Year Commitments are Reasonably Compatible • Simple model of performance-based aid allocation, calibrated on IDA rules and performance (CPIA) trends, 1999 – 2003. • Compare three approaches: • annual (optimal) allocations of aid • 5 year pre-commitment of aid (needs longer IDA funding horizon than at present) • flexible pre-commitment: aid changes if country rating changes a lot (+- 0.33 points, > 92% CPIA est. error).

7. Does “CPIA-Performance” relate to Development Outcomes? • Simple model over decade averages: D (outcome) = f (CPIA, D CPIA, initial outcome, Africa dummy, HIV/AIDS) • Estimated for Outcomes: Human Development Index, Infant Mortality, Growth • Results: Africa and HIV/AIDS effects negative; conditional convergence, • Levels and changes in CPIA significant positive • Adjusting for “exogenous factors (NEEDS) ”, IDA allocation is”performance based”.

8. HDI Paths Over Time: Three Country Trajectories IEG Project Ratings also reflect CPIA scores and trends

9. Losses from Misallocating Aid Assume: • Optimal aid distribution among countries independent of total quantity of aid. • Marginal product of aid declines with more aid, but more slowly in better-managed countries • Marginal product of $1 in aid = $1 for the optimal distribution (can be relaxed) • At some multiple of current aid levels m, marginal product of aid falls to 0 given policies and institutions What is real value of m? Optimists (2) skeptics (1.25) Realists 1.5 ??

10. Efficiency losses from misallocation Type I and Type II losses: too much, too little aid

11. For “realist” assumptions and calibration on IDA’s PBA IDA allocation approximates to Aid = k.CPIA cubed. • Significant improvements in aid stability… • IDA’s pure PBA cuts volatility by 2/3 on average compared to historical levels; • flexible pre-commitment further halves volatility. • At reasonable efficiency cost… • Pre-commitment for 5 years is costly:11% of aid flow; 4.2% Type I, 6.5% Type II; • But flexible pre-commitment cuts losses to 2%: this is less than misallocation losses caused by CPIA performance measurement error (3-4%).

12. Volatility of PBA Flows by CPIA quintile PBA Stabilizes flows, except for the volatile bottom CPIA quintile

13. Flexible Pre-commitment: • Fully stabilizes flows to more stable countries—those that need stable aid • Is less effective for volatile / low-performing countries, but limits misallocation losses from committing to them • Most countries take several years to make major policy/iinstitutional changes: implications for performance measurement and review cycles: 3-4 years

14. Applying to the design of Budget Support Instrument • How to set budget support as a share of total aid by country? Why not simply as a share (based on CPIA) of aid? Possible, but: • Budget support can be seen as an investment in the improvement of country systems which are not yet at the “comfort” quality threshold • Unconditional support only to countries above this level.

15. Budget Support: Benefits and Risks With Capacity should improve faster with budget support (ABD)

16. The Core Review Cycle • Enhanced CPIA-type review focusing on + / - budget and financial management, procurement (actionable governance indicators), quality of service delivery • About every 3 – 4 years (enough time to expect discernable change). Light interim reviews, with modest incentives (+/- 10%) • Plus “catastrophic” triggers for major review of entire program not just budget support (CPIA +- 0.33) • Actions and outcomes complementary, yet imperfect, indicators of performance.

17. Need to Benchmark Outcomes • If outcomes are to be used as indicators for support, have to benchmark. How? Performance-based norms? • Example (Clemens): long trends: primary enrollment: at 50% enrollment, annual norm gain is 0.95% with 95% confidence interval [1.12%, 0.78%] • Example: infant mortality: shorter trends and qualtile regression. At initial level of 150, annual 10th percentile reduction is 0; annual 90th percentile reduction is 3.9% • More work needed in this area

18. Performance-based norm for Infant Mortality

19. Conclusions • Short-term aid shocks can be dealt with – with reserves and an agreed performance plan. • Aid can be performance-based yet less volatile than at present, especially for more stable, better-performing countries. But need a longer funding horizon than 3 years • Similar principles can be applied to budget support, with modest incentive components combined with “catastrophic” re-contracting.

20. END

21. Back-up Slides

22. Do IDA Funds go to Better-Performing Countries? • IDA allocations, commitments, disbursements over decade. Separate out country groups: • Africa Hi and Lo IDA; non-Africa Hi and Lo IDA, post-conflict countries, capped countries • Development results (change in results indicators): • better in Hi IDA groups than Lo IDA groups. • Better in non-Africa relative to Africa • Strong in capped countries • Very weak in post-conflict countries over the decade • Conclusion: IDA is selective normally but anti-selective in last two groups

23. HDI Change and IDA Allocations HDI change is adjusted also for initial level and HIV/AIDS

24. Anatomy of Major Shocks

25. Ratings for 4370 Projects and CPIA Average Scores Ordered Logit Estimates