Evidence on Changes in Aid Allocation Criteria

1. Evidence on Changes in Aid Allocation Criteria Stijn Claessens, Danny Cassimon, and Bjorn Van Campenhout Bicocca University- Leandro Yagust and Chrystelle Tanti 23/05/2014

2. OUTLINE : 1- Introduction and Motivation 2- Data sources and Methodology 3- Empirical Results 4- Conclusions and extended analysis

3. 1- Introduction and Motivation • OBJECTIVES • Explain the behavior of donors countries, how the way aid provided is affected : • By the characteristics of the countries • By the time : how this has varied over time

4. RELATED PAPERS • Burnside and Dollar (2000) found aid works better in good policy and istitutional environment • Consequences • Easterly, Levine and Roodman (2004) using the same specifications find that results do not stand up over a long time period. • Rajon and Subramian (2008) found little robust evidence of a positive (or negative) relationship between aid inflows into a country and its economic growth.

5. FINDINGS • The aid is allocated better recently • Moreover after the fall of the Berlin wall in 1989 Why is it important ? • Aid flows are large • Aid ought to be considered only for countries that are deserving and in need • Alesina and Dollar (2000) show that non-economic factors greatly influence aid allocation in addition to economic and development considerations.

6. Evolution in the mid-1990s (geopolitical and economic) • Geopolitical because of : • The fall of Berlin wall in 1989 • The end of major communist governments • Have changed motivation for aid • Economic because of : • The end of central planning • The increase in private capital flows • Have allowed different form of external financing

7. Evolutions in the mid-1990s (rules and development approach) • Rules under which aid is being provided have changed • Greater emphasis on coordination among donors and with recipients countries • Greater transparency • Growing importance of alternative donors • Changes in development approach • Stronger recipient country ownership of development programs • Greater use of Poverty Reduction Strategy Papers • Enumeration of the objective of scaling up

8. CONSEQUENCES • Increase development efficiency an effectiveness of aid • Affect the amount and distribution of bilateral aid flows • Fewer coordinations problems among donors

9. STUDIES • Donor's selectivity toward country need and policies has improved over time (Barthélemy and Tichit 2004, Roodman 2005,...) • Contrary view (Easterly 2007) expresses finding no consistent evidence of increased selectivity with respect to policies and only temporarily increased selectivity in the late 1990s with respect to corruption. Main question of the paper is Whether changes have led over time to donor providing aid in more rationnal manner ?

10. 2- Data sources and Methodology • DATA • Bilateral aid flows reported by the OECD and DAC Aid Statistic database • Does not include all bilateral donors • Cover the bulk of international aid flows for 1970-2004 • Recipient countries are restricted to developing countries • Are a three-dimensional panel of aid flow to 147 countries from 22 bilateral donors over the period

11. VARIABLES • Dependent Variables • Net aid transfer per capita Independent Variables • Lagged GDP per capita • Population • CPIA • Burnside-Dollar • Present value of external debt • Net aid others • Donor sum of net aid transfers • Lagged bilateral trade

12. CONCEPT OF NET AID • The DAC statistics generally focus on the concept of net aid that is the total ressources provided by donors as grants, loans, any debt relief, net of any loan principal repayments • But unlike many earlier studies that use the net aid data directly, this study transsform the data into net aid transfers.

13. TOTAL NET AID TRANSFER CONCEPT Net aid transfer = total (bilateral) official development assistant grants + total (bilateral) official development assistance loans extended to recipient - official development assistance loan amortization by recipients - interest paid by recipient

14. HOW THE POLICY SELECTIVITY OF AID IS INVESTIGATED ? • By using the World Bank Country Policy and Institutional Assessment (CPIA) score for the recipient countries • This index is a composite rating of 16-20 aspects of countries' policy and istitutions • Another index of countries' policies (Burnside and Dollar 2000) is also used for robustness.

15. STUDIES • Have found that small countries get more aid per capita • Why ? • Small countries tend to be more open = more vulnerable to external shocks • motivating for aid flows • May receive aid for political economy reasons • More easily swayed for a given amount of aid

16. TIME DESCRIPTION • To check whether aid allocations have changed over time relative to need and policy selectivity measures, the sample is divided into 3 subsamples : • Period before the fall of Berlin wall 1970-1989 • Post-Berlin wall era is split into 2 periods : • 1990-1998 • 1999-2004 • The break point 1998 coincides roughtly with the start of the new litterature on aid effectiveness and major changes in the international aid architecture.

17. METHODOLOGY • Panel data have 3 dimensions • Donors • Recipients • Time • Empirical model tries to explain bilateral aid flows between : • Donors i • Recipients j • At time t • Using a matrix of explanatory variables • A fixed donor effect • A fixed recipient effect • A time dummy variable

18. PROPOSED MODEL 1 • To investigate such changes, the coefficient on the four variables of interest are allowed to change over time. • Specifically the four aid-determining variables... • Poverty • Policy • Small country effect • Debt burden • … Are interacted with dummy variables for each of the 3 periods to capture strutural breaks.

19. PROPOSED MODEL 1 • This is done in one regression, thus keeping the coefficients for the other fixed effects and for the other independent variables constant across the 3 periods. • This way, changes in each of the four relationships are analyzed concurrently overtime while keeping other factor constant.

20. PROPOSED MODEL 2 • A random effect model could be used instead of a fixed effect model. • Assume that all explanatory variables are uncorrelated with the individual specific effect. • The random effects panel regressions are also reported for robustness.

21. BUT … OTHER ISSUES • Facing all aidstudiesisthat for manydonor-recipient country aidflows are zero • Or one can use onlynonzero observations in a simple ordinary least squares (OLS) • But Barthélemy and Tichit (2004) and Barthélemy (2006) using a 3 dimensional panel, show that for aidflows the difference are smallbetweenfixedeffectusing observations only, randomeffect and OLS.

22. SO … • ADOPTION OF AN INTERMEDIATE APPROACH • Distinguish case where : • A donor never provided aid to a recipient • A donor provided aid but not every year • Zero bilateral flows for the whole period are excluded • This seems a more robust way of running regressions • All the regressions are run with all observations and with nonzero observations only, and the results are reported in the base regression in robustness tests.

23. Empirical Results Evolution of Bilateral Net Aid Transfers Over Time (in 2000 u$s dollars)

24. Figure 1 shows the evolution of bilateral net aid transfers over time, disaggregated by grant, loan, and debt relief components on a recipient country basis. • Descriptive Statistics Results • Net aid transfers increased in the 1980s, dropped in the mid 1990s and recovered after 1998. • In 2004 although total aid per capita was still below the early 1990s peak in real terms. • Overall, aid per capita remains within a bound of $6-$8 per person for the whole period. • Grants have replaced loans, with net loans transfers becoming negative in the last years of the period. • Debt relief largely accounts for the peak in aid volume in 1991 and for the last years of the period.

25. Table 1 data for all variables for 1970-2004 period except CPIA data, available for 1977 onwards

26. Table 1 (continued)

27. Table 1 statistics • Dependent variable • Net aid transfers per capita • Independent variables • Lagged GDP per capita, proxiefor the need (poverty) and selectivity dimension of aid • Population • CPIA • Burnside-Dollar INDEX • Present value of external debt • Net aid other • Donors sum of net aid transfers • Lagged bilateral trade

28. Dependent variables • Average net aid transfers was $2.4 per year but with large variations from $-138 to $ 9,025. • Excluding the cases with zero observations, average aid per capita per donor Is $4. • Including the zero observations of total net aid transfers, grants per donor were the largest component, averaging $ 2.2 per capita per year, net loans were $0.16 per capita per year, and debt reliefper donor was $ 0.04 per capita per year.

29. Independent variables • Recipient’s GDP per capita averages $ 3,800, varying from less than $500 to $23,266 • Average population is 2.8 millions, but the standard deviation is high, at 11.7 millions. The smallest country has 20,000 people and the largest 1.3 billion (China).There are no countries in the segment between 300 million and 1 billion people. For this reason population population is used in log terms in the regressions. • Average CPIA index is 3.46, ranges from 0.72 to 6. • Debt burden in present values averages 182% of exports, with great variations as well. • Total aid provided by other donors averages $ 35 per capita per year. • Donors provided an average of $313 in net aid tranfers per capita to all other countries in the same year, • Bilateral donor-recipient country trade averages 2.1% of country GDP.

30. Regression • Sample 49.804 obs. with 2,384 specific donor-recipient combinations • Column 1 and 2 present de fixed and random estimates for the whole period , keeping the coefficients for the four main variables constants. • Columns 3 and 4 allows the coefficients for the four main variables to change for each of the three subperiods, again with fixed and random effects.

31. Table 2. Regression Results Poverty aid Small country bias Policy responsive aid

32. Table 2. Debt burden responsive aid

33. Results • The model with constant coefficients finds that income level of the recipients countries matters (significant an 1% level), with poorer countries receiving more aid. This suggest that donors do care about poverty. • The size of the recipient country also matters with bigger countries reciving less aid per capita. • CPIA is not significant for the whole period this means that donors do not care about the institutional enviroment and the quality of the policies. • The total debt burden does not significant affects aid transfers, suggesting that neither concern about debt overhang nor defensive lending drove aid flows. • Control variables show that the more aid a donor gives in general, the less it gives to a specific country, likely because donors an overall budget constrain.

34. Results (continued) • Aid flows of one donor are positively affected by aids of other donors (althought the relation is not statistically significant) possibily due signaling effect for the quality of the recipients country policies. • Donors give more aid to important trade partners although is not significant, prehaps because donors tend to support their own exports. • Changes over time in the key relationships for the three subperiodsshows an increase in the responsiveness of aid to recipients country income from -0.535 to -0.720. • Although the F-test can reject only at the 16 level these coefficients are different from each other, this is evidence that donors have become more focused to providing aid to poor countries rather than say their political allies.

35. Results (continued) • The small country bias has diminished over time with coefficient for population falling from -3.544 to -3.020. Significant at 0.05 level that this coefficients are not different from each other. This changes can be attribute to the fact that with the end of the cold war donors lost their interest in buying political favor in the UN. • In general results confirm the improvements in the quality of aid allocations. • Aid become much mire responsive to policy: the coefficient, negative and statistically insignificant in the first period, rises to 0.215 and to 0.919 in the third, becoming statistically significant in the most recent period. • CPIA is not significant in the whole period since aidbecome sensitive to policy and institutional environment only in the last period. • Debt burden are no longer an obstacle to aid flows. In the two recent periods the estimated coefficient is positive and statistically significant at 0.1 level.

36. Table 3. Robustness Tests

37. Table 3.

38. Robustness Tests • Results • The model uses the Burnside and Dollar (2000) policy index instead of the CPIA. This is to avoid potenciallyendogedenity if World Bank staff adjust CPIA to affect IDA lending patterns.This would lead to a false conclusion of increased selectivity. • The CPIA scores could also have been affected by the prospective lendind behavior of other donors, with the WB staff raising CPIA scores for countries for which they expect more aid flows. • The Burnside and Dollar (2000) index uses three indicators of economic policy: • Log of 1 + inflation rate: weights -1.4 • Budget balance as % of GDP: weights 6.85 • Sachs-Warner (1995) trade openness variable (1,0): weights 2.16 • Is a linear combination of the three policy variables • While it is a more objective measure and less subject to biases, does a poor job capturing institutional environment since is mostly outcome based.

39. Results (continued) • The differences in the regression when using the Burnside and Dollar Index instead of the CPIA are attributable to the different samples. • The coefficients are estimated using first-differenced general method of moments GMM because there can be dynamics in the aid determination. Also, aid projects may involve lumpy disbursements, leading to autocorrelation. • Balanced sample in the regression, since the unbalanced sample may have biases arising from its change in composition over time. • All observations also were run only with the non-zero observations and with all observations • The model was also estimated using a lagged dependent variable, but using a fixed effect model instead of GMM. • The panel regression results are dependent on a certain degree of data homogeneity. Homegeneity can be considered in all three dimensions: over time, across donors, and across recipients. • Most results confirmed the base panel results, although generally with reduced statistical significance.

40. Changes Over Time Among Donors • To sum up this paper documented a general improvement in aid allocation. • Also resent research have has highlighted differences among donors, with some behaving more altruistically and other focusing more on their geopolitical interests(Dollar and Levin 2006) • The general idea is that donors also vary in how much they improved their selectivity and quality of their aid. • Using a panel approach, keeping all control variables the same for all donors, but allowing the coefficients for each donor to differ over the three time periods, table 4analysed the elasticitiesof individual donors to the four key selectivity measures.

41. Table 4. Donors-specific Sensitivities

42. Results • Large differences remain among donors. • For GDP per capita average sensitivity suggest that the USA aid is much more geared towards the poorest countries than is aid from Italy. • The UK is more geared to small countries aid than Canada. • France shows a bigger sensitivity to policy -CPIA index- than Italy. • Finally for debt burden France aid flows are the most negative sensitive. • Although not all these coefficients are statistically significant, the result show large differences among donors.

43. Conclusions • The study observed behavioral changes over time in actual aid flows toward more optimal allocations across countries. • The role of poverty and institutional environment increased. • The small country bias and debt burden diminished. • This changes in part are attributable to reforms of the international aid architecture. • Although is unclear which institutional changes at the international or bilateral level have driven the changes in behavior. • The IMF, WB, Paris Club have introduce many changes which likely have affected the behaviour of bilateral aid flows.

44. Conclusions (continued) • Changes such as the HIPC Debt Initiative and the Poverty Reduction Strategy Papers process diminished the influence of debt on donors flows and increased donors selectivity • Nevertheless this paper have not been able to document specific evidence of their impact. • However this study observes large remaining differences among donors in reveled selectivity that appear’s to be related to institutional environments. • This suggests that reforms will have to be multifaceted and include further changes to the political economy and accountability in donors countries as well.

45. Final Remarks • Robert Barro and Jong-Wha Lee (2005) found evidences that IMF’s lending decisions were influence by the quota and staff that a country has on the IMF. • They concluded that the probability and size of IMF loans were larger when a country had a bigger quota, more national working on the professional staff and more proximity to the USA and the western europeancountries. • Moreover they find that IMF greater program participation seems to retard economic growth. • Another important result was that they found evidence that IMF loans programs have a negative effect on the rule of law. • This findings are also consistent with the one’s reported in Svensson (2000)and Alesina and Weder (2002). This studies found that an increase in foreign aid led to a rise in official corruption ”voracity effect”.

46. So, Why do countries in political favored positions participated in IMF programs if these appeared to retard economic growth? • Barro and Lee proposed a few answers: • IMF lending may be bad for the economy but good for the governments and individual politicians who arrange the lending.