1. Bo Sjo

2. Development per Aid Dollar Data Envelopment Analysis applied to aid efficiency Bo Sjö Email: bo.sjo@sadev.se March 2009

3. The Aid Efficiency Literature Focus on economic growth: “Does aid cause economic growth in the long run?” If not, aid becomes a matter of income distribution only Two conclusions: –“+1 % in poor countries” taking a correct definition and condition on correct variables –“Significance is the result of data mining, remove or add countries, extends sample and the significant results are gone”

4. The typical aid efficiency regression: Outcome = factors that drives this outcome + special factors (dummies) + initial level of outcome + aid + aid  policy + randomness Special factors ex: HIV/AIDS frequency, Africa, size, etc Aid flow measured in previous period “Aid  policy”, ‘policy’ is supposed to enhance the effects of aid Outcome is typically the change (improvement) in whatever you chose to study, most frequently economic growth.

5. Critique against the growth literature Aid has more objectives than economic growth Poverty is more than lack of economic resources –Democracy –Health –Education –Gender issues –Lack of capacities etc..

6. Limitations of the regression approach Only one outcome at a time Need to know “factors that drive this outcome”, Need to know the technology or the functional form of whatever drives the outcome in order to say something about the effects of aid, and what actually works.

7. An alternative to regression - DEA There are specific problems related to the aid efficiency that DEA can handle, both on macro and sector levels. Aid has multiple goals and multiple output/outcomes A straightforward measure of efficiency that can be used in decision making The ‘technology’ behind aid  development is complex. Think ODA  Development

8. DEA A general definition of efficiency: Efficiency = Output/Input Relative ranking of aid efficiency across countries rather than absolute measures Calculate efficiency scores for aid efficiency with multiple outputs –A ranking of ‘decision making units’ between 0 and 1.0, where 1 most efficient. All those that get 1.0 represent the best practice frontier.

9. DEA i: number of units E = Outputs /Inputs w: J outputs (y) v: K inputs (x) Max E Subj. to E between 1 and 0

10. Pros and Cons of DEA Sensitive to outliers but quite forgiving regarding “technology” behind E = output/input. –Thus, filter for outliers, work with averages. Number of inputs and outputs are limited (of course) But, No need to impose the same weights on different outputs, or the different inputs. Inputs can be substituted for each other Outputs can be substituted for each other “Every unit is put in their best light”

11. Aid Efficiency - ODA E = (Improvements in Development) / Total ODA p.p Development: 4 common indices ODA at time t, outcome time t+1 A pure accounting exercise: to capture “Development per aid dollar”

12. A major point in this analysis I do not have to know the technology. I can assume that the amount of total ODA to a country was optimally composed cross sectors to have the biggest impact wherever possible.

13. Data issues I ODA countries –A huge number of countries has officially received aid –Adjust for humanitarian aid –Filter out too rich, too small, too little ODA, extreme changes in outputs, etc. to create a more homogeneous sample without outliers. Around 60 countries left Four broad development indices: Poverty/ Economy: PPP-adjusted GDP Health: Under 5 morality rate Education: Net primary school enrolment Democracy & governance: Voice and Accountability index

14. Data issues II Outcomes are measured as relative changes between periods. Net primary school enrolment requires additional data sources and some interpolation

15. 2 samples, 2 periods and 2 set-ups Samples: “All donor countries” and Sweden Long period (10+10 year): Total ODA received 1985-1995, outcomes 1996-2005. Short period (5+5 year): Total ODA received 1997- 2001, outcomes 2002-2006.

16. Set-up 1: 4 outcomes /ODA A clear inverse relation between ODA received and development. The more aid the less the country develops compared to other ODA receiving countries There are some big exceptions, but not more than randomness could create Holds for both “All donor countries” and Sweden “Why are you allocating aid in the way you do?”

17. Set-up 1: Conclusions 1)Lack of aid is not a restriction for development 2)Aid could be negative for development – not tested 3)Donors could be super-selective, – not tested 4)The problems of finding aid efficiency is not solved simply by saying that aid has different objectives 5)Aid efficiency must be identified on the margin, or in a context

18. Set-up 2: Efficiency against Need and Ability In each period ODA is given, and must be allocated across countries to receive expected outcomes. Evaluate against two balancing factors; –Need for Aid (GDP) –Ability or Capacity ( governance, performance ) E = outcomes / Input 3 Inputs: Total ODA, Mean PPP-adjusted GDP, and Mean V&A index, 1997-2001 2 Outcomes: Improvements in PPP-GDP and V&A, 2002-2006

19. Results – There are differences Top countries Bangladesh Burundi Colombia Ethiopia Mozambique Ruwanda B Top countries Bottom Countries Bolivia Jamaica Namibia Mauritius Botswana

20. Set-up 2: Results for Sweden The bottom list. Similar results “all donors” and Sweden. Sweden 51 countries in the sample, 1997-2006 The bottom list. Compared to the development in other countries, and how much ODA these received the outcome was relatively bad. 38Zambia0.536176.55 39Albania0.5329.87 40Ghana0.53121.29 41Mongolia0.46211.95 42Ecuador0.43916.29 43India0.439201.78 44Peru0.42424.92 45Guatemala0.42192.81 46Honduras0.39864.70 47El Salvador0.37552.52 48Nicaragua0.366211.28 49Philippines0.35256.77 50Bolivia0.342152.71 51Namibia0.334115.23

21. Set-up 2: Result the top - Sweden Top 13 countries First 5 are the relatively most efficient (1.0) Yes, Zimbabwe is there. Conclusion: In relation to the initial conditions, the allocation of money was optimal with respect to the outcome. Remember the amount of aid is fixed – and it must be allocated across countries given initial relative conditions. NoCountryCRSSUMODA 1Burundi1.00014.72 2Rwanda1.00042.62 3Sudan1.00010.51 4Togo1.0003.37 5Zimbabwe1.000171.40 6Nigeria0.9573.83 7Ethiopia0.926213.86 8Angola0.907116.26 9Mali0.8767.93 10Guinea-Bissau0.86938.22 11Viet Nam0.865303.39 12Mozambique0.828391.14 13Morocco0.8264.40

23. IDA’s CPR and DEA ? Is IDA’s Country Performance Index predicting the results in Set-up 2? Answer not really. Further research … What about Sweden’s aid to poor performing countries? How is it motivated? Are there differences is modalities, areas etc.? Here is a basis for asking questions. What about Sweden's land ‘focusation’ from 2007? Sweden picked a little bit more countries at the top, and excluded many at the bottom But, still long term development partnership countries might be a mixed bunch. More questions about differences in policies etc. Has Sweden clearly identified Need and Ability? And the consequences there-off?

24. Set-up 2: Conclusions It is relevant to ask “How did you choose to allocate your ODA budget across countries, ex ante?” And, “How can we judge that the allocation was good or bad, ex post?” Remember: The link between the amount of aid and development is inverse. Aid is allocated across countries on expected outcome, and initial conditions. (Do we know which?)

25. Model 2 Conclusions Mozambique and Viet Nam have done well, and received large amount of aid. Easy to motivate. But, Zimbabwe did not so good? Or did it? In relation to other countries development, the null that the relatively large amount of aid was well balanced cannot be rejected by the results here. Is this situation captured by the Government’s letter of instructions to Sida?

26. Final DEA is a very ‘soft’ way of measuring development efficiency, no a priori weights reflecting minimum or acceptable standards or benchmarks. All results are based in “constant returns to scale”. Relative efficiency is measured on a straight line. Switch to “variable returns to scale” and the differences are less pronounced. (The scale is reduced). This means that a more theoretically based, “cause and effects” model might is necessary. Say, aid is another source of capital that improves productivity in the economy. If we are satisfied with the setup of the DEA model. This exercise leads to a basis for asking deeper questions about the allocation of aid budgets. It is possible to ask further question not only about the allocation, but about the efficiency ranking. What exogenous factors might explain the ranking which are not captured by the “ability” variable. These factors are important for aid agencies’ accountability reporting. If there are systematic factors that predict aid outcomes, these needs to be accounted for in evaluation and in donors’ performance.