Bo Sjo

Development per Aid DollarData Envelopment Analysis applied to

aid efficiency

Bo SjöEmail: bo.sjo@sadev.se

March 2009

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”

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.

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..

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.

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

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.

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

K

k

ki

ki

J

j

ji

ji

i

xv

yw

E

1

1

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”

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”

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.

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

Data issues II

• Outcomes are measured as relative changes between periods.

• Net primary school enrolment requires additional data sources and some interpolation

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.

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?”

Set-up 1: Conclusions1) 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

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•

Results – There are differences

Top countries

Bangladesh

Burundi

Colombia

Ethiopia

Mozambique

Ruwanda

B Top countries

Bottom CountriesBoliviaJamaicaNamibiaMauritiusBotswana

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.

38 Zambia 0.536 176.55

39 Albania 0.532 9.87

40 Ghana 0.531 21.29

41 Mongolia 0.462 11.95

42 Ecuador 0.439 16.29

43 India 0.439 201.78

44 Peru 0.424 24.92

45 Guatemala 0.421 92.81

46 Honduras 0.398 64.70

47 El Salvador 0.375 52.52

48 Nicaragua 0.366 211.28

49 Philippines 0.352 56.77

50 Bolivia 0.342 152.71

51 Namibia 0.334 115.23

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.

No Country CRS SUMODA

1 Burundi 1.000 14.72

2 Rwanda 1.000 42.62

3 Sudan 1.000 10.51

4 Togo 1.000 3.37

5 Zimbabwe 1.000 171.40

6 Nigeria 0.957 3.83

7 Ethiopia 0.926 213.86

8 Angola 0.907 116.26

9 Mali 0.876 7.93

10 Guinea-Bissau 0.869 38.22

11 Viet Nam 0.865 303.39

12 Mozambique 0.828 391.14

13 Morocco 0.826 4.40

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?

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?)

•

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?

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.