More aid to Needy Countries? Woojin Jung, Ph.D. Student, University of California Berkeley,
[email protected], 510-725-5680, 425 Liberty Ship Way, #303, Albany, CA
More aid to Needy Countries?
Woojin Jung
Abstract
The critical question in the allocation of development aid is to what extent recent aid has
been allocated based on a developing country’s needs, and if so, then what kinds of “needs”
are being prioritized. Are more grants going to countries with low income, or to countries with
large numbers of poor, or to countries deprived of basic health and education services? By
examining predictors of global aid distribution, this study tries to understand the extent to
which recipient needs, as measured by various development indicators, are significant
determinants of the global aid distribution.
In particular, a question arises as to whether both monetary and multidimensional
poverty measures have explanatory power of aid inflow. To verify this assumption, the
difference in coefficients of aid predictors before and after 2000 is analyzed. Sector-based
aid allocation is also examined considering that assistance targeted to a specific sector
accounts for various facets of poverty such as deprivation in education and health. The
paper uses a synthesized dataset from OECD, UNDP, and the World Bank. It contains net
aid amount of 135 countries for the past 10 years and 30 variables illustrating distinct
economic, political and geographical dimensions of recipient needs and profiles.
The results from the OLS imply that the “needs” of the global South are predominantly
measured by national revenue, population as well as poverty headcount. The OLS
estimations do not provide strong evidence that countries with larger numbers of poor
receive more development aid. Instead, a country’s low income and large population are
positively correlated with a higher amount of aid. The results shows that 10% increase in the
log of GNI per capita explains about 4.7% decrease in the total aid a country receives. Under
the OLS, the log coefficient of poverty headcount is not statistically significant, holding other
variables constant.
In the OLS analysis of sector designated aid, some evidence of progressive distribution
is found in the health sector and its STD subsector. Countries with higher HIV/AIDS
prevalence rate tend to receive higher amounts of aid, confirming that HIV prevalence rate is
a powerful predictor of aid volume to health sector. An increase of 10% in the average HIV
prevalence would result in a 4.61% change in average aid to the health sector. Results are
mixed for a policy shift driven by the MDGs, but it is noted that the tendency of favoring
countries with high human development has slightly weakened since 2000.
The finding implies that the “needs” of the global South are predominantly measured by
national revenue and population rather than by absolute number of poor people or by nonmonetary deprivations. Another implication would be that highly unequal and low-income
countries with a large numbers of the poor do not necessarily receive more aid than similar
income countries with less poverty. The results also confirm performance-based aid
allocation given that more aid have been allocated to countries with less political risks and
more capacity.
1
More aid to Needy Countries? Woojin Jung, Ph.D. Student, University of California Berkeley,
[email protected], 510-725-5680, 425 Liberty Ship Way, #303, Albany, CA
Questions
The purpose of this analysis is to explore factors that drive a larger amount of
development aid in a given country. Aid allocation generally depends on a host of distinct
factors besides recipient needs, such as donor countries’ political, commercial and security
interests. Nevertheless, the analysis focuses on recipient-country characteristics excluding
donor side determinants. It discusses whether recipient countries’ needs, as measured by
various development indicators, are significant determinants of the global aid distribution. In
particular, a question arises as to whether poverty is valid predictor of aid inflow. Poverty is
commonly understood as a lack of material necessities but also as a deprivation of the basic
capabilities needed to obtain and convert resources into well-being. Monetary poverty is
measured by poverty headcount, or in other words, by the number of people living under
$1.25 PPP a day. Multidimensional poverty measure combines the measure of health and
education outcomes such as life expectancy at birth and years of schooling.
Data
For the purpose of the analysis, three dataset are selectively synthesized. First, aid data
from the OECD contains entire project-level database over one million aid activities funded
by 80 donors from the years 1940 to 2012. To construct a new dataset, aid flows are
aggregated by recipient countries for 10 year period. Second, data on income poverty and
country classification are drawn from the World Bank development indicators. Third, the
UNDP Human Development statistical table provides sources for multi-dimensional poverty.1
The new dataset for this analysis contains 47 variables for 135 non high-income countries.
All the countries have received official development assistance (ODA) from 2013-2012.
The main dependent variable of interest is net aid received per country for the past 10
years from 2003 to 2012. Outcome variables include aid in support of health and education,
as well as 10 years of aid before and after the Millennium Declaration (MD) in 2000. Key
predictors include the number of people living under the international or multidimensional
poverty threshold. Other independent variables intend to capture country characteristics,
which might influence variance in aid amount. They include income, population, region,
indebtedness, vulnerability, political instability, and inequality. Education and health related
development indicators are also analyzed. [
Table 1] presents descriptive statistics for key variables.
1
Aid data http://aiddata.org/aiddata-research-releases, the World Bank
http://wdi.worldbank.org/table/2.8, Human Development statistical table by the UNDP
http://hdr.undp.org/en/data
2
More aid to Needy Countries? Woojin Jung, Ph.D. Student, University of California Berkeley,
[email protected], 510-725-5680, 425 Liberty Ship Way, #303, Albany, CA
Table 1: Descriptive statistics with selected variables
Var.
Group
Measurement
Y1
Y2
Continuous
Y3
X1
X2
X3
X4
X5
X6
X9
X10
Binary
Categorical,
binary
X8
Median
Samp
le
129
Recent aid from
2003 to 2012
(USD billion
Recent
dollar) sector
aid
from 2003 to
2012
(USD dollar)
The sum of the past 10-year
aid per country
1 6.071
2.991
Recent 10-yr aid to education
Recent 10-yr aid to basic
education
Recent 10-yr aid to health
Recent 10-yr aid to HIV/AIDS
8.14e+08
247081679
373944945
273796638
4.449e+09
3
93164414
1.072e+08
93164414
129
128
135
135
10-yr aid before
MDGs
from 1990 to
1999
(USD dollar)
10-yr aid after
MDGs
from 2000 to
2009
(USD dollar)
Monetary
Poverty
Multidimensiona
l poverty
Income
Before MDG Aid
Before MDG Aid to education
Before MDG Aid to health
Before MDG aid to HIV/AIDS
After MDG Aid
After MDG Aid to education
After MDG Aid to health
After MDG aid to HIV/AIDS
3.356e+09
425096812
269598616
18831026
713117858
442504920
442504920
237653104
1.954e+09
1.777e+08
124202842
2.757e+09
3.974e+08
2.193e+08
442504920
237653104
129
129
129
90
129
129
129
128
Poverty headcount
(thousands)
Poverty headcount
(thousands)
GNI
per capita (USD, 2013)
Headcount (millions)
Gini Coefficient
Difference between the
Multidimensional Poverty
Index and income poverty
rate
HIVA/ADIS
prevalence rate
among adults
9,421
16,056
3,877
42.82
41.58
7.229e-11
1,289.6
2,883.46
3,220
9.50
41.0
-4.170e-18
111
91
128
135
104
82
2.602
0.70
99
Population
Gini Coefficient
Discrepancy
between
monetary and
capability
HIV
AID
measures Rate
Prevalence
X7
Mean
Indebtedness
IMF/WB Heavily Indebted
Poor Countries (HIPC) list
Baseline
Low debt
(95, 70%)
High debt
(40, 30%)
99
Vulnerability
UN Least Developed
Countries (LDC) list
Not LDC (88,
65%)
LDC (47,
35%)
135
Political
instability
Fragile states indices
Not fragile
(83,61%),
Fragile (52,
38%)
135
3
*
More aid to Needy Countries? Woojin Jung, Ph.D. Student, University of California Berkeley,
[email protected], 510-725-5680, 425 Liberty Ship Way, #303, Albany, CA
X11
Geography
Regions
East Asia
& Pacific (23,
17%)
Europe &
Central
Asia (18,
13%),
Latin
America &
Caribbean
(26, 19%),
Middle East
& North
Africa (13,
*
Word Bank Harmonized list of fragile states, Fund for Peace Fragile States Index 2013
10%),
South Asia
(8, 6%),
SubSaharan
Africa (47,
Method
35%)
135
The selection of a linear regression model is grounded in the literature. Collier (2006)
offers descriptive evidence suggesting that low income countries with low institutional quality
receive a considerable share of loans. Johansson also claims that the poorest countries
receive a higher share of grants rather than loans (2011). Although previous studies did not
focus on poverty ratio or headcount as a predictor of aid, it can be assumed that pro-poor aid
targeting is taking place. In this line of thought, a positive relationship between indicators of
poverty and indicators of external resource flows can be assumed.
Next, multivariate linear regression suits the purpose of the analysis and the constraints
of the current dataset. The main goal in this analysis is to be able to make statistical
inference on aid targeting as opposed to make precise prediction, which would require
greater number of observations. Country samples represent almost entire populations of aid
recipients, and thus the normality of E[YǀX] distribution is not an issue. On this front, a simple
linear regression is expected to work well. Yet, considering the moderate number of samples
and variables as well as the presence of missing data, the use of a data adaptive algorithm
would not work well.
Log transformation of both independent and dependent variables helps satisfy the
linearity assumption. Such transformation is reasonable considering the shape of scatter
plots and histograms of net aid [Figure 1]. Aid in the past 10 years is heavily skewed to the
right with mean (16 billion) more than five times larger than median (about 3 billion). Its
functional form resembles a log function with y=1/lnx (y>0) rather than a first-degree
polynomial function. Poverty head count variables also has the functional form of y=ln(x) in
that the relationship between y and x displays diminishing marginal returns.
4
More aid to Needy Countries? Woojin Jung, Ph.D. Student, University of California Berkeley,
[email protected], 510-725-5680, 425 Liberty Ship Way, #303, Albany, CA
Figure 1: Histogram of the sum of aid from 2003 to 2012
Results
(1) What are the possible need-based models of aid allocation?
An analysis of correlation coefficient suggests a high degree of multicollinearity among X
variables. A certain pairs of predictors such as poverty headcount vis-à-vis multidimensional
poverty headcount (0.95); and population vis-à-vis multidimensional poverty headcount (0.92)
have more than 0.90 of co-linearity. From this point of view, any individual predictor, or
predictors that are redundant with respect to others, are dropped. Scatter plots of X (GNI,
poverty headcount, multidimensional poverty headcount) and Y (aid10) variables also
suggested variables such as GINI that only exhibit marginal relationship with respect to Y
[Figure 2]. Additionally, PCA results gave some insight on sub categories of independent
variables.2
2
The cumulative percentage of the total sample variance explained by the two components are
0.5194505, 0.677741. PCA was performed based upon a subject of continuous variables and
countries with available data. Multidimensional poverty measures such as HDI and MPI contributes
more to the first component (≈0.3). In the second component, population related variables contributed
significantly more (>=0.5) than others.
5
More aid to Needy Countries? Woojin Jung, Ph.D. Student, University of California Berkeley,
[email protected], 510-725-5680, 425 Liberty Ship Way, #303, Albany, CA
Figure 2: paired plots
Table 2 presents model specification. The first and the second analysis explore the effect of
monetary and multidimensional poverty respectively. In both cases, poverty headcounts do
not provide sufficient evidence on the level of aid flows. Moreover, residual plots [1] and [2]
are slightly concave up. The inclusion of recipient characteristics increases the explanatory
power of the model as shown in model [3]. Yet, simpler model [4] considerably increases F
statistics from 18 to 56. Coefficients of log GNI and log headcounter also slightly increased.
Not surprisingly, Model [4] with smaller sets of variables have lower adjusted R squared than
model [3] (0.61 vs 0.65). Model [5] excluded two influential points, Iraq and Palestine. In fact,
aid to Iraq was exceptionally high in 2005 due to large debt relief operations. After
eliminating unusual pledges driven by the war on terrorism, model [5] sees increase in the
coefficients of log GNI from -0.04 to -0.51 and the adjusted R squared from 0.659 to 0.678.
The final model [6] introduces an interaction term of the vulnerability and state fragility,
thereby enhancing R-squared. F-statistics become statistically significantly lowered, as
demonstrated by ANOVA between nested [5] and full model [6], coefficients of log GNI is
larger in model [6].
6
More aid to Needy Countries? Woojin Jung, Ph.D. Student, University of California Berkeley,
[email protected], 510-725-5680, 425 Liberty Ship Way, #303, Albany, CA
Table 2: Regressions table
Dependent variable: Ln(net aid from 2003-2012)
[1] multi
[2] income
poverty
poverty
Ln(GNI/capita)
0.05750
[0.70033]
Ln(Income poverty
headcount)
Ln(population)
Ln(Multidimensional
poverty head count)
Debt (low debt)
Region
Fragility (Not
Fragile)
Least Developed
(Not LDC)
0.395958
[0.00315]
**
0.06669
[0.59388]
-0.75540
[0.00539]
**
0.84743
[0.04051]
*
Middle
East
-0.32508
[0.10545]
[3] full
GNI+control
-0.3323479
[0.041550] *
0.10182
[0.31318]
0.34122
[0.00257]
**
0.0001326
[0.999023]
0.4334210
[0.000345]
***
-0.73859
[0.00350]
**
-0.63197
[0.04029] *
Latin Am.
-0.4695017
[0.129703]
Latin Am.
-0.37453
[0.06057] .
0.28459
[0.23178]
-0.3519082
[0.085298 .]
0.4949415
0.059772 .
[4] simple
GNI+pop
-0.45902
[5.87e-05]
***
-0.08127
[0.347]
0.53629
[7.17e-08]
***
[5]
outlier
-0.44841
[2.20e-05]
***
-0.03894
[0.626]
0.48806
[9.98e-08]
***
[6]
Interaction
-0.51119
[6.22e-05]
***
-0.04172
[0.60755]
0.48034
[2.69e-07]
***
0.23360
[0.35769]
0.83934
[0.00469]
**
-0.85000
[0.01564 *]
Interaction
(Not LDC*Not
fragility)
Observation
86
110
107
107
107
107
Adjusted R-sq.
0.6571
0.6214
0.6403
0.6101
0.6595
0.6865
F-statistics
17.29
18.89
18.15
56.29
68.14
38.05
P-value
8.916e-16 < 2.2e-16
< 2.2e-16
< 2.2e-16
< 2.2e-16 < 2.2e-16
Notes: OLS estimation with standard errors. P values in brackets.
significance at 10% , significant at 5% *, significant at 1% **, significant at 0.1% ***. Constant not
reported. For regional dummy, only the estimate of the highest p-value reported.
The effect of log of income poverty is not significant and ranges from -0.08 to 0.1.
The effect of multidimensional poverty also does not show significant explanatory power
itself. However, it may be indirectly reflected by the LDC status in model [6], with coefficient
0.83934, considering that LDC takes into consideration human capability aspects. It seems
that GNI and population capture the effect of poverty indicators. The GNI coefficient, varying
from 0.34 to 0.5, is consistently negative and significant. The population coefficient is also
positive and significant in all models3. The coefficient estimates of GNI and population
3
Taking out coefficient of the log of population from the equation makes the coefficient of log of
7
More aid to Needy Countries? Woojin Jung, Ph.D. Student, University of California Berkeley,
[email protected], 510-725-5680, 425 Liberty Ship Way, #303, Albany, CA
change up to 18% in response to changes in the model. Therefore, how well the entire
bundle predicts the outcome variable, and what the directions (signs) of predictors are, might
be more important than the coefficient of individual predictors.
Based on model [6], 10% increase in GNI per capita of a recipient country is
associated with a 4.76% (=1.01-0.5119-1.0) decrease in net aid in the past decade. Graduating
from the Least Developed Countries group, one expect to receive about 1.31 times (exp.
(0.83934)-1.0) more of aid than a LDC country. Compared to fragile states, non-fragile states
that are not LDCs are likely to receive 195% ({exp. (0.85+0.2336)-1.0}*100) more aid.
Figure 3: Residual plots
Model [6]’s residual versus fitted values plot [Error! Reference source not found.]
does not show non-linear relationship. It is clustered around 0 though its distribution seems
to be concentrated largely in the middle. It appears that linearity and a constant variance
assumption is valid. In addition, the Normal Q-Q plot follows the diagonal straight line, thus
indicating that the assumption of normality in the errors is not violated.
poverty headcount significant, but models without population show non-linear trends of residual plot.
8
More aid to Needy Countries? Woojin Jung, Ph.D. Student, University of California Berkeley,
[email protected], 510-725-5680, 425 Liberty Ship Way, #303, Albany, CA
(2) Is the consideration of multidimensional poverty evident in sector-allocable aid?
In overall aid distribution, non-monetary facets of poverty do not have strong explanatory
power. Yet, it is possible to hypothesize that donors would take into consideration the
multidimensional nature of poverty in the distribution of sector allocable aid4. If that is the
case, there should be an association between lower levels of sector-specific development
indicators and higher levels f sector targeted aid. This analysis explores education and
health sectors considering that UNDP’s human development statistical table offers at least
two development indicators for these two sectors. As the best measures of educational
development, it looks at average years of schooling and preschool enrollment rates.
Comparably, life expectancy at birth and HIV/AIDS prevalence rate among adults are used
as proxy indicators for health-related poverty. Available data includes contribution of
educational and health deprivation in dimension to overall poverty.
[Table 3] presents the relationship between sector-specific poverty and aid. After
controlling for two important predictors of overall aid flow, GNI and population, only the
health sectors show a correlation between a lower development indicator and the amount of
aid a country received. In particular, HIV prevalence rate among adults is powerful predictor
of both aid to health sector and aid to Sexually Transmitted Disease (STD). A 10 % increase
in HIV/prevalence rate is associated with a 4.61% percent change in total commitments to
health and 8.29% increase in commitments STD. Life expectancy at birth is marginally
significant at the 0.1 level, and it shows a rather regressive aid distribution. In other words,
countries with better life expectancy receive more health assistance. With a 10% increase in
life expectancy, countries expect about a 0.089 % increase in total flow targeted to health. In
the education sector, none of the predictors are significant in explaining variation of
assistance in support of education. Model [8] indicates that one year of low average years of
schooling is associated with 10.79% increase in aid to basic education, but the estimate is
not statistically significant.
Table 3. Sector-specific poverty and aid
Model
Independent variable
Dependent
variable
Estimates [p-value]
Education
[7]
-Ln(GNI)
-Ln(population)
-Avg years of schooling
-Educational
+
deprivation
-Preschool enrollment
rate
Ln(Aid to
education)
0.1811608 [0.134]*
0.5534079 [<2e-16] ***
-0.0395045 [0.433]
0.0141844 [0.125]
-0.0005652 [0.859]
Fstatistics
[p-value]
2
Adj R
39.33
[< 2.2e16]
0.7269
4 Sector-allocable aid is the sum of aid that is designed to assist specific sectors such as education,
health, agriculture, civil society and governance, or multisector activities. The OECD DAC’s Creditor
Reporting system Code offers detailed sector classification directives.
9
More aid to Needy Countries? Woojin Jung, Ph.D. Student, University of California Berkeley,
[email protected], 510-725-5680, 425 Liberty Ship Way, #303, Albany, CA
Basic
education
[8]
-Ln(GNI)
Ln(Aid to
-0.035666 [0.878]
16.08
-Ln(population)
basic
0.600794 [1.28e-10]
[2.434e-Avg years of schooling
education)
***
10]
-Educational
-0.102492 [0.288]
0.515
+
deprivation
0.025207 [0.153]
-Preschool enrollment
-0.001938 [0.750]
rate
Health
-Ln(GNI)
Ln(Aid to
-0.235182 [0.0473 ] *
54.23
[9]
-Ln(population)
health)
0.684673 [< 2e-16] ***
[< 2.2e-Life expectancy at birth
0.009400 [0.5826]
16]
++
-Heath deprivation
-0.006690 [0.2187]
0.7894
- Ln(HIV prevalence
0.473402 [2.19e-08]***
rate)
HIV/AIDS
-Ln(GNI)
Ln(Aid to
-0.373333 [0.0041] **
62.03
[10]
-Ln(population)
STD
0.681657 [2.94e-12]
[< 2.2e-Life expectancy at birth including
***
16]
++
-Heath deprivation
HIV/AIDS)
0.034910 [0.0617].
0.8112
- Ln(HIV prevalence
0.004464 [0.4453]
rate)
0.836133 [1.47e-15]***
+ Contribution of educational deprivation in dimension to overall poverty
++ Contribution of health deprivation in dimension to overall poverty
P values in brackets. significance at 10% , significant at 5% *, significant at 1% **, significant at 0.1%
***. Constant not reported. For regional dummy, only the estimate of the highest p-value reported.
(3) Has the focus on poverty shifted after the MDGs?
Adopted by 189 nations in September 2000, the Millennium Development Goals (MDGs)
have galvanized global pledges for poverty reduction. With the MDGs, the elimination of
poverty became the central objective of development aid. An assumption that can be made
is that donors have strengthened poverty-oriented aid allocation after the MDGs. To verify
this assumption, the paper explores the difference in coefficients of aid predictors before and
after 2000 when the critical policy change occurred. The paper adopts primary models from
the previous two analyses. It should be noted that in 1990s, concepts such as fragile states
were absent, and donors were unable to factor such country classification into their aid
allocation formulas. Nevertheless, it is still possible to discuss whether certain povertyrelated indicators have gained more explanatory power in the 2000s, when donors pledge on
poverty reduction became stronger and recognition on the multidimensionality of poverty
were strengthened.
The analysis on income poverty yields mixed results. In
Table 4, simple model [4] indicates that the tie between low income and large aid flow
has been reinforced whereas the more complicated model [3] tells the opposite story. In
model [4], a 10% decrease in GNI is associated with a 3.1% increase in aid in the 1990s, but
the effect has expanded to 4.0% in the 2000s. This means that the effect of low income on
the net aid received is larger during the post-MD term than during the pre-MD term. Based
on the simple income poverty model [3], however, data indicates that income-based
allocation has been weakened. In model [3], 10 % decrease in national income is associated
with 4.7% increase in aid in the 1990s but with only 4.1% increase in aid in the 2000s.
10
More aid to Needy Countries? Woojin Jung, Ph.D. Student, University of California Berkeley,
[email protected], 510-725-5680, 425 Liberty Ship Way, #303, Albany, CA
Table 4. Comparison of Aid before and after the 2000 Millennium Declaration
Model
Income
poverty
:Full model
[3]
Income
poverty
: Simple
model
[4]
Education
sector
[7]
Health
sector
[9]
+
HIV/AIDS
[10]
Variables
Ln(GNI)
Ln(pop)
Aid in 1990s (1990-1999)
-0.4738397 [0.000237]***
0.4700556 [2.33e-05] ***
Aid in 2000s (2000-2009)
-0.41286 [0.003983] **
0.43725 [0.000518] ***
Ln(poverty
headcount)
LDC Not LDC
Latin America &
Caribbean region
Fragility Not FS
-0.0549197 [0.575636]
0.11215
0.5549138 [0.022331] *
-0.5895154 [0.048033] *
-0.06256 [0.42802 ]
0.0137424 [0.941203]
-0.35283 [0.095336 ]
F-statistics [P-value]
Adjusted R-squared
Ln(GNI)
19.17 [< 2.2e-16]
0.6359
-0.33142, [0.00116] **
18.72 [< 2.2e-16]
0.6324
-0.42861, [0.000303 ]***
Ln(pop)
0.50621, [2.75e-08] ***
0.50621, [2.75e-08 ]***
Ln(poverty
headcount) [P-value]
F-statistics
Adjusted R-squared
Ln(pop)
Preschool enrollment
rate
F-statistics [P-value]
Adjusted R-squared
Ln(GNI)
Ln(pop)
Life expectancy
Health deprivation
Ln(HIV rate)
-0.06256, [0.42802]
19.17, [< 2.2e-16],
0.6359
0.528556, [1.16e-12] ***
0.018935, [0.000132] ***
-0.09730, [0.289085]
51.31, [< 2.2e-16],
0.5944
0.546672 [<2e-16] ***
0.001073 [0.737]
22.64, [3.174e-13]
-0.6004
0.046749 [0.6937]
0.619700 [< 2e-16] ***
0.041584 [0.0190] *
-0.028888 [4.12e-05] ***
0.183489 [0.0181] *
38.38, [2.2e-16]
0.7219
--0.168060 [0.0774] 
0.639138 [<2e-16]***
0.008792 [0.5230]
-0.012115
[0.0229] *
0.091276 [0.1328]
F-statistics
R squared
38.11, [ 2.2e-16]
0.7289
60.86 [< 2.2e-16]
0.8127`
Ln(GNI)
-0.51712 [0.04835 ]*
-0.435480, [0.00174] **
Ln(pop)
Ln(HIV rate)
Life expectancy
F-statistics [P-value]
Adjusted R-squared
0.84257 [1.15e-08] ***
1.14720 [1.71e-08 ]* **
0.11741 [0.00529] **
16.34 [9.897e-10]
0.5694
0.731139 [8.34e-16] ***
0.858138 [3.10e-13] ***
0.036308 [0.08702]
46.37 [< 2.2e-16]
0.7964
0.46735 [0.085816 ]
-0.52616 [0.078483 ]
+Many missing values and only 94 counts
Overall, evidence from sector-based allocation signifies positive shifts towards
11
More aid to Needy Countries? Woojin Jung, Ph.D. Student, University of California Berkeley,
[email protected], 510-725-5680, 425 Liberty Ship Way, #303, Albany, CA
multidimensional poverty-oriented thinking. Most of all, a regressive distribution tendency to
favor countries with better indicators has been weakened. In the 1990s, a 1% increase in
preschool enrollment rate is associated with a 1.91% increase in aid. The positive
relationship between preschool enrollment and more aid is not statistically significant after
MDGs. Similarly, countries with higher life expectancy and lower deprivation in health5 were
likely to receive more aid in the 1990s but their effects were no longer statistically significant
in the 2000s. To be specific, a decrease of one year in the average of life expectancy at birth
would result in a 2.8 and a 0.8 percent change in the average aid received per country in the
1990s and in the 2000s respectively. Absolute values of coefficients of health deprivation
also went down from -0.0288 in the 1990s to -0.012 in the 2000s.
The prevalence of HIV/AIDS is a powerful progressive indicator in the health sector but
its explanatory power has rather weakened after the Millennium Declaration. Model [9]
explores an association between HIV prevalence (multidimensional poverty and sector
specific needs) and aid designated to the health sector; model [10] explores an association
between HIV prevalence and aid targeted to STD control. Although the post-MDG model fits
better to data6, the point estimates of HIV prevalence in the 2000s are smaller than that in
the 1990s. Compared to the pre-MDGs term, a percentage change in aid to health
associated with 10% increase in average HIV prevalence rate was declined from 1.76% to
statistically insignificant 0.85 % during the last decade. An increase of 10% HIV prevalence
rate among adults would only yields 8.5% increase in aid to STD during the decade ending
2009 in comparison to 11.55% increase during the decade ending 1999.
Conclusions
The estimations in this study fail to provide evidence that countries with larger numbers
of poor receive more development aid. Instead, a country’s low income and large population
are positively correlated with a higher amount of aid. The finding implies that the “needs” of
the global South are predominantly measured by national revenue and population rather
than by absolute number of poor people or by non-monetary deprivations. Another
implication would be that highly unequal and low-income countries with a large numbers of
the poor do not necessarily receive more aid than similar income countries with less poverty.
The human capability approach to poverty is not apparent in the overall aid distribution
model, but may be reflected in sector allocable aid and in a policy change after the
Millennium Declaration. An underlying assumption is that assistance targeted to a specific
sector accounts for various facets of poverty such as deprivation in education and health. In
the analysis of sector designated aid, some evidence of progressive distribution is found in
the health sector and its STD subsector. Countries with higher HIV/AIDS prevalence rate
tend to receive higher amounts of aid. Results are mixed for a policy shift driven by the
MDGs. In general, it should be noted that the tendency of favoring countries with high
human development has slightly weakened since 2000.
The analysis also shows that the categorical country classification (such as LDC, fragile
states, and regions) has some power in explaining the variability of the total aid level. It can
be hypothesized that donors take into consideration country classification and grant more aid
to developing countries with low fragility and vulnerability. This finding may reflect two
5
6
One year increase in life expectancy at birth is related to 4.6% increase in aid
R2 for the pre-MDG terms is only 0.5694 while R2 for the post Millennium Declaration is 0.79.
12
More aid to Needy Countries? Woojin Jung, Ph.D. Student, University of California Berkeley,
[email protected], 510-725-5680, 425 Liberty Ship Way, #303, Albany, CA
primary rules of allocation: more aid provided to not only to low-income countries (needbased allocation) but also countries with less political risks and more capacity (performancebased allocation). The trend is consistent with the current criterion for aid allocation based
on Dollar and Levin (2006)’s research, which calls for more aid to low-income countries
pursuing good policies.
13
More aid to Needy Countries? Woojin Jung, Ph.D. Student, University of California Berkeley,
[email protected], 510-725-5680, 425 Liberty Ship Way, #303, Albany, CA
References
Paul Collier, Loans and Grants: Coherence in Aid Instruments [Oxford: Oxford University
Press, 2006]. The New Public Finance: Responding to Global Challenges.
William Easterly, “Are Aid Agencies Improving?”, Economic Policy 52 (2007): 633-78.
Pernilla Johansson , ”Grants to Needy Countries? A Study of Aid Composition between 1975
and 2005”, Development Policy Review. 2(2011): 185-209
David Dollar and Victoria Levin, “The Increasing Selectivity of Foreign Aid, 1984-2003’”,
World Development 12 (2006): 2034-46.
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