1. Does Humanitarian Aid Crowd Out Development Aid?
A Dynamic Panel Data Analysis
Delwar Hossain
PhD Student
Arndt-Corden Department of Economics, ANU
Crawaford PhD Conference 2013
04 November 2013

2. Outline of the Presentation
• Motivation/Contribution
• Related literature
• Trends of development aid and humanitarian aid
flows to the developing countries
• Model specification, data sources and variable
construction
• Estimation methods
• Results
• Policy inferences and scope for further research

3. Motivation/ Contribution
• Growth impacts of two types of aid are different
• Strong international commitment for
humanitarianism
-Due to increased attention to disaster prevention and
‘political-economy’ reasons, donors are now providing
more aid in the form of humanitarian aid
• Concerns in policy circles that emphasis on
humanitarian aid can crowd out ‘development
aid’ (discussed in next section)
• This is the first study to empirically test this
possibility

4. Motivation/ Contribution
• The country programmable aid, which best reflects
the actual amount of aid transfer from donors to
recipient countries, is used as the proxy for
development aid
• A newly constructed panel dataset covering 23
OECD-DAC donor countries and 117 aid recipient
developing countries over the period of 2000-2011
has been used
• The econometric analysis is undertaken within the
standard gravity modelling framework

5. Related Literature
• Macrae (2002) shows that in spite of overall decline in DAC ODA between
1992 and 2000 due to wider budget cuts in OECD countries, the assistance for
humanitarian activities has increased each year from 1997.
• The UN General Assembly resolution 2816 strongly urge all member states
and development agencies that the complementary assistance for emergency
purposes should be given without prejudice to the normal development
assistance (UN, 1971). UN also reiterates its earlier commitments in
resolution 46/182 in 1991.
• Jayasuriya and McCawley (2008) show that though the Tsunami disaster aid is
estimated at around US$ 14.00 billion to be spent over the period of 20052011, the actual addition of Tsunami aid to total aid flows was only US$ 3.5
billion.
• The Tsunami Evaluation Coalition (2007) states that the financial assistance
pledges for the Tsunami response were almost all new pledges, whereas the
response to Hurricane Mitch of 1998 was mostly old or already pledged
money.
• Kharas (2007, 2008, 2009) studies insinuate the crowding out hypothesis of
development aid due to increased flow of humanitarian aid.

6. Trends of Various Types of Aid to Developing Countries (2000-2011)
Year
DAC Countries, Total (in billion 2010 USD)
All Donors, Total (in billion 2010 USD)
CPA
(Devt. Aid)
Humanitarian
Aid
Total Aid
CPA
(Devt. Aid)
Humanitarian
Aid
Total Aid
2000
38.61
5.63
64.54
60.81
7.36
92.71
2001
40.03
5.52
65.94
66.97
7.38
101.68
2002
41.02
6.12
73.99
71.79
8.01
114.78
2003
41.03
8.48
81.82
66.92
10.18
118.03
2004
43.26
10.13
80.74
71.76
10.98
121.81
2005
47.75
10.87
106.85
76.18
12.44
151.09
2006
47.11
8.50
100.19
78.02
9.87
198.88
2007
47.72
7.82
90.62
82.09
9.11
145.66
2008
53.85
10.25
100.67
92.74
11.69
155.25
2009
55.81
10.35
96.70
97.92
11.70
157.68
2010
57.08
10.72
103.67
95.98
12.45
163.62
2011
54.76
10.46
101.98
94.20
13.21
159.44
568.04
104.86
1067.71
955.36
124.37
1680.64
47.34
8.74
88.98
79.61
10.36
140.05
6.68
2.05
15.08
12.81
2.04
30.54
708.74
426.85
590.15
621.36
506.85
458.67
Total
(2000-11)
Mean
SD
CV (in %)
Note: SD and CV indicate standard deviation and coefficient of variation, respectively.

7. Bilateral ODA Composition: DAC Countries, total, 2011

8. Model Specification
𝐥𝐧⁡ 𝑫𝑨 𝒊𝒋,𝒕 ) = 𝛽0 + 𝛽1 ln⁡ 𝐷𝐴 𝑖𝑗 ,𝑡−1 ) + 𝛽2 ln⁡ 𝐻𝐴 𝑖𝑗𝑡 ) +
(
(
(
𝛽5 ln⁡𝐺𝐷𝑃𝑃𝐶 𝑗𝑡 )+𝛽 ln⁡𝐺𝐷𝑃𝑔 𝑗𝑡 ) +
(
(
6
𝛽7 ln⁡ 𝐷𝐼𝑆
(
𝑖𝑗
𝛽3 ⁡ ( 𝑇𝑅𝐴𝐷𝐸 𝑖𝑗 ,𝑡 ) + 𝛽4 ln⁡𝐺𝐷𝑃𝑃𝐶 𝑖𝑡 ) +
ln⁡
(
) +𝛽8 ln⁡𝐺𝐶𝑂𝑁 𝑗𝑡 ) +𝛽9 ln⁡𝑃𝑂𝑃𝑗𝑡 ) +
(
(
𝛽10 ln⁡𝐹𝑅𝐸𝐸 𝑗𝑡 ) +𝛽11 (𝐶𝑂𝐿𝑂𝑁𝑌𝑖𝑗 ) + 𝛽12 (𝐶𝑂𝑀𝐿𝐴𝑁 𝑖𝑗 ) + 𝜇 𝑖 + 𝛾 𝑗 + 𝜆 𝑡 + 𝜀 𝑖𝑗 ,𝑡
(
𝑓𝑜𝑟 𝑖, 𝑗 = 1, 2, … . , 𝑁; 𝑡 = 1, 2, … . , 𝑇

9. Definitions and Construction of Variables
Variable
Definition and Construction
Source
Development Aid(CPA)
Derived by netting out the following components of OECD.StatExtracts database 2013
ODA from the gross ODA: i) unpredicted nature of aid
<http://stats.oecd.org/Index.aspx?
(humanitarian aid and debt relief) ii) aid that doesn’t
DataSetCode=CPA#>
have cross-border flow (administrative costs, imputed
student costs, promotion of development awareness,
and research and refugees in donor countries);iii) aid
beyond the
co-operation agreements between
governments (food aid and aid from local
governments); and iv) aid that is not country
programmable by the donor (core funding of NGOs).
(In 2010 constant million USD)
Humanitarian Aid
Sum of emergency/disaster relief, emergency food aid,
relief coordination, protection and support services,
reconstruction relief and rehabilitation and disaster
prevention and preparedness activities. (In 2010
constant million USD)
Total bilateral trade between a donor and a recipient
(in 2010 constant million USD)
Real Per Capita GDP in PPP term (in 2010 constant
USD)
Growth rate of real per capita GDP (annual %)
Trade
Per Capita GDP
Per Capita GDP growth
OECD.StatExtracts database 2013
<http://stats.oecd.org/index.aspx?
r=298880#>
UN
COMTRADE
through WITS, 2013
WDI, 2013
WDI, 2013
database

10. Variable
Definition and Construction
Source
Distance
Simple distance between two capital cities The GeoDist database, CEPII, 2013
(capitals, km)
Government
Consumption
Total Government Consumption (as % of GDP)
Population
Total population of a recipient country (in million WDI, 2013
number)
Freedom
The unweighted average of two indices: political
right and civil liberty. Each index is rated from 1
to 7 with 1 representing the most free and 7 the
least free.
Colony (dummy)
Dummy variable equal to 1 for the recipient The GeoDist database, CEPPI, 2013
country if it is a former colony of the donor,
otherwise 0.
Common
(dummy)
Disaster Loss
WDI, 2013
Foredoom House, 2013
Language 1 if a language is spoken by at least 9% of the The GeoDist database, CEPII, 2013
population in both countries
Estimated damage Cost (as % of GDP)
EM-DAT database, Centre for
on the Epidemiology of
(CRED), 2013
Affected
(Dummy: Number of disaster affected people (dummy: EM-DAT database, Centre for
Affected 1, 2)
Affected1 & Affected2 are1 if total number of on the Epidemiology of
disaster affected people is equal to or higher than (CRED), 2013
50,000 or 100,000, respectively in a given year;
otherwise 0)
Research
Disasters
Research
Disasters

11. Estimation Methods
• POLS
• Fixed Effects and Random Effects
• Hausman-Taylor IV Estimation
• Robustness checks
- System GMM
- 2SLS estimation with external IVs for
humanitarian aid

12. Choice of Estimation Technique
• The pooled OLS estimator ignores country specific effects.
• The fixed effects (FE) estimator does not allow for including time-invariant
variables. Additionally, in the dynamic panel set-up correlation between
country-specific effects and the lagged dependent variable might cause
endogeneity in the independent variables, yielding inconsistent estimates
(Caselli et al., 1996).
• Random effects (RE) estimator can accommodate time-invariant variables, but
the exogeneity assumption i.e., the residuals are independent of the
covariates, does not hold in many standard random effects models which leads
to biased estimates.
• Although dynamic panel structure minimizes the reverse causation problem,
still there might be some other types of endogeneity problem in our
development aid function.
• To incorporate both time-varying and time-invariant variables and address the
endogeneity issues finally we use the Hausman and Taylor (1981) instrument
variable approach as our preferred estimation technique along with the SGMM
and 2SLS IV approaches.

13. Other Concerns about Estimation Technique
•
•
•
Several empirical studies (e.g., Ahn and Low, 1996 and Mitze, 2009) argue that the
HT model is not as good in time-invariant estimates as in time-varying estimates.
As an alternative to HT, recently Plümber and Troeger (2007) and Mitze (2009)
advanced fixed effects vector decomposition (FEVD) model. But, several recent
studies (Breusch et al., 2011a, b; Greene, 2011a, b, 2012 etc.) argue that the
standard errors are likely to be incorrect in FEVD approach.
A sizeable number of recent literature on panel analyses (e.g., Pesaran 2006;
Hoyos and Sarafides, 2006; Eberhardt and Teal, 2009 & 2010; Moscone and
Tosetti , 2010) question about the parameter homogeneity and cross-sectional
independence assumptions in macro panel data models. They argue that ignoring
these two properties will yield biased and inconsistent estimates. Therefore, we
also apply the cross-sectional dependence consistent Driscoll-Kraay (1998)
technique to get the CD-robust standard errors.
Silva and Tenreyro (2006) argue that the traditional empirical analyses are
inappropriate in case of log-linearized gravity structure because of presence of
large number of zeros as well as heteroscedasticity problem. They propose
possion psedu-maximum likelihood (PPML) technique to address the problem of
log of gravity. However, our data structure is well-fitted with the log-linearization
model and HT technique can address the heteroscedasticity problem.

14. Crowding-out Effect of Humanitarian Aid on Development Aid: Hausman-Taylor Estimation
Independent
Variables
log(DAt-1)
log(HA)
log(gdppcre)
log(consumption)
log(population)
(1)
0.396***
(33.58)
0.0165**
(2.301)
-0.240**
(2.468)
0.338***
(5.365)
0.318***
(8.198)
(3)
1.477***
(7.401)
0.397***
(24.17)
0.0172**
(1.964)
-0.176
(1.247)
0.343***
(3.898)
-0.0148
(0.282)
5.950***
(6.672)
1.677***
(5.958)
0.02
0.701
0.396***
(28.68)
0.0175**
(2.085)
-0.289***
(2.597)
0.339***
(4.591)
0.390
(1.517)
3.513**
(2.231)
1.581***
(5.421)
0.0496
(0.204)
0.572
(0.887)
(0.402)
(0.449)
log(distance)
Colony
(dummy)
Com. language
(dummy)
Sargan-Hansen
Statistic
(p-value)
(2)
(4)
(5)
0.396***
(31.87)
0.0156**
(2.083)
-0.377***
(4.172)
0.329***
(4.965)
0.821***
(8.197)
0.647**
(2.215)
1.295***
(5.807)
0.393***
(2.746)
3.42
0.396***
(32.04)
0.0160**
(2.108)
-0.391***
(4.663)
0.330***
(4.990)
0.810***
(11.51)
0.659**
(2.389)
1.301***
(5.921)
0.386***
(2.787)
3.58
(0.181)
(0.167)
Note: Numbers in parentheses are the absolute values of robust t-ratio with significance level: *** p<0.01, **
p<0.05 and * p<0.

15. Robustness Checks

16. Crowding-out Effect of Humanitarian Aid on Development Aid: 2SLS Estimation
Ind. Variables
REM with IV
FEM with IV
NOSA with IV
log(DAt-1)
0.832***
0.392***
0.833***
(80.38)
(30.57)
(80.42)
0.145***
0.179***
0.146***
(2.891)
(3.065)
(2.897)
0.0738***
0.0199
0.0735***
(8.001)
(0.879)
(7.983)
-0.124***
-0.00294
-0.123***
(3.729)
(0.0203)
(3.711)
-0.0566***
-0.120
-0.0566***
(4.578)
(0.397)
(4.590)
-0.184***
-0.154
-0.184***
(3.539)
(1.461)
(3.538)
log(HA)
log(trade)
log(gdppcre)
log(population)
log(free index)
Colony
0.190***
0.189***
(dummy)
Sargan-Hansen
Statistic
(p-value)
(3.248)
(3.239)
IV Set
0.215
1.82
0.216
(0.643)
(0.178)
(0.642)
(affected2,
lossgdp)
(affected2,
lossgdp)
(affected2,
lossgdp)

17. Relation between Development Aid and Humanitarian Aid: SGMM Estimation
log(HA)
(1)
(2)
(3)
(4)
(5)
0.874***
0.879***
0.881***
0.892***
0.888***
(49.60)
Independent
Variables
log(DAt-1)
(52.77)
(57.92)
(67.30)
(68.25)
0.0859*** 0.0684*** 0.0914*** 0.0768*** 0.0638***
(3.651)
log(gdppcre)
(3.913)
(3.850)
(3.459)
0.0831*
0.0839**
0.0704*
0.0546*
0.0752**
(1.928)
log(trade)
(3.299)
(2.032)
(1.940)
(1.761)
(2.374)
-0.202*** -0.215*** -0.166*** -0.175*** -0.202***
(3.171)
(3.501)
(3.079)
(3.608)
(4.007)
-0.0642*
-0.0588*
-0.0594*
-0.0470*
-0.0557**
(1.782)
(1.715)
(1.959)
(1.863)
(2.170)
-0.340**
-0.264**
-0.295**
-0.231***
-0.172**
(2.481)
(2.043)
(2.264)
(2.808)
(2.130)
0.243***
0.162**
0.218***
0.179**
0.152**
(dummy)
(2.913)
(2.092)
(2.783)
(2.527)
(2.143)
AR(2) test
0.42
0.43
0.42
0.43
0.42
0.133
0.411
0.133
0.41
0.164
232
286
232
286
340
Log(population)
log(free index)
Colony
Hansen test (p-value)
No. of instruments

18. Inferences
• Our findings with all econometric techniques strongly
demonstrate that humanitarian aid, on average, crowds in,
rather than crowds out the development aid in the recipient
countries. However, the extent of crowding-in is not very large.
• Among other forces that increase the flow of development aid
are past aid disbursement, historical colonial tie with donors,
strong trade relations, government consumption, and common
language. Additionally, donors seem to be more generous to
poor and politically freer countries.
• The small country bias and distance variables give ambiguous
results in our analysis.

19. Conclusions and Scope for Further Research
• All econometric approaches including HT suggest that the
additional flow of humanitarian aid due to any natural calamity or
other causes help outpouring the overall development aid
disbursement in the developing countries. In other words, donors
are, in general, more generous during the crisis period of a recipient
country.
• Overall, our findings rule out the crowding out hypothesis and
support the donors’ commitments towards humanitarian
responses.
• This study is confined only to 12 years due to limitation of
disaggregated (pairwise) aid data. A more sensible analysis could
have been done, if longer time series data were available.
• Both donor- and recipient- specific case studies can provide more
insights in this line of research.
• Multi-lateral donors, non-DAC donor countries, and fragile states
contexts can be extensions to this study.
• Regarding the 2SLS estimation, finding stronger IV(s) can give more
efficient estimates.
• Exploring time-series properties with longer time-series data would
be another worthwhile exploration.
19

20. Thanks