Transcript PhD Seminar Topic: Differential Impacts of Foreign Capital

Does Humanitarian Aid Crowd Out Development Aid?
A Dynamic Panel Data Analysis
Delwar Hossain
Arndt-Corden Department of Economics, ANU
2014 Australasian Aid and International Development Policy Workshop
13 February 2014
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
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
Motivation/ Contribution (con…)
• 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
Related Literature
• Terry (1998) notes that due to increased occurrence of humanitarian
disasters in 1990s, development assistant has stagnated in many parts
of the world, whereas humanitarian assistance has become the most
common form of aid flow to the affected countries.
• 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 in several occasions 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, 1991).
• OECD (2006) reports that though the food aid has declined in absolute
value and relative importance, the share of food aid for humanitarian
relief and crisis-related emergency assistance has increased at the
expense of development aid.
Related Literature(Con..)
• 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.
• 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 2005-2011, the actual addition of Tsunami aid to total
aid flows was only US$ 3.5 billion.
• By using data for a set of thirteen countries over the period of 1992
to 2007, Celasun and Walliser (2007) also show that rise in
emergency aid is associated with large shortfalls in project aid (i.e.,
development aid).
• Kharas’s (2007, 2008, 2009) studies insinuate the crowding out
hypothesis of development aid due to increased flow of
humanitarian aid.
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.
Bilateral ODA Composition: DAC Countries, total, 2011
Major Donors and Recipients of HA (2001-10)
Donor (10 Years Total) (In million USD)
34,140
USA
14,624
EU
8,474
UK
6,334
Germany
4,951
Sweden
4,773
Netherlands
4,442
Japan
4,156
Norway
3,534
France
3,357
Spain
3,226
Italy
3,215
Canada
2,794
Australia
2,252
Switzerland
2,202
Denmark
2,138
Saudi Arabia
1,505
Belgium
1,178
Finland
1,107
Ireland
869
UAE
Source: GHA Report 2012.
Recipient (10 Years Total) (In million USD)
9,735
Sudan
6,488
Palestine
5,605
Afghanistan
5,256
Ethiopia
5,246
Iraq
4,565
Pakistan
3,708
Haiti
3,690
DRC
2,744
Somalia
2,434
Indonesia
1,887
Kenya
1,814
Sri Lanka
1,749
Lebanon
1,688
Zimbabwe
1,565
Uganda
1,407
Chad
1,266
Jordan
1,188
Angola
1,159
Burundi
995
Myanmar
Model Specification
𝐥𝐧⁡
(𝑫𝑨𝒊𝒋,𝒕 ) = 𝛽0 + 𝛽1 ln⁡
(𝐷𝐴𝑖𝑗 ,𝑡−1 ) + 𝛽2 ln⁡
(𝐻𝐴𝑖𝑗𝑡 ) + 𝛽3 ⁡
ln⁡
(𝑇𝑅𝐴𝐷𝐸𝑖𝑗 ,𝑡 ) + 𝛽4 ln⁡
(𝐺𝐷𝑃𝑃𝐶𝑖𝑡 ) +
𝛽5 ln⁡
(𝐺𝐷𝑃𝑃𝐶𝑗𝑡 )+𝛽 ln⁡
(𝐺𝐷𝑃𝑔𝑗𝑡 ) + 𝛽7 ln⁡
(𝐷𝐼𝑆
6
𝑖𝑗
) +𝛽8 ln⁡
(𝐺𝐶𝑂𝑁𝑗𝑡 ) +𝛽9 ln⁡
(𝑃𝑂𝑃𝑗𝑡 ) +
𝛽10 ln⁡
(𝐹𝑅𝐸𝐸𝑗𝑡 ) +𝛽11 (𝐶𝑂𝐿𝑂𝑁𝑌𝑖𝑗 ) + 𝛽12 (𝐶𝑂𝑀𝐿𝐴𝑁𝑖𝑗 ) + 𝜇𝑖 + 𝛾𝑗 + 𝜆𝑡 + 𝜀𝑖𝑗 ,𝑡
𝑓𝑜𝑟 𝑖, 𝑗 = 1, 2, … . , 𝑁; 𝑡 = 1, 2, … . , 𝑇
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 GDP (annual %)
Trade
Per Capita GDP
GDP growth rate
OECD.StatExtracts database 2013
<http://stats.oecd.org/index.aspx?
r=298880#>
UN
COMTRADE
through WITS, 2013
WDI, 2013
WDI, 2013
database
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
Estimation Methods
• POLS
• Fixed Effects and Random Effects
• Hausman-Taylor IV Estimation
• Robustness checks
- System GMM
- 2SLS estimation with external IVs for
humanitarian aid
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.
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 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.
Due to short span of time our data series does not encounter CD problem.
• 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 loglinearization model and HT technique can address the heteroscedasticity
problem.
Econometric Result Analysis
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.
Robustness Checks
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)
Relation between Development Aid and Humanitarian Aid: SGMM Estimation
Independent
Variables
log(DAt-1)
log(HA)
log(trade)
log(gdppcre)
(1)
(2)
(3)
(4)
(5)
0.874***
0.879***
0.881***
0.892***
0.888***
(49.60)
(52.77)
(57.92)
(67.30)
(68.25)
0.0859*** 0.0684*** 0.0914*** 0.0768*** 0.0638***
(3.651)
(3.299)
(3.913)
(3.850)
(3.459)
0.0831*
0.0839**
0.0704*
0.0546*
0.0752**
(1.928)
(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
Crowding-in Effects of Humanitarian Aid on Development Aid
Estimated through Different Approaches
POLS
FE
RE
HT
RE with FE with
IV
IV
0.0373*** 0.0179** 0.0365*** 0.0160** 0.145*** 0.179***
(6.879) (2.263) (5.942) (2.108) (2.891) (3.065)
NOSA SGMM
with IV
0.146*** 0.0638***
(2.897) (3.459)
Note: Numbers in parentheses are the absolute values of the robust t-ratio with significance level: *** p<0.01,
** p<0.05 and * p<0.1.
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.
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 time 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 examined.
• 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.
23
Thanks