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

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

• 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 • 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 2005-2011, 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.

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

Total

(2000-11) 568.04 104.86 1067.71 955.36 124.37 1680.64

Mean 47.34 8.74 88.98 79.61 10.36 140.05

SD 6.68 2.05 15.08 12.81 2.04 30.54

CV (in %) 708.74 426.85 590.15 621.36 506.85 458.67

Note: SD and CV indicate standard deviation and coefficient of variation, respectively.

Bilateral ODA Composition: DAC Countries, total, 2011

Model Specification

𝐥𝐧(𝑫𝑨𝒊𝒋,𝒕) = 𝛽0 + 𝛽1 ln(𝐷𝐴𝑖𝑗 ,𝑡−1) + 𝛽2ln(𝐻𝐴𝑖𝑗𝑡 ) + 𝛽3ln(𝑇𝑅𝐴𝐷𝐸𝑖𝑗 ,𝑡) + 𝛽4ln(𝐺𝐷𝑃𝑃𝐶𝑖𝑡) +

𝛽5ln(𝐺𝐷𝑃𝑃𝐶𝑗𝑡 )

+𝛽6

ln(𝐺𝐷𝑃𝑔𝑗𝑡 ) + 𝛽7 ln(𝐷𝐼𝑆 𝑖𝑗 ) +𝛽8ln(𝐺𝐶𝑂𝑁𝑗𝑡 ) +𝛽9ln(𝑃𝑂𝑃𝑗𝑡 ) +

𝛽10ln(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

ODA from the gross ODA: i) unpredicted nature of aid

(humanitarian aid and debt relief) ii) aid that doesn’t

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)

OECD.StatExtracts database 2013

<http://stats.oecd.org/Index.aspx?

DataSetCode=CPA#>

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)

OECD.StatExtracts database 2013

<http://stats.oecd.org/index.aspx?

r=298880#>

Trade Total bilateral trade between a donor and a recipient

(in 2010 constant million USD) UN COMTRADE database

through WITS, 2013

Per Capita GDP Real Per Capita GDP in PPP term (in 2010 constant

USD)

WDI, 2013

Per Capita GDP growth Growth rate of real per capita GDP (annual %) WDI, 2013

Variable Definition and Construction Source

Distance Simple distance between two capital cities

(capitals, km)

The GeoDist database, CEPII, 2013

Government

Consumption

Total Government Consumption (as % of GDP) WDI, 2013

Population Total population of a recipient country (in million

number)

WDI, 2013

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.

Foredoom House, 2013

Colony (dummy) Dummy variable equal to 1 for the recipient

country if it is a former colony of the donor,

otherwise 0.

The GeoDist database, CEPPI, 2013

Common Language

(dummy)

1 if a language is spoken by at least 9% of the

population in both countries

The GeoDist database, CEPII, 2013

Disaster Loss Estimated damage Cost (as % of GDP) EM-DAT database, Centre for Research

on the Epidemiology of Disasters

(CRED), 2013

Affected (Dummy:

Affected 1, 2)

Number of disaster affected people (dummy:

Affected1 & Affected2 are1 if total number of

disaster affected people is equal to or higher than

50,000 or 100,000, respectively in a given year;

otherwise 0)

EM-DAT database, Centre for Research

on the Epidemiology of Disasters

(CRED), 2013

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 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.

Crowding-out Effect of Humanitarian Aid on Development Aid: Hausman-Taylor Estimation

Independent

Variables

(1) (2) (3) (4) (5)

log(DAt-1) 0.396*** 0.397*** 0.396*** 0.396*** 0.396***

(33.58) (24.17) (28.68) (31.87) (32.04)

log(HA) 0.0165** 0.0172** 0.0175** 0.0156** 0.0160**

(2.301) (1.964) (2.085) (2.083) (2.108)

log(gdppcre) -0.240** -0.176 -0.289*** -0.377*** -0.391***

(2.468) (1.247) (2.597) (4.172) (4.663)

log(consumption) 0.338*** 0.343*** 0.339*** 0.329*** 0.330***

(5.365) (3.898) (4.591) (4.965) (4.990)

log(population) 0.318*** -0.0148 0.390 0.821*** 0.810***

(8.198) (0.282) (1.517) (8.197) (11.51)

log(distance) 5.950*** 3.513** 0.647** 0.659**

(6.672) (2.231) (2.215) (2.389)

Colony 1.477*** 1.677*** 1.581*** 1.295*** 1.301***

(dummy) (7.401) (5.958) (5.421) (5.807) (5.921)

Com. language 0.0496 0.393*** 0.386***

(dummy) (0.204) (2.746) (2.787)

Sargan-Hansen

Statistic

0.02 0.701 0.572 3.42 3.58

(p-value) (0.887) (0.402) (0.449) (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)

log(HA) 0.145*** 0.179*** 0.146***

(2.891) (3.065) (2.897)

log(trade) 0.0738*** 0.0199 0.0735***

(8.001) (0.879) (7.983)

log(gdppcre) -0.124*** -0.00294 -0.123***

(3.729) (0.0203) (3.711)

log(population) -0.0566*** -0.120 -0.0566***

(4.578) (0.397) (4.590)

log(free index) -0.184*** -0.154 -0.184***

(3.539) (1.461) (3.538)

Colony 0.190*** 0.189***

(dummy) (3.248) (3.239)

Sargan-Hansen

Statistic

0.215 1.82 0.216

(p-value) (0.643) (0.178) (0.642)

IV Set (affected2,

lossgdp)

(affected2,

lossgdp)

(affected2,

lossgdp)

Relation between Development Aid and Humanitarian Aid: SGMM Estimation

Independent

Variables

(1) (2) (3) (4) (5)

log(DAt-1) 0.874*** 0.879*** 0.881*** 0.892*** 0.888***

(49.60) (52.77) (57.92) (67.30) (68.25)

log(HA) 0.0859*** 0.0684*** 0.0914*** 0.0768*** 0.0638***

(3.651) (3.299) (3.913) (3.850) (3.459)

log(trade) 0.0831* 0.0839** 0.0704* 0.0546* 0.0752**

(1.928) (2.032) (1.940) (1.761) (2.374)

log(gdppcre) -0.202*** -0.215*** -0.166*** -0.175*** -0.202***

(3.171) (3.501) (3.079) (3.608) (4.007)

Log(population) -0.0642* -0.0588* -0.0594* -0.0470* -0.0557**

(1.782) (1.715) (1.959) (1.863) (2.170)

log(free index) -0.340** -0.264** -0.295** -0.231*** -0.172**

(2.481) (2.043) (2.264) (2.808) (2.130)

Colony 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

Hansen test (p-value) 0.133 0.411 0.133 0.41 0.164

No. of instruments 232 286 232 286 340

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 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.

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