Does Humanitarian Aid Crowd Out Development Aid? A Dynamic Panel Data Analysis

Delwar HossainArndt-Corden Department of Economics, ANU

2014 Australasian Aid and International Development Policy Workshop13 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 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

Major Donors and Recipients of HA (2001-10)Donor (10 Years Total) (In million USD) Recipient (10 Years Total) (In million USD)

USA 34,140 Sudan 9,735EU 14,624 Palestine 6,488UK 8,474 Afghanistan 5,605Germany 6,334 Ethiopia 5,256Sweden 4,951 Iraq 5,246Netherlands 4,773 Pakistan 4,565Japan 4,442 Haiti 3,708Norway 4,156 DRC 3,690France 3,534 Somalia 2,744Spain 3,357 Indonesia 2,434Italy 3,226 Kenya 1,887Canada 3,215 Sri Lanka 1,814Australia 2,794 Lebanon 1,749Switzerland 2,252 Zimbabwe 1,688Denmark 2,202 Uganda 1,565Saudi Arabia 2,138 Chad 1,407Belgium 1,505 Jordan 1,266Finland 1,178 Angola 1,188Ireland 1,107 Burundi 1,159UAE 869 Myanmar 995

Source: GHA Report 2012.

Model Specification

𝐥𝐧(𝑫𝑨𝒊𝒋,𝒕) = 𝛽0+𝛽1ln(𝐷𝐴𝑖𝑗,𝑡−1)+𝛽2ln(𝐻𝐴𝑖𝑗𝑡) +𝛽3ln(𝑇𝑅𝐴𝐷𝐸𝑖𝑗,𝑡)+𝛽4ln(𝐺𝐷𝑃𝑃𝐶𝑖𝑡) +𝛽5ln(𝐺𝐷𝑃𝑃𝐶𝑗𝑡) +𝛽6ln(𝐺𝐷𝑃𝑔𝑗𝑡) +𝛽7ln(𝐷𝐼𝑆𝑖𝑗)+𝛽8ln(𝐺𝐶𝑂𝑁𝑗𝑡)+𝛽9ln(𝑃𝑂𝑃𝑗𝑡)+ 𝛽10ln(𝐹𝑅𝐸𝐸𝑗𝑡)+𝛽11(𝐶𝑂𝐿𝑂𝑁𝑌𝑖𝑗) +𝛽12(𝐶𝑂𝑀𝐿𝐴𝑁𝑖𝑗) + 𝜇𝑖 + 𝛾𝑗+𝜆𝑡+𝜀𝑖𝑗,𝑡 𝑓𝑜𝑟𝑖,𝑗= 1,2,….,𝑁;𝑡 = 1,2,….,𝑇

Definitions and Construction of Variables

Variable Definition and Construction SourceDevelopment 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

GDP growth rate Growth rate of GDP (annual %) WDI, 2013

Variable Definition and Construction SourceDistance 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 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 log-linearization 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

(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 IVlog(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 EstimationIndependentVariables

(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

Crowding-in Effects of Humanitarian Aid on Development Aid Estimated through Different Approaches

POLS FE RE HT RE with IV

FE with IV

NOSA with IV

SGMM

0.0373*** (6.879)

0.0179** (2.263)

0.0365*** (5.942)

0.0160** (2.108)

0.145*** (2.891)

0.179*** (3.065)

0.146*** (2.897)

0.0638*** (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.

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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.

Thanks