






































DEM_2015_89to109


 

© 2015 Nicolaus Copernicus University. All rights reserved.  
http://www.dem.umk.pl/dem 

D Y N A M I C  E C O N O M E T R I C  M O D E L S  
DOI: http://dx.doi.org/10.12775/DEM.2015.005  Vol. 15 (2015) 89−109 

Submitted September 29, 2015  ISSN (online) 2450-7067 
Accepted December 15, 2015 ISSN (print) 1234-3862 

Katarzyna Andrzejczak, Agata Kliber*  
 

The Model of French Development Assistance  
– Who Gets the Help?∗∗ 

A b s t r a c t. Development cooperation is an important element of international relations 
because it influences the power balance between major players on the world markets and in 
the political debate. The aim of the article was to analyze the French development assistance 
model based upon the amount of help sent to Africa over the period 2001–2012. The motiva-
tion of donor country is a crucial factor of development assistance, which influence not only 
the relations between donors and recipients, but also the effectiveness of aid. We estimated 
a series of dynamic panel models to assess whether the poverty-related factors play a domi-
nant role in the distribution of help. On the contrary, we found that the most important varia-
bles appeared to be the political and economic dependencies, among others: colonial history 
and oil/gas reserves.  

K e y w o r d s: development assistance; bilateral aid; development cooperation; developing 
country; France, dynamic panel model;   

J E L Classification: F53, F54, I32, O15. 

Introduction  

 The role and effectiveness of development aid have been repeatedly 
questioned ever since the system was established. The growing income dis-

                                                 
* Correspondence to: Katarzyna Andrzejczak, Poznan University of Economics e-mail: 

katarzyna.andrzejczak@ue.poznan.pl, Agata Kliber, Poznan University of Economics, e-mail: 
agata.kliber@ue.poznan.pl. 

∗∗ This work was supported by the Polish Ministry of Science and Higher Education 
through the Project SONATA: 2013//09/D/HS4/01849 (Mechanizmy wykorzystania 
technologii w Afryce Subsaharyjskiej).  



Katarzyna Andrzejczak, Agata Kliber 

DYNAMIC ECONOMETRIC MODELS 15 (2015) 89–109 

90 

parities between the North and the South, energy crisis in 1970s, debt crisis 
and structural adjustment in the late 1980s, numerous political and military 
conflicts in the 1990s, the number of people living below the poverty line, as 
well as interference in political situation in beneficiary countries are only 
a few examples of system dysfunction, repeatedly analyzed in the develop-
ment literature (e.g. Chenery, Carter, 1973; Bonne, 1996; Kosack, 2003; 
Amprou et al., 2007; Easterly, Pfutze 2007; Doucouliagos, Paldam, 2008; 
Roodman, 2008). At the same time, the economic and social context, as well 
as the main character of development cooperation changed over the years. 
The critics of aid and donors as well as the appearance of new donors on the 
scene lead to official redefinition of development cooperation in year 2000 
(United Nations Millennium Development Goals Declaration). The malfunc-
tioning of the development cooperation system has been officially recog-
nized and the aid has been declared to serve development issues, not the 
donors’ interests. In the meantime, the emergence and spreading of global 
communication means and channels allowed more open debate on migra-
tions, detriment of environment, health and natural disasters as well as ter-
rorism threats issues. This increased public opinion’s role in evaluation of 
the Northern countries approach towards less developed regions. As a con-
sequence, the role of grass root level of cooperation has been noticed and its 
importance raised in the development agenda. A philosophy to direct the 
support towards local communities in need with the omission of state admin-
istration often affected by corruption and ineffectiveness (Tanburn, 2008) 
has been widely recognized 
 The landscape of development cooperation has undergone major changes 
in the last decade especially due to the changes in the “donors’ club”. This 
means that the donors represent very different (not only political) contradic-
tory interests and approaches, and that they start to “compete” as donors. 
The appearance of the “generous” Nordic states and multilat-eral organiza-
tions, such as European Union, then the so called South countries, compelled 
the “old donors” to revise their international policy in development coopera-
tion (McEwan, Mawdsley, 2012). The appearance of new actors in the  
system was a main driving force of its evolution. The architecture of aid 
relations known in 20th century and based on Organization for Economic 
Cooperation and Development Assistance Committee (hereafter DAC), 
World Bank and International Monetary Fund is therefore being replaced by 
a more complex system of actors and approaches (Gore, 2013). The appear-
ance of emerging economies and private actors and organizations has 
changed the international scene and requires rethinking of the traditional 
development assistance schemes (Eyben, 2012). Main aid donors are no 



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91

longer only the Triada countries. Old donors, which placed the themes of 
democracy and the market economy as pivotal for aid policy agenda are now 
only a group in a more complex donor group (Kim, Lightfoot, 2011). The 
DAC-ability concept, which embraced the philosophy of aid giving and the 
very definition of what aid is (Official Development Aid term), is now chal-
lenged by the non-DAC donors (NDD). There is an increasing volume of 
development aid provided by the emerging economies. This aid influences 
international relations between states and redesigns the cooperation scene, 
influences and interests. Among the symbolic signs of change, the Fourth 
High Level Forum on Aid Effectiveness was organized at Busan in South 
Korea. 
 The changes in aid system are undeniable, however, apart from the opti-
mistic declarations, it is debatable to say that the system has evolved for the 
benefit of aid recipients. It is therefore doubtful that the donors stopped fo-
cusing on realization of their external policy goals in the scope of develop-
ment cooperation. Because of this, the dilemmas that undermine the very 
concept of development cooperation need to be addressed. The critics of aid 
donors persist. The ineffectiveness problem remains unresolved, as the Paris 
Declaration agenda of 2005 does not seem to be fulfilled. The relations be-
tween development agents tend to rely on one-sided domination, not partner-
ship. Rather than development aid creating growth, Doucouliagos and 
Paldam (2013), suggest it is possible that economic growth itself may influ-
ence donor aid allocation decisions. Since donor dysfunctions burden aid 
efficiency (Simplice, 2014), in the analysis of aid effects in recipient coun-
try, the issue of donors motivation in decision making process is crucial. 
Development assistance system is given, so the research should concentrate 
on finding the means to make it more effective and enhance countries’ ca-
pacities to develop. For these effects to appear, we assumed that they are 
more probable, once the aid is provided with such an aim.  
 The goal of this paper is, therefore, to analyze the bilateral development 
cooperation in the context of the donor motivation to provide assistance. We 
chose France as a model country representing traditional donors, since it has 
the characteristics of traditional donors. However, we are aware that the 
other traditional countries need to be tested for compliance with the model in 
further research. We concentrate on the aid for African countries. First, we 
discuss current literature on development assistance cooperation and next we 
introduce the traditional model of aid, based on qualitative analysis of coop-
eration dimensions. Methods of comparative and system analysis were ap-
plied in order to elaborate theoretical foundations for the models of coopera-
tion. We used the system-GMM approach for dynamic panel models to veri-



Katarzyna Andrzejczak, Agata Kliber 

DYNAMIC ECONOMETRIC MODELS 15 (2015) 89–109 

92 

fy the motivation of the donor in the aid distribution over the period 2001– 
–2012. It appeared that the poverty-related factors do not play any signifi-
cant role in the aid distribution, but rather the political and economic ones. 
The robustness of the results was confirmed by the non-linear correlation test 
(Kendall’s tau). In the last section we discuss the implication of the results. 

1. Traditional Development Aid Donors 

 Development cooperation system is a post-Second World War phenom-
enon, which was initiated by the USA as a response to the economic situa-
tion and Cold War in Europe (overseas development assistance). It’s further 
evolution was a consequence of the decolonization process and the inde-
pendence of African states (Williams, 2014). In the OECD terminology, the 
aid is associated with Official Development Assistance (ODA) public flows 
to beneficiaries placed on the DAC (Development Assistance Committee) 
List of ODA Recipients and to multilateral development institutions. These 
transfers are concessional in character – they convey a grant element of at 
least 25 per cent (calculated at a rate of discount of 10 percent per annum) 
and are administered with the promotion of the economic development and 
the welfare of developing countries as the main objective (OECD, 2014). 
That leaves out a great deal of development initiatives by private entities 
(enterprises, NGOs) but also public actors (non-ODA initiatives, local com-
munities cooperation).  

Moreover, currently some major donors, such as China, do not report 
their flows to OECD at all. The character of these flows is often unknown, 
so there is no possibility to conclude if they meet the requirements of ODA 
or not. Because of that, the term development cooperation should not be 
limited purely to ODA transfers recognized by the DAC anymore. Develop-
ment aid gains a wider meaning, which include ODA and other development 
purposes transfers, not reported to DAC. In a broad sense, aid is neither lim-
ited to the type of transfer nor to the type of agents in the relation. However, 
it still does not include some “foreign aid” elements, such as military sup-
port. In this article we concentrate on development aid, treated as an instru-
ment of a state foreign policy towards less developed countries. 

The broadening range of donor countries is a consequence of major 
changes in World economy. While the group of aid recipients is shrinking, 
the group of donors grows (the good news about the aid system is just about 
it). The enlarged donor club is currently an amalgam of different countries. 
Verifying whether their aid is instrumentally subordinated to the long and 
short term strategic goals of foreign policy is an important aim. Analysis of 



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DYNAMIC ECONOMETRIC MODELS 15 (2015) 89–109 

93

donors motivation requires more than dividing them into DAC and NDD 
groups, because there are differences between the donors in both of the 
groups. The division logic may be based on the geography, their public will-
ingness to adhere to DAC standards, level of economic development, politi-
cal regime, and others. There are  OECD countries that are not members of 
DAC–Mexico, Turkey and several European countries; new EU Members; 
Middle East and Organization of the Petroleum Exporting Countries, and 
non-OECD donors from neither of these groups: Brazil, China, India and 
Russia (Kim, Lightfoot, 2011). In the DAC there are traditional donors, 
Scandinavian states (generous), new-DAC members, including new EU 
Members. Such classifications are not exhaustive, but sufficient to under-
stand the complexity of interests, which may be related with development 
cooperation. All these groups are specific in character and realize different 
development aid policy.  

In this paper we concentrate on the practice of traditional DAC donors, 
based on French example. We are interested to see, weather the emergence 
of new donors affected their actions or only the declarations. We suspect, 
that as Cumming and Chaffer wrote: “European member states are driven 
more by their desire to enhance their own relative power within the interna-
tional system than by any overriding need to help African countries”  
(2011: 212).  

Traditional DAC donors based their foreign aid policy mainly on bilat-
eral relations. Major contributors such as USA, France and UK were there-
fore criticized for supporting dictators in favor of their geopolitical goals in 
the bipolarized reality of the second half of XXth century (Williams, 2014). 
Today, their image of Cold War players is still in force, but the war against 
terrorism and the peace maintenance in strategic regions of the World are of 
increased importance (Chou, 2012). The altruistic motivation of traditional 
donors involvement in any international security issue is questioned, as well 
as their true willingness to decrease poverty. 

Traditional donors approach has been first contested by the aid agenda of 
Scandinavian countries and multilateral organizations. Generally, Sweden 
and other Nordic states are considered not only most generous donors 
(Barczak, 2008) but also donors more focused on the idea of effective use of 
help, on building a civil society in developing countries, and on directing 
their assistance towards realization of Millennium Development Goals 
(Thiele et al., 2007). However, even the Nordic states are claimed to enhance 
their relative power and punch above their weight in international donor 
circles (Cumming, Chaffer, 2011). Multiparty organizations, on the other 
hand, are theoretically devoid of egoistic national interests and oriented to-



Katarzyna Andrzejczak, Agata Kliber 

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94 

wards democracy promotion and human rights protection in recipient states 
(Der-Chin, 2003). Their policy stance was supposed to reflect the coherent 
interests of developed and developing countries. Because of this, the in-
creased share of multilateral disbursements in the total donors’ aid volume 
was considered as higher aid quality. In reality, multilateral aid is biased for 
the Western World with the omission of national labels (see: Charbonneau, 
2008). Ohler and Nunnenkamp (2014) found that the multilateral institutions 
do not take regional needs into account while directing aid, instead favorit-
ism plays an important role for location choices. Traditional donors are in-
fluential in the international organizations and may profit from their activi-
ties indirectly. 

Therefore, the emergence of South-South development cooperation has 
influenced the position of traditional donors in a much serious way, than the 
recommendation to increase the multiparty aid element. Some research val-
ues South-originated aid as multidimensional and encompassing the intrinsic 
and non‐economic roles of development and hence very practical for African 
economies (Babaci‐Wilhite et al., 2013). Realization of sustainable devel-
opment goals investments in joint investment promotion mechanisms, joint 
programs for absorptive capacity and joint public-private partnership models 
are said to be more likely to happen in the scope of regional and South-South 
cooperation (UN WIR, 2014). However, the Chinese development coopera-
tion practice is in many ways similar to French or American model 
(Chaponniere et al., 2009). The major accusations towards traditional donors 
are their limited involvement in cooperation and self-oriented approach – 
despite helping to close savings gap, based on the neo-classical theory, aid 
serves donors’ interests (Page, te Velde, 2004; Cumming, Chaffer, 2011). 
However, for China and India energy, land and raw materials imports from 
Africa are equally important (Bearce et al., 2009; Walker, 2008, p. 21). What 
is especially interesting, is that the appearance of new donors, which negoti-
ate with African countries without the colonial history burden, and their 
increased presence in the region, influenced the need of a change in the tra-
ditional donors’ policies. 

Both individually and as a member of organizations, France has always 
played leading role in development cooperation system. In the past France 
was realizing its neo-colonial Françafrique strategy, partly created by Jaques 
Foccart and supported by the “cellule Africaine” in Presidential Palace, who-
ever was its resident since Charles de Gaulle. Historically France kept close 
relations with the countries of the so called champ and even closer with the 
group of pre carré, especially in Africa. The strategy embraced economic, 
political and diplomatic goals (Fuchs, 1993). For Africa – or more precisely 



The Model of French Development Assistance – Who Gets the Help? 

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95

– for African leaders these relations have been a source of material, political 
and diplomatic power, while for France – more literally of resources and 
comparative advantage in international relations (Lancaster, 1999). France 
was more direct in their policy goals realization than the United Kingdom, 
for which African policy was a source of relative power – soft influence. 
French were less liberal than the British and took a more interventionist 
stance in African relations (Cumming, Chaffer, 2011). The element shared 
by France and UK was the establishment of language communities, which 
were enforcing the relations and creating another dimension for a closer link 
(Hugon, 2008).  
 Today, according to the French development policy, main components of 
development agenda: economy, society and environment are subordinated to 
fighting with poverty and sustainable development promotion. Francphone 
terrirtories are the main area of development aid focus. French cooperation 
policy aims to address four mutually-supportive issues, promoting peace, 
stability, human rights and gender equality (1); Equity, social justice and 
human development (2); sustainable, job-rich economic development (3); 
protecting the environment and global public goods (4). Strategic partners of 
French development cooperation are: Benin, Burkina Faso, Burundi, Djibou-
ti, Comoros, Ghana, Guinea, Madagascar, Mali, Mauretania, Niger, 
Centralafrican Republic, Democratic Republic of Congo, Chad, Togo, Sene-
gal (MAE, 2015). Among them only two not being a former French colony, 
both rich in natural resources. Such a selection of partners indicates that the 
realization of national interests can actually remain a major issue for the 
development policy. In the next sections, the analysis of French practice will 
be studied in order to evaluate the motivation of France as a traditional aid 
donor.  

2. Methodology 

Panel data modeling is a statistical methodology allowing to use infor-
mation included both in time and cross-sectional dimension. The regression 
is therefore run on the two (or more) dimensions. Depending on the assump-
tions made about the error term, one can talk about fixed- or random-effect 
models (see e.g. Hsiao, 2014; Greene, 2011). Let us consider a panel model 
of the following form: 

,,,, tititi uy += βX   (1) 



Katarzyna Andrzejczak, Agata Kliber 

DYNAMIC ECONOMETRIC MODELS 15 (2015) 89–109 

96 

where ity  is the dependent variable observed at time t for the i-th cross-

sectional unit, while ti ,X – the matrix of explanatory variables. Depending 

on the error decomposition we receive different model specification. For 
instance: 

,itiitit uy ++= ηβX                                                                           (2) 

is a “fixed-effect” panel model, that can be estimated e.g. through least-
square dummy variable (LSDV) estimator. Such specification is justified 
when we assume that apart from the time-varying explanatory variables 
there are also time-invariant individual effects that influence the dependent 
variable.  
Another possible specification is the random-effect one: 

.itiitit uy +++= νηβX   (3) 

In this formulation iν  is a group-specific random element similar to itu , 
except that for each group there is but a single draw that enters the regres-
sion identically in each period (Greene, 2011). If a lagged dependent varia-
ble is included in the model, we talk about the dynamic panel data models. 

In order to utilize all available information: both changes of variables in 
time, as well as across countries, we composed a panel of the data, including 
12 time periods (2001–2012) and 53 cross-section units (53 countries). Since 
the number of  time periods in our model was relatively small (T=12) com-
pared to the number of cross-sectional units (N=53), we used the system-
GMM approach for dynamic panel models. Let us consider a dynamic panel 
model of the form: 

tiitititi uηyy ,,1,, +++= − βXα ,  (4) 

where tiy ,  denotes amount of financial help sent from donor (France) to the 

i- th African country at time t. In our case t = 1,…,12, while i= 1,…,53. β is 
a 1×k vector of the coefficients, α  is the vector of autoregressive coeffi-
cients, ti ,X  is a  k ×1 vector of explanatory variables observed for country 

i at time t, iη  are so called individual effects (time-invariant), while itu  – the 
disturbance specific for country i at time t. The model can be estimated using 
the so called “difference estimator”. The main idea of the approach is to first 
difference the data. Then, one obtains: 

titititititi ,,,,1,, uγWuXβyαy ∆+=∆+∆+∆=∆ − .    (5) 



The Model of French Development Assistance – Who Gets the Help? 

DYNAMIC ECONOMETRIC MODELS 15 (2015) 89–109 

97

 The error term of the equation (2) is by definition autocorrelated and also 
correlated with the lagged dependent variable. All values of ktiy −,  with k>1 

can be used as instruments for 1, −∆ tiy . One-step estimator of equation (5) 

amounts to computing (Cottrel, Lucchetti, 2014): 

















∆
































= ∑∑∑∑

==

−

==

N

i
iiN

N

i
ii

N

i
iiN

N

i
ii

1

'

1

'

1

1

'

1

'ˆ yZAZWWZAZWγ , (6) 

where: 
[ ]

.

,

0...000

..................

0...0

0...00

,
...

...

,,...,

1

1

'

,

4,2,1,

3,1,

,3,

1,2,

,3,

−

=

−









=





















∆

∆
∆

=










∆∆
∆∆

=

∆∆=∆

∑
N

i
iiN

Ti

iii

ii

i

Tii

Tii
i

Tiii

x

xyy

xy

xx

yy

yyy

HZZA

Z

W

 

 Once the 1-step estimator is computed, the 2-step estimated are obtained 
through replacing the matrix H with the sample covariance matrix of the 
estimated residuals. The 2-step estimator is consistent and asymptotically 
efficient.  
 In our paper we used the so-called “system” estimator that complements 
the differenced data with data in levels, so the lagged differences are used as 
instruments (see: Blundell and Bond, 1998). The key equation of the system 
estimator is as follows (Cottrel, Lucchetti, 2014): 

,~~~~~~~~~

1

'

1

'

1

1

'

1

'























∆











































= ∑∑∑∑

==

−

==

N

i
iiN

N

i
ii

N

i
iiN

N

i
ii yZAZWWZAZWγ  (7) 

  



Katarzyna Andrzejczak, Agata Kliber 

DYNAMIC ECONOMETRIC MODELS 15 (2015) 89–109 

98 

where: 
[ ]

.~~

,

...00...000

...........................

0...00000

...........................

0...0...000

...........................

0000...0

0...00...00

~

,
......

......~

,......~

1

1

*'

,1,

3,2,

,2,

4,2,1,

3,1,

,3,,3,

1,2,1,2,

,3,,3,

−

=

−

−

−−








=

































∆

∆

∆

∆
∆

=










∆
∆

=

∆∆=∆

∑
N

i
iiN

TiTi

ii

TiTi

iii

ii

i

TiiTii

TiiTii
i

Tiitiii

xy

xy

xy

xyy

xy

xxxx

yyyy

yyyy

ZHZA

Z

W

y

 

The choice of matrix *H  is not trivial. The details are presented for instance 
in Roodman (2009) or Hsiao (2014). See also: Dańska-Borsiak (2009). For 
more detailed description of panel data concept and modelling we refer the 
Readers to e.g. Hsiao (2014),  Longhi and Nandi (2014) or Gruszczyński et 
al. (2012). 

3. French ODA – the Data 

 The amount of French ODA sent to Africa is very heterogeneous and it 
seems to depend both on the period and on the receiver country. We observe 
an enormous growth over the period 2004–2006 of the amount of help re-
ceived by Nigeria, as well as high transfers to the Democratic Republic of 
Congo in 2003, to Congo in 2005 and 2010 and periodical higher transfers to 
Ivory Coast. 
 In order to select a relatively homogeneous group, we excluded from the 
analysis those countries, where the high jumps in data were present (more 
precisely: we deleted from the sample those countries, where the standard 
deviation value was higher than the mean value). We filtered out: Congo, the 
Democratic Republic of Congo, Cote d’Ivore, Liberia, Mozambique, Nige-
ria, Seychelles, Sierra Leone, the United Republic of Tanzania and Zambia.  



The Model of French Development Assistance – Who Gets the Help? 

DYNAMIC ECONOMETRIC MODELS 15 (2015) 89–109 

99

 In Table 1 We present the descriptive statistics of ODA sent from France 
to Africa over the period 2001–2012 in the filtered group of countries. The 
mean value of the help in US 2012 mln dollars amounted to 54.168, but the 
median only to 20.159. We divided the full sample into two subsamples: 
COLONY and NON-COLONY countries as well as OIL/GAS vs. NON-
OIL/GAS ones. The differences in the amount of help received in each group 
is striking. In the case of the previous colonies the mean value of help 
amounted to 104.380 mln USD, while in the case of the non-colonies – only 
to 9.868. In the case of the countries possessing oil/gas reserves the amount 
of ODA received  equaled 73.246, while in the case of the remaining ones – 
36.247. The Cochran-Cox test for equality of mean values rejected the null 
hypothesis in the case of both pairs. Therefore, we can say that the amount 
of financial help sent to African countries is larger in the case of the ones 
that possess natural resources. The analysis of the “within” and “between” 
standard deviations reveals that the non-colony as well as the NON-
OIL/GAS group are more homogeneous than the remaining ones. In all sub-
samples the data is right-skewed. 

Table 1. Descriptive statistics of financial help from France to the African countries 

  FULL SAMPLE COLONY NON-COLONY OIL/GAS NON-OIL/GAS 

Mean 54.168 104.380 9.868 73.246 36.247 
Median 20.159 74.089 5.779 44.145 13.754 

Minimum 0.025 1.350 0.025 0.580 0.025 
Maximum 811.650 811.650 121.390 811.650 310.700 
std.dev. 84.443 101.430 13.032 101.650 59.037 

vol.factor 1.559 0.972 1.321 1.388 1.629 
skewness 3.780 3.167 4.451 3.566 2.624 
Kurtosis 22.150 15.013 29.524 18.033 7.094 

5% percentile 0.670 14.011 0.469 2.195 0.445 
95% percentile 210.170 268.570 27.311 232.060 189.310 

Q3–Q1 66.111 88.910 12.570 93.550 38.716 
missing obs. 4 0 4 4 0 

obs. No. 512 240 272 248 264 
within s.d. 49.744 71.795 10.320 68.146 20.964 

between s.d. 70.302 76.229 8.596 79.419 56.705 

3.1. Explanatory Variables 

 In order to model the amount of financial help received from France, we 
chose the following set of explanatory variables: 

− MIGRATION – number of immigrants in a given year; 
− FDI – Foreign Direct Investment of the donor country in the receiver 

country; 



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100 

− IMPORT_FUEL – value of import of fuels, lubricates and related prod-
ucts (import from Africa); 

− EXPORT_FUEL – value of export of fuels, lubricates and related prod-
ucts (export from France/Great Britain); 

− IMPORT_CRUDE – value of import of crude materials, inedible except 
fuel; 

− EXPORT_CRUDE – value of export of crude materials, inedible except 
fuel; 

− IMPORT – total import from the receiver country; 
− EXPORT – value of the total export to the receiver country; 
− POLITICAL_STABILITY – index of political stability in the receiver 

country; 
− EXTERNAL_DEBT – value of the external debt of the receiver country 

(as % of GDP); 
− CORUPT_CONTROL – value of the index of corruption control; 
− GIRLS_OUT_OF_PS – number of girls out of primary school; 
− MORTALITY_RATE – infant mortality rate (deaths per 1000 live 

births); 
− LIFE_EXPECTANCY – expected length of life in the receiver country; 
− GDP_PER_CAPITA – value of the GDP per capita in the receiver coun-

try; 
− OIL – binary variable, taking one for the 12 African countries that have 

documented oil reserves: Algeria, Angola, Chad, Egypt, Equatorial 
Guinea, Gabon, Libya, ,Sudan1; 

− OIL_GAS – binary variable taking value 1 for the African countries that 
have documented gas and/or oil reserves, the gas-producers are: Angola, 
Benin, Cameroon, Chad, Equatorial Guinea, Ethiopia, Gabon, Ghana, 
Kenya, Liberia, Madagascar, Malawi, Mauritania, Namibia, Niger, Sen-
egal, Sudan, Uganda. 

The source of the data were the following databases: OECD, and AFMI (Af-
rican Financial Markets Initiative). The descriptive statistics of the explana-
tory variables are given in Table 8 in the Appendix. The values of import, 
export and GDP are given in 2012 US dollars. The dependent variable was 
ODA – the amount of help received from the donor. All the data were col-
lected for the time period from 2001 to 2012. The computations were per-
formed using GRETL (Cottrel, Lucchetti, 2014, Kufel, 2011). 

                                                 
1 We excluded South Sudan from the whole analysis due to the lack of data for most of 

the indicators. 



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4. Results 

 In Table 2 we present the results of the estimation of the model for the 
full sample. We observe that the amount of help sent from France to Africa 
depends on political factors and trade links. First of all, the higher the migra-
tion rate from a given country, the higher the amount of help. The amount of 
help sent seems to depend positively on the amount of export sent to the 
given country. This suggests that the trade partners receive higher support. 
Eventually, the historical colonies receive significantly more than the other 
countries.  

Table 2. Results of the dynamic panel model estimation for France – the full sample 

Variable Coefficient Std.error t statistics p-value 

ODA(–1) 0.318 0.070 4.572 0.000 
constant  −62.219 24.406  −2.549 0.011 

migration (–1) 2.972 0.920 3.231 0.001 

colony 38.331 9.309 4.118 0.000 

log(eksport)(–1) 3.970 1.448 2.742 0.006 

Note:  Sargant test statistics of overidentification amounted to 28.4 (p-value: 1), z-statistics for AR(1) 
test: –1.77 (p-value=0.07), for AR(2): –0.17 (p-value:0.87). Joint Wald test statistics: 168 (p-value:0). 
Standard error of residuals: 69.5. 

Table 3. Results of the dynamic panel model for France – the oil&gas countries 

Variable name Estimate std.error t-statistics p-value 

ODA(–1) 0.294 0.064 4.575 0.000 
Const −93.3005 47.477 −1.965 0.049 

Log(IMPORT) 5.424 2.583 2.100 0.036 
COLONY 78.742 18.543 4.247 0.000 

Note:  Sargant test statistics of overidentification amounted to 16.24 (p-value: 1.00), z-statistics for AR(1) 
test: –1.73 (p-value=0.08), for AR(2): –0.28 (p-value:0.77). Joint Wald test statistics: 84.27 (p-value:0). 
Standard error of residuals: 79.19. 

 In Table 3 we present the results obtained for the group of countries that 
have proven oil and gas reserves. The group is not homogeneous, as the de-
scriptive statistics in Table 1 show. The heterogeneity of the group can be 
explained by the political instability and internal conflicts, large debt for-
giveness events, and the delayed wealth effect. Moreover, the countries that 
exploit natural resources since many years are more wealthy than the coun-
tries in which the oil/gas reserves have been found only recently. Thus, the 
explanatory power of the model is very weak. However, it shows the main 
drivers for the help distribution. It seems that the factors that influence gen-
erosity is again the amount of import from France. Supporting outlets for 
French products is an important goal, which at the same time serves internal 



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policy, by the promotion of French industry and companies. Moreover, the 
countries that used to be French colonies receive on average 70 mln USD 
more than the other countries. Being a former colony increases the benefit of 
possession of natural resources. 
 In Table 4 we present the results obtained for the more homogenous 
group – the countries that do not possess natural resources. From Table 1 we 
know that the amount of help received by them is significantly lower than 
those of the countries that have oil and/or gas. We observe again that politi-
cal factors do play a role in generosity of the donor – the amount of ODA 
received grows together with the migration rate. The residuals from the 
model have the smallest standard deviation from all estimated ones – 29.51. 
Providing help to the countries of origin of diasporas living in France allows 
to realize domestic policy goals at the same time indeed.  

Table 4. Results of the dynamic panel model for France – the non-oil&gas countries 

Variable name Estimate std.error t-statistics p-value 

ODA(–1) 0.228 0.103 2.207 0.027 
Const 18.660 5.422 3.441 0.001 

Migration2 0.333 0.047 7.125 0.000 

Note:  Sargant test statistics of overidentification amounted to 21.41 (p-value: 1.00), z-statistics for AR(1) 
test: –1.82 (p-value=0.07), for AR(2): 1.34 (p-value:0.18). Joint Wald test statistics: 1749.29  
(p-value:<0.001). Standard error of residuals: 29.51. 

Next, we estimated the models for the groups: historical colonies and others. 
In Table 5 we present the results of the model for colonies. We again ob-
serve that the amount of help depends on political dependencies (migration) 
and trade (the more France imports from the donor, the more help is sent in 
return there). As the reason for the colonies was to ensure the access to cer-
tain resources, the same logic explains the development agenda.  

Table 5. Results of the dynamic panel model for France – the historical colony 
group 

Variable name Estimate std.error t-statistics p-value 

ODA(–1) 0.311 0.063 4.969 0.000 
Const −113.774 60.898  −1.868 0.062 

Migration(–1) 2.326 1.386 1.679 0.093 
Log(IMPORT)(–1) 9.452 3.772 2.506 0.012 

Note:  Sargant test statistics of overidentification amounted to 14.7 (p-value: 1.00), z-statistics for AR(1) 
test: –1.91(p-value=0.05), for AR(2): –0.12 (p-value:0.90). Joint Wald test statistics: 67.7 (p-value:0). 
Standard error of residuals: 82.36 

Eventually, in the case of the group of non-colonies we estimated the static 
fixed-effect model, since the data did not exhibit any autocorrelation. This 



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can suggest that French policy is not consequent in this group of countries. 
The LSDV R2 amounted to 0.47 (the “within” one – to 0.07). In Table 6 we 
present the fixed-effect model with time-effect. The time effect for the peri-
od 2009–2012 was insignificant, so we conclude that the average value of 
ODA received by the group was the same as in 2001 (in real value). Moreo-
ver – the amount of help received by the countries from the group depended 
on the recipient and the year (this model was the best one from all other es-
timated models, including those with additional explanatory variables). Each 
recipient received different and specific amount of help which varied in dif-
ferent years but did not depend on any other factors – neither political nor 
poverty-related ones. We observe that in 2004 and 2006 the average amount 
of ODA was higher, while the lowest – in 2002. All the values presented in 
the table should be interpreted as the surplus in comparison with 2001. 

Table 6. Results of the static fixed-effect panel model with time effect for France –
the non-colony group. 

Variable name Estimate std.error t-statistics p-value 

const 6.457 1.179 5.476 0.000 
dt_ 2002 1.444 0.462 3.124 0.002 
dt_2003 2.286 0.845 2.706 0.007 
dt_2004 7.234 2.845 2.543 0.012 
dt_2005 6.921 4.040 1.713 0.088 
dt_2006 5.308 2.769 1.917 0.057 
dt_2007 5.673 1.733 3.273 0.001 
dt_2008 4.311 1.423 3.029 0.003 
dt_2009 6.400 4.787 1.337 0.183 
dt_2010 0.672 0.947 0.710 0.478 
dt_2011 0.884 0.919 0.962 0.337 
dt_2012 −0.320 0.909 −0.352 0.725 

Note:  LSDV R2 amounted to 0.47, while the within R2 to 0.08. LSDV F(33,238)=6.43(p-value= 0), F-test 
statistics for named regressors: F(11,238) =1.8 (p-value=0.05).Durbin-Watson statistics to 1.77. The null 
hypothesis for common constant in groups was rejected at p-value 4.22e-20. The p-value of the Wald test 
for common significancy of dummy time effects was equal to 1.75e-13. Standard error of residuals 
amounted to 10.14. 

4.1 Robustness Check – Kendall tau 

 In Table 7 we present the Kendall τ computed for the value of financial 
help from France and the remaining variables in the set. The τ is the rank-
based correlation, indicating the non-linear dependencies in the data. The 
data in table is sorted according to the decreasing value of τ. According to 
this simple analysis, the political factors influence the amount of help sent 
the most. France seems to support mainly its own historical colonies and 
trade partners or – more precisely – the countries of the highest value of 



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import from France. A very important factor seems to be also the migration 
number, as well as the fact of being a historical colony of France. The fact of 
possessing oil reserves is also significant, but of lesser importance. France is 
using the nuclear power to quite a large extend, so this may be one of the 
explanations. From the poverty-related factors only the illiteracy is signifi-
cantly related to the amount of ODA received.  Political stability factor is 
also significantly related to the amount of ODA, but the relationship is in-
verse (the less stable the country, the higher amount of help should it re-
ceive). This is neither coherent with the Burnside and Dolar paradigm of aid 
effectiveness in sound policy environment nor with the general conditionali-
ty of help based on democracy promoted by the DAC. The conclusions from 
the panel models seem to be robust. 

Table 7. Kendall-tau for financial help and other considered variables 

 Correlation between ODA and: Tau p-value 

colony 0.576 <0.001 
Migration 0.551 0 

export_crude 0.531 0 
export 0.529 0 

export_fuel 0.362 0 
import_crude 0.361 0 

import 0.323 0 
FDI 0.328 0 

oil/gas 0.238 0 
girls out of primary school 0.272 0 

life expectancy 0.073 0.006 
GDP_per_capita 0.046 0.086 

mortality rate 0.017 0.517 
corruption control –0.036 0.172 

import_fuel –0.05 0.246 
external debt –0.03 0.206 

political stability –0.116 0 

Note:  Insignificant variables (p-value higher than 0.05) are put in italics. 

5. Conclusions 

 Most of development aid research concentrate on the aid effectiveness in 
the context of recipient performance. However, the motives of donors are 
also important. The non-profit and development-driven character of foreign 
aid flows are questionable. From the early stages of development coopera-
tion former colonial powers enjoyed the access to the commodities from 
newly created states. The economic ties established during colonialism were 
not sealed off. Despite the reluctance towards former powers, the reconstruc-
tion of the economy towards independence was a more complicated process, 



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than assumed. Moreover, the increasing political instability in many coun-
tries and numerous military conflicts, made many African states fragile and 
explained traditional aid donors presence in the politics.  
 Establishment of aid structures in African states, allowed donors’ gov-
ernments to insert national business (especially of the key branches such as 
telecommunication, energy, minig etc.) on the key local markets. Companies 
without government support or local experience wouldn’t easily decide to 
invest due to high risks. The countries which were politically involved with 
African leaders, either for the past reasons (France) or the new ones (USA), 
were automatically predestinated to gain access to the resources of the re-
gion. The traditional development cooperation model, represented here by 
France, is characterized by longtime relationship at relatively high intensity. 
The evolution of development assistance is the evolution of traditional mod-
el policy declaration – from neocolonial relations to development agenda 
and sustainable development. This policy means both: keep using aid as an 
instrument of their foreign policy (votes in UN, peacekeeping, access to 
natural resources), and engage in the initiatives of Millennium Development 
Goals and the democracy building. They claim to be driven by moral obliga-
tion for the past wrongdoing, but at the same time are perceived to use their 
superior position in negotiations.   
 Our study suggest, that the traditional approach to development coopera-
tion is based on donors’ interests in 21st century, just as it was in the prece-
dent one. The analysis of recipient structure shows that the aid volume is 
correlated positively with oil reserves, both export and import and with mi-
gration. To a lesser extent literacy rate and mortality rate were depicted as 
important. The motivation of traditional donor is therefore self-oriented. 
Development cooperation remains instrumental to realization of foreign and 
internal policy goals of donors. Governments of donor countries, responding 
to their voters, tend to subordinate development cooperation to increase real-
ization of national and regional interests. Securing the access to markets and 
low cost imports supports both donor country’s entrepreneurs going abroad 
as the consumers inside the country. The orientation towards “migration 
countries” may imply the will to keep stable relations, possibly i.a. for the 
security reasons. Also, the importance of cultural relations for the develop-
ment cooperation, especially because of the use of common language is rec-
ognized. We conclude, that despite the official redefinition of the develop-
ment cooperation goals, the system tends to serve the traditional donor’s 
agenda in the first place, and next the recipient’s needs are considered. Prac-
tically, for the African states, it means that a stronger negotiation approach 
with the donors is needed. At the same time the improvement of internal 



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transparency to ensure local politicians accountability for the development 
cooperation agreements could positively influence the system functioning in 
the future. 

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Appendix 

Table 8. Descriptive statistics of the explanatory variables 

  Mean Median Min Max Std.dev. 
Within 

s.d. 
Between 

s.d. 

Migration 1.726 0.207 0 31.113 4.584 0.719 4.571 
FDI 91.711 6.276 –7532.6 12918 789.420 827.64 144.22 

Import  5.12e+08 3.55e+07 1841.5 7.36e+09 1.17e+09 5.15e+08 1.06e+09 
Import fuel 7.01e+08 9.60e+07 19 7.05e+09 1.33e+09 7.70e+08 9.90e+08 

Import crude 2.06e+07 4.03e+06 3 2.68e+08 3.75e+07 1.59e+07 3.31e+07 
Export 5.51e+08 1.18e+08 37316 8.42e+09 1.21e+09 3.78e+08 1.16e+09 

Export fuel 3.29e+07 1.75e+06 185 8.61E+08 9.72E+07 7.4e+07 6.43e+07 
Export crude 6.16e+06 8.40e+05 1157.9 2.54E+08 2.15E+07 1.05e+07 1.83e+07 

Political stability –0.539 –0.36 –3.3 1.190 0.93 0.355 0.873 
Corruption control –0.607 –0.67 –1.92 1.260 0.58 0.193 0.555 

Girls out of PS 3.47e+05 1.57e+05 52 5.07e+06 7.32e+05 1.34e+05 7.63e+05 
Life expectancy 56.282 55.708 12.3 114.4 13.203 11.612 7.170 

Mortality rate 64.701 63.95 12.2 138.5 27.265 9 26.092 
GDP per capita 2024.2 729.88 110.5 24355 3153.8 1434.8 2863.8 
External debt 65.195 46.575 0.65 881.95 85.792 58.625 65.423 

Model francuskiej pomocy rozwojowej – kto dostaje pieniądze? 

Z a r y s  t r e ś c i. Artykuł przedstawia analizę dystrybucji francuskiej pomocy rozwojowej 
wśród krajów afrykańskich, w latach 2001–2012. Na podstawie wyników uzyskanych przy 
pomocy dynamicznych modeli panelowych autorki stwierdzają, że pomoc nie była kierowana 
do krajów najbardziej potrzebujących, ale do tych, z którymi łączą Francję więzi polityczne 
i gospodarcze (m.in. byłe kolonie oraz kraje zasobne w ropę i gaz).  

S ł o w a  k l u c z o w e: pomoc rozwojowa, Deklaracja Milenijna, ubóstwo, Francja, dyna-
miczny model panelowy  




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