Russian Journal of Agricultural and Socio-Economic Sciences, 2012, №4 (4) Апрель

Ф
ФЕЕДДЕЕРРААЛЛЬЬННААЯЯ ССЛЛУУЖ
ЖББАА ППО
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ФЕЕРРЕЕ ССВВЯЯЗЗИИ,, ИИННФ
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ТТЕЕХХННО
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РРО
ОССССИИЙЙССККИИЙЙ Ж
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ОЗЗЯЯЙЙССТТВВЕЕННННЫ
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ОЦЦИИААЛЛЬЬННО
О-
ЭЭККО
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МИИЧЧЕЕССККИИХХ ННААУУКК 
 

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СОДЕРЖАНИЕ
CONTENT 
 
 
 
Российский журнал

сельскохозяйственных и социальноэкономических наук 

Russian Journal 
of Agricultural and Socio-Economic 
Sciences 
 
выпуск 
4(4) 
issue 
апрель 2012
April 
 
 
 

М
М..ДД.. ТТааммббии 
The decomposition of poverty: 
a distributive approach  to living standards 

3 
M
M..DD.. TTaammbbii 
The decomposition of poverty: 
a distributive approach  to living standards 
 
 
 

М
М.. ААббддеелльь--ГГааннии 
Identifying opinion leaders using social network 
analysis, a study in an egyptian village 

12 
M
M.. AAbbddeell--GGhhaannyy 
Identifying opinion leaders using social network 
analysis, a study in an egyptian village 
 
 
 

НН..НН.. ЛЛииссююттччееннккоо,, АА..АА.. ППооллууххиинн 
Организационно-экономические основы 
энергосбережения в сельском хозяйстве 

20 
NN.. LLiissjjuuttcchheennkkoo,, AA.. PPoolluuhhiinn 
Organizational and economic bases of energy 
conservation in agriculture 
 
 
 

АА..ВВ.. ААллттууххоовв 
Экономический анализ функционирования 
рынка зерна Орловской области 

27 
AA.. AAllttuukkhhoovv 
Economic analysis of functioning of the Orel 
region grain market 
 
 
 

ВВ..ТТ.. ЛЛооббккоовв,, СС..АА.. ППллыыггуунн,,

НН..ИИ.. ААббааккууммоовв,, Ю
Ю..АА.. ББооббккоовваа 
Роль обработки почвы и применения 
гербицида «Тризлак» при выращивании 
озимой пшеницы на качество зерна 

32 
VV.. LLoobbkkoovv,, SS.. PPllyygguunn,,
NN.. AAbbaakkuummoovv,, YY.. BBoobbkkoovvaa 
Role of tillage and application of herbicide 
«Trizlak» for growing of winter wheat 
on grain quality 
 
 
 

ИИ..ВВ.. ЧЧееррввоонноовваа,, ВВ..СС.. ББууяярроовв 
Научно-практическое обоснование 
использования препарата «Экофильтрум» 
в бройлерном птицеводстве 

38 
II.. CChheerrvvoonnoovvaa ,, VV.. BBuuyyaarroovv 
Scientific-practical basis of preparation 
«Ekofiltrum»  in broiler production 

 

M.D. TAMBI, University of Dschang 

3 
 

THE DECOMPOSITION OF POVERTY: 
A DISTRIBUTIVE APPROACH TO LIVING STANDARDS 
 
Mbu Daniel Tambi, Department of Agricultural Economics 
 
University of Dschang, West Region – Cameroon, Central Africa 
E-mail: mbunicoda@yahoo.com 
 
Received April 19, 2012 
 

ABSTRACT 

This study attempts to carry out a comprehensive analysis of the evolution of poverty trends using national household consumption survey I and II collected in 1996 and 2001 respectively. The theoretical 
decomposition frameworks propelling the study are motivated mainly by the Shapley value while empirical estimates are obtained from DAD 4.4. From our findings, we observe that Rural forest and Rural highlands regions were hardest hit by poverty and inequality trends in Cameroon. The result 
shows that the within-regions effects were found to be more instrumental in accounting for changes in 
all the classes of poverty measures than the inter-sector population shift effects in the period under 
review. While the between-region effects were systematically contributing in alleviating poverty in the 
Rural forest and Rural highlands and at the same time aggravating poverty in Yaounde, Douala. 
Based on our result, we suggest that policies and strategies for reducing poverty/inequality should 
place particular emphasis on the countryside and on a region-by-region approach such as decentralization, increase provision of rural extension services (roads, electricity, markets, portable water). 
 
KEY WORDS 

Decomposition; Poverty; Distribution; Standards of living; Approach.  

The problem of poverty is a major concern 
for all governments and the struggle to alleviate 
poverty is leaving no government indifferent. 
Poverty and inequality are actually a world wide 
phenomenon which is spreading in rich and underdeveloped countries in different ways and is 
destined to worsen unless new approaches are 
developed and new scientific knowledge about its 
causes is discovered   (Townsend, 1993). In Cameroon, overall poverty deepened within the period 1984/1996 with rural poverty remaining 
more widespread, deeper and more severe than 
urban poverty (see Baye 2005a, Fambon et al, 
2004). Despite the improve macro economic situation, public education and health indicators have 
remained poor and Cameroon is still perceived as 
a very corrupt country on the basis of surveys 
undertaken in 1998 and 1999 by transparency 
international. 
The Poverty Reduction Strategy Paper 
PRSP, (2003) also confirms that, nearly four out 
of every ten Cameroonians in 2001 were living 
with an annual income below the poverty line of 
CFAF 232.547. This represents the estimated 
annual income necessary for an individual in 
Yaounde to buy a ‘minimal basket’ of essential 

food and non food items. In 2001, eight poor 
people out of ten were living in the country-side 
and the incidence of poverty there is more than 
double the incidence in the cities. More so, transparency international, (1998 and 1999) state that 
the index for the cost of living rose by 60% within 1998 and 1999, however, nominal wages remained unchanged. From December 1992 to December 1995, real wages of senior civil servant 
fell by 75/80 %, and this had a deleterious impact 
on civil servants motivation and fuelled corruption as well as poverty and inequality.  
As noted in GOC (2003), the increasing level of poverty in rural communities induced many 
young people to migrate to large towns where 
they expected to find better conditions. They 
ended up in a net work  of relatives and friends 
who initially supported them against the worst 
hardships; eventually some succeed in making 
ends meet, while others are exposed to unemployment or under-employment, crime and social- behaviour, which posed insecurity problems 
to both the authority and other  city dwellers. 
More generally, as argued by Baye and 
Fambon, (2002), the joint effects of the economic 
crises and structural adjustment programmes 

Russian Journal of Agricultural and Socio-Economic Sciences, No. 4 (4) / 2012 

4 
 

(SAPs) forced many Cameroonians to adopt coping devices such as moonlighting, seeking  for 
survival in the informal sector. Also, they engaged in occupational and geographical mobility, 
changing regional patterns of activities and productivities, and adopting ‘‘behavioural innovations’’ like corruption and other malpractices for 
survival. These adaptations are thought to have 
modified the pattern of welfare among households in the different regions and sectors of activities. 
According to Baye (2006b), the adverse International environment as   reflected in the overvaluation of the CFAF against the dollar and the 
sagging world market prices of commodity exports in the late 1980s and early 1990s, and its 
implications for government revenue, production, 
consumption and relative prices, led to 50% of 
devaluation of the CFAF in January 1994.  Being 
a centre-piece of adjustment, the devaluation was 
intended to perform two functions: (1) reduce 
expenditure on imports and (2) re-allocate resources away from non-tradable commodities 
with a view to propping up the global competitiveness of the economy subsequent to the 1994  
devaluation of the CFAF, Cameroon achieved 
macro-economic stability. Yet, rural incomes 
were slow to improve because much of the 
acreage under coffee and Cocoa had been abandoned, in addition to the typically low short run 
elasticities of supply of these commodities. 
As a subject of debate, many authors have 
approached the study on poverty change in the 
living standard of Cameroon. Among these, are 
pioneer authors such as Araar (2003), Baye 
(2005a), NIS (2002), Fambon et al (2004), Njinkeu et al (1997). However, very little is known 
about the exact contributions of intra and inter 
sectoral components to changes in aggregate poverty using the 1996 and 2001 household survey. 
Yet, such knowledge is required for public policy, especially in an era when poverty eradication 
is gaining prominence in the policy menu. 
The objective of this study include; I) to examine the evolution of poverty between 1996 and 
2001, 2) to assess the relative importance of the 
Within and Between sector effects to changes in 
aggregate poverty, 3) To derive policy implications on the basis of the analysis. 
The rest of this work is divided into four 
sectors: section II covers the theoretical framework, section three exposes the methodology, 
section four presents the results and discussions 
while section five submit the general conclusions 
of the study. 

THEORETICAL REVIEW 
 
The poverty measure used in this work, is 
that suggested by Foster, Greer and Thorbecke 
FGT, (1984) and reviewed by the World Bank 
(1990), Lipton and Ravallion, (1995) and Fields, 
(1997).These include the headcount index, the 
poverty gap and the squared poverty gap. FGT 
(1984) shows that these three poverty measures 
may all be calculated using the following formula: 

α

α
∑
=






−
=

M

i

i
z
y
z
N
P

1

1
, where
0
≥
α
, 

and yi is the average real spending of i household, 
z is the poverty line, N is the number of adult 
equivalent households, M is the number of poor 
adult equivalent households, α can be interpreted 
as a measure of poverty aversion or coefficient 
reflecting different degrees of importance, which 
a government might accord to the depth or severity of poverty. 
As reviewed in Baye (2005), Ravallion and 
Huppi (1991), we made use of the Pα class of 
poverty measures to identify the factors underlying the observed changes in aggregate poverty 
between two dates, t and t+n. This class of poverty measures is sub-group consistent and additively decomposable (FGT, 1984, Balisacan, 
1995; Foster and Shorrocks, 1991). The factors 
explored were the intra and inter subgroup contributions to any observed changes in poverty. If 
fk and Pαk represent the population share and 
poverty level of subgroup k∈K, the property of 
subgroup decomposability of the Pα class of poverty measures enables us to write the expression
∑
∈
=

K
k
t
k
t
k
t
P
f
P
,
,
,
α
α
. 

The aggregate change in poverty between 
period t and t+n yields: 
=
−
=
∆
+
t
P
P
P
n
t
,
,
α
α
α

[
]
∑
∈
+
+
−

K
k
t
k
t
k
n
t
k
n
t
k
P
f
P
f
,
,
,
,
α
α
 (1) 

The goal here is to account for the overall 
change in poverty, ∆Pα, in terms of changes in 
poverty within subgroups, ∆P αk = P αk,t+n – P 
αk,t, k ∈K, and the population shifts between  
subgroups, ∆fk = fk,t+n – fk,t, k ∈K. 
Ravallion and Huppi (1991) exploit the additive decomposability of the Pα class of poverty 
measures to throw light on the relative importance of changes within sectors versus changes 
between them, such as due to the between sector 

M.D. TAMBI, University of Dschang 

5 
 

population or work-force shifts.1 This decomposition of the aggregate poverty change is not exact because it requires an interaction term to establish its identity. Using the above notations, the 
Ravallion-Huppi decomposition of an aggregate 
change in poverty can be expressed as:  
 

(
)
t
k
K
k
t
k
n
t
k
n
t
f
P
P
t
P
P
P
,
,
,
,
,
∑
∈
+
+
−
=
−
=
∆
α
α
α
α
α
 

(Within sector effects) 

(
)
∑
∈
+ −
+

K
k
t
k
t
k
n
t
k
P
f
f
,
,
,
α
 

(Between sector population shift effects) 
+
(
)(
)
t
k
n
t
k
K
k
t
k
n
t
k
f
f
P
P
,
,
,
,
−
−
+
∈
+
∑
α
α
 

(Interaction effects) (2) 
 
The within-sector effects are simply the contribution of poverty changes within sectors, controlling for their base period population shares. 
The between-group population shift effects are 
the contribution of changes in base period poverty due to changes in the distribution of the population across sectors between the based and terminal periods. The residual or interaction effects 
arise from the possible correlation between population shifts and within sector changes in poverty. 
It has been suggested that the interaction 
term can be made to vanish by taking the average 
of the results got by using the initial and terminal 
periods as base periods. The problem with the 
averaging method is that it is not based on any 
theoretical underpinning. But this gap is filled 
when we appeal to the Shapley Value approach. 

 
METHODOLOGY AND DATA SETTING 
 
Methodology. 
With 
regards 
to 
Baye 
(2005b), the methodology proposed here, performs exact decomposition of changes in aggregate measure poverty into within and between 
sector components that hinge on Shapley Value.2 
An important issue in distributive analysis 
would be how to assign weights to the factors 
that contribute to an observed level or change in a 

                                           
1 As observed by Shorrock, (1999) and reviwed by Kaboré, (2002), 
standard decomposition techniques typically confront four major 
problems: (1) The contribution assigned to each specific factor does 
not always have an intuitive clear meaning, (2) Decomposition 
produced use only applicable to certain poverty and inequality indices; (3) The type of contributing factors considered are usually 
limited, (4) Above all, conventional decomposition methods lack a 
shared theoretical framework. 
2 The exposition of the Shapley Value Submitted here draws heavily on the Succinct Discussion in Baye (2006b, 2007). 

measure of living standards. For instance, the 
level and/or change of a distributive index between two dates may be attributable to factors 
such as within-sector and between-sector effects 
and analysts are interested in quantifying the relative importance of each component. There are 
different methods to perform the attribution, all 
of which must have to deal with the fact that the 
contribution of a factor depends on the presence 
of the other factors. This issue is similar to problems that arise in cooperative game theory, and 
recent literature in distributive analysis is proposing and applying an attribution according to the 
Shapley Value (see Shorrocks, 1999; Kabore, 
2002; Rongve, 1995; Chantreuil and Trannoy, 
1997; Baye, 2006b). We first appeal to cooperative game theory before applying the solution set 
to decomposed changes in poverty. 
A typical question to address is what each 
player might reasonably expect to receive (or 
pay) as his or her share of the reward (or cost) in 
a cooperative game. The solution concept widely 
used in the theory of cooperative games to answer such questions is the Shapley Value (see 
Owen, 1977, Moulin 1988), which provides a 
recommendation for the division of the joint profits or costs of the grand coalition, while satisfying some reasonable properties. 
For instance, let K = {1, 2, …, k,…, m} be a 
finite set of players. Non-empty sub-sets of K are 
called coalitions. To accomplish the division 
process, the players may form coalitions and the 
strength of each coalition is expressed as a characteristic function v. For any coalition or subset
K
S ⊆
, v(S) measures the share of the surplus 
or loss that the coalition, S, is capable of appropriating without resorting to agreements with 
players belonging to other coalitions. 
For each player k, K
S
∉
, Shapley (1953) 
proposes a value based on the player’s marginal 
contribution – defined as the weighted mean of 
the marginal contributions v(S∪ {k}) - v(S) of 
player k in all coalitions S ⊆  K- {k}. That is, 
player k is attributed the extra amount that he 
brings to the existing coalition of players. To 
identify this value, we imagine that the m players 
are randomly ranked in some order, or join the 
game in a random order, defined by σ, 










=

−
−

+
−
43
42
1
4
4 3
4
4 2
1
1

1
1
2
1
,...,
,
,
,...,
,

s
m

m
k
k

s

k
σ
σ
σ
σ
σ
σ
σ
    (3) 

and then successively eliminated in that order. 
The elimination of players reduces the share accruing to the group of those not yet eliminated. 

Russian Journal of Agricultural and Socio-Economic Sciences, No. 4 (4) / 2012 

6 
 

When the coalition, S, is composed of s elements, 
we can only find the value they will obtain, v(S), 
when the first s elements of σ are exactly the 
elements of S. The weight of the coalition S is 
measured by the probability that the first s elements of σ are all elements of S. This probability 
is found by dividing the number of ordered arrangements of which the first s elements are all in 
S by the total number of possible ordered arrangements. The numerator can be obtained by 
imagining that the first s players are orderly arranged in a sequence and the remaining m-s-1 
players are also orderly arranged in another sequence. 
The number of possible ordered arrangements is the number of permutations of m players 
taken m at a time, which is m!. By the same reasoning, since the first s players yield s! number of 
permutations, the remaining m-s-1 players would 
yield (m-s-1)! Number of Permutations. The 
number of ordered arrangements in which the 
first s players are all elements of S is thus given 
by s!(m-s-1)!. 
The weight (or probability) that the first s 
elements of σ are all elements of S is thus defined 
by s!(m-s-1)!/m!, where s is the size of the coalition S. This weight also measures the probability 
that the player before player k will be in S. The 
Shapley Value of player k, denoted by 

(
),
,v
K
sh
k
ϕ
 is thus the weighted mean of his 
marginal contributions v(S∪ {k}) -v(S) over the 
set of coalitions S ⊆  K- {k} given by:  
 

(
)
(
)

{ }

{ }
(
)
( )
[
]
S
v
k
S
v
m
s
m
s
v
K

m

s
k
K
S

sh
k

s
S
−
∪
−
−
= ∑ ∑

−

=
−
⊆
=

1

0
!
!
1
!
,
ϕ
 (4) 

by convention, 0! = 1 and v(φ ) = 0 
 
To apply the Shapley Value in distributive 
analysis instead; of considering m players as in 
cooperative game theory, we now consider m 
factors that contribute in the explanation of an 
observed phenomenon. The Shapley Value given 
in Equation 4 satisfies all three of Shapley’s 
axioms. They state that: (1) the expression 

(
)
v
K
sh
k
,
ϕ
 should be symmetric (or anonymous) 
in the sense that the contributions assigned to any 
given factor should not depend on the way in 
which the factors are labelled or listed. In order 
words, 
(
)
v
K
sh
k
,
φ
 should be independent of the 
factor’s label, 1, 2, …, m; and (2) the decomposition should be efficient, that is, it should be exact 
and 
additive, 
so 
that, 
for 
K
k ∈
∀
, 

(
)
(
)
φ
φ
φ
=
∩
∈
∀
+
+
v
K
v
K
K
sh
k
sh
k
k
,
,
,
1
1
 and 

(
)
( )
K
v
v
K

m

k

sh
k
=
∑
=1
,
φ
. 

That is, the intuitively appealing contributing factors should form a partition, so that there 
is no need for vague concepts such as residual or 
interaction terms to secure the identity of the decomposition. 
Since by the additivity axiom the set of factors completely determine the aggregate indicator, which could be at levels or changes, it is 
convenient to assume that v(φ ) = 0, in the sense 
that the aggregate indicator is zero when all the 
factors are extracted. 
Applying the Shapley approach to sectoral 
decomposition, we denote the within sector factors by W and the between sector population shift 
factors by B. This implies that Equation 2 can 
also be expressed using the characteristic function v as ∆P α = v α(W,B). Here we have only 
two factors and the two elimination sequences are 
given by {W, B} and {B, W}. 
Following Baye (2006b, 2007), from Equation 1, 
aP
∆
 explains the overall change in poverty and which can now be rewritten in terms of 
exactly two components: changes in poverty 
within-sector and between-sector population shift 
effects as: 
 

(
)

k
n
t
k
K
k
t
k
k
n
t
k
K
k
t
k

sh
B
sh
W
f
P
P
P
f
f

v
v
P

∆
+
+
∆
+
=

+
=
∆

+
∈
+
∈
∑
∑
]
[
5.0
]
[
5.0

,2
)
,2
(

,
,
,
,
α
α
α

α
α
α
φ
φ
 (5) 

= Within-Sector Effects + Between-Sector 
Population Shift Effects 
 
In contrast with the standard sectoral decomposition in Equation 2, as suggested by Ravallion 
and Huppi (1991), there is no interaction term in 
the Shapley decomposition in Equation (5). 
Data Setting. Our approach to poverty in 
this study is based on the method of basic food 
and non food needs, identified using data from 
the two Cameroonian household surveys; ECAM 
I and ECAM II that were conducted nationwide 
by the national institute of statistics in 1996 and 
2001 respectively. They provided a clear picture 
of poverty and living conditions in Cameroonian 
households. This statistics is defined as a snap 
shot of activities (economic, social, demographic) 
in a particular place at a particular time. The 
household prices of ECAM 1 and ECAM 11 
were harmonised in other to make them comparable. 

M.D. TAMBI, University of Dschang 

7 
 

Also 1996 total expenditures were scaled up, 
employing consumer price indices, to express 
them in terms of 2001 prices to enable us use the 
poverty line computed from the 2001 survey for 
the two periods (See, NIS 2002). The welfare 
indicator used is expenditures per adult equivalent. Since the composition of households by age 
was captured by the surveys, we followed previous studies in Cameroon to adopt a hybrid of 
the Oxford Equivalent Scale by attributing adult 
equivalent scales of 0.5 for household members 
aged below 15 years and 1 for those aged 15 and 
above. This adult equivalent scale is consistent 
with 2400 kcal per adult per day to exercise normal activity (See, Araar 2006, Baye 2007). 
The standard of living indicator used for determining the poverty threshold is annual household consumption. The poverty threshold was 
thus set as 232547CFAF in 2001 versus 148000 
in 1996. For purposes of comparing the poverty 
situation between 1996 and 2001, a new threshold of 185 490 CFAF per year per adult equivalent was estimated by the National Institute of 
Statistics. This is the poverty line used in the 
computation of this study. 

EMPIRICAL RESULTS AND POLICY 
RECOMMENDATION 
 
Evolution of the Head Count Index (
0
P
∆
). 
As seen in Table 1, the incidence of poverty in 
1996 and 2001 at the national level was 53.3% 
and 40.2% respectively, using a poverty line of 
185 490CFAF. These results show that the prevalence of poverty retreated by some 13.1% within 
five years. Disaggregating the prevalence of poverty at regional levels depicts similar tendencies. 
The incidence of poverty at the regional level between 1996 and 2001 is highest in the forest 
region and in the Rural highlands. In these areas, 
however, there has been a noticeable decline in 
poverty: the incidence was 55.4% and 50.7% respectively in 2001 compare to 72.5% and 62.9% 
in 1996 representing a decline of 17.1% and 
12.2% points respectively. On the contrary, the 
phenomenon has accentuated in the savannah 
region (especially in the North and Extreme 
North Provinces), where the incidence rose by 
1.3% points. The survey results also shows that 
within this period, poverty is more pronounce in 
rural than in urban areas. 

Table 1 – Regional incidence of poverty in 1996 and 2001 in Cameroon 

 

Region 
1996 
2001 
Difference in Contribution 

Proportion 
Po 
ACi 
Proportion 
Po 
ACi 
(2001)-(1996) 

Yaounde 
0.071 
(0.010) 
0.490 
(0.041) 
0.035 
(0.006) 
0.087 
(0.006) 
0.183 
(0.020) 
0.016 
(0.002) 
-0.307 
(0.007) 

Douala 
0.098 
(0.014) 
0.373 
(0.059) 
0.036 
(0.008) 
0.097 
(0.006) 
0.185 
(0.016) 
0.018) 
(0.002) 
-0.188 
(0.009) 

Other towns 
0.129 
(0.019) 
0.363 
(0.043) 
0.047 
(0.010) 
0.164 
(0.017) 
0.262 
(0.019) 
0.043 
(0.005) 
-0.101 
(0.011) 

Rural forest 
0.182 
(0.022) 
0.725 
(0.028) 
0.132 
(0.018) 
0.145 
(0.022) 
0.554 
(0.039) 
0.080 
(0.015) 
-0.171 
(0.023) 

Rural highlands 
0.279 
(0.040) 
0.629 
(0.058) 
0.176 
(0.034) 
0.0262 
(0.028) 
0.507 
(0.027) 
0.133 
(0.016) 
-0.122 
(0.037) 

Rural savannah 
.0.242 
(0.031) 
0.444 
(0.097) 
0.108 
(0.025) 
0.245 
(0.028) 
0.457 
(0.033) 
0.112 
(0.016) 
-0.013 
(0.029) 

Cameroon 
1.000 
(0.000) 
0.533 
(0.033) 
- 
1.000 
(0.000) 
0.402 
(0.015) 
- 
0.131 
(0.036) 

 
Source: Computed by the author from ECAM I and ECAM II Survey Data. Notes: Poverty line = 185.490 CFA francs per adult 
equivalent per year. Figures in parenthesis represent standard errors, Po is head count index, ACi is the absolute contribution. 
 

Decomposition of 
0
P
∆
to Within-and Between-Group Effects. Table 2 summits a sectoral decomposition of 13.1% points degrees of the 
head count index between 1996 and 2001. The 
absolute contributions of Yaounde, Douala and 
other towns to alleviating the incidence of poverty were favourable, but much lower in both cases.  
 

In a nutshell while all the intra-sector effects 
contributed favourably, the inter sector population shift effects lessened the Yaounde, Douala, 
other towns and Rural savannah contributes to 
the declining incidence of poverty. 
 
 
 
 

Russian Journal of Agricultural and Socio-Economic Sciences, No. 4 (4) / 2012 

8 
 

Table 2 – Regional decomposition of ∆Po into within and between group effects: 
Shapley Decomposition Approach

Region 
1996-2001 

Intra-sector effects 
Inter-sector effects 
Impact on ∆Po 

Yaounde 
-0.024 (0.000) 
0.005 (0.000) 
-0.019 (0.007) 

Douala 
-0.018 (0.000) 
-0.000 (0.000) 
-0.018 (0.009) 

Other towns 
-0.015 (0.000) 
0.011 (0.000) 
-0.004 (0.011) 

Rural forest 
-0.028 (0.000) 
-0.024 (0.000) 
-0.052 (0.023) 

Rural highlands 
-0.033 (0.000) 
-0.009 (0.000) 
-0.042 (0.037) 

Rural savannah 
0.003 (0.000) 
0.001 (0.000) 
0.004 (0.029) 

Cameroon 
-0.115 (0.000) 
-0.016 (0.000) 
-0.131 (0.000) 

Source: Computed by the author from ECAM I and ECAM II Survey Data. Notes: Poverty line = 185 490 CFA francs per 
adult equivalent per year, figures in parenthesis represent standard errors, ∆Po is change in head count index.

Evolution of the Poverty-gap Index (
1P
∆
). 
With respect to the results presented in table 3, an 
analysis of the depth of poverty shows that the 
two largest cities, Douala and Yaounde that accounts for about 20% of the country’s total population, contributes only 13.3% points and 8.6% 
points as those classed as poor using a poverty 

line of 185 490 CFAF per adult equivalent. The 
fall in income gap disparity of Cameroon as a 
whole was felt in all the regions, while the fall in 
income gap disparity of the poor from the poverty line was largest in Yaounde and Douala, the 
savannah region had the least percentage fall. 

 
Table 3 – Regional depth of poverty in 1996 and 2001 in Cameroon

Region 
1996 
2001 
Difference in Contribution 

proportion 
P1 
ACi 
proportion 
P1 
ACi 
(2001)-(1996) 

Yaounde 
0.071 
(0.010) 
0.184 
(0.023) 
0.013 
(0.003) 
0.087 
(0.006) 
0.051 
(0.007) 
0.004 
(0.001) 
0.133 (0.003) 

Douala 
0.098 
(0.014) 
0.134 
(0.027) 
0.013 
(0.004) 
0.097 
(0.006) 
0.048 
(0.005) 
0.005 
(0.001) 
0.086 (0.004) 

Other towns 
0.129 
(0.019) 
0.121 
(0.018) 
0.016 
(0.003) 
0.164 
(0.017) 
0.078 
(0.00 
0.013 
(0.002) 
0.043 (0.004) 

Rural forest 
0.182 
(0.022) 
0.266 
(0.043) 
0.048 
(0.007) 
0.145 
(0.022) 
0.209 
(0.028) 
0.030 
(0.007) 
0.057 (0.010) 

Rural highlands 
0.279 
(0.040) 
0.229 
(0.043) 
0.064 
(0.016) 
0.262 
(0.028) 
0.209 
(0.020) 
0.055 
(0.008) 
0.02 (0.018) 

Rural savannah 
0.242 
(0.031) 
0.152 
(0.037) 
0.037 
(0.009) 
0.245 
(0.028) 
0.140 
(0.014) 
0.034 
(0.005) 
0.012 (0.010) 

Cameroon 
1.000 
(0.000) 
0.191 
(0.017) 
– 
1.000 
(0.000) 
0.141 
(0.009) 
– 
0.05 (0.019) 

Source: Computed by the author from ECAM I and ECAM II Survey Data. Notes: Poverty line = 185 490 CFA francs per adult 
equivalent per year. Figures in parenthesis represent standard errors, PI poverty-gap index, ACi is the absolute contribution.

Decomposition of ∆P1 into Within- and 
Between-Group. The results presented here are 
basically tracing the same story line as revealed 
in the analysis of the head count index. Here, 
there is a national decline in the poverty gap of 
4.9% points, of this percentage decline, Yaounde 
accounted for 0.9%, Douala 0.8%, Other towns 
0.2%, Rural forest 1.8%, Rural highlands 0.1% 
and Rural savannah 0.3% points.  
A clear observation of regional decomposition of changes in the Poverty gap index into 
within and between group effects shows that between 1996 - 2001. In the same line the intra sector effects in the country was 4.3%. As in intrasector contribution, the rural sector effects con
tributed more in the period 1996-2001 with contribution effects of 2% for Yaounde, 0% each for 
Douala and Rural Savannah, 0.4% each for Other 
towns and Rural highlands 0.9% for Rural forest 
respectively. 
The inter sector effects with 0.7% contributes more favourably in explaining changes in 
the poverty-gap as compare to the intra sector 
effects with 4.3% points. The impact of povertygap is felt more in Rural forest (1.8%) and least 
felt in Rural savannah with 0.2% points. On the 
general scale there is an absolute decline in the 
poverty-gap between 1996 and 2001 and the 
same applied in both the intra sector effects and 
inter sector effects. 

M.D. TAMBI, University of Dschang 

9 
 

Table 4 – Regional Decomposition of ∆P1 into Within-and Between-Group Effects: 
Shapley Decomposition Approach

Source: Computed by the author from ECAM I and ECAM II Survey Data. Notes: Poverty line = 185.490 CFA francs per 
adult equivalent per day. Figures in parenthesis represent standard errors; ∆P1 is change in poverty-gap index.

Evolution of the Squared Poverty-gap Index (∆P2). In Table 5, one notice that at the national level the severity of Poverty stood at 9.0% 
for 1996 and 7.2% for 2001. However despite 
these values, the severity of poverty retreated 
between this 1996-2001 periods for about 2%. 
From above statistical presentation, one can see 
clearly that in this period (1996-2001) Rural for
est and Rural highlands experience more of poverty severity as compared to other regions. It 
can equally be notice that despite the weight of 
poverty inequality, poverty at the regional level 
retreated between 1996-2001 with Yaounde scoring 6.8%, Douala 4.3%, Other towns 2.1% Rural 
forest 1.5%, Rural highlands 0.3% and Rural Savannah by 1% respectively. 
 
Table 5 – Regional inequality of poverty in 1996 and 2001 in Cameroon 

 

Region 
1996 
2001 
Difference in Contribution 

proportion 
P2 
ACi 
Proportion 
P2 
ACi 
(2001)-(1996) 

Yaounde 
0.071 
(0.010) 
0.089 
(0.013) 
0.006 
(0.002) 
0.087 
(0.006) 
0.021 
(0.003) 
0.002 
(0.000) 
0.068 (0.002) 

Douala 
0.098 
(0.014) 
0.063 
(0.015) 
0.006 
(0.002) 
0.097 
(0.006) 
0.020 
(0.003) 
0.002 
(0.000) 
0.043 (0.002) 

Other towns 
0.129 
(0.019) 
0.055 
(0.009) 
0.007 
(0.002) 
0.164 
(0.017) 
0.034 
(0.003) 
0.006 
(0.001) 
0.021 (0.002) 

Rural forest 
0.182 
(0.022) 
0.124 
(0.011) 
0.022 
(0.003) 
0.145 
(0.022) 
0.109 
(0.023) 
0.016 
(0.005) 
0.015 (0.006) 

Rural highlands 
0.279 
(0.040) 
0.109 
(0.026) 
0.031 
(0.009) 
0.262 
(0.028) 
0.112 
(0.015) 
0.029 
(0.005) 
-0.003 (0.010) 

Rural savannah 
0.242 
(0.031) 
0.072 
(0.019) 
0.017 
(0.005) 
0.245 
(0.028) 
0.062 
(0.008) 
0.015 
(0.003) 
0.01 (0.005) 

Cameroon 
1.000 
(0.000) 
0.090 
(0.009) 
– 
1.000 
(0.000) 
0.070 
(0.006) 
– 
0.020 (0.011) 

Source: Computed by the author from ECAM I and ECAM II Survey Data. Notes: Poverty line = 185 490 CFA francs per adult 
equivalent per year. Figures in parenthesis represent standard errors, P2 is squared poverty-gap index, ACi is the absolute contribution.

Decomposition of ∆P2 into Within-and 
Between-Group Effect. Table 6 traces the same 
story as Table 4 and 5. Thus considering intrasector effects between 1996 and 2001, Yaounde 
contributed 0.5%, Douala 0.4%, Other towns 
03%, Rural Savannah 0.2% respectively while 
the entire Cameroon economy contributed 1.7%. 
In the same line, the inter sector effects at the 
regional level was 0.1% for Yaounde, Douala is 
0.0%, Other towns 0.02%, Rural forest 0.4% Rural highlands 0.2%, Rural Savannah 0.0% and 
0.4% for the national territory respectively. From 
observation, though the percentages might 

change, the inter sector effects contributes favourably though the contribution of intra sector 
effects is non negligible.  
However, considering the intra and the sector effects one can observed that regionally 
Yaounde contributed 0.4%, Douala 0.4%, Other 
towns 0.1%, Rural  forest 0.6%,  Rural highlands 
0.1%, Rural Savannah 0.2% respectively for 
1996-2001. Also during this same period the national territory of Cameroon contributed 0.1% to 
the intra and inter effects. 
 
 

Region 
1996-2001 

Intra-sector effects 
Inter-sector effects 
Impact on 
1P
∆
 

Yaounde 
-0.011 (0.000) 
0.002 (0.000) 
-0.009 (0.003) 

Douala 
-0.008 (0.000) 
-0.000 (0.000) 
-0.008 (0.004) 

Other towns 
-0.006 (0.000) 
0.004 (0.000) 
-0.003 (0.004) 

Rural forest 
-0.009 (0.000) 
-0.009 (0.000) 
-0.018 (0.010) 

Rural highlands 
-0.006 (0.000) 
-0.004 (0.000) 
-0.009 (0.018) 

Rural savannah 
-0.003 (0.000) 
0.000 (0.000) 
-0.002 (0.010) 

Cameroon 
-0.043 (0.000) 
-0.007 (0.000) 
-0.049 (0.000) 

Russian Journal of Agricultural and Socio-Economic Sciences, No. 4 (4) / 2012 

10 
 

Table 6 – ∆P2 into Within-and Between Group-Effect: Shapley Decomposition Approach

Source: Computed by the author from ECAM I and ECAM II Survey Data. Notes: Poverty line = 185 490 CFA francs per 
adult equivalent per year, figures in parenthesis represent standard errors ∆P2 is squared poverty-gap. 
 
Based on our analysis and in conformity with 
Baye (2006b), this observation is attributed to the 
importance of migration in the fight against poverty by the poor themselves. He suggested two possible transmission mechanisms that can explain 
this: 
1. Remittances made by rural-urban migrants, who generally leave part of their family in 
rural areas and maintain active ties with them. 
2. The rural consumption increasing effects 
of migration in the face of underemployment in 
rural agriculture, with or without remittances. 
More so, we can outlined that the important 
result emanating from this study is that Rural forest and Rural highlands regions were hardest hit 
by poverty and inequality trends in Cameroon. 
This observation means that the income gap (dif
ferences) was so wide in Other towns, Rural forest 
and Rural highlands as compare to other regions. 
This difference can be explained as follows: 
1. Natural heritage, whereby those who inherited properties receive an additional advantage 
which put them ahead of life as compare to those 
without this initial wealth. 
2. Income distribution in this region is highly 
skewed this explains why the gap between the 
poor and the non poor is more pronounced. From 
our result, we suggest that policies and strategies 
for reducing poverty/inequality should place particular emphasis on the countryside and on a region-by-region approach such as decentralization, 
increase provision of rural extension services 
(roads, electricity, markets, portable water and 
etc). 

 

REFERENCES 

Araar, A. (2003). “The Shapley Value”, paper 
presented at the Sisera Training Workshop 
on Poverty Dynamics, 22-30 January, 
Kampala, Uganda.  
Araar, A. (2006). On the Decomposition of the 
Gini Coefficient; an Exact Approach with 
an illustration using Cameroonian data. 
Working Paper 06-02. 
Balisacan, A.M. (1995). Anatomy of Poverty 
During Adjustment: The Case of the Philippines. Economic Development and Cultural Change 44(1) 33-60. 
Baye, M.F. (2007). Exact Configuration of Poverty, inequality and Polarization Trends in 
the Distribution of well-being in Cameroon. Final report submitted to African 
Economic Research Consortium. 
Baye, M.F. (2006b). Growth, Redistribution and 
Poverty changes in Cameroon: A Shapley 
Decomposition Analysis, journal of African economics, Vol. 15, N° 4 pp 543-570. 

Baye, M.F. (2005a). Structure of Sectoral Decomposition of aggregate. Poverty changes 
in Cameroon. Paper presented at the international conference on shared Growth in 
Africa-Accra. Organised by Cornell University/ISSR/The WORLD Bank, PP2122. 
Baye, M.F. and Fambon, F. (2002). Decomposition of Inequality in the Distribution Living 
Standard in Cameroon,  African journal of 
economic policy, Vol. 9, N° 2, pp 51-75 
Chantreuil, F. and Trannoy, A. (1997). “Inequality Decomposition Values” mimeo, universitè de Cergy – Pointoise. 
Duclos, J.Y. (2002). Poverty and equity: theory 
and estimation. CREFA, Universitè Laval, 
Canada, Preliminary Version, January 
2002. 
Fambon, S. Baye, F.M. Noumbar, J. Tamba, I. 
and Amin, A.A. (2004). Dynamique de la 
pauvreté et de la Répartition des Revenues 

Region 
1996-2001 

Intra-sector effects 
Inter-sector effects 
Impact on 
2
P
∆

Yaounde 
-0.005 (0.000) 
0.001 (0.000) 
-0.004 (0.002) 

Douala 
-0.004 (0.000) 
-0.000 (0.000) 
-0.004 (0.002) 

Other towns 
-0.003 (0.000) 
0.002 (0.000) 
-0.001 (0.002) 

Rural forest 
-0.002 (0.000) 
-0.004 (0.000) 
-0.006 (0.002) 

Rural highlands 
0.001 (0.000) 
-0.002 (0.000) 
-0.001 (0.006) 

Rural savannah 
-0.002 (0.000) 
0.000 (0.000) 
-0.002 (0.005) 

Cameroon 
-0.017 (0.000) 
-0.004 (0.000) 
-0.021 (0.000)