WPS5464
Policy Research Working Paper 5464
Accounting for Heterogeneity in Growth
Incidence in Cameroon
B. EssamaNssah
Léandre Bassolé
Saumik Paul
The World Bank
Poverty Reduction and Economic Management Network
Poverty Reduction and Equity Group
November 2010
Policy Research Working Paper 5464
Abstract
This paper presents counterfactual decompositions the price effect. Observed gains at the bottom of the
based on both the Shapley method and a generalization distribution are due to returns to endowments. The rest
of the OaxacaBlinder approach to identify proximate of the gains are accounted for by the composition effect.
factors that might explain differences in the distribution Further decomposition of these effects shows that the
of economic welfare in Cameroon in 19962007. composition effect is determined mainly by household
In particular, the analysis uses recentered influence demographics while the structural effect is shaped by the
function regressions to link the growth incidence sector of employment and geography. Finally, analysis of
curve for 20012007 to household characteristics and the ruralurban gap in living standards shows that, for
account for heterogeneity of impact across quantiles the poorest households in both sectors, differences in
in terms of the composition (or endowment) effect household characteristics matter more than the returns
and structural (or price) effect. The analysis finds that to those characteristics. The opposite is true for betteroff
the level of the growth incidence curve is explained households.
by the endowment effect while its shape is driven by
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author may be contacted at bessamanssah@worldbank.org.
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Produced by the Research Support Team
Accounting for Heterogeneity in Growth Incidence in Cameroon
B. EssamaNssah, Léandre Bassolé and Saumik Paul
The World Bank Group and African Development Bank
Washington, D.C.
Keywords: Cameroon, counterfactual distribution, Shapley decomposition, economic
growth, inequality, OaxacaBlinder decomposition, poverty, Recentered Influence
Function (RIF) regression, social evaluation.
JEL Classification Codes: C14, C31, D31, I32, O55, R11
The authors are grateful to Abdoulaye Seck for providing background information and insightful
comments on an earlier version of this paper, to Prospere R. BackinyYetna for his help with data issues, to
Andrew Dabalen for bringing to their attention the literature on RIF regression analysis used in this work,
to Nicole M. Fortin for sharing her most recent work on decomposition methods in Economics and
providing clarification on some technical issues, to Francisco H. G. Ferreira, Peter J. Lambert and Jan
Walliser for insightful comments on an earlier draft and for encouragement. The views expressed herein
are entirely those of the authors or the literature cited and should not be attributed to the World Bank or to
its affiliated organizations. * Tel.: +1 202 473 7564; fax: +1 202 522 3283 Email address:
bessamanssah@worldbank.org
1. Introduction
For the past twenty years or so, Cameroon has been battling a severe and
persistent socioeconomic crisis that can be traced back to a termsoftrade shock in the
mid 1980s and the associated policy response. Prior to that crisis, the country enjoyed
steady economic growth and relative social stability. For about 20 years following
independence in 1960, the average annual growth rate of Gross Domestic Product (GDP)
hovered around 5 percent. That growth was driven mainly by the agricultural sector
which employed more than 80 percent of the labor force and accounted for 32 percent of
GDP. This sector was also a major contributor to export earnings through mainly cocoa
and coffee (Benjamin and Devarajan 1986). The manufacturing sector accounted for
about 25 percent of GDP and was mainly involved in importsubstituting activities.
Cameroon became an oil producer in 1978 following the discovery of oil off the
west coast of the country. This presented policymakers with a new set of opportunities
and challenges. At that point in time, poor infrastructure and low levels of human capital
were considered serious obstacles to development efforts. Some of the oil revenues could
then be invested in capital formation. At the same time, there was a risk of Dutch
disease1 whereby traditional exports such as cocoa and coffee would lose competitiveness
in the world markets as a result of domestic inflation induced by a rapid spending of oil
revenues. In the early 1980s, the oil sector began to take over from the agricultural sector
as the engine of growth. Between 1977 and 1981 the average rate of economic growth
was about 14 percent and dropped to about 7.5 percent per year between 1982 and 1986
(Blandford et al. 1994). The share of the oil sector in GDP grew steadily from 1 percent
in 1978 to 20 percent in 1985. During the same period the share of agriculture declined
from about 29 percent to about 21 percent. Furthermore, the share of petroleum and oil
products in exports increased from 3 percent to 65 percent while that of agricultural
products plummeted from 87 percent to 27 percent.
The constant and steady growth achieved throughout the 1970s and 1980s earned
Cameroon the title of middleincome country, a World Bank classification it shared with
1
This term refers to the deterioration of the Netherlands' export competitiveness associated with the
exploitation of natural gas fields in the 1970s (Benjamin and Devarajan 1985).
1
countries such as Indonesia, Morocco, Thailand and Tunisia. Cameroon's per capita
GNP in 1988 dollars was estimated at US $1,010 (World Bank 1990). These positive
achievements in economic growth were generally attributed to fiscal prudence and
political stability. The World Development Report of 1988 did praise Cameroon along
with Indonesia for managing cautiously the windfall from the 19791981 oil boom2.
The fact that Cameroon did enjoy high and sustained economic growth
throughout 19651985 has been abundantly documented (Bradford et al. 1994, World
Bank 1995). However, little is known about trends in inequality and poverty during those
"good" times for lack of data. Based on the 1983 Household Expenditure Survey, the
World Bank (1995) found evidence of high levels of inequality in the distribution of
income and rural poverty. The same report discusses factors indicating that the situation
may not have been much better in years prior to the 1983 survey. While acknowledging
that many urban residents did benefit from this growth episode, the report points to the
following factors as contributing to high rural poverty: (1) an incentive structure that
favored capitalintensive methods of production over laborintensive ones; (2) an urban
bias in the selection of public investment; and (3) the lack of human capital development
in the rural areas.
In 1985, the economy was hit by a collapse of world prices of the country's major
export commodities, namely oil, cocoa and coffee. This was further complicated by a 40
percent appreciation of the CFA franc between 1985 and 1988, and gains in
competitiveness by Nigeria since 1985. The export price index fell by 65 percent for oil,
24 percent for cocoa, 11 percent for coffee and 20 percent for rubber (Bradford et al.
1994). Faced with this difficult international environment, the government adopted
initially a strategy of internal adjustment3 between 1985 and 1993. This entailed cutting
back on public spending (mainly investment spending) and building up arrears. This
policy choice was in part dictated by the fact that, as a member of the franc zone,
Cameroon did not have the option of adjusting the nominal exchange rate to deal with the
terms of trade shocks. Early 1989, Cameroon entered a structural adjustment supported
2
It is reported that Cameroon saved up to 75 percent of the oil revenues abroad, and after the boom,
ensured that expenditure grew slower than revenues in order to avoid deficits (World Bank 1988).
3
This point in time also marks the abandonment of fiveyear plans for socioeconomic management. The
last one was the 5th Five Year Development Plan covering the 19821986 period.
2
by the International Monetary Fund (IMF), the World Bank and the African Development
Bank.
The crisis and the initial response to it led to a severe recession and increased
poverty (World Bank 1995). It is reported that by 1990, real GDP stood 20 percent
below its 1985 level. Furthermore, per capita income fell by about 50 percent between
1986 and 1993. The loss of competitiveness also led to the loss of export markets for
agricultural products and made it hard for domestic food crops and industrial products to
compete with imports. This squeeze implied a decrease of demand for labor both for
tradable and nontradable goods with adverse effects on living standards for both rural
and urban areas. Also, reduced economic activity combined with a slackening of tax
collection crippled the ability of the state to provide services, thus worsening the
impoverishment.
In 1994, the Central African Economic and Monetary Community4 of which
Cameroon is a member devalued the CFA franc by about 50 percent in nominal terms (30
percent real), and implemented additional trade and fiscal reforms. This presented
Cameroon with an opportunity to reverse the socioeconomic downturn. The country did
experience some positive growth after the devaluation, but it was only in mid 1996, after
some failed stabilization and adjustment efforts, that the government showed strong
commitment to meaningful policy reforms. The successful implementation of these
reforms led to macroeconomic stability and an average growth rate of real GDP in the
neighborhood of 5 percent between 1997 and 2000. On the basis of the 1996 and 2001
household surveys, it is estimated that the incidence of poverty fell by 13 percentage
points from about 53 percent to about 40 percent. However, income inequality remained
high with the Gini index of inequality decreasing only by 3 percentage points, from 44 to
41 percent. Furthermore, other social indicators have not shown such an improvement.
A shift in borrowing strategy around 1986 combined with the severity of the
socioeconomic crisis left the country saddled with an unsustainable debt burden. The
stock of external debt increased from less than 33 percent to more than 75 percent of
GDP between 1985 and 1993 (Government of Cameroon 2003). In October 2000,
4
Mostly known under its French acronym CEMAC for Communauté Economique et Monétaire d'Afrique
Centrale.
3
Cameroon became eligible for debt relief under the Enhanced HIPC5 Initiative. In this
context, the government adopted a Poverty Reduction Strategy (PRS) in 2003. The
strategy is designed to cut the number of poor by half by 2015 through strong and
sustainable economic growth. Cameroon reached the Completion Point in May 2006,
after three full years of implementation of the 2003 PRS. This achievement signals the
satisfaction of Cameroon's development partners with the implementation of this
strategy.
How much poverty reduction has this improved policy environment brought
about? Preliminary analysis by the National Statistical Office based on the most recent
household survey (2007) indicates that the overall incidence of poverty is still around 40
percent, about the same level as in 2001. The Gini index of inequality seems to have
dropped a couple of percentage points from 41 percent in 2001 to 39 percent in 2007.
These observations raise some interesting evaluative questions in terms of the social
impact of economic growth in Cameroon. To what extent has the growth process been
inclusive in Cameroon? What are the sources of observed variations (over time and
across socioeconomic groups) in the distribution of economic welfare?
The purpose of this paper is to use available household level data, particularly the
2001 and 2007 surveys, to try to answer these questions using counterfactual
decomposition of changes in the distribution of economic welfare. To put things into
perspective, we present in section 2 a profile of growth, inequality and poverty for the
period 19962007. In that section we use the Shapley decomposition to explain
5
HIPC stands for Heavily Indebted Poor Countries. This initiative was launched in 1996 by the
International Development Association (IDA, the World Bank's fund designed to provide concessional
credits and grants to the poorest countries) and the IMF. The initiative was enhanced in 1999 to tighten its
link with poverty reduction and to widen its scope and make it more efficient (in terms of speed of relief
delivery). Eligibility is based on three criteria: (1) qualify only for concessional assistance from IDA, (2)
debt situation remains unsustainable after full application of traditional relief mechanisms, and (3) a track
record of reforms combined with the development of a Poverty Reduction Strategy (presented in a
document known as Poverty Reduction Strategy Paper or PRSP). The whole process entails reaching a
Decision Point and a Completion Point. Two conditions must be met by a country to reach the Decision
Point: (1) satisfactory preparation of an interim PRSP, and (2) satisfactory performance under the IMF's
Poverty Reduction and Growth Facility (PRGF). At this point, the country gets conditional (on continued
good performance) interim relief. At the Completion Point debt relief becomes irrevocable. Reaching this
point requires the following: (1) maintain macroeconomic stability under a PGRF; (2) satisfactory
implementation of a full PRSP for one year; (3) implementation of structural and social reforms agreed
upon at the Decision Point.
4
variations in poverty in terms of changes in per capita expenditure and changes in
inequality.
In section 3 we apply a novel approach to counterfactual decomposition of
outcome distributions (Fortin, Lemieux and Firpo 2010; Firpo, Fortin and Lemieux 2009
a&b). In particular, using recentered influence function (RIF) regressions, the approach
allows us to link the relevant growth incidence curve to house characteristics and to
perform OaxacaBlinder type decomposition across quantiles. This way we can tell
whether different factors (such as the distribution of characteristics or the returns to those
characteristics) have different impacts at different points of the outcome distribution. We
also use the same methodology to decompose the ruralurban gap in the distribution of
economic welfare. For policymaking purposes, we need to understand the nature of the
changes in the distribution of welfare associated with the process of economic growth.
While the Shapley decomposition limits this understanding to changes in mean welfare
and inequality, the generalized OaxacaBlinder decomposition allows a much richer
analysis (Bourguignon and Ferreira 2005, Fortin, Lemieux and Firpo 2010)6. However,
both methods base the identification of the determinants of differences across
distributions of economic welfare on the comparison of counterfactual distributions with
observed ones. Concluding remarks are made in section 4.
2. A Profile of Growth, Inequality and Poverty
In this section, we present a summary of the three datasets we use in the analysis.
We also discuss the observed poverty outcomes and try to link them to changes in per
capita expenditure and inequality.
Table 2.1. Distribution of Per Adult Equivalent Annual Expenditure in Cameroon (19962007)
Mean Lowest 2nd 3rd 4th 5th 6th 7th 8th 9th 10th
Decile
1996 243262.2 2.89 3.78 4.88 6.66 7.26 8.04 7.99 10.45 13.94 34.10
2001 372742.6 2.64 4.00 5.19 6.79 6.67 8.59 10.07 11.56 15.59 28.90
2007 432894.2 2.70 3.95 4.74 6.22 7.74 9.30 10.65 12.87 16.64 25.19
Source: Authors' Calculations (using data from the 1996, 2001 and 2007 household surveys)
6
Within this framework outcome differentials are explained in terms of individual (or household)
endowments (or characteristics) and the returns to those assets.
5
2.1. Evolution of Per Capita Income and Inequality
Table 2.1 presents a summary of the distribution of per adult equivalent7
expenditure based on the 1996, 2001 and 2007 household surveys conducted by the
National Statistical Office. All these surveys follow the sampling frame of the 1987
population census. The samples are stratified and the 1996 survey has the smallest
sample size with 1,728 observations 36 percent of which represent the rural sector. The
National Statistical Office (2002) has noted this underrepresentation of the rural areas in
the 1996 household survey. For the other two surveys, the sample size is 10,992
observations for 2001 and 11,391 observations for 2007.
On the basis of the means reported in the second column of table 2.1, we find that
(see table 2.2) the average per adult equivalent expenditure grew 5.4 percent per year
over the period of 19962007 in nominal terms. Looking within subperiods, the mean
per adult equivalent expenditure grew by about 9 percent per year between 1996 and
2001, and by about 2.5 per year between 2001 and 2007. In real terms, these average
rates of growth fall respectively to 1.9 percent, 4.1 percent and 0.5 percent. National
account statistics tell a different story. The real per capita GDP is believed to have grown
only by 1.57 percent per year between 1996 and 2001, and by 0.57 percent between 2001
and 2007 (National Statistical Office 2002, 2008).
Table 2.2. Growth in Average per Adult Equivalent Expenditure in Cameroon
(19962007)
Period Average Growth Rate (percentage)
Nominal Real
19962001 9.0 4.1
20012007 2.5 0.5
19962007 5.4 1.9
Source: Authors' Calculations
7
The underlying scale assigns weights to individual members of the household according to their age and
gender. However there is no gender differential for children up to the age of 10. Thus children who are at
most 1 year old get a weight of 0.255. Those with age between 1 and 3 years get assigned a weight of 0.45.
Between the age of 4 and 6, the weight is 0.62 while it is 0.69 for the 710 age group. Starting from age 11,
males get assigned the following weights: 0.86 between 11 and 14, 1.03 between 15 and 18, 1 between 19
and 50 and 0.79 above 50. All females between 11 and 50 get a weight of 0.76 and those above 50 get a
weight of 0.66.
6
According to the National Statistical Office, there are at least five factors that
explain the level of economic growth achieved between 1996 and 2001. These include:
(1) a good performance of the export sector, particularly coffee, cocoa and cotton; (2)
investments associated with the privatization program; (3) the expansion of the timber
industry; (4) increased salaries in the public sector8; and (5) job creation and multiplier
effects associated with the construction of the ChadCameroon pipeline. The National
Statistical Office also explains that the poor performance of the economy between 2001
and 2007 is due mainly to the fact that growth occurred in low productivity sectors such
as the urban informal sector and traditional agriculture.
The data presented in table 2.1 also reveal a significant amount of inequality in
the distribution of per adult equivalent expenditure. The share of the richest decile is
equal to almost 12 times that of the poorest decile in 1996, about 11 times in 2001 and
9.3 times in 2007. Furthermore we note that, for all three years, the share of expenditure
of every decile up to the sixth is strictly less than its population share (10 percent). For
the seventh decile, the share of expenditure is about 8 percent in 1996, and a little over 10
percent in 2001 and 2007. Table 2.3 shows that the Gini measure of overall inequality
has hovered around 40 percent in 1996 and 2001 and declined slightly to about 39
percent in 2007.
2.2. Changes in Poverty over Time
Figure 2.1 presents a picture summarizing the evolution of aggregate poverty
from 1996 to 2007 based on TIP curves associated with poverty measures which are
members of the FosterGreerThorbecke (FGT) family. The acronym TIP stands for the
three I's of poverty because the curve provides a graphical summary of incidence,
intensity and inequality dimensions of aggregate poverty based on the distribution of
poverty gaps (Jenkins and Lambert 1997)9. These dimensions are shown as follows: (1)
the length of the nonhorizontal section of the curve reveals poverty incidence ; (2) the
intensity aspect of poverty is represented by the height of the curve; and (3) the degree of
8
No indication is provided as to whether this salary increase reflected gains in productivity.
9
This curve is constructed in four steps: (1) rank individuals from poorest to richest on the basis of the
welfare indicator y; (2) compute the relative poverty gap of individual i as gi=max{(1yi/z), 0} where z is
the poverty line; (3) form the cumulative sum of the relative poverty gaps divided by population size; and
(4) plot the resulting cumulative sum of poverty gaps as a function of the cumulative population share.
7
concavity of the nonhorizontal section of the curve translates into the degree of
inequality among the poor.
Figure 2.1. A Picture of Poverty in Cameroon, 19962007
20 14
2001
12 2007
16
Cumulative Poverty Gaps
10
Cumulative Poverty Gaps
12 1996 8
6
8
4
4
2
0
0
0 10 20 30 40 50 60 70 80 90 100
0 10 20 30 40 50 60 70 80 90 100
Cumulative Percentage of the Population
Cumulative Percentage of the Population
Table 2.3. A Profile of Poverty and Inequality, 19962007
Overall Urban Rural
1996 2001 2007 1996 2001 2007 1996 2001 2007
Headcount 53.26 40.18 39.90 41.39 17.88 12.17 59.62 52.08 55.04
Poverty Gap 19.09 12.79 12.31 14.67 4.28 2.81 21.46 17.32 17.50
Squared Poverty Gap 9.00 5.55 5.03 6.92 1.59 0.96 10.12 7.67 7.24
Watts 26.66 17.38 16.11 20.55 5.48 3.51 29.94 23.72 22.99
Atkinson (1) 23.84 24.00 21.94 28.72 24.31 18.59 17.81 16.63 15.35
Atkinson(2) 38.16 38.82 35.82 45.63 38.25 31.85 30.38 29.72 25.93
Gini 40.63 40.41 38.96 44.91 40.71 35.19 34.60 33.15 32.23
MLD 27.23 27.45 24.77 33.86 27.85 20.56 19.61 18.19 16.66
Theil 31.75 33.75 27.88 37.64 35.39 22.87 21.61 19.36 18.76
Source: Authors' Calculations (MLD stands for Mean Log Deviation).
Figure 2.1 is consistent with the poverty outcomes presented in table 2.3, showing
that poverty incidence dropped from about 53.3 percent in 1996 to about 40.2 percent and
40 percent in 2001 and 2007 respectively. The other three measures reported in that same
table (the poverty gap, the squared poverty gap and the Watts measure) show a similar
decline. These other three measures are members of the additively decomposable10 class
of poverty measures.
10
This class of poverty measures is defined by the following expression:  where z is
the poverty line, f(y) is the frequency density function of the welfare indicator y, and  is a convex
and decreasing function measuring individual deprivation. The indicator of individual deprivation is equal
to zero when the welfare level is greater or equal to the poverty line. The poverty measures are additively
separable because the deprivation felt by an individual depends only on a fixed poverty line and her/his
level of welfare and not on the welfare of other individuals in society. When the population is divided
exhaustively into mutually exclusive socioeconomic groups, this class of measures allows one to compute
8
To begin to uncover some of the factors that might explain the observed changes
in poverty between 1996 and 2007, we start from the fact that poverty indices are
computed on the basis of a distribution of living standards which is fully characterized by
its mean and the degree of inequality (as represented by the associated Lorenz curve).
Any poverty measure therefore is a function of these two factors. Formally we write this
as Pt P( t , Lt , z ) . In other words, poverty at time t is a function of the mean, t, the
Lorenz function, Lt, and the poverty line, z, (assumed constant over time). We can use
counterfactual decompositions to sort out the contribution of each of these factors to
changes in overall poverty. The basic idea underlying such decompositions is to compare
observed poverty outcomes to what they would have been under some counterfactual
state defined by letting only one factor vary while holding all other factors fixed. In
particular and given a fixed poverty line, we use the Shapley decomposition11 method to
identify the contributions of changes in the mean and relative inequality to the overall
change in poverty.
To see clearly how this works in the context of poverty outcomes, we note that the
marginal impact of the change in the mean of the distribution is equal to the change in
poverty that would have been observed had relative inequality remained constant. The
computation of this marginal effect is based on two counterfactual distributions. The first
is obtained by scaling up the initial distribution of welfare (y) by a factor equal to the
t
ratio . This distributionneutral transformation produces a counterfactual
t 1
distribution with the same Lorenz function as the initial distribution and the same mean
the overall poverty as a weighted average of poverty in each group. The weights here are equal to
population shares. Such indices are additively decomposable.
11
The Shapley decomposition is based on a microeconomic approach to distributive justice where the key
issue is a fair assessment of the productive contributions of partners in a joint venture. The Shapley value of
a participant is in general a solution to a cooperative game. If players join the game sequentially, the value
of a player is her net addition to overall payoff when she joins. The Shapley value is the average
contribution to the payoff over all possible orderings of the participants. The Shapley decomposition rule
respects the following restrictions: (1) Symmetry or anonymity (the contribution assigned to any factor
should not depend on its label or the way it is listed; (2) the rule should lead to exact or additive
decomposition; and (3) the contribution of each factor is taken to be equal to its (first round) marginal
impact. For more on the use of the Shapley value in inequality and poverty analysis, see Shorrocks (1999).
Kakwani (2000) proposes a similar decomposition using an axiomatic approach. Datt and Ravallion (1992)
offer a decomposition technique that splits a change in poverty between two dates into a growth
component, a redistribution component and a residual. They interpret this residual as an interaction term.
9
as the endperiod distribution12. The corresponding marginal effect is obtained by
comparing poverty outcomes under this counterfactual with those observed in the base
period. The second counterfactual is obtained by multiplying the level of welfare in the
end period by the inverse of the above ratio. The value of the marginal effect associated
with this counterfactual is based on the comparison of observed outcomes in the end
period with the counterfactual ones. In order to respect anonymity, the Shapley
contribution of changes in the mean to change in poverty is equal to the average of these
two marginal effects. We refer to this term as the scale component of the Shapley
decomposition.
Similarly, the computation of the contribution of changes in inequality to change
in poverty, ceteris paribus, is based on transformations that are sizeneutral to the extent
they hold the mean of the distribution constant while changing the Lorenz function. This
computation relies on the same counterfactuals discussed above13.
Table 2.4. Shapley Decomposition of Poverty Outcomes, 19962007
Overall Scale Inequality
19962001
Headcount 13.08 12.57 0.51
Poverty Gap 6.30 6.18 0.13
Squared Poverty Gap 3.45 3.47 0.02
Watts 9.29 9.35 0.06
20012007
Headcount 0.28 0.12 0.16
Poverty Gap 0.47 0.06 0.41
Squared Poverty Gap 0.53 0.03 0.49
Watts 1.27 0.09 1.17
19962007
Headcount 13.36 12.32 1.04
Poverty Gap 6.78 6.23 0.55
Squared Poverty Gap 3.98 3.52 0.46
Watts 10.55 9.39 1.16
Source: Authors' Calculations
12
See Lambert (2001) and Kakwani and Son (2008) for applications of this transformation.
13
In particular the contribution of changes in inequality, ceteris paribus, is equal to the average of the
following two counterfactual comparisons. First, poverty outcomes for the distribution defined by the base
mean and the end period Lorenz function are compared with baseline poverty outcome. Second, end period
poverty outcomes are compared with those for the counterfactual defined by base Lorenz and the end
period mean.
10
The results of our decomposition over the period 19962007 are reported in table
2.4. Those associated with the overall period, 19962007, suggest that on average both
changes in the mean per adult equivalent expenditure and in relative inequality associated
with the growth process have led to poverty reduction. The comparison of the
magnitudes of the Shapley contributions indicates that the pure growth or scale effect
dominates the inequality effect, except for the subperiod 20012007. The meager
reduction in poverty observed in 20012007 is mostly due to the modest reduction in
inequality.
2.3. Regional Disparity
Aggregate outcomes such as those discussed above can often hide a great deal of
heterogeneity in the incidence of the growth process on poverty. This heterogeneity in
impact also means that we can expect losers during spells of growth, even when poverty
falls on average as we have observed above (Ravallion 2001). At this stage we limit our
consideration of this issue to regional disparities14. Table A1 through A4 in the appendix
present a profile of poverty and inequality for 12 regions of Cameroon (the two major
cities Douala and Yaoundé, and the 10 provinces) for 2001 and 2007. The identification
of winners and losers at the regional level is made on the basis of a comparison of
regional outcomes to national outcomes. Focusing for instance on poverty incidence, we
note that four provinces (Adamaoua, East, North and Far North) experienced a significant
increase in poverty incidence between 2001 and 2007 while the trend in overall poverty
was declining (although slightly). The two Northern provinces (North and Far North)
saw the biggest increase. Poverty incidence increased by 13.6 and 9.6 percentage points
respectively in the North and Far North. The increase was 6.4 for the Eastern province
and 4.5 points for Adamaoua.
For each of the two years, 2001 and 2007, we also observe a deviation of regional
poverty levels from the national average. It turns out that we can also use a twoway
Shapley decomposition to identify proximate explanations for these poverty differences
across regions (Kolenikov and Shorrocks 2005). Just as in the case of overall poverty,
14
Later on we present some econometric results which will help us identify household characteristics that
might explain outcomes described in this section.
11
regional poverty levels are fully determined by average real income and inequality in its
distribution. Therefore, the Shapley contributions now indicate the influence of
deviations of mean (real) income and inequality from the national level. This
decomposition allows us to uncover the dominant factor between these two.
Our results for some important members of the class of additively decomposable
poverty measures are presented in table 2.5 (a&b for 2001 and 2007 respectively15).
There are six regions (the two major cities, and the coastal, western, southern and south
western provinces where poverty is generally below the national average in both 2001
and 2007. Poverty is above the national average for the other six regions. The overall
pattern that emerges from these results is that, except for the western, southern and south
western provinces, the real income (scale) effect dominates (in magnitude) the inequality
effect in 9 regions. Thus regions (among these 9) with lower poverty rates than the
national average tend to have average real income higher than the national average.
Similarly, average real income tends to be lower than the national average for those
regions (out of 9) with higher poverty rates than the national average. Poverty levels in
the West, South and SouthWest tend to be lower than the national average due to lower
inequality.
The above results suggest that regional disparity in Cameroon is mostly due to
differences in average real income, an indication of significant betweengroup inequality.
The results of similar analysis applied to ruralurban differences for 1996, 2001 and 2007
are presented in table A5A7 in the appendix. These results confirm the urban bias noted
earlier to the extent that urban poverty is consistently below the national average while
rural poverty is consistently above. A close look at the Shapley contributions reveals that
rural poverty would be much higher than the national average if rural inequality were not
lower than the national average. For instance in 2007, the incidence of rural poverty
would have been about 21 percentage points higher than the national average if rural
inequality had been at the same level as overall inequality. The observed difference
stood at 15 points because the inequality effect was 6 percentage points.
15
Here we focus on these two years because the data from the 1996 survey are organized around 4 regions
only in addition to the 2 major cities.
12
Table 2.5a. Shapley Decomposition of Regional Differences in Poverty for 2001
Headcount Poverty Gap Squared Poverty Gap Watts
Difference Scale Inequality Difference Scale Inequality Difference Scale Inequality Difference Scale Inequality
Douala 29.29 29.85 0.56 10.71 10.52 0.19 4.84 4.64 0.19 14.76 14.32 0.45
Yaoundé 26.84 30.08 3.24 10.13 10.56 0.43 4.70 4.73 0.04 14.10 14.45 0.35
Adamaoua 8.20 14.54 6.34 2.60 7.02 4.42 0.83 3.70 2.88 2.94 10.27 7.33
Center 8.00 15.07 7.07 2.19 6.42 4.24 1.08 3.28 2.21 3.67 9.40 5.73
East 3.80 9.34 5.54 2.58 5.70 3.12 1.20 3.07 1.88 3.48 8.43 4.95
FarNorth 16.11 22.73 6.62 6.05 11.11 5.05 2.62 5.96 3.33 7.97 16.45 8.48
CoastT 4.70 2.47 7.17 2.70 0.71 3.40 1.38 0.36 1.74 3.95 1.02 4.97
North 9.90 13.30 3.40 2.71 6.36 3.65 0.81 3.30 2.49 3.05 9.24 6.19
NorthWest 12.30 12.69 0.39 8.11 7.08 1.03 5.15 4.10 1.05 13.45 11.00 2.45
West 0.15 10.17 10.02 1.69 4.33 6.02 1.36 2.21 3.57 3.19 6.22 9.41
South 8.63 3.94 12.57 5.43 1.61 7.04 3.13 0.76 3.89 8.34 2.24 10.58
SouthWest 6.36 2.49 3.86 2.28 1.05 1.23 1.04 0.54 0.50 3.25 1.53 1.72
Source: Authors' Calculations
Table 2.5b. Shapley Decomposition of Regional Differences in Poverty for 2007
Headcount Poverty Gap Squared Poverty Gap Watts
Difference Scale Inequality Difference Scale Inequality Difference Scale Inequality Difference Scale Inequality
Douala 34.40 26.66 7.73 11.44 8.57 2.87 4.81 3.51 1.30 15.10 11.23 3.88
Yaoundé 33.96 26.34 7.62 11.35 8.54 2.80 4.79 3.55 1.23 14.99 11.25 3.73
Adamaoua 13.05 17.73 4.68 2.17 7.41 5.23 0.39 3.69 3.31 2.35 10.51 8.16
Center 1.29 14.76 13.46 2.83 5.76 8.59 1.93 2.69 4.62 4.43 7.99 12.43
East 10.51 16.43 5.92 3.37 7.98 4.61 1.20 4.25 3.05 4.14 11.57 7.44
FarNorth 25.97 24.77 1.20 12.26 14.59 2.33 6.18 8.43 2.25 17.23 22.02 4.79
Coast 8.82 3.09 11.91 4.66 1.34 6.00 2.32 0.65 2.97 6.51 1.87 8.38
North 23.76 24.15 0.39 8.67 12.63 3.96 3.55 6.68 3.13 11.32 18.35 7.03
NorthWest 11.10 9.66 1.44 4.30 5.61 1.31 1.81 3.00 1.19 5.67 8.15 2.48
West 10.95 2.85 13.80 5.68 0.98 6.66 2.76 0.47 3.22 7.87 1.36 9.23
South 10.64 2.96 7.68 4.94 1.27 3.67 2.38 0.62 1.76 6.80 1.77 5.03
SouthWest 12.39 4.46 7.93 5.45 1.78 3.67 2.55 0.85 1.70 7.46 2.47 4.99
Source: Authors' Calculations
13
To assess the extent of betweengroup inequality in the distribution of economic
welfare in Cameroon, we perform a threefold decomposition of the overall Gini measure
of inequality following the framework proposed by Lambert and Aronson (1993). These
authors explain that three basic components account for the overall inequality as
measured by the Gini coefficient namely: (1) between group inequality, GB, (2) within
group inequality, GW (3) the extent of overlapping among subgroup distributions, GO
Let GY be the overall Gini for an income distribution for a population partitioned in m
groups, then we have the following expression: GY G B GW GO . The within group
component is known to be equal to a weighted sum of within group Gini coefficients
where the weight of each group is equal to the product of its population share and its
income share.
Our computation is based on a simple threestep procedure which Lambert and
Aronson (1993) use to reveal the interrelation between these three components of the
Gini coefficient. Like other decompositions used in this paper, this one also relies on a
counterfactual comparison of distributions. Suppose that we start from a position of
perfect equality where every individual (household) receives the overall mean income.
We can introduce between group inequality by giving everybody, not the overall mean,
but the mean income of her group. The Gini coefficient for this new distribution
measures between group inequality.
Next consider the distribution obtained as follows. Keep individuals lined up by
increasing order of group means. Thus all people from the poorest group will appear first
in the income parade and members of the richest group will all appear last. Then, within
each group, give people their actual incomes and sort them by increasing level of income
within each group. The resulting distribution is such that the richest person in group (k
1) finds herself standing next to the poorest person in group k. By construction, this
distribution accounts for both between group and within group inequality. We can net
the between group component out by subtracting GB from the concentration coefficient of
this "lexicographic income parade"16. This operation yields an estimate of the within
group component, GW.
16
This terminology is from Lambert and Aronson (1993)
14
Finally, consider sorting individuals by increasing order of their actual income
with no attention paid to group membership. People are now ranked from the overall
poorest to the overall richest. To the extent that there is overlapping between subgroup
distributions, some people will shift ranks relative to their positions in the lexicographic
parade. The extent of this overlapping is measured by subtracting the concentration
coefficient of the lexicographic distribution (which embeds both the between and within
group components) from the overall Gini coefficient.
Table 2.6. A Threefold Decomposition of the Gini Measure of Inequality:19962007
Level (in Relative (in
percentage) percentage)
1996 2001 2007 1996 2001 2007
BetweenGroup 16.02 17.46 19.38 39.43 43.21 49.75
WithinGroup 12.17 8.26 1.25 29.95 20.45 3.21
Overlapping 12.44 14.69 18.33 30.62 36.35 47.05
Overall 40.63 40.41 38.96 100 100 100
Source:Authors' Calculations
Figure 2.2. Relative Contribution of Gini Components:19962007
60
50
40
Share in Percentage
30
20
10
0
Between W ithin Overlap
Y_1996 Y_2001 Y_2007
Source: Authors' Calculations
15
Our application of this procedure to data for 1996, 2001 and 2007 led to results
reported in both table 2.6 and figure 2.2. The decomposition for 2001 and 2007 is based
on the same groups listed in table 2.5. As noted earlier, the data from the 1996 survey
has a different grouping. These results confirm the conclusion we reached earlier on the
basis of Shapley analysis of regional differences in Poverty. Between group inequality is
indeed a major component of overall inequality (as measured by the Gini Coefficient) in
Cameroon. This component has increased from 39 percent of the total in 1996 to almost
50 percent in 2007. It represented 43 percent of total inequality in 2001. These results
also reveal that there is significant overlapping between regional distributions and a low
level of within group inequality. In addition, within group inequality has been declining
significantly over time. It accounted for about 30 percent of total inequality in 1996, 20
percent in 2001 and dropped to about 3 percent in 2007.
Table 2.7. Contribution of Location to Income Inequality
RuralUrban Region RuralUrban &Region
1996 0.03 0.10 0.10
2001 0.18 0.17 0.22
2007 0.30 0.35 0.40
Source: Authors' Calculations
Finally, we use simple regression analysis to decompose the variance of the
logarithm of per adult equivalent expenditure. To do this, we run regressions the
logarithm of per adult equivalent expenditure only on a set of dummy variables indicating
the area of residence of the household. It is known that the Rsquared from such a
regression measures the proportion of the variation in the dependent variable (log of
expenditure) explained by the location dummies (Benjamin, Brandt and Giles 2005). We
consider three different specifications for each of the three years: the ruralurban divide
alone, the regions only and the interaction between regional dummies and the ruralurban
indicator. The results are presented in table 2.7. These results confirm that there is
significant regional disparity in Cameroon and it has been growing over time. In 1996,
ruralurban location accounted only for 3 percent of the variance of log per adult
equivalent expenditure. In 2007, this proportion has increased to 30 percent. The
regional dummies account for 10 percent of the variation in 1996 versus 35 percent in
16
2007. The interaction between the two types of location dummies explains 10 percent of
the variation in log per adult equivalent expenditure in 1996 and 40 percent in 2007.
3. A Counterfactual Decomposition of Growth Incidence
Figure 3.1.Growth Incidence Curve, 20012007
Total (years 2001 and 2007)
7 Growthincidence 95% confidence bounds
Growth in mean Mean growth rate
6
5
Annual growth rate %
4
3
2
1
0
1 10 20 30 40 50 60 70 80 90 100
Expenditure percentiles
Figure 3.1 presents the Growth Incidence Curve17 (GIC) for the period 2001
2007. This curve shows how the distribution of expenditure changes at each quantile
between 2001 and 2007. Presumably this is an outcome of the underlying Poverty
Reduction Strategy. The curve reveals some heterogeneity in the impact of growth on the
living standards. People located at the bottom of the distribution up to the 10th percentile
have experienced an income growth greater than average and so have most of the people
above the median, except at the very top of the distribution. Between the 10th and about
the 30th percentiles, incomes grew at a rate below average. Finally the segment of the
population located between the 30th and the 50th percentiles experienced an income
growth rate equal to the growth rate of the average income. In this section we use
influence functions to link this pattern of growth to household characteristics and to
perform OaxacaBlinder type decompositions. This decomposition framework is
designed to help identify the effects of household (or individual) characteristics and the
17
As defined by Ravallion and Chen (2003), the Growth Incidence Curve shows the growth rate of an
indicator of the living standard (e.g. income or expenditure) at the pth quantile of the size distribution of that
indicator. It is formally defined by the following expression where , and f(·)
is the density function characterizing the distribution of the living standard indicator.
17
returns to those characteristics on the distribution of economic welfare18. We first
explain the structure of the framework along with its empirical implementation19. We
then discuss the results of its application to the data at hand.
3.1. The OaxacaBlinder Decomposition Framework
Just as in the case of the Shapley decomposition, the main objective of the
OaxacaBlinder method is to identify the factors that might account for changes in the
distribution of outcomes from one state of the world to another. In the context of policy
impact analysis, individual outcomes are viewed as payoffs to participation and type,
where type is defined by observable and unobservable characteristics. Differences in
outcome distributions therefore reflect differences in payoff structure and differences in
the distribution of characteristics. The OaxacaBlinder decomposition method is
commonly used to split the overall difference in the distribution of outcomes between
two different states of the world into a component attributable to differences in payoff
structure and another due to differences in the distribution of observable characteristics.
Within this framework, we need a model linking the outcome of interest to
individual (or household) characteristics. We therefore maintain the assumption that the
welfare indicator y (e.g. real per capita expenditure in our case) has a joint distribution
with household characteristics (such as age, education and occupation of the head of
household, area of residence and family size) represented by a vector x. The approach
applies to both changes in summary statistics and in whole distributions. More
specifically, we are interested in comparing features of an outcome distribution under two
mutually exclusive states of the world say, j and s. We formally write the outcome
equation as follows.
, , , . (3.1)
18
In particular Ravallion (2001) argues that disparities in access to human and physical capital, and
differences in returns to such assets are the main determinants of income inequality. Furthermore these
disparities are most likely to inhibit overall growth prospects.
19
Our presentation of the structure of this framework follows closely Fortin, Lemieux and Firpo (2010).
18
where represents unobservable factors. This specification implies that the outcome
distribution can vary between the two states due to: (1) differences in the outcome
structure functions gt(·), (2) differences in the distribution of observable characteristics
(x), and (3) differences in unobservable characteristics ().
Like many other decomposition techniques, the OaxacaBlinder method relies on
estimating some counterfactual distribution of outcomes such as the distribution of
outcomes that individuals observed in state s would have experienced under the
conditions prevailing in state j. Let t stand for an observable indicator of the prevailing
state,  and  represent counterfactual outcomes for state s and state j
respectively. Distributional statistics such as the mean, the variance, various quantiles,
and measures of inequality such as the Gini coefficient or members of the generalized
entropy family can be thought of as realvalued functionals of the relevant distributions20.
Let  stand for the distribution of the (potential) outcome yj for individuals in state
s. We will express any distributional statistic associated with this distribution as:
 . The overall difference in the distribution of outcomes between the states j
and s can be written in terms of this statistic as follows (Fortin, Lemieux and Firpo 2010).
  (3.2)
Splitting this overall difference in outcomes between the two states into a component
attributable to differences in observed characteristics of agents, and a component
attributable to the outcome structure, entails a comparison of actual and counterfactual
outcome distributions. In particular we used the above counterfactual for state s to obtain
the following aggregate decomposition.
    (3.3)
Following Fortin, Lemieux and Firpo (2010) we note this decomposition as:
. The first component of this aggregate decomposition ( ) is known as the outcome
structure effect or the structural effect of moving from the outcome distribution
prevailing in state j to the one in state s. The second component ( ) is the composition
20
A functional is a rule that maps every distribution in its domain into a real number (Wilcox 2005)
19
effect. Bourguignon and Ferreira (2005) refer to these two effects respectively as the
pricebehavioral effect (or price effect for short) and the endowment effect.
The outcome model (3.1) suggests that conditional on the observable
characteristics, x, the outcome distribution depends only on the function gt(·) and the
distribution of the unobservable characteristics . If the composition effect represents
that part of the outcome differential due to observable characteristics only, for things to
add up, the structural effect must account for differences in gt(·) and in the distribution of
. The identification and estimation of these two effects rest on a factorization of the
joint distribution of outcomes and characteristics and a ceteris paribus condition which is
satisfied if there are no general equilibrium effects and unobservable factors are
conditionally independent of the state of the world, given the observables21.
DiNardo, Fortin and Lemieux (1996) show that the counterfactual distribution,
 , can be estimated by properly reweighing the distribution of covariates in state j.
Using a slightly simplified notation, one can express this counterfactual as follows.
   w (3.4)

where the reweighing factor is equal to: w · . These weights

are proportional to the conditional odds of being observed in state s. The proportionality
factor depends on which is the proportion of cases observed in state s. One can easily
21
To see clearly what is involved, note that the law of total probability implies that one can derive the
distribution of yjt=j from a factorization of the conditional joint distribution yj and the covariates x as
follows:   ,  ·  . The counterfactual distribution which underpins
the aggregate decomposition in (3.3) is the distribution of outcomes that would prevail in state s if
observable characteristics were rewarded as in state j, ceteris paribus. It is equal to the following:
  ,  ·  . This counterfactual can be obtained by replacing in the
above factorization the distribution of observables in state j (  ) with that of state s (  ), while
holding constant the conditional outcome distribution of state j (  , ). Given that this conditional
outcome distribution depends on both the outcome structure gt(·) and the distribution of , if there are no
general equilibrium effects, the outcome structure would be invariant to changes in the distribution of
covariates. In addition, if the distribution of unobservables is the same in both states of the world (i.e.
conditional independence holds), changing the distribution of the observed characteristics would not affect
that of the unobservables. Under these conditions therefore, the terms of the aggregate OaxacaBlinder
decomposition are identifiable and can be consistently estimated (Fortin, Lemieux and Firpo 2010).
20
compute the reweighing factor on the basis of a probability model such as logit or
probit22.
The classic OaxacaBlinder decomposition applies only to differences in the mean
of the outcome variable and assumes that the outcome variable is a linear function of
individual characteristics (observable or not). In addition, this variant of the method
assumes that the conditional mean of the unobservables given observables is equal to
zero. These assumptions imply that the conditional expectation of the outcome variable
is also a linear function of the covariates while the unconditional expectation of the same
variable is a linear combination of the expected values of the covariates. The coefficients
spanning this combination are obtained from a regression of the outcome variable on the
covariates. The expected values of the covariates are estimated by the corresponding
sample means23.
The linearity assumption makes it easier to compute the contribution of each
covariate to each component of the aggregate decomposition. Fortin, Lemieux and Firpo
(2010) explain that a decomposition approach provides a detailed decomposition when it
allows one to apportion the composition effect or the structural effect into components
attributable to each explanatory variable24. The contribution of each explanatory variable
22
In general the aggregate decomposition procedure follows three basic steps (Fortin, Lemieux and Firpo
2010): (1) pool the data for states j and s to run a logit or probit model for belonging to state s; (2) estimate
the reweighing factor w(x) for observations in j using the predicted probability of belonging to j and s; (3)
compute the counterfactual statistic of interest using observations from the j sample reweighted by the
estimated reweighing factor.
23
In terms of the general framework discussed in this section, linearity combined with the assumption of
mean independence implies that the expected value of the outcome variable in state t is equal to: 
 , , . The mean of the counterfactual distribution  can therefore be written as:
  . The corresponding structural effect is:  
 , and the composition effect is:  
  .
24
Let xk and k reperesent the kth element of x and respectively. Then to structural effect can be written
µ
as the sum of individual contributions : S E x t s . Similarly, the compostion effect
µ
can be written as the sum of partial composition effects: X E x t s E x t j . Firpo,
Fortin and Lemieux (2007) note two limitations of the classic OaxacaBlinder decomposition. The
contribution of each covariate to the structural effect is highly sensitive to the choice of the base case.
Furthermore, the decomposition provides consistent estimates only when the assumption of linearity is
valid. One should therefore consider using reweighing even in this classical case to protect against
misspecifications.
21
to the composition effect is analogous to what Rothe (2010) calls a "partial composition
effect"25.
For the purpose of our study, we would like to account for impact heterogeneity
along the growth incidence curve depicted in figure 3.1. We note that in the absence of
panel data spanning the growth episode under consideration, it is impossible to identify
impact for a particular individual or household26 , the identification and computation of
the local impact of growth relies therefore on the assumption of anonymity27 and
compares growth rates across quantiles28 based on crosssectional data. This approach is
essentially the same as that underlying the identification of quantile treatment effects
(QTE) in the context of treatment effect analysis. Here, the anonymity assumption plays
the same role as that of rank preservation across treatment status in the case of QTEs29.
To try to uncover what might be driving the pattern of growth incidence we need
a way of linking marginal (unconditional) quantiles to household characteristics that also
allows us to perform OaxacaBlinder type decompositions. Recentered influence
function (RIF) regression offers a simple way of establishing this link and performing
both aggregate and detailed decompositions for any statistic for which one can compute
an influence function (Fortin, Lemieux and Firpo 2010). An influence function is the
derivative of a functional. Thus the derivative of a distributional statistic (F) is called
25 This is the effect of a counterfactual change in the marginal distribution of a single covariate on the
unconditional distribution of an outcome variable, ceteris paribus. Rothe (2010) interprets the ceteris
paribus condition in terms of rank invariance. In other words, the counterfactual change in the marginal
distribution of the relevant covariate is constructed in such a way that the joint distribution of ranks is
unaffected.
26
This is an instance of missing data problem characterizing the identification issue in the context treatment
effect analysis.
27
This assumption, also referred to as symmetry, implies that when comparing distributions of outcomes
the position of a particular individual in one distribution is irrelevant (Carneiro, Hansen and Heckman
2002).
28
Quantile (or fractile) is a cutoff value of a variable such that a given fraction of values lie at or below
the cutoff point (Freund and Williams 1991). For instance, the performance of a student on a standardized
test is said to be at the th quantile if a proportion of scores in the reference group are less than or equal to
hers. Formally, let y be a random variable with probability distribution function . The th
quantile of y is the smallest value of y, say q() such that: ,0 1. Equivalently we write:
: .
29
Rank preservation across two alternative states of the world (or rank invariance) means the outcome at
the th quantile in the outcome distribution for one state has its counterpart at the same location in the
outcome distribution for the other states. When rank preservation fails, the QTE approach identifies and
estimates the difference between the quantiles and not the quantiles of the difference in outcomes (Bitler et
al. 2006).
22
the influence function of at F (where F is the distribution function of the random
variable y). We note this function as IF(y;, F). The influence function of the th
quantile of the distribution of y is given by the following expression (Firpo, Fortin and
Lemieux 2009a).
; (3.5)
where the distribution function is kept implicit, I(·) is an indicator function for whether
the outcome variable is less than or equal to the th quantile, and is the density
function of y evaluated at the th quantile.
Fripo, Fortin and Lemieux (2009a) define the recentered or rescaled influence
function (RIF) as the leading terms of a von Mises (1947) linear approximation of the
associated functional. It is equal to the functional plus the corresponding influence
function. Given that the expected value of the influence function is equal to zero, the
expected value of the RIF is equal to the corresponding distributional statistic. In other
words, ; , . The rescaled influence function of the th quantile of
the distribution of y is:
; ; (3.6)
By the law of iterated expectation the distributional statistic of interest can be
written as the conditional expectation of the rescaled influence function (given the
observable covariates). This conditional expectation is known as a RIF regression. We
express the RIF regression for the th quantile of the distribution of y, as:
;  so that the unconditional or marginal quantile is equal to:
; , (3.7)
In the empirical implementation of this approach, we follow Firpo, Fortin and
Lemieux (2007) and work with a linear approximation of the RIF regression of the th
quantile. These authors explain that, since the expected value of the approximation error
is zero, the expected value of the linear approximation of the RIF regression is equal to
the expected value of the true conditional expectation. This fact makes the extension of
the standard OaxacaBlinder decomposition to RIF regressions both simple and
meaningful.
23
To be more specific, let q be the estimated coefficients from a regression of
RIF(y;q) on x. Following the standard OaxacaBlinder approach, the structural effect
can be written as
 · (3.8)
The composition effect is
  · (3.9)
This decomposition may involve a bias since the linear specification is only a local
approximation that may not hold in the case of large changes in covariates30. The
solution to this problem is to combine reweighing with RIF regression and compute the
structural effect as follows
 · (3.10)
where is the vector of coefficients from a RIF regression on state j sample reweighted
to have the same distribution of covariates as in group s. Reweighing ensures that
reflects a true change in the outcome structure.
Now, the expression for the composition effect includes the approximation error
  ·  (3.11)
The use of a linear approximation of the RIF regression also makes it easier to
separate out the contribution of different subsets of covariates to the various elements of
the aggregate decomposition. For the composition effect, we have
   (3.12)
Similarly, the structural effect can be written as.
 (3.13)
30
In particular, and may differ just because their estimation is based on different distributions of
the covariates x, even if the outcome structure remains unchanged (Firpo, Fortin and Lemieux 2009a).
24
3.2. Empirical Results
In this section of the paper, we focus on three sets of empirical results. First, we
examine the coefficients of both the OLS and unconditional quantile regressions of log
expenditure on household characteristics. As noted earlier, RIF regressions allow us to
link the growth incidence curve to household characteristics, and to account for
heterogeneity of impact across quantiles. Next, we consider both the aggregate and
detailed decompositions of the growth incidence curve into composition and structural
effects. Finally, we take a closer look at the ruralurban differential in living standards to
try to identify the proximate determinants of the difference in welfare between the rural
and urban sectors.
Figure 3.2 A Decomposition of Growth Incidence between 2001 and 2007
.5
.4
Change in Log Expenditure
.3
Composition Effect
Overall Incidence
.2
.1
Structural Effect
.0
.1
10 20 30 40 50 60 70 80 90
Returns to Selected Covariates
We consider four broad categories of characteristics: (1) Demographics (gender
of household head, age of household head, and household composition in terms of
proportions of various age groups up to age 25); (2) Household and community assets
(years of schooling of head of household, land ownership, access to credit, at least one
migrant in household, distance to nearest hospital, distance to nearest tarred road); (3)
25
Sector of employment (public sector, formal private sector, smallholder agriculture,
informal nonagriculture, unemployed; and (4) Area/province of residence31.
Our estimates of the marginal impact of each characteristic on household welfare
in 2001 and 2007 are reported in tables B1 and B2 in the appendix. These tables show
the coefficients and the associated standard errors for OLS and selected unconditional
quantile regressions. We focus first on the OLS results. All demographic variables are
statistically significant. As expected, an increase in any component of household
membership reduces welfare. The male dummy variable has a negative sign in 2001 and
a positive one in 2007. However, the 2007 coefficient is not significantly different from
zero. Thus maleheaded households do not necessary fare better than the reference
femaleheaded households in either year, other things being equal. Among the remaining
nongeographical characteristics, the following have the highest positive and statistically
significant impact on household welfare: (1) formal sector employment (public or
private), (2) access to credit and (3) years of schooling of the head of household. Having
at least one migrant in the household has no significant impact on welfare in either year.
Similarly, land ownership does not seem to make any difference, on average. The
coefficient for agricultural employment is statistically significant in both years but has a
negative sign. This is certainly another manifestation of urban bias noted earlier. Indeed,
these regression results confirm that urban residence has a strong positive impact on
welfare.
The OLS results discussed above give only the average impact of the
characteristic of interest on household welfare. We now consider results from RIF
regressions to learn how these impacts vary across quantiles. It is much easier to deal
with plots of the coefficient estimates at various quantiles rather than the estimates
themselves. To keep our story manageable, we focus on three groups of covariates
namely household assets (education of head, access to credit, land ownership and having
at least one migrant), sector of employment and area of residence (urbanrural). The
effects of these characteristics are plotted in figures B1 through B5 (in appendix B).
31
Our choice of dummy variables implies that the reference household (conditional on characteristics
represented by continuous variables) lives in the rural area of the central province and has head who is
female and out of the labor force, no access to credit and no migrant.
26
Figure B1 shows plots of the unconditional quantile regression coefficients for
education and access to credit. Returns to education (in terms of real per adult equivalent
expenditure) are positive and statistically significant across all quantiles. Not
surprisingly, economic welfare increases with education over the whole distribution. In
2001, the impact of education increases faster at the lower end of the distribution up to
the 17th percentile. After that point the quantile function remains more or less flat up to
the median. It assumes a Ushape in the topend of the distribution (between the median
and the 95th percentile). This profile implies that, for the year 2001 education enhances
inequality in the lower and upper ends of the distribution. The pattern is different for the
year 2007 where the impact of education is more or less increasing over the entire
distribution. Thus education is thoroughly inequality enhancing in 2007. Comparing
both years, we note that (with the exception of the lower end of the distribution and the
segment between the 66th and 87th percentiles) the impact of education is significantly
higher in 2001 than in 2007. This could be a manifestation of the lack of economic
growth experienced by the country over that period. Indeed the lack of employment
opportunities for the educated is a latent source of social tension in Cameroon.
The quantile plot for the returns to access to credit has an inverse Ushape in the
lowend of the distribution (up to the 56th percentile) in 2001. Thus access to credit in
2001 increases inequality in the lower end of the distribution (up to the 25th percentile)
and dampens inequality between the 25th and the 56th percentiles. In 2007, the effect has
a Ushape in the same range of the distribution. The 2001 curve dominates the 2007 one.
The effect of having access to credit is flat at the upper end of the distribution (i.e. past
the median) and there is no significant difference between the two years.
Figure B2 show the marginal impacts of land ownership and migration. Overall,
land ownership has a very small positive impact on household welfare in the lowend of
the distribution, particularly in 2007 and at the upper end of the distribution. Having at
least one migrant in the household in 2001 made no significant difference for most
households over the entire distribution of welfare except in the neighborhood of the 10th
percentile where the impact is statistically significant and negative. No other coefficient
underlying the quantile curve is significantly different from zero in a statistical sense.
But most of these coefficients are different from zero and statistically significant in 2007.
27
In particular having a migrant in the household in 2007 has a positive impact on welfare
in the lowest end of the distribution (up to the 10th percentile) and between the 60th and
the 97th percentile. In addition, it contributes to increasing inequality in the lower parts of
those segments of the distribution of welfare in 2007.
The effects of formal sector of employment are presented in figures B3 and B4.
As far as employment in the private sector is concerned, the left panel of figure B3
reveals that returns to this attribute are positive in 2001 for households located beyond
the 12th percentile. In 2007, these returns are negative for most of that range up to the
75th percentile. For the lowest end of the distribution, private sector employment brings
positive returns only in 2007. The pattern of returns is similar for the public sector
except that returns for 2007 are negative only over a very short range (from the 13th to the
24th percentile). A comparison of the two sectors in figure B4 shows that there is a
reversal in the relative pattern of the returns to public and formal private sector
employment between 2001 and 2007. In 2001 the curve for the private sector dominates
that for the public sector. In 2007, the two curves are basically indistinguishable up to
the 25th percentile then the public sector overtakes the private sector all the way up to the
95th percentile and both curves merge again. The configuration of these quantile curves
suggests two things. First, there is no advantage for the poor to be engaged in the formal
sector. Second the sluggish growth experienced between 2001 and 2007 may have hurt
households engaged in the private sector more that those in the public sector.
We note from figure B5 that households engaged in agriculture are worse off
across quantiles and years, than those employed in the other sectors of the economy32. In
2001, the returns to agriculture are positive only between the 14th and 36th percentiles. In
2007, this impact is positive only from the 96th percentile. The configuration of the two
curves implies that the penalty associated with being engaged in agriculture hurts the
households at the lower end of the distribution more than those at the top. The same
figure reports the marginal impact of urban residence on welfare. In both years, the
quantile curves have generally an inverted Ushape (first rising and then falling). This
suggests that urban residence increases inequality in the low end of the distribution and
decreases it in the top end. This pattern is more pronounced in 2007 than in 2001. The
32
The results for the informal sector, not shown, have the same pattern as those for smallholder agriculture.
28
curve for 2007 dominates that for 2001 in the 35th to 97th percentile range. This pattern
of unconditional quantile regression coefficients confirms that urban households are
generally better off than their rural counterparts and that this urban bias has been
increasing over time.
Decomposition of the Growth Incidence Curve
Figure 3.2 shows a decomposition of the total variation in the distribution of log
per capita expenditure (essentially the GIC) into two components. The first component is
due to changes in the distribution of characteristics while the second represents the
contribution of changes in the distribution of returns to those characteristics. These two
components pull in opposite directions from the lowest percentile to the 76th. Past this
point, both effects are more or less the same. Overall, the structural effect has a Ushape
while the composition effect has an inverted Ushape. The structural effect dominates at
the lowest end of the distribution while the composition effect dominates in the middle,
from the 12th to the 76th percentile. Thus, the structural effect tends to decrease inequality
at the lowest end of the distribution while the composition effect tends to increase it.
Figure 3.3: Composition Effects
.24 .25
Full Composition Effect
.20 Full Composition Effect
.20
Change in Log Expenditure
Change in L og E xpenditure
.16
.15
Demographics
.12
.10
.08
.05
.04
Household Assets
.00 .00 Geography
.04 Sector of Employment
.05
10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90
Quantile Quantile
Once we account for the composition effect, the profile of the growth incidence
curve is closer to that of the structural effect than that of the composition effect. The
effect of characteristics is positive and shows a slight decline across quantiles. The effect
of returns to those characteristics is negative for households located between the 14th and
the 39th percentiles. The configuration of the three curves implies that the level of the
29
GIC is driven by the composition effect while the shape of the same GIC is explained
mostly by the structural effect. In particular, the fact that people located at the bottom of
the distribution up to the 10th percentile have experienced an income growth greater than
average is due to the structural effect while the gains beyond that point are mainly due to
the composition effect. Recall that, according to the GIC depicted by figure 3.1, most of
the people above the median, except at the very top of the distribution also experienced
an income growth above average. This outcome is due to the composition effect.
Figure 3.4: Composition Effects of Household Assets
.015
.010 Education
.005
Change in Log Expenditure
Credit
.000
Land
.005
.010 All Assets
.015
.020
.025
10 20 30 40 50 60 70 80 90
Quantile
What drives the composition and the structural effects? To try to understand the
potential factors that determine these two components of the aggregate decomposition,
we further disaggregate these two components on the basis of sets of covariates. The
results are presented in figures 3.33.5. The left panel of figure 3.3 compares the full
composition effect to the contribution of household demographics to this effect. The
right panel compares the same full effect and the contributions of household assets, sector
of employment and geography. These results clearly show that both the level and the
dispersion of the full composition effect are mostly accounted for by household
demographics.
30
Figure 3.4 shows the contributions of various household assets to the composition
effect. The figure reveals that the contribution of assets to the composition effect is
mostly accounted for by changes in the distribution of years of schooling.
Figure 3.5: Structural Effects
.6 1.6
.4
1.2
Demographics
Ch an ge in L o g E xp en ditu re
C h an g e in L o g E x p en d itu re
.2 Overall Structural Effect
0.8
.0
Household Assets
.2
0.4
.4
0.0 Overall Structural Effect
.6 Sector of Employment Geography
.8 0.4
10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90
Quantile Quantile
Finally, figure 3.5 presents results of a decomposition of the structural effect.
These results suggest that the overall shape of the structural effect is determined by the
sector of employment and, to a certain extent, geography. These are the two
characteristics that explain the negative values of the structural effect in some range in
the lower end of the distribution. This negative contribution is mitigated to some extent
by household demographics.
A Closer Look at the RuralUrban Differential in Living Standards
Figure 3.6 Change in RuralUrban Differential in Living Standards
Total Differential
1.2
1.1
1.0
Difference in Log Expenditure
0.9
0.8
2007
0.7
0.6 2001
0.5
0.4
0.3
10 20 30 40 50 60 70 80 90
Quantile
31
Given the importance of urban bias in the pattern of economic growth in
Cameroon, we take a closer look at how the ruralurban differential has changed over
time and across quantiles. We also apply the generalized OaxacaBlinder decomposition
to this differential both in 2001 and 2007 to try to identify some key factors that might
explain the observed differences between the rural and the urban sector. Figure 3.6
shows a comparison of the ruralurban differential for 2001 and 2007. The fact that the
curve for 2007 dominates that for 2001 confirms the finding in section 2 of this paper that
the gap between the rural and urban sector has been growing over time. This dominance
results also reveals that the gap has been widening across all quantiles. In both years the
total differential generally increases across quantiles, implying that dispersion increases
at all points of the distribution. However, in 2001 the increase is steeper at the top end of
the distribution, while in 2007 it is steeper at the low end. This observation implies that,
in 2001 urban residence increased inequality more at the top of the distribution compared
to the bottom. The opposite happened in 2007.
Figure 3.7 A Decomposition of the RuralUrban Differential
2001 2007
1.2 1.0
1.0 0.8
Total Differential
D iffe re n c e in L o g E x p e n d itu re
D iffe re n c e in L o g E x p e n d itu re
0.8 0.6
Composition Effect
Total Differential 0.4
0.6
Structural Effect Structural Effect
0.4 0.2
0.2 0.0
Composition Effect
0.2
0.0
10 20 30 40 50 60 70 80 90
10 20 30 40 50 60 70 80 90
To further explore what may lie behind this configuration of urban bias in
Cameroon, we use the same decomposition technique that we applied to the growth
incidence curve above. Figure 3.7 shows the results for both years under consideration.
Focusing first on 2001, we notice that overall, the curve depicting the structural effect
tends to follow a Upattern while that representing the composition effect has, more or
less, an inverted Ushape. Furthermore the structural effect dominates the composition
effect over the whole range of the distribution, except between the 76th and the 88th
32
quantiles. This clearly shows that the greater increase in inequality at the top of the
distribution in 2001 is due to the structural effect. The composition effect is pulling in
the opposite direction.
Figure 3.8 Changes in Composition and Structural Effects over Time
Composition Effect Structural Effect
.5 1.1
2007 1.0
.4
0.9
Difference in Log Expenditure
Difference in Log Expenditure
.3 0.8
2001 0.7
.2
0.6
2007
0.5
.1
0.4 2001
.0 0.3
0.2
.1 10 20 30 40 50 60 70 80 90
10 20 30 40 50 60 70 80 90
Quantile
Quantile
In 2007 the inverted Ushape of the composition effect is more pronounced than
in 2001. The curve representing the structural effect has more or less the same shape as
in 2001. It tends to fall until the 31st quantile then increases monotonically afterwards.
The composition effect dominates the structural effect between the 11th quantile and the
median. Both curves are very similar between the median and the 60th quantile. Past that
point the structural effect clearly dominates the composition effect. Considering the
overall profile of the total ruralurban differential, we note that the effect of urban
residence on inequality is mostly driven by the composition effect at the low end of the
distribution and by the structural effect (with some dampening by the composition effect)
at the top end of the distribution.
Finally, figure 3.8 shows how each of these two components evolved between
2001 and 2007. The composition effect increased significantly across quantiles up to
about the 73rd quantile. In addition, its slope became steeper and peaked earlier (at the
33rd percentile compared to the 85th in 2001). This implies that the inequality enhancing
effect associated with composition has become more concentrated at the lower end of the
distribution in 2007 compared to 2001. There is a similar shift in the profile of the
structural effect. While the curve has more or less a Ushape in both years, it bottoms out
around the 30th percentile in 2007 and increases monotonically thereafter up to the 98th
percentile. In 2001 the structural effect bottoms out around the 85th percentile and
33
increases steeply afterwards. The key point that emerges from these observations is that
composition contributes proportionately more to the increase in the ruralurban gap at
lower quantiles while structure accounts for more of this increase at higher quantiles. In
other words differences in household characteristics matter more for the poorest
households (particularly in 2007) than returns to those characteristics. The reverse is true
for betteroff households. This finding suggests that prevailing social arrangements treat
the people at the bottom of the distribution alike whether they live in urban areas or not.
At the top of the distribution social arrangements in urban areas reward better the set of
characteristics than arrangements in the rural areas 33.
4. Concluding Remarks
The Government of Cameroon has declared poverty reduction through strong and
sustainable economic growth the central objective of its development policy. This paper
therefore seeks to characterize the pattern of economic growth in Cameroon focusing on
factors that might account for the observed heterogeneity in growth incidence. Our
analysis of available data shows poverty fell by about 13 percentage points between 1996
and 2001. But, between 2001 and 2007, growth weakened significantly due to the fact
that it was driven by low productivity sectors in the informal segment of the economy.
Poverty incidence fell only by 1 percentage point over that period, and the Gini
coefficient decreased by about 1.5 percentage point.
These aggregate changes in growth, inequality and poverty between 2001 and
2007 hide a great deal of heterogeneity in growth incidence in that period. A
decomposition of changes in poverty outcomes over time shows that the pure growth
effect dominates the inequality effect, except for the subperiod 20012007. Furthermore,
the meager reduction in poverty observed in 20012007 is mostly due to a modest
reduction in inequality. An application of the same methodology to deviations of
33
Nguyen et al. (2007) find a similar pattern for the case of Vietnam using a quantile regression
decomposition method proposed by Machado and Mata (2005). They explain their results by noting that,
in accounting for ruralurban gap in wellbeing, one should not expect the structural effect to be important
at the bottom of the distribution because poor people tend to work in jobs that pay little above the
subsistence level. However, at the very top of the distribution, urban markets pay more for the same bundle
of attributes than rural markets. One can therefore expect the structural effect to be more important than
the composition effect in the upper end of the distribution. This explanation is also relevant to our case.
34
regional poverty from the national level reveals significant variation in the poverty
impact of economic growth. Four regions out of 12 experienced significant increases in
poverty between 2001 and 2007 while overall poverty tended to decline. We also find
that, except for three regions, the real income effect dominates the inequality effect in
explaining the divergence between regional and national poverty.
We use RIF regressions to link the growth incidence curve for the 20012007
period to household characteristics and perform counterfactual decomposition and thus
account for heterogeneity of impact across quantiles. We find that the level of the GIC is
explained by the composition effect while its shape is driven by the structural effect. The
fact that the structural effect is negative or very close to zero on a wide segment of the
distribution reveals that the weak performance of the economy over the period under
consideration was mainly driven by the effect of the returns to household endowments.
This supports the view that growth did not occur in high productivity sectors of the
formal economy nor in the smallholder agriculture which employed 70 percent of rural
heads of household in 2001 and 74 percent in 2007. Indeed, our RIF regression results
show that returns to employment in smallholder agriculture were mostly negative in both
years. Yet agriculture once was the main engine of growth in Cameroon even though its
contribution to poverty reduction is debatable. The relationship between the composition
effect and the structural effect over the whole distribution indicates that the observed
gains at the lower part of the distribution are due to the structural effect while gains
beyond the 10th quantile are due mainly to the composition effect.
A further decomposition of the composition and structural effects reveals that the
level and the dispersion of the full composition effect are accounted for by household
demographics. Furthermore the contribution of assets to the composition effect is mostly
driven by changes in the distribution of years of schooling. A similar decomposition of
the structural effect shows that the overall profile of this effect is shaped by the sector of
employment and, to a certain extent, by geography. These are the two dimensions that
explain the negative values of the structural effect over some range of the lower end of
the distribution.
One finding that stands out above all else is that urban bias and regional disparity
are significant and have been increasing over time. A RIF regression decomposition of
35
the urbanrural differential shows that it has been increasing over time across all
quantiles. In 2001, the structural effect almost dominates the composition effect and thus
accounts for both the level and shape of the total differential. The increase in inequality
observed along the entire distribution in 2001 is thus due to the structural effect. In 2007,
the configuration of the composition and structural effects shows that the former
increases inequality at the lower end of the distribution while the latter does so in the
upper end.
What is the policymaker to make of these findings? Fundamentally, the living
standard achieved by an individual is an outcome of the interaction between opportunities
offered by society and the readiness and ability of the individual to identify and exploit
such opportunities. The perspective of development as opportunity equalization
promotes a level playing field where individuals have equal opportunities to pursue freely
chosen life plans and to be spared from extreme deprivation in outcomes. Our findings
suggest that the pattern of growth in Cameroon is characterized by urban bias, regional
disparity and a decline of the agricultural sector. This is hardly an outcome of
opportunityequalizing growth, and makes one question the effectiveness of the
underlying development strategy. There is therefore a need to reexamine (and possibly
reform) the mechanisms governing the allocation of public resources (e.g. investment in
infrastructure, health and education) designed to support individuals' efforts to improve
their standard of living, both in the rural and the urban sectors.
36
Appendix A: Poverty and Inequality by Region
Table A1 Regional Distribution of Poverty in 2001
Headcount Poverty Gap Squared Poverty Gap Watts Population Share
Douala 10.89 2.07 0.72 2.61 9.70
Yaoundé 13.34 2.66 0.86 3.27 8.72
Adamaoua 48.38 15.39 6.38 20.31 4.47
Center 48.18 14.97 6.63 21.05 7.85
East 43.98 15.37 6.75 20.85 4.81
Far North 56.29 18.84 8.18 25.34 17.74
Coast 35.48 10.09 4.17 13.43 4.88
North 50.08 15.50 6.36 20.43 7.26
North West 52.48 20.90 10.70 30.83 11.52
West 40.33 11.10 4.19 14.19 12.06
South 31.55 7.35 2.43 9.04 3.45
South West 33.83 10.50 4.51 14.13 7.53
Cameroon 40.18 12.79 5.55 17.38 100.00
Source: Authors' Calculations
Table A2 Regional Distribution of Poverty in 2007
Headcount Poverty Gap Squared Poverty Gap Watts Population Share
Douala 5.50 0.87 0.21 1.01 9.96
Yaoundé 5.94 0.97 0.24 1.13 9.60
Adamaoua 52.95 14.49 5.41 18.46 5.18
Center 41.19 9.48 3.10 11.68 7.63
East 50.40 15.69 6.22 20.25 4.66
Far North 65.87 24.58 11.21 33.35 18.11
Coast 31.08 7.65 2.71 9.60 3.50
North 63.66 20.99 8.58 27.43 9.85
North West 51.00 16.61 6.83 21.78 10.14
West 28.95 6.64 2.27 8.24 10.58
South 29.25 7.37 2.65 9.31 3.24
South West 27.51 6.87 2.47 8.65 7.55
Cameroon 39.90 12.31 5.03 16.11 100.00
Source: Authors' Calculations
37
Table A3 Regional Inequality in the Distribution of Welfare, 2001
Gini Atkinson1 Atkinson2 Mean Log Deviation Theil
Douala 42.46 26.16 39.33 30.33 41.17
Yaoundé 42.59 26.00 39.88 30.11 37.79
Adamaoua 33.82 16.87 28.81 18.48 20.14
Center 34.62 18.55 34.61 20.52 22.06
East 34.21 17.66 31.33 19.43 20.26
Far North 32.97 16.05 27.77 17.49 18.69
Coast 34.19 17.62 31.36 19.39 20.24
North 36.16 19.23 31.11 21.36 25.62
North West 40.55 24.40 41.68 27.98 29.96
West 31.21 14.69 25.49 15.89 17.61
South 29.76 13.27 23.19 14.24 15.45
South West 38.02 21.41 35.88 24.09 26.81
Cameroon 40.41 24.00 38.82 27.45 33.75
Source: Authors' Calculations
TableA4 Regional Inequality in the Distribution of Welfare, 2007
Gini Atkinson1 Atkinson2 Mean Log Deviation Theil
Douala 33.87 17.07 28.37 18.72 21.72
Yaoundé 33.15 16.60 28.02 18.15 21.07
Adamaoua 33.75 16.70 27.25 18.27 21.20
Center 28.07 11.91 20.72 12.68 14.13
East 32.88 15.79 26.63 17.19 18.99
Far North 36.52 19.14 30.28 21.24 25.07
Coast 31.86 15.33 25.71 16.64 19.26
North 35.33 18.22 28.57 20.12 24.65
North West 38.24 20.98 33.32 23.54 27.66
West 29.73 13.39 23.41 14.37 15.80
South 34.58 18.02 29.79 19.87 23.61
South West 33.24 16.54 28.67 18.08 19.69
Cameroon 38.96 21.94 35.82 24.77 27.88
Source: Authors' Calculations
Table A5 Shapley Decomposition of Urban Rural Differences in 1996
Difference Scale Inequality
Headcount 11.87 16.12 4.26
Poverty Gap 4.43 8.51 4.09
Urban Squared Poverty Gap 2.09 4.96 2.87
Watts 6.12 13.24 7.12
Headcount 6.36 13.12 6.76
Poverty Gap 2.37 6.91 4.53
Rural Squared Poverty Gap 1.12 4.10 2.98
Watts 3.28 10.80 7.52
Source: Authors' Calculations
38
Table A6 Shapley Decomposition Urban Rural Differences in Poverty in 2001
Difference Scale Inequality
Headcount 22.30 22.25 0.05
Poverty Gap 8.51 8.20 0.31
Urban Squared Poverty Gap 3.96 3.80 0.16
Watts 11.90 11.39 0.50
Headcount 11.90 19.87 7.97
Poverty Gap 4.54 9.40 4.86
Rural Squared Poverty Gap 2.11 5.03 2.91
Watts 6.35 13.94 7.59
Source: Authors' Calculations
Table A7 Shapley Decomposition Urban Rural Differences in Poverty in 2007
Difference Scale Inequality
Headcount 27.73 22.05 5.68
Poverty Gap 9.50 7.69 1.81
Urban Squared Poverty Gap 4.06 3.44 0.62
Watts 12.60 10.42 2.18
Headcount 15.14 21.41 6.27
Poverty Gap 5.19 10.47 5.28
Rural Squared Poverty Gap 2.22 5.64 3.42
Watts 6.88 15.34 8.46
Source: Authors' Calculations
39
Appendix B: Returns to Household Characteristics
Table B1: OLS and Unconditional Quantile Regression Coefficients on Log Expenditure,
2001
Eq Name: OLS Quantile 10 Quantile 25 Quantile 50 Quantile 75 Quantile 90
Dep. Var: LPCEXP RIFQT_10 RIFQT_25 RIFQT_50 RIFQT_75 RIFQT_90
Constant 13.159472 12.210145 12.571805 12.924253 13.729915 13.827996
(0.0639)** (0.1606)** (0.1110)** (0.0797)** (0.0861)** (0.1278)**
Male 0.174076 0.059530 0.191985 0.206050 0.269449 0.170897
(0.0150)** (0.0377) (0.0261)** (0.0187)** (0.0202)** (0.0300)**
Age of Head 0.011417 0.006544 0.005325 0.007807 0.013364 0.009057
(0.0022)** (0.0055) (0.0038) (0.0027)** (0.0030)** (0.0044)*
Age Head
Squared 0.000087 0.000110 0.000011 0.000061 0.000117 0.000104
(0.0000)** (0.0001)* (0.0000) (0.0000)* (0.0000)** (0.0000)*
Age<5 (% of
Household) 0.007902 0.007698 0.009874 0.006203 0.008073 0.010204
(0.0005)** (0.0012)** (0.0008)** (0.0006)** (0.0006)** (0.0009)**
Age 5 to <10
(%HH) 0.012690 0.015400 0.014570 0.010822 0.013067 0.013608
(0.0004)** (0.0011)** (0.0008)** (0.0006)** (0.0006)** (0.0009)**
Age 10 to < 15
(%HH) 0.009995 0.008390 0.012397 0.010170 0.011347 0.011913
(0.0005)** (0.0012)** (0.0008)** (0.0006)** (0.0006)** (0.0010)**
Age 15 to <20
(%HH) 0.008513 0.006559 0.010314 0.007059 0.008955 0.009859
(0.0005)** (0.0012)** (0.0008)** (0.0006)** (0.0006)** (0.0009)**
Age 20 to <25
(%HH) 0.006930 0.007858 0.008141 0.005454 0.006481 0.007318
(0.0005)** (0.0013)** (0.0009)** (0.0006)** (0.0007)** (0.0010)**
Schooling
(Years) 0.035724 0.023833 0.036002 0.036836 0.036160 0.048525
(0.0015)** (0.0039)** (0.0027)** (0.0019)** (0.0021)** (0.0031)**
Land 0.000040 0.000327 0.000266 0.000542 0.000681 0.002758
(0.0002) (0.0005) (0.0003) (0.0002)* (0.0002)** (0.0004)**
Access to Credit 0.272581 0.344990 0.502057 0.167755 0.174148 0.203044
(0.0187)** (0.0469)** (0.0324)** (0.0233)** (0.0251)** (0.0373)**
Has Migrant (s) 0.003390 0.093183 0.035803 0.008674 0.007870 0.013549
(0.0110) (0.0277)** (0.0191) (0.0137) (0.0148) (0.0220)
Distance to
Nearest Hospital 0.007699 0.018778 0.005528 0.003056 0.001005 0.000380
(0.0010)** (0.0024)** (0.0017)** (0.0012)* (0.0013) (0.0019)
Distance to
Nearest Tarred
Road 0.000842 0.003488 0.000158 0.001588 0.000488 0.000673
(0.0002)** (0.0004)** (0.0003) (0.0002)** (0.0002)* (0.0003)
Public Sector 0.137185 0.261937 0.280543 0.158812 0.164989 0.327043
(0.0247)** (0.0619)** (0.0428)** (0.0307)** (0.0332)** (0.0493)**
Private Sector
(Formal) 0.268188 0.061321 0.392699 0.266786 0.271007 0.346203
40
(0.0249)** (0.0626) (0.0433)** (0.0311)** (0.0336)** (0.0498)**
Agriculture 0.046850 0.240546 0.204856 0.078711 0.125636 0.046576
(0.0183)* (0.0459)** (0.0317)** (0.0228)** (0.0246)** (0.0365)
NonAgriculture
Informal 0.003375 0.098520 0.169686 0.053357 0.062985 0.025741
(0.0200) (0.0503) (0.0348)** (0.0250)* (0.0270)* (0.0400)
Unemployed 0.074510 0.096578 0.368856 0.163483 0.251011 0.256259
(0.0365)* (0.0915) (0.0633)** (0.0454)** (0.0491)** (0.0728)**
Urban 0.358252 0.327079 0.411417 0.424686 0.326439 0.367371
(0.0173)** (0.0434)** (0.0300)** (0.0215)** (0.0233)** (0.0345)**
Adamaoua 0.070304 0.056012 0.011889 0.119813 0.035959 0.011751
(0.0321)* (0.0806) (0.0557) (0.0400)** (0.0432) (0.0642)
East 0.020939 0.209054 0.159996 0.025619 0.010703 0.038472
(0.0264) (0.0663)** (0.0459)** (0.0329) (0.0356) (0.0528)
FarNorth 0.122657 0.072381 0.034542 0.153209 0.141260 0.151345
(0.0197)** (0.0494) (0.0342) (0.0245)** (0.0265)** (0.0393)**
Coast 0.110042 0.052038 0.033475 0.104869 0.143391 0.102709
(0.0226)** (0.0569) (0.0393) (0.0282)** (0.0305)** (0.0453)*
North 0.171011 0.148245 0.012795 0.116118 0.155918 0.255212
(0.0241)** (0.0606)* (0.0419) (0.0301)** (0.0325)** (0.0482)**
NorthWest 0.097031 0.273372 0.206432 0.040859 0.032324 0.018093
(0.0202)** (0.0507)** (0.0351)** (0.0252) (0.0272) (0.0403)
West 0.095338 0.421776 0.038442 0.088763 0.011295 0.047624
(0.0193)** (0.0485)** (0.0336) (0.0241)** (0.0260) (0.0386)
South 0.118213 0.391487 0.347940 0.290900 0.165893 0.140165
(0.0359)** (0.0901)** (0.0623)** (0.0447)** (0.0483)** (0.0717)
SouthWest 0.040321 0.027439 0.043129 0.149609 0.123667 0.124648
(0.0222) (0.0558) (0.0386) (0.0277)** (0.0299)** (0.0445)**
Observations: 11000 11000 11000 11000 11000 11000
Rsquared: 0.3659 0.0970 0.1992 0.2909 0.2555 0.1558
Fstatistic: 218.3177 40.6275 94.0738 155.1603 129.8469 69.7986
Source: Authors' Calculations (Standard Errors in Parentheses)
41
Table B2: OLS and Unconditional Quantile Regression Coefficients on Log Expenditure,
2007
Eq Name: OLS Quantile 10 Quantile 25 Quantile 50 Quantile 75 Quantile 90
Dep. Var: LPCEXP RIFQT_10 RIFQT_25 RIFQT_50 RIFQT_75 RIFQT_90
Constant 13.304263 12.197708 13.120938 13.238181 13.718763 13.899547
(0.0629)** (0.1408)** (0.1113)** (0.0882)** (0.1018)** (0.1419)**
Male 0.070325 0.154760 0.224476 0.071366 0.401929 0.244113
(0.0367) (0.0822) (0.0650)** (0.0515) (0.0595)** (0.0829)**
Age of Head 0.013158 0.008564 0.016820 0.010976 0.014366 0.005371
(0.0018)** (0.0041)* (0.0033)** (0.0026)** (0.0030)** (0.0041)
Age Head
Squared 0.000114 0.000121 0.000139 0.000089 0.000095 0.000041
(0.0000)** (0.0000)** (0.0000)** (0.0000)** (0.0000)** (0.0000)
Age<5 (% of
Household) 0.005484 0.000316 0.005747 0.005578 0.007783 0.010410
(0.0004)** (0.0009) (0.0007)** (0.0005)** (0.0006)** (0.0009)**
Age 5 to <10
(%HH) 0.007811 0.003733 0.007685 0.007811 0.011619 0.011752
(0.0004)** (0.0009)** (0.0007)** (0.0006)** (0.0007)** (0.0009)**
Age 10 to < 15
(%HH) 0.008318 0.011155 0.009006 0.007767 0.007190 0.008996
(0.0004)** (0.0009)** (0.0007)** (0.0006)** (0.0007)** (0.0009)**
Age 15 to <20
(%HH) 0.007076 0.005308 0.006029 0.006312 0.009886 0.010217
(0.0004)** (0.0009)** (0.0007)** (0.0005)** (0.0006)** (0.0009)**
Age 20 to <25
(%HH) 0.001457 0.000448 0.000346 0.000942 0.002167 0.002464
(0.0004)** (0.0009) (0.0007) (0.0006) (0.0007)** (0.0010)**
Schooling
(Years) 0.027913 0.022405 0.020552 0.027924 0.031056 0.038209
(0.0014)** (0.0031)** (0.0025)** (0.0020)** (0.0023)** (0.0032)**
Land 0.000909 0.001197 0.001421 0.000104 0.000284 0.002935
(0.0003)** (0.0006) (0.0005)** (0.0004) (0.0005) (0.0006)**
Access to Credit 0.121824 0.326021 0.003454 0.153329 0.183045 0.187816
(0.0164)** (0.0367)** (0.0290) (0.0230)** (0.0265)** (0.0370)**
Has Migrant (s) 0.007313 0.059992 0.047730 0.027361 0.053349 0.025172
(0.0092) (0.0206)** (0.0163)** (0.0129)* (0.0149)** (0.0208)
Distance to
Nearest Hospital 0.001734 0.003170 0.003044 0.002540 0.000407 0.001568
(0.0005)** (0.0011)** (0.0009)** (0.0007)** (0.0008) (0.0011)
Distance to
Nearest Tarred
Road 0.000625 0.001278 0.001463 0.000091 0.000473 0.000360
(0.0001)** (0.0003)** (0.0002)** (0.0002) (0.0002)* (0.0003)
Public Sector 0.101134 0.145912 0.018378 0.033898 0.294486 0.314242
(0.0396)* (0.0886) (0.0700) (0.0555) (0.0641)** (0.0893)**
Private Sector
Formal 0.011229 0.121331 0.017644 0.156668 0.013729 0.243870
(0.0404) (0.0905) (0.0716) (0.0567)** (0.0655) (0.0913)**
Agriculture 0.253115 0.142792 0.362748 0.348374 0.149680 0.090588
(0.0358)** (0.0801) (0.0633)** (0.0502)** (0.0579)** (0.0808)
NonAgriculture 0.144915 0.126827 0.050514 0.181781 0.160270 0.221364
42
Informal
(0.0350)** (0.0783) (0.0619) (0.0490)** (0.0566)** (0.0789)**
Unemployed 0.190645 0.026608 0.141813 0.232494 0.135369 0.072094
(0.0403)** (0.0903) (0.0714)* (0.0566)** (0.0653)* (0.0911)
Urban 0.428321 0.075966 0.304728 0.548580 0.683162 0.563511
(0.0162)** (0.0362)* (0.0286)** (0.0227)** (0.0262)** (0.0365)**
Adamaoua 0.011547 0.053626 0.002316 0.066617 0.082621 0.123803
(0.0233) (0.0521) (0.0412) (0.0327)* (0.0377)* (0.0526)*
East 0.158765 0.413217 0.303400 0.148873 0.105910 0.029665
(0.0240)** (0.0537)** (0.0424)** (0.0336)** (0.0388)** (0.0541)
FarNorth 0.176944 0.743800 0.448753 0.088092 0.002126 0.115887
(0.0170)** (0.0381)** (0.0301)** (0.0239)** (0.0275) (0.0384)**
Coast 0.013519 0.084526 0.149400 0.024614 0.250750 0.208432
(0.0338) (0.0756) (0.0597)* (0.0473) (0.0546)** (0.0762)**
North 0.137783 0.232887 0.397939 0.157968 0.000971 0.079831
(0.0182)** (0.0408)** (0.0323)** (0.0256)** (0.0295) (0.0412)
NorthWest 0.112079 0.402653 0.311221 0.095947 0.022289 0.059885
(0.0207)** (0.0463)** (0.0366)** (0.0290)** (0.0335) (0.0467)
West 0.104418 0.079892 0.222665 0.153857 0.000626 0.078137
(0.0200)** (0.0447) (0.0353)** (0.0280)** (0.0323) (0.0451)
South 0.096130 0.032081 0.203152 0.174641 0.095795 0.113482
(0.0295)** (0.0661) (0.0522)** (0.0414)** (0.0478)* (0.0666)
SouthWest 0.149720 0.147742 0.191804 0.264295 0.024753 0.043076
(0.0203)** (0.0454)** (0.0359)** (0.0284)** (0.0328) (0.0458)
Observations: 11388 11388 11388 11388 11388 11388
Rsquared: 0.4994 0.1991 0.3137 0.3804 0.3316 0.1744
Fstatistic: 390.7395 97.3824 179.0128 240.4643 194.2671 82.7593
Source: Authors' Calculations (Standard Errors in Parentheses)
43
Figure B1: Returns to Education and Access to Credit
Education Credit
.07 .6
.06 .5 2001
.4
.05
2001 .3
.04
C o effic ien t
Coefficient
.2
.03 2007
2007 .1
.02
.0
.01 .1
.00 .2
.3
.01
10 20 30 40 50 60 70 80 90
10 20 30 40 50 60 70 80 90
Quantile
Quantile
Figure B2: Returns to Land and Migration
Land Migration
.006 .20
2007
.005 .15
.004
.10
.003
.05
C o e ffic ie n t
C o e ffic ie n t
.002 2001
2007 .00
.001
2001 .05
.000
.10
.001
.002 .15
.003 .20
10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90
Quantile Quantile
Figure B3: Returns to Formal Sector Employment
Private Sector Public Sector
.6 .6
2001
.4
.4 2001
.2
.2
Coefficient
C o e fficie n t
.0
2007
.0
.2 2007
.2
.4
.4 .6
10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90
Quantile Quantile
44
Figure B4: Private and Public Sectors Compared
2001 2007
.6 .5
Private
.4
.4
.3
.2
.2 Public
Public
Coe ffic ie nt
.1
C oe ffi c i e nt
.0 .0
.1
.2
Private
.2
.4 .3
.4
.6 10 20 30 40 50 60 70 80 90
10 20 30 40 50 60 70 80 90
Quantile
Quantile
Figure B5: Returns to Smallholder Agriculture and Urban Residence
Agriculture Urban Residence
.4 .8
2001
.7
.2 2007
.6
.0
.5
C o e ffic ie n t
C oe ffi c i e nt
2001
.2 .4
2007
.3
.4
.2
.6
.1
.8
.0
10 20 30 40 50 60 70 80 90
10 20 30 40 50 60 70 80 90
Quantile
Quantile
45
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