Economics Department, University Buea,
Buea
Cameroon
The Mediation Effect of Informal sector growth
on the Relationship between Poverty and
Inequality in Sub-Saharan Afri
Ngepah Belina and Johannes Tabi

PLAN OF PRESENTATION

INTRODUCTION
Recently, there have been growing interests on
informal economy and its resulting effect on
inequality and poverty dynamics (Sharma and
Adhikari, 2020).
It is generally accepted that economic growth
increases income which in turn reduces poverty
(Bergstrom, 2020; Cerra et al., 2021) but its impact
on inequality and poverty is confusing and depends
on the underlying sources of growth (Cerra et al.,
2021).

INTRODUCTION CON’T
Research (Fosu 2010b) indicates that that the
responsiveness of poverty to income is a
decreasing function of inequality (Fosu 2010c).
High initial levels of inequality limit the
effectiveness of growth in reducing poverty while
growing inequality increases poverty directly for a
given level of growth (Fosu 2011).

INTRODUCTION CON’T
SSA is the only area where poverty has been rising
over time (Atangana, 2022). Average poverty rate
for SSA stands at about 41%.
Out of the world’s 28 poorest nations, 27% are
from SSA with a poverty rate of 30%. Thus,
majority of extremely poor persons now live and
die in SSA(Ines et al; 2022).

INTRODUCTION CON’T
 Rising income inequality has been the tendency in SSA
(Gimba et al., 2021).
Inequality and growth are important factors to be
considered in formulating strategies and policies to
achieve the SDG 1(Ines et al; 2022).
 Kunawotor et al., (2020) revealed that no matter how
much growth is boosted, it may not have any
significant impact on poverty reduction except there is
an equivalent drop in inequality

INTRODUCTION CON’T
 Resolving inequality problem is a key to solving
poverty trap in Africa
Despite previous propositions that the informal
sector is temporary, there is acknowledgement in
recent time that this sector is dynamic in nature
and progressively becoming persistent and growing
across many countries of the world (Esaku, 2021).

OBJECTIVES AND
LITERATURE
 To Assess the mediation effect of informal sector
growth on the nexus between poverty and
inequality.
A number of studies have tried to quantify the
effects of informality on poverty and inequality
(Canelas, 2018; Delbiso et al.; 2018; Nuhu and
Abdullahi, 2018, Elgin et al.; 2021, Elgin and
Elveren 2019).

OBJECTIVES AND
LITERATURE CON’T
 The literature has identified both theoretical and
empirical channels through which output growth
can influence inequality and poverty.
This could be observed from an understanding of
the dualistic labour market theory.
The concern here is the effect of informal sector
growth on the nexus between poverty and
inequality.

METHODOLOGY
 This work covers a period of 29 years, running from 1990 to 2018 and the focus is
on 35 countries in SSA
 However, 29 years for model are too many. Accordingly, the GMM model is
appropriate for N>T and N (i.e. 35) should be substantially higher than T (i.e. 29).
 However, it is also important to note that the minimum T for a GMM is five (i.e. T
must be >=5).
 With a T=29, data averages in terms of overlapping intervals with the 7th year
having 5 years is adopted and this prevents instrument proliferation.

METHODOLOGY CON’T
 A dynamic panel data model estimated using the Generalized Methods of
Moments (GMM) was adopted for the analysis.
 Model specification :
• POVit = 𝛼0 + λ1POVit-1 + λ2INFit + λ3GINIit + λ4UEMPit + λ5TOit + λ6lnPOPit +
• λ7lnGOVCit + λ8lnGDFCFt +λ9(INF *GINI)it +λ10lnGDPPCίt +
• vi +Ԑit (1)
 We transform equation (1) into a dynamic form and estimate it using the
two step procedures and the two equations below in levels and first
difference

METHODOLOGY CON’T
• POVit = 𝛼0 + 𝛼1POVit-1 + 𝛼2INFit +𝛼3GINIit + 𝛼4 (INF *GINI)it +𝛼5UEMPit +𝛼6TOit + 𝛼
7lnPOPit + 𝛼8lnGOVCit + 𝛼9lnGDFCFit +𝛼10lnGDPPCίt +ẟi +θt + eit
(2)
POVit - POVit-1 = 𝛼1(POVit-1 - POVit-2) +𝛼2(INFit- INFit-1) +𝛼3(GINIit - GINIit-1)+ 𝛼4 {(INF *GINI)it-(INF
*GINI)it-1} + 𝛼5(UEMPit - UEMPit-1) +𝛼6(TOit - TOit-1)+ 𝛼7(lnPOPit - lnPOPit-1) +𝛼8(lnGOVCit -
lnGOVCit-1) + 𝛼9ln(GDFCFit - GDFCFit-1)+ 𝛼10(lnGDPPCίt - lnGDPPCίt-1)+ (θt - θt-1) + eit- eit-1)
( 3)
 We adopted the current GMM-centric literature for a strong specification, and
identification strategy by using years as instruments.
 Strictly exogenous as years cannot become endogenous after a first difference.
Otherwise it is not easy to find strictly exogenous variables because almost all variables
are characterised by simultaneity or reverse causality ( Roodman (2009a, 2009b) and
this limit the proliferation of instruments.

DATA DEFINITION AND
SOURCE
Variables Designation Description Source
Informal sector growth infmimic1 multiple indicators multiple causes
model-based (MIMIC) estimates of
informal output (% of official GDP)
Informal world development indicator
(IWDI)
Inequality Gini Coefficient Consumption inequality Global Consumption and Income
Project ( GCIP)
poverty POVit Poverty head count GCIP ,Global
Consumption and Income Project
Interactive variable inter_term informal sector output interacted with
inequality
(infmimic1* Gini)/100
Unemployment uemp1 It is the total percentage of the labour
force that is not working. It consists of
individuals without work but who are
able and willing to work.
World Development Indicators from
the World Bank (WDI)
Trade openness to1 It is estimated by the summation of
imports and exports divided by the
level of GDP.
World Development Indicators from
the World Bank (WDI)
Log of gross domestic
product per capital
lrgdppc Annual GDP per head World Development Indicators from
the World Bank (WDI)
Log of total Population lpop It is the total number of
people in a country
World Development Indicators from
the World Bank (WDI)
Log of Gross domestic
fixed capital formation
lgdfcf Annual total investment World Development Indicators from
the World Bank (WDI)
Log of Government
consumption expenditure
lgovco Government expenditure on good and
services
World Development Indicators from
the World Bank (WDI)

DETECTION OF
MULTICOLLINEARITY
Pairwise correlation matrix
Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
(1) infdge1 1.0000
(2) cgini1 -0.2058 1.0000
(3) pov1 0.5383 -0.1441 1.0000
(4) uemp1 -0.3742 0.5795 -0.4189 1.0000
(5) to1 -0.2985 0.2533 -0.3271 0.5900 1.0000
(6) lpop 0.1636 -0.2574 0.3837 -0.3542 -0.3832 1.0000
(7) lgovco -0.2369 0.0593 -0.1971 0.1742 0.0088 0.6755 1.0000
(8) lgdfcf -0.0494 0.0554 -0.1496 0.1202 0.1154 0.1227 0.2562 1.0000
(9) lrgdppc -0.3379 0.3459 -0.7719 0.6019 0.4159 -0.2473 0.4396 0.3158 1.0000
(10) Inter _term 0.7186 0.5162 0.3524 0.0496 -0.0657 -0.0893 -0.2191 -0.0268 -0
Pairwise correlation matrix on variables after differencing the interaction term
Variables (1) (2 (3) (4) (5) (6) (7) (8) (9) (10)
(1) nfdge1 1.0000
(2) cgini1 -0.2071 1.0000
(3) pov1 0.5381 -0.1440 1.0000
(4) uemp1 -0.3746 0.5860 -0.4204 1.0000
(5) to1 -0.3054 0.2506 -0.3289 0.5919 1.0000
(6) lpop 0.1662 -0.2516 0.3867 -0.3566 -0.3808 1.0000
(7) lgovco -0.2360 0.0687 -0.1956 0.1795 0.0233 0.6749 1.0000
(8) lgdfcf -0.0491 0.0653 -0.1516 0.1164 0.1276 0.1288 0.2671 1.0000
(9) lrgdppc -0.3366 0.3492 -0.7720 0.6045 0.4208 -0.2475 0.4438 0.3228 1.0000
(10) D. inter_term 0.0539 0.0776 0.0035 -0.0127 -0.0425 0.0106 -0.0063 0.0153 0

REGRESSION RESULTS
Mediating Effect of Informal sector growth on Poverty through inequality
(1) (2) (3) (4)
VARIABLES pov1 pov1 pov1 pov1
L.pov1 0.920*** 0.873*** 0.873*** 0.893***
(0.0238) (0.0345) (0.0428) (0.0481)
lag2 0.0575* -0.0782 0.0547
(0.0325) (0.0597) (0.0788)
lag3 0.117 -0.209
(0.0710) (0.139)
lag4 0.192**
(0.0784)
cgini1 25.91*** 26.14*** 24.06*** 31.10***
(7.755) (6.641) (7.016) (8.341)
infmimic1 0.296*** 0.280*** 0.252*** 0.309***
(0.0953) (0.0649) (0.0694) (0.0840)
inter_term -36.85** -34.55*** -31.42** -43.12***
(17.32) (10.24) (12.60) (14.82)
uemp1 0.0658 0.0576 0.0820 0.109*
(0.0503) (0.0451) (0.0660) (0.0642)
to1 -0.0187** -0.0233*** -0.0254*** -0.0326***
(0.00738) (0.00796) (0.00856) (0.00799)
lrgdppc -3.178*** -3.177*** -3.807*** -3.816***
(0.523) (0.537) (0.693) (0.679)
lpop -0.287 -0.517 -0.726 -0.547
(0.556) (0.531) (0.548) (0.527)
lgdfcf -0.0115 -0.0128 0.000782 0.0305*
(0.0146) (0.0119) (0.0160) (0.0174)
lgovco 0.222** 0.251*** 0.323*** 0.276***
(0.103) (0.0870) (0.0965) (0.0950)
yr1 0.969 1.164** 2.036*** 2.328***
(0.637) (0.552) (0.740) (0.762)
yr2 0.346 0.0958 0.134 0.357
(0.618) (0.507) (0.488) (0.455)
yr3 0.181 0.0332 0.0316 0.297
(0.488) (0.400) (0.413) (0.375)
yr4 0.181 0.0532 0.0581 0.324
(0.444) (0.355) (0.376) (0.340)
yr5 0.303 0.267 0.257 0.545*
(0.394) (0.273) (0.307) (0.277)
yr6 0.0395 0.0965 0.0387 0.122
(0.306) (0.267) (0.277) (0.259)
Time effects Yes Yes Yes Yes
Constant 10.87 13.70 22.52* 15.10
(12.48) (11.55) (12.15) (12.17)

REGRESSION RESULTS
F-stat
Prob > F
230281.55
0.000
124654.36
0.000
278439.64
0.000
72911.61
0.000
AR(1):(Pr > z) 0.002 0.001 0.001 0.000
AR(2):(Pr > z) 0.052 0.079 0.027 0.989
Sargan OIR Prob >
chi2
0.000 0.000 0.000 0.000
Hansen OIR Prob >
chi
0.847 0.815 0.678 0.865
DHT for instruments
(a)Instruments in levels
H excluding group 0.026 0.017 0.006 0.014
Dif(null, H = exogenous
(b) IV (years, eq(diff)
1.000 1.000 1.000 1.000
H excluding group 0.574 0.513 0.509 0.464
Dif(null, H =
exogenous)
0.999 1.000 0.808 1.000
Instruments 45 45 45
Countries 35 35 35 35
Observations 965 1964 963 962
*** p<0.01, ** p<0.05, * p<0.1
***, **, *: significance levels at 1%, 5% and 10% respectively. DHT: Difference in Hansen Test for Exogeneity of Instruments Subsets. Dif:
Difference. OIR: Over-identifying Restrictions Test. F: test for overall significant of the finding. AR(1): First order autocorrelation test &
AR(2):Second order autocorrelation test and tandard errors in parentheses

CONCLUSION
 Findings revealed a growing informal sector and of course inequality is
associated with increased poverty,
 Secondly, poverty falls as a result of the interaction between informal sector
output and inequality.
 The implication is that a growing informal sector can modulate poverty
through its confounding effect on inequality

THANK YOU