STICKY FLOOR OR GLASS CEILING? INVESTIGATING GENDER
WAGE GAP IN THE NIGERIAN LABOUR MARKET
Emmanuel Nwosu
Department of Economics, University of Nigeria
and Anthony Orji

ADRODEP Conference, Kigali, Rwanda
STICKY FLOOR OR GLASS CEILING? INVESTIGATING GENDER WAGE
GAP IN THE NIGERIAN LABOUR MARKET
21st -23rd March, 2023
by
Emmanuel Nwosu and Anthony Orji
Department of Economics, University of Nigeria, Nsukka

Introduction and Motivations
Interest in the magnitude, pattern and causes of gender wage gap has
been growing both within and across countries and African countries are
not left out (Ntuli & Kwenda, 2020 and Mohanty 2021).
 While labour unions across the globe have continued to push for higher wages, the
issue of gender differences in earnings (Metcalf, 2009; Mamiko, 2021) has
continued to be of great interest.
 The results of the analysis of gender disparity in labour markets in Africa is
dominated by sharp contrasts and has remained a serious challenge in
many countries (ILO, 2019).
 Even though the number of women in paid employment and non-agricultural
sectors in Africa has increased by more than 3.5% over the past decade, they still
earn lower than men.
 Female workers have been found to earn about twenty to thirty percent below
their male colleagues (ILO, 2019).

Introduction and Motivations
Gender earnings differential could be higher in developing countries if in-
depth analyses are carried out.
There are concerns that the Nigerian labour market exhibits
significant gender wage disparities (e.g. Temesgen, 2008 and Ajefu,
2019).
 Though research exists to explain gender wage gap in Nigeria, the focus has mostly
been on mean gap with very little done to explain wage inequality along the wage
distribution by gender.
 The issue of glass ceiling and/or sticky floor which are relevant to design of gender
sensitive policies (Temesgen, 2008; Ajefu, 2019; among others) have also not be
adequately addressed by existing literature in Nigeria.
• Glass ceiling, for example, is a labour market condition whereby females are pushed down to the
lower cadre of an organization and it seems difficult for women to attain the highest position in
the organization while sticky floor is a discriminatory employment pattern that keeps workers,
mainly women, in the lower ranks of the job scale, with low mobility and invisible barriers to career
advancement.

The Research Issue
Existing literature on labour market studies in Nigeria has made
attempts to explain gender wage discrepancies with particular focus
on mean decompositions (Okpara, 2004, Oyelere, 2007,Onwudiokit,
2009, Agu and Evoh, 2011, Oginni et al,2014).
 However, limited or no in-depth empirical study to the best of our knowledge has
been carried out to determine if significant gender differences exists along the
wage distribution in Nigerian and how individual attributes explain the disparity on
various parts of the distribution.
 We employ two waves of Living Standards and measurement Surveys (2003/2004
and 2018/2019) for an in-depth analysis of gender wage bias in wage employment
and temporal explanations to the gap.

Research Questions
1. Are there significant gender differences along the wage
distribution in the Nigerian labour market?
2. How do individual characteristics explain gender wage gap
at various points of the wage distribution?
3. How has gender wage gap and factors underpinning it in
the Nigerian labour market changed over time?

The Nigerian Labour Market
 The labour market in Nigeria is characterised by both formal and informal
sectors, private and public sectors, rural and urban sectors.
 Available statistics from the NBS (2019) shows that the informal market has about 65%
of Nigeria’s workforce who dominate most of the activities in the economy even
though it is characterised by low wages.
The Nigerian labour market is also characterized by high rate of
unemployment and under employment.
 Nigeria’s rate of unemployment rose to 22.6% in 2018 from 10.6 % in 2012. NBS (2021)
report further shows that, for fully employed and underemployed labour force,
females accounted for 40% and 45.9% respectively in 2020.
 Furthermore, Nigerian labour force statistics as of 2019 show that out of the
62,447,230 labour force population, 45.57 percent (i.e 28,457,207) women make up
the total, while 54.43% are men.
 On the other hand, for the labour force with advanced education, the percentage of
the male working-age population with advanced education is 74.76%, while that of the
female population is 73.68%.

OVERVIEW OF THE LITERATURE
 Theory
Neoclassical theory of distribution and Becker Theory of Gender Gap in
Labour Markets of Becker (1957 and 1965).
Empirics
Three are existing studies that related gender to labour market
outcomes in Nigeria (See for example Aderemi, and Alley 2019,
Abekah-Nkrumah, Asuming, and Yusif 2019; Kyoore and Sulemana,
2019; Akono 2018; Akekere and Yousuo, 2013; Fapohunda, 2013; Autor,
et al, 2006; Bradley and Nguyen, 2004)

Contributions to Knowledge
Our paper contributes to the extant literature in three ways:
• First, previous studies on the gender wage gap in Nigeria concentrated
on measuring and decomposing the gender wage gap at the mean.
• This study departs from analysis at the mean by considering the entire
wage distribution using the extended decomposition approach proposed
by Firpo et al. (2009).
• Second, we attempt to provide temporal analysis of gender wage gap In
Nigeria using waves of household surveys.
• Third, this paper sheds light on the factors underpinning observed gender
wage gaps in Nigeria by decomposing the gender wage gap into the ‘price’
and ‘composition effects’.
• The composition effect also called the endowment effect shows how labour
market characteristics of individuals influence the gender wage gap. The price or
wage structure effect explains how pricing or utilization of individual
characteristics affects the gender wage gap.

METHODOLOGY
Theoretical Framework and Model Specifications
The theory which has been used by researchers for decades to explain
wage determination is the Mincer (1974) model.
In this framework, wage is expressed basically as a function of
education and experience.
However, the model has been extended by researchers to account for
other covariates that are related to individual demographic
characteristics, productivity and location (Bils and Klenow, 2000)

METHODOLOGY
Model Specifications
Taking cognizance of the Nigerian context, we specify the
wage equation in vector form as:
where is the log wage expressed in natural logarithm.
 The way labour market is structured in Nigeria is that except for
labourers who are rewarded on daily or piece meal basis, majority are
paid on monthly basis.

METHODOLOGY
Model Specifications
the full specification of the wage equation we estimated is given by:
)
2
.
1
..(
..........
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..........
South_West
h
South_Sout
South_East
North_West
North_East
others
ing
manufactur
ted
sales_rela
ervices
clerical_s
tion
administra
al
al_technic
profession
ndary
above_seco
secondary
primary
union
widowed
separated
divorced
married
urban
age
lnwage
20
19
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which is estimated for two groups (g=male, and female) so that the contribution
of each covariate to differences in the wage distributions of males and females
can be divided into composition and wage structure effects.
Wage growth can be affected by the characteristics of those in employment. For example, if the
age of those in the workforce increases, average wages are likely to increase as older workers
are on average paid more. These are known as 'compositional effects'.

METHODOLOGY
Decomposition of Wage Distribution
We applied an extension of OB decomposition technique that relies on the
re-centered influence function (RIF) regression in order to estimate the
effects of covariates on the distribution of wages between males and
females and over time.
The distribution of interest are quantiles (Firpo, Fortin, and Lemieux 2009;
2011; and 2018).
 The main attractive feature of using the RIF-regression approach to
compute the Oaxaca-Blinder type decomposition is that it gives a linear
approximation of highly non-linear functional such as the quantiles (which
of interest to us to in this research).
 We followed Firpo, Fortin, and Lemieux (2018) and applied unconditional
quantiles in our decomposition of gender wage gap in Nigeria and what
explains gap over time.

Following from FFL (2018), the th

 quantile of the distribution F is define as the functional of
the form: .
q
or
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(
|
inf(
)
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 quantile is
given by:
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Our Approach: Decomposition of Wage Distribution

The decomposition of (unconditional) quantiles follows the same approach as mean
decomposition. In stage one, the estimation of 1,0
g
, 
g
q and C
q
ˆ are obtained by reweighting as:
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and
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Software packages such as Stata can easily compute g
q
ˆ and C
q with appropriate reweighting
factor. Hence, the gaps can be calculated as:
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ˆ
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and
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Our Approach: Decomposition of Wage Distribution

In stage two, the linear RIF-regressions will be estimated and this can be done for each observation
by inserting the sample estimate of the quantile, r
q̂ , and using it to estimate the density of the
quantile, ).
ˆ
(
ˆ

q
f For example if ,
1
|
1 
T
Y the RIF-regressions can be estimated by substituting the
usual dependent variable, Y, with the estimated value of )
,
;
(
ˆ
1 F
q
y
F
I
R  which can also be done
with standard software packages such as Stata. The estimated coefficients will therefore be:
0
.
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,
;
(
ˆ
)
,
(
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,
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This can be decomposed as in the case of mean gap as follows:
2
.
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).........
ˆ
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]
1
|
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
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



Our Approach: Decomposition of Wage Distribution

DATA
We used the 2003/2004; and the 2018/2019 Nigeria Living Standards
Surveys (NLSS).
In the 2003/2004 survey, the number of households sampled was
21,900 while a total of 18, 861 was used for the final analysis.
The most recent household survey is the 2018/2019 survey which was
published by the National Bureau of Statistics of Nigeria in 2020.
In this survey, the total number of households captured was 22,110 out
of the 22,200 originally proposed.
 A total of 116,320 individuals were sampled and our tabulations
show that about 49,780 the individuals captured were in paid
employment.

DATA
 For the key variable earnings, respondents indicated the frequency of payment
i.e. daily, weekly, biweekly, monthly, quarterly and annually. To facilitate
comparability, we converted all earnings to reflect monthly income. This is
consistent with the structure of labour market Nigeria where, except for
labourers who are rewarded on daily or piece meal basis, majority are paid on
monthly basis.
 We converted these earnings to natural logarithm for the analysis and plotted
the kernel densities presented in Figures 1 for the two surveys. The density
function for log of wage in 2003/2004 and that of 2018/2019 are approximately
normally distributed.
 However, while the density function is left skewed in the 2003/2004 compared
to that of 2018/2019 suggesting that the mean is less than that of 2018/2019 (in
nominal terns anyway).
 However, in order to further facilitate comparison earnings over time and using
2009 as the base year, we deflated the nominal wages by the consumer price
index in order to obtain real wages for both males and females as reported in the
descriptive statistics table.

Descriptive Statistics
Table 1 shows the descriptive statistics, it can be seen that average for
males was higher than that of females in the two periods.
For example, in 2003/4 and 2018/19, average wage for males were
N16,698.1 and N42,822.29 respectively. The average wage for females
over the two periods were NN11,249.51 and N33,453.74 respectively.
The wage gap between them was N 5448.59 in 2003/4 and the by
2018/19 the average wage gap was N 9368.58.
The ratio of male nominal wage income to that of female workers that
was 1.48 in 2003/4 had dropped to 1.28 (or dropped by 1.22 in real
wages) in 2019/18. This seems to suggest a reduction in relative gender
wage gap over the two periods.

Table 1: Descriptive Statistics of the Variables by Gender and Time Period
2003/2004 2018/2019
male female Male Female
variable mean sd mean sd mean sd mean sd
wage 16,698.1 37449.9 11,249.5 31917.3 42,822.3 31293.4 33,453.7 30890.3
Real wage (2009 base year) 25,795.0 59336.3 17,500.7 50527.7 15,760.7 25663.1 12,878.0 15960.3
lnrealwage 9.2287 1.4240 8.7123 1.4287 8.8495 1.5087 8.7962 1.3775
Demographics/location
age 37 14.12 34 12.830 33 14.18 33 13.49
urban 0.230 0.421 0.222 0.415 0.303 0.459 0.296 0.457
never married 0.310 0.462 0.153 0.360 0.465 0.499 0.269 0.443
married 0.652 0.476 0.748 0.434 0.512 0.500 0.634 0.482
divorced 0.007 0.084 0.008 0.087 0.005 0.068 0.010 0.098
separated 0.016 0.125 0.018 0.133 0.010 0.099 0.017 0.131
widowed 0.015 0.123 0.073 0.261 0.008 0.086 0.070 0.256
wage_emp 0.392 0.488 0.245 0.430 0.149 0.356 0.062 0.241
union 0.059 0.236 0.023 0.151 0.055 0.229 0.024 0.154
Education
none 0.126 0.332 0.171 0.376 0.171 0.377 0.233 0.423
primary 0.365 0.481 0.400 0.490 0.188 0.391 0.208 0.406
secondary 0.369 0.483 0.336 0.472 0.492 0.500 0.451 0.498
above_secoondary 0.140 0.347 0.093 0.291 0.149 0.356 0.108 0.310
Occupation
Professional or technical 0.055 0.228 0.035 0.184 0.253 0.435 0.556 0.497
Administration 0.010 0.099 0.005 0.071 0.148 0.355 0.115 0.319
Clerical/services and related 0.089 0.284 0.249 0.433 0.145 0.352 0.127 0.333
Sales and related 0.077 0.267 0.199 0.399 0.040 0.197 0.047 0.213
agriculture 0.686 0.464 0.463 0.499 0.128 0.334 0.088 0.284
Manufacturing/processing/ 0.060 0.238 0.046 0.210 0.256 0.436 0.052 0.222
agriculture 0.686 0.464 0.463 0.499 0.509 0.500 0.343 0.475
others 0.022 0.147 0.003 0.052 0.030 0.170 0.015 0.121
Number of observations 18,063 17,262 29,193 31,563
Descriptive Statistics

RESULTS AND DISCUSSION ON FINDINGS
Table 2 shows that there is a significant gender mean wage gap in
Nigerian and also at all points of wage distribution.
Thus, women earn less than men and this result is consistent over time
though not in the same magnitude.
This is in line with the finding by Aderemi, and Alley (2019) who shows
wide variation in the wages of males and females in favour of male
workers in private and public sectors in Nigeria.
The total gaps show an inverted U-shape. This implies that gender
wage gap was wider at the middle income, but as wage increases, the
gap becomes narrower beginning from the 50th quantile.

RESULTS AND DISCUSSION ON FINDINGS
2003/2004 2018/2019
Total Difference Total Explained total Unexplained Total Difference Total Explained total Unexplained
Mean 0.485*** -0.0255 0.510*** 0.357*** -0.0115 0.369***
(0.000) (0.349) (0.000) (0.000) (0.491) (0.000)
q10 0.445*** -0.0311 0.476*** 0.433*** -0.0105 0.443***
(0.000) (0.572) (0.000) (0.000) (0.765) (0.000)
q20 0.477*** -0.0150 0.492*** 0.538*** -0.0259 0.564***
(0.000) (0.759) (0.000) (0.000) (0.282) (0.000)
q30 0.567*** -0.0232 0.590*** 0.384*** -0.132*** 0.516***
(0.000) (0.474) (0.000) (0.000) (0.000) (0.000)
q40 0.571*** 0.00204 0.569*** 0.494*** -0.0454* 0.540***
(0.000) (0.937) (0.000) (0.000) (0.025) (0.000)
q50 0.575*** 0.000466 0.575*** 0.359*** -0.00717 0.366***
(0.000) (0.984) (0.000) (0.000) (0.725) (0.000)
q60 0.523*** 0.0131 0.509*** 0.285*** 0.0445* 0.240***
(0.000) (0.568) (0.000) (0.000) (0.035) (0.000)
q70 0.437*** -0.00854 0.445*** 0.229*** -0.00208 0.231***
(0.000) (0.731) (0.000) (0.000) (0.925) (0.000)
q80 0.408*** -0.0586* 0.467*** 0.205*** -0.00397 0.209***
(0.000) (0.030) (0.000) (0.000) (0.863) (0.000)
q90 0.368*** -0.0954* 0.463*** 0.122*** -0.0119 0.134***
(0.000) (0.022) (0.000) (0.001) (0.644) (0.000)
N: Males
Female 5191
2783
3978
1807
p-values in parentheses
Table 2: Decomposition Results for Total, Composition and Wage Structure Effects for the
2003/2004 and 2018/2019 Surveys
p-values in parentheses
"* p<0.05 ** p<0.01 *** p<0.001"

RESULTS AND DISCUSSION ON FINDINGS
 Furthermore, there appears to be double inverted U-shape for the raw gap over
the 2018/2019 period. The non-overlapping confidence intervals at all the quantile
points show pictorially the significance of gender wage gap in Nigeria both across
time and space (see figure 3).
 First, both in the 2003/4 and 2018/19 periods, the total gap increased along the
distribution up to the 50th quantile and began to decline consistently up to the
90th quantile but the decline at the top quantile was more rapid since 2018/19.
During this period, most of the quantile-specific gender pay gaps were accounted
for by the wage structure effect
 For the 2018/19, the gaps were also larger at the bottom than at the top of the
income distribution while for the 2003/04 period the gaps were lower at the
bottom and top end of wage distribution and are found to be statistically different
from zero overall.

RESULTS AND DISCUSSION ON FINDINGS
 Moreover, at the top of wage income the composition effect was significant for
the 2003/04 period while it was significant at the middle of wage income in the
2018/19 period.
 In any case the composition effect works to reduce gender wage gap (see Figure
2. This suggests that if certain female characteristics approach that of males,
gender wage gap in Nigeria would decline significantly).
 These findings overall suggest that wage structure effect has been dominant in
explaining gender wage gap in Nigeria since the 2003/2004 period.
 This is shown pictorially in Figure 3 by the non-overlapping confidence intervals for the wage
structure effect component of the gap decomposition.

RESULTS AND DISCUSSION ON FINDINGS
Figure 2: Decomposition results – Raw Wage Gap

RESULTS AND DISCUSSION ON FINDINGS
 Figure 3: Decomposition results – composition and Wage Structure effects

RESULTS AND DISCUSSION ON FINDINGS
 The findings further show that demographic characteristics, human capita
(education level), occupational factors and location explain the composition effect
of wage gap as well as the wage structure effect. Example:
 Age:
 Marital status:
 Urban/rural location:
 Unionisation:
 Education:
 Occupation:
 As a general note and in corroboration with the findings by FFL (2018), no single
factor appears to be able to fully explain the polarization of the wage distribution
over time.

CONCLUSION AND RECOMMENDATIONS
 The findings in general suggest that the pricing of labour in terms of the attributes
act to reduce gender wage gap to the advantage of women at higher wages; while
at lower wages, these characteristics do not reduce the gap as much.
 The findings further show that raw gaps for the two surveys appear to show
inverted U-shape suggesting evidence of both sticky floor and glass ceiling effect
in both periods in the Nigeria labour market.
 In terms of the contributions of individual covariates on gender pay gap in Nigeria,
we found that urban residence, unionization, education (human capital variables)
and occupation variables exhibit major influence, and some of them such as urban
residence, marital status, higher education, and union membership have remained
consistent over time how the affect gender wage gap especially through the wage
structure effect.

CONCLUSION AND RECOMMENDATIONS
 This suggest that for wage gap to be reduced over time, education level above
secondary for girls and women should be taken seriously.
 Efforts should be geared towards promoting higher educational
qualifications for women even when they are already in the labour market
 Women should be given more opportunity in urban employment where wages
are higher and the gap is lower.
 Invariably, there is the need to promote policies that would reduce urban
unemployment for women since urban residence appears to favour women by
reducing wage gap than in rural employment

CONCLUSION AND RECOMMENDATIONS
 Urbanization of labour tends to cause higher pay gap at the top of wage
distribution. Therefore, policy makers should design mechanisms to ensure that
urban wage gap is reduced especially in top positions where women should get
equal pay with men.
 Women are also encouraged to unionize though not radically in order to benefit
from the influence of unions especially in securing higher wages where
necessary.
 Finally, policies to address gender wage gap can be focused on specific
occupations or sub-sectors where women are disadvantaged.

THANK YOU
Acknowledgements
We wish to express our deep appreciation to African Economic
Research Consortium (AERC) for the financial support to carry
out this research