Gender Wage differences; the case of Nigeria

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Gender Wage differences; the case of Nigeria

NWEKE NOTTINGHAM NNANYIBU

Submitted to Swansea University in fulfilment of the requirements for MSc IN INTERNATIONAL BANKING, FINANCE AND ECONOMICS

DEPARTMENT OF ECONOMICS

SWANSEA UNIVERSITY [email protected] 2014

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DECLARATION This work has not previously been accepted in substance for any degree and is not being concurrently submitted in candidature for any degree. Signed……………………………………………………. (Candidate) Date ………………………………………………………

STATEMENT 1 This dissertation is being submitted in partial fulfilment of the requirements for the degree of MSc in International Banking, Finance and Economics. Signed………………………………………………………. (Candidate) Date …………………………………………………………

STATEMENT 2 This Dissertation is the result of my own independent work/investigation, except where otherwise stated. Other sources are acknowledged by footnotes giving explicit references. A bibliography is appended. Signed………………………………………………………. (Candidate) Date …………………………………………………………

STATEMENT 3 I hereby give my consent for my Dissertation, if relevant and accepted, to be available for photocopying and for inter-library loan, and for the title and summary to be made available to outside organisations. Signed ………………………………………………………… (Candidate) Date ……………………………………………………………

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ACKNOWLEDGEMENTS

I am indeed heavily indebted to my supervisors Dr. Nigel O’Leary and Dr. Reza Arabsheibani for their immense contribution. The study would not have existed if not for their valuable support and guidance. Their indefatigable contribution in labour economics and especially their interest on wage gap decomposition motivated my curiosity for this research topic. Each of time I had a supervisory meeting with them, the urge to do more research sparks within me. The challenges of research were so frustrating at times but those challenges brought out the best in me.

My regard goes Mr Matthew Welch, of The World Bank micro data library and Mr Leo Sanni of Nigerian National Bureau of Statistics for making the data available for this study, and Vanessa for her priceless support.

Lastly, I am grateful to God Almighty for making this study a success; I would like to thank my parents, my siblings, my love (Blessing) and my little angels (Franklin, David and Naomi) for their immeasurable support and guidance. I dedicate this dissertation to them all.

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ABSTRACT

Wage discrimination especially against women has remained a focal point of several literatures in Africa and even on the global perspective, hence a call for equity and sustainable national development and poverty reduction. This dissertation will seek to examine gender wage gaps, if it does exist between the wages of male and female workers in Nigeria, the differences within industries and occupation, and the reasons for these gaps. It will further examine the regional and sectorial impacts to these gaps, as well as the individual characteristics, access to education, finance, health service, high profile jobs and other social infrastructures.

The Data source of this study will be the 2010 general household survey and 2012-2013 general household panel wave2 survey from the Nigerian National Bureau of statistics and the world bank, as an integrated living standard measurement survey (LSMS) for Nigeria. Sample selections of households were taken which includes men and women with different occupations, employed within various sector of the economy; however those with negative wage were removed from the sample to avoid further lowering of the mean wage of both groups.

Key words: Gender wage gap, wage decomposition, enumerated area, earnings and wage were used interchangeably, human capital characteristics and wage differences.

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TABLE OF CONTENT PAGE

TITLE PAGE …………………………………………………………..I DECLARATION……………………………………………………….II ACKNOWLEDGEMENTS…………………………………………...III ABSTRACT…………………………………………………………...IV CONTENTS…………………………………………………………....V LISTS OF TABLES…………………………………………………...VI LIST OF TABLES…………………………………………………….VI CHAPTER ONE

INTRODUCTION ……………………… ……...1

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Organisation study structure…………………….……………...4

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Institutional background....……………………………………..4

CHAPTER TWO LITERATURE REVIEW………………………......7 2.1 Differences in preferences ………………………… ………..8 2.2 Differences in comparative advantage………………………....9 2.3 Differences in human capital investment……………………...11 2.4.1 Earnings……………………………………………………..12 2.4.2 Stereotype………………………………………………..…14 2.5 Factors summarising wage differential……………………….16 2.6 Theories of wage discrimination……………………………...19 CHAPTER THREE METHODOLOGY AND DATA DESCRIPTION..21

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CHAPTER FOUR EMPIRICAL RESULTS………………………..30

CHAPTER FIVE CONCLUSION AND RECOMMENDATIONS....44

BIBLOGRAPHY ……………………………………………….…...46

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LIST OF TABLES

PAGES Table 1; Definition of variable and mean estimation…………………..31 Table 2; Log wage regression……………………………….… ……..32 Table 3; Occupational participation……………………………………33 Table 4; Estimation with the exclusion of occupational variables…….38 Table 5; Oaxaca Ransom estimation…………………………….……..40 Table 6; Adjusted Oaxaca Ransom estimation…………………….…..41 Table 7; Nopo matching estimation…………………………………...42 Table 8; Adjusted Nopo matching estimation…………………….…...43

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Chapter one

1 INTRODUCTION Labour-market analyst (Hicks, 1963), in his study, defined wage as the price of labour, he also stated that these price are determined by the forces of demand and supply in the market for labour. However, a lot of studies including (Altonji & Blank, 1999) have shown that the wages of most minority groups including women are determined by the individual group characteristics rather than the conditions of labour market. Hence the right of Women that guarantees Freedom and Equity has been the focus of so many struggles in the 20th century, including the Beijing Declaration, the Equality Act and the Child Act (UNDP, 1995), and so many other legislations made in Nigeria and the world over. From the nineteenth century to the pre-colonial era the role of the Nigerian woman has continued to evolve within the household, in governance and the society as a whole. Due to their extraordinary ability and skill, the story of Nigeria would never be told without the enviable contribution of women, their skills and achievement. However, despite all these efforts gender wage differences have remained stubbornly persistent even when there seems to be some convergence within the gap (Altonji & Blank, 1999). Above all the Nigerian women have not been able to transfer these wonderful skills, character and qualification to their level of earnings as a group compared to their male counterpart, hence they are considered as second citizen in Nigeria due to their role in the family and society (Sudarkasa, 1986). Gender wage gap, is defined as the differences associated with the average gross earnings of men and women within the same occupation, industry and economy (Metcalf, 2009). Gender inequality in Nigeria has been reviewed by several studies including (Tinuke, 2013) and (Opeke, 2002) to be highly correlated with economic, political and socio-cultural inequalities. The United Nations through its agency (UNDP, 2009), have observed that gender inequality in Nigeria is traditionally associated with religious and cultural practices, and with great dismay have observed the widening of this gap in Nigeria from 0.43 to 0.49 between 1985 and 2008. It further highlight that inequality in Nigeria is systemic, having the country

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ranked as one of the highest inequality countries in the world, meaning that inequality in Nigeria spreads across regions, sectors and occupations. Therefore, people within the lower bounds do not have the opportunity for upward mobility. A close examination according to (Tinuke, 2013), (UNDP, 2009) shows that the level of poverty in the country is partly a feature of high level of inequality, which is fully manifested in unequal distribution of income and limited access to all basic infrastructure which includes education, credit facility, good road, training, job opportunities and health services which at the end hinders poverty reduction and economic growth. After a lengthy military rule and the transition process that brought in a democratic administration in Nigeria, the democratic processes enabled Nigerian women to increase their labour force participation, improved their educational attainment, occupational location, nonwage compensation, job mobility, higher training, access to justice through the numerous activities of women activist, better representation, greater political participation as well as gaining high profile employment and becoming chief executive of highly reputable firms. However, an overall assessment of these efforts and results have shown that the gap between wages of both groups are still wide and are in favour of the men compared to the results from developed countries that have had similar efforts from women, (Christofides, et al., 2010)further revealed that men and women with same job could be paid same wage but on average the wage of men are higher than that of women. With the expectation of closing these gap, the Nigerian government with the support of the United Nations development program rolled out a developmental road map title “growth and equality” (UNDP, 2009) looking at this plan and the previous ones, it could be recall that forty years after the first Nigerian bill for the introduction of equality wage pay and an equality act 2010 that was made to reinforce the 40 years act, nevertheless the authorities are faced with several limitations that hinder the implementation of this laws and its acceptance, they also revealed factors responsible for wage gap, which include differences in human capital, occupation segregation, institutional barriers and labour market discrimination. Following these, a significant attention has been drawn from national to international bodies including several research work to the issues of gender wage gap in the Nigeria labour market (UNDP, 2009), with the institution of a democratic government in 1999, the initial stages to achieve equality and the protection of women right includes the Child Rights Acts of 2003, with the coverage of 18 states out of the 36 states of Nigeria, the gender equality duty 2007,

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to be implemented by all public enterprises and government ministries, the equality act and the creation of the ministry for women affairs and social development to cater for the needs of women, these perhaps were all done to close the gap. To achieve the various expectations of the government including poverty reduction and economic growth, there must be equality in the workplace and other areas within the economy. These could be done by providing for equal wage and opportunity to all without gender consideration; without doubt this will increase labour force participation and lead to increase in productivity and an overall increase in the total labour force output. According to (Thomas, 1990), wage as a of sources motivation for any worker has failed to motivate the Nigeria woman, hence they are discouraged from participating in the labour force or prefer casual and family jobs. According to (Lundberg & Richard, 1983) the bargaining power of the Nigerian women are highly influence by their position in the family, education attainment, health, employment status and access to credit, they suggested anti-discriminatory policies that will reflect societal acceptance. (Christofides, et al., 2010) In their studies argues that “glass ceiling”1 were the actual reason for wage differential, because women are limited from attending a certain height in the society, and organisations that employ their services even when they are the most qualified; evidently 95% chief executives of big firms in Nigeria are men even when they are less qualified (World Bank, 2010). The average wage of Nigerian female work force within the same industry and occupation with their male counterpart has not been determined by productivity but gender characteristics, as against the standards of wage theory according (Gerhart & Rynes, 2003), which states that wages ought to be measured by productivity. This study is therefore, tempted to ask if the Nigerian labour wage is determined by the forces of labour market or some institutional arrangement. This dissertation will above other things wish to established the possible reasons for gender wage differences in Nigeria, it will not only take a mere descriptive analysis but will employ the use various statistics measurement using the integrated living standard measurement survey (LSMS) for Nigeria according to (Bureau of Statistics, 2013), the use of OLS regressions will be considered essential and other form of statistics measurement that will enable us to control individual characteristics, hence the purpose of this literature is to fill the literature gap about gender wage differences in Nigeria, by establishing if wage discrimination against women in the 1

The norm that do not allow women to certain position in the society or organisations were they work

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Nigerian labour market, thirdly the study will also look at estimation of possible gaps if there are any, fourthly it well make use of quantitative measurement to establish male-female wage differential sources.

1.1 Organisation of the study The layout of this study will open with chapter one, which serves as an introduction to the entire research. As noted earlier, chapter one relates to the disparity between the average earnings of the male and female workers in Nigeria and the impact on poverty reduction and economic growth, key to the conclusion of the chapter show that gender wage differences remain wide with increased in women labour force participation and institutional changes. Chapter two will outline focus on the Nigerian labour market with different individual characteristics that fuel inequality and it will consider the various previous empirical works on gender wage gap in both developed and developing economies of the world drawing a conclusion on the studies and the gaps that exist. In chapter three, the context will involve the analytical explanation of various methodologies to be use; chapter four will establish all the empirical result and explanations. While chapter five will draw up the conclusion of the entire study by summarising each of the chapters and the overall findings of the study.

1.2 Institutional backgrounds Before the amalgamation of Southern and Northern protectorate to the present day Nigeria in 1914, (Attoe, 2002), in his study highlight that the contribution of Nigerian women were so immense that the name Nigeria was given by a woman, a British journalist Flora Shaw who was till then, the wife of the Governor-general of Nigeria, Lord Lugard.

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independence, the Nigerian women with a lot of skills controlled certain occupation and held several key positions including female chiefs and age grade leaders, but women in Nigeria were not allowed to vote till 1970’s. Substantially over the decade it is evident that efforts made to curtail gender wage gap were more obvious after the years’ of prolong military administration and the transition process that brought the democratic governance in Nigeria. However, it was on record that these efforts yielded some positive results that led to some

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opening within the political arena, e.g. increased number of political appointment quota for women by Dr. Goodluck Jonathan led administration. These openings were also evident with the increase in political representation of women in some of the states including the federal capital territory, among which 9 senators and 32 honourable members of federal house of representative were elected, and there were so many other achievements within the state and federal level. Furthermore, these developments necessitate the promulgation of numerous bills passed by

both arms of the national and

states house of assemblies, as well as the numerous contributions of some International organisations which includes the United Nations human development report, the World Bank development indicators and the international Labour organisation (ILO) to mention but few. Several other monitoring agencies were as well created to ensure the implementation of the various Acts, Bills and Declarations. (Attoe, 2002), revealed that the history of women in Nigeria can be traced back to the nineteenth century with host of achievements, evidently the contributions Fumilayo Ransom Kuti, the first female activist that spearheaded the fight for equal right for the Nigerian woman in the 1950’s and Madam Alimotu Perewuru the then chairwoman of then Lagos market women union, whose economic and political influence was a huge challenge to then colonial administration, as well as the organisers of the Aba women riot of 1929 in which more than 30,000 women defiled the resistance of the colonial army and match nude against the unfair tax imposition on women, from Aba to Ibibio land. Studies have shown that despite this long-term effort the gaps are still wide, (Christofides & Vrachimis, 2007). Nigeria subdivided into two major religious group, the North dominated by Islamic religion and the South dominated by Christian religion, according (UNDP, 2008) the foundation of every kind of inequality in Northern Nigeria against women appear not to be justifiable in Islamic theology nevertheless these laws are implemented without consideration, the same applicable to the South with the issue of widowhood. The team revealed a notable evidence of death sentence pronounced on a lady called Safiya’s for adultery, while her male counterpart was made to walk away freely, this case is one too many. It is indeed a cause for concern as the decline within the gaps are still slow over the decades despite the numerous effort by female activist, human right lawyers and the various declarations that guarantee the provision of equal opportunities including the Beijing

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Declaration for a step-ups in the protection of women right and full political participation especially at the decision-making level.

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Chapter two

2 Literature review To delineate the subject of gender wage differences (Altonji & Blank, 1999), suggests that it will be logical for one to “compare the different attribute that different individuals bring to their occupation or workplace, which includes performances and other human capital characteristics, but with differences in gender, race or ethnicity. (Altonji & Blank, 1999), defined labour market discrimination as situations in which persons that provide labour force services with equally productive, are treated unequally in a way that relates to their individual observable characteristics on average within the same labour market or occupation”. The analysis of gender wage differences because of its economic impact have continue to occupy the attention of so many literatures as it provide a guide in formulating policies that promote equal opportunity, poverty reduction, access to income distribution and basic infrastructures. The pre-neoclassical theory of labour market states that women inferiority position in the labour market are based on wage differences and institutional constraints, while (David & Tzannatos, 1993) revealed that gender segregation were the earliest form of discrimination and were the products of Atlantic side of Europe, he further explain that the other forms of discrimination were associated with race, dominated in America and geographical segregations which were seen with the introduction of the colonies (slave trade).

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2.1 Differences in preferences The model of labour market economics which covers gender wage gap according to (Becker, 1965), have associated

lower wage earnings of women to time allocation based on

preferences and public opinions which might be viewed as gender specifics, (Blau & Kahn, 2000) have shown the most striking issue of women status for many decades now are the fairly small number of female predominate jobs with very lower wages, such jobs includes clerical and services occupations, they revealed that in the 1970’s about 53 percent of women were in such jobs with only 15 percent of men. The choice of women to work has been largely influenced by several factors including the allocation of time, marriage, role definition and custom. According to (Altonji & Blank, 1999) professional women were only predominately employed in female professions, such as basic teaching, nursing, care services etc. with low wage, they also highlight that women were highly under-represented in white-collar jobs with high wages while it was male predominate job. People differ in their choices of participation in the labour market or nonprofit verses private establishment, the distribution of choices and their characteristics across a group will determine the wage distribution of the group. Considering how preferences affect wages and why the effect remains, (Webb, 1919) argued that the employment of women could be dependent on other variables such as marital status, level of education, work experience, training, family background, time allocation etc. He further illustrates that family rationality influences labour market choices of women and create labour market participation constraint which are most crucial to explain gender wage difference. He examined that pre-labour market discrimination of family that reduces the human capital investment of the girl child and their role in the family; hence the quality of education women receive, their field of study and access to higher education affects the level of wage now and in the future. Parental discrimination should be considered as one of the sources of gender discrimination in human capital attainment. Some evidence has shown that discrimination originates from institutional settings that hinder women from the pursuit of their desired career and constraint their upward progression, (Reskin & Hartmann, 1986) and (Royalty, 1996). However, (Blau, et al., 1998) have shown that there has been a reduction in the gap over the years following the higher human capital investment on women, and the occupational shift preference of women to predominately male occupations, it is also evident

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that technological innovation have altered the effects of physical strength of men and increase the average productivity of women and preferences. It is worthy to note that research findings of (Solberg & Laughlin, 1995) revealed that occupational preferences are the foundations or determining factors of gender wage gap. They show that women tend to go for less human capital investment hence they are likely to go for lower wage-paying occupations that provides flexibility for work and family. They further state that women care more about their home than their career, and are less likely than men to choose high paying career jobs, because such jobs lack flexibility. However, (Witt, 1992), and (Weaver, 1998) critics the ideas of (Solberg & Laughlin, 1995) stating that maledominated occupations have more flexibility as against the notion that women are allowed more leave time, but rather that men have more leave time in their occupation than femaledominated occupation. (Bourshey, 2007), argued that men have more access to flexibility and view the preference of women as a myth, that women choose less paying jobs because it allowed them to manage work and family. (Blau & Kahn, 2006) Suggested that preferences are significant factors therefore they cause a large portion of gender wage gap to be left unexplained. It is remarkable that even in occupations with the full representation of both genders, the concept of glass ceiling concentrate women within the lower wage paying firms and positions (Blau, 1977). However Blau, Simpson and Anderson (1998) have shown that, there is a significant change in the composition of several occupations, and that women have had tremendous success in labour force participation by entering into job that were before now male-dominated high wage paying jobs.

2.2 Differences in comparative advantage The standard model of human capital investment predicts that human capital investment are acquired in accordance with the returns they are anticipated to generates and the comparative advantages that exists, therefore is expected to have lower investment with a gender group that tend to work for fewer hours and stay fewer years over their career life, hence their productivity and wages will be lower compare to those groups that tend to stay longer in their career, (Altonji & Blank, 1999).

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(Becker, 1991), (Mincer & Polachek, 1974), analysing the economics of family argues that biologically based differences in reproduction creates traditional division of labour between gender, giving women comparative advantage in home production and child-bearing/rearing, while physical strength have given men advantage in certain area of labour force participation that involves physical strength. Research have shown that parents tend to invest less in the human capital investment of their girl child compared to the boy child because returns to such investment are lower compared to the later. The tendencies of Nigerian women leaving the labour market more frequently or to have a short time career leads to lowering of their average wage earns compared to the men with more hours of work and long stay in the labour market, according to (Tinuke, 2013) this are one major reason for gender wage gap in Nigeria. With lower labour market experience and their anticipated short and discontinuous career, it is predictable to anticipate the wage of women to compulsory plunge lower. (Becker, 1991),argues that the comparative advantage of women are further amplified by stereotype and parental investment in the skill preference for their daughters as housewives, because they belief that the household production skills of women would be rewarded in marriage in a market populated by men who are prepared for labour market and tend to provide for the household, therefore women are less likely to go into careers and trainings that require longer time and resource investment, thus results in smaller human capital investment and thereby lower their wage earnings relative to men with heavy human capital investment. (Becker, 1985), revealed that the more women choose to spend time taking care of the household and less hours and years they tend to spend in the labour market. (Becker, 1993), revealed that the family traditional form of division of labour are at the detriment of women with regard to labour market participation, hence it cause women to devote more of their time non-pay and domestic works with less time for occupational work. (OECD, 2009), in its report has indicated that women are obliged on average to work few hours because of household responsibilities which includes unpaid domestic works and child-rearing which the male partner is exempted from on average. Therefore, is important to stress that the growing importance of science and technology with cognitive and interpersonal skills that have further reduce gender differences and comparative advantage. But the family have played an important role of skill preferences in the acquisition

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of occupation-specific human capital investment hence group differences in comparative advantage might persist.

2.3 Differences in Human capitals investment (Bergmann, 1984) in his work argued that gender wage gap can only be explained partially through human capital factors and work patterns, he further examined that particularly high earnings partially reflect past education, experience, status, family background and the labour market structure but however returns to education can only be seen through labour force participation. (Mincer & Polachek, 1974), in their study of economics of family have revealed that discrimination affects human capital development and individual preferences about jobs therefore, individual characteristics and human capital has a very massive role in explaining gender wage gap. (Altonji & Blank, 1999), have shown with preferences and Human capital hypothesis that segregation in wages, occupation and employment patterns were the consequences of preferences and skills differences and not discrimination. They further highlight that preference and human capital null hypothesis were the underlying factor of most empirical research on gender wage differences, on this instance therefore they stressed that discrimination is assume to be part of the residual differences that exist in labour market outcome which cannot be observed But (Altonji & Blank, 1999) recalled that earning differences are related to inter-related factors, including the combination of pre-labour market training, work and family life as well as discrimination and occupational segregation. (Tinuke, 2013), (Altonji and Blank, 1999), and (Backer, 1993), highlights that preference evolves overtime with pre-labour market gender discrimination in child-rearing practice/education system, should be viewed as one of the major source of wage differential. The preference of boy to girl rational response by parents even some altruistic ones who tend to shape the preference of the girl child to avoid future discrimination of their daughters in predominately male occupation and to be in line with the status tradition and custom. (Becker,1993) argued that a greater percentage of women are engaged in casual jobs including unpaid household farm works and domestic jobs than the men, and that this tend to lower the average wage earning of women compared to the wage received by higher percentage of men in full time jobs.

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He also reviewed that greater percentage of women are forced to leave the labour market for some issues that includes child-bearing, geographical relocation of spouse, care of the elderly and the household care in general, (Mason, 2004) argued that men are likely to attach more importance to their career and wage payment than the women do, most working women are seen as a special group of people and cannot be seen as a sample for women because they attached more importance to family values than the men do, he further analyse that religious and traditional status of women hinder the full participation of women in the labour force. (Bommer, 1984), observed that even in countries with greater level of equality like Sweden where parents equally are given 16 months paid parental leave, men only spend on average 20% of these time with their kids while they transfer the remaining to their parents wellbeing and other issues. Nigeria with a greater level of inequality, marriages in general affects the female wage earnings, because women are left with so much household responsibility with the accompanied maternity leave. In Nigeria, child care is seen as the sole responsibility of women leading to fewer hours in their occupational job. (Soetan, 2002), has argued that the reasons why fewer women are in higher paying occupations and higher management level are because of their frequent exit and entry into labour market. It is important to highlight that education might lead to a greater difference in comparative advantage and human capital development.

2.4.1 Earnings Earnings defined as reward for labour and other entitlement are ought to be structured according to productivity, however, Bound and Freeman (1992) highlights that local labour markets differ and are mostly immobile therefore depress wages of a particular group, while so many series of recent studies including (Altonji, 1995), (Tinuke,2013) and couple of others literatures have tried with recent evidence to show that wage structure was one factor that were directly related to wage differences of male and female workers. They also highlight experience as one of the determinate of the level of earnings; hence it gave edge to workers with longer stay in labour market and the reverse to those with shorter stay. But if these gaps exist due to discrimination and other factor such education, childcare, occupation, field of study and motivation, it could be seen that women-dominated occupations with lower wage structured or vertical and horizontal segregation according

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(Tinuke, 2013), would definitely lead to a wider gap and play a big role in determining the wage structure with a lot of details to explain the gap. They also linked this evidence with the gap in earning base on individual characteristics such as race, gender, ethnicity, educational level, family status, and occupation, access to well-paid job, region, access to credit, and access to arable land, health and religious background. However (Blau, Ferber and Winkler, 1998), in their work affirmed that age, experience, occupation, education and the length of time in the labour force are not the only variable, but they also revealed that associated to wage gap are child care, average number of hours worked per week and the workers academic grades while in school as some of the possible explanatory variables to gender wage differences and their relationship to wage structure. In another instance (Blau and Kahn, 2006), revealed that perhaps the overall wage gap has decreased somehow overtime but some proportion of female to male wage gap has somehow remain unexplained by human capital variables and that they are actually increasing, they stressed that measuring gender wage differences should include statistical decomposition which contains explained variables which includes gender measured characteristics and unexplained components relating to discrimination and unobservable characteristics. However (OECD 2009), acknowledges that these factors mentioned above though explains a larger proportion of the factors responsible for gender wage differences but some of its general analysis have failed to explain some part of gender wage differences, perhaps the unexplained proportion reflects the influence and contribution of the unobservable factors which includes labour market discrimination against women, some independent variables that were not included and some other crowing-out discriminations variables. Group qualification differences that were not included in the measurement can be seen as a possible tool to explain the residuals. They argued therefore that if the omitted variable belongs to highly endowed men, meaning that discrimination would have been overestimated but if they are controlled in a way to reflect the impact of discrimination it would have been underestimated. They further argued that institutional barriers suffered by women in pre-labour labour market period and their entry to certain jobs has highly unmeasured productivity of women compared to men hence it reveals the case of underestimation of discrimination against women. They went further to highlights the difficulties experience in trying to measure the actual size and contribution of these variables to the gap; thereby creating measurement problem.

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The impact of negative wage to women in Nigeria have had an enormous consequence on the individual households, the society and economy at large, the position of women in the Nigerian society as the custodian of children and care-taker of the household at large reflects so much on poverty reduction and economic growth. According to (Thomas 1997), (World Bank, 2001) the bargaining power of women has a significant impact on children education, health and nutrition. (Klasen & Wink, 2002) and (The World Bank, 2001), with some evidence have shown that women empowerment will certainly lead to improved governance and hence reduces corruption, the research reveals that women have a lower propensity to corruption and similar behaviours, and this show why gender gap in education and employment leads to lower productivity.

2.4.2 Stereotype Jobs dominated by males such as construction, managerial, administration, science and engineering are often affected and restructured by bias because of imperfect information. (Smith, 1993), have shown that female scientist and engineer with great achievements are not accorded equal credit as their male counterpart, if they to be accorded such respect they have to double their achievement. (Oloko, 2002), in his work indicates that gender stereotype tend to be the forces propelling occupational segregation, because people tend to consider what generation before them have done and what society expects from them, and these will go a long way to influence their career and educational decisions, in Nigeria it is belief that men are more competent and valued in certain jobs than women, thereby leaving women to few occupation. He further argued that particular stereotype on this ground affects both gender perception of their ability to perform certain task; hence men could evaluate their ability to be higher than women even when they are on the same level, and this bias assessment tend to influence a lot of career and educational decisions within the Nigerian society. The (OECD, 2010), have shown that women’s labour market behaviour in Nigeria are highly influenced by religious, cultural and social values. With jobs that are anchored on stereotypes

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and lifestyle that are fundamentally discriminatory against women. It is therefore arguable that women educational preference and choices are made based on some certain assessment that some type of employment opportunity and occupations are not meant for them and this is very prevalent in the Nigerian society. (Oloko 2002) and (Bommer, 2003) observed that men are unnecessarily favoured against members of the lower-status group including women to negative stereotype and this is attributed to the work-related assumed competence, while members of the higher-status group like men receives unmerited favourable evaluation of competence because of positive stereotype. Rather than looking at what is achievable with their education skills, career stereotype have been the force that shape these decisions. (Collinson & Collinson, 2003), have arguably shown the tendencies to devalue the productivity of women, with biases against women in male-dominated jobs or roles even when they are the most qualified, they further reviewed that women that escape “glass ceiling” and enter the higher status or male dominated jobs often times meet with more unfriendly situations than equally qualified men do. While (Virginia, 2001), have shown that managers believe those gender stereotypes are real therefore, associate higher competence with male than female, (Okpara, 1997) examines that the general assessment of both gender are influenced by gender stereotype.

He also show that women are not given equal

opportunity to become leads hence men are always favoured at their expense. Negative stereotype against women makes it difficult to recognise the achievement of women but rather find faults and transfer such appreciation to men. It is well established that discrimination breeds inequality, and it commonly exist against the minority groups which includes women, this study considers to carry out few analysis for both groups in order to measure discrimination and its impact on individual differences. Several studies including (Altonji & Blank, 1999) have shown that wage discrimination against women runs across industries and occupations which are applicable to Nigeria, to proof wage discrimination and its existence in the Nigerian labour market, this study will employ the use of (Oaxaca, 1973) and wage decomposition model and further extend to (Nopo, 2004) matching technique. These models will help us to decompose wages into explained and unexplained components based on the average earnings of men and women who are in the same occupation and industry with all necessary prerequisite. However few literatures including (Tinuke, 2013) and (Opeke, 2002) have argued that it is obvious that status attainment, gender stereotype and human capital investment influence the wages of

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men and women in Nigeria and therefore should be taken into consideration while decomposing wage gap.

2.5 Factors summarising wage differential in Nigeria Sowell (1984) in his examination of wage gap have argued that a greater proportion of gender wage gap exist because of marital status and not glass ceiling, he further revealed that earning for men and women with the same job characteristics tend to be the same but with women having lower level labour force participation. The difference in job characteristics are one of the principal reasons for gender wage differences because it explain a substantial part of gender wage differences, (Blau & Kahn, 1997), in their study have shown that the addition of industry, occupation, education and training to wage regressions will reduce the unexplained proportion of the differences from 22% to 13% in 1988, (Macpherson & Hirsch, 1995) in their study revealed that the exclusion of these variables of job characteristics will reduce the unexplained gap from 17% to 12% in 1983 to 1993 pooled data. (Soetan 2002), observed that occupational segregation as one of the main factors responsible for gender wage gap in Nigeria involves the total domination of certain highly paid jobs by a particular gender e.g. men, while women’s dominance are seen only in few lowly paid jobs, he reviewed that 50% of women in Nigeria are strictly involve in seven occupations which are commonly within clerical occupation and services occupation which includes sales, nursing, social care and teaching profession, while the men’s dominance spread throughout the managerial, administrative, engineering, science and the technical professions, including the manual and manufacturing professions. However, (Ojo, 1998) revealed that within period of military administration in Nigeria, occupation segregation were more pronounced within lower skilled workers and women with children than it is seen in other jobs including the up range of earnings but on average the gap is still wide. The (OECD, 2009) noted that female workers with higher education were found with more number of occupations than less educated female workers while it reviewed that nursing mothers or women with children are more likely than single women to work as service/sales workers, this is viewed as a result of self-selection (preference) of mothers into occupation compatible with family responsibility, or employers who are willing to offer

17

mother career employment opportunity. While men tend to maintain their dominance in the male-dominated occupations with higher wages thereby helping to widen the gap, however it was noted that gender occupational choice concentration are due to differences in preferences largely among women while others are not. (Okpara 1997), (Oloko 2002) and (Weaver, 1998) argued that female-dominated occupations are few and pay lower wages despite workplace and occupational characteristics. With women under-represented in managerial, administrative, sciences and technology jobs, because they are male-dominated occupation especially at the high levels, thereby making room for a vertical component to occupational segregation. These automatically places women under “glass ceiling” meaning they cannot reach the upper level of corporate ladder, and within the private sector they are only limited to the entry and middle class levels even when they are the most qualified. (UNDP 2009) reviewed that Nigerian women on average held only 9.2% of corporate board seats in 2007 business year. It is arguable to say that these are the reasons for high entry and exit spell of women within an organisation and across sectors. (Blau, Ferber and Winkler 1998), have arguably shown in their work that occupational segregation were declining but wage differences between men and women in comparison has remained wide, while the overall average is declining due to the decline in occupational segregation, they were not convinced that discrimination against women in labour market are decreasing. While (Goldin & Cecilia, 1997) argued that wage differential pattern in the US between men and women have shown that the ratio of women to men have increased secularly as well as the presence of women in different occupation improved, however the discrimination against women coincide with the movement of women from manufacturing to service and other occupations, with higher human development index (HDI), rather than that what were seen in developing countries were the reserve. While (Cohen & House, 1993) examined that little or no care were given to labour market discrimination against women in developing countries including Nigeria. There were evidence of direct discrimination, and it has been argued by several researchers to be one of the greatest sources for occupational segregation, suggesting that women are indirectly forced into lower wage paying occupation and men into higher paying occupations. (Opeke, 2002), indicates that in the labour market there are statistically wage differences between jobs that defined male and female workers and he further show that in such

18

occupations consideration of wages were based on gender discrimination, leading to occupational stereotype and the devaluation women labour productivity. (Correll & Benard, 2007) have shown with evidence that women with children were less likely to be hired and if they were hired they will be paid lower wage because of the flexibility given to them to manage family and job, assisted with the negative stereotype of assessment women are assumed to be less likely competent than men, however such discrimination against mothers were not related to men that have children. The wages of women were highly influenced by their marital and social status that shows the depression of female wage as a function of gender stereotype. Consciousness of stereotype as well influences the negotiating power of women to perform competitive task, Sowell (1984) revealed that women were less likely to negotiate for better wage than men when they are given equal opportunity to negotiate. (Nwankwo, 2010) highlights that Gender inequality were rooted in customary and religious laws, and as conservatives thus hampers growth and poverty reduction, (Ravallion & Datt, 2002) with their findings in India have shown that gender inequality is inimical to growth, perhaps through the reduction of productive assets and the productivity of highly talented females. They further show that gender inequality leads to the restriction of highly qualified girl child in the favour of lowly qualified boy child thus leading to lower human capital development and reducing the average productivity of the total workforce. While (Abu-Ghaida & Klasen, 2002) have shown that any country that are able to eliminate gender gap in educational enrolment by 2005, will reap benefit from the terms of such indicators, and this will in turn determine economic growth, perhaps these were the reason why gender gap in education leads to a reduction economic growth. The analysis of most of these literatures have shown that the consequences of institutional constraint, preferences, social norms and employers discrimination are behind the crowding out of certain groups in the labour market to a certain occupation, however major weakness of the formal model were the inability of the model to analysis the mechanism that sustain the four causes. (Altonji and Blank, 1999), in their study have revealed that recent studies have shown some direct evidence of discrimination on gender wage differences including the differences in human capital investment, occupational location, occupational segregation, experience and labour market discrimination. They revealed that human capitals investment are likely related to labour market returns, differences in wage and employment, this variable are related to

19

differences in characteristics, differences in labour market treatment given characteristics. They also highlight that the root cause of difference between labour market participation and the effects of gender were the lower number of week and hours women work, they also stressed that women wages were highly affected by labour participation and educational differences which culminates with preferences and choice of poor proxy experience of women due to child-bearing.

2.6 Theories of discrimination Economics theories of discrimination revealed that discrimination can arise in different ways that are subdivided into competitive models with individual agents and collective models with groups acting collectively against each other (Becker, 1957)refer to the first as taste by some members of a majority groups against interaction with members of a minority group or the discriminatory taste of some employers. (Becker, 1971) modelled prejudice as taste for employers’ discrimination, he highlight that in a situation that some employer are prejudice against members of a particular minority group, e.g. female workers in favour of another especially their male counterpart, taste parameter of the firm were called coefficient of discrimination, the gap increase as less demand are made of the supply of the female workers. The model highlight that if female workers were employed by the least prejudice firm, the group therefore, are separated from others in the labour market. (Becker, 1971) and other researchers have shown that discriminating employers will earn lower profit than nondiscriminating employers, since non-discriminating employers will pay less to hire the services of the female workers and they would not be willing to work for the prejudice employer, leading to the excessive supply labour, therefore depressing the wages of the group, however there will be segregation in the labour market. But employee discrimination cannot lead to wage gap if there were no search cost. Secondly (Durlauf, 1992), show that statistical discrimination by employers in the presence of incomplete information about the skills or behaviour of the majority and minority groups, he examine that employers often hire, promote and pay employees base on stereotype and as well as the consequences of the action of a majority group against another (the minority) using both legal system and violence as means of enforcement which positively predict productivity of such group. The emphasize on the role of imperfect information were great

20

about a workers attribute, (Durlauf 1992) and (Benabou, 1996), reveals that past labour market and pre-labour market discrimination against a group might have effects on the human capital investment of the future generation of that group and would lead to persistent group difference in skills.

21

Chapter three

Methodology The (Oaxaca and Blinder,1973), gender wage decomposition before now were the key tools for explaining wage gap, but the model requires the estimation of linear regression of earning equation for men and women and this generates counterfactuals: these counterfactuals are divided into differences attributable to individual characteristics, male advantage and female disadvantage. The problem of mis-specification also arouse with the decomposition model, these occur due the differences in supports of empirical distribution of individual characteristics for both groups, otherwise called gender differences in support. According to (Rubin, et al., 1985), groups with separate individual combination of characteristics cannot be comparable therefore extensions of the model were required. (Oaxaca & Ransom, 1994), gender wage gap decomposition will be use as against the traditional Oaxaca Blinder, this will enable us decompose gross unadjusted wage differences of men and women into discrimination and productive components, as pioneer by (Oaxaca 1973) and (Blinder 1973). However (Cotton, 1988) in his studies developed a models that attempt to separate the effects of discrimination to individual groups as “cost impose on minority and benefit gained by majority”, and in attempt to strike a balance through unbiased estimation, he stressed that effective non-discriminatory wage structure could only be achieved by the estimation of weighted average observed wage structure of both groups. According to (Oaxaca & Ransom, 1994) in the absence of discrimination the differences between the wages of men and women will be due to pure productivity differences which reflect pre-labour market discrimination. According to (Neumark, 1988) non-discriminatory wage in natural log could be estimated for each labour by the weighted average of each gender group wage and the combination of both will gives us a common wage structure in the absence of discrimination. Oaxaca Ransom decomposition model established that the average log of earnings can be created separately for the both groups; which involves male and female earnings and their decomposition. To remove the effect of index number problem, the estimation of the non-discriminatory coefficient vector will be needed, (Oaxaca and Blinder, 1973), fail to establish gender differences in supports by estimating the wages of all working male and females without restricting the comparison to the appropriate sample of individuals with same characteristics. This study therefore will further incorporate (Nopo, 2004);

22

matching technique that will help divide the decomposition equation into four addictive components and give the appropriate support to individual differences, this model will attempt to match variables with similar characteristics or linear combination of them.

3.1 Oaxaca Ransom wage decomposition estimation equation;

Earnings equation for men estimated with OLS ln𝑌𝗆=𝑿𝗆𝘣𝗆+ 𝘜𝗆

(1)

The equation above can be describe as follows; ln𝑌𝗆 is the natural logarithm of hourly wage of Nigerian men and any other advantageous group, while 𝑿𝗆 is the vector of the above observed characteristics use to explain earnings, 𝘣𝗆 is the coefficient that are same for each group but might vary across groups and 𝘜𝗆 is the error term of the equation. Where the earnings equation of women thus; ln𝑌f = 𝑿𝒇𝘣𝒇 + 𝘜𝒇

(2)

Thus; ln𝑌𝗆 is the natural logarithm of hourly wage of Nigerian women and any other disadvantageous group, while 𝑿𝗆 is the vector of the above observed characteristics uses to explain wages, 𝘣𝗆 is the coefficient that are same for each group but might vary across groups and 𝘜𝒇

is the error term of the equation.

However according to (Reza & Manfor, 2002), the consideration for the earning equation for women are bit complicated, because working women may not be a true random sample of women, therefore they should be considered as a special group of women. Hence working women should be seen as self-selected and this might lead doubtful outcome and make an uncorrected earning equation to be biased, they further highlight that there could be factors responsible for the increase of women’s labour participation as well as high or lower wage

23

earnings. Therefore additional repressor’s of earnings function will be needed to prevent such from happening and to correct sample selection bias according to James Heckman’s two step model which is outside the scope of this study, hence matching technique will be used to remedy the biased condition. (Oaxaca & Ransom, 1994) Intuitive assumption states that these differences can only arise as a result of the differences in individual characteristics, while many other theories arguably suggested that discrimination lower the wage of minority groups and leads to a higher pay for majority groups. Thus, the gap can be explained following the gap in the earning of both groups. = 𝘣𝗆 𝑿𝗆 - 𝘣f𝑿𝒇

(3)

The process of adding and subtracting the term 𝘣f 𝑿𝗆 or 𝘣𝗆𝑿𝒇 help us to produce the decomposition of average gross differences in wages, because differences in reward is included plus the quantity of this rewarded characteristics that are held by each group, they are the counterfactual. Considerations were to what wage of women (men) in average characteristics would be they rewarded as men (women), with algebra computation. Thus; 𝑙𝑛𝑌 ⏟ ̅ 𝑚 − 𝑙𝑛𝑌̅ 𝑓

=

𝑡ℎ𝑒 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝑠 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑡ℎ𝑒 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑒𝑎𝑟𝑛𝑖𝑛𝑔𝑠

̅ 𝑓) + 𝑏̂ 𝑓 (𝑥 ⏟̅ 𝘮 − 𝑥 𝑡ℎ𝑒 𝑝𝑎𝑟𝑡 𝑒𝑥𝑝𝑙𝑎𝑖𝑛𝑒𝑑 𝑏𝑦 𝑡ℎ𝑒 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝑠 𝑖𝑛 𝑐ℎ𝑎𝑟𝑐𝑡𝑒𝑟𝑖𝑠𝑡𝑖𝑐𝑠

𝑥̅ 𝑚 (𝑏 ⏟̂ 𝑚 − 𝑏̂𝑓)

(4)

𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝑠 𝑖𝑛 𝑎𝑣𝑎𝑒𝑟𝑎𝑔𝑒 𝑟𝑒𝑤𝑎𝑟𝑑 (𝑢𝑛𝑒𝑥𝑝𝑙𝑎𝑖𝑛𝑒𝑑 𝑝𝑎𝑟𝑡)

The first component of equation (4) describe the average logarithm of male and female wages, the second component are that part of earnings components that are explained by the differences in average characteristics between men and women while the third part is that part of the gap that is divided into the preferences of women in labour force participation and the remaining part are the effects of unobserved average characteristics that are rewarded by labour market which may include discrimination. Naturally [- 𝑥̅ 𝒇], gives the differences in the average characteristics between males and females, while [𝑏̂𝗆 -] is the differences in average rewards to individual characteristics. Market discrimination only reflects the relative effect of labour market discrimination; therefore most of the adjusted differences can be attributable to overestimation of male wage and the disadvantage (underestimation) of female wage. To have an unbiased competitive wage in the absence of discrimination which will reveal what men and women earn without discrimination, we have to construct a non-discriminatory wage structure.

24

β̽ =Ω 𝛽̂m + (1-Ω) 𝛽̂f,

(5)

Where Ω is the weight attached to both male and female wage; (Oaxaca 1973) argued that (Ω =1) equals the current estimated structure of men’s wage or (Ω = 0) which are the current wage of women and should be taken as a non-discriminatory wage structure as supported by many studies including (Blinder 1973), he also suggested that these wage structures should lie between the men and women wage in this instance, and that the only way to measure differences would be to include gender dummies in the standard regression. Some few contradicting issues highlights what happen if female were paid same as male or male paid as female that will lead to index number problem, the estimation of non-discrimination coefficient vector β̽ is needed and to know why discrimination is directed only on one of the groups which lead to index number problem. However (Dolton & Makepeace, 1987), have shown the information limitations of Oaxaca and Blinder approach. They revealed that the approach only analyse the average unexplained differences and had no information for the distribution of this unexplained differences, one of the solution for overcoming this limitation was the matching estimation by (Nopo 2004). The potential problem of Oaxaca Ransom that was also linked to mis-specification due to differences in support of the distribution of male and female characteristics called “gender differences in support”. Oaxaca Blinder model fails to identify these issues while estimating earning equations and the differences in support of males and females, their comparison were not restricted to those with similar characteristics. The decomposition was assumed to overestimate the component of the gaps link to differences in rewards while they only analyse the average unexplained differences. (Nopo,2004) with his evolutionary literature introduced the matching technique to fix the problem of differences in the out-of- support assumption without requiring any estimation of the earnings equations and provided information for the distribution of the unexplained component of the gap. This model uses matching comparison to sorts the problem of sample selection, gender variables were use as treatment through matching technique to select subsamples of males and females in a way that the samples matched had no differences in their observable characteristics.

25

3.2 Nopo matching

Thus matching and wage gap decomposition follows; matching technique E [Y |M ] = ∫𝑆𝑚 𝑔 𝑚(𝑥) dF M (𝑥), E [Y |F ] = ∫𝑆𝑓 𝑔 𝑓(𝑥) dF F (𝑥), Where; Y stands for random variable that controls individual earnings, X stands for the vector of individual characteristics such work experience, firm size etc. that influences earnings. Thus FM (-) and FF

(-)

represents the conditional cumulative distribution functions of

individual characteristics X, that are conditional on being male or female. dF M (-) and dF F () stands for the implied probability measures, SM stands for the support of the distribution for male characteristics and SF stands for the support for the characteristics of females. Therefore 𝜇𝐹 (S) = ∫𝑆 represents the probability measure of the set S under the distribution of dF

F

(-), hence 𝜇𝐹 (S) = ∫𝑆 = dF

modelled by gM

F

(x) and 𝜇𝑀 (𝑆) = ∫𝑆 dF M(x). The entire relationship is

(-) and gF (-) random variables; however they represents the expected

earnings conditional on characteristics and gender. Thus E [Y |M, X ] = gM (X) and E [Y |F,X ] = gF (X). 𝚫 ≡ E [Y |M] - E [Y |F ], Expressed as 𝚫 = ∫𝑆𝑚 𝑔 𝑀(𝑥) dF M (𝑥) - ∫𝑆𝑓 𝑔 𝐹(𝑥) dF F (𝑥)

(6)

After several mathematical transformations, we will have the components as 𝚫M = [

𝑑𝐹𝑀 (x)

∫𝑆𝑓 𝑔 𝑀 (𝑥) 𝜇𝑀 (𝑆𝑓) − ∫𝑆𝑓 𝑔 𝑀 (x)

𝑑𝐹𝑀 (x) ] 𝜇𝑀 (𝑆𝑓)

𝛍M , ̅̅̅ 𝑆𝐹

(7)

The gap can now be explained by the differences between the two groups of males whose characteristics can be compared to that of the two groups of women those that match and those that cannot. That leads to the second component,

26 𝑑𝐹𝑀

𝚫X = ∫𝑆𝑚∩𝑆𝑓 𝑔𝑀 (𝑥) [𝜇𝑀 (𝑆𝑓) −

𝑑𝐹𝑓 ] 𝜇𝐹 (𝑆𝑀)

(x),

(8)

The second component, describes that part of the wage gap that can be explained by the differences in the distribution of characteristics between males and females over common support. Third component, 𝑑𝐹𝑓 (x)

𝚫0 = ∫𝑆𝑚∩𝑆𝑓[𝑔𝑀 (𝑥)- 𝑔𝑓(𝑥)] [ 𝜇𝐹 (𝑆𝑀)] ,

(9)

The third component corresponds to the unexplained component, which is that part of the wage gap that cannot be attributed to differences in characteristics of individuals but is attributed to the existence of both unobservable characteristics that explains earnings differences and discrimination,

In Oaxaca Blinder it has the following; 𝑿𝒇[𝘣𝗆 - 𝘣𝒇 ]

The fourth component,

𝚫F = [

𝑑𝐹𝑓 (x)

∫𝑆𝑀 𝑔𝑓 (𝑥) 𝜇𝐹 (𝑆𝑀) − ∫𝑆𝑀 𝑔𝑓 (x)

𝑑𝐹𝑓 (x) ] 𝜇𝐹 (𝑆𝑀)

𝛍F ̅̅̅̅ 𝑆𝑀

(10)

This component describes that part of the gap which are explainable by the differences in characteristics between the two groups of the females, those with the characteristics that are comparable to the male characteristics and those that do not, on the reverse this part of the gap would disappear should all the females end up in one set of characteristics and will not be accounted for. The wage gap from the above instance has been divided into four additive components, two for males and two for females; 𝚫 = 𝚫M + 𝚫X + 𝚫0 + 𝚫F

(11)

The following (𝚫X, 𝚫M and 𝚫F ) is attributed to the existence of differences in individual characteristics that are rewarded by the labour market, while 𝚫0 is the existence of the combination of unobservable differences in characteristics

that are rewarded by labour

market, which may include discrimination. Therefore wage gap is expressed as,

27

𝚫 = (𝚫M + 𝚫X + + 𝚫F) + 𝚫0

(12)

If divided into the traditional Oaxaca Blinder of two components, one part is attributable to differences in observable characteristics of individuals and the other to the unexplained component of the gap. With matching technique in order to estimate the four components, we re-sample all females without replacement and we match each observation to one male with similar characteristics.

The only pioneering empirical evidence of gender wage gap in Nigeria, (Tuneke 2013) in his research revealed that 39 percent of wage gap were due the effects of different reward structures of human capital and differences in the average percentage of women in individual occupations. He also revealed that 28.2 percent of the gaps were aroused due to gender stereotype and overt discrimination; this study however will seek to compare this outcome with latest figures.

Data Description

3.3 The Data Resource and (GHS) survey The collection of this data followed a request for permission which was granted by the Nigeria National Bureau of Statistics and the World Bank. Having downloaded and clean up the data, there were need to pick the actual variables of interest, the most informative to this study were the post-planting data because they had more information and seems to be more coherent than the post-harvest data. There were important variable that were meant to be included but were not, such as experience instead age squared were used by proxy, firm size and unionism because they lack reasonable number of observation and consistency. This study employ these panel data from the Nigerian National Bureau of statistics in conjunction with the World Bank, with funding from the Bill and Melinda Foundation (BMGF) for the strengthening of the productivity for the household level, they were life standard measurement survey (LSMS) which were made towards poverty reduction,

28

innovation, and foster efficiency in various sectors of the economy. BMGF is a non-profit donor organisation with the aim of improving standard of living around the world. The surveys covered the 36 states of the federation including the federal capital territory. Covered were socio-economic topics with three different questionnaires for household, agriculture and community. The questionnaires were in depth with the aim of capturing each household selected and members of such household with their various individual characteristics within the time under consideration at both individual and family levels. The Enumerated areas (EA’s) of the 2006 housing and population census were use as the master plan for the survey, 30 master samples (EA) were created in each local government area (LGA) of all the states, multiple by the number of LGA’s in that state, for the federal capital territory it was 40 (EA’s) per each LGA, 10 households were selected per each EA, a total of 500 EA’s were selected from the states including the federal capital territory making a total of 5000 household that were interviewed nationwide, though in some cases the number of EA/household varies from state to state. Each members of the selected household within five years and above are required to answer for themselves, the questions boarder around agricultural, community and household activities. The questionnaires started with the household head and then each members of the household were interviewed through the household list, those at home first and appointment were made for the expected date of those who are away from the house, and those who have been in the house for just five months or less were recorded as visitors. A total of 27,533 household members were interviewed, on average 93% response were recorded while 6.1% attrition were recorded and 0.3% of non-response. The surveys were made in two phases, the first were between February/March post-planting visit and the secondly the post-harvest visits within March/April. According (Bureau of Statistics, 2013) the survey was carried out on the basis of a sample for the entire population which comprises the six geographical zone of Nigeria including the federal capital territory, a mixture of the urban and rural populace constitute the sample and the survey. For the regression equation, it was divided into binary and continuous variables in the wage equation; the dependent variables were the natural logarithm of hourly wage measured in Naira, which represents the Nigerian currency. The hourly wage were calculated using the two questions, how much were you paid in your last period pay and what period did this payment covered. The wage variable were then computed by multiplying the last period

29

payment by 5 days of the week, then divide by the period which the payment covered which were eight, including hourly pay, daily pay, weekly pay, fortnight, monthly, quarterly, half a year and yearly. The explanatory variables include the various individual characteristics in the data set and the two groups of workers the males and females. The regressions included the following independent variable that had the minimum and maximum values of between 0 and 1: schooling divided into 6, region divided into 6, industries divided into 7, occupation divided into 8, gender 2, and marital status 3, sector 4, one each were excluded from each variable group. As the customary expectation in the study of this literature, men earn higher than women, age squared by proxy were use as experience.

30

Chapter Four

Empirical Results and analysis

The summary statistics for both groups were computed to highlight the gap in earnings and source of such differences, also shown in table 1 below are the definition of the various variables that were used in the computation. Hourly wage were the basic wage variable, those who were paid on weekly basis, their wages were computed using the number of hours they work per week, then divided by the total wage earn, same goes to those on monthly basis, which are the case in Nigeria. Looking at the two groups, from table 1 the average natural log of hourly wage of men were higher than that of women by 0.67%, this result supports the arguments of (Tinuke, 2013) and (Sowell, 1984), which reveals that on average men earn more than women. Looking at the age variation married men tend to stay longer in the labour market than women do, this supports the reason why there are preference high human capital investment on the boys than girls, because the returns from the later are lower compared to the former (Altonji & Blank, 1999). The education variables revealed that men had higher education (degree) and therefore earn more; it also shows that 80% of these men were married, the above result supports the argument of (Altonji & Blank, 1999), which reveals that a direct evidence of discrimination were linked to differences in human capital investment that favour men over women. The results also revealed that married women had more domestic training, primary and religious education as a result according to (Albrecht, et al., 2009) the gap became wider, this could be viewed as institutional barrier and stereotype. Lower level of education and less experience of women tend to crowd out the technical, education and health, logistics, skilled and manufacturing occupations which linked to the problem of “sticky floor and glass ceiling” according to (Albrecht, et al., 2003) and (Metcalf, 2009). The results also highlights that separated and divorced women tend to earn more than married women; reasons might be the access to inheritance, experience and their long stay in the labour market.

31 Definition of Variables and data description

Table 1

Variable

Male Mean

description of variables

Male Female Std. Dev. Mean

Female Std. Dev.

lnhourly The hourly wage of workers 4.984 1.225 4.319 1.204 age The age range of workers 45.312 14.558 42.793 14.078 age2 Age squared 2264.974 1418.517 2029.300 1320.569 Education (reference - non-qualification) highdegree Those with high degree 0.138 0.345 0.079 0.269 ondalevel Holders of ordinary diploma 0.125 0.331 0.123 0.328 olevel Olevel certificate 0.304 0.460 0.260 0.439 Quaranic Regeligious education 0.026 0.159 0.028 0.166 secondary first cycle of education 0.307 0.461 0.379 0.485 househead The head of each household 0.847 0.360 0.188 0.391 Occupation (reference - Elementary occupation) managers1 Managerial occupation 0.026 0.158 0.005 0.074 profession~1 Professional occupation 0.126 0.332 0.090 0.286 technicians1 Technical occupation 0.155 0.362 0.346 0.476 salesworke~1 sales occupation 0.015 0.121 0.013 0.113 skilled1 Skilled occupation 0.050 0.218 0.144 0.352 unskilled1 Unskilled occupation 0.321 0.467 0.160 0.367 Industries (reference - Finance and services) Agricmining Agriculture and Mining industry 0.331 0.471 0.247 0.431 manufactur~g Manufacturing Industry 0.044 0.204 0.089 0.285 profession~s Professional Industry 0.103 0.304 0.008 0.092 Logistics Transport and Communication indistry 0.174 0.380 0.419 0.494 EducationH~h Educational Industry 0.106 0.308 0.107 0.309 PublicAdmi~n The services Industry 0.097 0.296 0.045 0.207 OtherSector The Other general Industries 0.016 0.126 0.008 0.089 Region (Reference-Northwest region) Northcentral North central region 0.120 0.325 0.096 0.294 Northwest North western region 0.136 0.343 0.086 0.281 Southeast South eastern region 0.186 0.389 0.243 0.429 Southsouth South south region 0.206 0.405 0.230 0.421 Southwest South west region 0.234 0.424 0.265 0.441 Marital status (reference - Single) married Married workers 0.803 0.398 0.706 0.456 widdivsep Widows, Divorces and Seperated couples 0.038 0.191 0.204 0.403 Urban/Rural reference -Rural) urban Urban Dwellers 0.413 0.492 0.393 0.488 sector (reference - otheremployers) government Workers Employed by the government 0.223 0.416 0.120 0.325 privateEmp~r Workers Employed by private firms 0.151 0.358 0.070 0.255 selfemployed Self employed 0.623 0.485 0.808 0.394 Source: Authors tabulation from National Bureau of statistics Nigeria

32

Women from the Southern region earn more than women from the Northern region because of their educational characteristics and the level of endowment in the region, the Southern women attend more western (formal) education while women in the Northern attend more of religious education and get married too early, their lack of formal education and experience therefore drive down lower their wages. The proportion of women who are self-employed were so alarming close to 20% compared to percentage of men in that occupation, while women were unrepresented close to 0.05% in managerial and professional occupations, revealing the indication that occupational segregation leads to wilder gap. This figure backed up the argument of (Altonji & Blank, 1999) which states that professional women only were only employed in predominantly female jobs which are very few with low wage, as shown in table 2 below. Table 2 below highlights the percentage participation in the Nigerian labour market.

Table 2: Adult (15+) Labour Force Participation Rate (%) by Sex. Gender

1990

2010

Female

37

40

Male

75

69

Source: Nigeria bureau of statistics (2010)

The results in table 3 below highlights the possible influence of individual characteristics that determines the wage level of each group. This study wills above other things attempt to address the issue of gender wage differences existence in the Nigeria labour market.

33

OLS Regression for the earnings of male and female Lnhourly Age age2 (Reference-none qualification) Highdegree Ondalevel Olevel Secondary Qur’anic Househead (Reference-Elementary occupation) managers1 profession~1 technicians1 salesworke~1 skilled1 unskilled1 Reference-Finance Health industries) Agricmining manufactur~g profession~s Logistics EducationH~h PublicAdmi~n OtherSector Reference- North-east) Northcentral Northwest Southeast Southsouth Southwest Reference- Singles) Married Widdivsep (Reference-Rural area) Urban (Reference-Othersectors) Government privateEmp~r Selfemployed _cons R-squared Adj R-squared

male Coef.

Table 3 male Std. Err.

female Coef.

Female Std. Err.

0.058 -0.001

0.013 0.000

0.061 -0.001

0.014 0.000

0.982 0.418 0.273 0.305 -0.297 -0.119

0.121 0.118 0.096 0.091 0.181 0.118

1.008 0.548 0.278 0.061 -0.691 0.227

0.147 0.127 0.102 0.089 0.199 0.115

0.304 0.176 0.129 0.078 0.139 -0.341

0.163 0.107 0.084 0.200 0.119 0.137

0.426 0.368 0.034 -0.030 -0.126 -0.250

0.326 0.159 0.114 0.231 0.116 0.251

0.056 0.030 0.406 0.128 0.107 0.135 -0.257

0.140 0.135 0.101 0.090 0.134 0.133 0.206

-0.083 -0.050 0.356 -0.006 -0.022 -0.025 0.355

0.256 0.151 0.287 0.113 0.174 0.187 0.311

-0.006 -0.173 -0.071 0.308 0.064

0.110 0.112 0.103 0.099 0.101

0.201 -0.293 0.008 0.350 0.033

0.162 0.176 0.147 0.143 0.147

0.144 -0.277

0.121 0.172

-0.088 -0.254

0.098 0.144

0.064

0.058

-0.034

0.064

0.537 0.178 0.357 2.816

0.435 0.430 0.430 0.518 0.238 0.2238

0.118 -0.672 -0.346 3.108

0.595 0.595 0.593 0.670 0.311 0.2954

34 Source: Authors tabulation from the Nigerian bureau of statistics Note: schooling are in years while other are (0,1) dummies.

The

R-squared in this regression indicates that co-variables were explained by 24%/31% of

the variability of male and female earnings. The earnings equation of females were computed using the ordinary least squared regression in table 3 above, the coefficient of 1.008 for women with higher degree, show that the earnings of women increases as their educational level increases, this increase occur more in with the women’s wages than the increase in the men’s wages, supporting the argument of (Blau, et al., 1998), which highlight that increase in the human capital investment of women will reduce gender wage gap. It is also evident that the increases were related to those with higher degree and not those with lower education. However a coefficient of -0.691 for women with Qur’anic indicates that women with lower level of education earn less than those without qualification, this can be related to institutional barrier of attaching less value to nonformal and Qur’anic education leading to a an adverse impact on the wage of women and this supports the view of (Royalty, 1996), (OECD, 2010) and (Blau & Kahn, 2006) . On the regional effects, the coefficient of women in the North-central region were 0.201, this shows that women in that region earns more than those in the North-eastern region of the country; however those in the North-west with a coefficient of -0.293 earn much less than those in the North eastern region due to lower school enrolment and religious limitations (OECD, 2010). The earning coefficients of women in the entire Southern region are statistically significant and therefore had a positive impact on the wages of women. Considering the regions in the South with better wage structure, the coefficients indicates that those in the South-east region with a coefficient of 0.008 earn less than those in the South-west with 0.033 and even worse compared to those South-south regions with a coefficient of 0.350. The coefficient of the South-south region shows that women in that region earn far better than women in the Southeast region and more than the women in the South-west region, the reasons for high level of significance in South-south is due to the endowment level of the region. The results for the occupational categories were significant in some of the occupations and it show that women with higher education (higher degree) had a coefficient of 1.008, and were more likely to gain employment as managers, professionals and technical workers and they

35

likely to earn more than women in other occupations especially those with lower qualification (Blau, et al., 1998). The results from the occupational category also had a surprisingly results showing that women with elementary occupation earn more than those in sales work, skilled and unskilled occupation, reasons for this outcome were the dominance of men in such occupations and the case of “sticky floors and glass ceiling”, indicating that the occupational concentration (segregation) of women were within the lower cadre of the industry and they were not allowed to move to certain positions because of their gender characteristics thereby leads to a negative impact on the earnings of women (Collinson & Collinson, 2003) and (Oloko, 2002). The coefficient of -0.083, -0.006, -0.022, -0.025 and -0.050 for women in agricmining, logistics, education-health, public administration and manufacturing industries show that women in the above industries earn less than those in finance and health industries, while those in the group called other-industries had 0.355, earn as more as those in professional industries, this happened because few women have the preference of working in such industries, therefore they earn very high wage. (Mason, 2004), have shown that a lot of women have the preference of doing jobs that allow them the flexibility to combine work and household which align with those industries with a high concentration of women and lower wage pay. The coefficient also reviewed that married, widows, divorced and separated women earn less than single women, reasons for this were the more number of hours single women put into the labour market which tend to be higher, they therefore earns higher than t married, widow, separated and divorced women, most of them are withdrawn from the labour market. The results also show that women in the urban areas had a coefficient of -0.034 therefore unexpectedly earn less than women in the rural areas, this result were not supported by any empirical finding, therefore calls further investigation. However, reasons for this might be that the rural women easily have access to employment than women in the urban area. This is possible because most agricultural farms project, manufacturing firm and self-employment opportunities are found most likely in the rural areas, it is also evident that most women in the urban areas, are in such urban area because of spouse and some find it hard get job with the flexibility. In the sectorial coefficient it is evident that women in the government sector earn more than those who were self-employed, the implementation of Beijing declaration were part of the reasons behind the high level of earning in the sectorial variable. Those

36

working for private firms earn less than those in the group called other sectors of the economy due to the case of sticky floors and glass ceiling. The results for men show that the coefficient of men who are house heads were lower, -0.119 compared to men who do not head any household, reasons for the lower earnings of house heads are due the enormous challenge of taking care of the entire household and numerous hours lost off work to look after the family or work in the family farm. Men with higher education (degree), had a coefficient of 0.982 and were likely to be employed as managers, professionals, sales workers and skilled men, and therefore had the probability of earning higher wage more than men who were employed within the finance and education occupations, supporting the views of (Altonji & Blank, 1999) and (Becker, 1991). While those unskilled occupations with -0.341 earn less than those with elementary occupation. The industrial coefficient of 0.056, 0.030, 0.406, 0.128, 0.107 and 0.135 shows that men in agaric-mining,

manufacturing,

professional,

logistics,

education-Health

and

public

administration earn more than those in finance and health industries. Men in other sector industry, industries that were not mentioned earn less than those in health and finance industries. While the coefficient of married men were 0.144 showing that married men earn more than single men because they are more likely to be committed to their jobs due to family responsibility and likely work more hours than the single men. However, the coefficient of -0.277 show that single men earn more than widowers, divorced and separated men because as single parents’ have to care for their children and this will tend to reduce the number of hours they put in their jobs. Men in the urban centres earn more than those in the rural areas as expected because they have more opportunity for good jobs than those in the rural area. Men in government sectors with coefficients of 0.537 earn almost twice than those who were self-employed with 0.178, while those in private firm employment with 0.178 earn less than those who were self-employed with 0.357, is arguable that the self-employed people often time tend to falsify their records, therefore the result are not reliable. However those who were employed in private firm earn more than those in other sectors of the economy grouped as others.

Further estimations were carried out, some adjustment were made with the exclusion of occupational variables, it help to identify the factors leads to wage gap, from table 4 below

37

the results show that with the exclusion of occupational variable the gap within wage of women and men were reduced close to 50%. In table 1 the differences between the wages of men and women were 0.665,(4.984 for men and 4.319 for women). With the exclusion of occupational variable in table 4, the differences were reduced to 0.341. The results show that a greater part of the gap between the wages of men and women were explained by the occupational differences, it highlight that men were concentrated within the managerial and professional occupations with high wages while women were concentrated within occupations with low wages and that this leads to differences in wages. This result justifies the findings of (Bergmann, 1984) which states that inequality arise due to differences in characteristics. The findings of (Macpherson & Hirsch, 1995), (Oloko, 2002), (Opeke, 2002) and (Soetan, 2002), revealed that the reason for gender wage differences in Nigeria were due to occupational domination of high paying jobs by men and low paying jobs for women.

38

Estimation excluding Occupational Variables Variable lnhourly age age2 househead highdegree ondalevel olevel secondary Quaranic Northcentral Northwest Southeast Southsouth Southwest Agricmining manufactur~g profession~s Logistics EducationH~h PublicAdmi~n OtherSector married widdivsep urban government privateEmp~r selfemployed

male Mean 4.660 44.082 2149.907 .525 .111 .124 .285 .340 .027 .108 .112 .214 .218 .249 .290 .066 .057 .294 .107 .071 .012 .755 .119 .403 .173 .111 .713

male Std. Dev. 1.259 14.379 1376.455 .499 .315 .330 .451 .474 .162 .311 .315 .410 .413 .433 .454 .248 .232 .456 .309 .257 .110 .430 .324 .491 .378 .315 .452

Table 4 female female Mean Std. Dev. 4.319 1.204 42.793 14.078 2029.3 1320.569 .188 .391 .079 .269 .123 .328 .260 .439 .380 .485 .028 .166 .096 .294 .086 .281 .243 .429 .230 .421 .265 .441 .247 .431 .090 .286 .009 .092 .419 .494 .107 .309 .045 .207 .008 .089 .706 .456 .204 .403 .393 .488 .120 .325 .070 .255 .808 .394

Source: Authors tabulation from the Nigerian bureau of statistics

The coefficient also shows that the differences between the earnings of men and women with higher degree were not wide; the same results were applicable to the manufacturing industry. Women in the entire Southern region earn as higher as men, in the North women in the North-central region earn more than women in the other parts of the North. In the sectors women a bit less wage compared to the earlier results in table 3. These results according to the OLS estimate show that most of the gap can be explain with differences in occupation.

39

Oaxaca Blinder wage decomposition are based on OLS estimates and does not resolve any of the problems inherited with inconsistent of parameter estimation which were somehow misleading hence Oaxaca Ransom decomposition is required. The Oaxaca Ransom wage decomposition runs as fellows: the first parts of the gap were the explained components by group differences in predictor or characteristics, as shown in table 5 below. The second parts of the gap are the unexplained part of the component, and this attributable to unobservable characteristics such as the preferences of women to participate in labour force while the remaining part were attributable to discrimination in labour market. Sometimes the unexplained components were further decompose into favourable (positive) and unfavourable (negative) discrimination depending on the sign. However several research works including (Cotton, 1988), (Durlauf 1992) and (Benabou, 1996), have shown that the direction of discrimination towards one group leads to index number problem, therefore the estimation of the non-discriminatory coefficient vector β* were needed. According to (Cotton, 1988) and (Reimers, 1983) the devaluation of one group leads to the overestimation of the other group, hence he propose the use of average coefficient for the estimation of nondiscriminatory coefficient as seen below;

40

Oaxaca Ransom wage decomposition

Table 5; Coef. Differences

P Value

0.610 (.0426404)

Explained

0.479

12.960

(.0369399) Unexplained

0.131

6.07

(.021603) Standard error in parenthesis. (Sources: Authors tabulation, Nigeria Bureau of statistics) Oaxaca Blinder decomposition as shown in table 5 reveals that the total differences between the wages of males and females were approximately 0.61%, out of this 0 .47% were explained by the differences in individual characteristics, while 0 .13% were treated as residuals were divided into parts of some unobservable individual characteristics that are rewarded by the labour market. This unobservable characteristic includes women preference and choices to participate in the labour force or work in certain occupation that gives the flexibility of combining work with family works. The remaining parts of the unexplained component are labour market discrimination that linked to institutional barrier; however, looking at this results and comparing it with the existing results of (Tinuke, 2013), it were observed that the gaps between the wages of men and women were underestimated. Considering the outcome the explained component were positively significant therefore the male individual characteristics were more superior to the female characteristics, while the unexplained component relates to female disadvantage which were as a result of institutional setting according to (Blau and Kahn 2006). However, the problem of mis-specification and the case of in and out of non-support of the sample selections lead to overestimation and underestimation of the gaps; hence we need a better measurement therefore, matching technique was required. Before Nopo matching some adjustment were with the exclusion of the occupational variables.

41

Oaxaca Ransom with the exclusion of occupation variables Table 6 Variable Differences

Coef. 0.613

P.value 14.47

(.042) Explained

0.473

12.97

(.037) Unexplained

0.139

6.40

(.022)

The results show that the differences increased with a decrease in the explained component while the unexplained increased indicating overestimation and underestimation of the gaps. Therefore we consider Nopo matching.

42

Wage Decomposition with Nopo matching: Table 7 D

D0

DM

DF

DX

.15382261

.11918504

.11926713

-.08500521

.00037565

percM =.16098485 percF =.29230769 (Nigeria GHS-Panel Wave 2 PP HH Section 1 - Roster) Source: Author tabulation from the Nigerian bureau of statistics.

According to (Nopo, 2004), to decompose the gap between the male and female wage using the matching technique, the matching technique aim to match variables with similar characteristics or a linear combination of them for a better decomposition. Having controlled for observed characteristics, we measure the impact of treatment on these under different set of identifying assumption. Group computations of the gap have to be done by the decomposition of the unexplained component of the gap. As shown in table 6 above, D is the total difference between the average earning of males and female, D=15 that is the outcome of variables for both groups. DO=0.119, is the unexplained part of the gap, while DM=0.119 is that part of the gap that can be explained by the differences in individual characteristics that is in and out common support with the variables=1,DF=-0.085 are that part of the gap that can be explained by the differences in individual characteristics that are in and out of support with the variable=0.

percM=0.161 are the percentage of individuals with the

variable=1 that are similar with matched variables, while percF= 0.292 are the percentage of individuals with the variable=0 that are common with matched variables. Therefore, in decomposing the wage gap the total difference in gap is divided by the unexplained part of the gap, 0.77 is the unexplained component of the gap between males and females wage according to (Nopo, 2004). Further decomposition takes the addition of all the parts in the gap that are explained by individual characteristics, which are DM+DF+DX / D, therefore, the explained component of the gap is 0.29. Total differences in male and female wage difference, 77% were explained by the individual differences in human capital characteristics, while the 29% were unexplained component of

43

the gap which can be explained partly by unobservable individual characteristics which includes women preferences which are rewarded by the labour market and the remaining part which is residuals called labour market discrimination. Further adjustments were carried with occupational variables. Nopo matching with the exclusion of occupation Table 8 D

DO

DM

DF

DX

.15382261

.11292568

.12877622

-.08787929

.0

percM =.1500947 percF =.28337469 The results show that the differences were same but the unexplained part decreased from 0.119 to 0.113, the explained gap increased from 0.119 to 0.129. The result supports the finding of (Macpherson & Hirsch, 1995) and (Soetan, 2002), that the exclusion of occupational variable will lead to a decrease in the gap.

The results from the Oaxaca Ransom decomposition were little misleading because it had the problem of mis-specification, again the model only analyse the average of the unexplained part and could not analyse the distributions of this components. With Nopo matching technique, variables matched and compared to similar variable from the opposite group, it also help us to solve the problem of sample selection and analyse the distribution of the unexplained components, with the adjustments the sources of gender wage gap was traced to differences in human capital, occupational segregation and labour market discrimination

44

Chapter five

Conclusion and Recommendations

In the context of this study, the reasons behind wage gap were differences in individual characteristics, differences in employment characteristics and preferences of women to work rather than differences in pay rates of men and women in similar job. The empirical results show that the gaps were wider within urban areas than rural areas, because the levels of women unemployment were higher in the urban areas. To understand the gaps that exist, this study looked at the earnings and hourly rate of men and women in Nigeria and considered their average differences. To trace the sources of this gaps adjustments were made, the adjusted gaps were crucial because it aid to identify the real factors which affect pay gap. My research results support the arguments of (Addison & Conia, 2001) , (Macpherson & Hirsch, 1995) and (Heath & Cheung, 2007), they argued that if persons with the same characteristics in their jobs are paid the same wage for their equality characteristics, like the case in Nigeria because of the prevailing actions of labour union, the difference in their characteristics will often be the product of inequality. Adjustments were made for occupational variables, because these variables incorporate discriminatory values (gendered), men were likely concentrated within managerial and professional jobs that pays high wage, therefore they earn higher wages than women with teaching and nursing jobs that rewarded lower than male dominated jobs. Academic qualifications were rated more than vocation that has a higher concentration of women; with the adjustments it was seen that women earn higher than men in most of the variable. The answers to the differences job were segregation, human capital and stereotype, which explained a greater part of the gap. Several literatures including (Altonji & Blank, 1999) and (Blau & Kahn, 2000) have shown that discrimination alone cannot account for the unexplained part of the gap, but could only account for a part of the unexplained component while the remaining part relates to preferences made by certain members of the female group that would allow them flexibility to manage their home and occupational jobs.

45

Gender wage differences were largely explained by the gender characteristics differences that were rewarded by the labour market and not by the distribution of characteristics that leads to women experience of both glass ceiling effect and sticky floor effect. (Blau and Kahn, 2006) have shown that the unexplained component could be explained by the preferences of women leaving labour market more frequently. (Altonji and Blank, 1999), have shown that most of hypothesis of gender wage gap uses human capital differences and preferences as the cause of gender wage gap. (Dolton and Makepeace 1987), revealed that Oaxaca Ransom decomposition lacked information, out of assumption show that the gaps were either overestimated or underestimated. In this study it was underestimated. They also highlight that the model fail to provide for the distribution of unexplained differences, but only analyses the average. The results from (Tinuke, 2013) underestimated the differences between male and female wages. Results from matching technique show that the explained differences in individual characteristics, reinforces the argument of (Altonji and Blank, 1999) that the differences in wage exists do to individual preferences of women and difference in human capital. To reduce the gap between wages of men and women in Nigeria, the government should endeavour to make sure that national and international laws made against inequality are enforced and that these laws are socially acceptable within the populace. The government should promote equal opportunity within the workplace and monitor differences in wage payment. Career development programs for women should be develop to encourage the participation of women in labour market, the provision of child care services should be efficient and effective in order to relief women from the burden of home care, the government should as well provide parental leave for both parents for fair balance family care. Policies should be made to increase the human capital investment of women, provide mandatory quota for women in boardrooms, in management and in governance. With the development of science and technology women should be made to move into male-dominated jobs. Finally the government should organise a re-orientation program to change the perspective of employers and the general public, and make sure every trace of glass ceiling and sticky floor are eliminated.

46

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