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Impact of wage discrimination by age in Pakistan

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Published: Mon, 5 Dec 2016

1.1 GENDER DISCRIMINATION

‘Gender Discrimination’ is discrimination against people, groups and gender identity. It is considered that men are physically strong, must suppress vulnerable emotions (such as remorse and uncertainty), and have economic independence and authority over women and other men. On the other hand, characteristics such as gentleness, patience, kindness and submission to male-dominance, ego-massaging, sensitivity, nurturance of children, and sociability. Gender discrimination is social differentiation of individuals which determines the role of individuals in a particular society. Every individual has different roles to play in his or her society and thus the role of gender is changeable in accordance with the prevailing social set up of that individual.

1.2 WAGE DISCRIMINATION

The main type of gender discrimination that is prevailing in Pakistan is wage discrimination between male and female workers. The term wage discrimination can be explained as a difference in the wage levels offered to male workers and female workers for the same level of work they do. It occurs when an individual in a labour market suffers from a decreased wage level for the same job and performance.

Studies to determine the wage differential have evoked considerable interest in the developing and industrialized countries .The common factor which the studies have is that there is indeed a wage discrimination-favouring man. Gender wage differences seem to exist in many countries to some extent. In order to consider gender differences, researchers look at the ratio of female and male wages. Studies on wage differences shows that female/male earnings ratios are usually less than one, indicating that women usually earn less than men do. Depending upon the characteristics of their labour markets, factors producing wage differentials in those studies varied from race, gender, education, job status occupation, type of sector (public vs. private) to type of industry, among the others.

Two major trends in the world have worked to widen the gender gap: increases in the pay premium associated with higher “skills” (i.e., higher levels of education and labour market experience) and increased pay differences across industries and occupations. This has served to widen the gender gap because female workers continue to have less labour market experience, on average, than male workers, and are, on average, in lower-paying occupations. The rising wage inequality and increasing economic returns to skills slowed women’s progress and alone would have increased the gender pay gap.

All of these factors interact in complex ways. Hence it is difficult to determine precisely how much of the difference in female/male pay is due to discrimination and how much is due to differential choices and preferences by female workers i.e., if women have less experience than men, they may choose occupations where extensive experience is less necessary. If women consistently choose different occupations than men, stereotypes about women’s abilities may be reinforced and discriminatory behaviour by employers may be perpetuated. If employers make it difficult for women to enter certain occupations, women’s incentives to invest in training for those occupations may be reduced.

The male-female difference in wages is also visible in fringe benefits. Much of the female-male gap in pension coverage can be accounted for by differences in their labour market histories and is much smaller among younger workers. In addition, among those who have pensions, the gender gap in benefit levels is largely explained by gender differences in income. Therefore, lower wages, and hence, lower lifetime earnings, result in lower pension benefits upon retirement.

1.3 CAUSES OF WAGE DISCRIMINATION

The empirical investigation of wage discrimination by gender has gathered a lot of importance in economic literature. There have been numerous theories which have tried to figure out the causes of wage discrimination, the common economic theories concerning the reasons for the pay gap are explained below:

Human capital theory attributes income differences to variations in education, experience and commitment to the labour force. Female workers has always been underestimated when it comes to education and experience and they always has been treated as an inferior in terms of experience and education as compared to male workers. Wage discrimination occurs because male workers are more experienced as compared to female workers and based on their education and experience they are earning more than the opposite sex.

Jobs which involve more specialization and experience are valued very high and offers a lot of earnings but such jobs are always kept for men because it is believed that men would performance better than females and jobs with low value and low specialization are kept for females workers. Companies always give first priority to men while recruiting.

Dual labour market theory suggests that the workforce is divided into primary and secondary sectors, the first consisting of skilled, unionized, well-compensated, stable jobs, and the second consisting of temporary, low-paying jobs with little upward mobility, and few benefits. When we talk about the skills possessed by the work force, male workers are always given priority because it is believed that men are equipped with better skills than women. While working in an organization wages are given on the level of skills an individual has, wage gap exists between male and females because male workers are believed to be more skilled than female workers thus they earn more. In the business world job which is dominated by women are not valued in the same manner as jobs dominated by men.

One of the factors is the marital status of an individual, married women tend to earn lesser amount of money because their working hours are comparatively less to men. This is due to the responsibilities of women at home like taking care of children, household management etc. However, married men have higher average earnings as compared to all the other groups; married, unmarried, and separated women, be it young or old.

Wage discrimination can also be due to the disruption of careers that is the break which an individual takes while doing a job. Female workers take leaves from their jobs more as compared to male workers such leaves may include the maternity leaves or a leave which incorporates the reason associated with spouse or children. Therefore, the seriousness and dedication of working women is in question resulting in lower wages.

Another factor is the unexplained portion that is the general perception of people towards women, as women in our society have been given specific roles and are perceived as individuals who need male dominance for their own good. Thus, there are few bodies in our society who do not like the concept of women as a part of the working population, as a result, this general perception adds to the discrimination in its own self. There have been a lot of increase in the number of women workers in our economy but still it does not lead to any major change towards general perception towards women.

Laws structured for the abolishment of wage discrimination exist but they are not strong. Over the years, the Government has begun to pay more attention towards this social issue as percentages of working women are increasing in the total work force of the country. However, present laws lack the ability to eliminate this discrimination from the society and need to be moderated so that the organizations are forced to develop their wage structures without any discrimination and biasness towards any gender.

1.4 SITUATION IN PAKISTAN

Pakistan from its time of existence it has kept strong beliefs in limited social circuit for women. Specific roles for women and men are assigned, which the society must abide. Stereotypically, men are aggressive, competitive and instrumentally oriented while women are passive, cooperative and expressive. However, the perceptions of those living in urban areas are changing till today, but the majority that lives in the rural areas still abides by the strict gender roles prescribed.

The conservative beliefs discourage female workers to compete with male workers and take active part in the development of labour market. The status of women in society is rooted in social structures that prevent the realization of their full potential and their due place in society. Lower position in society manifests in differentiated impacts on women in the field of education, health, labour force participation, political participation, access to assets and resource Apart from these social issues there has been a tremendous change in demographics of our country. Female population has increased as compared to male population, and it has given an alarming situation in the labour market.

Moreover, trends and conditions of working women present a depressing image in Pakistan. Their problem is not only of segregation in the society but also of invisibility of women’s work and inadequate recognition of their contribution, glass ceilings and poor position. Besides the low literacy levels (parents still prefer spending more for the education of their boys than girls since a boy’s education is seen as an investment for the parents’ future), lack of skills and freedom there is a general perception about the female workers in Pakistan, which negatively affects the female workers and do not let them take any power in the society.

It has been identified two facets of undervaluation: women tend to be paid less than men for the same performance in the same job and the jobs that they do tend to attract lower wages than men’s jobs. Gender segregation, which we consider below, plays a role, because it is harder to challenge (or even to notice) the differentials between men’s and women’s work when the two are separated. Visibility, Valuation, Vocation, Value added, and Variance are the five V’s that are identified as being involved in creating lower pay:

Visibility; women’s skills are not recognised by “large and undifferentiated” pay and grading bands that conceal differences in skills and experience. These large bands also result in there being little room for promotion.

Valuation; even when skills are recognised, there is a long tradition in our culture of not giving a high value to women’s skills. Pay and grading systems are still likely to be based on a male skills model and undervalue communications and other ‘soft’ skills.

Vocation; it is assumed that women’s skills are ‘natural’, which underlies their low valuation.

Value added; the fact that man’s jobs are more likely to involve high value added processes or services leads to their being more highly rated, even when there is little difference between the actual skills involved.

Variance; the existence of women’s caring responsibilities underscores the idea that women’s work is in a separate sphere. “Part-time work is often seen as synonymous with unskilled work by both employers and women themselves.”

Today in Pakistan, female workers are engaged in all the sectors-from manufacturing to the service sector- and they play a major role in the economic activity.

1.5 GENDER PAY DIFFERENCES

There is both good news and bad news with regard to gender pay differences. The bad news is that there remains a significant differential between women’s and men’s pay even after controlling differences in skills and job characteristics, women still earn less than men. While there are a variety of interpretations of this remaining unexplained differential, one plausible interpretation is that gender wage discrimination continues to be present in the labour market. This interpretation is reinforced by other more direct studies of pay discrimination, which also show continuing gender differences in pay that are not explained by productivity or job differences.

On the other hand, the good news is that these differences have decreased in recent decades. This is true not only for the raw gap in average female/male pay, which has decreased but it is also true for the unexplained difference in female/male pay once factors that affect pay are controlled for. This suggests both those women’s skills and job choices are becoming more similar to those of men, and that discrimination may be lessening as well.

1.6 EMPLOYMENT IN PAKISTAN

Employment is generated in Pakistan through three major sectors; primary, secondary and tertiary. Table 1 shows the size of Pakistan’s labour force was 51.78 million out of which 40.82 million were males and only 10.96 million were females in 2007. Amongst which, the total number of employed workforce was 49.09 million out of which 39.06 million were males and only 10.03 were females. The remaining 2.69 million were unemployed amongst which 1.76 million were males and 0.93 million were females.

However, the size of total labour force of Pakistan in 2006 was 50.33 million which increased to 1.45 million in 2007. As far as employment is concerned, only 9.54 million women were employed as compared to 38.11 million employed men in 2006. This shows 20 percent less participation of females as compared to males in 2006. (Economic Survey 2007-2008).

TABLE 1: LABOUR FORCE SURVEY

Indicators

2006-07

2007-08

Labour force (in millions)

Total

50.33

51.78

Male

39.92

40.82

Female

10.41

10.96

Employed

Total

47.65

49.09

Male

38.11

39.06

Female

9.54

10.03

Unemployed

Total

2.68

2.69

Male

1.81

1.76

Female

0.87

0.93

Source: Economic Survey (2007-2008)

1.7 IMPORTANCE OF MANUFACTURING SECTOR IN PAKISTAN

Agriculture sector employs the largest number of labour. However, over the years, manufacturing, trade and services sectors have been absorbing a growing share of the work force.

The manufacturing sector comprises establishments engaged in the mechanical, physical or chemical transformation of materials, substances or components in to new products. The manufacturing industry of Pakistan falls under the secondary sector and employs 21% of the total employed labour force. Manufacturing sector is one the main sectors in Pakistan’s economy and contributes 18% to the Gross Domestic Product every year (Economic Survey 2007-2008).

Wage discrimination can be observed in the manufacturing sector of Pakistan as this is one of the largest sectors existing in our society and the number of male and female workers is enormous.

RATIONALE

Wage discrimination by gender is an important issue socially as well as in the business environment. Gender discrimination has taken varieties of forms, from practices that reduce the chances of a woman being recruited to differences in pay for men and women who work together, performing the same tasks with similar efficiency and productivity. The research studies the wage discrimination by gender in the manufacturing sector of Pakistan, as it is one of the largest sectors in Pakistan.

PROBLEM STATEMENT

What is the impact of wage discrimination by gender on the manufacturing sector of Pakistan?

CHAPTER 2

LITERATURE REVIEW

Hirsch and Schumacher (1992) put forward that the racial composition in the labour market is one of the most important factors in determining the wage levels in the United state of America. Both the models, containing the model of discrimination and the statistical model showed a consistency in the hypothesis of quality sorting and fractional consistency in the theories of racial crowding and language discrimination. It can be assumed that the wage rates can be estimated through racial composition within an organization or in the economy.

Solberg and Laughlin (1995) examined that fringe benefits make considerable differences in the analysis of earnings differentials. Thus, any measure which excludes fringe benefits may produce misleading results. After the statistical approach it was concluded that women will experience glass ceiling later in their career. If male and female jobs lead to different fringe benefits, pay and other opportunities the gap may become wider. Simultaneously, if the fringe benefits provided by the organization are different for males and different for females for the same level of work and job experience, then a fall in productivity in one of the gender is most likely to happen. It is usually said that, men workers are given more fringe benefits because management puts job enlargement and job rotation in the job of a male more as compared to female. Therefore, the productivity and efficiency level by the female worker tends to fall down.

Ashraf (1996) estimated wage differentials between male and female workers. After the statistical approach it has been concluded that the wage discrimination have showed a large decrease, both the general perception component as well as the wage difference components has shown a tremendous fall in the previous years. The result shows that the gap has been narrowed down from 84 percent to 47 percent in the years 1968 to 1990. It has been deduced that the decline in the unexplained portion- the general perception of the people- of the differential has helped in the fall of gender gap in the labour market.

Roxelle, Dong and Zhang (2002) discussed wage gaps in rural China. Gender earning gap were determined by using two cross-sections of data for 1988 and 1995. Wage regressions are run separately for men and women for each of the two sample periods. The explanatory variables are education, age, and employment sector (light and heavy industry). Comparing the estimates between 1988 and 1995, some interesting changes were noticed such as an education variable has positive values that display a strengthening of importance of education in wage discrimination. Wage differentials between age group narrows between 1988 and 1999. The fall in the wage gap between young and the middle age groups is expected to have positive effect on wage equality between men and women.

Kara (2006) investigated gender based wage differences by schooling and occupations to estimate the occupational gender wage discrimination in Turkey. Multiple regression is used to estimate the earning equations for males and females by using Turkish Household Expenditure and Income Survey. The results indicate that gender wage gap decreases with education and varies across occupations.

Wang and Cai (2006) showed that there was an immense gender wage gap that exists within different sector. The study shows that by running regression on the selected variables i.e. wage discrimination as dependent variable and performance as independent variable. The results indicate that the employers appreciate female workers less regardless of their performance on the job. Also, female workers have little incentives to be promoted even they perform excellent on the job. This leads to lower pays for the female workers.

Aeberhart and Pouget (2007) studied the national origin of wage differential in France. Data was selected from employer-employee through survey which was conducted in 2002. Regression was run on the selected variables i.e. wage discrimination as dependent variable and occupational employment, education ,experience as an independent variables. The results indicated that wage differentials exist when there are different type of jobs done by the workers, different level of experiences and education.

Temesgen (2008) investigated gender pay gap, with a particular focus on analyzing the effects of labor market institutions. A regression analysis is used in analyzing the varying impacts of labor market institutions and firm level characteristics on gender wage gaps in the Nigerian urban labor market, using information from workers and establishment level survey data. Primarily it was found that labor market institutions such as unions, and firm characteristics such as ownership, affect the level of gender wage inequality at the firm level. It was also found that unions have significant influence on firm level gender wage gaps in Nigeria. Specifically the paper shows that wage gaps are higher in unionized firms in Nigeria because women are generally less likely to join unions, thus being less likely to benefit from union-induced wage.

OBJECTIVES OF THE STUDY

The objectives are listed below:

To analyze the impact of wage discrimination by gender that is prevailing in the manufacturing sector of Pakistan.

To analyze the differences in the fringe benefits provided to male workers as compared to female workers, also if it is true that male workers are given priority when it comes to fringe benefits distribution or not.

To examine the general perception of the society towards female workers.

CHAPTER 3

METHODOLOGY

This study analyses the Wage discrimination by gender in the manufacturing sector of Pakistan. For this OLS estimation technique is applied using wage discrimination as a dependent variable and fringe benefits, education, experience, general perception, wage structures and performance as an independent variables.

3.1 SOURCES OF DATA

This study is based on the primary data. The information was collected using the survey method through questionnaires in the manufacturing sector considering total 10 companies.

The companies considered for the study were textiles, leather and the surgical instruments in the manufacturing sector of Pakistan. The sample includes 40 observations from the employees in this sector. Furthermore the study has been conducted in 2009 whereas; sampling technique used is non random sampling.

3.2 DATA DESCRIPTION

List of variables

W.D = Wage discrimination

W.S = Wage structures

F.B = Fringe benefits

EXP = Experience

EDU = Education

G.P =General perception

PER = Performance

Et = Error term.

t = Time period

SPSS (Statistical Package for Social Sciences Version 14) has been used to analyze the impact of wage discrimination by gender in the manufacturing sector of Pakistan.

3.3 HYPOTHESIS FORMULATION

Hypothesis 1:

H0: There is no wage discrimination by gender in the manufacturing sector of Pakistan.

H1: There is wage discrimination by gender in the manufacturing sector of Pakistan.

3.4 THEORETICAL FRAMEWORK

The theoretical framework generated for this study lists the relationships that are possible between the set of independent variable and dependent variables that have been listed.

3.5 SIMPLE LINEAR FUNCTIONAL FORM OF MODEL

Multiple linear regression technique using Ordinary least square (OLS) estimation has been applied to see the wage discrimination by gender in the manufacturing sector .For this wage discrimination is used as dependent variable and fringe benefits, education, experience, general perception, wage structures and performance as independent variables. Using the above mentioned variables the regression equation will be:

W.D = βo + β1 (F.B) + β2 (EDU) + β3 (EXP) + β4 (PER) + β5(G.P) + β6 (W.S) + Et

However, in the above equation βo is the intercept term whereas; (β1, β2, β3, β4, β5 and β6) are the slope coefficients of the respective variable. The expected signs of β’s are positive i.e. all the slope coefficient of independent variable are positive (β’s>0). This shows that an increase in any of these variables will cause wage discrimination to increase β times. Whereas, Et is the error term that captures the effect of other variables which are not explicitly brought in to the regression analysis.

CHAPTER 4

RESULTS AND INTERPRETATION

4.1 RESULTS OF ESTIMATED MODEL

TABLE 4.1 MODEL SUMMARY

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.952(a)

.906

.888

.26569

A. Predictors: (Constant), Fringe benefits, Performance, Wage structures, Experience, Education, General perception

B. Dependent Variable: Wage discrimination

The goodness of fit of model is measured by the coefficient of determination (R2). R2 explains the proportion of variation in the dependent variable explained by the independent variables in the model. The value of R2 lies between 0 and 1. The closer the value of R2 to 1, the model proves to be a good fit of the model. The value of R-square in this regression has turned out to be 90.6 percent which shows 90.6 percent variability is caused by most of the independent variables in the analysis. It implies that 90.6 percent variability in wage discrimination is caused by education, experience, fringe benefits, general perception, performance, and the wage structures. The adjusted R2 which has been adjusted for the degree of freedom to overcome the short comings of R2, has the value of 0.888, again showing that the model fitted is a good one. The standard error of estimates has turned out to be 0.26569 which is quite small and it shows that the observations had minimum chance of error in them while estimating the regression model.

TABLE 4.2 ANOVA TEST

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

25.166

7

3.595

50.931

.000(a)

Residual

2.612

37

.071

Total

27.778

44

a) Predictors: (Constant), Fringe benefits, Performance, Wage structures, Experience, Education, General perception

b) Dependent Variable: Wage discrimination

The overall significance of the model is measured by F-statistic. Here the value of F statistic is 50.931 at 0.000 level of 99 percent significance. This significance implies that the results obtained are statistically significant.

4.2 RESULTS OF ESTIMATED COEFFICIENTS

TABLE 4.3 RESULTS OF ESTIMATED COEFFICIENTS

Model

Unstandardized Coefficients

Standardized Coefficients

t

B

Std. Error

Beta

1

(Constant)

.635

4.133

6.510

Education

.167

-1.131

-.598

-6.784

Experience

.105

.690

.497

6.555

Performance

.072

-.524

-.663

-7.232

Wage structure

.288

-1.064

-.426

-3.690

General perception

.168

1.097

.580

6.519

Fringe benefits

.083

-.170

-.108

-2.052

a) Dependent Variable: Wage discrimination

Table 4.3 shows the results of estimated coefficients. All the estimated coefficients are consistent with economic theory, i.e. an increase in any of the independent variable will cause wage discrimination to change. Education is positively related to wage discrimination which means that an increase in education level will cause 16.7 percent increase in the wage discrimination. It means that if any of male or female workers will be more educated, there will be wage discrimination by gender in the manufacturing sector.

Experience of the worker contributes a lot in increasing the industrial production. The results of experience as an independent variable is positively related to wage discrimination, which shows that one year increase in experience of a worker increases wage discrimination by 10.5 percent.

Better performance of a worker will increase efficiency in productive activity in the manufacturing sector. Performance is positively related to wage discrimination, causing 7.2 percent increase in the wage discrimination which is highly significant. Wage structure of an organization attracts and motivates a worker to perform well. The results of wage structures show a positive and a significant impact on wage discrimination.

General perception refers to the general attitude and perceptions of people towards women, which shows 16.8 percent increase in wage discrimination.

Fringe benefits make considerable differences in the analysis of wage discrimination. If different fringe benefits are being offered to male or female workers having the same experience and nature of job, it will cause wage to be discriminated. The results of fringe benefits prove to be statistically significant causing 8.3 percent discrimination in the wages of male and female workers in the manufacturing sector. Thus, all of the six independent variables are highly significant.

4.3 HYPOTHESIS TESTING

Hypothesis 1:

H0: There is no wage discrimination by gender in the manufacturing sector of Pakistan.

H1: There is wage discrimination by gender in the manufacturing sector of Pakistan.

As the p value is 0.000, it shows significance at 99 percent confidence level. So we can reject H0 i.e., is the null hypothesis and accept H1 i.e., the alternative hypothesis. This means that there is wage discrimination by gender in the manufacturing sector of Pakistan.

CONCLUSION

The study concludes that wage discrimination exists in the manufacturing sector of Pakistan. Using the primary data for ten manufacturing companies i.e., textiles, leather and surgical instruments. Overall 40 observations were selected. OLS estimation technique was applied considering general perception, education, experience, performance, fringe benefits and wage structures as an independent variable and wage discrimination was considered a dependent variable. The results indicate a highly significant relationship among the selected variables. It shows that any increase in the independent variable causes wage to be discriminated in the manufacturing sector of Pakistan.

LIMITATIONS

The research work that had already been conducted on Wage discrimination by gender in the manufacturing sector was mostly done for the developed countries and comparatively less amount of research had been carried out for the developing countries. Therefore, the research for the relative literature proved to be a difficult task.

Majority of the organizations in Pakistan are male-dominant, to get the questionnaires filled, it was difficult to get equal amount of inputs from men and women workers.

RECOMMENDATIONS

Gender discrimination is a very complicated issue, which has been plaguing the society as well as the business environment. Following are the recommendations which can resolve the issue of wage discrimination to some extent.

Wage structures and organizational policies should be modified to make them same for both the genders.

Discriminatory Practices evolved in processes such as training, promotion and hiring should be eliminated.

A large scale motivational campaign needs to be launched for educating the general public at large and in particular parents, teachers, children, employers, employees, urging both genders to take equal part in the process of economic development. This campaign should aim at changin


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