Abstract

Gender inequality is still evident in Malaysian labour market despite the enactment of Employment Act 1955. Females' advantaged position in educational achievement prior to joining the labour market does not really grant them an advantaged standing in the labour market. The Act is in many ways outdated and proper revision is in pressing need. While theoretical framework from the economic perspective attributes this irony to gender differences in human capital and discriminatory practice in the labour market, theoretical framework from the psychological perspective justifies the role of occupational preferences. It follows that an integration of the two perspectives in studying gender inequality is thus relevant. This paper attempts to postulate the scenario of gender inequality in Malaysian labour market through descriptive statistics to propose for a revision to the existing Employment Act 1955 pertinent to female workers. It is also hoped that the discussion generated in this paper would provide insight to policy makers in devising a new strategy to promote gender equality in the labour market and gender-related development. Therefore, an overview of the issue of gender inequality from the economic and psychological perspectives is thus relevant.

Keywords: Gender inequality; Human capital; Discrimination; Occupational preferences.

Introduction

Gender inequality is not only a recent concern in the study of contemporary labour economics. The issue is as pertinent to economists as it is to sociologists and psychologists more than three decades ago. Theoretical framework explaining gender inequality and empirical research justifying its validity has since been the research domain of economics and psychology disciplines. Notwithstanding that, by reviewing the literature from these two disciplines one may notice that most of the empirical studies lingered around only either discipline, with cross-disciplinary research being less attempted. It is worth noted that while accounting for, say, gender wage differentials to explained and unexplained portions by measuring percentages of each portion, an investigation into the psychological aspects contributing to differences in each portion is also necessary. The implication is as significant for gender-related development as it is for addressing inadequacy in current labour law provisions.

In terms of statutory provision, the United States (US) government has taken two approaches to addressing the issue of gender inequality rooted from discrimination, namely non-discrimination approach, and affirmative action (Ehrenberg & Smith, 2009). The former resulted in the Equal Pay Act of 1963 and the Title VII of the Civil Rights Act of 1964, while the latter led to the establishment of The Federal Contract Compliance Program in 1965. In Malaysia, workers' well-being in the labour market is governed by the Employment Act of 1955, to which employers' compliance is monitored by the Department of Labour (Maimunah, 2006). Any injustice cases pertaining to labour market discrimination that are brought before the Department will be investigated and any suggestions resulted will serve as points of reference to the parties involved, albeit a specific statutory provision that works to outlaw discrimination is nonexistent in the Employment Act of 1955.

However, as charged by Maimunah (2006), the Employment Act of 1955 does not reflect the real picture of the current labour market. In fact, the Act is in many ways out-of-date. For instance, statutory provision that prohibits females from working in underground mining operation is outdated because such an activity is a rarity in Malaysian society nowadays. Besides that, the provision that grants female workers the rights to choose not to work between 10pm and 5am in industrial and agriculture sectors is indeed a form of discrimination. Such restriction on the types of industry women are allowed to be involved and on working hours is indeed a camouflage of discriminatory treatment working to the disadvantage of female workers, especially in the era of promoting gender equality nowadays.

As predicted in enormous empirical studies on the effects of gender discrimination on gender differences in labour market outcomes (e.g. Oaxaca, 1973; Brown, Moon & Zoloth, 1980; Kidd and Shannon (1994); Rahmah & Zulridah, 2005), gender discrimination that works to the disadvantage of female workers would result in females receiving lower wages compared to that received by male workers. Therefore, it is insufficient to protect working women without legitimately requiring for equal pay for equal work in order to outlaw discrimination against women. In fact, there are no such acts as the Equal Pay Act, Comparable Worth Policy, and Affirmative Action in Malaysian labour law, as does the US.

Despite numerous proposals are brought forward to revise the existing Act, however, the lack of consensus among interested parties renders the proposals unfruitful. In fact, clarifications made by the Court or changes in public policies will partly alter the interpretations of a specific provision under the Act as noticed by Maimunah (2006). But the questions arise: to what extent the Act could be revised and the interpretations would be altered along with the change of public policy? What will then be the driving forces for such a change? Answering these questions requires a holistic economic analysis supported by statistical evidence to highlight the real scenario of gender discrimination in Malaysian labour market. However, advanced statistical analysis procedures might somehow prompt researchers to put mathematical elegance before a more important theoretical reasoning. Perhaps, a conspicuous statistical analysis through descriptive statistics would shed some light to policy makers on the overall picture of the extent of gender inequality in Malaysia. It is also pertinent to researchers in justifying the use of an appropriate economic model and econometric model when explaining the causes of, say, gender wage differentials.

Therefore, this paper attempts to postulate the scenario of gender inequality in Malaysian labour market through descriptive statistics as a means of proposing for a revision to the existing statutory provision for women workers under the Employment Act 1955. Apart from revising the Act, it is hoped that the discussion generated in this paper would serve as a platform for policy makers in devising a new strategy to promote gender equality in the labour market and gender-related development. To achieve this end, an overview of the issue of gender inequality from the economic and psychological perspectives is thus relevant. The following section discusses about the ironic factual statistics that highlight the possibility of gender inequality in Malaysian labour market. Policy implications and recommendations for future research will be followed and a conclusion will come next to wrap up the paper.

Ironic Factual Statistics in Malaysian Labour Market

The following factual statistics portray a disadvantaged position of female workers compared to their male counterparts in Malaysian labour market. It is termed ironic because the statistics also show an advantaged position of the females as opposed to the males in the stage prior to joining the labour market. Some of the factual statistics shown in this paper are available at the official website of the Ministry of Women, Family and Community Development, while others are courtesy of the Department of Statistics.

Lower Labour Force Amidst High Working Age Population Among Female

Data source from the Department of Statistics of Malaysia shows that the percentages of male and female population under the age group of 15 to 64 - an age group to which labour force is referred - are almost similar within the period of 2000 - 2008, i.e. approximately 63 percent of total male and female population, respectively (refer to Figure 1). The evidence should have implied a comparable labour supply by gender which is characterised by equal appearance of male and female workers in the labour market. However, the male's labour force participation rate is still dominating the female's by almost twice as much even from as early as the 1990s towards the 21st century. Meanwhile, female's unemployment rate has all-time been higher than that of the male's (refer to Figure 2).

Could this ironic statistic be due to males' superiority in human capital accumulation compared to female's as suggested by Becker and Chiswick (1966)? Or could it be a result of shorter duration of labour force attachment among females to facilitate performing household responsibility as observed by Mincer and Polachek (1974), Polachek (1981), and Becker (1985)? Most importantly, does discriminatory treatment practiced against female workers exist in the labour market as contended by Becker (1971), Oaxaca (1973) and Blinder (1973)? In the effort of unveiling the truth, one must not also overlook the possibility of a non-discrimination-inflicted explanation that traditionally serves as a domain of studies within the theory of vocational development - i.e. occupational preferences (Gill, 1994; Solberg, 1999, 2004).

Figure 1: Mid-year Population by Age Group 15 - 64 and Sex, Malaysia, 2000 - 2008

Year

Total

(‘000)

% of

Total

Male

(‘000)

% of

Male

Female (‘000)

% of Female

2000

14,681.5

63.1

7,534.1

63.2

7,147.1

63.0

2001

14,956.7

62.9

7,565.8

62.7

7,390.7

63.0

2002

15,318.3

62.5

7,798.9

62.5

7,519.4

62.5

2003

15,702.3

62.7

7,992.5

62.7

7,709.8

62.7

2004

16,090.8

62.9

8,189.0

62.9

7,901.9

62.9

2005

16,483.0

63.1

8,387.8

63.1

8,095.1

63.1

2006

16,858.6

63.3

8,576.8

63.2

8,282.0

63.3

2007

17,238.1

63.4

8,767.9

63.4

8,469.9

63.5

2008

17,620.3

63.5

8,961.8

63.5

8,658.5

63.6

Source: Social Statistics Bulletin Malaysia, Department of Statistics, various years.

Figure 2: Employment: Summary Statistics, 1990 - 2008

Indicators

1990

(%)

1995 (%)

2000

(%)

2005 (%)

2006 (%)

2007

(%)

2008

(%)

Labour Force Participation Rate

66.5

64.7

65.4

63.3

63.1

63.2

62.6

Male

85.3

84.3

83.0

80.0

79.9

79.5

79.0

Female

47.8

44.7

47.2

45.9

45.8

46.4

45.7

Unemployment Rate

5.1

3.1

3.1

3.5

3.3

3.2

3.3

Male

4.0

2.8

3.0

3.4

3.3

3.1

3.2

Female

5.4

3.8

3.2

3.7

3.4

3.4

3.7

Source: Extracted from the Department of Statistics, Malaysia.

Females' Superiority in Educational Achievement

It is, as proposed in human capital theory, reasonable to unfavourably impart biases against females in token of lower level of educational attainment among them as opposed to their males' counterparts. However, it is deemed unreasonable from the perspective of theory of labour market discrimination had females not been offered the similar access to the labour market as males, given their superior achievement in education compared to their males' counterparts. Figure 3, 4, and 5 postulate females' higher achievement in terms of education.

Females' superiority in education is evidenced from their higher average enrolment rate in post secondary (Form Six and Matriculation) education between year 2000 and 2007, which is approximately 66.2 percent. Apart from that, a comparable average enrolment rate is also observed in college enrolment between both sexes within that period. Most strikingly, higher average percentage is also evident among females even for university enrolment rate (approximately 60.3 percent), compared to that of the male from 2000 to 2007. Among all the three levels of education mentioned above, females have recorded almost twice as much percentage growth of university enrolment rate as the males during the period under study with an increase of 43.6 percent compared to only 22.3 percent for the males (refer to Figure 3).

Figure 3: Enrolment in Government and Government-assisted Educational Institutions by Level of Education and Sex, 2001 - 2007

Level of Education

2001

2002

2003

2004

2005

2006

2007

Post Secondary (Form Six & Matriculation)

Male

27,570

35,382

43,157

52,781

56,450

53,410

46,115

Female

55,685

70,783

87,232

104,492

109,909

100,484

86,180

Total

83,255

106,165

0,389

157,273

166,359

153,894

2,295

% Female (over total)

66.9

66.7

66.9

66.4

66.1

65.3

65.1

College

Male

47,237

53,959

54,075

61,944

65,264

72,908

78,863

Female

43,488

54,269

54,167

59,914

63,858

70,756

77,888

Total

90,725

108,228

108,242

121,858

129,122

143,664

156,751

% Female (over total)

47.9

50.1

50.0

49.2

49.5

49.3

49.7

University

Male

103,747

116,591

110,645

116,799

121,157

119,304

126,836

Female

142,242

166,615

172,594

179,412

191,008

189,787

204,189

Total

245,989

283,206

283,239

296,211

312,165

309,091

331,025

% Female (over total)

57.8

58.8

60.9

60.6

61.2

61.4

61.7

Source: Extracted from the Ministry of Education Malaysia and Economic Planning Unit.

Besides that, analysing by student enrolment in public higher learning institutions at first degree by field of study between 2004 and 2006, one can explicitly notice that females have substantially dominated the enrolment percentage in fields of study such as arts, and science and technology, despite a reverse domination by males' counterparts in technical and vocational field (refer to Figure 4). The fields of study where females' enrolment rate is in dominance are those intensified areas pertinent to transforming Malaysian workers into knowledge-based workers to achieve the status of a developed country by the year of 2020.

Figure 4: Student Enrolment in Public Higher Learning Institutions at First Degree by Field of Study and Sex, 2004 - 2006

Sex

Field

Male

Female

2004

2005

2006

2004

2005

2006

Arts (%)

29.0

26.9

31.4

71.0

73.1

68.6

Science & Technology (%)

31.9

31.4

33.8

68.1

68.6

66.2

Technical & Vocational (%)

58.8

60.0

61.0

41.2

40.0

39.0

Source: Ministry of Higher Education, Malaysia.

Meanwhile, taking a closer look at the student enrolment in public higher learning institutions by level of study between 2004 and 2007, it is easily visible that female students are still the major human capital investors in every level of study except for Ph.D. study (refer to Figure 5). Females' lower enrolment in Ph.D. study could be attributed to the shorter working lifespan perceived by them compared to their male counterparts to give way for marriage and child-bearing. Besides that, female workers might also believe that they have shorter period of time to recoup the investment costs, which has shaken their motivation to invest in higher educational level in the way predicted by Mincer and Polachek (1974), Polachek (1981), and Becker (1985). Notwithstanding that, female's educational achievement should have indeed adequately equipped them for a labour market that is full of competition.

Figure 5: Student Enrolment in Public Higher Learning Institutions by Level of Study and Sex, 2004 - 2007

Male

Female

2004

2005

2006

2007

2004

2005

2006

2007

Diploma (%)

41.2

41.8

40.4

41.8

58.8

58.2

59.6

58.2

First Degree (%)

35.9

35.5

35.8

38.1

64.1

64.5

64.2

61.9

Post Graduate Diploma (%)

--

45.5

42.4

35.2

--

54.5

57.6

64.8

Masters (%)

46.3

45.9

45.6

47.1

53.7

54.1

54.4

52.9

Ph.D. (%)

61.4

61.3

60.9

61.9

38.6

38.7

39.1

38.1

Source: Adapted from the Ministry of Education, Malaysia.

Based on the statistical evidence shown above, it is convincingly true that females' achievement in education in terms of their enrolment in government and government-assisted educational institutions by level of education, enrolment in public higher learning institutions at first degree by field of study, and enrolment in public higher learning institutions by level of study, is way ahead of their males' counterparts. If gender discrimination that works to the disadvantage of females does not exist in Malaysian labour market, females' access to all occupational categories should by all means be at least comparable to, if not exceeding, that of the males'. However, the phenomenon of occupational and industrial segregation by gender is still evidenced in Malaysian labour market as discussed below.

Gender Inequality in Occupational Distribution: Discrimination or Preference?

Analysing the number of employed persons by occupation and sex between 2005 and 2007 where occupation categories are classified according to Malaysian Standard Classification of Occupations (MASCO) 1998, one can easily observe that male workers constitute the largest share of the pool of employed persons in all occupation except for clerical workers which are deemed to be female-dominated. The top three occupation categories with the highest magnitude of percentage difference between male and female workers are the ‘craft and related trade workers', ‘legislators, senior officials and managers', and ‘skilled agricultural and fishery workers' which are reckoned with being male-dominated. Moreover, occupational distribution among female workers as a percentage of total female employed further confirms the scenario above. Lowest proportions of females are employed as ‘craft and related trade workers' and ‘legislator, senior officials and managers' with an annual average of 4.3 percent and 5.2 percent, respectively, between 2005 and 2007. Most of the female workers find their employment as ‘service workers and shop and market sales workers' and ‘clerical workers' (annual average of 19.2% and 18.8%, respectively). Figure 6 below presents this ironic difference. 

Figure 6: Number of Employed Persons by Occupation and Sex, 2005 - 2007

Occupations (Occ)1

Year

Total (‘000)

Male (M) (‘000)

% M of total

M (% of total M hired)

Female (F) (‘000)

% F

of total

F (%

of total F hired)

Predicted

no. of female

(If Female % = Male %) (‘000)

No. of female needed to change Occ (‘000)a

Number of employed persons

2005

10,045.6

6,470.7

64.4

100.0

3,574.9

35.6

100.0

3,574.9

0

2006

10,275.4

6,618.6

64.4

100.0

3,656.8

35.6

100.0

3,656.8

0

2007

10,538.1

6,747.1

64.0

100.0

3,791.0

36.0

100.0

3,791.0

0

Legislators, senior officials & managers

2005

777.4

579.4

74.5

9.0

198.0

25.5

5.5

320.1

122.1

2006

829.6

635.7

76.6

9.6

193.8

23.4

5.3

351.2

157.4

2007

770.4

593.1

77.0

8.8

177.3

23.0

4.7

333.2

155.9

Professionals

2005

555.1

315.9

56.9

4.9

239.3

43.1

6.7

174.5

-64.8

2006

565.9

314.9

55.6

4.8

251.0

44.4

6.9

174.0

-77.0

2007

596.8

326.5

54.7

4.8

270.3

45.3

7.1

183.5

-86.8

Technicians & associate professionals

2005

1,266.8

784.4

61.9

12.1

482.5

38.1

.5

433.4

-49.1

2006

1,307.5

797.7

61.0

12.1

509.8

39.0

.9

440.7

-69.1

2007

1,400.5

852.8

60.9

12.6

547.8

39.1

14.5

479.2

-68.6

Clerical workers

2005

992.3

314.1

31.7

4.9

678.2

68.3

19.0

173.5

-504.7

2006

968.3

289.7

29.9

4.4

678.6

70.1

18.6

160.1

-518.5

2007

1,029.5

320.3

31.1

4.7

709.2

68.9

18.7

180.0

-529.2

Service workers and shop and market sales workers

2005

1,483.7

833.0

56.1

12.9

650.8

43.9

18.2

460.2

-190.6

2006

1,597.1

888.5

55.6

.4

708.6

44.4

19.4

490.9

-217.7

2007

1,705.6

952.0

55.8

14.1

753.5

44.2

19.9

534.9

-218.6

Skilled agricultural & fishery workers

2005

1,268.6

936.3

73.8

14.5

332.3

26.2

9.3

517.3

185.0

2006

1,335.9

993.4

74.4

15.0

342.5

25.6

9.4

548.9

206.4

2007

1,355.3

1,002.6

74.0

14.9

352.7

26.0

9.3

563.3

210.6

Craft and related trade workers

2005

1,145.5

985.3

86.0

15.2

160.2

14.0

4.5

544.4

384.2

2006

1,154.8

1,002.0

86.8

15.1

152.7

.2

4.2

553.6

400.9

2007

1,3.2

973.7

85.9

14.4

159.5

14.1

4.2

547.1

387.6

Plant & machine-operators and assemblers

2005

1,427.5

1,023.2

71.7

15.8

404.4

28.3

11.3

565.3

160.9

2006

1,408.0

1,004.4

71.3

15.2

403.6

28.7

11.0

554.9

151.3

2007

1,347.4

966.1

71.7

14.3

381.4

28.3

10.1

542.8

161.4

Elementary occupations2

2005

1,128.3

699.1

62.0

10.8

429.2

38.0

12.0

386.2

-43.0

2006

1,108.4

692.3

62.5

10.5

416.1

37.5

11.4

382.5

-33.6

2007

1,199.3

760.0

63.4

11.3

439.3

36.6

11.6

427.0

-12.3

a Index of Dissimilarity is calculated as the percentage of female workers to be moved out from such occupations as Professionals, Technicians and Associate Professionals, Clerical Workers, Service Workers and Shop and Market Sales Workers, and Elementary Occupations, over the total number of female workers employed.

1 Occupation is classified according to MASCO (Malaysian Standard Classification of Occupations) 1998

2 Elementary occupations perform simple and routine tasks, which mainly require the use of handheld tools and in some cases considerable physical effort. Most occupations in this major group require skills at the first skill level.

Source: Department of Statistics, Malaysia; percentage of total for male and female, and the Index of Dissimilarity, are author's calculations.In accounting for the phenomenon of gender inequality in occupational distribution, the theory of labour market discrimination provides an explanation from the dimension of personal prejudice among employers (Becker, 1971; Hellerstein, Neumark & Troske, 2002), employees (Ragan & Tremblay, 1988; Baldwin, Butler & Johnson; 2001), and customers (Holzer & Ihlanfeldt, 1998). The issue might also find its roots from what statistical discrimination theory has suggested on how a job candidate's expected productivity is assessed based on the group data to which the candidate belongs (Aigner & Cain, 1977) and the information readily available to employers pertaining to hiring, eg., educational level and race (Altonji & Pierret, 2001). Following labour market discrimination that is brought about by personal prejudice and statistical discrimination, workers from different gender groups might be refused of certain jobs and be crowded into another that depresses their marginal productivity and wages. This line of reasoning could explain the incidence of gender occupational segregation evidenced from Figure 6 in the way Bergmann (1971) tested for her crowding hypothesis, which was in return confirmed by Sorenson (1990).

While attributing the causes of gender occupational segregation to labour market discrimination, be it of pre-market or post-market nature, one should also be aware of the possible explanatory power of a non-discrimination-inflicted factor - occupational preference. If gender occupational segregation observed in Figure 6 is the result of pure labour market discrimination working to the disadvantage of females, the possibility of such a job preference effect could be safely dismissed. However, if it is females' own choice as to which occupational category to enter - probably to give way to performing household responsibility - then the explanatory power of occupational preference to gender occupational segregation highly regarded. This dimension of labour market decision has been the research domain both from the economic (Gill, 1994; Solberg, 2004) and psychological perspective (Super, 1953; Brown, 2002). In fact, the research works on occupational preference from the psychological perspective have been carried out more extensively than that in the economic framework. One might account for gender occupational preferences, and thus, gender occupational segregation, by the role of interest and self-efficacy (Lofquist & Dawis, 1991; Tracey & Hopkins, 2001), the role of occupational sex-role stereotypes (Gottfredson, 1981; O'Connor & Goodwin, 2004), the role of job knowledge (Miller and Hayward, 2006), the role of cultural and work value (Brown, 2002), and the role of gender-specific factors (Hakim, 1996, 2000; Marks & Houston 2002). By taking into consideration the role of such psychological factors in explaining gender occupational segregation observed in Figure 6, it is indeed pertinent to government policy making and educational curriculum development.

Index of Dissimilarity

Whether the phenomenon of unequal occupational distribution by gender is a result of human capital accumulation, labour market discrimination, or even occupational preferences, a more equal distribution is desirable for sustainable economic growth generated from the contribution of both gender groups. To facilitate national manpower planning and human capital investment, a country should know the amount of manpower needed in each occupational category in order to strategically channel government appropriation for manpower training and development. A calculation of the Index of Dissimilarity may serve the purpose.

Index of Dissimilarity can be constructed based on the occupational distribution by gender in Figure 6 to indicate the percentage by which female workers should be reallocated from certain occupations to enable equal occupational distribution as their male's counterparts. From the last column of Figure 6, one may notice that a total of about 852,200 or 23.8 percent of total female workers should be moved out from such occupations as ‘Professionals', ‘Technicians and Associate Professionals', ‘Clerical Workers', ‘Service Workers and Shop and Market Sales Workers', and ‘Elementary Occupations' in 2005 - Clerical Workers being most excessive of females. They should have moved into such occupations as ‘Legislators, Senior Officials and Managers', ‘Skilled Agricultural and Fishery Workers', ‘Craft and Related Trade Workers', and ‘Plant and Machine-Operators and Assemblers'. It follows that the Index of Dissimilarity is therefore 23.8 percent. However, the total number of female workers who should have moved out from their existing occupations has been increased to about 915,900 persons in 2006, driving up the index to 25.0 percent. The same measurement for female workers has slightly reduced to about 915,500 persons in 2007, sliding the index to 24.1 percent.

The idea embedded in the Index of Dissimilarity calculated above is that a certain percentage of female workers should be moving out of their existing occupations to facilitate more equal occupational distribution by gender. The occupational categories from which they have to move out are characterised by female overrepresentation. The causes of this overrepresentation might not necessarily be rooted from unjust treatment in the workplace. If in any way females own occupational preference is accounted for, then government policy should not be merely oriented towards eliminating pre-market or post-market gender discrimination. Perhaps, this should be an agenda as pertinent to the Ministry of Human Resources as it is to the Ministry of Women, Family and Community Development as far as the non-market factor is concerned, e.g. performing household responsibility and child-rearing.

Unequal Distribution of Labour by Gender and Industry: Fewer Females in Services

When analysing the number of employed persons by industry and sex between 2005 and 2007, one can identify that industrial segregation by gender is evidenced more explicitly in the three consecutive years (refer to Figure 7). Male workers occupy the largest share of total employed persons in almost all industries categorised as manufacturing, services, and agriculture sectors, except for such industries as ‘education', ‘health and social work', and ‘private households with employed persons'. ‘Fishing' industry witnesses the highest rate of male's dominance over the females, followed by such industries as ‘construction', ‘mining and quarrying', and ‘electricity, gas and water supply'. Male's dominance in those industries is reasonable owing to the fact that the jobs categorised in those industries traditionally require physical strength and innate ability that is pertinent to males rather than females. Meanwhile, percentage distributions between male and female workers are almost comparable in such industries as ‘hotels and restaurants', and ‘financial intermediation' where workers of both sexes are regarded as equally important.

Occupational distribution among female workers as a percentage of total female employed further confirms the scenario above. Lowest proportion of females is employed in ‘fishing', ‘mining and quarrying industries', and ‘electricity, gas and water supply'. Miller and Budd (1999) provide an excellent explanation to this scenario by concluding that females typically prefer traditional ‘female' occupations to traditional ‘male' occupations, and vice versa for males.

It is, however, interesting to observe that despite its male-dominant nature, majority of females are found to have secured their jobs in the manufacturing industry between 2005 and 2007 (annual average percentage of 21.6% of total female employed), soothing the palpable tension of gender inequality in industrial distribution. Notwithstanding that, females are still less represented in the services industry within that period. Such services subsectors as ‘electricity, gas and water supply', ‘transport, storage and communication', ‘financial intermediation', and ‘real estate, renting and business activities' still comprise of a rather low percentage of female involvement. Given females' outperformance in educational achievement over their males' counterparts, they should have been equally represented in those subsectors. Moreover, services industry has been in dominance of other industries in serving the role as the key engine of economic growth in Malaysia since 2001. Growth momentum in the services sector is forecasted to remain strong in 2009 with the growth rate of 6.9 percent and account for 55.1 percent of the Gross Domestic Product (GDP). This sector is also expected to contribute 3.7 percentage point to the overall GDP growth in 2009, mostly driven by the performance in such services subsectors as the ‘finance and insurance', ‘real estate and business services', ‘transport and storage', ‘communication', and ‘wholesale and retail trade' (Economic Report, 2008/2009). Therefore, economic growth of Malaysia could have been achieved more tremendously in the past and future should female workers successfully channel their human capital to productive use in the services sector. Failing to do so, females' human capital accumulation will not be effectively utilised, while the whole economy in general, and labour market in specific, might not flourish due to allocative inefficiency.

Figure 7: Number of Employed Persons by Industry and Sex, 2005 - 2007

Industry

Year

Total (‘000)

Male (M) (‘000)

% M

of total

Female (F) (‘000)

% F

of total

Female

(% of total female employed)

Agriculture, hunting & forestry

2005

1,355.2

996.6

73.5

358.6

26.5

10.0

2006

1,375.3

1,0.9

73.7

361.4

26.3

9.9

2007

1,437.3

1,064.5

74.1

372.8

25.9

9.8

Fishing

2005

115.2

110.3

95.8

4.9

4.2

0.1

2006

128.2

123.2

96.1

5.0

3.9

0.1

2007

120.9

115.4

95.5

5.4

4.5

0.1

Mining & quarrying

2005

36.1

31.5

87.5

4.5

12.5

0.1

2006

42.0

35.9

85.5

6.1

14.5

0.2

2007

39.4

33.7

85.5

5.7

14.5

0.2

Manufacturing

2005

1,989.3

1,201.5

60.4

787.8

39.6

22.0

2006

2,082.8

1,270.4

61.0

812.4

39.0

22.2

2007

1,977.3

1,196.6

60.5

780.7

39.5

20.6

Electricity, gas & water supply

2005

56.6

49.0

86.6

7.6

.4

0.2

2006

75.4

63.4

84.1

12.0

15.9

0.3

2007

60.8

52.0

85.5

8.8

14.5

0.2

Construction

2005

904.4

832.1

92.0

72.3

8.0

2.0

2006

908.9

834.4

91.8

74.5

8.2

2.0

2007

922.5

854.5

92.6

68.0

7.4

1.8

Wholesale & retail trade; repair of motor vehicles, motorcycles and personal & household goods

2005

1,620.3

1,063.5

65.6

556.8

34.4

15.6

2006

1,650.5

1,081.0

65.5

569.6

34.5

15.6

2007

1,712.1

1,120.2

65.4

592.0

34.6

15.6

Hotels & restaurants

2005

671.8

349.3

52.0

322.5

48.0

9.0

2006

721.3

385.5

53.4

335.8

46.6

9.2

2007

760.7

397.7

52.3

363.0

47.7

9.6

Transport, storage & communication

2005

544.7

464.9

85.4

79.7

14.6

2.2

2006

539.7

449.9

83.4

89.8

16.6

2.5

2007

538.2

450.2

83.6

88.0

16.4

2.3

Financial intermediation

2005

247.4

120.3

48.7

127.0

51.3

3.6

2006

242.3

120.5

49.7

121.8

50.3

3.3

2007

282.2

142.2

50.4

140.0

49.6

3.7

Real estate, renting & business activities

2005

459.0

284.8

62.1

174.2

37.9

4.9

2006

508.4

317.8

62.5

190.6

37.5

5.2

2007

558.1

343.5

61.5

214.7

38.5

5.7

Public administration & defence; compulsory social security

2005

728.5

525.8

72.2

202.8

27.8

5.7

2006

674.1

486.5

72.2

187.6

27.8

5.1

2007

716.1

514.6

71.9

201.5

28.1

5.3

Education

2005

607.1

219.6

36.2

387.4

63.8

10.8

2006

600.1

209.7

34.9

390.4

65.1

10.7

2007

632.7

221.7

35.0

411.0

65.0

10.8

Health & social work

2005

212.6

69.8

32.8

142.8

67.2

4.0

2006

223.2

70.6

31.6

152.6

68.4

4.2

2007

238.9

74.9

31.4

164.0

68.6

4.3

Other community, social & personal service activities

2005

234.9

4.0

57.1

100.9

42.9

2.8

2006

247.1

5.2

54.7

111.9

45.3

3.1

2007

266.5

144.9

54.4

121.7

45.7

3.2

Private households with employed persons

2005

260.6

16.1

6.2

244.5

93.8

6.8

2006

254.7

19.6

7.7

235.0

92.3

6.4

2007

272.7

19.5

7.2

253.2

92.8

6.7

Extra-territorial organisations & bodies

2005

1.7

1.3

76.5

0.4

23.5

0.01

2006

1.2

1.0

83.3

0.2

16.7

0.01

2007

1.7

1.0

58.8

0.7

41.2

0.02

Total

2005

10,045.3

6,470.5

64.4

3,574.8

35.6

100.0

2006

10,275.4

6,618.6

64.4

3,656.8

35.6

100.0

2007

10,538.1

6,747.1

64.0

3,791.0

36.0

100.0

Source: Department of Statistics, Malaysia; percentage of total for male and female are author's calculations.

Unequal Number of Registered Professionals by Gender: Sex-role Stereotype?

Another scenario of unequal occupational distribution by gender can be signalled by analysing the number of registered professionals by sex in the period between 2005 and 2008. Despite females' outperformance in education over their male counterparts in terms of level and field of study, they are less dominant in all professions requiring high level of educational investment, except for dentists profession where females' involvement merely outweighs the males' (although improving over years) during the period. On the contrary, males are especially seen in dominance of such professions as land surveyors, professional engineers, graduate engineers, and professional architects between 2005 and 2008 (refer to Figure 8).

While economists generally attribute this observation to labour market discrimination that works to the disadvantage of females, Miller and Hayward (2006) speak from the perspective of educational psychology by associating this scenario to occupational sex-role stereotype perceived by teenagers ever since they were in schooling. As observed by them, both males and females prefer jobs that they perceive as stereotypically gender-appropriate and dominated by their own sex. They show through their findings that boys generally express a more stereotyping view than do girls in considering such occupations as engineers and architects as masculine jobs; while girls or even boys generally stereotype a profession in dentistry as either sex neutral or feminine by nature. Based on such psychological reasoning, it is self-explanatory for an observation of unequal number of registered professionals by gender in Malaysia.

Figure 8: Number of Registered Professionals by Sex, 2005 - 2008

Profession

Year

Total

Male

Male

(% of total)

Female

Female

(% of total)

Accountants

2005

21,289

12,145

57.0

9,144

43.0

2006

22,460

12,578

56.0

9,882

44.0

2007

23,558

12,986

55.1

10,572

44.9

2008

25,019

,369

53.4

11,650

46.6

Professional Architects

2005

1,652

1,426

86.3

226

.7

2006

1,621

1,389

85.7

232

14.3

2007

1,629

1,395

85.6

234

14.4

2008

1,648

1,402

85.1

246

14.9

Architects

2005

1,334

1,000

75.0

334

25.0

2006

1,271

925

72.8

346

27.2

2007

1,281

916

71.5

365

28.5

2008

1,217

866

71.2

351

28.8

Professional Engineers

2005

11,523

11,2

97.3

310

2.7

2006

12,598

12,245

97.2

353

2.8

2007

12,844

12,466

97.1

378

2.9

2008

--

--

--

--

--

Graduate Engineers

2005

37,678

32,866

87.2

4,812

12.8

2006

40,608

34,728

85.5

5,880

14.5

2007

42,893

36,596

85.3

6,297

14.7

2008

--

--

--

--

--

Dentists

2005

2,601

1,8

43.8

1,463

56.2

2006

2,841

1,244

43.8

1,597

56.2

2007

2,998

1,252

41.8

1,746

58.2

2008

3,2

1,292

40.2

1,921

59.8

Veterinary Surgeons

2005

1,399

935

66.8

464

33.2

2006

1,462

960

65.7

502

34.3

2007

1,464

962

65.7

502

34.3

2008

1,524

977

64.1

547

35.9

Medical Doctors

2005

15,574

9,775

62.8

5,799

37.2

2006

--

--

--

--

--

2007

--

--

--

--

--

2008

--

--

--

--

--

Land Surveyors

2005

405

404

99.8

1

0.2

2006

423

422

99.8

1

0.2

2007

447

445

99.6

2

0.4

2008

436

434

99.5

2

0.5

Quantity Surveyors

2005

1,878

1,265

67.4

6

32.6

2006

1,408

931

66.1

477

33.9

2007

1,457

946

64.9

511

35.1

2008

1,050

849

80.9

201

19.1

Lawyers

2005

11,841

6,519

55.1

5,322

44.9

2006

12,229

6,639

54.3

5,590

45.7

2007

12,391

6,699

54.1

5,692

45.9

2008

12,471

6,702

53.7

5,769

46.3

Notes: 2005 & 2006; 2007 & 2008 - as at mid-year

-- Not available

Source: Malaysian Institute of Accountants, Board of Architects Malaysia, Board of Engineers Malaysia, Malaysian Dental Council, Malaysian Veterinary Council, Bar Council of Malaysia, Board of Land Surveyors of Peninsular Malaysia and Sabah, Board of Quantity Surveyors of Malaysia, and Ministry of Health Malaysia.

Unequal Occupational Distribution by Gender in Academic Line

Unequal occupational distribution by gender is not only evidenced in corporate sector, it is indeed also apparent in education industry. Analysing the number of premier officers and academic staffs in public higher learning institutions in the period between 2004 and 2008, one can observe that males still largely dominate such positions as the vice chancellor, deputy vice chancellor, registrar, professor, and associate professor (refer to Figure 9). So far as the data shown below is concerned, an ironic question is left unanswered: given females' remarkable achievement in education, why would their occupation in such positions not palpably and fairly reflected in public higher learning institutions? Whether or not this phenomenon is attributed to household responsibility taken up by female workers that in turn, to certain extent, causes a lack of seniority effects on them (Rummery, 1992) as frequently believed, a multidisciplinary research integrating economics and psychology perspectives is thus warranted to unveil the truth as far as the issue of gender discrimination is concerned.

Figure 9: Number of Premier Officers and Academic Staffs in Public Higher Learning Institutions, 2004 - 2008

Gender

Year

Position

Vice Chancellor

Deputy Vice Chancellor

Registrar

Male

2004

17

44

15

2005

17

45

15

2006

18

46

15

2008*

18

54

18

Female

2004

0

1

2

2005

1

2

3

2006

2

4

3

2008*

2

6

2

Total

2004

17

45

17

2005

18

47

18

2006

20

50

18

2008*

20

60

20

% Female (over total)

2004

0.0

2.2

11.8

2005

5.6

4.3

16.7

2006

10.0

8.0

16.7

2008*

10.0

10.0

10.0

Gender

Year

Position

Professor

Associate Professor

Lecturer

Male

2004

1,081

2,554

6,260

2005

866

1,988

6,358

2006

908

2,000

6,585

2008

1,077

1,974

7,301

Female

2004

342

1,469

6,202

2005

245

1,0

6,343

2006

250

1,121

6,824

2008

323

1,2

8,282

Total

2004

1,423

4,023

12,462

2005

1,111

3,118

12,701

2006

1,158

3,121

,389

2008

1,400

3,106

15,583

% Female (over total)

2004

24.0

36.5

49.8

2005

22.0

36.2

49.9

2006

21.6

35.9

51.0

2008

23.1

36.4

53.1

Note: * As at July 2008.

Source: Adapted from the Ministry of Higher Education, Malaysia.

Gender Inequality Moving Up the Job Ladder

Following a survey done by the Ministry of Women, Family and Community Development (MWFCD) on fifty companies listed under Bursa Malaysia (formerly known as Kuala Lumpur Stock Exchange), it is noticeably evident that women are less influential at decision-making level in the corporate sector between 2001 and 2008. Women's occupation in such positions as the members of board of directors, president, vice-president, managing director, chief executive officer, chief operation officer, senior general manager, and general manager ranges only from 5.3 percent to 26.2 percent of the total amount of decision makers during that period (refer to Figure 10). However, with the exception for members of board of directors, the proportion of women decision makers in other positions has registered a continuous growth within that period despite the composition is still relatively lower than that of men.

This finding is consistent with that of Baldwin, Butler and Johnson (2001) and Bertrand and Hallock (2001). The latter analyses gender compensation differences among top executives to examine how well women were doing in top corporate jobs. They observe that there are only 2.4 percent of female possessions of top executive positions, with women executives earning about 45 percent less than their men counterparts since they were under-represented in large corporations. Such inferiority in terms of compensation received by women top executive could be explained on the ground of employees' discrimination against female superior as argued by Bertrand and Hallock (2001). They rationalise their argument by advocating that capability and quality between women and men should be homogenous when they arrive at such higher positions in the job ladder. Therefore, it follows that any unexplained gender differences in compensation could be discriminatory-inflicted. To the extent that this explanation is applicable in Malaysian context, further research on this respect is worth attempted.

Figure 10: Women at Decision-Making Level in the Corporate Sector, 2001 - 2008

Year

2001

2002

2003

2004

2005

2006

2007

2008*

Position

% Female

Members of Board of Directors

10.1

10.5

10.1

9.9

10.2

5.9

5.3

6.1

President, Vice-President, Managing Director, Chief Executive Officer, Chief Operation Officer, Senior General Manager, General Manager

12.0

12.1

12.3

.5

.9

14.3

24.0

26.2

Note: * As at June 2008

Source: Survey by MWFCD on 50 companies listed under Bursa Malaysia.

Gender Wage Differentials in Services Sector

A common measure of examining gender inequality in the labour market is through analysing gender wage differentials. The most fundamental method of analysis is proposed by Oaxaca (1973) in decomposing gender wage differentials into explained and unexplained portions, where the former is basically attributed to gender differences in productive-related characteristics while the latter, discriminatory treatment working to the disadvantage of one of the gender groups.

In Malaysia, among the earlier studies of gender wage differentials are those of Chua (1984) and Latifah (1998). Their studies employ aggregate data that includes all economic sectors in Malaysia and result in a larger extent of discriminatory effect working against females' interest. However, Rahmah and Zulridah (2005) subsequently focus on examining gender wage differentials in Malaysian manufacturing sector and a rather smaller discriminatory effect is found (25.7% of gender wage differentials is due to unexplained variables). In fact, male employees are paid approximately 18.4 percent more than their female counterparts in the manufacturing sector. They argue that a more structured wage scheme offered in the manufacturing sector could serve as the explanation for such lower extent of discriminatory practice as opposed to investigating the issue in all sectors. Comparing the findings of Chua (1984), Latifah (1998), and Rahmah and Zulridah (2005), it follows that gender wage differentials in services and agricultural sectors could be higher in extent than that of manufacturing sectors. However, studies on gender wage differentials incorporating the effects of explained and unexplained measures in services sector and agricultural sector alone are still less attempted in Malaysia, making it hardly possible for an appropriate statutory provision to be available in Malaysian labour law pertaining to the sectors. Moreover, the fact that services sector remains to be the key engine of economic growth in Malaysia in recent years justifies an investigation into the sector to examine if such sectoral growth is well reflected through gender equality in pecuniary term. Series of descriptive data from the Department of Statistics provide a preliminary overview of the extent of gender wage differentials in Malaysian services sector, with particular reference to the computer and telecommunications ICT services subsectors.

Within the overall computer services subsector, it is observed that the average monthly salary ratio of male to female for all occupational categories fluctuated within a range of 1.0 and 1.4, indicating males' superiority in terms of monetary compensation (Figure 11). Notwithstanding that, this trend of males' superiority was slightly weakened between 2001 and 2006 when female's average monthly salary was slightly improved within that period. Among all, general workers registered a rather huge fluctuation in the ratio between 2001 and 2003. Monetary improvement had been evidenced among females since then towards 2005 before wage equality by gender was almost reached.

Similar to computer services subsector, the overall telecommunication services subsector too observed male superiority in monetary compensation with its average monthly salary ratio of male to female for all occupational categories fluctuating within a range of 1.0 and 1.4 between 2001 and 2006 (Figure 12). Monetary improvement had also been observed among females general workers and females technical and supervisory workers, indicated by a rather huge fluctuation in the ratio within these two jobs. However, a reverse trend of the ratio was evident since 2004 in the telecommunication services subsector as opposed to the computer services subsector when the average monthly salary of male workers had been continuously raising far above that of female workers' within that period.

Following the observations in the abovementioned two services subsectors, two conclusions can be drawn with respect to gender differences in monetary remuneration - male superiority and female improvement in labour market outcomes. This observation would call for attention of the potential explanations to male-female wage differentials in Malaysian services sector. An analytical tool that can clearly isolate the effects of gender differences in productivity-related characteristics, labour market discrimination, and individual occupational preferences is thus necessary. To the extent where such a wage differential by gender is attributed to labour market factors and non-labour market factors, an empirical investigation into the issue is thus warranted.

Note:

F = Full-time employees, M = Managerial, professional and executive, T = Technical and supervisory, C = Clerical and related occupations, G = General workers

Note:

F = Full-time employees, M = Managerial, professional and executive, T = Technical and supervisory, C = Clerical and related occupations, G = General workersPolicy Implications and Recommendations for Future Research

Existing econometric models employed in decomposing gender wage differentials do not account for the potential sources of the unexplained portions of the differentials. These portions could be due to discriminatory treatment practiced by employers in the labor market. However, following Becker (1971), discriminatory treatment that is resulted from personal prejudice could be rooted from employees and customers, besides employers. By incorporating these three sources of discrimination into the analysis of gender wage differentials, it is pertinent for policy making.

If employers' prejudice constitutes the largest share of the unexplained portions of gender wage differentials, a revision of the existing Employment Act 1955 is thus justifiable on the ground of setting forth a specific statutory provision that outlaws any forms of discrimination exerted by employers in hiring, wage determination, and promotion. Such acts as the Equal Pay Act, Title VII of the Civil Rights Act, and the Federal Contract Compliance Program enacted by the US government could serve as points of reference for Malaysian government in setting a law that prevents gender wage discrimination for otherwise equally qualified workers, a law that renders any forms of prejudicial practices in hiring and promotion as unlawful, and a law that requires employers intended to bid for government contracts to comply with nondiscriminatory hiring requirement set forth by the government. Besides that, if employees' prejudice accounts for much of the unexplained portions of gender wage differentials, a special clause can be included in the employment contract pertaining to the code of conduct applicable to the respected employees. Any breach of this code of conduct can be dealt with in much similar way to the offence of sexual harassment taking place at the workplace. Meanwhile, if customers' prejudice made up the largest share of the unexplained portions, employers should consider reshuffling the proportion of workforce serving customers who belong to the same demographic group as the workforce. Hiring decision should also be encompassed by the proportion of customers. In conclusion, future researchers examining gender wage differentials in Malaysia may want to consider developing an econometric model that adequately incorporates the effects of employers, employees and customers prejudice.

The standard Oaxaca wage decomposition model is no longer a valid mechanism to rely on when proposing for revision to the existing Employment Act 1955. This model is devoid of the possibility of gender wage differential that is due to within- and across-occupational differences (Brown et al., 1980). A clearer distinction between these two causes is important in justifying if the revision of the Act should be oriented towards eliminating pre-market discrimination or post-market discrimination (Teo, 2003). If within-occupational differences outweigh across-occupational differences, the Act should reflect equal pay for equal work or comparable worth; if it is the other way around, the Act should outlaw any forms of discrimination practiced at the point of hiring to address the issue of gender occupational segregation explained by crowding hypothesis. However, policy makers should be made aware that not all the unexplained portions of gender wage differentials are discrimination-inflicted. Gender differences in occupational preferences could be as important as discriminatory practices in accounting for gender wage differentials. It follows that the econometric model proposed by Gill (1994) would be more appropriate to render pre-market and post-market policies justifiable after controlling for the effects of occupational preferences. In conclusion, future researchers may want to consider employing Gill's model for a better policy justification.

The use of Gill's model provides insights not only to the revision of Employment Act 1955, it is also pertinent to devising government policy aiming at promoting gender-related development. If gender differences in occupational preferences constitute a larger proportion of the gender wage differentials, future researchers may want to further analyse the determining factors for such differences. Since the study of occupational preferences is as relevant to economists as it is to psychologists, an introduction of the psychological perspective in the study of gender wage differentials is thus necessary. If gender differences in occupational preferences are due to gender differences in interest, self-efficacy and the amount of job knowledge possessed, government policy could be oriented towards educational curriculum development and human capital enhancement. More rigorous teaching approach could be employed to integrate classroom learning and industrial relevance to generate interest among learners in reinforcing their self-efficacy and enhancing their job knowledge. If the gender-specific factors are relevant, government policy could be focused on better family and community development. This includes awarding better pecuniary incentive to facilitate family planning for efficient household division of labour. Meanwhile, if occupational sex-role stereotypes and cultural and work values are the factors, positive values should be inculcated among females and males when they are still young. This, among all, is definitely the research domain from the psychological perspective.

As observed by Miller and Hayward (2006), occupational sex-role stereotypes among United Kingdom (UK) teenagers are highly associated with their job preference. This pool of teenagers was in their high school during the survey period and it is found in the study that this association decreases with age for female students. It follows that future researchers may want to consider conducting a longitudinal study to trace the same pool of respondents' occupational preferences in the labour market. This endeavour should be particularly of interest among economists and psychologists to examine the effects of perceived occupational sex-role stereotypes formed at younger age on gender occupational preferences and segregation, and thus, gender inequality in monetary remuneration in the labour market. This is definitely an important research agenda that integrates expertise carrying the title of economists and psychologists.

Conclusions

All in all, the series of evidence observed in the figures above have explicitly reflected the issue of gender inequality with respect to labour market outcomes in Malaysia. Contemporary economic theoretical frameworks partly attribute this issue to gender differences in human capital accumulation and discriminatory treatment that work to the disadvantage of female workers. Theoretical frameworks developed from the psychological perspective highlight gender differences in occupational preferences as a possible explanation for unequal occupational distribution by gender. If such occupational preference is justifiable on the ground of differences in interest and self-efficacy, occupational sex-role stereotypes, job knowledge, cultural and work value, and gender-specific factors, then multidisciplinary research cutting across economics and psychology should be relevant. Therefore, it serves as a solid research platform for future researchers to examine different elements of gender inequality in Malaysian labour market. The implication will be as important in devising government policy as it is in expanding the corpus of knowledge on gender inequality in Malaysia.

Source: Essay UK - http://turkiyegoz.com/free-essays/economics/gender-inequality.php


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