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EPPE6264/EPPE8154 ADVANCED LABOUR ECONOMICS Semester 1, Session 2022/2023

发布时间：2023-01-10

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Semester 1, Session 2022/2023

EPPE6264/EPPE8154 ADVANCED LABOUR ECONOMICS

Continuous Assessment: 40% (EPPE6264); 20% (EPPE8154)

Continuous Assessment #1 (60 marks) ~ Due Date: 20 January 2023 by 12pm Malaysian time via UKMFolio

1) How are longer unemployment spell and gender occupational segregation related to the wage level of an otherwise disagreeable job?

Answer the above question using graphical approach and support your arguments with previous

studies in the format of a literature review. (16 marks)

2) (a) Based on the table below, calculate and interpret the Duncan Index to examine the degree of dissimilarity on the gender distributions across occupations in Malaysia in the year of 2020. (10 marks)

Occupation Classification	Year	Men ('000 persons)	Women ('000 persons)
Managers	2020	607.5	200.9
Professionals	2020	781.9	1085.8
Technicians and associate professionals	2020	1063.2	484
Clerical support workers	2020	384	858.1
Service and sales workers	2020	1861.8	1770.4
Skilled agricultural, forestry and fishery workers	2020	719.9	196.4
Craft and related trades workers	2020	1174.9	297.6
Plant and machine operators, and assemblers	2020	1277.8	412.2
Elementary occupations	2020	1257.8	522.5
Total		9128.8	5827.9

(Source: ILOStat, International Labour Organization)

(b) Explain two non-discrimination-related reasons for males’ overrepresentation in high-pay jobs, and females’ overrepresentation in low-pay jobs. Support your arguments with previous studies

in the format of a literature review. (14 marks)

3. Oaxaca (1973) proposed the following gender wage decomposition model:

− n̅ = (âm − ) + F̂m(̅ − ) + (F̂m − )

Reference:

Oaxaca, R. (1973). Male-Female Wage Differentials in Urban Labor Markets. International Economic Review, 14(3), 693–709.https://doi.org/10.2307/2525981

where n̅ is the average of natural logarithm of monthly wages received by male (m) or female (f) workers; is the average of vector of productivity-related characteristics possessed by m or f; is the estimated constant value from the wage equation for m or f; while F̂ is the estimated vector of coefficients for productivity-related characteristics from the wage equation for m or f.

Male wage equation and female wage equation are estimated for Malaysian workers in 2015, with the descriptive statistics and regression results shown below . Monthly wages for Malaysian workers are determined by their year of schooling, work experience (and its squared term), workplace core skills, workplace process skills, aggressive behaviour (i.e. type A behaviour), and adventurous behaviour (i.e. sensation seeking).

Calculate each of the percentage of total gender wage differentials that is due to explained factors, and unexplained factors. Based on the results, what are the two major sources of gender wage discrimination that work to the disadvantage of women? Support your arguments with previous

studies in the format of a literature review. (20 marks)

Regression (Male Wage Model):

Descriptive Statistics

Mean		Std. Deviation	N
lnWage	7.629936678	0.5793332	567
Year of schooling	13.506172840	2.41964	567
Work Experience	8.851322751	7.62208	567
Work Experience Squared	136.339488536	240.60384	567
Workplace Core Skills	0.673662551	0.16720	567
Workplace Process Skills	0.692592593	0.17166	567
Type A behaviour	0.584038801	0.15016	567
Sensation Seeking	0.678012934	0.16573	567

Coefficientsa

Unstandardized Coefficients Model B Error				Standardized Coefficients Beta	t	Sig.	Collinearity Statistics Tolerance VIF
1	(Constant)	4.946099985	0.128		38.674	0.000
	Year of schooling	0.149470300	0.008	0.624	18.330	0.000	0.746	1.340
	Work Experience	0.054563973	0.006	0.718