Mohammed Al-Waqfi – UAE University Ibrahim M. Abdalla – UAE University

Gender-based differences in employment conditions in the GCC context: The case of the United Arab Emirates

Mohammed Al-Waqfi – UAE University Ibrahim M. Abdalla – UAE University

Nationalization of the Workforce in the GCC CountriesWORKSHOP II

New York University Abu Dhabi Institute10-11 April 2010

Outline

1. Introduction: Overview of UAE labor market

2. Objectives and methodology - The Study Objectives - Research Methodology - Sources of Data

3. Results4. Conclusions and implications

UAE Labor Market – Key Facts

1. Low proportion of national workers in the total workforce (10%).

2. Unique labor market structure and labor policy - related to reliance on foreign workers

3. Segmentation of the labor market by sector (public vs. private) and nationality of workers.

4. Concentration of the local workforce in the public sector.

5. Generally, foreign workers hold jobs that citizens do not accept or jobs that require a level of expertise that citizens do not have

6. Unemployment among national workers.

7. Wage structure and wage-setting policy.

8. Low overall labor productivity.

9. Restricted labor mobility of foreign workers (changed in 2011).

The Study Main Objectives

1. Are there gender-based differences in employment conditions in the UAE labor market?

2. Are their gender-based differences in pay levels between employees in the UAE labor market?

3. Are there gender-based differences in access to employment opportunities in the UAE labor market?

4. Are their gender-based differences in promotion opportunities to management positions (Glass Ceiling) between males and females in the UAE labor market?

Sample We utilize a data set from the Dubai Labor Market Survey (DLM).

DLMS is a stratified random sampling survey, designed to explore a range of workplace issues relating to employees.

DLM randomly selects a sample of establishments and draws a sample of employees within these establishments.

The Employee Questionnaire explores issues related to employees’ general characteristics, formal training, earnings, employment conditions, and use of technology.

The sample utilized consists of 282 establishments (workplaces) and 1455 employees. Stratification of the sample in the private sector intended to ensure representation of all workers in the labor market (excluding laborers and unskilled workers) and was based on nine job categories (financial & business services, wholesale & trade, manufacturing & industry, construction, transport & communication, education & health services, tourism, hotels & restaurants, oil & gas and others).

Data analysis

Data is analyzed using two methods:

First, descriptive statistics and application of chi-square tests on cross-tabulations of the data by employee gender and several characteristics of employees. This analysis enable us to assess gender-based differences in employment conditions of workers including job categories, benefits, training received, promotion opportunities, etc.

Second, simple two-equation model of wage determination and access to employment was estimated in accordance with the methodology developed by Neuman and Oaxaca (2004).

Survey Sample by Establishment Main Activity and Number of Employees

Industrial Activity Establishments EmployeesNo. % No. %

Construction 27 9.6 98 6.7Financial and business services 13 4.6 63 4.3Manufacturing and other industry 35 12.4 151 10.4Health & Education 11 3.9 190 13.1Oil and gas 2 0.7 6 0.4Public administration 20 7.1 156 10.7Tourism, hotels and restaurants 40 14.2 166 11.4Wholesale and trade 76 27.0 393 27.0Transport and communication 25 8.9 96 6.6

Other activities 33 11.7 136 9.3Total 282 100% 1455 100%

Demographic Characteristics of the Sample

Gender Frequency Percent

Male 960 68.4

Female 443 31.6

Total 1403 100.0

Marital Status Frequency Percent

Valid Married 899 64.7

Not married 490 35.3

Total 1389 100.0

Missing System 16

Total 1405

Nationality Frequency Percent

UAE 232 16.6

Othe GCC 13 .9

Other Arab countries 344 24.6

Asian 778 55.6

Western (N. America,

Europe, Australia, ...)

21 1.5

Other nationality 8 .6

Total 1400 100.0

Education Level Frequency Percent

Less than high school 88 6.3

High school or equivalent 188 13.4

Some post high school 135 9.6

College/university degree 804 57.3

Graduate degree (Master's

and above)

188 13.4

Total 1403 100.0

Job Category Frequency PercentValid Manager 161 11.7

Professional 296 21.4

Technician 107 7.8

Office/clercal/sales/customer service

705 51.1

Production employee 111 8.0

Total 1380 100.0

Missing System 25

Total 1405

Distribution of Respondents by Education level, Gender and Nationality

Gender

Chi-square Test

Male Female

Count Column % Count Column %Citizenship of employee

Non - citizen

Education level

High school or less 216 24.8% 17 5.8% Chi-square =

57.020df= 3

Sig.= 0.000

Some post high school 87 10.0% 22 7.5%

College/Univ. degree 460 52.8% 198 67.3%

Graduate (Master or above)

108 12.4% 57 19.4%

Total 871 100 294 100

UAE- citizen

Education level

High school or less 17 20.2% 21 14.3% Chi-square = 6.498df= 3

Sig.= 0.090*

Some post high school 14 16.7% 12 8.2%

College/Univ. degree 47 56.0% 97 66.0%

Graduate (Master or above)

6 7.1% 17 11.6%

Total 60 100 147 100

Distribution of Respondents by Gender, Job Category and Nationality

Chi-square

GenderMale Female

Count Column % Count Column %Citizenship of employee

Non - citizen

Job category

Manager 90 10.5% 20 7.0% Chi-square = 35.072

df = 3Sig. = .000

Professional or technician 254 29.6% 79 27.6%

Clerk/office/sales ... 416 48.4% 182 63.6%

Production worker 99 11.5% 5 1.7%

UAE- citizen

Job category

Manager 22 26.2% 29 20.1% Chi-square =

12.440df = 3

Sig. = .006

Professional or technician 17 20.2% 52 36.1%

Clerk/office/sales ... 41 48.8% 63 43.8%

Production worker 4 4.8% 0 .0%

Distribution of Respondents by Average Wages, Gender and Nationality

Gender

Male Female

Mean Median Count Mean Median Count

Nationality UAE Amount of monthly pay in Dirhams 14059a 12000 84 13236a 11650 147

GCC Amount of monthly pay in Dirhams 7222a 6000 10 3500a 3500 3

Other Arab Amount of monthly pay in Dirhams 6485a 5000 280 6738a 4750 64

Asian Amount of monthly pay in Dirhams 3888a 3000 567 4362a 3500 210

West Europe & US Amount of monthly pay in Dirhams 11800a 7200 10 14067a 18000 15

Other Amount of monthly pay in Dirhams 6375a 6750 6 9250a 9250 2

Note: Values in the same row and subtable not sharing the same subscript are significantly different at p< 0.05 in the two-sided test

of equality for column means. Tests assume equal variances.1

1. Tests are adjusted for all pairwise comparisons within a row of each innermost subtable using the Bonferroni correction.

Distribution of Respondents by Average Wages, Gender, Job Category and Nationality

Gender

Male Female

Mean Median Count Mean Median CountNationality UAE Job category Manager Monthly Pay 21894a 23000 22 20564a 20000 29

Professional or technician Monthly Pay 15496a 12240 17 12609a 12000 52

Clerk/office/sales ... Monthly Pay 9854a 10000 41 10258a 10000 63

Production worker Monthly Pay 32501 3250 4 .1 . 0

GCC Job category Manager Monthly Pay .1 . 0 .1 . 0

Professional or technician Monthly Pay 84201 5000 5 35002 3500 1

Clerk/office/sales ... Monthly Pay 57251 6000 5 35002 3500 2

Production worker Monthly Pay .1 . 0 .1 . 0

Other Arab Job category Manager Monthly Pay 11165a 7000 38 8875a 10000 4



Production worker Monthly Pay 21601 1600 20 40002 4000 1

Asian Job category Manager Monthly Pay 7655a 7000 47 7071a 6500 10



Production worker Monthly Pay 1823a 1375 79 2633a 3000 4

West Europe & US

Job category Manager Monthly Pay 150002 15000 4 220001 22000 5




Other Job category Manager Monthly Pay 85002 8500 1 145002 14500 1

Professional or technician Monthly Pay 80501 9150 3 40002 4000 1

Clerk/office/sales ... Monthly Pay 28001 2800 2 .1 . 0


Note: Values in the same row and subtable not sharing the same subscript are significantly different at p< 0.05 in the two-sided test of equality for column means. Tests assume equal variances.3,4

1. This category is not used in comparisons because there are no other valid categories to compare

Table 2: Maximum Likelihood Estimates of Probit Model of Access to Employment by Gender

Parameter Estimate S. E. P-value

Intercept 1.174 .346 .001

Age .021 .005 .000Citizenship of employee -.945 .149 .000Public sector -.339 .142 .017Arabic oral skills .241 .091 .008Test knowledge for job match? .158 .087 .069

Education level:

High school or equivalent

-.793

.324

.014Some post high school -1.130 .325 .001College/university degree -1.445 .303 .000Graduate (Master's or above) -1.578 .346 .001

PROBIT model: PROBIT(p) = Intercept + BX Dependent variable = 1 if worker is male, and 0 if worker is female. Reference group for Citizenship of employee is “Non-citizen”. Reference group for Public sector is “Private”. Reference group for Arabic oral skills (worker has oral communication skills in Arabic) is “No”. Reference group for Test knowledge for job match? is “no”. Reference group for Education level is “Less than high school”. Source: Dubai labour market survey (2007)

Results - Access to Employment by Gender

1. Age - which is used as a proxy of work experience - is positively related to male workers’ employment chances in the labor market. Experienced males have better chances of employment relative to females.

2. Similar results as in 1 above are found with respect to workers who have oral communication skills in Arabic and those who are tested by employers on knowledge related to the job.

3. Females have higher probability of employment in the public sector compared to males (p-value=0.017).

4. Nationals have lower chances of getting employment compared to foreign workers (employers do not favor nationals).

5. increased education level reduces males’ employment chances in the UAE labor market compared to females (this reflects the labor intensive nature of the market which also favor male workers – labor intensive industries are male dominated).

6. Females are subjected to more strict selection criteria based on educational qualifications (if you are a female you need higher qualifications to get the job).

7. Education gives an advantage for females compared to males – It improves their chances of employment more compared to males.

Table 3: Log Monthly Wage Regressions, Male and Female Full-time Workers.

Parameters

OLS Estimates Selectivity bias Corrected EstimatesMale Female Male Female

B p-value B p-value B p-value B p-value(Constant) 6.413 .000 7.719 .000 6.949 .000 4.924 .000Hours of work per week -.013 .000 -.002 .556 -.010 .000 .001 .795Citizenship of employee .449 .000 .538 .000 1.083 .000 1.342 .000Public sector .537 .000 .548 .000 .660 .000 .750 .000Age .068 .000 .008 .797 .046 .009 .005 .880Age2/1000 -.660 .002 .075 .857 -.520 .021 -.159 .704Education level:

High school or equivalent

.190

.022

.238

.457

.340

.000

1.140

.002Some post high school .494 .000 .098 .760 .775 .000 1.256 .002College/university degree .653 .000 .387 .211 1.111 .000 1.841 .000Graduate (Master's or above) .818 .000 .820 .010 1.348 .000 2.497 .000Job category:

Manager

.900

.000

.475

.005

.883

.000

.346

.044Professional or technician .481 .000 -.110 .468 .470 .000 -.200 .199Office/clerical/sales … .260 .000 -.122 .401 .260 .000 -.238 .116Lambda -- -- -- -- -1.080 .020 -1.397 .000R2 (adjusted) 0.54 0.58 0.56 0.62Sample size 831 362 752 322

Dependent Variable: Log( monthly wages) Reference group for Citizenship of employee is “Non-citizen”. Reference group for Public sector is “Private”.Source: Dubai labour market survey (2007)

Reference group for Education level is “Less than high school”.Reference group for Job category is “Production worker”.

Results – Determinants of Wage Levels by Gender

1. The model shows high selectivity bias factor (Lambda) which is negative and statistically significant. This means that workers who are selected into the labor market on average have less productivity but higher wages. In other words, selection into higher wage brackets is not dependent only on human capital factors but other hidden factors that are important to employers (these could be related to social networks and favoritism (Wasta)). This is true for both males and females.

2. The selectivity bias might reflect the structural segmentation in the UAE labor market (Abdalla et al, 2010) or inefficiency caused by favoritism in hiring – possibly an agency problem!

3. The selectivity bias affect females more than males – probably indicating that this bias might involve gender discrimination against females.

4. Females get lower return on job category. Male managers get more return on their job category than females – This is an indication of gender-based wage discrimination.

5. For females, return on professional or office administration jobs has no significant difference from production worker job (reference group). This is also an evidence of wage discrimination.

6. Return on age (proxy for experience) is higher for males than females (0.06 vs. 0.01) – Female workers are mainly young and well educated and senior workers tend to be males (this probably indicate a glass-ceiling phenomenon in the UAE labor market)

7. Return on citizenship is positive and significant for both males and females but is higher for females (females benefit more than males from being a citizen in the UAE labor market).

Conclusions and Implications

Thank you

Mohammed Al-Waqfi – UAE University Ibrahim M. Abdalla – UAE University

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