1. 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 Countries
WORKSHOP II
New York University Abu Dhabi Institute
10-11 April 2010

2. Outline 1. Introduction: Overview of UAE labor market
2. Objectives and methodology
- The Study Objectives
- Research Methodology
- Sources of Data
3. Results
4. Conclusions and implications

3. UAE Labor Market – Key Facts
Low proportion of national workers in the total workforce (10%).
Unique labor market structure and labor policy - related to reliance on foreign workers
Segmentation of the labor market by sector (public vs. private) and nationality of workers.
Concentration of the local workforce in the public sector.
Generally, foreign workers hold jobs that citizens do not accept or jobs that require a level of expertise that citizens do not have
Unemployment among national workers.
Wage structure and wage-setting policy.
Low overall labor productivity.
Restricted labor mobility of foreign workers (changed in 2011).

4. The Study Main Objectives
Are there gender-based differences in employment conditions in the UAE labor market?
Are their gender-based differences in pay levels between employees in the UAE labor market?
Are there gender-based differences in access to employment opportunities in the UAE labor market?
Are their gender-based differences in promotion opportunities to management positions (Glass Ceiling) between males and females in the UAE labor market?

5. 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 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).

6. 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).

7. Survey Sample by Establishment Main Activity and Number of Employees
Industrial Activity
Establishments
Employees
No. %
Construction 27
9.6 98
6.7
Financial and business services 13
4.6 63
4.3
Manufacturing and other industry 35
12.4
151
10.4
Health & Education 11
3.9
190
13.1
Oil and gas 2
0.7 6
0.4
Public administration 20
7.1
156
10.7
Tourism, hotels and restaurants 40
14.2
166
11.4
Wholesale and trade 76
27.0
393
Transport and communication 25
8.9 96
6.6
Other activities 33
11.7
136
9.3
Total
282
100%
1455

8. Demographic Characteristics of the Sample
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)
Total
1403
100.0
 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
1405
Job Category 
Frequency
Percent
Valid
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
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

9. Distribution of Respondents by Education level, Gender and Nationality
Gender
Chi-square Test
Male
Female
Count
Column %
Citizenship of employee
Non - citizen
Education level
High school or less
216
24.8% 17
5.8%
Chi-square =
df= 3
Sig.=
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
UAE- citizen
20.2% 21
14.3%
Chi-square =
Sig.= 0.090* 14
16.7% 12
8.2% 47
56.0% 97
66.0% 6
7.1%
11.6% 60
147

10. Distribution of Respondents by Gender, Job Category and Nationality
Chi-square 
Gender
Male
Female
Count
Column %
Citizenship of employee
Non - citizen
Job category
Manager 90
10.5% 20
7.0%
Chi-square =
df = 3
Sig. = .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 22
26.2% 29
20.1%
Chi-square =
Sig. = .006 17
20.2% 52
36.1% 41
48.8% 63
43.8% 4
4.8%
.0%

11. Distribution of Respondents by Average Wages, Gender and Nationality
Gender
Male
Female
Mean
Median
Count
Nationality
UAE
Amount of monthly pay in Dirhams
14059a
12000 84
13236a
11650
147
GCC
7222a
6000 10
3500a
3500 3
Other Arab
6485a
5000
280
6738a
4750 64
Asian
3888a
3000
567
4362a
210
West Europe & US
11800a
7200
14067a
18000 15
Other
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.

12. Distribution of Respondents by Average Wages, Gender, Job Category and Nationality
Gender
Male
Female
Mean
Median
Count
Nationality
UAE
Job category
Manager
Monthly Pay
21894a
23000 22
20564a
20000 29
Professional or technician
15496a
12240 17
12609a
12000 52
Clerk/office/sales ...
9854a
10000 41
10258a 63
Production worker
32501
3250 4 .1 .
GCC
84201
5000 5
35002
3500 1
57251
6000 2
Other Arab
11165a
7000 38
8875a
7729a 92
10564a
7500
4798a
4800
126
4951a
4000
21601
1600 20
40002
Asian
7655a 47
7071a
6500 10
4967a
152
5384a
3000 55
3315a
279
3658a
3300
134
1823a
1375 79
2633a
West Europe & US
150002
15000
220001
22000
20750a
20750
10200a
9000
6525a
6100
10000a
Other
85002
8500
145002
14500
80501
9150 3
28001
2800
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

13. 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
.000
Citizenship of employee
-.945
.149
Public sector
-.339
.142
.017
Arabic oral skills
.241
.091
.008
Test knowledge for job match?
.158
.087
.069
Education level:
High school or equivalent
-.793
.324
.014
Some post high school
-1.130
.325
College/university degree
-1.445
.303
Graduate (Master's or above)
-1.578
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)

14. Results - Access to Employment by Gender
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.
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.
Females have higher probability of employment in the public sector compared to males (p- value=0.017).
Nationals have lower chances of getting employment compared to foreign workers (employers do not favor nationals).
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).
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).
Education gives an advantage for females compared to males – It improves their chances of employment more compared to males.

15. Selectivity bias Corrected Estimates
Table 3: Log Monthly Wage Regressions, Male and Female Full-time Workers.
Parameters
OLS Estimates
Selectivity bias Corrected Estimates
Male
Female B
p-value
(Constant)
6.413
.000
7.719
6.949
4.924
Hours of work per week
-.013
-.002
.556
-.010
.001
.795
Citizenship of employee
.449
.538
1.083
1.342
Public sector
.537
.548
.660
.750
Age
.068
.008
.797
.046
.009
.005
.880
Age2/1000
-.660
.002
.075
.857
-.520
.021
-.159
.704
Education level:
High school or equivalent
.190
.022
.238
.457
.340
1.140
Some post high school
.494
.098
.760
.775
1.256
College/university degree
.653
.387
.211
1.111
1.841
Graduate (Master's or above)
.818
.820
.010
1.348
2.497
Job category:
Manager
.900
.475
.883
.346
.044
Professional or technician
.481
-.110
.468
.470
-.200
.199
Office/clerical/sales …
.260
-.122
.401
-.238
.116
Lambda --
-1.080
.020
-1.397
R2 (adjusted)
0.54
0.58
0.56
0.62
Sample 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”.

16. Results – Determinants of Wage Levels by Gender
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.
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!
The selectivity bias affect females more than males – probably indicating that this bias might involve gender discrimination against females.
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.
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.
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)
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).

17. Conclusions and Implications

18. Thank you