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

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

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).

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?

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 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

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

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 = 57.020 df= 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 UAE- citizen 20.2% 21 14.3% Chi-square = 6.498 Sig.= 0.090* 14 16.7% 12 8.2% 47 56.0% 97 66.0% 6 7.1% 11.6% 60 147

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 = 35.072 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 = 12.440 Sig. = .006 17 20.2% 52 36.1% 41 48.8% 63 43.8% 4 4.8% .0%

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.

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

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)

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.

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”.  

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).

Conclusions and Implications

Thank you