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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

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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 Countries

WORKSHOP II

New York University Abu Dhabi Institute

10-11 April 2010


Outline

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

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

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

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 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

Survey Sample by Establishment Main Activity and Number of Employees


Demographic characteristics of the sample

Demographic Characteristics of the Sample


Distribution of respondents by education level gender and nationality

Distribution of Respondents by Education level, Gender and Nationality


Distribution of respondents by gender job category and nationality

Distribution of Respondents by Gender, Job Category and Nationality


Distribution of respondents by average wages gender and n ationality

Distribution of Respondents by Average Wages, Gender and Nationality


Distribution of respondents by average wages gender job category and n ationality

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


Table 2 maximum likelihood estimates of probit model of access to employment by gender

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

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

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.


Table 3 log monthly wage regressions male and female full time workers

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


Results determinants of wage levels by gender

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

Conclusions and Implications


Mohammed al waqfi uae university ibrahim m abdalla uae university

Thank you


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