Perception of Lebanese working women towards the barriers preventing them from running for...

Perception of Lebanese working women towards the barriers

preventing them from running for high-level policymaking offices

Abdulrazzak Charbaji, Ph.D.

Professor of Applied Statistics and Business Research Methods

College of Business Administration and Economics

Lebanese University

[email protected]

mailto:[email protected]

2

Perception of Lebanese working women towards the barriers

preventing them from running for high-level policymaking offices

ABSTRACT

Lebanese women are very much underrepresented in power positions. Given the dearth of

empirical research in this area, the present paper investigates the possible explanation to

this situation and determine the relative importance of the discriminating variables

between two barriers preventing Lebanese women from running for high-level

policymaking offices: (1) Stereotypes and the roles our society expect women to conform

to and; (2) women lack of skill to advance in power positions.

INTRODUCTION

Based on review of literature it is found that at a global level “Women have succeeded in

large numbers in fields such as physiology, biology, and social sciences, and they are

having increasing success in starting small businesses” (Wadhwa, 2006). In several

countries, many women are becoming entrepreneurs and the number of female business

owners are growing rapidly (Woldie and Adersua, 2004). According to the United

Nations Economic Commission for Europe (UNECE), the share of women’s

entrepreneurship increased in many countries of Western Europe. In the EU, 70 per cent

of self-employed women operate businesses that employ 5 or fewer people (UNECE,

2004). This picture does not hold true in quite few developing countries, many countries

of Eastern Europe, and also in a number of Arab countries where women may face

obstacles of both legal and socio-culture nature. Adriana and Manolescu reports that

“So far, women are not well represented in all the sectors of the Romanian economy and

they are less likely than men to work in the private sector and their own businesses.”,

(Adriana and Manolescu, 2006). Employers in various Arab countries give priority to

men in terms of employment and promotion, even where women have the same

qualifications (McElwee and Al-Riyami, 2003). In several Arab countries, employment

policy, education and training, labor regulations are not easy (Al- Madhi and Barrientos,

2003). Family law norms in more than a few Arab countries limit Arab women’s freedom

of movement, and Arab women have to be accompanied by their husband or male

relative if they want to participate in meetings or if they decide to launch their own

business. Likewise, Arab women access to credit is very limited due to their cultural

stereotypes. The obstacles encountered by Arab women entrepreneurs are usually socio-

cultural rather than legal. While education and law in Arab countries prohibit gender

discrimination and different articles of labor codes in Arab countries explicitly state that

norms of training apply to both men and women, we find that theory is not consistent

with reality and in practice, the situation is different. Arab societies and governments

are not homogenous and speaking about women in Arab countries is fundamentally

complex. Arab woman living in Lebanon or in Tunisia differ in political, social and

economic status than their counterparts in the Gulf, Levant or Africa. “In Jordan, Syria,

Egypt and most other Arab countries, a man who murders his female relative to defend

http://www.businessweek.com/bios/Vivek_Wadhwa.htm

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family “honor” receives a reduced penalty — or may not be sent to prison at all” (Barron,

2007). Al-Mandhry reported that the low percentage of women participating in the labor

force is mainly attributable to lack of work opportunities rather than lack of interest (Al-

Mandhry, 2000). The point of all this is that the present image of Arab woman in the west

seems to be of uneducated, homebound and veiled who needs help. This image

undoubtedly does not exist in Lebanon. Lebanese woman is different from other Arab

women who are not liberated because Arab men are fundamentally not liberated there.

Woman in Lebanon may share similar problems with woman living in advanced

societies. Women in these countries are less likely than men to become employers or

self-employed workers and a need has always been there for an action to be taken to

support and promote self employed women at different levels including financial and

empowerment, and “If professionally inclined; their participation is expected to be in the

areas of education, health (mainly nurses) and other support or clerical job; leadership

positions are typically reserved for men” (Al-Lamky, 2007). Jamali and her colleagues

report that “Although women in Lebanon are increasingly recognized as full-fledged

partners in the family economy, decision-making positions in Lebanon continue to be

monopolized by men”, (Jamali et all, 2005). The presence of a role model or mentor can

influence women in their decisions and choices (Brynin and Schupp, 2000). The

American university commitment to educating Lebanese girls dates back 90 years.

Today, there are more Lebanese girls studying post graduate studies than boys.

Lebanese suffered through many difficult years of civil war but their spirit has always

been as eternal as the Cedar trees in the Lebanese mountains. But when it comes to self

employment, even though self employment requires no prior experience, qualifications or

C.V type evidence ; Lebanese women may not start their own business because they

lack managerial skills. Few people would deny that lack of support and training to be a

major obstacle to self employed women in Lebanon. If a Lebanese woman seeks self

employment then, she may become humble trying to find answers to questions such as:

Where do I go? What do I do? What must I bring? How long will it take? and How

much will it cost?

Nowadays, the grounds for traditional Lebanese woman have been shaken by

globalization. Currently, Lebanese women are more able to work and manage conflict.

Their presence at work and university is accelerating and most of them are not facing

traditional problems any more, but they have been facing new challenges. The question

that arises, then; is why policy making and politics remain a man’s world in Lebanon?

The objective of this study is to understand the obstacles that prevent women in our

country from assuming positions of leadership and policy making.

SIGNIFICANCE OF THE STUDY

On one side, civil war in Lebanon is over and the war between supporters of the

governing coalition and Hezbollah –led opposition has also ended. Therefore, we expect

Lebanese government to offer jobs to the Ex-militiamen to prevent them from forming

their gangs in the streets. On the other side, The U.S. dollar depreciation against Euro,

and the Lebanese high rate of unemployment are pushing the young educated men to

seek work in other countries. Lebanese girls inhibited by family ties are left behind and

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the question that arises, then; what are the new challenges that face Lebanese women and

how to help them use the maximum of their potential to satisfy their need for self-esteem

and self actualization? In Oct, 2007. Danial Williams reported that “In an April survey

of 1,600 Lebanese -- half who want to emigrate and half who have -- 31 percent blamed

their exit on political instability, 24 percent cited politics and 18 percent said a need to

secure their future. Just over 73 percent didn't plan to return permanently” (Williams,

2007). Apparently, with boys leaving Lebanon then, the question that arise is: whether

the boys’ drain provide opportunities for women to alter the balance of political and

social power between men and women. Historically speaking, Lebanese mothers have

always succeeded in transferring social values to their kids but have been deprived

from their rights to reach power positions and transfer corporate values to others.

Women will never be able to create changes in our governmental system unless they are

given the opportunity and responsibility to do so. That is why it is imperative to

investigate if stereotype or lack of skills are hindering women from reaching policy

making positions and what are the variables leading to such situation?

PURPOSE OF THE STUDY

Although Lebanese women have made large strides professionally over the last few

decades, the question that arises is: why is there no gender balance in policymaking

positions in Lebanon? The objective of the present study is to investigate if Lebanese

working women lack skills and perceive themselves as not ready to stand for policy-

making positions office or if they perceive stereotype and the roles our society expect

women to conform to as the main factor that prevent them from reaching positions of

power.

LITERATURE REVIEW

Based on review of literature on women and leadership Rhode states that there are two

areas of focus for most research in this area. The first is on gender differences in

opportunities and generally finds that glass ceilings exclude women from leadership

positions. The second involves gender differences in the exercise of leadership such as

different styles, effectiveness, and priorities of men and women. Rhode concludes that

too little work has been done on the interrelationship between gender and situational

forces such as race (Rhode, 2003, p. 4-5).

. Arab women have always been viewed as out of place in professional environments.

Historically, the primary job of Arab women is raising children and their participation in

leadership positions has been limited. Today, the picture is different. UND Arab Human

Report states that:

By early 2006, women held 25.5 percent of the seats in Iraq’s parliament,

while in Tunisia’s last elections in 2004 women claimed 23 percent of the

seats. In Morocco, the percentage of women in parliament jumped from 1%

in 1995 to 11% in 2003; in the same eight-year period in Jordan it went from

2.5% to 5.5%, while in Tunisia, women’s representation in the legislature rose

from 6.8% to 11.5%.

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Based on review of literature it is found “that women still get paid less and few make

it to the top of companies.” (The Economist, 2006). According to Soin, the reason “that

women are absent is because gender stereotypes establish leadership as a masculine

activity.” ( Soin, 2008). To expand on this, Ireland believes that “women moving into

positions of power dose not mean that women do really have the power to change

(Ireland, 2003). Nisha Varia notes that “Lebanese women are caught in an unenviable

position. While their participation in the workforce has increased, gender stereotyping

and discrimination mean that they have retained the primary burden of household work.”

( Varia, 2008). Russeau believes that “In a country where over half of the population are

women, Lebanon lacks political representation even more than some of its other Arab

neighbors.” ( Russeau, July 16, 2008).

PROCEDURES AND METHODOLOGY

INSTRUMENTATION

Through a review of literature, informal discussion with university colleagues in

management, and this researcher's personal research experience, this researcher

constructed a pilot instrument that was distributed on a trial basis. Since the pilot

questionnaire was found to be lengthy, wordy, and inappropriately scaled, the final

draft was revised to correct for these problems. Working women were asked to

respond to attitude items related to cultural and social status of women in

Lebanon using Likert –five point scale format. The present study asked Lebanese

employed women, for the barriers preventing them from running for high-level

policymaking offices. The dependent variable in this study measures the perception

of Lebanese working women towards the barriers preventing them from running for

high-level policymaking offices. Each working woman was asked to choose one of the

two reasons behind women remaining underrepresented at the top and

overrepresented at the bottom in both private and public sectors: (1) Stereotypes

and the roles our society expect women to conform to, (2) women lack of skills and

knowledge to advance in power positions. Questions of a more personal nature,

were reserved for later questioning at the end of the questionnaire.

FACTOR ANALYSIS AND CONSTRUCT VALIDATION

Factor analysis was carried out as a data reduction technique. Two statistical tests were

conducted in order to determine the suitability of factor analysis. First, the Kaisers-

Meyer-Olkin (KMO) measure of sampling adequacy score of 0.637 was well above the

recommended level of 0.5. Second, the Bartless test of sphericity was significant ( Chi

Square = 2376.06, P < 0.01), indicating that there are adequate inter-correlations between

the items which allow the use of factor analysis. Principal axis factoring was used as an

extraction method and oblique rotation was used as a rotation method. Five factors were

extracted using Eigenvalue greater than one criterion. The five factor solution accounted

for 72.792 per cent of the total variance. The five factors were easy to label ( See

TABLE I). The first factor accounts for 20.17 percent of total variance and is defined by

five items. I call factor one "Women Empowerment". The second factor accounts for

18.42 percent of total variance and is defined by its three items with factor loadings

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greater than 0.70. I call this factor "Social Barriers". The third factor accounts 14.08

percent of total variance and is defined by two items with factor loadings greater than

0.70. I call factor three "Lack of Confidence". The fourth factor accounts 10.694

percent of total variance and is defined by two items with factor loadings greater than

0.70. I call factor four " Job Satisfaction and Commitment". The fifth factor accounts

9.43 percent of total variance and is defined by two items with factor loadings greater

than 0.70. I call factor five " Financial Barriers". As shown in TABLE 1 below:

Insert Table 1 about here

SAMPLE SELECTION

Since the researcher in this study did not have access to the names of employees in

Lebanon and was unable to randomly select a representative sample of women working

in Lebanon therefore, the researcher decided to use a very large judgmental sample

which consisted of 520 working women in Lebanon. Factor analysis was based on 438

valid responses while Discriminant Analysis was based on 401 observations due to the

missing values on demographic variables. Two thirds of the respondents (61.8%) were

less than 40 years old. Less than one third of the respondents (27.2%) were single.

Almost half of the sample (49.3%) had 3 children or below and just (25.1%) don’t use

internet.

DISCRIMINANT ANALYSIS

A discriminant analysis to distinguish between the two segments of the dependent

variable: (1) stereotype and (2) the lack of skills to advance Lebanese women in power

positions was used using SPSS. Table s 2 indicates that

CONCLUSION AND RECOMMENGATTIONS

While Lebanese working women have made a mark on history as they relate to

professional environments, most of them have not acquired leadership positions in their

own right.

two agendas that need to be addressed in order for change to occur. The first is to make

certain that women have equal access to leadership opportunities. Secondly, to enlist and

empower women in using their leadership to advance the public interest in general and

women’s equality

further research whether barriers regarding career opportunities can be removed, to what

exactly leadership means to different women in different situations.

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Create organizational change to Breaking the Barriers to Gender Equality

that if a woman does not have the necessary leadership skills they should then be offered

the training. The second frame is to create equal opportunities by fixing the policies and

practices that block women’s advancement.

Strategies for Developing White Men as Change Agents for Women Leaders”

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Ireland, P. (2003). Progress versus equality: Are we there yet? In D. L. Rhode (Ed.),

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ope

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TABLE 1

Factor Analysis

KMO and Bartlett's Test

.637

2376.060

91

.000

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy.

Approx. Chi-Square

df

Sig.

Bart lett 's Test of

Sphericity

http://www.businessweek.com/bios/Vivek_Wadhwa.htm

http://www.businessweek.com/smallbiz/content/mar2006/sb20060314_760860.htm

http://www.bloomberg.com/apps/news?pid=20601085&sid=aBJEdCZC2AOg&refer=europe

http://www.bloomberg.com/apps/news?pid=20601085&sid=aBJEdCZC2AOg&refer=europe



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Total Variance Explained

2.824 20.171 20.171 2.824 20.171 20.171 2.494

2.578 18.417 38.588 2.578 18.417 38.588 2.468

1.971 14.076 52.664 1.971 14.076 52.664 1.828

1.497 10.694 63.358 1.497 10.694 63.358 1.915

1.321 9.434 72.792 1.321 9.434 72.792 1.911

.913 6.521 79.313

.596 4.256 83.568

.586 4.189 87.757

.402 2.869 90.626

.369 2.638 93.264

.289 2.063 95.327

.229 1.635 96.962

.220 1.574 98.536

.205 1.464 100.000

Component

1

2

3

4

5

6

7

8

9

10

11

12

13

14

Total % of Variance Cumulat iv e % Total % of Variance Cumulat iv e % Total

Initial Eigenvalues Extract ion Sums of Squared Loadings Rotation

Sums of

Squared

Loadingsa

Extract ion Method: Principal Component Analysis.

When components are correlated, sums of squared loadings cannot be added to obtain a total v ariance.a.

11

Structure Matrix

.816 .028 -.056 .136 -.168

.796 .040 .003 .169 -.145

.693 .092 -.216 .193 -.024

.559 .054 .245 -.114 -.210

.551 -.094 .397 -.239 -.249

-.077 -.912 .032 .056 .085

.054 .893 .099 -.090 -.040

.036 .878 .068 -.131 -.053

-.015 .037 .872 -.175 .108

.009 -.068 -.845 .073 -.186

.088 -.061 -.097 .929 -.085

.171 -.153 -.130 .900 -.106

.170 .099 -.139 .101 -.919

-.147 -.039 .157 -.109 .913

My work requires that I

work hard

Self -employment requires

skills in f inance and

marketing

Self emloyed woman has

control at work of her

working hours

I can balance work and

f amily responsibility

Women are good at

communicating and

building relations at work

Polygamy in Lebanon is

now unthinkable

Young girls are

persuaded into early

marriage

A man can murder his

f emale relat iv e to "def end"

f amily honor

Girls in our society lack

conf idence to start -up

their own business

Girls should be f orced to

marry their assailants to

"protect" the f amily 's

reputation

I would be very happy to

spend the rest of my lif e in

my business

I f eel as if my business

problems are my own

It is legal f or a bnak not to

give loan to a woman

without a man co-sign

Women lack the

necessary start-up

f inance

1 2 3 4 5

Component

Extraction Method: Principal Component Analy sis.

Rotation Method: Oblimin with Kaiser Normalization.

12

TABLE 2

Discriminant Analysis

Model Summary

.733a .538 .528 .344

.733b .538 .529 .343

.733c .537 .530 .343

.733d .537 .531 .342

Model1

2

3

4

R R Square

Adjusted

R Square

Std. Error of

the Est imate

Predictors: (Constant), Do you use internet internet,

REGR factor score 3 f or analy sis 1, Children, REGR

f actor score 5 f or analysis 1, REGR factor score 2 f or

analysis 1, REGR factor score 4 for analy sis 1, REGR

f actor score 1 f or analysis 1, Marital Status

a.


Children, REGR f actor score 5 f or analysis 1, REGR


analysis 1, REGR factor score 1 for analy sis 1, Marital

Status

b.




analysis 1, REGR factor score 1 for analy sis 1

c.




analysis 1

d.

13

ANOVAe

53.131 8 6.641 56.284 .000a

45.665 387 .118

98.795 395

53.131 7 7.590 64.491 .000b

45.665 388 .118

98.795 395

53.086 6 8.848 75.295 .000c

45.710 389 .118

98.795 395

53.047 5 10.609 90.443 .000d

45.749 390 .117

98.795 395

Regression

Residual

Total

Regression

Residual

Total

Regression

Residual

Total

Regression

Residual

Total

Model

1

2

3

4

Sum of

Squares df Mean Square F Sig.

Predictors: (Constant), Do you use internet internet, REGR f actor score 3 f or

analysis 1, Children, REGR f actor score 5 f or analysis 1, REGR factor score 2 for

analysis 1, REGR factor score 4 for analy sis 1, REGR f actor score 1 f or analysis

1, Marital Status

a.

Predictors: (Constant), Do you use internet internet, Children, REGR f actor score 5

f or analy sis 1, REGR f actor score 2 for analysis 1, REGR factor score 4 f or

analysis 1, REGR factor score 1 for analy sis 1, Marital Status

b.



analysis 1, REGR factor score 1 for analy sis 1

c.



analysis 1

d.

Dependent Variable: Factors prev ent ing working women f rom reaching top

positions

e.

14

Coefficientsa

.428 .076 5.619 .000

-.324 .019 -.630 -17.239 .000

.013 .018 .025 .696 .487

-7.4E-006 .018 .000 .000 1.000

-.159 .018 -.317 -8.858 .000

-.042 .018 -.083 -2.332 .020

-.028 .045 -.025 -.613 .540

-.016 .005 -.112 -3.025 .003

.087 .044 .077 1.973 .049

.428 .076 5.626 .000

-.324 .019 -.630 -17.328 .000

.013 .018 .025 .698 .486

-.159 .017 -.317 -9.070 .000

-.042 .018 -.083 -2.342 .020

-.028 .045 -.025 -.618 .537

-.016 .005 -.112 -3.029 .003

.087 .044 .077 1.977 .049

.424 .076 5.600 .000

-.324 .019 -.630 -17.335 .000

.010 .018 .020 .577 .564

-.158 .017 -.315 -9.061 .000

-.041 .018 -.082 -2.316 .021

-.015 .005 -.106 -2.973 .003

.076 .040 .067 1.899 .058

.430 .075 5.738 .000

-.323 .019 -.628 -17.344 .000

-.158 .017 -.316 -9.094 .000

-.042 .018 -.083 -2.365 .019

-.015 .005 -.106 -2.976 .003

.072 .039 .064 1.838 .067

(Constant)

REGR factor score

1 f or analy sis 1

REGR factor score

2 f or analy sis 1

REGR factor score

3 f or analy sis 1

REGR factor score

4 f or analy sis 1

REGR factor score

5 f or analy sis 1

Marital Status

Children

Do you use

internet internet

(Constant)

REGR factor score

1 f or analy sis 1

REGR factor score

2 f or analy sis 1

REGR factor score

4 f or analy sis 1

REGR factor score

5 f or analy sis 1

Marital Status

Children

Do you use

internet internet

(Constant)

REGR factor score

1 f or analy sis 1

REGR factor score

2 f or analy sis 1

REGR factor score

4 f or analy sis 1

REGR factor score

5 f or analy sis 1

Children

Do you use

internet internet

(Constant)

REGR factor score

1 f or analy sis 1

REGR factor score

4 f or analy sis 1

REGR factor score

5 f or analy sis 1

Children

Do you use

internet internet

Model

1

2

3

4

B Std. Error

Unstandardized

Coef f icients

Beta

Standardized

Coef f icients

t Sig.

Dependent Variable: Factors prev ent ing working women f rom reaching top positionsa.

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Excluded Variablesd

.000a

.000 1.000 .000 .935

.002b

.069 .945 .003 .947

-.025b -.618 .537 -.031 .740

.003c

.089 .929 .004 .948

-.019c -.477 .634 -.024 .777

.020c

.577 .564 .029 .971

REGR factor score

3 f or analysis 1

REGR factor score

3 f or analysis 1

Marital Status

REGR factor score

3 f or analysis 1

Marital Status

REGR factor score

2 f or analysis 1

Model

2

3

4

Beta In t Sig.

Part ial

Correlation Tolerance

Collinearity

Stat istics

Predictors in the Model: (Constant), Do y ou use internet internet, Children, REGR factor

score 5 for analysis 1, REGR f actor score 2 f or analysis 1, REGR factor score 4 f or

analysis 1, REGR factor score 1 f or analy sis 1, Marital Status

a.



analysis 1, REGR factor score 1 f or analy sis 1

b.



analysis 1

c.

Dependent Variable: Factors prev enting working women f rom reaching top positionsd.

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