Post on 24-Mar-2023
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
Abdulrazzak.charbaji@lau.edu.lb
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
3
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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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
10
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.
Predictors: (Constant), Do you use internet internet,
Children, REGR f actor score 5 f or analysis 1, REGR
f actor score 2 f or analysis 1, REGR factor score 4 f or
analysis 1, REGR factor score 1 for analy sis 1, Marital
Status
b.
Predictors: (Constant), Do you use internet internet,
Children, REGR f actor score 5 f or analysis 1, REGR
f actor score 2 f or analysis 1, REGR factor score 4 f or
analysis 1, REGR factor score 1 for analy sis 1
c.
Predictors: (Constant), Do you use internet internet,
Children, REGR f actor score 5 f or analysis 1, REGR
f actor score 4 f or analysis 1, REGR factor score 1 f or
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.
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
c.
Predictors: (Constant), Do you use internet internet, Children, REGR f actor score 5
f or analy sis 1, REGR f actor score 4 for analysis 1, REGR factor score 1 f or
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.
15
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.
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
b.
Predictors in the Model: (Constant), Do y ou use internet internet, Children, REGR factor
score 5 for analysis 1, REGR f actor score 4 f or analysis 1, REGR factor score 1 f or
analysis 1
c.
Dependent Variable: Factors prev enting working women f rom reaching top positionsd.