1. GENDER DIVERSITY IN THE BOARD
ROOM AND FIRM PERFORMANCE
SUBMITTED BY .
DR GHULAM MUJTABA
PRESENTED BY.
BUSHRA,ZAINAB,NOSHEEN

2. Now a days
Gender diversity is fast
becoming an emerging issue in
the corporate world
(Julizaerma M.k., Zulkarnain
Mohamad Sori 2012).
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3. Percentage of female
director on European
companies
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4. DEFINITION:
According to Dutta and Bose (2006), the
definition of gender diversity in the
boardroom refers to the presence of women
as the board of directors which is an
important aspect of board diversity. Gender
diversity could bring board functioning that
eventually could influence firm performance
(Carter, Simskinsand Simpson (2003).
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5. IMPORTANCE:
 Gender diversity in the boardroom and in the top
executive positions has been the focus of public
debate, academic research, government
consideration and corporate strategy for more
than a decade now (Joana Marinova, Janneke
Plantenga and Chantal Remery 2010).
 Despite debate actions programs and media
attention, however, women in the EU represent
only 11% of boards of directors and supervisory
board (Desvaux, Devillard- Hoellinger and Meaney
2008). According to the monitor, the average
European board consists of 15.1 members, of
which 1.5 are women (European PWN 2008)
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6. Aim of article
The aim of this article is to
contribute limited Pakistani
evidence on gender diversity and
firm performance as until now
most empirical research has
focused on the companies
situated in the developed
countries, after that it will
contribute to the scientific debate
by applying a methodology that
allow for correct analysis of the
relationship between board
gender diversity and firm
performance.
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7. MEASUREMENT OF VARIABLES
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8. Page 8

9. DEPENDENT VARIABLE:
 IN ORDER TO MEASURE PERFORMANCE
THAT IS DEPENDENT VARIABLE WE USE
ACCOUNTING BASE MEASURE THAT IS
ROA AND ROE.
 IN ORDER TO MEASURE GENDER
DIVERSITY THAT IS INDEPENDENT
VARIABLE WE USE PERCENT OF WOMEN
ON THE BOARD.
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10. CONTROL/DUMMY
VARIABLES
 FIRM SIZE: NATURAL LOGARTHM OF
THE TOTAL ASSET OF THE FIRM.
 FIRM AGE: NUMBER OF YEARS THE
COMPANY IS INCORPORATED IN 2014.
 BOARD SIZE: TOTAL NUMBER OF
BOARD OF DIRECTORS.
 REGRESSION EQUATION
Y = a + B1X1 + B2D1 +B3D2+B4D3 +e
 Firm performance= a + b1percentage
of women directors+b2 firm size
+b3firm age+ b4 board size + e
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11. TYPE OF DATA:
 We have to use Quantitative Panal Data
 Our population of Listed company in the
stock of Pakistan
 Total no of firms= 550
Sample size= 16
 Constant analysis is Companies Annual
Report from 2011 to 2014.
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12. Results :
 Descriptive statistics
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FIRMAGE FIRMSIZE FIRMSIZE01 ROA ROE WOMEN
Mean 10.00000 22146.31 6.578671 0.259068 0.445723 0.130100
Median 10.00000 23123.28 4.363704 0.098860 0.448060 0.160200
Maximum 10.00000 25699.51 13.36614 0.819310 0.451840 0.200000
Minimum 10.00000 16639.18 4.221132 0.019240 0.434930 0.000000
Std. Dev. 0.000000 3418.897 3.927257 0.330378 0.006661 0.077142
Skewness NA -0.725824 1.153900 1.032508 -0.782357 -0.985551
Kurtosis NA 2.014999 2.332725 2.223132 1.983439 2.223976
Jarque-Bera NA 31.28804 58.67388 49.48948 35.39755 45.62244
Probability NA 0.000000 0.000000 0.000000 0.000000 0.000000
Sum 2440.000 5403700. 1605.196 63.21247 108.7563 31.74440
Sum Sq. Dev. 0.000000 2.84E+09 3747.873 26.52334 0.010781 1.446064
Observations 244 244 244 244 244 244

13. Analysis
 Skewness: is a measure of symmetry, or more
precisely, the lack of symmetry. A distribution, or
data set, is symmetric if it looks the same to the
left and right of the center point.
 Kurtosis: a measure of whether the data are
peaked or flat relative to a normal distribution.
 That is, data sets with high kurtosis tend to have a
distinct peak near the mean, decline rather
rapidly, and have heavy tails. Data sets with low
kurtosis tend to have a flat top near the mean
rather than a sharp peak. A uniform distribution
would be the extreme case.
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14. Regression equation:
Dependent variable :ROA
Dependent Variable: ROA
Method: Panel Least Squares
Date: 01/22/16 Time: 06:09
Sample: 2011 2014
Periods included: 4
Cross-sections included: 61
Total panel (balanced) observations: 244
Variable Coefficient Std. Error t-Statistic Prob.
C 0.972833 0.093743 10.37770 0.0000
WOMEN -3.797214 0.251755 -15.08295 0.0000
FIRMSIZE -9.92E-06 5.68E-06 -1.746785 0.0819
R-squared 0.976120 Mean dependent var 0.259068
Adjusted R-squared 0.975922 S.D. dependent var 0.330378
S.E. of regression 0.051266 Akaike info criterion -3.091377
Sum squared resid 0.633385 Schwarz criterion -3.048379
Log likelihood 380.1480 Hannan-Quinn criter. -3.074060
F-statistic 4925.500 Durbin-Watson stat 3.555484
Prob(F-statistic) 0.000000 Page 14

15.  R-squared.
It means that our independent variable that is
percentage of women director explain
97.6% the dependent variable that is
ROA.
 Adjusted R-squared
Adjusted R-squared gives the percentage of
variation explained by only those
independent variables that in real affect
the dependent variable Page 15

16. Dependent Variable :ROE
Dependent Variable: ROE
Method: Panel Least Squares
Date: 01/22/16 Time: 06:11
Sample: 2011 2014
Periods included: 4
Cross-sections included: 61
Total panel (balanced) observations: 244
Variable Coefficient Std. Error t-Statistic Prob.
C 0.503665 0.002017 249.7590 0.0000
WOMEN 0.260365 0.005416 48.07513 0.0000
FIRMSIZE -4.15E-06 1.22E-07 -33.92733 0.0000
R-squared 0.972812 Mean dependent var 0.445723
Adjusted R-squared 0.972586 S.D. dependent var 0.006661
S.E. of regression 0.001103 Akaike info criterion -10.76965
Sum squared resid 0.000293 Schwarz criterion -10.72666
Log likelihood 1316.898 Hannan-Quinn criter. -10.75234
F-statistic 4311.598 Durbin-Watson stat 3.555484
Prob(F-statistic) 0.000000
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17. Analysis
 R-squared.
It means that our independent variable that is
percentage of women director explain 97% the
dependent variable that is roe.
 Adjusted R-squared
Adjusted R-squared gives the percentage of variation
explained by only those independent variables that
in reality affect the dependent variable
 F-TEST
F-test determines whether this relationship is
statistically significant.
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