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On Gender Discrimination in Wages and the Feminization of Poverty
The Case of Israel 1997-2010
Miri Endeweld1 and Daniel Gottlieb2*
Abstract
In this paper we analyze the socio-economic situation of women in Israel. More
specifically we study the development of the dimensions of poverty of households
headed by women over the observation period. We discuss poverty calculated from
economic cash income and from net cash income. The difference between them
reflects the effort of poverty reduction by government intervention through payment
of social benefits and taxation. These developments are shown for various population
groups, including the old-aged and single mothers. The poverty dimensions include
poverty incidence, the relative income gap and poverty severity (as measured by the
FGT index).
In the second part we estimate the gender effect in a microeconomic model of
determination of hourly wages, which may be interpreted as an indication for gender
discrimination in the labor market. This is done by estimation of a wage equation for
the beginning and the end of the observation period – 1999 and 2010. We also added
an estimate for 2011 in order to check for the robustness of the 2010 results. The
hourly wages are explained by demographic and socio-economic variables, like the
economic branch and occupation of the wage earner’s, and more general data such as
the geographic area and ethnic origin, which are also important determinants in
Israel’s highly heterogeneous society. Such estimations typically encounter the
problem of the self-selection bias. This is particularly true in economies which have a
high percentage of people in working age who do not participate in employment. We
thus estimate the two-stage model including a “Heckman-correction” and compare it
with the OLS estimates.
Our analysis indicates that the poverty indices for women as heads of households are
significantly higher than for households headed by men. However the gender gap in
poverty rates is found to decline over time. In the simple wage equation we find
gender discrimination to be significant and more or less stable over the decade . After
correcting for self-selection we find that the gender bias was lower in all three years,
but remained stable between 1999 and 2010. However it increased in 2011 compared
to the other two years we examined..
Jerusalem, April 2013
Keywords: Poverty; Gender discrimination, Income Distribution.
JEL Classification codes: I32, J16, J31, J7
*Corresponding author: [email protected]; Tel.: +972-505298555;
1
2
National Insurance Institute, Jerusalem, Israel
National Insurance Institute, Jerusalem, Israel and Hebrew University of Jerusalem
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Introduction
There are many disadvantaged groups in any society. However it seems that women
constitute the biggest disadvantaged group, since they account typically for about half
the society.
A particularly outrageous expression of gender discrimination is certainly the
phenomenon of ‘missing women’, as portrayed by A.K. Sen.3
In most Western countries, as opposed to the situation for example in Africa and
India, the number of women exceeds that of men by about 5-6%. In Israel the
situation is similar to that of Western countries with the female population exceeding
the male population by some 2%. At birth the ratio is 0.96 but the ratio increases with
age. At the age of 31 the number of women begins to exceed the number of men by
1% and at the age of 69 the number of women exceeds that of men by 70% (see
appendix figure 1).
The feminization of poverty describes a trend of worsening poverty dimensions over
time for households headed by women compared to those of male headed
households.4
The purpose of this paper is to estimate and analyze the feminization of poverty and
more generally the gender gap in wages for Israel over the period 1997 to 2011. The
data are based on the household income surveys which have been compiled by a
consistent methodology since 1997 by the Central Bureau of Statistics.
One difficulty of evaluating women’s socio-economic situation is that a considerable
part of their economic activity is not channeled through the market and is therefore
underestimated in a longstanding tradition of national accounting practice. The
neglect of home production in official statistics has lately been reconsidered in the
report by Stiglitz, Sen and Fitoussi (2009). The report, especially in its fifth
recommendation, strongly advocates to measure home production for a better account
of income and consumption. This will have a direct bearing on the accounting and
analysis of the society’s well-being, income distribution and efficiency of resource
allocation. Notwithstanding, the Nordic social model stresses the importance of
3
See Amartya K. Sen, 1990.
The sociologist Diana Pearce was probably the first to use the term of feminization of poverty in the
beginning of the 1970s, describing a worsening trend of the gender gap in poverty incidence. See
Pearce (2011).
4
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channeling economic activity as much as possible through the market mechanism,
thus ensuring the full benefit of economies of scale (see Andersen et al., 2007).
1. The socio-economic situation of women and men 5
The data presented here show that poverty incidence of economic income among
women is significantly higher than that of men. Economic income refers to the market
income collected by households, i.e. income earned from work, pensions or capital.
This is the income before government intervention and transfers among households.6
The gender distinction in the present analysis is done by identifying households by the
gender of the head of the household.7
Figure 1: Poverty incidence by gender for economic income: 1997 to 2010**
36%
34%
34.6%
32.8%
32.6%
32%
30.9%
26%
31.3%
30.8%
30%
27.7%
28%
31.8%
32.1%
31.5%
27.6%
28.2%
26.8%
28.0%
26.3%
25.6%
24%
26.7%
Women
Men
22%
20%
1997 1998 1999 2002 2003 2004 2005 2006 2007 2008 2009 2010
**The following comment applies to all figures:
The data are from the yearly income surveys of the Israeli Central Bureau of Statistics. Since the data
for the years 2000 and 2001 could not be collected on the Arab population of East Jerusalem, these
years are not included in the analysis.
Figure 1 shows that women’s incidence of economic poverty exceeds that of men by
some 15 to 20%. Figure 2 indicates that poverty incidence of net income of women,
men exceeds that of men by 5-10%. During the observation period all categories
experienced a rise in poverty incidence with a particularly sharp increase in child
poverty. This is not surprising since the correlation between poverty and family size is
a well established fact. Women’s poverty incidence is about 1 to 1.5 percentage points
higher than for men. While child poverty increased already in 1998 women’s poverty
5
Throughout the paper we refer to women and men aged 18+.
The poverty of economic income is calculated here by use of the official net equivalised household
income.
7
This is a common choice, although an alternative would be to identify individuals by gender.
6
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accelerated between 2003 and 2005 with the implementation of the harsh social
policy, after which it stabilized at around 19 to 20%.
Poverty incidence of net income is the combined outcome of poverty of economic
income and of government intervention through taxation and benefits. Economic
poverty reached 35% in the beginning of the observation period and after a certain
decline it remained relatively high at about 30%. Male economic poverty was quite
stable at around 26 to 27%.
Figure 2: Poverty incidence by net income, 7991-0272
40%
35.3%
35%
30%
Children
Women
25%
20%
15%
Men
19.9%
18.2%
10%
1997 1998 1999 2002 2003 2004 2005 2006 2007 2008 2009 2010
Government intervention in the form of benefits and taxation reduced the gender gap
(figure 3). However in recent years the intervention became less effective and the
gender gap in net incomes increased, despite the fact that the poverty gap due to
market forces has been falling. This result was brought about by a severe cut in social
benefits, particularly of income support, child benefits and unemployment benefits for
the young, as well as a freeze of inflation adjustments of all benefits. By 2006 this
anti-social policy was further intensified by a regressive tax reform. Since the single
mother families represent a significant group among families receiving income
support, these cuts hurt families headed by women more than those headed by men.
Unsurprisingly therefore the main increase in poverty incidence of both men and
women occurred in the early 2000s. The rapid economic growth thereafter dampened
female poverty incidence. However it did not manage to reverse the trend of increased
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poverty incidence. It is not surprising that poverty among women has become more
sensitive to economic growth, a fact due mainly to the continued increase in women’s
employment ratio. There remains the question why this development has not
succeeded in further reducing the gender poverty gap. The answer seems to be related
to the fact that many of the women joining employment did so at low wages, relative
to their qualifications.
A further reason for the slow adjustment of the gender related poverty gap could be
due to the discrimination of women in wages, an issue taken up in section 2.1.
Our first conclusion is therefore that the reduction of the phenomenon of feminization
of poverty was due to favorable market forces. The reduction of the gender gap in
poverty requires active government policy. However, as can be seen from the data the
reduction in the gender gap has been declining over time. The intensity of the policy
correction has been declining from some 6% in the late 90s to about half of that in
2010 (figure 4).
Figure 3: Reduction of poverty incidence through government intervention*,
1997-2010
20%
Women
Men
18%
17.8%
16.8%
16%
14.6%
14%
13.9%
13.9%
12%
12.0%
12.5%
12.5%
12.5%
11.8%
10.3%
10%
11.6%
11.9%
11.7%
11.4%
10.8%
10.3%
9.6%
9.5%
8.6%
8.7%
8.7%
9.2%
8.5%
8%
1997
1998
1999
2002
2003
2004
*The figures also include transfers between households.
2005
2006
2007
2008
2009
2010
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Figure 4: The impact of government policy on the reduction of the gender related
poverty gap, 1997 -2010
6.0%
5.5%
The gender gap in
the impact of
government policy
on poverty incidence
5.0%
4.5%
4.0%
Log. (The gender gap
in the impact of
government policy
on poverty
incidence)
3.5%
3.0%
2.5%
2010
2009
2008
2007
2006
2005
2004
2003
2002
1999
1998
1997
2.0%
Income from work is the main source of income of both women and men. However,
table 1 shows that there is still a considerable difference in the composition of income
sources between households headed by men compared to those headed by women: in
2010 income from work was 10 percentage points higher for men than for women,
while the share of social benefits was twice as high for women than for men.
Table 1: Income sources by gender
Households headed by:
Source of income Men
%
Women
All sources
15,878 100.0%
11,804
Work
13,028
82.0%
8,151
Benefits
1,499
9.4%
2,189
Capital
558
3.5%
426
Pensions
793
5.0%
1037
%
100.0%
69.1%
18.5%
3.6%
8.8%
2. Gender and the labor market
Gender discrimination in the labor market has been studied all over the world and
over various periods.8 The UN’s human development report for example includes the
labor force participation rates by gender in its measure of gender inequality. However,
this is an imperfect measure since it does not reflect gender differences in wages.
8
A comprehensive discussion of gender inequality in the labor market can be found in the UN’s
Human Development Report.
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These differences are quite persistent over time.9
Figure 5 presents the development of employment participation rates over time by
gender. The gender difference in Israeli participation rates has been quite stable over
time, though dipping in 2009, when the world economic crisis hit Israel’s economy
with some delay. The male employment rate dipped by 6.8% whereas women’s
employment rate dropped only by 3.7%.
In 2010 hours worked and the reported wages still show considerable differences.
Among wage earners average monthly hours worked as reported in the income survey
were about 27% higher for men than for women. The hourly wage, which takes
account of the difference between the sexes in hours worked, was still about 17%
higher for men.
Figure 5: Employment rates* by gender – 2001 to 2009
72.0
20.0
70.0
18.0
68.0
16.0
66.0
14.0
64.0
12.0
62.0
10.0
Men - all ages
60.0
Women - all ages
8.0
58.0
Gender difference in employment rates (RHS axis)
6.0
56.0
4.0
54.0
2.0
52.0
0.0
2001
2002
2003
2004
2005
2006
2007
*Employment rates are calculated as the share of the working age population.
Source: Administrative data of the tax authorities
9
See The Economist, 2009.
2008
2009
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Figure 6: Average monthly wages for men and women by age groups,
1999 and 2010
14,000
Monthly wages
12,000
10,000
8,000
6,000
Men, 2010
Men, 1999 (2010
prices)
Women, 2010
4,000
2,000
Women, 1999 (2010
prices)
0
15-24 25-29 30-34 35-39 40-44 45-49 50-54 50-59 60-64 65-69 70+
Age groups
Further evidence about women’s discrimination can be found in Endeweld (2012),
according to which wage mobility from 1990 to 2005 was significantly lower for
women than for men. This result indicates that the gender wage gap was not
diminished over that period.10
The wage curve by gender over the various age groups presented in figure 6 indicates
the change in the monthly wage over the life cycle for the years 1999 and 2010.
Figure 7 shows that the gender wage differential rises with age and culminates around
the age of 50 to 69. Since expertise and professional experience are expected to be
closely related to the wage level this implies that the more experienced the worker the
higher the absolute gender wage differential.
10
This result is based on the administrative panel data set of the tax authorities, a fact that reinforces
the evidence on the gender gap since it is based on different sources.
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Figure 7: Wage differential – male less female wages, by age groups
(2010 prices)
8,000
7,000
6,000
5,000
4,000
1999
3,000
2010
2,000
1,000
-
Wage discrimination of women may occur through the feminization of specific
economic branches. According to this argument the feminization would reduce the
general average wage, the higher the share of women employment in the industry. As
can be seen in figure 8, panels a and b, while there was a slight negative correlation
between the general average wage and the share of female employment in 1999 (as
reflected by the trend line), this effect turned into a positive slope by 2010. Of course
this does not yet exclude this effect to have taken place, since there may be additional
factors at work such as different levels of education etc. but we may conclude that the
feminization effect probably has not played a major role in wage determination.
Figure 8: Average hourly wage and female participation in economic branch
Panel b
80
80
70
70
60
60
hourly wage, 1999
hourly wage, 2010
Panel a
50
50
40
40
30
30
20
20
10
10
0
20
40
60
80
Share of women in economic branches
100
0
20
40
60
80
Share of women in economic branches
100
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2.1 An econometric model of wage discrimination
In order to estimate the possibility of wage discrimination, as many variables that
could cause wage-differentials need to be accounted for. In the following we shall
argue that there is wage discrimination only if a difference in hourly wages remains
after the maximum of objective determinants that can create wage differentials have
been taken into account. Of course the focus on hourly wages implies that if a person
works part time this reflects a choice and not a constraint in the availability of full
time jobs. The same holds for the effect of economic branch. Part of the
discrimination may manifest itself in a limited possibility to find a job in an economic
branch with high average wages.
With all these reservations in mind we use a simple linear model of wage
determination:
∑
where
is the log of individual i's hourly wage and the variables
represent
demographic variables and personal characteristics such as the wage earner’s age,
family status, number of children, ethnic affiliation, education, economic branch of
activity, occupation, geographic area etc. and ε the error term.
Such an OLS regression is presented in table 3.
The signs of the coefficients are in the expected direction: they suggest that the wage
increases with age though at a declining pace, the number of children add positively to
the wage, maybe due to a higher reservation wage. So does the number of school
years affect hourly wages. Working in one of the traditional economic branches
reduces the wage when compared to the branch of social and private services which
was excluded from the regression.
The choice of occupation affects wages significantly.
Being Arab or Haredi reduces the wage after having taken into account all other
determinants, suggesting, similarly to the possible gender discrimination also a bias of
belonging to one of the two groups. There is a striking similarity in the size of the
three biases. While the discount on Haredi labor increases over time the opposite
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happens to wages of new immigrants. For Arabs the average reduction is less stable
over time.
A slight though significant advantage is found in wages paid in the center whereas the
often stated bias towards Europeans or Americans and the parallel bias against people
of Sephardic descent seems to have become irrelevant towards the end of the
observation period.
The gender bias is estimated at some 18-19% for each of the three years, revealing
quite a stable coefficient. The R2 of the regression is around 0.4. Most variables have
a high statistical significance level.
A well-known problem with wage equations is the possible bias that arises from the
fact that a significant share of the population is not employed. This may lead to a bias
since also some of the people out of employment share similar characteristics as the
wage earners. This may thus lead to an exaggerated estimate of some of the
characteristics affecting the wage equation. We therefore apply the ‘Heckman
correction’ by adding a first stage of regressing an employment equation such as to
minimize the possibility of such a bias to appear in the coefficients we are interested
in.11
The coefficients we report in table 4 are adjusted by the ‘Heckman correction’. These
estimates take into account the possibility of self-selection. Indeed this correction
seems to be of particular importance when we analyze the gender bias. This bias gets
corrected downwards, leaving the possible discount due to discrimination at the level
of about 13.5% both for 1999 and for 2010. In 2011 the bias increases to some 17%.
11
See Heckman (1979).
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Table 3: OLS regressions for log hourly wages in 1999, 2010 and 2011
1999
Dependent variable - log of hourly wage
2010
2011
Women
Regression
coefficient
-0.191510
0.000
Regression
coefficient
-0.181000
0.000
Regression
p value
coefficient
-0.193990 0.000
Age
0.040920
0.000
0.039000
0.000
0.049030
0.000
Age squared
-0.000380
0.000
0.000000
0.000
-0.000460
0.000
Children
0.021400
0.000
0.027000
0.000
0.025180
0.000
Number of school years
0.037350
0.000
0.040000
0.000
0.039640
0.000
p value
p value
Economic branch; Excluded - Social and personal services
Industry, construction, agriculture (traditional sectors) -0.040020
0.037
0.034000
0.018
0.017340
0.227
Electricity and water
0.309080
0.000
0.376000
0.000
0.342100
0.000
Trade and food
-0.130300
0.000
-0.015000
0.306
-0.032510
0.025
Transportation and Communication
0.039300
0.112
0.046000
0.018
0.038590
0.044
Banking and Finance
0.025180
0.211
0.077000
0.000
0.087190
0.000
Public sector
0.062620
0.006
0.159000
0.000
0.134400
0.000
Occupation; Excluded - low skilled workers
-0.081730
0.000
-0.049000
0.000
-0.035880
0.007
Academic
0.518770
0.000
0.479000
0.000
0.464540
0.000
Technical, Free
0.391280
0.000
0.340000
0.000
0.334090
0.000
Management
0.538530
0.000
0.481000
0.000
0.514600
0.000
Clerk
0.183130
0.000
0.133000
0.000
0.110500
0.000
Sales personnel
-0.020860
0.226
-0.026000
0.095
-0.043390
0.005
Professional worker
0.053670
0.002
-0.005000
0.758
-0.011210
0.512
Europe, America
0.013130
0.359
-0.033000
0.015
0.019610
0.228
Asia, Africa
-0.046880
0.002
-0.024000
0.194
0.014720
0.416
Arab
-0.209790
0.000
-0.255000
0.000
-0.187830
0.000
Haredi
-0.198550
0.000
-0.236000
0.000
-0.267720
0.000
Jerusalem
-0.012420
0.512
-0.033000
0.048
-0.001850
0.912
Haifa and North
0.004540
0.746
-0.013000
0.308
-0.000730
0.953
Tel Aviv and Center
0.063130
0.000
0.060000
0.000
0.074950
0.000
New Immigrant
-0.343180
0.000
-0.212000
0.000
-0.191650
0.000
Constant
1.936780
0.000
2.096000
0.000
1.954080
0.000
Education and Health
Origin or ethnic group (Excluded - Jewish, born in Israel
Area of dwelling (Excluded - South)
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Table 4: Two stage regression for log hourly wages in 1999, 2010 and 2011
with a ‘Heckman-correction’ for possible self-selection bias
1999
Dependent variable - log of hourly wage
Women
2010
Regression
p value
coefficient
-0.13450
0.000
Age
2011
Regression
p value
coefficient
-0.13627
0.000
Regression
p value
coefficient
-0.16995
0.000
0.04212
0.000
0.03948
0.000
0.04917
0.000
-0.00036
0.000
-0.00032
0.000
-0.00044
0.000
Children
0.02685
0.000
0.02850
0.000
0.03248
0.000
Number of school years
0.03736
Age squared
0.000
0.04071
0.000
0.03990
0.000
Economic branch; Excluded - Social and personal services
Industry, construction, agriculture
-0.04151
0.030
0.03592
0.013
0.01606
0.263
Electricity and water
Trade and food
0.30815
0.000
0.38207
0.000
0.34608
0.000
-0.13357
0.000
-0.01738
0.236
-0.03514
0.015
Transportation and Communication
0.03950
0.108
0.04629
0.016
0.03731
0.050
Banking and Finance
0.02829
0.159
0.08244
0.000
0.09153
0.000
Public sector
0.06208
0.006
0.15695
0.000
0.13414
0.000
-0.08527
0.000
-0.04573
0.001
-0.03412
0.009
Education and Health
Occupation; Excluded - low skilled workers
Academic
0.40897
0.000
0.34271
0.000
0.34850
0.000
Technical, Free
0.39293
0.000
0.33523
0.000
0.33375
0.000
Management
0.53690
0.000
0.47680
0.000
0.51135
0.000
Clerk
0.18163
0.000
0.12619
0.000
0.10572
0.000
-0.02001
0.241
-0.02786
0.073
-0.04542
0.003
0.04774
0.005
-0.01589
0.349
-0.01695
0.317
0.01118
0.435
-0.03272
0.014
0.01919
0.238
Asia, Africa
-0.04585
0.002
-0.02668
0.147
0.01443
0.421
Arab
-0.12255
0.000
-0.12835
0.000
-0.09137
0.000
Haredi
-0.19574
0.000
-0.23207
0.000
-0.26603
0.000
-0.00842
0.656
-0.03200
0.055
0.00040
0.981
0.00542
0.698
-0.01297
0.291
-0.00178
0.885
Sales personnel
Professional worker
Origin or ethnic group (Excluded - Jewish, born in Israel
Europe, America
Area of dwelling (Excluded - South)
Jerusalem
Haifa and North
Tel Aviv and Center
0.06490
0.000
0.06111
0.000
0.07655
0.000
-0.34607
0.000
-0.21969
0.000
-0.19507
0.000
1.94557
0.000
2.15349
0.000
2.00735
0.000
Age
-0.02695
0.000
-0.01928
0.000
-0.02074
0.000
Women
-0.54968
0.000
-0.34314
0.000
-0.19093
0.000
Jewish
0.72411
0.000
0.84692
0.000
0.78594
0.000
Married
0.32287
0.000
0.24064
0.000
0.27291
0.000
Children
-0.09236
0.000
-0.04327
0.000
-0.09359
0.000
Academic
1.60062
0.000
1.53120
0.000
1.57627
0.000
Constant
1.00465
0.000
0.60923
0.000
0.66016
0.000
New Immigrant
Constant
Selection equation, Variable - Worker
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3. Summary and conclusions
In this paper we analyze the gender gap from the late 1990s to 2010 both in poverty
dimensions and in the labor market. The poverty of households headed by women is
found to exceed that of households with men at their head. We discuss poverty
calculated from economic cash income and from net cash income. The difference
between them reflects the effort of poverty reduction by government intervention
through payment of social benefits and taxation.12
The gender effect in hourly wages may be interpreted as an indication for a gender
bias in the labor market. Our OLS estimates of the wage equation show that the
gender bias seems to have been quite high and stable over some 13 years– nearly one
fifth of male hourly wages.
Such estimations typically encounter the problem of the self-selection bias. This is
particularly true in economies which have a high percentage of people in working age
who do not participate in employment. We thus estimate the two stage model
including a “Heckman-correction” and compare it with the OLS estimates.
When taking into account the possibility of self-selection the gender bias is somewhat
reduced – to about 13 percent. The gender bias is lower in all three years but increases
in 2011 compared to the years 1999 and 2010.13The hourly wages are explained by
demographic variables, personal characteristics, the economic branch of the wage
earner’s activity and her occupation, and more general data such as the geographic
area and ethnic origin, which are also important determinants in Israel’s highly
heterogeneous society.
Our analysis indicates that the poverty indices for women as heads of households are
significantly higher than for households headed by men. The gender gap in poverty
rates is found to decline over time.
12
In the next version we shall estimate the gender bias by a similar methodology to that applied in our
wage equation, except for the use of a logistic function which is more suitable for estimating the risk of
poverty.
13
In the next version we shall provide the robustness test for all the years in the sample. This will also
allow us to see if there is a tendency of the feminization of poverty or not. Judging from the heuristic
approach in the introduction it seems that there is no trend of an increasing poverty incidence or
severity of households headed by women.
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References
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Folbre Nancy, 1991, “The Unproductive Housewife: Her Evolution in Nineteenth
Century Economic Thought”, Signs 16 (3): 563-484.
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Sen Amartya, 1990, “More than 100 million women are missing, December, Volume
37, Number 20, http://ucatlas.ucsc.edu/gender/Sen100M.html
Stiglitz Joseph, 2012, “The Price of Inequality – How Today’s Divided Society
Endangers Our Future”, W. Norton and Company.
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inequality.
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Appendix
Appendix 1: (Ratio of Women/Men minus 1) by age
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
0
4
8
12
16
20
24
28
32
36
40
44
48
52
56
60
64
68
72
76
80
84
88
92
-10.0
Age
Source: Central Bureau of Statistics, Israel
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