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On Gender Discrimination in Wages and the Feminization of Poverty

The Case of Israel 1997-2010

Miri Endeweld1 and Daniel Gottlieb

2*

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: danielgt@nioi.gov.il; Tel.: +972-505298555;

1 National Insurance Institute, Jerusalem, Israel 2 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. 4 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).

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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 men5

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**

**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+.

6 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.

34.6%

32.6%

30.9%31.5%

32.8%32.1%

30.8%

31.8%

31.3%

26.8%27.6%

25.6%

27.7% 28.2%

26.3%

28.0%

26.7%

20%

22%

24%

26%

28%

30%

32%

34%

36%

1997 1998 1999 2002 2003 2004 2005 2006 2007 2008 2009 2010

Women

Men

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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

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

35.3%

19.9%

18.2%

10%

15%

20%

25%

30%

35%

40%

1997 1998 1999 2002 2003 2004 2005 2006 2007 2008 2009 2010

Children

Women

Men

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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

*The figures also include transfers between households.

17.8%

16.8%

13.9%

14.6%

13.9%

12.5%

11.8%

12.5%

11.6%11.9% 11.7%

11.4%12.0%

12.5%

10.3%10.8%

10.3%9.6% 9.5%

8.6% 8.7% 8.7%9.2%

8.5%8%

10%

12%

14%

16%

18%

20%

1997 1998 1999 2002 2003 2004 2005 2006 2007 2008 2009 2010

Women

Men

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Figure 4: The impact of government policy on the reduction of the gender related

poverty gap, 1997 -2010

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

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.

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

5.5%

6.0%

19

97

19

98

19

99

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

The gender gap inthe impact ofgovernment policyon poverty incidence

Log. (The gender gapin the impact ofgovernment policyon povertyincidence)

Households headed by:

Source of income Men % Women %

All sources 15,878 100.0% 11,804 100.0%

Work 13,028 82.0% 8,151 69.1%

Benefits 1,499 9.4% 2,189 18.5%

Capital 558 3.5% 426 3.6%

Pensions 793 5.0% 1037 8.8%

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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

*Employment rates are calculated as the share of the working age population.

Source: Administrative data of the tax authorities

9 See The Economist, 2009.

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

52.0

54.0

56.0

58.0

60.0

62.0

64.0

66.0

68.0

70.0

72.0

2001 2002 2003 2004 2005 2006 2007 2008 2009

Men - all ages

Women - all ages

Gender difference in employment rates (RHS axis)

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Figure 6: Average monthly wages for men and women by age groups,

1999 and 2010

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.

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

15-24 25-29 30-34 35-39 40-44 45-49 50-54 50-59 60-64 65-69 70+

Men, 2010

Men, 1999 (2010prices)Women, 2010

Women, 1999 (2010prices)

Mo

nth

ly w

age

s

Age groups

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Figure 7: Wage differential – male less female wages, by age groups

(2010 prices)

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 a Panel b

-

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

1999

2010

10

20

30

40

50

60

70

80

0 20 40 60 80 100

ho

url

y w

age,

20

10

Share of women in economic branches

10

20

30

40

50

60

70

80

0 20 40 60 80 100Share of women in economic branches

ho

url

y w

age,

19

99

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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

Regression

coefficientp value

Regression

coefficientp value

Regression

coefficientp value

Women -0.191510 0.000 -0.181000 0.000 -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

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

Education and Health

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

Origin or ethnic group (Excluded - Jewish, born in Israel

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

Area of dwelling (Excluded - South)

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

1999 2010 2011

Dependent variable - log of hourly wage

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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

Regression

coefficientp value

Regression

coefficientp value

Regression

coefficientp value

Women -0.13450 0.000 -0.13627 0.000 -0.16995 0.000

Age 0.04212 0.000 0.03948 0.000 0.04917 0.000

Age squared -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 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 0.30815 0.000 0.38207 0.000 0.34608 0.000

Trade and food -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

Education and Health -0.08527 0.000 -0.04573 0.001 -0.03412 0.009

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

Sales personnel -0.02001 0.241 -0.02786 0.073 -0.04542 0.003

Professional worker 0.04774 0.005 -0.01589 0.349 -0.01695 0.317

Origin or ethnic group (Excluded - Jewish, born in Israel

Europe, America 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

Area of dwelling (Excluded - South)

Jerusalem -0.00842 0.656 -0.03200 0.055 0.00040 0.981

Haifa and North 0.00542 0.698 -0.01297 0.291 -0.00178 0.885

Tel Aviv and Center 0.06490 0.000 0.06111 0.000 0.07655 0.000

New Immigrant -0.34607 0.000 -0.21969 0.000 -0.19507 0.000

Constant 1.94557 0.000 2.15349 0.000 2.00735 0.000

Selection equation, Variable - Worker

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

Dependent variable - log of hourly wage

20111999 2010

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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 2010.13

The 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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Endeweld Miri, 2012, Wage mobility and Inequality in Israel, 1990-2005, October,

National Insurance Institute, 1-66, (Hebrew with English abstract).

Folbre Nancy, 1991, “The Unproductive Housewife: Her Evolution in Nineteenth

Century Economic Thought”, Signs 16 (3): 563-484.

Heckman, James, 1979, "Sample selection bias as a specification

error", Econometrica 47 (1): 153–61.

Pearce Diana, 2011, “The changing faces of the feminization of poverty”, Lecture in

a Seminar on the Feminization of Poverty, Valparaiso, Chile, March, 1-8.

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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Commission on the Measurement of Economic Performance and Social

Progress”, http://www.stiglitz-sen-fitoussi.fr/en/index.htm.

The Economist, 2009, “Women at work – We did it”, December.

The Economist, 2013, “The Next Supermodel”, February.

United Nations, Human Development Reports, various years. Chapter on gender

inequality.

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Appendix

Appendix 1: (Ratio of Women/Men minus 1) by age

Source: Central Bureau of Statistics, Israel

-10.0

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

0 4 8

12

16

20

24

28

32

36

40

44

48

52

56

60

64

68

72

76

80

84

88

92

Age

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