A. GENERAL______________________________________________________17

B. MAIN FINDINGS_________________________________________________19

Income of Individuals Aged 15 and Over, by Work Status: Employees, Self-Employed, and Not Working (Table 1)_________________________________19

Income of Employees (Tables 2–13)__________________________________20Average Gross Income of Employees, by Occupation, Extent of Job Position, and Sex (Table 2)_________________________________________________20Gross Income per Married Employee, by Extent of Job Position, Number of Children in Household and Sex (Table 3)______________________________22Average Gross Income per Employee, by Occupation, Level of Education and Sex (Table 4)____________________________________________________24Gross Income per Employee, by Occupation and Sex (Table 5)_____________25Gross Monthly Income per Employee, by Decile, Number of Years of Schooling, Occupation and Sex (Table 7)_______________________________________26Gross Income per Employee, by District of Residence and Sex (Table 8)______27Gross Income per Employee, by Industry, Level of Education and Sex (Table 9)_______________________________________________________________28Gross Income per Female Employee, by Number of Her Children, Marital Status, and Extent of Job Position (Table 10)___________________________29Gross Income per Employee, by Population Group, Continent of Birth, Period of Immigration, and Sex (Table 11)_____________________________________30Gross Income per Employee, by Type of Locality (Table 12)________________30Gross Income per Employee, by Sub-District and Sex (Table 13)____________30

Indices of Income per Employee, by Occupation (Tables 2, 4–7)___________30

Indices of Income per Employee, by Industry (Table 9)___________________31

Indices of Income per Employee, by Continent of Birth (Table 11)_________32

Indices of Income per Employee, by Number of Years of Schooling (Tables 5 and 7)___________________________________________________________32

Gross Income per Employee, by Age Group (Table 6)____________________32

Gross Income per Self-Employed Person, by Occupation and Sex (Table 14)________________________________________________________________33

Gross Income per Self-Employed Person, by Industry and Sex (Table 15)___33

Income per Self-Employed Person, by Age Group and Sex (Table 16)______34

Gross Income per Self-Employed Person, by Quintile and Age Group (Table 17)______________________________________________________________35

Gross Income per Self-Employed Person, by District of Residence and Sex (Table 18)________________________________________________________35

C. TERMS, DEFINITIONS AND EXPLANATIONS_________________________37

D. METHOD OF INVESTIGATION______________________________________41

E. RESULTS OF THE FIELD WORK____________________________________42

F. PROCESSING THE SURVEY DATA_________________________________44

G. METHOD OF ESTIMATION_________________________________________45

H. RELIABILITY OF THE ESTIMATES__________________________________47

1. Sampling Errors_______________________________________________47

2. Non-Sampling Errors___________________________________________52

( 16 )

A. GENERAL

The data on income presented in this publication are part of the Household

Expenditure Survey, and include data on the current expenditures and income of

individuals and households in Israel. The main questions on income in the survey

relate to income of persons aged 15 and over from employee work and self-

employment and income from other sources at the individual level.

The publication includes data on monetary income before tax (meaning gross

income).

In recent years, there have been changes in the data collection on income. Between

1997 and 2011, the CBS investigated income in two separate surveys: The

Household Expenditure Survey and the Income Survey. The Income Survey was

conducted in combination with the quarterly ongoing Labour Force Survey, in which a

quarter of the households were also asked about their income. Data from the two

surveys were combined into one system and presented as integrated results. The

Integrated Income Survey sample included an average of 14,500 households,

annually (6,000 from the Household Expenditure Survey and 8,500 from the income

chapter of the Labour Force Survey).

In 2012, due to the transition of the Labour Force Survey from a quarterly format to a

monthly one, the Income chapter was removed from the Labour Force Survey, and

as a result the Household Expenditure Survey sample was expanded by

approximately 2,500 households.

As of 2012, in addition to the expansion of the survey sample, changes were made in

the survey population, questionnaire, and estimates, as follows:

(1) The sample population of the survey was expanded and now includes collective

moshavim and renewed kibbutzim (which have engaged in a process of

privatization). As a result, the population coverage was increased from about

95% to about 97%, but it still does not include collective kibbutzim and Bedouins.

(2) Definition of soldiers in compulsory military service as employees. Until 2012, the

income of soldiers in compulsory military service was included in “other income

( 17 )

from work”. However, in accordance with the ILO conventions and

recommendations, the CBS decided to measure the entire instead of the civilian

labour force, as in most other developed countries. This was done by adding

soldiers in compulsory military service to the labour force in Israel. Following this

decision, as of 2012, the income of employees also includes the income of

soldiers in compulsory military service.

(3) Expanding the sample among the Arab population of northern localities. The

sample was expanded to provide more reliable estimates of the Arab population.

(4) Changing the estimation method. The estimation method is intended to reduce

sampling errors as well as biases that can occur due to differences between the

characteristics of the households that did not respond to the questionnaire and

those that responded. In 2012, estimation of the population in the Household

Expenditure Survey was adjusted to the new structure of the monthly Labour

Force Survey.

( 18 )

B. MAIN FINDINGS

The findings are based on a sample of 8,903 households and 21,404 individuals

aged 15 and over in 251 urban and rural localities. The sample represents

approximately 2,497,000 households in the population, and approximately 5,902,000

individuals aged 15 and over. Of those individuals, approximately 3,316,000 were

employees, approximately 404,000 were self-employed, and approximately

2,182,000 were not working.

Income of Individuals Aged 15 and Over, by Work Status: Employees, Self-Employed, and Not Working (Table 1)

Analysis of the sources of income of individuals aged 15 and over revealed that

income from work constituted 81.1% of the total income of individuals aged 15 and

over, whereas 18.9% of this income was not from work (income from the National

Insurance Institute, from pension and provident funds, subsidies from government

institutions, and from other households and persons in Israel and abroad).

Distribution of Income Not from Work: 8.6% of the income not from work was from

the National Insurance Institute; 7.5% from pension and provident funds, 1.5% from

subsidies provided by governmental institutions; and 1.3% from subsidies provided

by other households and persons in Israel and abroad.

Income of employees: 94.1% of this income derived from work and 5.9% from other

sources.

Income of self-employed persons: 90.8% of this income derived from work and

9.2% from other sources (mainly from capital, pension, and provident funds).

Income of persons who are not working: 0.7% of this income derived from

temporary and informal work and 99.3% from other sources. The main sources of

income for individuals aged 15 and over who are not working were the National

Insurance Institute (45.7%), and pension and provident funds (42.1%).

Individuals who are not working comprise 37.0% of the entire population and they are

divided into two main age groups. The first one is individuals at working age, 15–66

( 19 )

years, who do not work regularly, and the second one of individuals not of working

age, 67 years and over. The average monthly income of the latter was NIS 5,776, 3.8

times higher than the income of younger persons who are not working, which was

NIS 1,523. In both groups the main income was not from work. 50.2% of the income

of persons aged 67 and over was from pension, and provident funds, and 41.4%

from the National Insurance Institute, compared with 28.2% and 53.1%, respectively,

in the younger group of the working persons.

See Table A in Appendix: Introductory Tables.

Income of Employees (Tables 2–13)

Average Gross Income of Employees, by Occupation, Extent of Job Position, and Sex (Table 2)

The average gross monthly income of employees from work in 2016 amounted to

NIS 9,724, whereas the median income amounted to NIS 7,030.

The number of work hours per week amounted to 41.0, and the average income per

work hour amounted to NIS 57.1.

Of all employees in the population, 78.4% worked full time (35 hours or more per

week), and their average monthly income was NIS 11,128; 21.6% of the employees

worked part-time (less than 35 hours per week), and their average monthly income

was NIS 4,637.

( 20 )

0

2,000

4,000

6,000

8,000

10,000

12,000

Managers Professionals Practicalengineers,technicians,agents, andassociate

professionals

Clericalsupportworkers

Service andsales

workers

Skilledworkers

Elementaryoccupations

NIS

DIAGRAM 1. GROSS MONTHLY INCOME PER EMPLOYEE ENGAGED IN A PART-TIME JOB, BY OCCUPATION (2011 CLASSIFICATION) AND SEX

2016

Women Men

32.0% of the women were employed part-time, and their average monthly income

was NIS 4,637; 12.1% of the men were employed in the same way, and their

average monthly income was NIS 4,639.

0

4,000

8,000

12,000

16,000

20,000

24,000

Managers Professionals Practicalengineers,technicians,agents, andassociate

professionals

Clericalsupportworkers

Service andsales

workers

Skilledworkers

Elementaryoccupations

NIS

DIAGRAM 2. GROSS MONTHLY INCOME PER EMPLOYEE ENGAGED IN A FULL-TIME JOB, BY OCCUPATION (2011 CLASSIFICATION) AND SEX

2016

Women Men

( 21 )

68.0% of the women were employed full time, and their income was NIS 9,040;

87.9% of the men were employed in the same way, and their average monthly

income was NIS 12,627.

The average monthly income of men who were employed full time was 1.4 times

higher than that of women. Among those employed part time, the average monthly

income of men was almost the same that of women.

Women and men who worked part and full time as managers earned the highest

income. Men who worked as managers earned more than their female counterparts

in both full- and part-time positions.

The income of men who worked full time as managers and professionals was more

than twice as high (over 100% higher) than that of men working part time in the same

occupation. The income of women working full time as managers was also higher,

but less than that of men. This is partly explained by the differences between

industries in which male and female managers are employed.

Gross Income per Married Employee, by Extent of Job Position, Number of Children in Household and Sex (Table 3)

Married persons constituted 58.8% of all employees in the population, and their

average gross monthly wages amounted to NIS 11,894 and higher by 18.2% than the

average monthly income of the total employees – NIS 9,724.

A breakdown by number of children reveals that employees with 2 children had the

highest monthly income (NIS 12,172); by comparison, employees with no child had a

gross monthly income lower by 4.3% – NIS 11,644.

( 22 )

0

4,000

8,000

12,000

16,000

20,000

Total No children 1 2 3 and over

NIS

Number of children

DIAGRAM 3. GROSS MONTHLY INCOME PER MARRIED EMPLOYEEENGAGED IN A FULL-TIME JOB, BY NUMBER OF CHILDREN AND SEX

2016

Women Men

Gender pay gaps: The more children a full-time worker has, the smaller the gender

gaps. For example, the pay gap among parents with one child is 36.9%, and by

contrast, the gap among parents with 3 children or more is 28.7%.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

Total No children 1 2 3 and over

NIS

Number of children

DIAGRAM 4. GROSS MONTHLY INCOME PER MARRIED EMPLOYEEENGAGED IN A PART-TIME JOB, BY NUMBER OF CHILDREN AND SEX

2016

Women Men

( 23 )

Among employees engaged in part-time jobs the pay gaps are decreasing, i.e., the

more children a male and a female employee have, the income get similar. Among

married employees with 3 children or more the trend even reverses and the women

among them earn more than men.

Average Gross Income per Employee, by Occupation, Level of Education and Sex (Table 4)

See Table B in Appendix: Introductory Tables.

Of the employees in the population, 35.2% had and 64.8% did not have an academic

degree. Among the former, the percentage of women was higher than the

percentage of men (19.0% and 16.2% of the total employees, respectively). Among

the latter, the percentage of men was higher than the percentage of women (35.6%

and 29.2% of the total employees, respectively). This difference reflects the

administrative education data: the rates of women who complete their studies for first

and second degrees are higher than the corresponding rates of men.

See Table C in Appendix: Introductory Tables.

The average gross monthly income of employees with an academic degree was 2.1

times higher than the income of employees without an academic degree (NIS 14,580

and 7,084, respectively).

The highest average income was found among Managers. A comparison by level

of education reveals that managers with an academic degree earned 1.7 times

more than other managers (NIS 21,754 and 13,026, respectively).

The average income per work hour for employees with an academic degree was NIS

84.2 per hour, compared with NIS 41.9 per hour for employee without a degree.

The average gross monthly income for a male employee with an academic degree

was NIS 18,815 – 1.7 times more than the average gross monthly income for the

female counterpart, which amounted to NIS 10,953.

( 24 )

Gross Income per Employee, by Occupation and Sex (Table 5)

0102030405060708090

100

Managers Professionals Practicalengineers,technicians,agents, andassociate

professionals

Clericalsupportworkers

Service andsales workers

Skilledworkers

Elementaryoccupations

NIS

DIAGRAM 5. GROSS INCOME PER EMPLOYEE PER WORK HOUR,BY OCCUPATION (2011 CLASSIFICATION) AND SEX

2016

Women Men Total

The income per work hour for men was higher than that of women in all occupations.

The largest gap was among “Sale and Service Workers” (women’s income amounted

to 67.5% of men’s income). The smallest gender pay gap was found among Clerical

support workers (women’s income amounted to 89.4% of men’s income).

( 25 )

0

10

20

30

40

50

60

70

80

90

100

Up to 8 9–12 13–15 16 and over

NIS

Years of schooling

DIAGRAM 6. GROSS HOURLY INCOME PER EMPLOYEE, BY NUMBER OF YEARS OF SCHOOLING AND SEX

2016

Women Men Total

At all levels of education, the income of men per work hour was higher than that of

women. The smallest gap was among those with up to 8 years of schooling, women’s

hourly income was 83.8% of men’s income; whereas the maximum gap was among

those with 16 and over years of schooling: women’s hourly income was 76.1% of

men’s income.

Gross Monthly Income per Employee, by Decile, Number of Years of Schooling, Occupation and Sex (Table 7)

Regarding the distribution of employees’ income by deciles, the findings revealed

that in the lowest decile, 55.9% of the employees were women and 44.1% were men.

In the highest decile, the ratio was reversed: 23.2% were women, and 76.8% were

men. In the four lowest deciles the rate of women was higher than the rate of men,

and as of the fifth decile the ratio reversed.

( 26 )

0

10

20

30

40

50

60

70

80

90

Perc

enta

ges

Decile

DIAGRAM 7. EMPLOYEE DISTRIBUTION, BY DECILE AND SEX2016

Women Men

The average gross income per individual employee in the highest decile was NIS

31,530 per month – 33.9 times more than the average income in the lowest decile

(NIS 931). Noteworthy, a part of the low incomes in the lowest decile are due to the

inclusion of soldiers in compulsory military service among the employees (see

Chapter A: “General”).

Gross Income per Employee, by District of Residence and Sex (Table 8)

The largest number of employees was in the Central District, where 27.7% of the

employees were concentrated. The Judea and Samaria Area had the lowest

percentage of employees – 4.1%. Similarly, the income of employees was highest in

the Tel Aviv District, and lowest in the Northern District.

The average total income of female employees was 34.6% lower than that of male

employees. In the Jerusalem District, the gap between the monthly incomes of

female versus male employees was the smallest. In this district the income of female

employees was 24.7% lower than that of their male counterparts.

( 27 )

0

10

20

30

40

50

60

70

80

Jerusalem Northern Haifa Central Tel Aviv Southern Judea andSamariaArea(1)

NIS

District of residence

DIAGRAM 8. GROSS HOURLY INCOME PER EMPLOYEE,BY DISTRICT OF RESIDENCE AND SEX

2016

Women Men(1) Israeli localities.

Gross Income per Employee, by Industry, Level of Education and Sex (Table 9)

The highest number of employed persons in the entire population was in "Education"

– nearly 433 thousand employees, in "Local, Public and Defense Administration and

Social Security" – nearly 388 thousand employees, in "Manufacturing; Mining and

Quarrying" – nearly 387 thousand employees, "Wholesale and Retail Trade and

Repair of Motor Vehicles" – nearly 380 thousand employees, and in "Human Health

and Social Work Activities" – nearly 353 thousand employees.

The highest monthly incomes were in "Electricity and Water Supply, Sewerage and

Waste Management" – NIS 17,946, and "Information and Communications" – NIS

16,488. The lowest monthly income was found in "Households as Employers" – NIS

4,323.

Among people with an academic degree the highest number of employed persons

was in the industries: “Professional, Scientific and Technical Activities, and Real

Estate Activities”, nearly 131,000 employees, with an average gross monthly income

of NIS 16,675, “Education”, nearly 248,000 employees, with an average gross

monthly income of NIS 10,672, and “Human Health and Social Work Activities”, with

nearly 153,000 employees, and an average gross monthly income of NIS 12,358.

( 28 )

Among people with no academic degree the highest number of employed persons

was in the industries: “Wholesale and Retail Trade and Repair of Motor Vehicles”,

nearly 310,000 employees, with an average gross monthly income of NIS 6,940, and

"Local, Public and Defense Administration and Social Security”, approximately

285,000 employees, with an average gross monthly income of NIS 4,537.

Gross Income per Female Employee, by Number of Her Children, Marital Status, and Extent of Job Position (Table 10)

The average gross monthly income of female employees was NIS 7,633, and their

average income per work hour was NIS 50.4. Women without children comprised

53.9% of all female employees, and their average monthly and hourly income and

was NIS 6,552 and 43.1, respectively.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

Total Married Nevermarried

Divorced Widowed Separated

NIS

Marital Status

DIAGRAM 9. GROSS MONTHLY INCOME PER FEMALE EMPLOYEE,BY MARITAL STATUS

2016

Never-married female employees earned NIS 4,860 per month for 37.9 weekly hours

on the average. Married female employees had the highest income – NIS 8,862 per

month for 36.5 weekly hours on the average.

( 29 )

Gross Income per Employee, by Population Group, Continent of Birth, Period of Immigration, and Sex (Table 11)

The average monthly income per Jewish male employee was NIS 12,734, compared

with NIS 7,384 for an Arab male employee – a gap of 42.0%. The gap in hourly

income decreased a little – to 41.6%: Jewish male employees earned NIS 67.8 per

hour on the average, compared with NIS 39.6 per Arab male employee.

The average monthly income of Arab female employees was NIS 5,004, compared

with NIS 7,928 for a Jewish female employee – a gap of 43.1%. The gap in average

hourly income was smaller – 36.9%.

Gross Income per Employee, by Type of Locality (Table 12)

The percentage of employees living in urban localities was 92.8%, and the remaining

7.2% lived in rural localities. Residents of non-Jewish localities comprised 10.0% of

all employees, and their average gross monthly income was 28.8% lower than the

corresponding income of all employees.

The highest gross monthly income per work hour was found in Tel Aviv-Yafo (NIS

12,674 and NIS 72.1, respectively). The gross income of women per work hour in all

localities except Jerusalem was lower than the income of their male counterparts.

Gross Income per Employee, by Sub-District and Sex (Table 13)

The distribution by sub-districts reveals that Tel Aviv had the highest gross income

levels, whereas Zefat, Kinneret and Golan had the lowest levels. In all sub-districts

there was a gender pay gap, both in terms of monthly income and income per work

hour.

Indices of Income per Employee, by Occupation (Tables 2, 4–7)

The Classification of Occupations was changed in 2013, and is now based on the

new International Standard Classification of Occupations. For further information, see

Chapter C: “Terms, Definitions, and Explanations” in this publication.

In 2016, the average gross monthly income per employee amounted to NIS 9,724,

and per work hour to NIS 57.1. The median gross monthly income per employee (i.e.,

( 30 )

the point at which half of the employees earn more per month and half earn less)

amounted to NIS 7,030 in 2016.

Ranking of the main groups of occupations by income level revealed that the highest

gross monthly income was earned by Managers (NIS 17,527) – 80.3% higher than

the average monthly income per employee. The lowest gross monthly income was

earned by employees in Elementary occupations – 57.0% lower than the average

gross monthly income per employee.

See Table D/1 in Appendix: Introductory Tables.

In 2013, there was a transition from the 1994 to the 2011 Standard Classification of

Occupations.

See Table D/2 in Appendix: Introductory Tables.

Indices of Income per Employee, by Industry (Table 9)

As mentioned, in 2013, there was a change in the classification of industries, and it is

now based on the new International Standard Industrial Classification. For further

information, see Section C: “Terms, Definitions, and Explanations” in this publication.

Examination of gross monthly income by industry revealed that in “Electricity and

Water Supply, Sewerage, and Waste Management” the gross income per work hour

of employees was 84.6% higher than that of all employees. By contrast, in “Activities

of Households as Employers” the gross hourly income of employees (NIS 26.5) was

less than half of the average income per work hour (53.6% lower).

See Table E/1 in Appendix: Introductory Tables.

In 2013, there was a transition from the 1994 to the 2011 Standard Classification of

Industries.

See Table E/2 in Appendix: Introductory Tables.

( 31 )

Indices of Income per Employee, by Continent of Birth (Table 11)

The highest income in 2016 was earned by Jewish Israeli-born employees whose

fathers were born in Europe or America. For that group, the average gross monthly

income (NIS 12,177) was 25.2% higher than the average income of all employees

(NIS 9,724).

See Table F in Appendix: Introductory Tables.

Indices of Income per Employee, by Number of Years of Schooling (Tables 5 and 7)

The higher the employees level of education, the higher their income. The average

gross income per work hour of employees with 16 and over years of schooling (NIS

84.4) was 47.8% higher than that of all employees (NIS 57.1).

Employees with low levels of education were concentrated in low-paying

occupations, such as “Elementary Occupations” and “Service and Sales Workers”.

Employees with 9–12 years of schooling were concentrated in jobs for “Service and

Sales Workers” and “Skilled Workers”. Employees with 16 and over years of

schooling were concentrated in high-paying occupations, such as: "Managers",

“Professionals”, “Practical Engineers, Technicians, Agents, and Associate

Professionals”.

See Table G, in Appendix: Introductory Tables.

Gross Income per Employee, by Age Group (Table 6)

Throughout the 20 years specified gross monthly and hourly income increase with

age, up to age 45–54. In the age group 55 and over, the income of individuals was

lower than the income of those in the younger age group.

See Table H, in Appendix: Introductory Tables.

( 32 )

Gross Income per Self-Employed Person, by Occupation and Sex (Table 14)

In 2016, the average monthly income of self-employed persons from work amounted

to NIS 11,480 – a gap of 15.3% compared with the average monthly income of

employees (NIS 9,724).

The distribution by sex indicates that 68.0% of all self-employed persons were men,

and 32.0% were women.

The average monthly income per self-employed woman was NIS 7,675 – 0.6%

higher than that of female employee.

The average monthly income per self-employed man was NIS 13,272 – 13.8% higher

than that of male employee.

The gap between the average monthly income per self-employed man and woman

was 42.2%. When the median income was calculated, the gap between self-

employed men and women declined slightly to 41.7%. The findings indicate that self-

employed women and men work fewer hours per week than those who are female

and male employees (31.6, 45.1, 36.9 and 44.6, respectively). However, the average

monthly income per self-employed man and self-employed woman was higher than

that of female and male employees.

See Table I, in Appendix: Introductory Tables.

Gross Income per Self-Employed Person, by Industry and Sex (Table 15)

The main industries in which self-employed persons worked were “Professional,

Scientific and Technical Activities” and “Real Estate Activities” (approximately 89,000

self-employed persons), “Wholesale and Retail Trade and Repair of Motor Vehicles”

(approximately 56,000), and “Human Health and Social Work activities”

(approximately 41,000). "Construction" (approximately 38,000) and “Other Service

Activities” (approximately 29,000),

There were substantial differences between industries in the income of self-employed

persons. The income of self-employed persons in “Financial and Insurance Activities”

( 33 )

(NIS 25,081) is higher compared with the income of their counterparts in “Education”

(NIS 6,984).

Among self-employed persons in most industries, there were also substantial gaps in

the income of men versus women.

Income per Self-Employed Person, by Age Group and Sex (Table 16)

There was a direct relationship between the age of self-employed persons and their

gross monthly and hourly wages in all age groups up to 45–54. However, the income

of self-employed persons aged 25–34 was lower than in the other groups. The

highest income per work hour among self-employed persons was found in the 45–54

age group. By contrast, the largest number of work hours per week was found among

those aged 35–44.

A comparison by gender revealed that the highest number of self-employed persons

was found among those aged 35–44.

The gender gap in gross monthly income of self-employed persons was found in all

age groups. However, the number of work hours per week was much higher for self-

employed men than women.

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

25–34 35–44 45–54 55–64 65 and over

NIS

Age group

DIAGRAM 10. GROSS MONTHLY INCOME PER SELF-EMPLOYED PERSON, BY AGE GROUP AND SEX

2016

Women Men

( 34 )

Gross Income per Self-Employed Person, by Quintile and Age Group (Table 17)

The division of self-employed persons into quintiles by gross monthly income

revealed that the average gross monthly income in the uppermost quintile per self-

employed person (NIS 30,798) was 2.7 times higher than the overall average income

per self-employed person (NIS 11,480).

See Table J, in Appendix: Introductory Tables.

Gross Income per Self-Employed Person, by District of Residence and Sex (Table 18)

The highest number of self-employed persons was found in the Tel Aviv and Central

Districts, and the lowest number in the Judea and Samaria Area. The average gross

monthly income per self-employed person was highest in the Central District (NIS

13,249), and was 15.4% higher than the overall average income per self-employed

person.

There was a gap between self-employed men and women in all districts. The

average income of self-employed men was 72.9% higher than the income of self-

employed women. The highest gender gap in average gross monthly income per

self-employed person was in the Central District, whereas the gender gap in income

of self-employed persons was smallest in the Southern District.

( 35 )

0

4,000

8,000

12,000

16,000

20,000

Total Jerusalem Northern Haifa Central Tel Aviv Southern Judea andSamariaArea(1)

NIS

District of residence

DIAGRAM 11. GROSS MONTHLY INCOME PER SELF-EMPLOYED PERSON, BY DISTRICT OF RESIDENCE AND SEX

2016

Women Men(1) Israeli localities.

( 36 )

C. TERMS, DEFINITIONS AND EXPLANATIONS

Individuals aged 15 and over: The survey sample was drawn from persons over

the age of 15, for whom employment data were filled out. In accordance with the

international definition and the World Labor Organization, working age is 15 and

over.

Employee: Any person interviewed in the survey who had some income from wages

or salaries during the three months preceding the interviewer’s visit.

Self-employed: Persons working in their own business or farm, including employers,

who reported in the survey on annual or monthly income of their business/farm

before tax is deducted.

A person who is not working: A person who did not work for even one day over the

three months preceding the visit of the interviewer.

The survey population: The survey population covered all households in Jewish,

Arab and mixed localities except for collective moshavim, kibbutzim and Bedouins

living outside localities.

The data for 2000–2001 did not include the population of East Jerusalem due to

enumeration difficulties. However, in other years the residents of East Jerusalem

were included in the survey population.

Full-time job: Over 35 hours of work per week as reported in the survey.

Part-time job: Less than 35 hours of work per week as reported in the survey.

Religion and population group: The classification by religion is Jews, Arabs and

Others. The group “Jews and others” includes Jews, non-Arab Christians, and those

not classified by religion, and the group “Arabs” includes Moslems, Arab-Christians,

and Druze.

Gross monthly income per employee: The income derived from the division by 3

of the quarterly gross income investigated in the survey. The quarterly gross income

is the gross income (i.e., before compulsory payments and taxes) from all places of

work where the participant in the survey was employed for wages or salary in the

( 37 )

three months preceding the interviewer's visit. This income includes all additions,

such as overtime, vacation pay, clothing allowance, and premium payments. This

division also applies to employees who worked only one or two out of the three

months about which they are being questioned. Therefore, the average monthly

income calculated in these cases is usually lower than their actual wages for the

months they did work.

Monthly income per self-employed person: Income from annual net profit of

owned business (divided by 12), as well as current monthly income that the person

receives from the business before deductions and tax payments.

Median income: A measurement of the statistical middle. Half the persons earn

more than the median and half earn less.

Gross income per employee per work hour: The income derived from dividing the

quarterly gross income (see above) by the total work hours for those three months.

Weekly work hours: Work hours as the employee/self-employed person reported in

the survey. It was difficult to find out the number of work hours in the field and

therefore it is subject to high sampling errors.

Sources of Income

Pension fund: One of the pension savings plans. In the pension fund, savings are

accrued for the benefit of the worker. Both the worker and the employer allocate

money to the fund. This savings plan is intended for payment of a monthly pension

when the beneficiary reaches retirement age (67 for men, 62 for women).

Allowances and subsidies: The item includes allowances and subsidies from the

National Insurance Institute and from other government institutions such as the

Ministry of Construction and Housing, the Ministry of Defense, and the Ministry of

Finance as well as current subsidies from other institutions such as yeshivas.

Child allowance: The National Insurance Institute pays a monthly child allowance to

families living in Israel with children up to age 18. The allowance is designed to help

families with expenses for raising children, and is paid irrespective of income.

Old-age pension: An allowance paid by the National Insurance, which is designed to

( 38 )

ensure that Israeli citizens receive a regular monthly income in old age.

Unemployment benefits and work injury allowance: The National Insurance

Institute provides unemployed persons with a source of subsistence until they find

work. The unemployment benefits are paid to persons who were salaried employees,

who are registered with the Employment Service as unemployed and who report to

the Employment Service to look for work. Persons injured at work also receive

assistance due to loss of income after an injury which rendered them unfit to work.

Income support benefit: A monthly allowance paid by the National Insurance

Institute to individuals who cannot or have difficulty integrating into the labour market,

as well as to those who integrated but their wages is lower than the minimum wage.

Income index: A number that describes the relationship or differences in surveyed

income between various groups in the population. Usually, the average income in the

population is regarded as 100%, and corresponding incomes in various groups are

compared with it.

For example: in Table D, the index of the monthly income for an employee = 100; by

comparison, the index for monthly income for an employee working in an academic

occupation = 142-100 = 42. That is, in 2016, the average monthly income of

employees working in academic occupations was nearly 42% higher than the

average monthly income per employee.

Deciles: A decile is a group composed of 10 percent of the surveyed population.

Deciles are arranged by income level (the “classifying income”), from the individual

with the lowest income in the lowest decile to the one with the highest income in the

uppermost decile.

The income by which the households are classified can be gross or net, as well as

income per household, per capita or per standard person. In this publication the

classification is by gross monetary income per person.

Quintile: A group comprised of 20% of the population (two deciles) according to a

classifying variable.

( 39 )

District and Sub-District: The districts and sub-districts are defined according to the

official administrative division of the State of Israel, which includes 6 districts and 15

sub-districts. In 1972, the Judea and Samaria and Gaza Areas were added to these,

to characterize the Israeli localities and their population, which are located in these

areas. As of August 2005 these are registered as the Judea and Samaria Area.

Classifications of Occupations and Industries

In 2013, there was a change in these classifications. Until 2012 inclusive,

occupations were classified in the Household Expenditure Survey according to the

1994 Classification, and as of 2013, the 2011 Classification is used. There are almost

no changes in the names of the major groups in the 1994 and 2011 Classifications.

However, the order in which the major groups appear has been adjusted to the

International Standard Classification of Occupations ISCO-08. The main differences

between the 1994 and 2011 Classifications are presented in the table below.

See Table K in Appendix: Introductory Tables.

In the process of adopting the International Standard Classification of Occupations,

Armed Forces Occupations was added as a specific major group. However, it should

be emphasized that the CBS does not code Armed Forces Occupations separately.

With regard to industries, until 2012, inclusive, the 1993 Standard Industrial

Classification was used in the Household Expenditure Survey. As of 2013, the 2011

Standard Industrial Classification is used. The main differences in the classifications

are presented in the table below.

See Table L in Appendix: Introductory Tables.

( 40 )

D. METHOD OF INVESTIGATION

Collecting the survey data: Data were collected from each household in an integrated

manner, as follows:

(1) A questionnaire on the structure of the household, which was filled out by the

interviewer. The questionnaire included basic demographic and economic data on

each member of the household (e.g., age, sex, country of birth, year of

immigration, work status).

(2) A bi-weekly diary, in which the household recorded each member’s daily

expenditures over a period of two weeks.

(3) A questionnaire that examined large or exceptional expenditures and income. The

questionnaire was filled out by the interviewer on the basis of reports from the

household relating to the 3-month or 12-month period preceding the date of the

interview (depending on the rarity of expenditures for the items investigated).

Survey period: The data were collected in the field over a period of approximately 13

months, beginning in January of the survey year and ending in January of the

subsequent year. Investigation of the sample was spread across the entire survey

period, so that all weeks in the investigation period would be represented.

Estimates of expenditures obtained from the diary are approximations of

expenditures made during the survey year. The estimates obtained from the

questionnaire pertain to a 15-month period (from October 2015 to December 2016),

or a 24-month period (from January 2015 to December 2016), according to the

purpose of expenditure.

( 41 )

E. RESULTS OF THE FIELD WORK

Of the 13,199 dwellings sampled, there were 1,396 (10.6%) that should not have

been investigated, as specified below:

DwellingsAbsolute numbersPercentages

Total dwellings that should not have been investigated1,396100.0Vacant62845.0

The occupants have another permanent address in Israel24517.6

The occupants are households that do not belong to the survey population715.1

Used as businesses, institutions, etc.18313.1

Demolished, abandoned, or under construction14310.2

Errors in sampling frames1269.0

The 11,803 dwellings that met the investigation criteria were occupied by 11,909

households belonging to the survey population. As expected, most of the dwellings

were occupied by one household, and only 0.81% were occupied by two households

or more.

Of the 11,909 households in the dwellings that met the investigation criteria, 25.2%

were not included in the survey estimates: 2,996 of these households were not

investigated, and 10 were disqualified at the editing stage.

( 42 )

Thus, a total of 3,006 households were not included in the estimates, as shown

below:

Households Absolute numbers

Percent-ages

Percent-ages

Total households for investigation 11,909 100.0 100.0Not investigated – total 2,996 25.2 100.0Refused 1,367 11.5 45.6

Not at home 472 4.0 15.8

Communication difficulties, illness, mourning, etc. 981 8.2 32.7

Not located and other difficulties 176 1.5 5.9

Investigated – total 8,913 74.8 100.0Disqualified in editing 10 0.1 0.1

Included in survey estimates 8,903 74.7 99.9

Of the households that were not investigated, some refused to participate in the

survey, some provided only limited information on household characteristics in

Questionnaire A, and a few began to fill out a diary but did not complete it.

Households Absolute numbers Percentages

Households not investigated – total 2,996 100.0Did not respond at all 2,096 70.0

Responded to Questionnaire A at least 900 30.0

( 43 )

F. PROCESSING THE SURVEY DATA

The questionnaires collected in the field undergo manual checking, editing and

coding. After the data are transferred to magnetic media, a series of logical checks

are conducted specifically to find and correct mistakes in the file. This includes:

checking relationships between different variables, checking for total amounts, and

so on. In addition, missing data are supplemented and questionnaires in which the

data are clearly defective and cannot be corrected are disqualified. The purpose of

the various checks is to correct mistakes as possible while also eliminating data from

the final survey that are deficient and cannot be corrected.

Price adjustment: All income data were presented at a standardized price level, i.e.,

at the average price level of the survey year (220.6 points in 2016 according to base

1993 = 100.0).

In addition, based on information from the National Insurance Institute, a further

revision was made for real changes in salary data of employees by industry. This

additional revision partially diminishes the distribution of data due to structural

changes. In that way the various average estimates are corrected, even if the sample

is not distributed equally over the survey year.

Imputation of missing incomes: Some respondents did not give full information

about the investigated variables. Therefore, different calculations are made at the

individual level, such as: imputing income for individuals aged 15 and over who

worked a few days and did not report a salary; imputing salaries for months for which

no data were reported, in accordance with reports for other months; calculation of

gross income for those cases where the net or the paid sum were reported.

( 44 )

G. METHOD OF ESTIMATION

The estimation method is designed to reduce both sampling errors and the biases

that may occur because non-responding households may have different

characteristics than households that participated in the survey.

To obtain estimates for the survey population at large, a “weighting coefficient” was

determined for each household that was interviewed and for all the persons

belonging to that household. The weighting coefficient of a household reflects the

number of households and the number of persons in the survey population whom this

particular household represents. In the calculation of these weighting coefficients, the

“non-response” cases were also taken into account (cases that should have been

investigated but were not), as well as cases that were disqualified due to missing

data about the respondents’ income or the extent of their work.

The set of weighting coefficients was determined in a multi-phase “raking” process, in

which the distribution of the weighted sample was adjusted to several external

distributions, according to selected distribution variables. The adjustment was made

separately to each of the distributions according to household characteristics, and

according to individual characteristics separately (not combined).

For the households, the adjustment was made to two groups:

a) The population of Jewish and mixed localities, excluding immigrants;

b) The population of non-Jewish localities.

For these distributions, the division varied according to the characteristics of the

household.

Groups of types of households, determined on the basis of the size and age

composition of the household (e.g., elderly persons living alone, young couples,

households with children).

Groups of households defined on the basis of the time they were investigated.

These groups are intended to balance the weighted sample over the survey year,

and to prevent biases that might result from the fact that groups were not evenly

( 45 )

distributed over the months of the year, due to field work constraints.

The distributions by household characteristics for which the survey data were

adjusted were obtained on the basis of the Labour Force Survey estimates, which

were based on a large sample.

As for individuals, the weighting coefficients for different groups of households were

also determined so that there would be full correspondence between the survey

estimates and the distribution of the survey population by sex and age groups in

geographic cross-sections, according to the current demographic data of the Central

Bureau of Statistics.

Regarding households, a different adjustment was made for those residing in Arab

localities, for new immigrants who settled in Israel up to four years prior to the survey,

and for the rest of the population, according to the following distributions:

(1) Homogeneous groups of households, as determined by statistical methods,

according to level of consumption expenditure;

(2) Groups of types of household, determined on the basis of the size and age

composition of the household (e.g., elderly persons living alone, young couples,

households with children, etc.);

(3) Groups of households defined on the basis of the time they were investigated.

These groups are meant to balance the weighted sample over the survey year,

and to prevent biases that might result from the fact that the survey sample was

not evenly distributed over the months of the year, due to fieldwork constraints.

( 46 )

H. RELIABILITY OF THE ESTIMATES

The estimates presented in this publication are based on a sample survey, and may

therefore be subject to errors of two main types:

1. Sampling errors: These errors arise from the fact that the survey investigated only

one sample of households and their individual members, and did not cover all the

households and individuals in the population.

2. Non-sampling errors: These errors result from other factors that may be present,

even when a full census of the entire population is conducted.

1. Sampling Errors

The sample that this survey is based on is one of many possible samples of the

same size that could have been drawn from the same population by the same

method.

Estimate X’ is the estimated value, based on the specific sample of this survey, for

the corresponding value, X, that would have been obtained had a full census been

conducted.

The sampling error of the estimate, (X), is the mean difference between all

estimates that could have been obtained from all possible samples of the same size

and the same method, and the value that would have been obtained had a full

census been conducted under the same data-collection conditions.

The confidence interval for the estimate is an interval that contains the census

value X at a given predetermined level of confidence. The estimate X, based on the

sample, and the estimate of its sampling error, (X), make it possible to create a

confidence interval at a predetermined confidence level, so that the interval contains

the census value X at the stipulated confidence level.

The confidence interval is usually presented at a confidence level of 95%. Therefore,

the boundaries of this confidence level are calculated as X '±2σ ' (X ' )

. For every

( 47 )

table of subgroups in this publication, the sign "" and the values of the two sampling

errors for this estimate are presented beneath the estimate (in small letters).

Example: According to Table 1, the estimated average gross monthly income from

work per individual in 2016 was NIS 6,340. The 95% confidence interval for this

estimate would be NIS 6,340134.0. That is, it can be claimed with 95% confidence

that the average gross monthly income of households in this group ranges from NIS

6,206 to 6,474.

The confidence level is usually set at 95%, but it can be set higher or lower, and the

confidence interval can be computed in the following way:

67% 80% 90% 95% 99.5%

() 1.0 1.3 1.7 2.0 2.8

where K (α )

(the number of sampling errors in either direction) is determined in

accordance with the requisite confidence level, .

Continuing with the previous example: If a higher confidence level of 99.5% (near

certainty) is desired, the value of the sampling error is divided by 2 (in order to obtain

the value of one sampling error), and the result is multiplied by 2.8 =K (α )

. In this

example, one sampling error of NIS 134 is obtained, and therefore a 99.5% level of

confidence will be:

6,340 2.8 * 67

That is: 6,340 187.6

It can therefore be argued with almost total confidence (99.5%) that the average

monthly income of households in the above-mentioned group ranges from NIS 6,152

to 6,528.

Notes

(a) The confidence intervals are usually symmetrical around the estimate, but they

are asymmetric for estimates based on a small number of cases in the sample

( 48 )

(less than 40). In these cases, both the estimate itself and the estimate of its

sampling error are subject to a high error. This publication presents the high value

of the confidence interval.

(b) In order to warn the reader against the use of estimates that are subject to high

errors, estimates with relative sampling errors between 15% and 30% are shown

in parentheses ( ). Estimates with relative sampling errors of over 30% are not

presented in this publication.

Comparisons of Estimates Related to Mutually Exclusive Groups

Sampling errors can be used to compare estimates related to mutually exclusive

population groups (e.g., men and women) and to determine whether the difference

between the two groups is statistically significant.

If the estimates for Group 1 and Group 2 are X (1) and X (2), respectively, the

estimated difference between the groups is D = X (1) - X (2).

In order to determine whether X (1) is different from X (2) in the population itself, it

is necessary to determine the sampling error of the estimated difference, D'

:

22 21 XXD , where one sampling error of the estimates is

obtained by dividing the values shown in the tables by 2.

If σ '(D ' )

is given, it is possible to determine a confidence interval for the difference at

a confidence level :D'±K ( α )σ '(D ' )

.

If the confidence interval contains the value 0, the difference D’ is not statistically

significant. In other words, on the basis of the specific sample in the survey at the

stipulated confidence level, it cannot be argued that X (1) is different from X (2) in

the population itself (even though the two values are different in the sample).

( 49 )

If the confidence interval does not contain the value 0, there is a statistically

significant difference between the two groups, and at the stipulated confidence level

the difference will be between D'−K ( α )σ '(D ' )

and D'+K (α )σ ' (D')

.

( 50 )

For the reader’s convenience, a chart is attached at the end of the Hebrew

introduction and can be used to obtain 95% confidence intervals for the difference

between mutually exclusive groups. The chart should be used in the following way:

For the estimates and sampling errors presented in this publication, the tables

present the value of two confidence intervals for the estimates X ' (1)

and X ' (2 )

.

These values are marked in the two columns at either end of the chart. If a line is

drawn between them, the value σ '(D ' )

will be found in the middle column in the

figure. (To facilitate use of the chart, the scale of the limits can be adjusted.) If, for

example, for two estimates, the values of two sampling errors are 120 and 150, they

can be divided by 2, with the results of 60 and 75, respectively. The line connecting

60 and 75 in the extreme columns intersects the middle column at the value of 96.

Therefore, the value of the two sampling errors of the difference is 192=2*96.

If the difference D’ is smaller than 2σ ' (D' )

in absolute terms, the difference is not

statistically significant. I.e., according to the specific sample in the survey, at the set

confidence level, it is impossible to state that X1 is indeed different than X2 in the

population itself (although in the sample they are different).

If the difference D’ is larger than 2σ ' (D' )

in absolute terms, the difference is

statistically significant and lies within the rangeD'±2σ ' (D ' )

.

Example: Based on Table 1, the average gross monthly income from work of

employees versus self-employed individuals will be compared at a 95% confidence

level. The average gross monthly income from work was:

NIS 9,815 211.2 per an employee;

NIS 11,925 898.4 per a self-employed person.

( 51 )

The question is whether the difference between these groups is statistically

significant. Based on these estimates alone, there would appear to be a difference in

the average gross monthly income of individuals in two groups above. This difference

is estimated as:

D’= NIS 11,925 – 9,815 = 2,110

To examine whether the difference is significant, the confidence interval needs to be

computed:

D'±2σ ' (D' )

A more accurate computation of the two sampling errors of the difference is based on

the following formula:

2σ ' (D' )=2∗√(898 .4/2 )2+(211.2 /2 )2=2∗461=923

One may calculate a 95% confidence margin for the estimate of the difference:

D '±2σ ' (D' )=2 ,110±923

This interval does not include the negative values or the value “zero” (the amount

ranges from 1,187 to 3,033); hence the difference is significant. Therefore, it can be

argued at a 95% level of confidence that there is a difference in the average gross

monthly income from work of employees versus self-employed individuals.

Ratio of estimates among mutually exclusive groups: The ratio R’ of X ' (1)

to

X ' (2 ) for two mutually exclusive groups, 1 and 2, is estimated as follows:

R'=X'(1 )

X ' (2)

( 52 )

And the estimate for the sampling error of the ratio estimate σ '(R ' )

will be:

σ ' (R' )=R '∗√( σ' X (1 )X ' (1 ) )

2

+( σ' X (2 )X ' (2 ) )

2

Therefore, a 95% level of confidence for R’ will be R'±2σ ' (R' )

.

If the confidence interval includes the value "1", the ratio is not significantly different

from 1.

If the confidence interval excludes the value "1", the ratio is significantly different from

1 and falls within the aforementioned confidence interval.

( 53 )

2. Non-Sampling Errors

The obtained estimate and its sampling error make it possible to deduce the value

that would have been obtained had a full census been conducted. However, this

value may be different from the real value for the population because it may be

affected by non-sampling errors. It is difficult or rather impossible to estimate these

errors. In this survey, these errors fall into the main following categories:

(a) Biases due to non-response or partial response: Approximately 10% of the

households out of all households that were supposed to have been in the survey

sample population did not participate in the survey for various reasons (see

section E: “Results of the Field Work”, below). Of the total number of households

that were included in the original survey sample (11,909) and belonged to the

survey sample, 3,006 did not participate. The group that did not participate in the

survey – i.e., the non-response cases – might have different characteristics than

the households that participated, and this can bias the estimates of the survey.

The method of estimation used in the survey substantially reduces errors of this

type, but does not eliminate all of them.

In addition, missing data on the variables examined in the survey can cause

errors. The missing information was supplemented by imputing income on the

basis of regression models, according to the reported income of each

investigation unit. For work hours per week, different types of imputation methods

were used. In that way, some of the bias errors that can derive from partial

responses were reduced. However, not all of those errors could be reduced,

because there is obviously no way of imputing the real value of all missing data.

(b) Response errors: The survey estimates were based on the responses of

interviewees to the items in the questionnaire, and only some of them were based

on documents such as salary slips. Hence, there might be some inaccuracies in

the responses – particularly with regard to income – in cases where the

interviewees did not have a salary slip and provided information based on their

memory. There may also be inaccuracies in the responses regarding other

variables examined in the survey, such as “weekly work hours”.

Another potential source of response errors lies in the survey method itself, where

( 54 )

one family member can report on another member (investigation through a proxy).

This method can lead to inaccurate reports for the family member who was away

from home at the time of the interview, because the family member who was

present at the interview might not have had all of the requested information.

These response errors can bias the survey estimates, despite the various tests

performed in an attempt to reduce the biases.

(c) Coding errors: Some of the data collected in the survey, such as data on

industries and occupations, were based on the interviewees’ verbal descriptions

and then recorded as numeric codes. Hence there might have been errors in the

codes assigned due to inaccuracies in the descriptions provided by the

interviewees.

(d) Other processing errors: Besides errors in coding, there is a potential for errors

in data entry and errors in other processing procedures such as logical checks,

which can affect the reliability of the estimates.

( 55 )