eradicating extreme poverty malaysian indicators … · and sex, malaysia, 2014 source: salaries...

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ERADICATING EXTREME

POVERTY:

MALAYSIAN INDICATORS

AND ISSUES Cecilia Ng and Yeong Pey Jung

UN ESCAP Expert Consultation Workshop

SDD for the SDG Indicators: What Now?

25-27 May, 2016

UNCC, Bangkok, Thailand

OUTLINE

The Context : SDG 1

Vulnerable Groups (women)

Malaysian Context

Malaysia: Available and Desirable Data

The ‘Other’

Summary, Issues and Challenges

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SDG GOAL 1: END POVERTY IN

ALL ITS FORMS EVERYWHERE

(SEVEN TARGETS)

Target 1.1: By 2030, eradicate extreme poverty

for all people everywhere, currently measured

as people living on less than $1.25 a day

Indicator 1.1.1: Proportion of population below the

international poverty line by sex, age, employment

status and geographical location (urban/rural)

Indicator is basically HH income-based – measuring those in

employment (available data in LFS, HIS, HES etc)

Income focused on regular income and stable employment,

leaving out the informal sector and casual workers

HH as black box: Assume shared goods, no gender inequality

POVERTY RATE IN SELECTED

ASIAN COUNTRIES (%)

0

5

10

15

20

25

30

35

Incid

en

ce o

f P

ov

erty

Countries

Incidence of Poverty

Source: ADB, Malaysia Human Development Report, 2013

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WHO GETS LEFT OUT?

WHO ARE FURTHEST BEHIND?

Women and girls tend to be more vulnerable to

extreme poverty compared to men

Women-headed HHs more than men-headed

Gender inequality/discrimination still pervasive

Vulnerable groups:

Ethnic minorities; indigenous groups

Rural communities

Informal workers

Migrants (documented and undocumented)

Refugees/asylum seekers

Disabled

IDPs (conflict areas, environmental disasters)

MALAYSIAN CONTEXT:

AN UPPER MIDDLE INCOME COUNTRY

Malaysia defines extreme poverty (hard core poverty)

to be half the poverty line index (PLI)

Income and consumption-based by households (HIS &

HES)

PLI for Peninsular Malaysia in 2014 is RM930 : twice

higher than MDG 1 indicator (Sabah: RM 1170,

Sarawak: RM990)

Reduction of absolute poverty from 16.5% in 1990 to

0.6% in 2014; extreme poverty largely eliminated

Persistent pockets of poverty prevail: in rural areas,

HHs in certain states, among certain ethnic groups,

genders and age groups

Source: MDGs Goals Report (2015): UN Malaysia, Kuala Lumpur

11th Malaysia Plan

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KEY FINDINGS OF MALAYSIAN HD

REPORT 2013 AND MDGS REPORT 2015

Head of household’s education, gender and

ethnicity correspond with household income

Relative household income deprivation more

acute among those with less formal education

Poor have primary/no formal education

Highest incidences of poverty among women-

headed households and (rural) ethnic

minorities

Persistent inequalities between men and

women (formal and informal sector)

VULNERABLE GROUPS: MALAYSIA

Rural HHs (agriculture, forestry and fisheries): 65% of

total poor HHs in 2014

Other Bumiputera have higher poverty rates (Orang

Asli at 34%, Bumiputera Sabah at 20.2% and

Bumiputera Sarawak at 7.3%)

Women-headed HHs (0.8%) at higher risk than men-

headed HHs (0.6%)

Children: 157,000 children under poverty line

(‘stateless’ children – 60,000 in Sabah)

World Bank proposed definition of vulnerable group as

those 2.5 times the PLI (if so, 15% in 2014)

Source: UNDP (2014), Malaysia Human Development Report, Kuala Lumpur,

Malaysia; United Nations Malaysia and Prime Minister’s Department (2016),

Malaysia: Millennium Development Goals Report 2015, UN Malaysia, Kuala

Lumpur.

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AVAILABLE DATA: HOUSEHOLD

INCOME SURVEY (HIS)

Annual statistics of the household income distribution, incidence of poverty and basic amenities

Principal indicators by:

Sex

Household income (mean and median)

Age group

Urban and rural

Educational attainment

Occupation

Industry

Basic amenities (distance to schools, hospitals, services and so on)

AVAILABLE DATA: HIS

Incidence of Poverty by Ethnic Group and Sex of Head

of Household, Malaysia, 2014

Source: Household Income Survey, 2014, Department of Statistics

Women headed households recorded a higher incidence of

poverty compared to men headed households

0.8

0.1

0.5

0.8

0.9

0.2

0.9

1.2

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Bumiputera

Chinese

Indian

Others

Incidence of Poverty

Sex

Female

Male

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AVAILABLE DATA: HIS

Median Gross Monthly Household Income by Ethnic

Group & Sex of Head of Household, Malaysia, 2014

Source: Household Income Survey, 2014, Department of Statistics

Women headed households recorded a significantly lower

median household income across all ethnicities

4331

5958

4820

4545

3525

4392

3618

3567

0 1000 2000 3000 4000 5000 6000 7000

Bumiputera

Chinese

Indian

Others

Income (RM)

Eth

nic

ity

Female

Male

DESIRED DATA

Household Income Survey, Malaysia

Incidence of Poverty by Gender, Age Group and Ethnicity

Median and Mean Household Income Data

by Sex, Age Group Ethnicity, Education, Marital Status

Further disaggregation of ethnicity by breaking down into ethnic (sub) groups, segregated by sex (e.g. more than 25 ethnic groups in Sabah and Sarawak,18 OA ethnic subgroups in Peninsular)

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AVAILABLE DATA: LABOUR FORCE

SURVEY

Annual statistics of the labour force,

unemployment and the structure of employment


Sex

Age group

Urban and rural


Occupation

Industry

AVAILABLE DATA – LABOUR FORCE SURVEY

Unemployment Rate by Age Group and Sex, Malaysia, 2015

Source: Labour Force Survey, 2015, Department of Statistics

Women recorded differential unemployment rates as compared

to men

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

18.00%

Total 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64

Un

em

plo

ym

en

t R

ate

Age Group

Male

Female

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AVAILABLE DATA – LABOUR FORCE SURVEY

Unemployment Rate by Education and Sex, Malaysia, 2015

Source: Labour Force Survey, 2015, Department of Statistics

• Unemployment rate for men with no formal education significantly

higher

• Unemployment rate for women with tertiary education higher

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

4.50%

5.00%

Total No formal Education Primary Secondary Tertiary

Un

em

plo

ym

en

t R

ate

Age Group

Male

Female

AVAILABLE DATA: INFORMAL SECTOR SURVEY

Statistics of the informal labour force and the

status of employment (employer, employee, own

account worker, unpaid family worker)

Principal indicators by

Sex

Age group


State

Urban and rural

Ethnicity

Occupation

Industry

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AVAILABLE DATA – INFORMAL SECTOR SURVEY Employment in the informal sector by status in employment

and sex, Malaysia, 2012-2013 (%)

Source: Informal Sector Workforce Survey, 2013, Department of Statistics

Women workers in the informal sector: higher % as employees

Own account and unpaid family workers, lower % as employers

3.1

33.0

59.4

4.4

1.0

10.2

78.1

10.0

4.1

32.0

59.6

4.3

1.4

8.8

81.7

8.1

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0

Employer

Employee

Own Account Worker

Unpaid Family Worker

Employer

Employee

Own Account Worker

Unpaid Family Worker

Mal

eFe

mal

e

Percentage

Sex

(S

tatu

s in

Em

plo

ymen

t)

2013

2012

AVAILABLE DATA – INFORMAL SECTOR SURVEY

Employment in the informal sector by educational attainment and sex, Malaysia, 2012-2013 (%)

Most have up to secondary education; about one quarter

have up to primary education Source: Informal Sector Workforce Survey, 2013, Department of Statistics

4.3

26.0

61.9

7.9

6.3

26.0

58.1

9.6

4.0

22.1

65.5

8.4

5.4

23.7

61.8

9.2

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0

No Formal Education

Primary

Secondary

Tertiary

No Formal Education

Primary

Secondary

Tertiary

Ma

leF

em

ale

Percentage

Sex

(Ed

uca

tio

na

l A

tta

inm

en

t)

2013

2012

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DESIRED DATA

Labour Force and Informal Sector Survey, Malaysia

Further disaggregation of ethnicity and employment by breaking down into ethnic (sub) groups and segregated by sex

Labour data for migrant workers – should be further segregated than just “non-citizens” – migrant workers (documented and otherwise)

Data on refugees and asylum seekers

Income data for informal sector workers segregated by sex

AVAILABLE DATA: SALARIES & WAGES SURVEY


Sex

Urban and Rural

Ethnic group

Occupation

Industry


State

NOT included: informal workers, casual workers

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Median and Mean Monthly Salary by Sex, Malaysia, 2013 -

2014

Source: Salaries & Wages Survey, 2014, Department of Statistics

It’s disconcerting to see a larger gender wage gap in 2014 (5.8%) as

compared to 2013 (4.5%) – is this a sign of regression that we should

be taking note of?

1 500

1 500

2 086

1 992

1 600

1 500

2 280

2 148

0 500 1 000 1 500 2 000 2 500

Male

Female

Male

Female

Med

ian

Mea

n

Income (RM)

Sex

2014

2013


Median and Mean Monthly Salary by Educational Attainment

and Sex, Malaysia, 2014


Significantly high gender wage gaps across all categories of educational

attainment – a 19.39% gender wage gap for tertiary education is very

high

945

1 009

670

710

1150

1282

800

881

1500

1837

1200

1465

3200

4079

3000

3288

0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500

Median

Mean

Median

Mean

Mal

eFe

mal

e

Income (RM)

Sex Tertiary

Secondary

Primary

No Formal Education

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AVAILABLE DATA: SALARIES & WAGES SURVEY Median Monthly Salary by Occupational Sector & Sex,

Malaysia, 2014


Unequal pay schedule still prevalent in Malaysia – women earn

less than men in all occupational sectors, notably in elementary

occupations

1080

1456

1330

1200

1400

2000

2640

4350

5080

800

1170

879

800

1000

1700

2600

3850

4500

0 1000 2000 3000 4000 5000 6000

Elementary Occupations

Plant and Machine Operators

Craft and Related Trades

Skilled Agricultural, Forestry and Fishery

Service and Sales

Clerical Support

Techicians and Associate Professionals

Professionals

Managers

Income (RM)

Occ

up

ato

nal

Sec

tor

Female

Male

DESIRED DATA

Salaries and Wages Survey, Malaysia

Further disaggregation of ethnicity, sex and income by breaking down into ethnic (sub) groups, segregated by sex and income (wages)

Income data for migrant/foreign workers, segregated by sex

Income data for informal sector workers, segregated by sex and status in employment

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AVAILABLE DATA: FOREIGN WORKERS Number of Foreign Workers in

Malaysia by country of origin, 2015

Source: Ministry of Home Affairs, Economic Planning Unit, Malaysia

No sex disaggregation data available for foreign workers in

Malaysia; no wage data; no data on undocumented migrant

workers

2015

Country Of Origin Total %

Indonesia 835,965 39.2

Bangladesh 282,437 13.2

Thailand 13,547 0.6

Philippine 65,096 3.0

Pakistan 72,931 3.4

Myanmar 145,652 6.8

Nepal 502,596 23.5

India 139,751 6.5

Others* 77,060 3.6

Total 2,135,035 100.0

2015

Sector Total %

Domestic Help 148,627 7.0

Manufacturing 450,364 21.1

Construction 745,131 34.9

Services 293,433 13.7

Agriculture 497,480 23.3

Total 2,135,035 100.0

• Number of Foreign Workers in Malaysia by sector, 2015

DESIRED DATA

Foreign Workers’ Data, Malaysia

No data disaggregated by sex is available

publicly

Further disaggregation of country of origin,

sex, salaries and wages (low-mid skilled

elementary occupations) in accordance to

each sector of employment

Include respondents in communal and group

housing

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THE ‘OTHER’: REFUGEES

Refugees: 158,510 refugees and asylum-seekers registered with UNHCR in Malaysia (2016)

69% are men, while 31% are women

34,300 children below the age of 18

Malaysia is not a signatory to the 1951 Refugee Convention or associated treaties and protocols; does not provide recognition of any particular rights e.g. employment and education

Unaccompanied women and girls, women heads of households and pregnant, disabled or older women face particular challenges, particularly with regards to sanitation, privacy and vulnerability to sexual abuse

STUDY OF AFGHAN REFUGEES

73 families interviewed

Many respondents unemployed

78.1% could barely afford the cost of food. Only 5.5%

were able to afford the cost of food “all the time” and

16.4% “most of the time”

Living on less than $1.25 per day

“The money for food is mainly for the children for milk.

For adults, we just don’t eat for a few days when there

is no money”

“I have thoughts of suicide – I don’t know what to do,

for I am afraid of my and my children’s future”

Source: Health Equity Initiatives (2012)

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THE ‘OTHER’: MIGRANT (FOREIGN WORKERS) -

DOCUMENTED AND UNDOCUMENTED

Estimates of two million documented migrant workers, and

another two million or more undocumented migrant workers

in Malaysia; 95% low and mid skilled

200,000 migrant domestic workers

Minimum wage (RM900) applies to migrant workers.

According to Giammarinaro, “migrant workers may be made to

work long hours, lack rest days, not being paid their salary, or

even suffer physical and sexual abuse ...are often exploited for

cheap labour by unscrupulous recruitment agencies and

employers ... trafficking of young foreign women and children

for the purpose of sexual exploitation is also prevalent in the

country” (Star March 9, 2015: Interview with UN Special

Rapporteur on Trafficking in Persons)

THE ‘OTHER’: IDPS

Risks faced by 59.5 million people who have been forcibly displaced by armed conflicts, and over 19.3 million newly displaced due to disasters worldwide (Norwegian Refugee Council, 2016)

Those who face discrimination because of their ethnicity, place of origin and gender, are more likely to become homeless and economic vulnerabilities

Rates of violence high among women IDPs

Increased number of widows and women-headed households among IDP populations

Children exposed to trafficking, sexual exploitation

Nepal (2011 Census): 75% of 659,837 displaced Nepalese are widows, many of them war widows; 52% below 40 years, 77% cannot read or write (Yadav, 2016)

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SUMMARY: DESIRABLE

DISAGGREGATION

To be made available: more in-depth statistical

representation of existing household and

individual income data

Further disaggregation of ethnicity to include ethnic

(sub) groups; ethnic minorities; indigenous

More sex-disaggregated data regarding

vulnerable groups (link to other indicators

including VAW and health indicators)

Refugees and asylum seekers

Migrant workers (documented and

undocumented)

Disabled

ISSUES AND CHALLENGES

Limitation of income-based measurement; need to be

multidimensional and go beyond numbers (technocratic)

Gender and poverty discourse/critique of power

Vulnerable groups and external forces: external shocks,

climate change, environmental disasters - falling into

extreme poverty and hunger

Gaps: resources, time, reaching out to ‘the other’

Complement with other studies (qualitative; NGOs,

think tanks)

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REFERENCES Cunial, Laura and Farmer, Kristie (Blog Post 8 March 2016, “Displaced

women at risk of homelessness – how to support displaced women’s rights”.

Department of Statistics (2015) Labour Force Survey Report, Malaysia.

Department of Statistics (2014) Salaries and Wages Survey Report, Malaysia.

Department of Statistics (2014) Household Income and Basic Amenities Survey Report, Malaysia.

Department of Statistics (2013) Informal Sector Work Survey Report, Malaysia.

Health Equity Initiatives (2012) Between a Rock and a Hard Place: Afghan Refugees and Asylum Seekers in Malaysia, Kuala Lumpur, Malaysia.

ILO (2016) Review of Labour Migration Policy in Malaysia, Bangkok, Thailand.

Khatiwada, Padma Prasad (2012) Internally Displaced Persons in Nepal: More Issues, Less Heard, Report to South Asians for Human Rights.

Ministry of Home Affairs (2015) Foreign Workers’ Data, Economic Planning Unit, Malaysia.

REFERENCES SUARAM (2015) Reports on Migrants and Refugees, Kuala Lumpur,

Malaysia.

UNDP (2014) Malaysia Human Development Report, Kuala Lumpur,

Malaysia United Nations Malaysia and Prime Minister’s Department.

UNDP (2016) Malaysia: Millennium Development Goals Report 2015,

UN Malaysia, Kuala Lumpur.

UN Women (2012) Women Working for Recovery: The Impact of Female

Employment on Family and Community Welfare After Conflict, New

York.

War on Want (2012) Restricted Rights: Migrant Women Workers in

Thailand, Cambodia and Malaysia, London.

World Bank (2015) Malaysia Economic Monitor: Immigrant Labour,

Washington.

Yadav, Punam (2016) ‘White Sari: Transforming Widowhood in Nepal”,

Gender, Technology and Development, 20 (1), 1-24.

Yadav, Punam (2016) Social Transformation in Post-conflict Nepal,

Routledge, Oxford: United Kingdom.

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