ILO global stimates e m w

ILO global stimates e m w
International
Labour
Organization
ILO global estimates
on migrant workers
Results and methodology
Special focus on migrant domestic workers
Labour Migration Branch
Conditions of Work and Equality Department
Department of Statistics
ILO global estimates on migrant workers
Results and methodology
Special focus on migrant domestic workers
INTERNATIONAL LABOUR OFFICE GENEVA
Copyright © International Labour Organization 2015
First published 2015
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ILO Global estimates of migrant workers and migrant domestic workers: results and methodology / International Labour
Office - Geneva: ILO, 2015
ISBN: 9789221304791 (print); 9789221304807 (web pdf)
International Labour Office, Labour Migration Branch, Conditions of Work and Equality
International Labour Office, Department of Statistics
migrant worker / domestic work / international migration / labour force participation / gender / trend / data collecting /
methodology
14.09.2
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Preface
In today’s globalized world, labour migration is a rising policy priority. Economic hardship and geopolitical crises
leading to the lack of decent work are resulting in growing and diverse migratory movements. In many
economies, including emerging economies, ageing populations and declining labour forces are also contributing
to the growing mobility of workers. Women are joining migration flows in growing numbers as independent
workers, with important consequences for gender equality in countries of origin and destination alike.
Migration flows have changed over the past few decades, growing significantly in some corridors and
between countries of the South. The governance challenges have increased in complexity. There is a need to
understand these dynamic migrant flows and their implications for labour markets, particularly in migrantdominated sectors.
New thinking and new approaches to the governance of labour migration are needed: a fair sharing of the
prosperity migrant workers help to create, and policies that respond equitably to the interests of countries of
origin and destination, as well as to migrant workers, employers and national workers.
To be effective, such policies must be grounded in strong evidence. For this, data on the number of migrant
workers, their distribution by sector and their employment patterns are badly needed.
While acknowledging the many challenges of data collection and analysis in this field, the present global
estimates developed by the ILO aim to fill in part of the current knowledge gaps.
This report is part of a broader ILO effort to improve the collection and production of labour migration
statistics at national, regional and global levels. These estimates will contribute to the implementation of
Resolution IV concerning further work on labour migration statistics, adopted by the 19th International
Conference of Labour Statisticians (ICLS) in 2013, which called upon the ILO to carry out preparatory work for
defining international standards on labour migration statistics, in close consultation with interested countries, the
social partners and civil society organizations. The results of this work will contribute to the next ICLS discussion
in 2018 and the development of international concepts and standards on labour migration statistics agreed
worldwide.
It is hoped that these estimates will help to advance the national and international debate on migration policy
and governance.
Manuela Tomei
Director,
ILO Conditions of Work and Equality Department
(WORKQUALITY)
Rafael Diez de Medina
Director,
ILO Department of Statistics
iii
Acknowledgements
This report was prepared under the overall coordination of Natalia Popova and Maria Gallotti from the Labour
Migration Branch in the ILO Conditions of Work and Equality Department and Mustafa Hakkı Özel from the ILO
Department of Statistics. The data analysis and methodology was formulated by Vijay Verma and Farhad
Mehran, both ILO consultants. Chantal Dufresne managed the production of the report from the start and
provided timely and valuable assistance at all stages, and Giuliana De Rosa helped to finalize the report. Research
assistance on data collection and processing by ILO consultants Eva-Francesca Jourdan and Ayşegül Tuğçe Beycan
is acknowledged.
Michelle Leighton, Chief of the ILO Labour Migration Branch, provided strong guidance and support
throughout the process, and contributed to the content of the report. Very helpful comments on draft versions
were received from (in alphabetical order): Coffi Agoussa, Ryszard Cholewinski, Tite Habiyakare, Steven Kapsos,
Samia Kazi-Aoul, Amelita King-Dejardin, Jaewon Lee, Malte Luebker, Maria Elena Valenzuela, Hans Van de Glind
and three anonymous peer reviewers. The report also benefited from further inputs from Manuela Tomei,
Director, ILO Conditions of Work and Equality Department (WORKQUALITY), Rafael Diez de Medina, Director,
ILO Department of Statistics and Charlotte Beauchamp, Office of the ILO Deputy Director-General for Policy.
Valuable comments on the estimates methodology formulation were provided by: Migration Section,
Demographic Analysis Branch, United Nations Population Division, Department of Economic and Social Affairs
(DESA); International Migration Division (IMD), Directorate for Employment, Labour and Social Affairs,
Organisation for Economic Co-operation and Development (OECD); Statistical Division, United Nations Economic
Commission for Europe (UNECE); and Migration Research Division, International Organization for Migration
(IOM).
This ILO report includes statistical approaches that were developed with support from ILO technical
cooperation projects funded by the Swiss Agency for Development and Cooperation (SDC). The estimates on
migrant domestic workers in this document were produced with the financial assistance of the European Union.
The views expressed herein can in no way be taken to reflect the official opinion of the European Union.
Data sources
This report is based in part on data from the UN Department of Economic and Social Affairs (UN DESA), the
Organisation for Economic Co-operation and Development (OECD) and the Integrated Public Use Microdata
Series (IPUMS).
iv

Contents
Preface................................................................................................................................................... iii
Acknowledgements...................................................................................................................................iv
Contents...................................................................................................................................................v
Acronyms and abbreviations.......................................................................................................................x
Executive summary...................................................................................................................................xi
1. Introduction...............................................................................................................1
PART I MAIN RESULTS................................................................................3
2. Global and regional estimates .....................................................................................5
2.1 Global estimates............................................................................................................5
2.1.1Overall picture.................................................................................................................5
2.1.2Gender differences..........................................................................................................5
2.1.3Distribution of migrant workers by broad branch of economic activity ..................................8
2.2 Estimates by country income group.................................................................................9
2.2.1 Overall patterns...............................................................................................................9
2.2.2Gender differentials ......................................................................................................11
2.3 Regional estimates.......................................................................................................15
2.3.1Migrant workers.............................................................................................................15
2.3.2Migrant domestic workers...............................................................................................17
3. Scope and definitions...............................................................................................25
3.1 Benchmark data..........................................................................................................25
3.1.1UN population data.......................................................................................................25
3.1.2UN international migration data......................................................................................26
3.1.3ILO labour force data.....................................................................................................27
3.2 International migrant ..................................................................................................27
3.3 Migrant worker............................................................................................................28
3.4 Scope of the global and regional estimates.....................................................................28
3.4.1Migrants.......................................................................................................................29
3.4.2Migrant workers.............................................................................................................29
3.4.3 Migrant domestic workers ..............................................................................................31
3.5 Breakdown by sector of economic activity ....................................................................32
3.6 Domestic worker..........................................................................................................32
3.7 Migrant domestic worker..............................................................................................34
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ILO Global estimates on migrant workers: Results and methodology
PART II ESTIMATE METHODOLOGY.............................................................35
4. Methodology, Phase 1: Data sources and input data....................................................37
4.1 Benchmark data..........................................................................................................37
4.1.1UN population data.......................................................................................................38
4.1.2UN international migration data......................................................................................38
4.1.3ILO labour force data.....................................................................................................39
4.2 National data..............................................................................................................40
4.2.1OECD migration databases.............................................................................................40
4.2.2ILO global and regional databases on labour migration......................................................41
4.2.3IPUMS international database on population censuses......................................................41
4.2.4Other national data........................................................................................................41
4.3 Constructing input data................................................................................................42
4.3.1Raw data......................................................................................................................42
4.3.2Edited data points.........................................................................................................42
4.3.3Standardized input data for 2013...................................................................................44
5. Data quality ............................................................................................................47
5.1 Dimensions of data quality...........................................................................................47
5.1.1Statistical accuracy .......................................................................................................47
5.1.2Consistency with other sources........................................................................................47
5.1.3Robustness of the results to the use of different imputation methodologies.........................48
5.1.4Completeness of the data...............................................................................................49
5.1.5Internal consistency of the results ..................................................................................49
5.1.6Data quality..................................................................................................................49
5.2 Completeness of available data.....................................................................................50
5.2.1Coverage of national data by income level........................................................................50
5.2.2Coverage of national data by broad subregion...................................................................52
5.3 Internal consistency requirements.................................................................................53
5.3.1Total = Male + Female....................................................................................................53
5.3.2 Inherent relationships among the variables......................................................................55
5.3.3Plausibility....................................................................................................................55
6. Methodology, Phase 2: Data imputation and production of global and regional estimates.57
6.1Introduction................................................................................................................57
6.2 Benchmark variables from standardized international datasets....................................... 57
6.3 Outline of the imputation procedure..............................................................................58
6.4 Constructing variable MW, migrant workers....................................................................59
vi

CONTENTS
6.4.1Countries with available data on MW...............................................................................59
6.4.2Countries with missing data on MW.................................................................................60
6.4.3Some details.................................................................................................................60
6.5 Constructing variable MW(sec), migrant workers by sector...............................................61
6.5.1Countries with available data on MW(sec)........................................................................61
6.5.2Countries with missing data on MW(sec)..........................................................................62
6.6 Constructing variable D, number of domestic workers......................................................62
6.7 Constructing variable MD, number of migrant domestic workers.......................................63
ANNEXES ..................................................................................................67
A
Geographical regions and income groups........................................................................69
B
Cross-classification of geographical regions and income groups........................................77
C
Countries covered, by domain (cross-classification of detailed subregion
and income group).......................................................................................................81
D
Data availability for different variables, by country and sex..............................................83
E
Data quality: Alternative imputation methods.................................................................89
F
Data quality: Comparison with ILO 2010 global and regional estimates
of the number of domestic workers................................................................................96
Bibliography........................................................................................................................99
Figures
2.1 Global estimates of the stock of migrants, migrant workers and migrant domestic workers, 2013 ....................... 5
2.2 Global distribution of migrant workers, by sex, 2013 (percentages).................................................................. 6
2.3 Global labour force participation rates of migrants and non-migrants, by sex, 2013 ......................................... 6
2.4 Global distribution of migrant domestic workers, by sex, 2013 (percentages).................................................... 7
2.5 Global distribution of non-migrant domestic workers, by sex, 2013 (percentages).............................................. 7
2.6 Global distribution of migrant workers, by broad branch of economic activity, 2013 (percentages)...................... 9
2.7 Migrant workers, by income level of countries, total (male + female), 2013 (percentages)................................ 10
2.8 Labour force participation rates of migrants (and non-migrants, by income level of countries, 2013 ................. 10
2.9 Migrant domestic workers, by income level of countries, 2013 (percentages).................................................. 11
2.10 Migrant domestic workers as a share of all migrant workers, by income level of countries, 2013 ...................... 11
2.11 Migrant domestic workers as a share of all domestic workers, by income level of countries, 2013 .................... 11
2.12 Migrant workers, by sex and income level of countries, 2013 (percentages).................................................... 13
2.13 Labour force participation rates of migrants and non-migrants , by sex and income level of countries, 2013 .... 14
2.14 Migrant domestic workers, by sex and income level of countries, 2013 (percentages)...................................... 14
2.15 Migrant domestic workers as a share of all migrant workers, by sex and income level of countries, 2013 .......... 15
2.16 Migrant domestic workers as a share of all domestic workers, by sex and income level of countries, 2013 ....... 15
2.17 Distribution of migrant workers, by broad subregion, total (male + female), 2013 (percentages)....................... 17
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ILO Global estimates on migrant workers: Results and methodology
2.18 Distribution of migrant workers, by sex and broad subregion, 2013 (percentages)............................................ 17
2.19 Labour force participation rates of migrants and non-migrants, by broad subregion, 2013................................ 19
2.20 Labour force participation rates of migrants and non-migrants, by sex and broad subregion, 2013 ................... 19
2.21 Distribution of migrant domestic workers, by broad subregion, 2013 (percentages).......................................... 20
2.22 Distribution of migrant domestic workers, by sex and broad subregion, 2013 (percentages) ............................. 20
2.23 Migrant domestic workers as a share of all migrant workers, by broad subregion, 2013.................................... 21
2.24 Migrant domestic workers as a share of all migrant workers, by sex and broad subregion, 2013........................ 22
2.25 Migrant domestic workers as a share of all domestic workers, by broad subregion, 2013 ................................. 22
2.26 Migrant domestic workers as a share of all domestic workers), by sex and broad subregion, 2013 .................... 23
3.1 Estimates of migrant workers, schematic representation................................................................................ 30
3.2 Estimates of migrant domestic workers, schematic representation.................................................................. 31
4.1 Data sources: Benchmark and national data................................................................................................. 37
4.2 Coverage of national data by reference year, 2005−14.................................................................................. 44
4.3 Coverage of national data by type of source.................................................................................................. 44
Tables
2.1 Global estimates of migrant workers and migrant domestic workers, 2013
(number of persons aged 15+, in millions)..................................................................................................... 6
2.2 Global estimates of migrant workers and migrant domestic workers, by sex, 2013 (percentages)......................... 7
2.3 Migrant workers and migrant domestic workers, ratios and labour force participation rates, by sex, 2013............ 8
2.4 Global distribution of migrant workers, by broad branch of economic activity and by sex, 2013.......................... 8
2.5 Migrant workers and migrant domestic workers, by income level of countries, total (male + female), 2013.......... 9
2.6 Migrant workers and migrant domestic workers, by sex and income level of countries, 2013 - Male.................. 12
2.7 Migrants and non-migrants: Labour force participation rate , and proportion of domestic workers among all
workers, by sex and income level of countries, 2013 .................................................................................... 13
2.8 Migrant workers and migrant domestic workers, by broad subregion, total (male + female), 2013..................... 16
2.9 Migrant workers and migrant domestic workers, by sex and broad subregion, 2013......................................... 18
3.1 ISIC groupings of economic activity............................................................................................................. 33
4.1 Countries or areas with at least one data source on international migrant stock, by age and sex, 1990, 2000
and 2010 (percentages)............................................................................................................................. 38
4.2 Summary of data availability, number of countries with information, by variable.............................................. 45
4.3 Calculation of standardized input data for 2013........................................................................................... 45
5.1 Coverage of countries with least one data point available, by income level...................................................... 51
5.2 Number of countries with data available on various items, by income level...................................................... 51
5.3 Proportion of the relevant population for which data are available, various items,
by income group (percentages)................................................................................................................... 51
5.4 Number of countries with data available on various items, by broad subregion................................................. 53
5.5 Proportion of the relevant population for which data are available, various items,
by broad subregion (percentages)................................................................................................................ 54
6.1 Variables to be estimated........................................................................................................................... 57
Boxes
1
viii
Number of domestic workers: Comparison with ILO 2010 global and regional estimates.................................. 48

CONTENTS
Annex tables
A.1 Income groups........................................................................................................................................... 69
A.1.1Income group............................................................................................................................................ 69
A.2 Standard geographical regions.................................................................................................................... 72
A.3 Number of countries in each major region.................................................................................................... 72
A.4 Number of countries in each broad subregion............................................................................................... 72
A.4.1Broad subregion........................................................................................................................................ 73
A.5 Number of countries in each detailed subregion........................................................................................... 76
B.1 Number of countries by broad subregion and income group........................................................................... 77
B.2 Size of the labour force, migrant workers and migrant domestic workers, by broad subregion
and income group, 2013............................................................................................................................ 78
B.3 Size of the male labour force, migrant workers and migrant domestic workers, by broad subregion
and income group, 2013............................................................................................................................ 79
B.4 Size of the female labour force, migrant workers and migrant domestic workers, by broad subregion
and income group, 2013............................................................................................................................ 80
C.1 Cross-classification of countries, by region, subregion and income group........................................................ 81
D.1 Data availabilty status for different variables - by country and sex.................................................................. 83
E.1 Estimated regression parameters and regression fit of relationship between labour force participation
rate of migrants and the national labour force participation rate, by sex and broad region................................. 90
E.2 Cross-classification of the working-age population by migrant status and labour force status............................. 91
E.3 Estimated cross-product ratio of relationship between migrant status and labour force status, by sex
and detailed subregion............................................................................................................................... 93
E.4 Alternative imputation of countries with missing data, by sex......................................................................... 94
E.5 Alternative imputation of countries with missing data, by major region........................................................... 94
E.6 Alternative imputation of countries with missing data, by income group.......................................................... 94
F.1
Comparison of global and regional estimates of domestic workers, 2010 and 2013......................................... 97
ix
Acronyms and abbreviations
ASEAN
Association of Southeast Asian Nations
DIOCDatabase on immigrants in OECD countries
EAPEPEstimates and projections of the economically active population (also EPEAP)
GCCGulf Cooperation Council
ICLSInternational Conference of Labour Statisticians
ILMSInternational Labour Migration Statistics (database)
IPUMSInternational Public Use Microdata Series
ISCOInternational Standard Classification of Occupations
ISICInternational Standard Classification of All Economic Activities
JPKEDepartment of Economic Planning and Development (Brunei Darussalaam)
ILOSTAT
ILO database on international labour statistics
LFPRlabour force participation rate
LFSlabour force survey
MOHRSS
Ministry of Human Resources and Social Security (China)
OECDOrganisation for Economic Co-operation and Development
x
UN DESA
United Nations Department of Economic and Social Affairs
UNECE
United Nations Economic Commission for Europe
UNHCR
United Nations High Commissioner for Refugees
UNWRA
United Nations Relief and Works Agency for Palestine Refugees in the Near East

Executive summary
The ILO estimates that 150 million people are
migrant workers
Global distribution of migrant workers, by sex, 2013
(percentages)
According to recent ILO estimates, there are 150.3 million
migrant workers in the world. Of these, 11.5 million
are migrant domestic workers. The term “migrant
worker” refers to all international migrants who are
currently employed or are unemployed and seeking
employment in their present country of residence.
The data on migrant workers that have been used
to calculate the estimates refer to migrant workers in
the country of destination and measure the migrant
stocks in 2013.
Global estimates of the stock of migrants, migrant workers and
migrant domestic workers, 2013
Migrants, especially migrant women, have higher
labour force participation rates than non-migrants
Migrants form 3.9 per cent of the total global
population (aged 15 years and over). However, migrant
workers constitute a higher proportion (4.4 per cent) of
all workers. This reflects a higher labour force
participation rate of migrants (72.7 per cent),
compared to that of non-migrants (63.9 per cent). This
Global labour force participation rates of migrants and nonmigrants, by sex, 2013
Among migrant workers, 83.7 million are men and
66.6 million are women, corresponding to 55.7 per
cent and 44.3 per cent of the total respectively.
xi
ILO Global estimates on migrant workers: Results and methodology
difference is associated with the fact that more
migrant women than non-migrant women work
(67.0 per cent versus 50.8 per cent), while there is
practically no difference between migrant and nonmigrant men in respect of their labour force
participation rate (78.0 per cent versus 77.2 per cent).
Labour migration is a phenomenon that concerns all
regions of the world
Almost half (48.5 per cent) of migrant workers are
concentrated in two broad subregions, Northern America
and Northern, Southern and Western Europe. These
subregions together make up 52.9 per cent of all female
migrant workers and 45.1 per cent of all male migrant
workers.
In the Arab States, by contrast, the gender
difference is reversed. While the region accounts for
11.7 per cent of all migrant workers, this corresponds
to 17.9 per of all male migrant workers and only
4.0 per cent of all female migrant workers.
These subregions are followed by Eastern Europe
(9.2 per cent) and South Eastern Asia and the Pacific
(7.8 per cent).
If each subregion is analysed individually, the Arab
States have the highest proportion of migrant workers
as a share of all workers, at 35.6 per cent. The
corresponding proportions are 20.2 per cent in
Northern America and 16.4 per cent in Northern,
Southern and Western Europe, followed by Central
and Western Asia (10.0 per cent) and Eastern Europe
(9.2 per cent). By contrast, in a number of subregions,
the proportion of migrant workers is below 2 per cent.
The lowest share, at 0.6 per cent, is in Eastern Asia
(which includes China), followed by Northern Africa,
Southern Asia (which includes India), and Latin
America and the Caribbean, all within the range of
1.0–1.5 per cent.
Migrant workers, by income level of countries, 2013
Distribution of migrant workers, by broad subregion, totals
(male + female), 2013
The vast majority of migrant workers are in highincome countries
Of the global total of 150.3 million migrant workers,
an estimated 112.3 million (74.7 per cent) were in
countries classified as high income, 17.5 million
(11.7 per cent) in upper-middle income countries and
16.9 million (11.3 per cent) in lower-middle income
countries. The lowest number of migrant workers was
xii

EXECUTIVE SUMMARY
in low-income countries, standing at 3.5 million
(2.4 per cent).
Migrants are concentrated in certain economic
sectors
The data show a concentration of migrants in certain
economic sectors, with notable gender differences.
The bulk of migrant workers in the world in 2013
were engaged in services, 106.8 million out of a total
of 150.3 million, amounting to 71.1 per cent. Industry,
including manufacturing and construction, accounted
for 26.7 million (17.8 per cent) and agriculture for
16.7 million (11.1 per cent).
Global distribution of migrant workers, by broad branch of
economic activity, 2013 (percentages)
every sixth domestic worker in the world was an
international migrant in 2013.
These estimates are an important contribution to
the ILO’s ongoing efforts to make decent work a
reality for all domestic workers worldwide, including
migrant domestic workers, who have specific needs
and face distinct vulnerabilities.
Most migrant domestic workers are women
About 73.4 per cent (or around 8.5 million) of all
migrant domestic workers are women. South-Eastern
Asia and the Pacific hosts the largest share, with 24.0
per cent of the world’s female migrant domestic
workers, followed by Northern, Southern and
Western Europe, with 22.1 per cent of the total, and
the Arab States with 19.0.
Male migrant workers are much less likely to be
domestic workers, with noteworthy regional
differences.
Global distribution of migrant domestic workers, by sex, 2013
(percentages)
Domestic work attracts more than 11 million migrant
workers
In 2010, following the adoption of the ILO Convention
on Domestic Workers, 2011 (No. 189), the ILO produced
the first global and regional estimates on domestic
workers. While these estimates did not distinguish
between national and migrant domestic workers, the
new estimates do make such a distinction.
Half of the world’s male migrant domestic workers
are in the Arab States
According to the current estimates, there are 67.1
million domestic workers in the world, of whom 11.5
million are international migrants. This represents 17.2
per cent of all domestic workers and 7.7 per cent of all
migrant workers worldwide. In other words, almost
The Arab States host 50.8 per cent of all male migrant
domestic workers. Over one in ten male migrant
workers is a domestic worker. This figure exceeds
5 per cent of the total only in Sub-Saharan Africa and
Southern Asia.
xiii
ILO Global estimates on migrant workers: Results and methodology
Distribution of migrant domestic workers, by sex and broad
subregion, 2013 (percentages)
Migrant domestic workers, by income level of countries, totals
(male + female), 2013 (percentages)
A very large proportion of migrant domestic workers are
concentrated in high-income countries
High-income countries accounted for 9.1 million of
the estimated 11.5 million migrant domestic workers
globally, amounting to nearly 80 per cent of the total.
Labour migration is rising globally, requiring new and
better data
The new global estimates show the magnitude of
labour migration in different regions and sectors. It is
hoped that they will contribute to a better
understanding of the interrelations between
migration, labour market policies and the future of
work more generally.
As migration patterns and dynamics grow in
complexity, high-quality, up-to-date and comparable
labour migration statistics are critical for well-informed
policy decisions that will maximize the development
gains for countries of origin and destination, as well as
for the migrants themselves, in line with the 2030
Sustainable Development Agenda.
xiv

1. Introduction
The ILO plan of action for migrant workers (2004)
called for the development of a global knowledge
base on international labour migration, including
“cooperation and exchange among countries to
improve migration statistics, particularly by expansion
of the ILO’s International Labour Migration Database”
(para. 33). Similarly, the ILO Tripartite Technical
Meeting on Labour Migration (2013) has urged for
more “evidence-based, policy-oriented research and
data development on how workers’ rights, wages and
other working and living conditions impact on
development outcomes for migrant workers and
countries of origin and destination” (ILO, 2013a, p.
29, para. 3 (v)). Further appeals for the development
of data on labour migration have been made in the
ILO Multilateral Framework on Labour Migration
(2006) and the Declaration of the UN High-level
Dialogue on International Migration and Development
(2013). At the 19th International Conference of
Labour Statisticians (2013), a resolution was adopted
recommending that the ILO “(a) set up a working
group with the aim of sharing good practices,
discussing and developing a work plan for defining
international standards on labour migration statistics
that can inform labour market and migration policy,
(b) prepare a progress report for discussion to the next
ICLS” (ILO, 2013b, p. 68, Resolution IV).
In parallel, following the adoption of the ILO
Convention on Domestic Workers, 2011 (No. 189), the
ILO produced global and regional estimates on
domestic workers revealing for the first time the
magnitude of the sector globally (ILO, 2013c).
Recognizing that in a number of regions and countries
across the world, domestic work is disproportionally
conducted by migrant labour and that migrants tend
to be more exposed than nationals to the risk of
exploitation and abuse because of their migrant
status, the ILO has begun a series of initiatives aimed
at better understanding the link between migration
and domestic work and addressing the specific needs
and vulnerabilities of migrant domestic workers.
Specifically, a Global Action Programme on Migrant
Domestic Workers was launched in 2013 which
included the development of survey methodologies to
collect data at the national level on domestic workers
and their working and living conditions, and in
particular on their migrant status. However,
information on the overall extent of the phenomenon
and the relative importance of migration for domestic
work globally and regionally remained unavailable.
To improve national data collection on labour
migration and on domestic workers, the ILO has
decided to start with the preparation of global and
regional estimates of migrant workers and migrant
domestic workers based on current methodologies
and existing national and international data. A main
purpose of global estimation is to provide information
on the order of magnitude of labour migration and
migrant domestic workers, and draw attention to the
economic and social issues involved. Another purpose
is to learn about the nature of the available data and
the national procedures used for collecting them. The
experience should help the development of sound
international statistical standards in the future.
However, challenges of data collection and analysis
in this field remain multiple; they relate to a variety of
factors ranging from the statistical definitions to the
weak capacities of authorities responsible. Part of the
challenge in analysing migration flows is that there is
no global consensus on who is a migrant worker.
Household-based surveys may collect this information
1
ILO Global estimates on migrant workers: Results and methodology
in different ways and based on varying definitions. ILO
work in this area will contribute to building consensus
around statistical definitions and methods with a view
to improving information sharing and consistency in
labour market and migration policy. This work will
support the successful implementation of the 2030
Sustainable Development Agenda adopted by the
United Nations, which includes a target on the
protection of migrant workers under the goal of
promoting decent work and economic growth.
Constructing appropriate policy responses in the
field of migration requires a good understanding of
the real and changing nature of the phenomenon
today, including its drivers, its magnitude and its main
characteristics.
The significance and changing patterns of labour
mobility today, including female participation in it,
requires new thinking and new approaches to
governance: a fair sharing of the prosperity migrant
workers help to create, and policies that respond
equitably to the interests of countries of origin and
destination, migrant workers, employers and nationals.
The report is organized in two main parts: Part I on
main results, and Part II on the estimate methodology.
In Part I, section 2 on main results presents global
and regional estimates of migrant workers
disaggregated by sex and broad branch of economic
activity, as well as the corresponding global and
regional estimates of domestic workers and migrant
domestic workers by sex. The reference year for all
estimates is 2013. Section 3 provides a short overview
of the scope and definitions used.
2
Part II is divided into three sections, describing the
statistical methodology followed. The methodology
can be divided into two fairly distinct phases. Phase 1
is the concern of section 4, which describes the
international and national data sources used for the
global and regional estimates, and the structure of the
input data obtained from them. Section 5 discusses
issues concerning data quality. Phase 2 of the
methodology − procedures for data imputation and
production of global and regional estimates − is
described in section 6.
Six annexes complement the material presented in
the main body of the report.
An initial version of this report was discussed at a
validation meeting at the ILO on 18 June 2015. The
next version of 27 August 2015 took into account the
comments of the meeting, in particular the
requirement for a more detailed description of the
methodology and its underlying assumptions; explicit
imputation for the countries with missing data; and
revision of the country groupings in line with the
provisional “ILO country groupings to be used for data
aggregation and dissemination purposes and a new
mechanism for disseminating global and regional
estimates of ILO labour market data” (ILO, 2014).
The current version implements more uniform
procedures for imputation of missing data and
construction of the final estimates, with strong
emphasis on transparency, replicability and
“institutionalization” of the methodology in future
applications.
PART I
MAIN RESULTS
3
2. Global and regional estimates
Global and regional estimates of the number of
migrant workers and migrant domestic workers for
2013 have been constructed by the ILO and an
overview of the main results is presented in this
section, highlighting the key global and regional
figures disaggregated by sex and also by main sector
of economic activity.1 Information on the scope and
definitions used for the estimates is described in
section 3.
FIGURE 2.1
Global estimates of the stock of migrants, migrant workers and
migrant domestic workers, 2013
2.1 Global estimates
2.1.1 Overall picture
It has been estimated that there were 232 million
international migrants in the world in 2013. According
to the results presented here, 207 million of them
were of working age, 15 years old and over. They are
referred throughout this report as the “aged 15+”
group. Of these migrants, 150 million were working or
economically active. As regards the estimated
67 million domestic workers in the world in 2013, over
11 million are estimated to be international migrants
(figure 2.1).
The ILO has published global and regional estimates
of domestic workers with 2010 as the reference year.
The definition of domestic worker in those earlier
estimates is similar to that adopted in the present study.
The two estimates are compared in Annex F. The results
show a considerably higher estimate of the number of
domestic workers in 2013 relative to the 2010 estimate:
1
The estimate figures have been rounded, which could lead to small
differences when summing the totals. All data on migrants refer to the
destination country.
67 million for 2013 compared to a little under
53 million in 2010, which is an increase of over 25 per
cent. A number of factors have contributed to this
increase, as summarized in box 1 in section 5.1 below,
and further elaborated in Annex F. Contributing factors
include availability of improved data for the 2013
estimates and the use of more precise methodology,
subject to less bias of underestimation.
2.1.2 Gender differences
There were more males than females among migrants
of working age (107.2 million versus 99.3 million).
Differences by sex were more marked among migrant
workers: 83.7 million male migrant workers versus
5
ILO Global estimates on migrant workers: Results and methodology
FIGURE 2.2
Global distribution of migrant workers, by sex, 2013
(percentages)
FIGURE 2.3
Global labour force participation rates of migrants and nonmigrants, by sex, 2013
66.6 million female migrant workers (table 2.1). This is
because male migrants, already more numerous than
female migrants, also have a higher labour force
participation rate (LFPR).
Among migrants, 48.1 per cent are female. Females
are a lower proportion (44.3 per cent) of migrant workers
(figure 2.2), but that is still higher than the corresponding
proportion (39.8 per cent) among non-migrant workers.
Nevertheless, this difference by sex among migrants
is less marked than that among non-migrants. As
noted, the difference between the numbers of male
and female migrant workers arises from two factors:
(a) there are fewer females among migrants; and (b)
female migrants have a lower labour force
participation rate. Essentially, only the second factor
applies in the case of non-migrants, but its effect is
stronger than the combined effect of the two factors
(a) and (b) for migrants.
Migrants form 3.9 per cent of the total population
(as noted, all numbers refer to population aged
15 years and over). However, migrant workers
constitute a higher proportion (4.4 per cent) of all
workers. This of course reflects the higher overall labour
force participation rate among migrants (72.7 per cent),
compared to that among non-migrants (63.9 per cent);
consequently, the proportion of migrant workers in all
workers is higher than the proportion of migrants in the
total population (figure 2.3).
TABLE 2.1
Global estimates of migrant workers and migrant domestic workers, 2013
(number of persons aged 15+, in millions)
Total
(male + female)
Female
Total population aged 15+
5 273
2 634
2 639
Migrant population aged 15+
206.6
107.2
99.3
Non-migrant population aged 15+
5 067
2 527
2 540
Total workers
3 390
2 035
1 356
Migrant workers
150.3
83.7
66.6
Non-migrant workers
3 240
1 951
1 289
67.1
13.4
53.8
11.52
3.07
8.45
55.6
10.3
45.3
Total domestic workers
Migrant domestic workers
Non-migrant domestic workers
6
Male
PART I MAIN RESULTS
2. GLOBAL AND REGIONAL ESTIMATES
FIGURE 2.4
Global distribution of migrant domestic workers, by sex, 2013
(percentages)
Among 13.4 million male domestic workers, 3.07
million are migrants, while among 53.8 million female
domestic workers, over 8.45 million are migrants.
Figures 2.4 and 2.5 show the global percentages of
migrant and non-migrant domestic workers. Table 2.2
shows the male−female breakdown of the various
categories in relative (percentage) terms, while
table 2.3 shows various rates computed from these
numbers.
The difference between migrants and non-migrants
is much sharper when we consider domestic work. As
many as 7.7 per cent of migrant workers are domestic
workers, compared with only 1.7 per cent of nonmigrant workers. Indeed, migrants account for 17.2
FIGURE 2.5
Global distribution of non-migrant domestic workers, by sex,
2013 (percentages)
per cent of all domestic workers: more than one in
every sixth domestic worker in the world was an
international migrant in 2013.
Turning to male−female differences in labour force
participation and domestic work rates, we find that
there is practically no difference in labour force
participation rates between migrant and non-migrant
men (77 per cent versus 78 per cent). While the rate is
lower for migrant women than migrant men (67 per
cent versus 78 per cent), it is much higher than that of
non-migrant women. The overall difference in migrant
and non-migrant rates arises only from the fact that
migrant women have a substantially higher labour
force participation rate than non-migrant women. This
TABLE 2.2
Global estimates of migrant workers and migrant domestic workers, by sex,
2013 (percentages)
Total
(male + female)
Male
Total population aged 15+
100
49.9
50.1
Migrant population aged 15+
100
51.9
48.1
Non-migrant population aged 15+
100
49.9
50.1
Total workers
100
60.0
40.0
Migrant workers
100
55.7
44.3
Non-migrant workers
100
60.2
39.8
Total domestic workers
100
19.9
80.1
Migrant domestic workers
100
26.6
73.4
Non-migrant domestic workers
100
18.5
81.5
Female
7
ILO Global estimates on migrant workers: Results and methodology
TABLE 2.3
Migrant workers and migrant domestic workers, ratios and labour force participation rates, by sex,
2013
Total
(male + female)
Male
Female
Migrants as a proportion of population aged 15+
3.9
4.1
3.8
Migrant workers as a proportion of all workers
4.4
4.1
4.9
Labour force participation rate for total population
64.3
77.2
51.4
Labour force participation rate for migrant population
72.7
78.0
67.0
Labour force participation rate for non-migrant population
63.9
77.2
50.8
Domestic workers as a proportion of workers in total population
2.0
0.7
4.0
Migrant domestic workers as a proportion of all migrant workers
7.7
3.7
12.7
Domestic workers as a proportion of workers, in non-migrant
population
1.7
0.5
3.5
17.2
22.9
15.7
Migrant domestic workers as a proportion of all domestic
workers
TABLE 2.4
Global distribution of migrant workers, by broad branch of economic activity and by sex, 2013
Numbers of workers (in millions)
Percentage distribution by sector
Agriculture
Industry
Services
Agriculture Industry
Services
MD/MW
Total
16.7
26.7
106.8
11.1
17.8
71.1
100.0
7.7
Male
9.3
16.6
57.8
11.2
19.8
69.1
100.0
3.7
Female
7.4
10.2
49.0
11.1
15.3
73.7
100.0
12.7
Note: MD/MW = Migrant domestic workers as a proportion of all migrant workers.
contrasts with men, for whom there is little difference
in the overall migrant and non-migrant participation
rates.
Over 80 per cent of non-migrant domestic workers
are female. Among migrant domestic workers, the
proportion of women is lower, at 73.4 per cent.
However, if we look at the proportion of of migrants
among domestic workers by sex, the share is higher
among men (22.9 per cent) than it is among women
(15.7 per cent), as shown in table 2.3. In other words,
one in every four to five male domestic workers in the
world in 2013 was an international migrant, while
under one in every six female domestic workers was
an international migrant. This is notwithstanding the
fact that domestic work forms a much higher
proportion of all work among migrants compared to
non-migrants, and that women workers are six times
more likely to be in domestic work compared to male
workers.
8
2.1.3 Distribution of migrant workers by broad
branch of economic activity
As shown in table 2.4, most migrant workers in the
world in 2013 were engaged in services: 106.8 million
out of a total of 150.3 million migrant workers,
amounting to 71.1 per cent. Industry, including
manufacturing and construction, accounted for 26.7
million (17.8 per cent) and agriculture for 16.7 million
(11.1 per cent). Among the 71.1 per cent of migrant
workers who are in the service sector, about 7.7 per
cent worked as domestic workers and the remaining
63.4 per cent in other services (figure 2.6).
It is interesting to note male−female differences in
the distribution of migrant workers by sector. For both
sexes, agriculture accounts for almost exactly the same
proportion (around 11 per cent). Men are more often
engaged in industry than women (19.8 per cent versus
15.3 per cent), and less in the service sector (69.1 per
PART I MAIN RESULTS
2. GLOBAL AND REGIONAL ESTIMATES
FIGURE 2.6
Global distribution of migrant workers, by broad branch of
economic activity, 2013 (percentages)
2.2 Estimates by country income group
2.2.1 Overall patterns
Table 2.5 shows the four groups into which countries
have been classified by income level (see Annex A).
The groups differ considerably in size, as can be seen
from the size of their total labour force. The high
income group of countries accounts for 20.3 per cent
and the low income group for 7.7 per cent of the
world labour force. The middle income groups are
much larger: the upper-middle income group accounts
for 38.1 per cent and the lower-middle income group
for 33.9 per cent of the total labour force. The former
group includes China (65.9 per cent of the group’s
labour force); the latter group includes India and
Indonesia (together accounting for 53.3 per cent).
cent versus 73.7 per cent). However, this difference in
relation to the service sector is more than accounted
for by markedly more engagement of women in
domestic work. There are in fact, in relative terms, a
higher proportion of male migrant workers engaged in
services other than domestic work compared to
female migrant workers (65.4 per cent of men versus
61.0 per cent of women).
When countries are grouped by income level, the
preliminary results show that the vast majority of
migrant workers were in high-income countries in
2013 (figure 2.7).
According to the data shown in table 2.5, out of
the world total of 150.3 million migrant workers, an
estimated 112.3 million (74.7 per cent) migrant
TABLE 2.5
Migrant workers and migrant domestic workers, by income level of countries, total (male + female),
2013
Lower- UpperLow
High All M+F
middle middle income
income income
income
Total workers
Total workers in %
260.2 1150.4
1293
686.6
3390.2
7.7
33.9
38.1
20.3
100
77.5
59.7
68.7
60.8
64.3
Migrant population aged 15+
6.0
24.3
24.8
151.5
206.6
Migrants as a proportion of population aged 15+
1.8
1.3
1.3
13.4
3.9
Migrant workers
3.5
16.9
17.5
112.3
150.3
Migrant workers in %
2.4
11.3
11.7
74.7
100
59.4
69.7
70.7
74.1
72.7
Migrant workers as a proportion of all workers
1.4
1.5
1.4
16.3
4.4
Total domestic workers
4.7
16.4
32.2
13.9
67.1
0.49
0.72
1.19
9.13
11.52
4.2
6.2
10.3
79.2
100
Migrant domestic workers as a proportion of all migrant workers
13.8
4.2
6.8
8.1
7.7
Migrant domestic workers as a proportion of all domestic workers
10.5
4.4
3.7
65.8
17.2
Labour force participation rate for total population
Labour force participation rate for migrant population
Migrant domestic workers
Migrant domestic workers in %
Note: Numbers in millions for the following categories: total workers, migrant population aged 15+, migrant workers, domestic workers and
migrant domestic workers.
9
ILO Global estimates on migrant workers: Results and methodology
FIGURE 2.7
Migrant workers, by income level of countries, total (male +
female), 2013 (percentages)
Labour force participation rates of migrants and non-migrants,
by income level of countries, 2013
workers were in countries classified as high income.
The estimated number of migrant workers in uppermiddle income countries was 17.5 million (11.7 per
cent), and in countries classified as lower-middle
income 16.9 million (11.3 per cent). The lowest
numbers of migrant workers were in low-income
countries at 3.5 million (2.4 per cent).
The picture for migrants shown in table 2.5 is rather
different. The labour force participation rate of
migrants declines with country income levels: from
around 74.1 per cent in the high income group, to
around 70.7 per cent in upper- and lower-middle
group countries, and to 59.4 per cent in the low
income group.
Table 2.5 also shows the estimated numbers and
distribution of migrants (as distinct from migrant
workers). The picture is of course very similar, except
for some effect of the lower labour force participation
rate among migrants in the low-income countries.
As a result, the labour force participation of migrants
is considerably higher than non-migrants in highincome countries, and higher in upper-middle income
countries as well. By contrast, the rate for migrants is
much lower than that for non-migrants in lower-middle
income countries. In low-income countries, participation
rates for migrants are practically identical to those of
non-migrants (figure 2.8).
A more telling picture is provided by the variation in
the proportion of migrant workers in the total
(migrant and non-migrant) workforce. One in six
workers in high-income countries is a migrant. In all
other groups, the proportions are very low and very
similar, between 1.4 per cent and 1.5 per cent of the
total workforce. There is no difference by income
level, with the exception of the high income group.
The labour force participation rate of the population
as a whole is low, at around 60.8 per cent in high
income and lower-middle income groups. It is much
higher (near 68.7 per cent) in the upper-middle
income group, and highest (77.5 per cent) in the low
income group. As noted above, the upper-middle
income group includes China with a relatively high
labour force participation rate, and the lower-middle
income group includes India with a relatively low
labour force participation rate, in particular among
women.
10
FIGURE 2.8
The above pattern of variation according to level of
income of the migration-receiving country deserves
further investigation. It is plausible that migration to
richer countries is more likely to be linked to work,
while migration to poorer countries more often
involves dependants. As for labour force participation
rates of non-migrants, they are high in low-income
countries, and also in upper-middle income countries.
The non-migrant labour force participation rates are
lower in high-income and also in lower-middle income
countries, in both cases largely as a result of low
participation rates among women.
An even larger proportion of migrant domestic
workers (nearly 79.2 per cent) than migrant workers in
general are concentrated in the high income group of
countries (figure 2.9). Indeed, this country grouping
PART I MAIN RESULTS
2. GLOBAL AND REGIONAL ESTIMATES
FIGURE 2.9
Migrant domestic workers, by income level of countries, 2013
(percentages)
accounts for 9.1 million of the estimated 11.5 million
migrant domestic workers globally. Also, unlike
migrant workers as a whole, there is a clear gradient
in the number of migrant domestic workers according
to countries’ income level. The shares are: 10.3 per
cent in the upper-middle income group; 6.2 per cent
in the lower-middle income group; and 4.2 per cent in
the low income group.
FIGURE 2.10
Migrant domestic workers as a share of all migrant workers, by
income level of countries, 2013
FIGURE 2.11
Migrant domestic workers as a share of all domestic workers, by
income level of countries, 2013
The share of migrant domestic workers among all
migrant workers has an interesting pattern. It is
similar, at 7 per cent and 8 per cent, in the high and
upper-middle income groups respectively, lower (4.2
per cent) in the lower-middle income group, but the
highest (13.8 per cent) in the low income group. One
in seven migrant workers in low-income countries is a
domestic worker (figure 2.10).
In high-income countries two-thirds (65.8 per cent) of
all domestic workers are migrants (figure 2.11). The
proportion is low (10.5 per cent) in low-income
countries, but very low (around 4 per cent) in uppermiddle and lower-middle income groups. These latter
groups include very large countries such as China and
India, where internal rather than international migration
prevails.
2.2.2 Gender differentials
The pattern by sex, as shown in figure 2.12 and table 2.6,
is of course similar in certain respects to the overall
pattern. However, there are some noteworthy differences.
Among the general population, female labour
force participation rates fall short of male rates by
large margins, but by amounts varying greatly
according to income group. Female participation
rates are lower by 11.4 percentage points in lowincome countries, by around 16.8 percentage points
in high-income countries, by around 19.6 percentage
points in upper-middle income countries, but by a
huge margin of 39.9 percentage points in lowermiddle income countries. The low female
11
ILO Global estimates on migrant workers: Results and methodology
TABLE 2.6
Migrant workers and migrant domestic workers, by sex and income level of countries, 2013
Male
Income level
Lower- UpperHigh
middle middle income
income income
Total
Male
137.5
772.0
742.7
382.3
2 034.6
6.8
37.9
36.5
18.8
100
83.3
79.5
78.4
69.4
77.2
Migrant population aged 15+
2.9
12.8
13.3
78.3
107.2
Migrants as a proportion of population aged 15+
1.8
1.3
1.4
14.2
4.1
Migrant workers
1.8
9.4
10.4
62.1
83.7
Migrant workers in %
2.1
11.3
12.4
74.2
100
61.2
73.5
78.1
79.4
78.0
Migrant workers as a proportion of all workers
1.3
1.2
1.4
16.3
4.1
Total domestic workers
1.2
4.5
4.3
3.5
13.4
0.25
0.42
0.21
2.2
3.07
8.1
13.6
6.7
71.6
100
Migrant domestic workers as a proportion of all migrant workers
14.1
4.4
2.0
3.5
3.7
Migrant domestic workers as a proportion of all domestic workers
21
9.4
4.8
63.2
22.9
Low
income
Total workers
Total workers in %
Labour force participation rate for total population
Labour force participation rate for migrant population
Migrant domestic workers
Migrant domestic workers in %
Female
Income level
Lower- UpperHigh
middle middle income
income income
Total
Male
122.7
378.4
550.3
304.3
1 355.7
9.1
27.9
40.6
22.4
100
71.9
39.6
58.8
52.6
51.4
Migrant population aged 15+
3.1
11.5
11.5
73.2
99.3
Migrants as a proportion of population aged 15+
1.8
1.2
1.2
12.7
3.8
Migrant workers
1.8
7.5
7.2
50.1
66.6
Migrant workers in %
2.7
11.3
10.8
75.3
100
57.7
65.3
62.2
68.4
67
Migrant workers as a proportion of all workers
1.5
2.0
1.3
16.5
4.9
Total domestic workers
3.5
12
27.9
10.4
53.8
0.24
0.3
0.98
6.93
8.45
2.8
3.6
11.6
82
100
Migrant domestic workers as a proportion of all migrant workers
13.5
4.0
13.7
13.8
12.7
Migrant domestic workers as a proportion of all domestic workers
6.9
2.5
3.5
66.7
15.7
Low
income
Total workers
Total workers in %
Labour force participation rate for total population
Labour force participation rate for migrant population
Migrant domestic workers
Migrant domestic workers in %
Note: Numbers in millions for the following categories: total workers, migrant population aged 15+, migrant workers, domestic workers and
migrant domestic workers
12
PART I MAIN RESULTS
2. GLOBAL AND REGIONAL ESTIMATES
FIGURE 2.12
Migrant workers, by sex and income level of countries, 2013 (percentages)
participation rate in the last-mentioned group is the
main factor behind the low overall participation rate.
The picture is very different among migrants, as
summarized in table 2.7.
Let us first consider gender differences in the labour
force participation rate; they are much smaller in the
migrant population compared to those in the nonmigrant population. As shown in figure 2.13, there is
only a small gender differential (3.5 percentage points)
in participation rate among migrants in low-income
countries. Migrant female participation rates are lower
by 11.0 percentage points than migrant male rates in
high-income countries, by 8.2 percentage points in
lower-middle income countries, but by as much as
15.9 percentage points in upper-middle income
countries.
The following picture emerges concerning migrant
versus non-migrant labour force participation rates for
males and females.
For men, as noted, the overall participation rates for
migrants and non-migrants are practically identical
(77 per cent and 78 per cent respectively). But this
TABLE 2.7
Migrants and non-migrants: Labour force participation rate, and proportion of domestic workers
among all workers, by sex and income level of countries, 2013
Labour force participation rate
Migrants
Domestic workers as % of all workers
Non-migrants
Migrants
Non-migrants
Total
Male Female Total Male Female Total Male Female Total Male Female
1 Low income
59.4
61.2
57.7
77.8 83.7
72.1
13.8 14.1
13.5
1.6
0.7
2.7
2 Lower-middle
income
69.7
73.5
65.3
59.6 79.6
39.3
4.2
4.4
4.0
1.4
0.5
3.1
3 Upper-middle
income
70.7
78.1
62.2
68.6 78.4
58.8
6.8
2.0
13.7
2.4
0.6
5.0
4 High income
74.1
79.4
68.4
58.8 67.8
50.3
8.1
3.5
13.8
0.8
0.4
1.4
72.7
78.0
67.0
63.9 77.2
50.8
7.7
3.7
12.7
1.7
0.5
3.5
Income group
Total
13
ILO Global estimates on migrant workers: Results and methodology
FIGURE 2.13
Labour force participation rates of migrants and non-migrants ,
by sex and income level of countries, 2013
masks very sharp migrant versus non-migrant
differentials for males within income groups. A similarity
between migrant and non-migrant rates is observed
only in the upper-middle income group. As income
levels decline, the rate of participation goes down for
male migrants. The migrant participation rate is lower
than that for the total male population by around
6.0 percentage points in lower-middle income countries,
reaching 22.1 percentage points in the low income
group. By contrast, in the high income group, the rate
for male migrants is higher than that for the total male
population by 10.0 percentage points.
For women, the overall participation rates of
migrants is higher than those of non-migrants, by
15.6 percentage points. But again there are sharp
differences in this respect when we consider income
groups individually. In upper-middle income countries,
14
FIGURE 2.14
Migrant domestic workers, by sex and income level of countries,
2013 (percentages)
the difference between female migrants and nonmigrants is small (the rate for migrants being a little
under 5.0 percentage points higher), rather similar to
the pattern noted above for males in this income
group.
The rate for female migrants compared to the total
female population is lower by around 14.1 percentage
points in the low income group but higher by
15.8 percentage points in the high income group.
The most pronounced contrast is in lower-middle
income countries. In this group, the migrant female
participation rate is higher than the non-migrant rate
by 16.3 percentage points. This is in sharp contrast to
the migrant/non-migrant differential noted above for
men. While for men, the migrant participation rate is
lower than the non-migrant rate in lower-middle
PART I MAIN RESULTS
2. GLOBAL AND REGIONAL ESTIMATES
FIGURE 2.15
Migrant domestic workers as a share of all migrant workers, by
sex and income level of countries, 2013
income countries, for women it is the other way
around and is very marked.
As for migrant domestic workers, as noted, nearly
three-quarters of them are female. The pattern by
income group for males and females is similar to the
overall pattern described above, namely the
predominance of the high income group, and a
decrease in numbers with declining income level
(figure 2.14). There are however some differences in
the case of male migrant domestic workers. The main
difference is a somewhat reduced predominance of
the high income group (accounting for just over 70
per cent rather than over 80 per cent of the total
migrant domestic workers), and some preference for
male migrant workers in lower-middle income group
countries.
There is no gender difference in the share of
migrant domestic workers among all migrant workers
in low income and lower-middle income countries
(figure 2.15). In upper-middle and high income
countries, however, a much higher proportion (around
14 per cent, or one in seven, in either group) of
female migrant workers are domestic workers, but
only 2 per cent and 3 per cent, respectively, in the
case of male migrant workers.
The pattern of migrant domestic workers as a
share of all domestic workers by income level and by
sex (figure 2.16) is quite different from the patterns
for migrant domestic workers as a share of migrants.
There is no gender difference in the high income
group, in which the ratio is around 65 per cent for
FIGURE 2.16
Migrant domestic workers as a share of all domestic workers,
by sex and income level of countries, 2013
both males and females. In all other income groups,
the ratio is much lower overall, but it is noteworthy
that it is three times higher for males than for
females.
2.3 Regional estimates
2.3.1 Migrant workers
Table 2.8 shows the 11 broad subregions into which
countries have been grouped. The groups differ
considerably in size, as can be seen from the the total
labour force. Two broad subregions, Eastern Asia (which
includes China) and Southern Asia (which includes India)
together account for half the global working population.
The smallest broad subregion is Arab States. However,
this region has a much greater importance in the
present context because of the number of migrants and
migrant workers it has (figure 2.17).
Two broad subregions, Northern America and
Northern, Southern and Western Europe, together
account for half (48.5 per cent) of global migrants or
migrant workers. The next most important subregion
is Arab States which accounts for over a tenth of the
world’s migrant workers.
As shown in table 2.8, the share of migrant workers
among all workers is the highest (35.6 per cent) in
Arab States. The corresponding proportion is 20.2 per
cent in Northern America and 16.4 per cent in
Northern, Southern and Western Europe, followed by
15
ILO Global estimates on migrant workers: Results and methodology
Central and Western Asia (10.0 per cent) and Eastern
Europe (9.2 per cent).
By contrast, in a number of subregions, the share of
migrant workers as a proportion of all workers is
below 2 per cent. The lowest, at 0.6 per cent, is
Eastern Asia (which includes China); followed by
Northern Africa, Southern Asia (which includes India),
and Latin America and the Caribbean, all within the
range 1.0–1.5 per cent.
The two broad subregions, Northern America and
Northern, Southern and Western Europe, host
relatively larger shares of female compared to male
migrant workers. These regions together account for
45.1 per cent of all male migrant workers, but for a
higher proportion (52.9 per cent) of all female migrant
workers (figure 2.18). The picture in Arab States is the
opposite: that region accounts for 17.9 per cent of all
male migrant workers, but for only 4.0 per cent of all
female migrant workers. Table 2.9 shows the
breakdown for migrant workers and migrant domestic
workers.
The pattern of higher labour force participation
among migrants relative to non-migrants (figure 2.19),
and larger differences among women than among
men observed for the world as a whole (figure 2.20),
is also observed in every region except Sub-Saharan
Africa, where the labour force participation rate of
TABLE 2.8
Migrant workers and migrant domestic workers, by broad subregion, total (male + female), 2013
Broad subregion
Northern SubLatin Northern Northern, Eastern Central
Africa Saharan America America Southern Europe
and
Africa and the
and
Western
Caribbean
Western
Asia
Europe
Total workers
Total workers in %
70.6 356.8 299.1 183.3
218 149.6
69.9
Arab
States
Eastern South- Southern
Asia Eastern Asia
Asia and
the
Pacific
All M+F
49.5 962.9 335.3 695.2 3 390.2
2.1
10.5
8.8
5.4
6.4
4.4
2.1
1.5
28.4
9.9
20.5
100
Labour force participation rate
for total population
49.1
70.6
66.5
63.9
57.9
60.0
57.7
51.1
72.0
70.1
56.6
64.3
Migrant population aged 15+
1.5
12.6
6.7
50.3
49.1
18.7
9.7
23.2
7.2
15.4
12.2
206.6
Migrants as a proportion of
population aged 15+
1.0
2.5
1.5
17.5
13.0
7.5
8.0
24
0.5
3.2
1.0
3.9
Migrant workers
0.8
7.9
4.3
37.1
35.8
13.8
7.0
17.6
5.4
11.7
8.7
150.3
Migrant workers in %
0.5
5.3
2.9
24.7
23.8
9.2
4.7
11.7
3.6
7.8
5.8
100
Labour force participation rate
for migrant population
52.3
63.1
65.0
73.7
72.9
73.9
72.3
76.0
75.2
76.5
71.0
72.7
Labour force participation rate
for non-migrant population
49.1
70.8
66.5
61.8
55.6
58.9
56.4
43.3
72.0
69.9
56.4
63.9
Migrant workers as a proportion
of all workers
1.1
2.2
1.5
20.2
16.4
9.2
10.0
35.6
0.6
3.5
1.3
4.4
Total domestic workers
0.9
8.4
17.9
0.9
4.1
0.3
0.8
3.8
14.6
9.1
6.4
67.1
0.07
0.58
0.75
0.64
2.21
0.08
0.26
3.16
1.1
2.24
0.44
11.52
Migrant domestic workers in %
0.6
5.0
6.5
5.5
19.2
0.7
2.2
27.4
9.5
19.4
3.8
100
Migrant domestic workers as a
proportion of all migrant
workers
9.0
7.3
17.2
1.7
6.2
0.6
3.6
17.9
20.4
19.0
5.0
7.7
Migrant domestic workers as a
proportion of all domestic workers
7.9
6.9
4.2
70.8
54.6
25.0
32.1
82.7
7.5
24.7
6.9
17.2
Migrant domestic workers
Note: Numbers in millions for the following categories: total workers, migrant population aged 15+, migrant workers, domestic workers and
migrant domestic workers.
16
PART I MAIN RESULTS
2. GLOBAL AND REGIONAL ESTIMATES
FIGURE 2.17
migrants is below the rate of non-migrants. There is a
(negligibly) small difference in the same direction in
Latin America and the Caribbean region.
Distribution of migrant workers, by broad subregion, total (male +
female), 2013 (percentages)
2.3.2 Migrant domestic workers
The distribution of migrant domestic workers by broad
subregion is shown in figure 2.21. Male migrant workers
are much less likely to be domestic workers than female
migrant workers. Still, in the Arab States, over one in ten
male migrant workers is a domestic worker. Among the
other regions, this figure exceeds 5 per cent only in SubSaharan Africa and Southern Asia.
Looking at the distribution of male migrant
domestic workers over regions (figure 2.22), we see
that the position of Arab States is very dominant: over
half (50.8 per cent) of all male migrant domestic
workers in the world are in the Arab States.
It is interesting to note the contrast with some other
regions. The South-Eastern Asia and the Pacific region
accounts for a small proportion (6.8 per cent) of all
male migrant domestic workers, but for a much larger
proportion (24.0 per cent, i.e. one in four) of all female
FIGURE 2.18
Distribution of migrant workers, by sex and broad subregion, 2013 (percentages)
17
ILO Global estimates on migrant workers: Results and methodology
TABLE 2.9
Migrant workers and migrant domestic workers, by sex and broad subregion, 2013
Broad subregion
Male
Total workers
Total workers in %
Labour force participation rate
for total population
Migrant population aged 15+
Migrants as a proportion of
population aged 15+
Migrant workers
Migrant workers in %
Labour force participation rate
for migrant population
Labour force participation rate
for non-migrant population
Migrant workers as a proportion
of all workers
Total domestic workers
Migrant domestic workers
Migrant domestic workers in %
Migrant domestic workers as a
proportion of all migrant workers
Migrant domestic workers as a
proportion of all domestic workers
Northern SubLatin Northern Northern, Eastern Central
Africa Saharan America America Southern Europe
and
Africa and the
and
Western
Caribbean
Western
Asia
Europe
Arab
States
Eastern South- Southern
Asia Eastern Asia
Asia and
the
Pacific
All M+F
53.0 191.6
2.6
9.4
174
8.6
98.2 119.4
4.8
5.9
78.0
3.8
43.1
2.1
40.7 537.2 191.5 507.8
2.0 26.4
9.4 25.0
2034.6
100
74.3
76.6
79.7
70.1
65.3
67.9
73.2
75.4
78.9
81.4
81
77.2
0.9
6.9
3.2
24.5
23.6
8.9
4.6
16.7
3.3
7.8
6.9
107.2
1.3
2.7
1.5
17.5
12.9
7.7
7.9
31.0
0.5
3.3
1.1
4.1
0.6
0.7
4.7
5.6
2.4
2.9
19.6
23.4
18.1
21.7
6.3
7.5
3.0
3.6
15.0
17.9
2.5
2.9
6.6
7.8
5.0
6.0
83.7
100
61.8
68.1
75.1
79.9
77.0
70.3
65.9
89.7
75.3
84.2
72.7
78.0
74.4
76.8
79.8
68.1
63.5
67.7
73.8
69
78.9
81.3
81.1
77.2
1.0
2.4
1.4
20.0
15.2
8.0
7.1
36.8
0.5
3.4
1.0
4.1
0.4
0.02
0.6
2.1
0.27
8.9
2.2
0.06
2.1
0.1
0.06
1.9
1.2
0.35
11.3
0.1
0.02
0.7
0.3
0.08
2.5
1.6
1.56
50.8
1.7
0.11
3.6
1.5
0.21
6.8
2.2
0.34
10.9
13.4
3.07
100
3.5
5.8
2.6
0.3
1.9
0.3
2.5
10.4
4.5
3.2
6.7
3.7
5.3
13
2.8
68.3
28.4
30.3
29.3
95.7
6.6
13.8
14.9
22.9
17.6 165.2 125.2
1.3 12.2
9.2
85.0
6.3
98.7
7.3
71.6
5.3
26.9
2.0
8.8 425.7 143.7 187.4
0.6 31.4 10.6 13.8
1355.7
100
24.2
64.8
54.1
57.9
50.9
53.3
43
20.5
64.9
59.1
31.2
51.4
0.6
5.7
3.5
25.8
25.5
9.8
5.1
6.5
3.9
7.6
5.3
99.3
0.8
2.2
1.5
17.6
13.2
7.3
8.1
15.1
0.6
3.1
0.9
3.8
0.2
0.3
3.3
4.9
1.9
2.9
17.5
26.3
17.7
26.6
7.6
11.4
4.0
6.0
2.6
4.0
2.9
4.4
5.2
7.8
3.7
5.5
66.6
100
37.6
57.1
55.7
67.8
69.2
77.2
78.1
40.7
75.0
68.4
68.8
67.0
24.1
64.9
54
55.8
48.1
51.4
39.9
16.9
64.9
58.8
30.8
50.8
1.2
2.0
1.6
20.6
17.9
10.6
14.8
30.0
0.7
3.6
2.0
4.9
0.5
0.05
0.6
6.3
0.31
3.6
15.7
0.69
8.1
0.8
0.58
6.9
2.8
1.87
22.1
0.3
0.06
0.7
0.5
0.18
2.1
2.2
1.6
19.0
12.9
0.99
11.7
7.5
2.03
24.0
4.1
0.1
1.2
53.8
8.45
100.0
23.0
9.4
35.3
3.3
10.6
0.8
4.5
60.8
33.9
39.2
2.8
12.7
9.8
4.9
4.4
71.0
65.8
23.6
33.4
73.1
7.6
26.9
2.5
15.7
Female
Total workers
Total workers in %
Labour force participation rate
for total population
Migrant population aged 15+
Migrants as a proportion of
population aged 15+
Migrant workers
Migrant workers in %
Labour force participation rate
for migrant population
Labour force participation rate
for non-migrant population
Migrant workers as a proportion
of all workers
Total domestic workers
Migrant domestic workers
Migrant domestic workers in %
Migrant domestic workers as a
proportion of all migrant workers
Migrant domestic workers as a
proportion of all domestic workers
Note: Numbers in millions for the following categories: total workers, migrant population aged 15+, migrant workers, domestic workers and
migrant domestic workers.
18
PART I MAIN RESULTS
2. GLOBAL AND REGIONAL ESTIMATES
migrant domestic workers. Similarly, the Northern,
Southern and Western Europe region accounts for
11.3 per cent of all male migrant domestic workers,
but for 22.1 per cent of all female migrant domestic
workers in the world. This indicates a strong preference
for female as opposed to male migrant domestic
workers in the above-mentioned regions.
Migrant domestic workers make up a large
proportion (between 17 per cent and 20 per cent) of
FIGURE 2.19
Labour force participation rates of migrants and non-migrants, by broad subregion, 2013
FIGURE 2.20
Labour force participation rates of migrants and non-migrants, by sex and broad subregion, 2013
19
ILO Global estimates on migrant workers: Results and methodology
FIGURE 2.22
all migrant workers in four regions: Eastern Asia,
South-Eastern Asia and the Pacific, Arab States, and
Latin America and the Caribbean (figure 2.23). In
another four regions (Northern America, Sub-Saharan
Africa, Southern Asia, and Northern, Southern and
Western Europe), their shares are between 5 per cent
and 10 per cent. In the remaining three regions, very
small proportions of migrant workers are domestic
workers.
Distribution of migrant domestic workers, by sex and broad
subregion, 2013 (percentages)
There are significant gender differences across the
subregions in the proportion of migrant domestic
workers in all migrant workers (figure 2.24). Since a
large proportion of domestic workers are female, the
pattern for females is similar to the overall pattern
(except for the fact that the ratio tends to be much
higher for females than the corresponding overall
value). The one exception is Southern Asia, where a
much lower proportion of migrant domestic workers is
female than male.
For men, the pattern of migrant domestic work to
migrant work tends to be quite different. Over
FIGURE 2.21
Distribution of migrant domestic workers, by broad subregion,
2013 (percentages)
10.4 per cent of male migrant workers are in domestic
work in the Arab States region. Otherwise, values
exceed 5 per cent only in Southern Asia and SubSaharan Africa, and are particularly low (0.3 per cent)
in Northern America and Eastern Europe.
20
PART I MAIN RESULTS
2. GLOBAL AND REGIONAL ESTIMATES
FIGURE 2.23
Migrant domestic workers as a share of all migrant workers, by broad subregion, 2013
FIGURE 2.24
Migrant domestic workers as a share of all migrant workers, by sex and broad subregion, 2013
21
ILO Global estimates on migrant workers: Results and methodology
While the share of migrant domestic workers
among all domestic workers (figure 2.25) is particularly
high in the Arab States region at 82.7 per cent, figure
2.26 shows that nearly all male domestic workers in
the region are migrants (MD/D = 95.7 per cent).
Though this figure may be subject to under-reporting
on non-migrant domestic workers, it is clearly
exceptional. Two-thirds (68.3 per cent) of male
domestic workers in Northern America are reported to
be migrants. Other regions with relatively high ratios
of around 30 per cent for male domestic workers
include Eastern Europe; Northern, Southern and
Western Europe; and Central and Western Asia.
The picture for female migrant domestic workers is
rather different. While three-quarters (73.1 per cent)
of female domestic workers in Arab States are
migrants, the proportion of migrant domestic workers
among domestic workers is also high in Northern
America (71.0 per cent) and Northern, Southern and
Western Europe (65.8 per cent).
FIGURE 2.25
Migrant domestic workers as a share of all domestic workers, by broad subregion, 2013
22
PART I MAIN RESULTS
2. GLOBAL AND REGIONAL ESTIMATES
FIGURE 2.26
Migrant domestic workers as a share of all domestic workers), by sex and broad subregion, 2013
23
3. Scope and definitions
This section explains the basic concepts and
definitions, as well as the scope of what is covered by
the estimates of migrant workers and migrant
domestic workers presented in this report, starting
with the data sources on the basis of which the
measures are defined and constructed.
3.1 Benchmark data
The benchmark data refer to the year 2013 and cover
176 countries and territories, representing 99.8 per
cent of the world’s working-age population (15 years
old and over). The countries are grouped into
geographic regions in line with the ILO field structure
(each region including countries covered by the ILO
regional office and non-ILO member countries in the
geographic region, together with broad and detailed
subregional groupings). The countries are also grouped
by level of income as defined in the World Bank’s
country income classification.2
The 176 countries and territories covered are listed
in Annex C, classified according to the detailed
subregion and income level group to which the
country belongs.
The global and regional estimates of migrant
workers and migrant domestic workers are based on
three sets of benchmark data for 2013, namely world
population (UN), stock of international migrants (UN),
and labour force (ILO). These are available by sex and
2
The World Bank updates its country income classification once a year. For
the purpose of ILO regional groupings, the latest World Bank income
classification is used to recreate consistent series over time (i.e. the same
country composition across years).
age group covering virtually all countries and
territories. The three sets of data are described below.
3.1.1 UN population data
The Population Division of the Department of
Economic and Social Affairs of the United Nations
Secretariat undertakes, on a regular basis, global
demographic estimates and projections of world
population. World Population Prospects: The 2012
Revision (UN, 2013a) covers 232 countries and
territories with at least 90,000 inhabitants in 2012.
The population data refer to mid-year and are available
by sex and five-year age group for each country and
territory for selected periods or dates between 1950
and 2100. Additional data on key demographic
indicators are also available for each development
group, major area, region and country.
The global population data are based on national
data, mostly derived from the latest population
censuses. In certain cases, the data refer to population
registers or official estimates, and in a few cases to
large-scale household surveys. The estimates and
projections are made based on certain assumptions
regarding fertility, mortality, international migration,
and in certain countries on HIV/AIDS prevalence rate
and modelling of mortality (UN, 2014).
Coverage and comparability
In ascertaining the size of a population it is necessary
to define what is meant by population of a certain
country or area. In census terms countries use two
different ways of defining the population – de facto
population or de jure population. The former is taken
25
ILO Global estimates on migrant workers: Results and methodology
to be the population actually present at some moment
of time, while the latter is a vaguer term referring to
the population which is usually and/or legally resident
in an area − the population which in some sense
“belongs” to the area. Worldwide, the de facto type
of census is considerably more common than the de
jure type, although for many policy purposes the de
jure population is more relevant. Sometimes it is
possible to estimate both the de facto and de jure
populations, and sometimes census counts fall
between the two.
An important element of a count of the population
enumerated in a census is a description of who is and
who is not included in the count. In order to improve
comparability between countries the United Nations
makes recommendations concerning this, but there
remain considerable variations in country practices.
3.1.2 UN international migration data
In the area of international migration, the United
Nations Population Division estimates the global
number of international migrants at regular intervals,
monitors levels, trends and policies of international
migration, and collects and analyses information on
the relationship between international migration and
development. Estimates of the stock of international
migrants across the world are prepared on a regular
basis. The latest edition of Trends in International
Migrant Stock (UN, 2013b) contains estimates for mid2013 by sex and five-year age group for 232 countries
and territories. Estimates of the number of
international migrants are available in the United
Nations Global Migration Database. The basic data are
obtained in the most part from national population
censuses. Some of the data are obtained from
population registers and nationally representative
surveys.
The Population Division indicates that:
Depending on the nature of the national data
available, country of origin is recorded either as
country of birth or as country of citizenship. In
estimating the international migrant stock,
international migrants have been equated with the
foreign-born whenever possible. In most countries
lacking data on place of birth, information on the
country of citizenship of those enumerated was
used as the basis for the identification of
26
international migrants, thus effectively equating
international migrants with foreign citizens.
The approach of equating international migrants
with foreign citizens when estimating the migrant
stock has important shortcomings. In countries
where citizenship is conferred on the basis of jus
sanguinis, people who were born in the country of
residence may be included in the number of
international migrants even though they may have
never lived abroad. Conversely, persons who were
born abroad and who were naturalized in their
country of residence are excluded from the stock of
international migrants when using citizenship as the
criterion to define international migrants. Similarly,
using country of citizenship as the basis for the
identification of international migrants has an
important impact on the age distribution of
international migrants, depending on whether
citizenship is conferred mainly on the basis of jus
sanguinis or jus soli.
Despite these drawbacks, information by country
of citizenship was used because ignoring it would
have resulted in a lack of data for 43 countries or
areas, equal to nearly 20 per cent of all countries
and areas of the world. (UN, 2013b, pp. 4−5)
Regarding the coverage of refugees, the Population
Division explains its principles as follows:
The coverage of refugees in population censuses
is uneven. In countries where refugees have been
granted refugee status and allowed to integrate,
they are normally covered by the population census
as any other international migrant. In such cases,
there is no reason to make a special provision for
the consideration of refugees in estimating the
international migrant stock. However, in many
countries, refugees lack freedom of movement and
are required to reside in camps or other designated
areas. In these cases, population censuses may
ignore refugees. Furthermore, when refugee flows
occur rapidly in situations of conflict, it is
uncommon for a population census to take place
soon after and to reflect the newly arrived refugee
population.
Consequently, for many countries hosting large
refugee populations, the refugee statistics reported
by international agencies are the only source of
information on persons who are recognized as
refugees or find themselves in refugee-like
situations. Figures on refugees reported by the
Office of the United Nations High Commissioner for
PART I MAIN RESULTS
3. SCOPE AND DEFINITIONS
Refugees (UNHCR) and the United Nations Relief
and Works Agency for Palestine Refugees in the
Near East (UNWRA) have been added to the UN
estimates of the international migrant stock for
most developing countries. For developed countries,
where refugees admitted for resettlement as well as
recognized asylum-seekers are routinely included in
population counts, no such adjustment was made.
(ibid. p. 4)
comparability of LFPR include: (i) country-reported
LFPR being derived from several types of sources,
often not comparable; (ii) differences in the age
groupings used in measuring the labour force; (iii)
limited geographic coverage; and (iv) other sources
such as differences in population coverage, concepts
or treatment of particular groups.
3.2 International migrant
3.1.3 ILO labour force data
ILO benchmark data on the labour force in 2013 are
part of the ILO Estimates and Projections of the
Economically Active Population (ILO, 2011). This
database is a collection of country-reported and ILOestimated labour force participation rates. The data
refer to 191 countries and territories including the 176
countries covered by the present study. The reference
period for the estimates is 1990−2010, while for the
projections it is 2011−20. For countries with historical
data prior to 1990 (but after 1979), estimates
concerning the period prior to 1990 are also provided.
The basic data are single-year labour force
participation rates by sex and age groups. The
historical estimates (1990−2010) are accompanied by
detailed metadata for each data point regarding the
source of collected data, the type of adjustments
made to harmonize them when needed, and the type
of imputation method used to fill missing data. The
projections are based on a range of models allowing
the capture of the impact of the economic
development on labour force. In certain cases, use is
made of projections recently published by national
statistical offices.
Data must be derived from either a labour force
(LFS) or household survey or a population census.
However, a strict preference is given to LFS-based
data, with population census-derived estimates only
included for countries in which no LFS-based
participation data exist. Data derived from official
government estimates are in principle not included in
the dataset, as the methodology for producing official
estimates can differ significantly across countries and
over time, leading to non-comparability.
A key objective in the construction of the database
is to generate a set of comparable labour force
participation rates (LFPR) across both countries and
time, with no missing values left unimputed. As
detailed in section 4.1, the main sources of non-
The UN recommendations on statistics of international
migration define the “stock of international migrants
present in a country” as “the set of persons who have
ever changed their country of usual residence, that is
to say, persons who have spent at least one year of
their lives in a country other than the one in which
they live at the time the data are gathered” (UN,
1998, para. 185).
This definition as it stands could be interpreted to
count as a migrant a citizen of a country currently
resident in that country, but who spent a year in
another country at some point in his/her life. In
practice, this definition is often not used. Since the
present report refers to estimates of immigrants in
destination countries, it could be preferable to refer to
the more conventional understanding of an immigrant
as a “person who moves to a country other than that
of his or her usual residence for a period of at least a
year (12 months), so that the country of destination
effectively becomes his or her new country of usual
residence” (ibid., para. 36).
The concept used in the current estimates is,
instead, that introduced in section 3.1.2 above. This
approach is adopted for the practical reason that the
UN Global Migration Database provides the necessary
information for all the countries included in the
present report.
This is a narrower definition formulated in terms of
citizenship (foreign population) or place of birth
(foreign-born population):
■■
Foreign population. All persons with usual
residence in a given country who are citizens of
another country. In the case of double or multiple
citizenships, the person is generally considered a
foreigner only if those citizenships do not include
that of his or her country of usual residence.
27
ILO Global estimates on migrant workers: Results and methodology
■■
Foreign-born population. All persons with usual
residence3 in a given country whose place of birth
is located in another country. Persons who have
remained in the territory where they were born but
whose “country of birth” has changed because
of boundary changes are not generally counted as
foreign-born.
Some countries such as Canada and the United
States that gather information on both place of birth
and mode of acquisition of citizenship use a restricted
definition of “foreign-born” for tabulation purposes:
they regard as “foreign-born” only those persons who
were born abroad and did not have a right to the
citizenship of the country concerned at the time of
their birth (in other words, persons who are not
citizens by birth). Certain countries also apply in their
national population censuses particular treatment for
short-term migrants such as cross-border and seasonal
migrant workers. The definitions used in several other
countries combine “citizenship” and “permanent
residency”. In these cases, the data typically also
include all persons who are not citizens of the country
and do not have a permanent residence permit in that
country.
A crucial concept affecting comparability of
migration statistics concerns “residence”. Normally,
immigrants are identified as non-residents who enter
the country with a view to establishing residence
(becoming a resident). Just as in the case of
determining the size of the population, the meaning
of residence in the context of international migration
can be taken from a legal (de jure) perspective, or
from a de facto perspective. However, the meaning of
these terms for the two purposes – of counting the
population in a census, and of identifying international
migrants − is not necessarily identical. In the context
of international migration, de jure residence normally
implies having a place of abode in a country and
acquiring certain benefits and obligations, but without
necessarily implying physical presence in the country at
any moment or interval in time. The de facto
perspective implies actually living or being present in
3
28
“Usual residence” is a complex concept and may be defined differently in
different national sources. The UN Department of Economic and Social
Affairs recommends “that countries apply a threshold of 12 months when
considering place of usual residence according to one of the following two
criteria: (a) The place at which the person has lived continuously for most
of the last 12 months (that is, for at least six months and one day) … (b)
the place at which the person has lived continuously for at least the last 12
months” (UN, 2008a, para.1.463, pp. 102−103). However, as noted, in
practice many countries have used a different length of reference period
for this purpose.
the country for more than a minimum length of time.
In practice, the minimum length of time for this
purpose varies from country to country, mostly in the
range of three to twelve months.
3.3 Migrant worker
According to the Migration for Employment
Convention (Revised), 1949 (No. 97), the term “migrant
for employment” means a person who migrates from
one country to another with a view to being employed
otherwise than on his or her own account. The scope of
Convention No. 97 excludes “frontier workers”,
“members of the liberal professions and artistes”, and
seafarers (Article 11.2). The Migrant Workers
(Supplementary Provisions) Convention, 1975 (No. 143)
provides a slightly broader definition: it also
encompasses persons who have migrated.4
The definition of “migrant worker” used in the
present estimates takes a different view, and is more
inclusive. It comprises all international migrants in the
sense described in the preceding section who are
currently employed or seeking employment in their
country of current usual residence. The intentions or
conditions of their entry into their current country of
residence are not relevant for this purpose.
The term “migrant worker” thus includes
unemployed migrant workers as well as migrant
workers whose status in employment is employer or
own-account worker or contributing family worker. It
excludes, of course, persons who are currently
employed in or are seeking employment in a country
other than their country of usual residence.
3.4 Scope of the global and regional
estimates
Clearly, a most important question is the following:
what is the “scope” of the estimates, i.e. the
population of migrants and migrant workers covered
in this report?
4
Article 11(1) of Convention No. 143, which applies to Part II of that
instrument, states: “… the term migrant worker means a person who
migrates or who has migrated from one country to another with a view to
being employed otherwise than on his own account and includes any
person regularly admitted as a migrant worker.’’ It should be noted that in
addition to the categories excluded under Convention No. 97, Convention
No. 143 also excludes students, trainees and employees of organizations
in a country who have entered that country temporarily for an assignment
and will leave on completion (Article 11(2)).
PART I MAIN RESULTS
3. SCOPE AND DEFINITIONS
Firstly, it should be noted that the present report is
concerned throughout with numbers and
characteristics of migrants in countries of destination.
A migrant, migrant worker or migrant domestic
worker is counted at the country of current residence.
3.4.1Migrants
The population of migrants covered by the estimates
presented here is defined by the nature of the data
used for the purpose, namely the database on the
stocks of international migrants produced by
Population Division of the Department of Economic
and Social Affairs of the United Nations Secretariat.
Since the basic data in these projections are obtained
in the most part from national population censuses,
the migrant population identified can be regarded as a
subset of the total population covered in the global
demographic estimates and projections of world
population, also undertaken on a regular basis by the
UN Population Division. These global population data
are based on national data, and again are mostly
derived from population censuses. Only in a few cases
do the data refer to population registers, other official
estimates, or nationally representative large-scale
household surveys. In short, the migrant population
covered in the present estimates is essentially confined
to the population covered in national population
censuses. Whether a particular category or type of
migrants can be included is determined by whether
they are eligible for inclusion, and are included in
practice, as residents in the national population
censuses.
Furthermore, the estimates are confined to the
adult population. In the vast majority of countries this
is taken as population aged 15 and over, but in a few
exceptional cases as 15−64.
3.4.2 Migrant workers
The term “migrant worker” as used in the present
report is defined in section 3.3 above. The procedure
used here to estimate the size of the population of
migrant workers is detailed in section 6. Briefly, it
involves the following two steps.
(i) From national data sources of the type described
in section 4.2, such as the OECD Migration
Database and the ILO Global and Regional
Databases on Labour Migration, estimates are
obtained of labour force participation rates of
migrants.
(ii) These LFPR estimates can be multiplied by
estimates of the total migrant population
as described above to obtain corresponding
estimates of the size of the population of migrant
workers.
A basic requirement in computing ratio (i) is that
the numerator (the number of migrant workers) and
the denominator (the number of migrants) should be
consistent in terms of the population covered and
ideally come from the same statistical source. In
coverage or numerical magnitude, the denominator of
(i) is not necessarily identical to the estimate of the
number of migrants in (ii).
The scope of the estimates of migrant workers
presented in this report is limited by the coverage of
both (i) and (ii), i.e. it is confined to the intersection of
coverage of the two sources. Thus, for example, even
if source (i) includes information on economic activity
of migrant children aged under 15, our estimates do
not cover that since (ii) has been restricted to
population aged 15 and over, as noted above.
Conversely, if (i) covers only the employed but not the
unemployed population in some group (such as
migrant domestic workers), the same limitation would
apply to the estimates of the number of migrant
workers for that group.
The total workforce engaged in a Country X is
divided into two parts – non-working and working
population. Migrants who are employed, or
unemployed and seeking employment, are part of the
working population of Country X and they fall within
the scope of the global estimation. Migrant domestic
workers fall within this category too.
Non-working migrants, i.e. persons who have
migrated for reasons other than work such as
dependants or students) are outside the scope of this
report. In practice, however, some of those who have
migrated for a reason other than work but who are
currently working in country X may in fact be counted
in the global estimate due to the nature and/or design
of the measurement tool. To what extent these
persons are covered by the global estimates is
however unknown.
29
ILO Global estimates on migrant workers: Results and methodology
Non-resident foreign workers cross borders to
perform work in Country X on a short-term basis;
these include daily workers in services or seasonal
workers in agriculture and construction, and so on. It
should be stated that not all cross-border migration is
necessarily of a seasonal nature. As shown in figure
3.1, cross-border migration for short-term work falls
outside the scope of the global and regional estimates
presented in this report.
Another group outside the scope of this report is
refugees and asylum seekers – persons who have fled
from persecution, war or other conditions of extreme
danger or hardship in their countries. These form a
separate category and are not covered in these
estimates concerning migrants. Again, as in the case
of those individuals who originally migrated for
reasons other than work, but are currently working,
they may be captured by the global estimates;
however the extent to which this has occurred is
unknown.
FIGURE 3.1
Estimates of migrant workers, schematic representation
migrants and migrant workers, irrespective of whether
or not they are economically active.
Returning ethnics.5 This refers to persons who are
admitted by a country of which they are not citizens
because of their historical, ethnic or other ties to that
country, and are immediately granted right to
permanent abode. That right makes them a part of
population P, and they are within the scope of the
present estimates until they acquire citizenship of their
new country.6
Temporary migrant workers. This may cover a
variety of arrangements, such as seasonal migrant
workers, migrant workers who are tied to specific
projects (and are not free to undertake other work),
contract migrant workers, and other temporary
migrant workers admitted for a limited period. These
include non-resident foreign workers who cross
borders to perform work at the country of destination
on a short-term basis such as daily workers in services,
seasonal workers in agriculture and construction, or
foreign business travellers receiving remuneration in
the country of origin (of course, not all cross-border
migration is necessarily of seasonal nature). Normally
such migrants would be excluded from the current
estimates. However, the determining factor is not the
condition under which such persons may have been
given the right to enter the country concerned, but
their de facto residential status at the current point in
time.
Migrants for family reunification. The status, and
hence potential inclusion in the estimates, of such
persons is normally determined by that of the “primomigrants” responsible for their permission to enter for
residence in the country concerned.
Some examples are provided below for further
clarification.
Returning migrants. These are persons who have
been abroad (i.e. in a country other than their own) as
migrants, and have returned to their own country to
settle in it. They are most likely to be citizens of their
“own” country and/or were born in it. They do not
belong to population M as defined above, and
therefore are excluded in the present estimates of
30
Foreigners admitted for special purposes, such as
foreign students, trainees, retirees. Often such persons
are not included as a part of the resident population,
especially when that is determined on a de jure basis
(which usually implies having a place of abode in the
country concerned and formally acquiring certain
benefits and obligations). If so, they remain excluded
from the present estimates.
5
The term is taken from Bilsborrow et al. (1997).
6
They will remain within the scope of the estimates if, in the country
concerned, migrant status is determined in terms of country of birth rather
than country of citizenship.
PART I MAIN RESULTS
3. SCOPE AND DEFINITIONS
The above two categories are examples of nonlabour migrants, that is, persons who have migrated
for reasons other than work. Again, it is important to
note that the factor determining their inclusion or
exclusion is not the condition under which such
persons may have been given the right to enter the
country concerned, but their de facto residential status
at the current point in time. In practice, some of those
who have migrated for a reason other than work may
in fact be currently working in the country of
destination. They should therefore be counted in the
global estimates. To what extent these persons are
actually covered by the present estimates is, however,
unknown.
Irregular migrants. These are persons who have
entered to stay in the country concerned, without fully
satisfying the conditions and requirements set by that
country for entry, stay or exercise of an economic
activity. Often it is correct to include such persons in
the estimates. However, many migrants and especially
migrant workers in such circumstances remain
undocumented. Dearth of data on undocumented
migrant workers undoubtedly results in
underestimation of their numbers.
Refugees, asylum seekers, and other persons
admitted for humanitarian reasons. The inclusion (or
exclusion) of such persons is again determined by their
right to residence and to undertaking work in the
destination country. An additional consideration is
whether they live in private households or in
institutions or camps. Available data sources often
cover only persons in private households. Persons with
other living arrangements often remain uncovered,
and hence outside the current estimates.
advantage for the objective of the present estimates.
The advantage is that all those who are economically
active should be recorded in official statistics, which is
closest to the concept we want to estimate.
Of concern also is that some groups such as
irregular migrants or those not resident in private
households (e.g. those living in asylum and refugee
reception centres) may not be recorded in official
statistics such as censuses or labour force surveys, so
they would be undercounted. This may be
unavoidable. Nevertheless, the problem needs
attention, as do the implications of omission of the
above-mentioned groups. In the future, it would be
very useful to have some idea of the numbers of
irregular or undocumented migrant workers and what
proportion of all migrant workers they form.
3.4.3 Migrant domestic workers
Figure 3.2 shows the scope of the global and regional
estimates of migrant domestic workers. As migrant
domestic workers are measured within the overall
framework of migrant workers, cross-border domestic
workers and other non-resident domestic workers are
not included in the present scope of estimation.
FIGURE 3.2
Estimates of migrant domestic workers, schematic representation
It should also be kept in mind that such persons,
who often have fled from persecution, war or other
conditions of extreme danger or hardship in their
countries, form a separate category with special
conditions, rights and obligations from the host
government. Statistical information on them thus
requires separate reporting in any case.
To summarize: “migrants for employment”, or
“economic migrants” may be distinguished from
family reunification migrants, and from asylum seekers
and refugees. However, in practice, most of the data
sources will be unable to take account of reasons for
migration and are likely to just record nationality/
country of birth. However, this can actually be an
31
ILO Global estimates on migrant workers: Results and methodology
3.5 Breakdown by sector
of economic activity
Estimates on migrant workers in this report are
disaggregated according to the main sector of economic
activity; the main sectors are agriculture, industry and
services. Table 3.1 shows the composition of these main
sectors in terms of the 21 sections defined in the latest
International Standard Industrial Classification of All
Economic Activities (ISIC), Revision 4 (UN, 2008b).
In principle, migrant workers may be classified by
branch of economic activity according to their main
job in the case of employed migrants, and according
to their latest job in the case of unemployed migrants
with past employment experience. This procedure is
admittedly flawed, in that it implicitly assumes that
unemployed migrant workers with past employment
experience have the same distribution by branch of
economic activity as employed migrant workers, for
whom the relevant data are more often available.
Unemployed migrants without past employment
experience are not classifiable by branch of economic
activity under these rules. However, for the purpose of
the present study, all migrant workers are classified by
branch of economic activity, including the unemployed
without past employment experience.
The disaggregation of all migrant workers according
to sector of economic activity is constructed as
follows. It is available (or can be imputed) for present
employment for migrants who are currently working,
or for most recent employment if the migrant has
worked before. The distribution obtained is then
applied to all economically active migrants, including
the unemployed with no past employment experience.
The resulting global estimates provide instructive
information on the broad sectors of economic activity
of migrant workers and pave the way for future
improvements to the estimates, especially if in the next
round of global estimates the ILO focuses attention on
the labour force status of migrant workers, deriving
separate global estimates on employed migrant
workers and unemployed migrant workers. The
breakdown by branch of economic activity may then
be more meaningfully limited to employed migrant
workers.
32
3.6 Domestic worker
The Domestic Workers Convention, 2011 (No. 189),
defines domestic worker in its Article 1:
(a) the term “domestic work” means work
performed in or for a household or households;
(b) the term “domestic worker” means any person
engaged in domestic work within an employment
relationship;
(c) a person who performs domestic work only
occasionally or sporadically and not on an
occupational basis is not a domestic worker.
In practice, there may be members or non-members
of the household carrying out the domestic tasks for
the household without having an obvious employment
relationship. Examples could include persons such as
foster children, orphans, distant relatives or unrelated
household members. Also there may be cases where
the domestic worker is considered as an own-account
worker if working for more than one household.
The term “domestic work” in the ILO Convention
refers to the tasks and duties of the domestic worker
such as cooking, cleaning house, laundering,
gardening, and so on. The tasks and duties define the
occupation of the domestic worker, but no specific
code or codes exist for exclusively identifying domestic
workers in the ILO International Standard Classification
of Occupations, ISCO-08 (ILO, 2012a) except for
certain cases.7
In most national data used in the present study,
domestic workers are instead identified on the basis of
their branch of economic activity. As shown above in
table 3.1, the International Standard Industrial
Classification of All Economic Activities (ISIC), Revision
4, classifies economic activities into 21 broad
categories (sections) subdivided into divisions, groups
and classes. Division 97 identifies Activities of
households as employers of domestic personnel (UN,
2008b). The corresponding category in the previous
7
The exceptions are Domestic helper (ISCO-08 code 9111), Domestic
cleaner (ISCO-08 code 9121), Housekeeper (ISCO-08 code 5152) and
Maid (ISCO-08 code 5162). Otherwise, the ISCO occupations are defined
broadly and do not refer to domestic work specifically. For example, the
occupational category Cook (ISCO-08 code 5120) may refer to both a cook
engaged by a household or a cook working in a restaurant or for that
matter in a hospital or in any another private or public institution. Similarly
for drivers, gardeners, guards or nurses. Thus, domestic workers cannot
be captured exhaustively in terms of occupations.
PART I MAIN RESULTS
3. SCOPE AND DEFINITIONS
TABLE 3.1
ISIC groupings of economic activity
Section
Divisions Description
Broad
category
A
01-03
Agriculture, forestry, and fishing
Agriculture
B
05-09
Mining and quarrying
Industry
C
10-33
Manufacturing
D
35
Electricity, gas, steam, and air conditioning supply
E
36-39
Water supply; sewerage, waste management,
and remediation activities
F
41-43
Construction
G
45-47
Wholesale and retail trade; repair of motor vehicles and motors
H
49-53
Transportation and storage
I
55-56
Accommodation and food service activities
J
58-63
Information and communication
K
64-66
Financial and insurance activities
L
68
Real estate activities
M
69-75
Professional, scientific, and technical activities
N
77-82
Administrative and support service activities
O
84
Public administration and defence; compulsory social security
P
85
Education
Q
86-88
Human health and social work activities
R
90-93
Arts, entertainment, and recreation
S
94-96
Other service activities
T
97-98
Activities of households as employers; undifferentiated goods
U
99
Activities of extraterritorial organizations and bodies
Services
Source: International Standard Industrial Classification of All Economic Activities (ISIC), Revision 4 (UN, 2008b).
Available at: http://unstats.un.org/unsd/cr/registry/regcst.asp?Cl=27.
ISIC Rev. 3.1 is Division 95: Activities of private
households as employers of domestic staff.8
For some countries, the data on domestic workers
are obtained from the relationship to the head or
8
Division 97 of ISIC Revision 4 defines activities of households as employers
of domestic personnel such as maids, cooks, waiters, valets, butlers,
laundresses, gardeners, gatekeepers, stable-lads, chauffeurs, caretakers,
governesses, babysitters, etc. It allows the domestic personnel employed to
state the activity of their employer in censuses or studies, even though the
employer is an individual. The product produced in this activity (e.g., cooked
food, clean house) is consumed by the employing household. The activity
excludes provision of services such as cooking, gardening, etc. by
independent service providers (companies or individuals).
reference of the household. In the case of a few other
countries, particularly in Latin America, the data on
domestic workers are obtained from a special category
of status in employment.
It should be mentioned that in all cases, domestic
workers are identified through their main job. Thus, to
the extent that some domestic workers are involved in
domestic work only in their secondary or subsidiary
jobs, the results based on main jobs underestimate the
total number of employed persons engaged in
domestic work. The data from the especially designed
33
ILO Global estimates on migrant workers: Results and methodology
survey on domestic workers conducted in the United
Republic of Tanzania in 2012 by the ILO indicate that
about 6 per cent were engaged as domestic worker in
their secondary job, their main job being other than
domestic work (Kahayarara, 2013).
Another source of bias is the age limit used for
estimation. The national data used here refer to the
working-age population, specified as persons 15 years
old and over. Child domestic workers below the age
set for measurement of economic characteristics in
national censuses and surveys are therefore excluded,
a limitations that also applies of course to all estimates
presented in this report. ILO global estimates on child
labour, however, indicate that some 6.3 million
children aged 5 to 14 years were engaged in domestic
work in 2012, a slight decrease from 7.4 million in
2008 (ILO, 2010a; Etienne, Diallo and Mehran, 2014).
3.7 Migrant domestic worker
Migrant domestic workers are international
migrants (in the sense described in section 3.2
above) who are engaged in their main job as
domestic workers by households. They also include
migrant domestic workers who are currently
unemployed, as well as those who may be engaged
in more than one household as an employee or
own-account worker. They exclude however crossborder domestic workers who are not residents of
the country in which they work. It is important to
spell out that the data on migrant domestic workers
presented here exclude domestic workers who have
migrated from one part of the country to another
(internal migration).9
Also, to the extent that migrant domestic workers
working irregularly are not reported in national
censuses and surveys, the data presented here
underestimate the global and regional number of
migrant domestic workers. This comment applies to
the global and regional estimates of migrant workers
as well. However, the degree of underestimation may
be relatively more important in the case of migrant
domestic workers, as their activity takes place inside
private houses and is therefore more likely to be
undocumented in many countries.10
9
This limitation of course applies to all estimates presented in this report,
which is concerned with international migration only.
10 On the other hand, there is anecdotal evidence that in some countries
workers entering on a migrant domestic worker visa may in fact end up
working elsewhere, possibly making themselves extra vulnerable to
exploitation.
34
PART II
ESTIMATE METHODOLOGY
35
4. Methodology, Phase 1: Data sources
and input data
The data sources used in the present study are of two
types, as shown in figure 4.1: (i) international data
sources that provide benchmark data on population,
stock of international migrants, and estimates and
projections of the economically active population
(labour force) for the reference year 2013; and (ii)
national data on migrant workers, domestic workers
and migrant domestic workers obtained from
population censuses, labour force surveys and other
large-scale household surveys with varied reference
years ranging with a few exceptions from 2005 to
2014. Most national data have been compiled from
international and regional databases and in a few
cases from national sources directly.
4.1 Benchmark data
As noted in section 3.1, the global and regional
estimates of migrant workers and migrant domestic
workers are based on three sets of benchmark data
for 2013: on world population (UN, 2013a), stock of
international migrants (UN, 2013b) and labour force
(ILO, 2011) covering virtually all countries and
territories. In the current estimates of migrant workers,
we take the benchmark data as complete and correct
for all the individual countries included. However, the
population figures are themselves estimates. The
quality of the estimates presented in this report is
affected by the degree of comparability of the
benchmark statistics across countries of the world.
With this in view, this section briefly describes how the
FIGURE 4.1
Data sources: Benchmark and national data
37
ILO Global estimates on migrant workers: Results and methodology
benchmark estimates have been constructed on the
basis of less-than-complete data.
used if available to evaluate the quality of census data.
If necessary, adjusted data are obtained or
adjustments are applied using standard demographic
techniques.
4.1.1 UN population data
3. Consistency checking and cross-validation. The
next step is to integrate the separate estimates for
fertility, mortality and migration. The estimates
obtained from the preceding steps are subjected to a
series of internal consistency checks on the
relationship between the enumerated populations and
their estimated intercensal demographic components.
For many countries, particularly in less developed
regions, empirical demographic information may be
limited or lacking and the available data can be
unreliable. In these cases, models and indirect
measures of fertility and mortality estimation have also
been used to derive estimates. In fact, the overall
analytical approach used in the 2012 Revision of
World Population Prospects consists of four major
steps:
4. Checking consistency across countries. Once all
the various components of each country’s estimates
are calculated, the results are aggregated by
geographical region and consistency checks
comparing the preliminary estimates against those
from other countries in the same region or at similar
levels of fertility or mortality are conducted. An
important component of the work at this stage is
ensuring the consistency of information on net
international migration, which for each five-year
period must sum to zero.
1. Data collection and estimation. For each country,
data from censuses, surveys, vital and population
registers, analytical reports and o ther sources are
collected, reviewed and used to estimate populations,
fertility, mortality and net international migration
components. In many cases, estimates derived from
different sources or based on different modelling
techniques can vary significantly, and all available
empirical data sources and estimation methods need
to be compared.
4.1.2 UN international migration data
2. Evaluation and adjustments. In a second step the
data are evaluated for geographical completeness and
demographic plausibility. Post-enumeration surveys are
Among the 232 countries or areas included in this
publication, 214 (representing 92 per cent of the total)
TABLE 4.1
Countries or areas with at least one data source on international migrant stock, by age and sex, 1990, 2000
and 2010 (percentages)
Countries with data available on migrant stock (%)
By sex
38
By age
No. of
countries
1990
2000
2010
1990
2000
2010
World
232
78
75
50
53
56
24
Africa
58
74
52
31
43
29
12
Asia
50
70
74
54
34
52
30
Europe
48
79
92
75
48
79
33
Latin America and the
Caribbean
48
83
77
38
79
65
21
Northern America
5
100
100
100
60
80
60
Oceania
23
91
96
57
74
61
17
PART II ESTIMATE METHODOLOGY
4. METHODOLOGY, PHASE 1: DATA SOURCES AND INPUT DATA
had at least one data source on the total migrant
stock by sex since the 1990 census round, while 76
per cent of countries or areas had at least one data
source on the age of international migrants.
In relation to coverage, 79 per cent of the total
migrant stock was based an empirical data source. In
relation to age, 55 per cent of migrant stock was
based on an empirical data source.
The availability of data on total migrant stock, as
well as on the age of international migrants, differs
significantly between countries and regions, as
summarized in table 4.1 showing census rounds
between 1990 and 2010.
For the 2010 census round, which was still ongoing
as of 2013, 31 per cent of countries in Africa had a
data source on total migrant stock, while 12 per cent
had recent data on the age of international migrants.
Asia, and Latin America and the Caribbean, also had a
relatively large number of countries or areas with no
data for the 2010 census round on international
migrants or their basic demographic characteristics; in
Asia, 54 per cent of countries had a recent data source
on total migrant stock and 30 per cent on the age of
international migrants; while in Latin America and the
Caribbean, 38 per cent of countries had a data source
on total migrant stock and 21 per cent had data on
the age of international migrants.
Data on the age of international migrants are
presented for standard five-year age groups commonly
used in demographic analysis, that is, 0 to 4, 5 to 9,
etc. In many cases, the available data required some
form of redistribution to ensure that the reported data
could be used for estimates by five-year age group.
Estimation procedures differed as follows,
depending on the number of data sources available in
a country.
1. Estimates for countries with two or more data
sources. For countries or areas with at least two data
points, interpolation or extrapolation was used to
estimate the migrant stock for the reference year. For
the total migrant stock, estimates were also adjusted
on the basis of other relevant information, including
the estimated size of the total population in the
country of destination based on the World Population
Prospects: The 2013 Revision. In relation to the age of
international migrants, the estimation method took
into consideration the change in the size of the
migrant stock, the ageing of the migrant stock, the
age distribution of newly arriving and departing
migrants, and the age distribution of the total
population in the country of destination. Certain
variations in these assumptions have been applied for
specific groups, such as refugees who tend to be
younger than other international migrants.
2. Estimates for countries with only one data
source. For countries or areas with only one data
source, different approaches were used. For total
migrant stock, the growth rates of the total migrant
stock in the relevant major area or region were
considered. In relation to the age of international
migrants, the estimation method also took into
consideration the change in the size of the migrant
stock, the ageing of the migrant stock, and the age
distribution of newly arriving and departing migrants
and of the total population in the country of
destination. Again, certain variations in these
assumptions have been applied for specific groups
such as refugees, who tend to be younger than other
international migrants.
3. Estimates for countries with no data. For
countries or areas without any data sources, another
country or group of countries was used as a model.
These “model” countries were selected on the basis of
various characteristics, including the use of the same
criterion for enumerating international migrants,
geographical proximity and migration experience.
4.1.3 ILO labour force data
The Estimates and Projections of the Economically
Active Population (EAPEP) database is a collection of
country-reported and ILO-estimated labour force
participation rates, constructed with the aim of
providing comparable LFPR across countries over time.
The main sources of non-comparability are as
follows:
1. Type of source. Country-reported LFPR are
derived from several types of sources including labour
force surveys, population censuses, establishment
surveys, insurance records and official government
estimates. Data taken from different types of sources
are often not comparable.
39
ILO Global estimates on migrant workers: Results and methodology
2. Age group coverage. Non-comparability also
arises from differences in the age groupings used in
measuring the labour force. While the standard age
groupings used in the EAPEP database are 15-19,
20-24, …, 65+, some countries report non-standard
age groupings, which can adversely affect broad
comparisons. For example, some countries have
adopted non-standard lower or upper age limits for
inclusion in the labour force, with a cut-off point at 14
or 16 years for the lower limit and 65 or 70 years for
the upper limit.
to the input data file; second, a simple interpolation
technique is utilized to expand the baseline data in
countries that report LFPR in some years; next, the
problem of non-response bias (systematic differences
between countries that report data in some years and
countries that do not report data in any year) is
addressed and a solution is developed to correct for
this bias; and finally, a weighted least squares
estimation model is used to produce the actual
country-level LFPR estimates.
3. Geographic coverage. Some country-reported
LFPR correspond to a specific geographic region, area
or territory such as “urban areas”. Geographicallylimited data are not comparable across countries.
4.2 National data
4. Other factors. Non-comparability can also arise
from the inclusion or non-inclusion of military
conscripts; variations in national definitions of the
economically active population, particularly with
regard to the statistical treatment of “contributing
family workers” and the “unemployed, not looking for
work”; and differences in survey reference periods.
The first step in the production of the EAPEP
database is to carefully scrutinize existing countryreported LFPR and to select only those observations
deemed sufficiently comparable. Two subsequent
adjustments are made to the national LFPR data in
order to increase the statistical basis (in other words,
to decrease the proportion of imputed values); that is,
harmonization of LFPR data by age bands, and
adjustment based on urban data.
In total, comparable data are available for 39,169
out of a possible 130,262 observations, or
approximately 30 per cent of the total. Response rates
vary substantially among the different regions of the
world. It is important to note that while the
percentage of real observations is rather low, 174 out
of 191 countries (91 per cent) reported LFPR in at least
one year during the 1980 to 2010 reference period.
Thus, some information on LFPR is known about the
vast majority of the countries in the sample.
All missing values have been imputed. The database
is a complete panel, that is, it is a cross-sectional time
series database with no missing values. The basic
missing value estimation model contains four
methodological steps: first, in order to ensure realistic
estimates of LFPR, a logistic transformation is applied
40
The national data on migrant workers, domestic
workers and migrant domestic workers were mostly
extracted from existing international databases.
Additional national data were collected from
publications or websites of national statistical offices.
4.2.1 OECD migration databases
The Organisation for Economic Co-operation and
Development (OECD) manages several databases
dedicated to international migration.11 The main ones
used for the present study were the database on
labour market outcomes of immigrants and the
database on immigrants in OECD countries (DIOC).
The database on labour market outcomes consists
of a series of statistical tables on quarterly rates of
labour force participation, employment and
unemployment, by sex and place of birth. The data are
mostly derived from national labour force surveys.
They cover twenty-nine OECD member countries and
include data for the period 2009 to 2013. The DIOC
database includes detailed information, mostly derived
from population censuses and population registers, on
demographic characteristics (age and gender),
duration of stay, labour market outcomes (labour
market status, occupations, sectors of activity), field of
study, educational attainment and place of birth. An
extension of DIOC covering a number of non-OECD
countries was not used here as it relates to the year
2000.
11 Available at: http://www.oecd.org/els/mig/oecdmigrationdatabases.htm.
PART II ESTIMATE METHODOLOGY
4. METHODOLOGY, PHASE 1: DATA SOURCES AND INPUT DATA
4.2.2 ILO global and regional databases on labour
migration
temporal comparisons. The data are disseminated free
and are available online upon registration.14
The ILO database on labour statistics (ILOSTAT)
provides statistics on international labour migration,
which cover indicators on international migrant stock,
international migrant flow and nationals abroad for
selected ASEAN and Arab countries from 2001 to
2013.12 The data are in the form of cross-tabulations.
At the time data were collected for this study, the
IPUMS covered 79 countries, 258 population censuses
and 560 million person records. The database included
variables on sex, age, employment status (employed,
unemployed, inactive) and nativity (native-born,
foreign-born). It also included variables on branch of
economic activity or industry according to the national
classification of industrial activities (IND) as well as
recoded (INDGEN) into twelve fairly consistent
groupings roughly conforming to the UN International
Standard Industrial Classification (ISIC). The third digit
of INDGEN retains important detail among the service
industries that permits, in many cases, the
identification of domestic workers as “Private
household services”.
The tables comprise information on stocks of the
total employed population and employed migrant
population by sex and country of origin, by occupation
and by status in employment, as well as inflows of
migrants by sex, country of origin, occupation and
economic sector. The database also includes three
tables on nationals abroad by sex and country of
destination, and outflows of nationals and employed
nationals by sex and country of destination.
More recent data were collected from databases
developed by the ILO Regional Offices, in particular
the International Labour Migration Statistics (ILMS)
databases for the Association of Southeast Asian
Nations (ASEAN) and the Arab States (2015 edition),13
and the 2012 Labour Overview for Latin America and
the Caribbean (ILO, 2012b). For global estimation of
migrant domestic workers, the present study also
made use of the database on domestic workers
developed for the 2013 ILO report on domestic
workers across the world (ILO, 2013c), which contains
harmonized data on the total number of domestic
workers in 2010 for 146 countries and territories, and
by sex for 137 countries and territories.
4.2.3 IPUMS international database on population
censuses
The Minnesota Population Center is a leading
developer of demographic data resources. It maintains
an International Public Use Microdata Series (IPUMS).
The data are samples from population censuses from
around the world taken since 1960. Names and other
identifying information have been removed. The
variables have been given consistent codes and have
been documented to enable cross-national and cross-
4.2.4 Other national data
To supplement the main databases described above,
country data were also collected directly from national
sources or reports, for example, the EU
Neighbourhood Migration Report 2013 (Fargues,
2013); Brunei Labour Force Survey 2014 (JPKE, 2014);
the Brazilian National Household Sample Survey 2009
(IBGE, 2009); the Namibia 2011 Population and
Housing Census (Namibia Statistical Agency, 2013),
and The Kuwaiti labour market and foreign workers:
Understanding the past and present to provide a way
forward (Salvini, 2014).
In the important case of China, the available data
were limited to domestic workers obtained as part of a
survey carried out by the Ministry of Human Resources
and Social Security (MOHRSS) in nine cities:
Chongqing, Nanchang, Nanjing, Qingdao, Shanghai,
Shenyang, Tianjin, Wuhan and Xiamen. The resulting
aggregate estimate in these cities for 2003 is 240,000
domestic workers.15 As part of its study on domestic
workers across the world (ILO, 2013c), the ILO
combined the MOHRSS data with other data to
estimate that there were 9,390,000 domestic workers,
or 1.2 per cent of total employment, in China in 2010.
12 Available at: http://www.ilo.org/ilostat/faces/help_home/data_by_subject?_
adf.ctrl-state=148yhq79k_9&_afrLoop=524817554597542.
14 Available at: https://international.ipums.org/international/samples.shtml.
13https://www.ilo.org/ilostat/faces/help_home/data_by_subject?_adf.ctrlstate=o4qkcx0ho_9&_afrLoop=401854832043421.
15 Asia Monitor Resource Centre: “Domestic work and rights in China” in
http://www.amrc.org.hk/content/domestic-work-and-rights-china, 2007.
41
ILO Global estimates on migrant workers: Results and methodology
The MOHRSS study further reported that as average
income increases, the demand for domestic help
should increase and consequently domestic work has
the potential of generating 20 million jobs and
600,000 domestic service agencies in China in the
long run. On the basis of this long-term projection, we
have a used a simple model to extrapolate the limited
empirical data available to obtain an estimate for 2013
of around 13 million domestic workers in China.
4.3 Constructing input data
The process of constructing global and regional
estimates can be divided into two fairly distinct
phases:
1. Construction of the input data file in a
standardized form.
2. Imputation, adjustments for consistency,
aggregation and production of global and
regional estimates.
In terms of implementation, the basic difference
between the two phases is that Phase 1 requires
expert involvement and judgement at almost every
step so as to be able to locate, select and edit data
from diverse international as well as national sources
resulting in as complete and as consistent an input
dataset as possible. The outcome of this phase is an
input data file in a standardized form at the level of
individual country by sex, and possibly also by age or
other classification variable(s) which may be
incorporated in the future.
Once the input data file is available in a
standardized form, the procedures for imputation,
adjustments for consistency, aggregation and
production of global and regional estimates in Phase 2
can be almost completely standardized.16 Software
can be developed to facilitate their repeated
application to different input datasets in the specified
form. They can form a tool for institutionalizing the
production and periodic updating of global and
regional estimates on migrant workers and migrant
domestic workers.
16 Of course, expert judgement may be called for in certain cases, e.g. in the
choice of “donors” when imputing across imputation “domains”. These
domains refer to cells in the cross-tabulation of detailed subregions and
income groups.
42
This section is concerned with Phase 1. The
different steps involved are described below.
For the present application, the construction of the
standardized input data file has been carried out in an
Excel file with three sheets, one storing the raw data,
the second editing the raw data and calculating
unique data points for countries with multiple data
points, and finally the third sheet standardizing the
data for the reference year 2013. These sheets have
been developed by the ILO, and may be modified,
updated and possibly made more detailed in future
applications of the procedures.
4.3.1 Raw data
The first sheet stored the input data obtained from the
national data sources. Each record corresponded to
one data source from a specific reference year and a
specific sex (male, female or total). In practice, there
may be multiple input records for a given country if
multiple data sources are used or if a single data
source is used for different years, or even if there is a
single data source for the same year but separate data
for men and women. The input data were unedited
and were recorded in the format of the national data
source, in absolute numbers or in percentages.
4.3.2 Edited data points
The input data were then edited and stored in a
second sheet called “Output”. Editing involved first
the calculation of data points for each record. A data
point is one of the five ratios:
(i)
the share of migrant workers in total labour force
or total employment;
(ii) the migrant-specific labour force participation
rate;
(iii) the share of domestic workers in total labour
force or in total employment;
(iv) the share of migrant domestic workers among
migrant workers; or
(v) the share of migrant domestic workers among all
domestic workers.
PART II ESTIMATE METHODOLOGY
4. METHODOLOGY, PHASE 1: DATA SOURCES AND INPUT DATA
If none of the data points could be calculated, the
country-by-sex record would be rejected. Only records
would be retained for which at least one data point
could be calculated. Where the data points are ratios,
an essential requirement is that both the numerator
and the denominator come from the same source, so
as to ensure that they are mutually consistent. As a
rule, it is preferable that a data point is in the form of
a ratio, rather than in the form of an absolute number,
e.g. migrant workers as a proportion of all migrants
aged 15+ rather than directly as an estimate of the
number of migrant workers. This is because a ratio is
less affected by coverage errors common to its
numerator and the denominator.
The next step in the editing process is choosing
between multiple data points referring to different
sources or different reference years for the same item
of information. An underlying factor in the choice
among different sources on the same item of
information always has to be expert assessment of the
relative “plausibility” of the different sources. Beyond
that, in general the more recent record containing the
data point(s) was retained. But in a few cases, the
decision was made in favour of the record with the
richer number of data points even if the record was
not the most recent. An alternative could have been to
choose the “best source” for each data point
independently, though that may increase somewhat
the number of different sources referred to for the
same country.
At the end of the editing process, there would be at
most three records for each country, one referring to
men, one referring to women and one referring to
both men and women. Also, each edited record may
contain at most five data points, if for that country-bysex record data were available on migrant workers,
domestic workers and migrant domestic workers, as
well as total labour force or total employment. A
national estimate of the total number of migrants is
not considered a data point for the present purpose,
as it is not labour-related data and does not add to the
information content of the study, given the existence
of the benchmark data on the stock of international
migrants covering all countries considered in the
present estimates. A specified item of relevant
information corresponds to a single data point – only
one is chosen when there are multiple sources for the
same item of information.
Hence, a data point means a country-by-gender
level estimate obtained from a national data source of
any one of the following:
(i)
the number or the percentage of migrant
workers among all migrants or all workers;
(ii) domestic workers among all workers; or
(iii) migrant domestic workers among all migrant
workers or all domestic workers.
As noted, the present analysis is based on 176
countries (representing 99.6 per cent of the global
working-age population) which are covered in the
benchmark data sources.
The present estimates also include the
disaggregation of migrant workers according to main
sector of activity (agriculture, industry, services).
However, having information available only on that
breakdown, without having information on any of the
five data points identified above, does not qualify a
record for inclusion. In practice no such cases occurred
in the input data. Information on breakdown by sector
was available only for a subset of cases with
information on total migrant workers (MW).
Additional data on migrant workers and migrant
domestic workers are available from national sources
or a large subset of the countries. The preliminary
results of the ILO global and regional estimation of
migrant workers and migrant domestic workers,
presented in this report, are based on national data
points from 134 countries and territories, covering
about 94 per cent of the global labour force.17
There were altogether 1,056 national data points
retained after editing. The figure includes national
data points on men and women. Information on the
presence or absence of data points by country and
subject is given in Annex D of this report. This gives an
average of (1,056/3x134)=2.6 data points per record
(out of a maximum of 5.0) for the 134 countries with
at least one data point available.
More information on data availability in terms not
only of the number of countries covered but also on
the share of the relevant population covered for
17 Seven data points and one country were subsequently deleted for reasons
of inconsistency or incompleteness.
43
ILO Global estimates on migrant workers: Results and methodology
FIGURE 4.2
Coverage of national data by reference year, 2005−14
different variables will be provided in the following
sections on data quality and estimation methodology.
Figure 4.2 shows the distribution of the national
data points by reference year. For ease of
interpretation the counts are presented in terms of
number of countries so that they add up to 134 − the
total number of countries with retained national data
points. It can be observed that the bulk of the data
refer to the past five years. The model year is
2010−11, covering the reference year of the ILO
global and regional estimates of domestic workers
(ILO, 2013c). For three countries (Brazil, Brunei
Darussalam and Kuwait) the dataset contains data for
2014.
Figure 4.3 shows the distribution of the retained
country data by type of source. Most of the data were
from population censuses, labour force surveys or
other household-based surveys. Most of the
population censuses were from two regions: the
Americas and Europe, and Central Asia. Most of the
LFS and other household-based surveys were from
Africa, and Asia and the Pacific. The data from
administrative sources were from China, Lebanon,
Russian Federation, Singapore and Thailand. The
national estimates were from Kuwait and the
Philippines. A total of 142 sources were used to obtain
the 1,056 retained national data points.
44
FIGURE 4.3
Coverage of national data by type of source
The distribution of the retained country data on the
main topics is shown in table 4.2. The figures are
summed from Annex D over the 176 countries.
Considering total (T), 97 countries and territories
had data on migrant workers, 126 on domestic
workers, 73 on migrant domestic workers, and 60 on
the breakdown of migrant workers by main sector.
Not all country data were available for men and
women separately. This applies in particular to data on
migrant workers (MW). For 96 countries there were
data on females (F), but only for 85 on males (M). In
112 countries, data on MW were available for at least
one of the three populations: total (male+female, T),
male (M), or female (F).
For domestic workers (D) data by sex were available
for 126 countries, with figures only for males in one
country. For migrant domestic workers (MD) for all
the 73 countries with any data, the data were always
available by sex. The same applied to migrant workers
by broad branch of economic activity: data were
found for 60 countries and territories, all of which
were also disaggregated by sex.
4.3.3 Standardized input data for 2013
The edited data points serve to calculate standardized
input data for 2013. The procedure is applied for each
country with available data points, separately for
PART II ESTIMATE METHODOLOGY
4. METHODOLOGY, PHASE 1: DATA SOURCES AND INPUT DATA
TABLE 4.2
Summary of data availability, number of countries with information, by variable
Migrant workers (MW)
Migrant domestic
workers (MD)
Domestic workers (D)
MW by main sector
T
M
F
Any
T
M
F
Any
T
M
F
Any
T
M
F
Any
97
85
96
112
126
127
126
127
73
73
73
73
60
60
60
60
Total
1 056
TABLE 4.3
Calculation of standardized input data for 2013
Variable
Name
Calculation
Benchmark data
Population aged 15+ years
P
UN World Population Prospects
Migrant population aged 15+
M
UN Trends in International Migrant Stock
Labour force aged 15+
W
ILO Estimates and Projections of the Economically Active
Population
Data points
Migrant workers
MW
Domestic workers
D
Migrant domestic workers
MD
(1) M x Edited data point [MW/M] or
(2) W x Edited data point [MW/W]
(3) W x Edited data point [D/W]
(4) MW x Edited data point [MD/MW] or
(5) D x Edited data point [MD/D]
males, for females, and for both sexes, as shown in
table 4.3.
(6)the number or proportion of migrant workers in
agriculture (AGR);
In addition to data points (1)-(5) above, at least one
of which must be available for a country to be
included in the database for estimation, there are
three more data points (again for total, and separately
by sex) concerning the distribution of migrant workers
according to the main sector of activity:18
(7)the number or proportion of migrant workers in
industry (IND);
18 As already noted, these additional three variables were available only in
situations where some information related to the total number of migrant
workers (MW) was also available. Hence these variables do not bring in
any additional countries to the list with at least one data point in terms of
variables (1)-(5) in table 4.3.
(8)the number or proportion of migrant workers in
services (SRV).
The use of the standardized input data for global
and regional estimation of migrant workers and
migrant domestic workers in the second phase of the
estimation process is described in section 6, following
the discussion of data quality in the following section.
45
5. Data quality
5.1 Dimensions of data quality
Data quality has a number of dimensions. In the
present context, the following six are particularly
relevant:
■■
Statistical accuracy
■■
Consistency with other sources
■■
Robustness of the results to the use of different
imputation methodologies
procedures do not account for short-term or crossborder work-related migration, particularly in
agriculture and construction as well as in domestic
work. Another source of underestimation is the likely
underreporting of irregular migration, not only in
administrative records but also in national censuses
and surveys.
5.1.2 Consistency with other sources
■■
Completeness of the input data
■■
Internal consistency of the results
■■
Data quality
5.1.1 Statistical accuracy
It is not possible to evaluate in any detail the statistical
accuracy of the estimates obtained, since the input
data used come from a great variety of national
sources which are very heterogeneous in data quality.
The estimates have been carefully constructed using
transparent procedures, and it is believed that the
results obtained are plausible and the best possible
under the given circumstances. Nevertheless, the
global and regional data presented in this report are
likely to be an underestimate of the number of
migrant workers and especially of the number of
migrant domestic workers, both globally and for the
various regions. The primary factor responsible for this
is the lack of complete information. Labour migration
across the world is also underestimated, as the
As an example of comparison with other sources,
estimates of the number of domestic workers given in
this study are compared with ILO 2010 global and
regional estimates. The results are summarized in
Annex F. The ILO’s global and regional estimates of
domestic workers in 2010 (ILO, 2013c) referred to 177
countries and territories, all included in the present
study except Netherlands Antilles. The underlying data
were obtained from national census and survey
sources and in a few cases from administrative
records. While the data used in the two studies
overlap to a considerable extent, the estimation
methodologies are rather different, as shown in box 1.
There are previous ILO estimates of migrant
workers: for example, that there were 36-42 million in
1995 (ILO, 1999, p. 3, table 1); 86.2 million in 2000;
and 105.5 million in 2010 (ILO, 2010b, p. 17, table
1.2). These previous estimates are not comparable to
the 2013 figures due to differences in definitions,
methodology and data sources used.
47
ILO Global estimates on migrant workers: Results and methodology
BOX 1
Number of domestic workers: Comparison with ILO 2010 global and regional estimates
Estimation of the number of all domestic workers is not the primary objective of this report. Nevertheless, the number
of all domestic workers is a parameter in the estimation of the number of migrant domestic workers and is therefore
produced as a byproduct of application of the present procedure.
In 2013, the ILO published global and regional estimates of domestic workers in 2010 (ILO, 2013c). The estimates
referred to 177 countries and territories, all included in the present study except Netherlands Antilles. The global
number of domestic workers in the present exercise is estimated at 67 million for 2013 compared to a little under 53
million in 2010, an increase of over 25 per cent.
The definition of domestic worker was similar to the one adopted in the present study, namely, branch of economic
activity codes 95 or 97 of the International Standard Industrial Classification of All Economic Activities (ISIC Rev 3,
Rev 3.1 or ISIC Rev 4) or its national equivalent. However, the 2010 global estimate covered currently employed
domestic workers, as opposed to the present study that in principle includes both currently employed and
unemployed domestic workers.
The differences at the global level may be the result of a number of general factors described in more detail in
Annex F of the present report. The most important include the following:
(i) Population growth between 2010 and 2013 is a factor contributing to the difference.
(ii) Additional contributions to increases over time may also come from socio-economic factors such as economic
development, increased inequality and urbanization.
(iii) In addition, a part of the difference is due to the additional component of unemployed domestic workers included
in principle in the 2013 estimate but not in the 2010 estimate.
(iv) There is more complete coverage of “industrialized” countries in the new estimates.
(v) The estimates for China have been revised upwards.
(vi) Corrections to the input data in order to improve their plausibility and consistency has resulted in revision
upwards in a number of other countries. An important contributing factor is the availability of more and possibly
better data for the 2013 estimates, not available for the 2010 estimates.
(vii) We believe that the present methodology is more precise and subject to less bias of underestimation.
5.1.3 Robustness of the results to the use of
different imputation methodologies
The method of imputation in the present application is
based on using regional averages to provide estimates
for countries with missing information in the region,
and using average values from neighbouring regions
when no information is available for the region
concerned. To evaluate the extent to which the global
and regional estimates of migrant workers depend on
the particular method of imputation adopted for
treating countries with missing values, two alternative
imputation methods have also been applied to the
datasets, one based on regressions and the other on
cross-product ratios.
In imputation using regressions, the method
assumes a relationship between the labour force
participation rate of migrant workers and the national
labour force participation rate. After fitting the data,
48
the parameters of the relationship are estimated and
used to derive estimates of the labour force
participation of migrants from the information on the
national labour force participation of the country.
In imputation using cross-product ratios, the
method used for the statistical treatment of countries
with missing data on migrant workers is based on the
calculation of cross-product ratios describing the
relationship between migrant status and labour force
status of the working-age population. The method
was also adapted to the case of migrant domestic
workers by considering the relationship between
migrant status and domestic workers status.
The two methods are described in detail and their
results compared in Annex E.
PART II ESTIMATE METHODOLOGY
5. DATA QUALITY
5.1.4 Completeness of the data
The lack of full information on all items in all countries
is a major issue in the current estimation procedure.
The problem is described in section 5.2, supplemented
by information in Annex D. The solutions adopted are
discussed.
5.1.5 Internal consistency of the results
There are a number of inherent relationships between
the variables used in this study that should be
reflected in the final estimates at any level of
aggregation. Some of these are discussed in section
5.3.
5.1.6 Data quality
For the purpose of quality assessment, the underlying
data used for global estimation of migrant workers
and migrant domestic workers may be grouped into
three parts: (1) international datasets on population,
stock of international migrants, and economically
active population or labour force;19 (2) national
datasets on migrant workers and migrant domestic
workers; and (3) national census data on migrant
workers by branch of economic activity.
1. Population, stock of international migrants and
labour force. Procedures and data quality aspects of
these sources have been described in sections 3.5 and
4.1. Sources of further information include World
Population Prospects, the 15th Revision (UN, 2015);
Trends in International Migrant Stock: The 2013
Revision: Migrants by Age and Sex: CD-ROM
documentation (UN, 2013c).
2. Migrant workers and migrant domestic workers.
The data on migrant workers and migrant domestic
workers were collected from a variety of national and
international sources by a team of statistical assistants
specially hired by the ILO over a period of about four
months from February to May 2015. The main criteria
used for data collection were the reference period of
the data (to the extent possible, not earlier than 2005)
and the possibility of calculating consistent
percentages such as the share of migrant workers in
total labour force, the labour force participation of
migrant workers, the share of migrant domestic
workers among domestic workers or the share of
migrant domestic workers among migrant workers.
The underlying national data were subject to a
number of errors affecting the aggregate regional and
global estimates. First, given the time constraint, the
data collected did not cover all possible countries with
available data on migrant workers and migrant
domestic workers. Second, because of the variety of
reference periods and definitions of migrant workers
and migrant domestic workers used in the available
national sources, the resulting data were in many
cases not fully comparable and hard compromises had
to be made in combining them.
3. Migrant workers by branch of economic activity.
The underlying national data on migrant workers by
branch of economic activity are obtained from the
Integrated Public Use Microdata Series (IPUMS),
described above in section 4.2.3. It is a collection of
sample microdata based on subsets of full population
data from countries around the world. The IPUMS
samples are either systematically drawn from fullcount data by IPUMS itself (or according to IPUMS
specifications) or by the statistical offices of the
country of origin according to a variety of complex
sample designs. Samples drawn by countries of origin
may include oversampling, clustering and stratification
with potential effects on multivariate standard error
calculation, and on weight computation to ensure
representative estimates. Another source of potential
error in the present context is the varied national
classifications used for classifying the working
population and migrant workers by branch of
economic activity. IPUMS-International maintains a rich
set of metadata on sample selections of census
records, as well as the national census questionnaire
19 In principle, the estimation procedure in this report takes the value of
“migrant-specific labour force participation rate” estimated from national
sources, and multiplies it by the corresponding UN estimates of the
number of migrants, to obtain an estimate of the number of economically
active migrants. In practice, however, limitations in the available data
result in departures from this ideal in some cases. This occurs when the
available data cover only the employed part of the population but exclude
the unemployed population. In such cases, the estimates are confined to
the employed population.
49
ILO Global estimates on migrant workers: Results and methodology
and enumeration instructions in the original language,
in pdf format and in English in html format.20
5.2 Completeness of available data
A major issue in the current estimation concerns the
lack of full information on all items in all countries. In
section 4 and Annex D some information is provided
on the availability of various items of information in
the input file, by country and sex. In this section
information is presented and analysed by country
income group and broad subregion.
5.2.1 Coverage of national data by income level
Table 5.1 shows the number of countries with at least
one data point available, classified by income level. It
shows that low-income countries are much less well
represented than middle-income and high-income
countries: less than half (47 per cent) of the lowincome countries and most (93 per cent) of highincome countries are covered.
The last column also clearly shows that the labour
force coverage of countries steadily increases with
income level. In low-income countries, the labour
force coverage is 59 per cent, against 94 per cent in
lower-middle income countries and 98 per cent in
upper-middle income countries; the labour force
coverage of high-income countries is virtually
complete.
Overall, the percentage of labour force covered is
considerably higher than the percentage of countries
covered. This is because data tend to be more readily
available for larger countries.
Table 5.2 shows the number of countries for which
information on various items by gender was available,
classified according to the countries’ level of income.
Firstly, considering the overall level:21
20 Available at: https://international.ipums.org/international/.
21 The figures given here show some minor differences from the figures given
in Annex D and summarized in the previous section. This is because of
some further editing of the data during the analysis phase. For instance, it
was possible to construct the figure for males if the information had been
recorded at the total level and for females. One or two cases were deleted
from the information on domestic workers and migrant domestic workers
due to inconsistency.
50
The highest proportion (70 per cent) of countries
have information on domestic workers (D). When
available in a country, it is always available separately
by sex.22 By contrast, information on migrant domestic
workers (MD) is missing in a much higher proportion
of countries: it is available in 30 per cent of the
countries with breakdown by sex, and in another 11
per cent only at the total level without breakdown by
sex.
Concerning data on migrant workers (MW),
information at the total level is available for 55 per
cent of countries; for females information is available
also for 55 per cent, and for males for 49 per cent of
countries. These are not necessarily the same set of
countries. In fact, 64 per cent of the countries have
either full data by sex, or only at the total level without
breakdown by sex, or in a few cases for only one of
the categories by sex.
Information on breakdown of migrant workers by
sector is available for a subset of these countries,
amounting to only 35 per cent of the total number of
countries. This means that of the countries for which
information on MW is available, breakdown by sector
is also available for around two-thirds of the cases
(35/55=64%).23 It should also be mentioned that in
countries for which data by sector, MW(sec) , were
available, the sector data were also available for male
and female migrant workers separately, and the data
for the three components were from the same source
and the same reference year.24
There are generally sharp differences by income
level. For low-income countries, information on any
variable is available only for a minority – all in the
range 23-33 per cent except for the higher figure (47
per cent) on domestic work. The proportion of
countries with information is much higher in highincome countries for three of the variables – MW, D
22 Even though variable D has the largest proportion of countries with
information recorded, in a number of cases it turned out to lack
consistency or plausibility, as detailed in section 5.3.3.
23 It is possible for a country to lack information on the number of migrant
workers (MW), but have information available on its breakdown by sector
(MW(sec)). This is because the latter information is in the form of the ratio
[MW(sec)/MW], where the numerator and the denominator come from the
same source. The “MW” in the denominator of the above does not
necessarily (and does not have to) correspond to the correct estimate of
the variable MW. The latter is normally estimated from national information
on LFPR of migrants [MW/M], multiplied by the number of migrants, M,
estimated from the standard international sources.
24 It may be considered surprising that more countries have data on the
number of migrant domestic workers than of migrant workers by sector,
since domestic work is only one part of the service sector. However, the
two items of information may come from different sources.
PART II ESTIMATE METHODOLOGY
5. DATA QUALITY
TABLE 5.1
Coverage of countries with least one data point available, by income level
Income group
Countries and territories
Labour force
Total no.
No. covered
% covered
% covered
Low income
30
14
47
59
Lower-middle income
44
33
75
94
Upper-middle income
44
33
75
98
High income
58
54
93
100
176
134
76
94
Total
TABLE 5.2
Number of countries with data available on various items, by income level
Total
number
Migrant workers
(MW)
MW by sector
Domestic workers
(D)
Migrant domestic
workers (MD)
T
%
M
F
T
%
M
F
T
%
M
F
T
%
M
F
Data available by
income group
1
Low income
30
10
33
9
11
9
30
9
9
14
47
14
14
7
23
7
7
2
Lower-middle
income
44
23
52
17
19
13
30
13
13
32
73
32
32
14
32
13
13
3
Upper-middle
income
44
21
48
19
22
21
48
21
21
30
68
30
30
17
39
16
16
4
High income
58
43
74
41
44
19
33
19
19
48
83
48
48
34
59
17
17
Total
176
97
55
86
96
62
35
62
62 124 70 124 124 72
41
53
53
%
100
55
49
55
35
35
35
30
30
70
70
70
41
TABLE 5.3
Proportion of the relevant population for which data are available, various items, by income group
(percentages)
Migrant workers
(MW)
MW by sector
Domestic workers (D)
Migrant domestic
workers (MD)
M(MW)/M
MW(Sec)/MW
W(D)/W
MW(MD)/MW
T
M
F
T
M
F
T
M
F
T
M
F
1 Low income
37
34
55
28
29
26
59
58
61
19
22
17
2 Lower-middle
income
67
11
11
7
8
6
92
94
90
7
7
5
3 Upper-middle
income
68
67
70
67
71
60
97
97
98
63
62
46
4 High income
88
94
87
63
59
68
97
97
97
71
54
55
82
79
75
57
55
59
93
93
92
62
49
47
Data available by
income group
Total
51
ILO Global estimates on migrant workers: Results and methodology
and MD. In contrast to MW, information on its
breakdown by sector, MW(sec) does not improve
much with increasing income level.
Table 5.3 shows the variation, for different
variables, of the “proportion of the relevant
population for which data are available”. The variables
involved are normally estimated as ratios, and the
relevant population is the denominator of the ratio.
For instance, in estimating the number of migrant
workers (MW) we normally estimate the LFPR of the
population (MW/M), and multiply that by the known
number of migrants, M. Hence the “proportion of the
relevant population for which data are available”
equals M, for which MW is known from the input
data, written here as [M(MW], divided by M for the
total population under consideration. For variable
MW, this ratio (for total=male+female) is 37 per cent
for low-income countries and 88 per cent for highincome countries – a sharp gradient by income level.
Similarly, for estimating the number of domestic
workers (D), we normally estimate the proportion of
domestic workers in the total labour force or all
workers [D/W], and multiply that by the known
number of workers W. Hence the “proportion of the
relevant population for which data are available”
equals W, for which D is known from the input data,
written here as W(D), divided by W for the total
population under consideration. For variable D, this
ratio (again, for total=male+female) is 59 per cent for
low-income countries and 97 per cent for high-income
countries – a somewhat less sharp gradient by income
level.
For both migrant workers by sector, MW(sec) and
migrant domestic workers, MD, the relevant
population is the number of migrant workers, MW.
For the first variable, the “proportion of the relevant
population for which data are available” equals MW,
for which breakdown by sector is known from the
input data, divided by MW for the total population
under consideration; and similarly for MD. 25
25 In computing these ratios for MW(sec) and MD, we have used the “full”
value of MW, meaning MW after imputation for missing values for it
(imputation procedures are described in the next section). In fact, the
amount of missing information on MW(sec) and MD given in the tables
here can be viewed as consisting of two components: the proportion of
information missing on the variable concerned where MW is available,
multiplied for the proportion of information missing on MW itself (as given
in the first column of the table). This gives 57/82=70% for MW(sec) and
62/82=76% for MD among cases with MW available in the input data.
52
For the two last-mentioned variables, the
proportion increases sharply as we move from low
income to high income groups, but with one major
exception: the proportion is extremely low for the
lower-middle income group of countries.
Overall, the ratios in terms of the base population
covered are higher than the proportion of countries
with available data, for instance 82 versus 55 per cent
for MW, and 62 versus 41 per cent for MD. This is
because, as already noted, data tend to be more
readily available for larger countries.
Finally, a brief comment on the differences in the
availability of breakdown by sex follows. Generally,
when information is available for the total population
it is also available separately for males and females. An
outstanding exception is information on migrant
workers (MW) in the lower-middle income group: it is
usually available only for the total population, without
breakdown by sex.
In the case of MD, the figure for females is notably
lower than that for males; this is connected with the
fact that a much higher proportion of female migrant
workers are domestic workers and the female-to-male
ratio of MW varies across countries.
5.2.2 Coverage of national data by broad subregion
Table 5.4 shows information on data availability
classified by broad subregion. In Sub-Saharan Africa,
the four variables shown are missing in a majority of
the countries; while the Eastern Asia and Southern
Asia subregions also include a high proportion of
countries where data are missing.
In Eastern Asia, there are no countries with
information on migrant domestic workers, MD.
In Southern Asia, information on both breakdown
by sector, MW(sec), and migrant domestic workers,
MD, is available for only one in ten countries.
Table 5.5 shows proportions of the relevant
population for which data are available on various
items, by broad subregion. The interpretation of these
measures is the same as that given above in the
discussion by income level.
PART II ESTIMATE METHODOLOGY
5. DATA QUALITY
TABLE 5.4
Number of countries with data available on various items, by broad subregion
Total
number
Migrant workers
(MW)
MW by sector
Domestic workers
(D)
Migrant domestic
workers (MD)
T
%
M
F
T
%
M
F
T
%
M
F
T
%
M
F
3
11 Northern Africa
6
3
50
3
3
3
50
3
4
67
4
4
3
50
3
3
12 Sub-Saharan
Africa
45
15
33
12
13
11
24
11 11 22
49
22
22
9
20
9
9
Latin America
21 and the
Caribbean
30
19
63
18
19
17
57
17 17 24
80
24
24
18
60
18
18
22 Northern
America
2
2
100
2
2
2
100
2
2
100
2
2
1
50
1
1
Northern,
31 Southern and
Western Europe
28
21
75
20
23
12
43
12 12 23
82
23
23
17
61
7
7
32 Eastern Europe
10
7
70
7
6
2
20
2
2
8
80
8
8
5
50
2
2
and
33 Central
Western Asia
11
4
36
5
5
3
27
3
3
9
82
9
9
3
27
2
2
41 Arab States
12
11
92
9
9
2
17
2
2
10
83
10
10
9
75
4
4
51 Eastern Asia
7
1
14
0
2
2
29
2
2
6
86
6
6
0
0
0
0
South-Eastern
52 Asia and the
Pacific
16
10
63
9
11
7
44
7
7
10
63
10
10
6
38
6
6
53 Southern Asia
9
4
44
1
3
1
11
1
1
6
67
6
6
1
11
1
1
Total
176
97 55
86 96
62 35
Again, the ratios in terms of the base population
covered are higher than the proportion of countries
covered, since data tend to be more readily available
for larger countries.
In Latin America and the Caribbean, Northern
America, and Northern, Southern and Western
Europe, the proportion of data available is around 90
per cent, with the exception of MW(sec) in the first of
the above-mentioned subregions. However, in the
Northern, Southern and Western Europe subregion,
breakdown of MD by sex is missing in a third of the
population. The pattern of availability by sex is uneven
also in some other cases, as can be seen in the table.
2
62 62 124 70 124 124
72 41
53 53
5.3 Internal consistency requirements
There are a number of inherent relationships between
the variables used in this study that should be
reflected in the final estimates at any level of
aggregation.
5.3.1 Total = Male + Female
A most obvious and important requirement for
consistency is that numbers of “male + female”
should be equal to the total population for any
53
ILO Global estimates on migrant workers: Results and methodology
TABLE 5.5
Proportion of the relevant population for which data are available, various items, by broad subregion
(percentages)
Migrant workers
(MW)
MW by sector
Domestic workers
(D)
Migrant domestic
workers (MD)
M(MW)/M
MW(Sec)/MW
W(D)/W
MW(MD)/MW
T
M
F
T
M
F
T
M
F
T
M
F
11 Northern Africa
41
36
48
40
36
48
91
91
90
40
36
48
12 Sub-Saharan Africa
47
37
39
34
35
32
71
71
71
30
32
27
America and
21 Latin
the Caribbean
92
91
92
63
65
62
98
98
98
91
92
90
22 Northern America
100
100
100
100
100
100
100
100
100
87
93
80
Southern
31 Northern,
and Western Europe
96
96
99
87
89
85
96
96
96
92
67
63
32 Eastern Europe
35
68
9
3
3
3
81
82
81
9
3
3
and Western
33 Central
Asia
41
44
45
40
40
41
79
79
78
40
20
23
41 Arab States
95
93
84
9
7
17
84
84
84
65
14
27
51 Eastern Asia
16
0
41
11
13
10
98
99
98
0
0
0
Asia
52 South-Eastern
and the Pacific
92
81
83
38
41
35
85
86
83
38
40
35
53 Southern Asia
82
20
29
14
23
1
99
99
99
14
23
1
82
79
75
57
55
59
93
93
92
62
49
47
Data available by
income group
Total
variable at the country level as well as at regional and
global levels.
There are many instances of input data based on
national sources used in the present exercise where
this requirement is not satisfied in the numbers
available for total and for male and female separately.
Generally the procedure used here did not attempt to
adjust individual input data items to conform to this
requirement. Rather, the relationship “Total = Male +
Female” has been built into the methodology so as to
ensure that it holds in all derived estimates at the
country, regional and global levels.
The above applies to the variables MW, MW(sec), D
and MD based on data from diverse national sources.
The three benchmark variables P, W and M, coming
from standard international sources and available by
sex for all countries, are expected to satisfy the
54
relationship “Total = Male + Female”. In the present
exercise, this indeed was found to be true in the case
of total population aged 15+ (P) and total number of
migrants aged 15+ (M), but not in the case of the
total labour force or working population (W). The
details were as follows.
For W, the requirement M+F=T was violated in the
input data in 38 of the 176 countries with data
available by 10 (‘000) or more, in 29 of the 176
countries by 20 (‘000) or more, in 19 countries by 50
(‘000) or more, and in 9 countries by 100 (‘000) or
more. The net difference over all the countries was
quite small, but gross differences were more
significant, and quite large in some countries.
In the data used for the construction of global and
regional estimates, this discrepancy was removed by
PART II ESTIMATE METHODOLOGY
5. DATA QUALITY
resetting the numbers total (say, T1), male (M1) and
female (F1) in each country as follows.
T2 = max(T1, M1+F1)
M2=M1*T2/(M1+F1)
F2 = F1* T2/(M1+F1)
so that M2+F2 = T2 is ensured.
The world total of T increased only by a very small
amount (by around 1.6 million, or 0.05 per cent), but
in some countries the correction was significant.
The above shows the importance of making even
such simple internal consistency checks. In
constructing the variables, it is often necessary to do
so repeatedly following steps involving other data
adjustments, as detailed in section 6 below.
useful, concept. Essentially, it implies that if the data
are clearly outside the range of values which can be
expected − on the basis of experience, comparison
with similar statistics, logic of the situation, or even
subjective expert assessment – then they are not
plausible.
It is on such basis that some of the input data have
had to be modified or rejected, and/or statistical
procedures chosen so as to reduce the risk of
obtaining results which appear implausible. The
following example illustrates this point.
Errors in the input data for domestic workers (D)
(i) In some cases input data on domestic workers were
implausible. For instance:
■■
In Australia, the input values of D were meaninglessly too low (practically =0), and have been
deleted (to be imputed along with other countries
with no data).
■■
In three other countries (Cape Verde, East Timor,
Trinidad) the data provided were too incomplete to
be useful, and have been similarly deleted (and later
imputed, of course, just as any other missing data).
■■
In a couple of other countries, the given value of
D is so small that it falls short of the given value
of MD (number of migrant domestic workers). We
substituted the latter value for the former. This is
the minimal correction required.
■■
After deleting Australian input data, only New
Zealand was left in domain 5224, which was too
limited a base to use for estimation (also the figure
for that country looked far too low, much like the
case of Australia). Therefore, that figure was also
not used and the estimate for domain 5224 was
obtained from domain 5214. This is a domain in
the same income group (‘4’ – high income), in the
same broad subregion, but in a different detailed
subregion (there are no other detailed subregions in
the broad subregion 522).26
5.3.2 Inherent relationships among the variables
There are a number of inherent relationships among
the variables that need to be satisfied and hence must
be built into the methodology. Specifically, the
following seven relationships must be satisfied by data
for each country, for the total population and
separately for males and females:
MW≤M
MW≤W
MW(AGR)+MW(IND)+MW(SRV)=MW
D≤W
MD≤MW
MD≤D
MD≤MW(SRV)
Variables MW(AGR), MW(IND), MW(SRV) refer to
migrant workers in, respectively, agriculture, industry
and services.
It is necessary to check the variables and make the
necessary corrections where possible, to ensure that
the above consistency requirements are satisfied.
Ideally this should be done for the input data, and
then subsequently at various stages during the
construction of the final estimates.
5.3.3Plausibility
(ii) The requirement ‘Total = Male + Female’ is
seriously violated in the input data, at the country level
and also at the regional and global levels. Total T in
The input data and the out estimates should be
“plausible”. Plausibility is a vague and complex, yet
26 Regions and subregions at different levels of detail have been defined in
section 3.
55
ILO Global estimates on migrant workers: Results and methodology
the given data fell short of (M+F) by nearly 20 per cent
in net terms. The gross discrepancy was nearer 25 per
cent. Though the estimation procedure has been
designed to ensure this condition at the end, it was
the input data which needed to be corrected
beforehand.
(iii) It is clear that the given values of D are too low
in comparison with MD values in a number of
countries. A consequence of this was that the “final”
values obtained after imputation of D were below
even the corresponding values obtained for MD in 16
of the 176 countries included in the analysis. Half of
these countries were from Eastern Europe including
Commonwealth of Independent States (CIS)
countries, and a quarter were from Eastern and
South-Eastern Asia. Such geographical clustering of
the pattern may reflect similar data situations in the
countries involved.
Consequently, a final correction to estimated D
values was introduced, in that they could not be
smaller than the estimated MD in the same country. In
fact, because of the shortcoming of data on D, we
removed the constraint
MD≤D
meaning that in the given data MD cannot exceed
D (and if so, the value of MD be revised downwards).
Rather, the constraint was now applied in reverse
D≥MD
meaning that D cannot be less than MD, and if so,
the determined value of D was revised upwards.
The same was applied to the final estimates after
imputation. If the estimate of MD exceeded that of D,
then the former was not adjusted downwards; rather
the latter was adjusted upwards so as not to be less
than estimated MD at the country-by-sex level.
In short, in a number of cases the information
compiled during the input phase (Phase 1) from
diverse national sources was incomplete and subject to
contradictions. Steps have been taken to improve
consistency where possible.27
27 It should be noted in particular that in the case of China there is a large
uncertainty in the number of domestic workers in the country. Fortunately,
the statistic of real interest in this report is the number of migrant domestic
workers. Given the very low migration rates into very large countries like
China (and similarly, India), estimates of the numbers of migrant domestic
workers (MD) are not likely to be greatly affected by the estimated
numbers of all domestic workers (D) in these cases.
56
6. Methodology, Phase 2: Data imputation and
production of global and regional estimates
6.1 Introduction
The objective of the imputation procedure is to
construct a set of variables at the level of individual
country (in the set of 176 countries included in the
analysis), for the total population and separately for
males and females (table 6.1).28
Section 6.2 considers the base variables, while
section 6.3 describes the general procedure used for
imputing missing values in the variables constructed
from national data sources. Sections 6.4-6.7 provide
details of the steps involved in the construction of
variables MW, MW(sec), D and MD in turn.
6.2 Benchmark variables from
standardized international
datasets
As noted in section 4, the benchmark data refer to
the year 2013 and cover 176 countries and
TABLE 6.1
Variables to be estimated
Variable
Name
Calculation
Benchmark variables from standardized international datasets
Population aged 15+ years
P
UN World Population Prospects
Migrant population aged 15+
M
UN Trends in International Migrant Stock
Labour force aged 15+
W
ILO Estimates and Projections of the Economically Active
Population
Variables constructed from national data
Migrant workers
MW
(1) M x Edited/imputed value [MW/M]
Domestic workers
D
(2) W x Edited/imputed value [D/W]
Migrant domestic workers
MD
(3) MW x Edited/imputed value [MD/MW] or
Migrant workers by main sector
ARG (agriculture)
IND (industry)
SRV (services)
(4) MW x Edited/imputed [MW(ARG)/MW]
MW(sec)
(5) MW x Edited/imputed [MW(IND)/MW]
(6) MW x Edited/imputed [MW(SRV)/MW]
28 Classification of the population by age and sex simultaneously is not
covered in the present estimates, but may be introduced in future
productions of the global and regional estimates of migrant workers and
migrant domestic workers.
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ILO Global estimates on migrant workers: Results and methodology
territories, representing 99.8 per cent of the world
working-age population (15 years old and over). The
three “benchmark variables” (P, W and M) coming
from standard international sources are available by
sex for all countries. Nothing more needed to be
done on input data for these variables, except to
verify that they satisfy the relationship ‘”Total = Male
+ Female’”.
In the present exercise, this indeed was found to be
true in the case of total population aged 15+ (P) and
total number of migrants aged 15+ (M), but not in the
case of the total labour force or working population
(W). In the data used for the construction of global
and regional estimates, this discrepancy was removed
by resetting the numbers total (say, T1), male (M1)
and female (F1) in each country as follows.
T2 = max (T1, M1+F1),
M2 = M1*T2/(M1+F1)
F2 = F1* T2/(M1+F1)
so that M2+F2 = T2 is ensured.
The above adjustment procedure to ensure the
consistency “Total = Male + Female” has in fact been
used repeatedly in the present procedure for the
construction of all variables.
6.3 Outline of the imputation
procedure
Variables MW, MW(sec), D and MD are constructed
from ratios involving them as specified in section 4.3.3
(table 4.3). For instance, D is constructed from ratio
[D/W] obtained from national data sources, multiplied
by W provided by the benchmark data; similarly MW
may be constructed from ratio [MW/M] obtained from
national data sources, multiplied by M provided by the
benchmark data.
In order to distinguish between information coming
from these two types of source, we will use the
following notation.
Quantities in square parentheses [..] such as
“[MW/M]” refer to ratios based on country-by-sex
level data where the numerator and the
denominator both come from the same source and
hence are compatible. Aggregate quantities (usually
58
in terms of numbers of adult persons in thousands)
have been obtained from standardized
international sources or are constructed using the
procedures to be described here. These are written
without parentheses, such as “M”, or their ratios in
round brackets (..) as in (W/M).
Generally, variables in this section refer to values at
the “case” (country-by-sex) level; for simplicity, no
subscript is used to identify an individual case.
Rather, we will use subscripts ‘INPUT’ and ‘IMPUTED’ to
distinguish between the given case values from
national data sources and the final values after
imputation for missing values. When necessary,
subscript ‘av’ is used to indicate values averaged
over a “domain” (a cell in cross-classification by
detailed subregion and income group).
In order to outline the imputation procedure in
general terms, let us use “Y” for a variable like MW or
MD to be estimated, and “X” for the variables in the
denominator of the ratio [Y/X] involved in its
construction using the relationship Y = X * [Y/X]. As
noted, quantity [Y/X] comes from national data
sources, and X comes from the benchmark databases.
Variable construction involves the following steps.
1. Obtain [Y/X]INPUT from national input data source
if this information is available, as described in section
4. This is at the country level, and for total and
separately for male and female populations.
2. Obtain X from benchmark database, or from
previous imputation of the variable (as for instance
MW for [MD/MW]), and where ratio [Y/X]INPUT is
available, compute
YINPUT = X * [Y/X]INPUT.
3. For cases (countries, by sex) for which [Y/X]INPUT is
available, sum up the YINPUT values and the XY=INPUT
values separately over the domain to which the
countries belong. Symbol XY=INPUT means that the sum
of X values is taken over cases for which [Y/X] and
hence Y is available. Domains refer to groupings of
countries which form the units for imputation. We
have used cross-classification of detailed subregions
and income groups to define 49 domains for this
purpose. Considering total, male and female
separately, we have a total of 49x3=147 domains each
containing one or more countries (see Annex C).
PART II ESTIMATE METHODOLOGY
6. METHODOLOGY, PHASE 2: DATA IMPUTATION AND PRODUCTION OF GLOBAL AND REGIONAL ESTIMATES
4. For each domain which has at least one country
with data available, the ratio of the above two sums
over countries with available data gives an estimate of
the average ratio in the domain, say
[Y/X]av = sum(YINPUT)/sum(XY=INPUT).
5. For a domain which has no countries with
information on the required ratio [Y/X], we have to
“borrow” the average value [Y/X]av from a
“neighbouring” domain. Ideally, a neighbouring
domain is taken to be a domain in the same detailed
subregion but in an adjacent income group. When
that is not possible (i.e. no such neighbour is
available), we have to search in a neighbouring
detailed subregion, and in exceptional circumstances,
even in a neighbouring broad subregion. Sometimes
the choice requires subjective judgement, such as
when the data in the closest available neighbouring
domain are based only on a small number of cases
and therefore cannot be taken as reliable.
6. With [Y/X]av so constructed for every domain,
ratio [Y/X]IMPUTED can be constructed for every case
(country-by-sex):
[Y/X]IMPUTED = [Y/X]INPUT where the latter is available;
otherwise
[Y/X]IMPUTED = [Y/X]av for the domain to which the
case belongs.
7. Finally, the values YIMPUTED estimated for each case
YIMPUTED = X * [Y/X]IMPUTED
are summed to the level of any reporting domain as
required. “Reporting domain” may refer, for instance,
to income groups, and/or to major regions, broad
subregions or detailed subregions.
6.4 Constructing variable MW,
migrant workers
population census or national labour force survey or
other large-scale representative household surveys
with a reference year not earlier than 2005. In a few
exceptions, countries for which the data found on
migrant workers were earlier than 2005 were also
accepted. In terms of the notation introduced in table
6.1, to be considered “available” the data on migrant
workers must include MW and either W or M from the
same source and the same reference year, such that
we can calculate one or the other of the two ratios:
[MW/W], migrant workers as a proportion of the total
labour force; or [MW/M], the share of migrant workers
in total working-age migrant population. In most
cases, the available data on migrant workers referred
to employed migrants and excluded unemployed
migrants. They were nevertheless used in the
calculations.
The estimation of migrant workers for 2013 for
countries for which data on migrant workers were
available in the sense described above was calculated
as follows:
MWINPUT = M * [MW/M]INPUT.
The notation introduced in section 6.3: [MW/M]INPUT
is migrants’ labour force participation rate (or
proportion working) where this information is
available in national input data, and M is the number
of migrants known from the benchmark data.
In cases where the available data was in the form of
[MW/W], the data was converted into [MW/M] using
the benchmark data on working-age migrants M and
national labour force W, actually their ratio (W/M):
[MW/M]INPUT = (W/M) * [MW/W]INPUT.
For countries for which both ratios [MW/M] and
[MW/W] were available, the first ratio [MW/M] was
used unless the country data on MW referred to the
desired concept of migrant labour force as opposed to
employed migrants. Indeed, this was the case for
almost all OECD countries for which data on
unemployed migrants as well as employed migrants
were available.
6.4.1 Countries with available data on MW
Availability of data for a given country means that
data were found on migrant workers from a
Normally the ratio [MW/M] is preferable because it
is more stable (uniform) across countries than ratio
[MW/W].
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ILO Global estimates on migrant workers: Results and methodology
6.4.2 Countries with missing data on MW
For cases (countries-by-sex) with data available we
have
MWINPUT = M * [MW/M]INPUT
and the quantities MWINPUT and MMW=INPUT are
summed over the domain. For each domain which has
at least one case with data available, the ratio of the
above two sums over cases with available data gives
an estimate of the average ratio in the domain:
[MW/M]av = sum(MWINPUT)/sum(MMW=INPUT).
For a domain which has no country with
information on the required ratio [MW/M], we borrow
the average value [MW/M]av from a neighbouring
domain. Hence we can construct for every case
(country-by-sex):
[MW/M]IMPUTED = [MW/M]INPUT where the latter is
available; otherwise
[MW/M]IMPUTED = [MW/M]av for the domain of the
country. This gives
MWIMPUTED = M * [MW/M]IMPUTED,
(iii) Estimates are improved using the following
relationships (already noted in section 6.2) which
ensure that the resulting T3 = M3 + F3:
T3 = max (T2, M2+F2)
M3 = M2 * T3/(M2+F2)
F3 = F2 * T3/(M2+F2)
(iv) For male and female separately, the condition is
imposed that MW does not exceed the corresponding
M (number of migrants) values, M-male and
M-female.
M4 = min (M3, M-male) 29
F4 = min (F3, M-female).
Finally, T4 is computed to be consistent with the
above:
T4 = M4 + F4.
quantities which can be summed up to the level of
reporting domains as required.
The next step is to impute for missing values of
[MW/M] using the procedure described earlier.
6.4.3 Some details
In carrying out this imputation separately for total
(T), male (M) and female (F), it is important to note the
following point so as to ensure consistency.
The actual algorithm involves some details which
are worth noting.
(i) For each country, the quantity MWINPUT defined
above is computed for the total (male+female)
population and for males and females separately. Let
us call these three respectively T1, M1 and F1. In terms
of data availability, logically there are five possible
patterns: all three of the above quantities are
available, only one of the three quantities (T1 or M1 or
F1) is available, or none of them is available. In the
present application, the situation was found to be as
follows. Of the 176 countries, full information was
available in 84, partial (only on T1 or M1 or F1) in 27,
and none in 65 countries.
60
(ii) The 84 “full information” countries included
four countries where two of the three quantities (T1,
M1, F1) were recorded. All three could be completed
using the relationship T1 = M1 + F1. Let us call the
quantities resulting after this simple step T2, M2 and
F2.
Imputation is made to ensure that for each country,
at least two of the three values (T, M, F) become
available. For consistency, at most two values are
imputed (never all three T, M, F) − if there is a
remaining unimputed value it is obtained from the
other two using the relationship T=M+F. Preferred
order of imputation where values are missing is F,
then M, and only then T as needed.
(v) The fifth improved version of MW is constructed
with the objective of using any information on
[MW/M] so far unused.
29 In general, symbol M refers to the number of migrants, with M-male and
M-female distinguishing it by sex when necessary. Total, male and female
for any variable have been referred to as Tn, Mn and Fn respectively,
n=1,2,3 … indicating successive refinements of the numbers.
PART II ESTIMATE METHODOLOGY
6. METHODOLOGY, PHASE 2: DATA IMPUTATION AND PRODUCTION OF GLOBAL AND REGIONAL ESTIMATES
F5 = F4 if already available;
else F5 = M*[MW/M] for female if the latter is
available;
else F5 remains blank.
M5 = M4 if already available;
else M5 = M*[MW/M] for male if the latter is
available;
else M5 remains blank.
T5 = M5 + F5 if both available from the above;
else T5 = T4 if already available;
else T5 = M*[MW/M] for total if the latter is
available;
else T5 remains blank.
(vi) The next steps give MW values by country and
sex, with all information completed. The objective is to
fill any gaps by using the relationship T6 = M6 + F6.
In the present application, such gaps existed only in
M6; these were filled using
M6 = T6 - F6, with T6 = T5, F6 =F5.
(vii) The obvious requirement that the number of
migrant workers does not exceed the total number of
workers in any country and sex group
MW ≤ W
has not been introduced so far because all the input
data were in the form [MW/M], which gave estimates
of MW as MW= M*[MW/M], without making any
reference to W.
In fact, the error MW>W happened to be rare and
negligibly small − 115 out of 150,368 (thousands), i.e.
0.07 per cent overall.30 In the final step this
contradiction was simply removed as follows.
M7 = min (M6, W-male)
F7 = min (F6, W-female)
T7 = M7 + F7.
30 This happened in three cases, all for the female group: Jordan MW=473,
W=382; Bahrain MW=152, W=146; Qatar MW=206 W=188.
6.5 Constructing variable MW(sec),
migrant workers by sector
This refers to the breakdown of MW (by country and
sex) according to main sector: agriculture (AGR),
industry (IND) and services (SRV).
The original input data contained a number of
obvious errors (e.g. repeated but different figures for
the same country in a few cases, some figures in single
numbers rather than in thousands as elsewhere).
These errors were corrected when identified. However,
the input numbers still lacked consistency in many
cases, for instance:
MW (overall and/or by sector) for “male” and
“female” did not add up to the value specified for
MW “total” in some cases.
In cases where MW was available in the input data,
it did not necessarily equal the sum of given values of
MW by sector, MW(AGR) + MW(IND) + MW(SRV).
In some other cases, the above given sum did not
agree with (and sometimes differed widely from) the
MW values estimated after imputation in section 6.4.
It was not considered necessary to try and correct
such errors individually. Instead, the following
procedure was used to produce consistent results.
6.5.1 Countries with available data on MW(sec)
(i) The given numbers for MW(AGR), MW(IND) and
MW(SRV) were used to construct percentage
distribution of MW by sector. Defining
MW(sum) = MW(AGR) + MW(IND) + MW(SRV)
the distribution is [MW(AGR)/MW(sum)], [MW(IND)/
MW(sum)] and [MW(SRV)/MW(sum)].
(ii) The percentage distribution values are multiplied
by the final MW values obtained in section 6.4 to
obtain corrected counts by sector:31
31 Note that following the imputation described in section 6.4, MW is now
taken as available for countries by sex. More precisely, the following
expressions should have been written as
MW(AGR)INPUT = MWIMPUTED * [MW(AGR)/MW(sum)]INPUT, etc.
The subscripts have been left out for simplicity when not necessary.
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ILO Global estimates on migrant workers: Results and methodology
MW(AGR) = MW * [MW(AGR)/MW(sum)]
MW(IND) = MW * [MW(IND)/MW(sum)]
MW(SRV) = MW * [MW(SRV)/MW(sum)].
The above is done only for “male” and “female” at
this stage; results for “total” will be obtained
subsequently by adding these two components.
At this stage, the above is done only for countries
where distribution of MW by sector is available.
(iii) The next step is to impute for missing values of
[MW(sec)/MW] using the procedure described earlier.
The imputation procedure is applied separately for
“male” and “female”. It is important to note that, in
order to ensure consistency, such imputation is not
made for “total” in its own right. It is possible to follow
this procedure because, in the present application, in
cases where information was available on MW(sec) it
was always available with breakdown by sex.
6.5.2 Countries with missing data on MW(sec)
(i) For countries with data available we have MW(sec)
= MW * [MW(sec)/MW]INPUT, and the quantities
INPUT
MW(sec)INPUT and MWMW(sec)=INPUT are summed over the
domain. To remind about the notation used : MW(sec)
refers to a case where MW(sec) value is known or
INPUT
can be computed from input data; MWMW(sec)=INPUT
refers to MW value of such a case.
For each domain which has at least one country
with data available, the ratio of the above two sums
over countries with available data gives an estimate of
the average distribution by sector in the domain:
[MW(sec)/MW]av = sum(MW(sec)INPUT)/
sum(MWMW(sec)=INPUT).
(ii) For a domain which has no country with
information on the required distribution [MW(sec)/
MW], we borrow the average value [MW(sec)/MW]av
from a neighbouring domain. Hence we can construct
for every case (country-by-sex):
[MW(sec)/MW]IMPUTED = [MW(sec)/MW]INPUT
where the latter is available; otherwise
[MW(sec)/MW]IMPUTED = [MW(sec)/MW]av
quantities which can be summed to the level of
reporting domains as required.
(iii) Finally, the distribution by country, for male and
female separately, are multiplied by the corresponding
final MW values to obtain counts by sector. The male
and female panels are added up to obtain counts in
the total panel, which are then converted into
percentage distributions.
6.6 Constructing variable D, number
of domestic workers
The estimation of domestic workers involves two
related steps: (a) estimation of all domestic workers;
and (b) estimation of migrant domestic workers. In
each case the estimation was carried out for males
and females separately.
This section considers the step of estimating the
number of domestic workers. The methodology for
estimating domestic workers by sex follows essentially
the same reasoning as the methodology described for
migrant workers.
(i) For countries with data available we have DINPUT =
W * [D/W]INPUT.
(ii) In order to obtain domain averages for D/W, we
use the combined ratio estimator. Quantities DINPUT and
WD=INPUT are summed over the domain. For each
domain which has at least one country with data
available, the ratio of the above two sums over
countries with available data gives an estimate of the
average ratio in the domain:
[D/W]av = sum(DINPUT)/sum(WD=INPUT).
(iii) For a domain which has no country with
information on the required ratio [D/W], we borrow
the average value [D/W]av from a neighbouring
domain. Hence we can construct for every case
(country, separately for male and female):32
[D/W]IMPUTED = [D/W]INPUT
where the latter is available; otherwise
for the domain of the country. This gives
MW(sec)IMPUTED = MW * [MW(sec)/MW]IMPUTED,
62
32 For consistency, this is done only for males and females, but not for total
in its own right. Values for total are obtained by addition; see the next step.
PART II ESTIMATE METHODOLOGY
6. METHODOLOGY, PHASE 2: DATA IMPUTATION AND PRODUCTION OF GLOBAL AND REGIONAL ESTIMATES
[D/W]IMPUTED = [D/W]av
share of migrant domestic workers among
domestic workers [MD/D]
for the domain of the country. This gives
DIMPUTED = W * [D/W]IMPUTED,
share of migrant domestic workers among migrant
workers [MD/MW]
quantities which can be summed to the level of
reporting domains as required.
can be calculated for the same source and the same
reference year.
(iv) Let us write the D values by country obtained
for male and female separately as M1 and F1,
respectively. These are added to obtain counts in the
total (male + female): T1 = M1 + F1.
In order to use ratio [MD/D] where available, it was
also necessary to have information on D available.
Though in principle one could use the values of D
constructed as in section 6.6, it was decided only to
use DINPUT, i.e. values given in the original input
dataset. This was a precaution in view of some
shortcomings of data on D, as noted earlier. In any
case, there were few cases with no data on DINPUT
when [MD/D]INPUT was available.
(v) It seemed that the given values of D in some
cases were too low in a number of countries in
comparison with MD values. Consequently, in some
(16 of the 176) countries there arose a small error in
satisfying the requirement that the number of
domestic workers must be at least as large as the
number of migrant domestic workers (the latter
estimated as described in the next section). There was
some pattern to this: half of these countries were from
Eastern Europe and CIS, and a fourth from Eastern
and South-Eastern Asia. We have introduced a
correction to the estimated D values, in that they
cannot be smaller than the final estimated MD in the
same country, described in the next section. The
estimates were corrected as follows.
T2 = max (MD-total, T1)
M2 = max (MD-male, M1)
F2 = max (MD-female, F1).
(vi) Finally, it is ensured that male and female add
up to total:
T3 = max (T2, M2+F2)
M3 = M2 * T3/(M2+F2)
F3 = F2 * T3/(M2+F2)
6.7 Constructing variable MD,
number of migrant domestic
workers
(i) Let us first consider countries for which data on
migrant domestic workers are available. Data
availability means the existence of national data on
migrant workers such that either of the ratios
In cases where only [MD/D] (and D) but no [MD/
MW] was available, total MD was estimated as
MDINPUT = MDfrom D = DINPUT * [MD/D]INPUT.
In cases where only [MD/MW] but no [MD/D] was
available, total MD was estimated as
MDINPUT = MDfrom MW = MW * [MD/MW]INPUT.33
In cases where the data permitted the calculation of
both ratios, after some experimentation it was decided
to retain the ratio that provided a higher estimate of
migrant domestic workers:
MDINPUT = max (MDfrom D, MDfrom M).
(ii) The first estimate of MD was obtained by
adjusting the above so as not to exceed the already
estimated number of migrant workers in the service
sector (section 6.5):
MD1 = min (MDINPUT, MW(SRV)).34
33 Note that, again, MW here refers to the final estimate for MW, i.e. to
MWIMPUTED, rather than only to the originally available values MWINPUT. This
differs from the use of DINPUT in the alternative estimate MDfrom D given
above.
34 Note that we have not introduced here the constraint that the number of
migrant domestic workers, MD, cannot exceed D, the total number of
domestic workers. As already noted, this is because of certain
shortcomings in data on D. Rather, the constraint has been applied in
reverse as noted in the construction of D, namely that D cannot be less
than MD; if so, the given data on D was revised upwards.
The same applies to the final estimates after imputation. If the estimate of
MD exceeded that of D, then the former was not adjusted downwards;
rather the latter was adjusted upwards so as not to be less than estimated
MD at the countryX sex level.
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ILO Global estimates on migrant workers: Results and methodology
The above, done for “total”, is repeated for “male”
and “female” in turn. Let us denote these three
estimates as T1, M1 and F1, respectively
for the domain of the country.
(iii) The above estimates are adjusted to ensure that
male and female sum to the total estimate:
[MD/MW]INPUT
(vi) Next, starting with cases where ratio
is available, the standard imputation procedure is
followed to construct ratio
T2 = max (T1, M1+F1)
M2 = M1 * T2/(M1+F1)
F2 = F1 * T2/(M1+F1)
(iv) We recheck that for males and females the
estimates do not exceed the corresponding MW(SRV):
M3 = min (M2, MW(SRV)MALE)
F3 = min (F2, MW(SRV)FEMALE),
and recompute “total” as
T3 = M3 + F3.
(v) For cases for which F3 is available, we compute
the ratio [F3/T3], proportion female among migrant
domestic workers.35
Now the standard imputation procedure is used to
estimate the ratio [F3/T3] for all countries.
Where available, quantities F3 and T3 are summed
over each domain. For each domain which has at least
one country with data available, the ratio of the above
two sums over countries with available data gives an
estimate of the average proportion of females in the
domain:
[F3/T3]av = sum(F3)/sum(T3).
For a domain which has no country with
information on the required ratio [F3/T3], we borrow
the average value [F3/T3]av from a neighbouring
domain. Hence we can construct for every country:
[F3/T3]IMPUTED = [F3/T3]INPUT
where the latter is available; otherwise
[F3/T3]IMPUTED = [F3/T3]av
35 The procedure has been made simpler at this point by the fact that, in the
present data, in all cases where F3 is available, T3 also happens to be
available. Modification (elaboration) would be required for a dataset
containing cases with F3 available but T3 missing.
64
[MD/MW]IMPUTED
for all countries. This is done for total and for
female in turn.36
(vii) An improved MD value for female (say, F4), is
computed as follows:
F4 = F3 if F3 is already available; otherwise
if T3 is available, then F4 = T3*[F3/T3]IMPUTED,
otherwise
if T3 is also not available, then F4 = MWFEMALE *
[MD/MW]IMPUTED-FEMALE.
(viii) The value is adjusted to ensure that F4 does
not exceed MW(SRV)FEMALE.
F5 = min (F4, MW(SRV)FEMALE).
(ix) An improved MD value for (male+female) =
total (say, T4), is computed as follows:
T4 = T3 if T3 is already available; otherwise:
T4 = F5/[F3/T3]IMPUTED, where F5 has been computed
in (viii) and the denominator [F3/T3]IMPUTED has been
computed in (vi) above.
(x) The value is adjusted to ensure that T4 does not
exceed MW(SRV)TOTAL:
T5 = min (T4, MW(SRV)TOTAL).
(xi) An improved MD value for male (say, M5), is
computed as:
M5 = T5 – F5.
36 The reason for choosing “female” rather than “male” for this operation is
that domestic labour and especially migrant domestic labour tends to be
predominantly female.
PART II ESTIMATE METHODOLOGY
6. METHODOLOGY, PHASE 2: DATA IMPUTATION AND PRODUCTION OF GLOBAL AND REGIONAL ESTIMATES
(xii) An alternative estimate for MD-total is:
and estimate for male is obtained by difference:
T6 = MWTOTAL * [MD/MW]IMPUTED-TOTAL
M7 = T7 – F7.37
and the larger of the two estimates is taken:
T7 = max(T5, T6).
T6 exceeding the original T5 happens in a minority
of the cases (37 out of 176 countries).
(xiv) The above adjustments can result in violating
the constraint MD≤MW(SRV) imposed earlier. This in
fact happened in the present application in some
countries, all of which happen to be in Eastern Asia,
and in all cases the violation concerned the female
subpopulation.38 Though this error is rare and mostly
negligibly small, it needs to be corrected:
(xiii) Estimate for female is adjusted proportionately:
F7 = F5 * (T7 / T5)
T8 = min(T7, MW(SRV)TOTAL); F8 = min(F7, MW(SRV)
); M8 = T8 – F8.
FEMALE
37 Note that for males, computation of quantities M4 and M6 is not involved
in the above procedure.
38 Since in the present application this error happen to occur only for female
(F), the correction has simply meant transferring the “excess” MW from F
in cases with error to male (M) in the same country, leaving total (T)
unchanged.
65
ANNEXES
67
Annex A
Geographical regions and income groups
Countries and territories have been grouped into four
classes according to income level as follows:
Income
group
No. of
countries
Countries
Mali
Mozambique
Nepal
TABLE A.1
Niger
Income groups
No. of countries
1 Low income
30
Rwanda
2 Lower-middle income
44
Sierra Leone
3 Upper-middle income
44
Somalia
4 High income
58
Tanzania, United Republic of
Total
Togo
176
Uganda
TABLE A.1.1
Income
group
Low income
Zimbabwe
No. of
countries
Countries
30
Afghanistan
Lowermiddle
income
44
Armenia
Benin
Bangladesh
Burkina Faso
Bhutan
Burundi
Bolivia, Plurinational State of
Cambodia
Cameroon
Central African Rep.
Cabo Verde
Chad
Congo
Comoros
Côte d’Ivoire
Congo, DR
Egypt
Eritrea
El Salvador
Ethiopia
Georgia
Gambia
Ghana
Guinea
Guatemala
Guinea-Bissau
Guyana
Haiti
Honduras
Korea, DPR
India
Liberia
Indonesia
Madagascar
Kenya
Malawi
Kyrgyzstan
69
ILO Global estimates on migrant workers: Results and methodology
Income
group
No. of
countries
Countries
Lesotho
Brazil
Mauritania
Bulgaria
Moldova, Rep. of
China
Morocco
Colombia
Myanmar
Costa Rica
Nicaragua
Cuba
Nigeria
Dominican Republic
Occupied Palestinian
Territory
Ecuador
Philippines
Senegal
Solomon Islands
Sri Lanka
Sudan
Swaziland
Syrian Arab Rep.
Tajikistan
Timor-Leste
Ukraine
Uzbekistan
Viet Nam
Yemen
Zambia
Albania
Fiji
Gabon
Guadeloupe
Iran, Islamic Republic of
Iraq
Jamaica
Jordan
Kazakhstan
Lebanon
Libya
Macedonia, The Former
Yugoslav Republic
Malaysia
Maldives
Mauritius
Mexico
Mongolia
Namibia
Panama
Algeria
Angola
Azerbaijan
Belarus
Belize
Bosnia and Herzegovina
70
Countries
Botswana
Papua New Guinea
44
No. of
countries
Lao PDR
Pakistan
Uppermiddle
income
Income
group
Paraguay
Peru
Romania
Serbia
South Africa
Suriname
ANNEXES
ANNEX A
Income
group
High income
No. of
countries
58
Countries
Income
group
No. of
countries
Countries
Thailand
Lithuania
Tunisia
Luxembourg
Turkey
Macau, China
Turkmenistan
Malta
Argentina
Martinique
Australia
Netherlands
Austria
New Zealand
Bahamas
Norway
Bahrain
Oman
Barbados
Poland
Belgium
Portugal
Brunei Darussalaam
Puerto Rico
Canada
Qatar
Chile
Réunion
Croatia
Russian Federation
Cyprus
Saudi Arabia
Czech Republic
Singapore
Denmark
Slovakia
Equatorial Guinea
Slovenia
Estonia
Spain
Finland
Sweden
France
Switzerland
Germany
Trinidad and Tobago
Greece
United Arab Emirates
Hong Kong, China
United Kingdom
Hungary
United States
Iceland
Uruguay
Ireland
Venezuela, Bolivarian Rep.
Israel
Total
176
Italy
Japan
Korea, Republic of
Kuwait
Latvia
For the purpose of this report the world has been
divided into standard geographical regions with three
levels of detail: five major regions and 11 broad
subregions, further divided into 20 finer subregions as
follows.
71
ILO Global estimates on migrant workers: Results and methodology
TABLE A.2
TABLE A.3
Standard geographical regions
Number of countries in each major region
Major regions
1 Africa
11 Northern Africa
111
Northern Africa
12 Sub-Saharan Africa
121
122
123
124
Central Africa
Eastern Africa
Southern Africa
Western Africa
No. of countries
1 Africa
51
2 Americas
32
3 Europe & Central Asia
49
4 Arab States
12
5 Asia & the Pacific
32
Total
176
2 Americas
21 Latin America and the Caribbean
211
212
213
Caribbean
Central America
South America
22 Northern America
221
Northern America
3 Europe & Central Asia
31 Northern, Southern and Western Europe
311
312
313
Northern Europe
Southern Europe
Western Europe
32 Eastern Europe
321
Eastern Europe
33 Central and Western Asia
331
Central and Western Asia
4 Arab States
Arab States
5 Asia & the Pacific
Eastern Asia
52 South-Eastern Asia and the Pacific
521
522
523
South-Eastern Asia
Australia and New Zealand
Pacific Islands
53 Southern Asia
531
72
Broad subregions
Southern Asia
No. of countries
11 Northern Africa
6
12 Sub-Saharan Africa
45
21
Latin America and the
Caribbean
22 Northern America
31
Northern, Southern and
Western Europe
30
2
28
32 Eastern Europe
10
33 Central and Western Asia
11
41 Arab States
12
51 Eastern Asia
7
South-Eastern Asia and the
Pacific
53 Southern Asia
Total
51 Eastern Asia
511
Number of countries in each broad subregion
52
41 Arab States
411
TABLE A.4
16
9
176
ANNEXES
ANNEX A
TABLE A.4.1
Broad
subregion
11 Northern
Africa
12 Sub-Saharan
Africa
Mali
No. of
Countries
countries
6
45
Mauritania
Mauritius
Algeria
Mozambique
Egypt
Namibia
Libya
Niger
Morocco
Nigeria
Sudan
Réunion
Tunisia
Rwanda
Angola
Senegal
Sierra Leone
Benin
Somalia
Botswana
South Africa
Burkina Faso
Swaziland
Burundi
Tanzania, United Rep.
Cameroon
Togo
Cabo Verde
Uganda
Central African Rep.
Zambia
Chad
Comoros
Congo
Congo, DR
Côte d’Ivoire
Equatorial Guinea
Eritrea
Ethiopia
Zimbabwe
21 Latin
America and
the
Caribbean
30
Argentina
Bahamas
Barbados
Belize
Gabon
Bolivia, Plurinational
State of
Gambia
Brazil
Ghana
Chile
Guinea
Colombia
Guinea-Bissau
Costa Rica
Kenya
Cuba
Lesotho
Dominican Rep.
Liberia
Ecuador
Madagascar
El Salvador
Malawi
Guadeloupe
73
ILO Global estimates on migrant workers: Results and methodology
Guatemala
Latvia
Guyana
Lithuania
Haiti
Luxembourg
Honduras
Macedonia, The Former
Yugoslav Rep.
Jamaica
Malta
Martinique
Netherlands
Mexico
Norway
Nicaragua
Portugal
Panama
Serbia
Paraguay
Slovenia
Peru
Spain
Puerto Rico
Sweden
Suriname
Switzerland
Trinidad and Tobago
Uruguay
Venezuela, Bolivarian
Rep. of
22 Northern
America
2
United Kingdom
32 Eastern
Europe
10
Bulgaria
Canada
Czech Republic
Hungary
United States
31 Northern,
Southern
and Western
Europe
Moldova, Rep.
28
Albania
Poland
Romania
Austria
Russian Federation
Belgium
Slovakia
Bosnia and Herzegovina
Croatia
Denmark
74
Belarus
Ukraine
33 Central and
Western Asia
11
Armenia
Estonia
Azerbaijan
Finland
Cyprus
France
Georgia
Germany
Israel
Greece
Kazakhstan
Iceland
Kyrgyzstan
Ireland
Tajikistan
Italy
Turkey
ANNEXES
ANNEX A
41 Arab States
12
Turkmenistan
Fiji
Uzbekistan
Indonesia
Bahrain
Lao PDR
Iraq
Malaysia
Jordan
Myanmar
Kuwait
New Zealand
Lebanon
Papua New Guinea
Occupied Palestinian
Territory
Philippines
Singapore
Oman
Solomon Islands
Qatar
Thailand
Saudi Arabia
Timor-Leste
Syrian Arab Rep.
United Arab Emirates
Yemen
51 Eastern Asia
52 SouthEastern Asia
and the
Pacific
7
Viet Nam
53 Southern
Asia
9
Afghanistan
China
Bangladesh
Hong Kong, China
Bhutan
Japan
India
Korea, DPR
Iran, Islamic Rep.
Korea, Rep.
Maldives
Macau, China
Nepal
Mongolia
Pakistan
Sri Lanka
16
Australia
Total
176
Brunei Darussalaam
Cambodia
75
ILO Global estimates on migrant workers: Results and methodology
TABLE A.5
Number of countries in each detailed subregion
Detailed subregions
111 Northern Africa
6
121 Central Africa
8
122 Eastern Africa
16
123 Southern Africa
5
124 Western Africa
16
211 Caribbean
10
212 Central America
8
213 South America
12
221 Northern America
2
311 Northern Europe
10
312 Southern Europe
11
313 Western Europe
7
321 Eastern Europe
10
331 Central and Western Asia
11
411 Arab States
12
511 Eastern Asia
7
521 South-Eastern Asia
11
522 Australia and New Zealand
2
523 Pacific Islands
3
531 Southern Asia
9
Total
76
No. of countries
176
Results are presented for four income groups (low
income, lower-middle income, upper-middle income
and high income) at the global level, and at the level
of the 11 broad subregions.
Some results are also discussed by cross-classifying
income groups and broad subregions. Ignoring empty
and very small cells, there are 22 categories in this
cross-classification.
All results are shown for the total population, and
for male and female populations separately.
The estimation procedure used involved the
construction of measures by individual country (for the
176 countries included in the database), and by 49
detailed country groups (domains) formed by crossclassification of detailed subregions and income
groups. These results formed the “building blocks” of
the estimation procedure used, but they are
considered too detailed to be included in this report.
These detailed results are available at the ILO for
internal use.
Annex B
Cross-classification of geographical regions and income groups
Geographical regions and groups of countries by
income level are highly correlated. In some regions,
such as Northern America and Northern, Southern and
Western Europe, all or nearly all countries are in the
high income group, while in others such as SubSaharan Africa a majority of countries are in the low
income group. Similarly, in Southern Asia the lowermiddle income group predominates.
Table B.1 shows how the 176 countries included in
the present analysis are distributed according to broad
subregion and income group. Out of the possible
11x4=44 cells of the cross-classification, 12 cells have
no countries in them.
Number of countries is however not a good
measure of the size or “importance” of a cell in the
cross-classification. In the study of workers, the total
labour force or number of workers in a cell is an
appropriate measure of its size. The first panel of table
B.2 shows this number. In addition to 12 empty cells
as already noted (dark shaded in the table), there are
five cells with under five million workers (light shaded),
and another five with under 10 million (under 0.3 per
cent of the total) workers. Excluding these empty or
very small cells, we are left with 22 (i.e. half the
potentially possible 44) groups of countries. Two of
the cells are very large: region 53, income level 2
(which includes India); and region 51, income level 3
(which includes China). There are only four other
regions with over 200 million workers.
TABLE B.1
Number of countries by broad subregion and income group
Number of countries
Income group
Subregion
1
2
3
3
3
25
12
6
2
45
1
6
14
9
30
2
2
4
24
28
11 Northern Africa
12 Sub-Saharan Africa
21 Latin America and the Caribbean
22 Northern America
31 Northern, Southern and Western Europe
4
All
6
32 Eastern Europe
2
3
5
10
33 Central and Western Asia
5
4
2
11
41 Arab States
3
3
6
12
2
4
7
4
16
51 Eastern Asia
1
52 South-Eastern Asia and the Pacific
1
8
3
53 Southern Asia
2
5
2
30
44
44
Total
9
58
176
77
ILO Global estimates on migrant workers: Results and methodology
TABLE B.2
Size of the labour force, migrant workers and migrant domestic workers, by broad subregion and
income group, 2013
TOTAL (M+F)
W (total number of workers, MW/W (Migrant workers as % MD/MW (Migrant domestic
millions)
of all workers)
workers as % of all migrant
workers)
Income group
Subregion
1
11
2
3
52
19
4
Income group
All
1
71
2
3
0.6
2.5
4
Income group
All
1
1.1
2
3
9.0
9.0
4
All
9.0
12
209
116
31
1
357
1.3
2.9
5.7
8.6
2.2
15.5
1.4
6.1
2.1
7.3
21
4
20
228
46
299
0.3
0.9
0.8
5.2
1.5
4.2
7.5
9.9
23.2
17.2
183
183
1.7
1.7
8
210
218
17.0
6.0
6.2
22
31
20.2 20.2
6.8
16.8 16.4
32
24
18
107
150
17.0
4.8
8.2
9.2
0.4
0.4
0.7
0.6
33
23
44
4
70
5.3
9.8
41.1 10.0
10.5
2.1
2.6
3.6
41
14
12
23
50
3.8
15.8
66.5 35.6
2.2
16.1
18.7
17.9
853
95
963
0.2
0.1
5.0
0.6
1.9
8.7
22.1
20.4
18
335
0.7
0.2
7.8
39.9
3.5
1.7
26.2
7.5
25.4
19.0
695
3.0
1.0
4.5
1.3
8.8
5.5
0.5
260 1 150 1 293 687 3 390 1.4
1.5
1.4
4.4
13.8
4.2
6.8
51
15
52
9
256
53
53
23
645
27
Total
16.3
5.0
8.1
7.7
Notes: * Data not shown for confidentiality reasons, since the cell contains only a single country. Shaded cells are empty (dark shaded) or are small in size (light
shaded). For names of subregions corresponding to the code in the first column, see table B.1.
Table B.2 provides three statistics by broad
subregion and income group:
(1) The total number of workers (W) in countries
in the cell, in millions
(2) Proportion of migrants among the workers
(MW/W)
(3) Migrant domestic workers as a proportion of
all migrant workers (MD/MW)
78
(Australia, Brunei Darussalam, New Zealand,
Singapore) and 33-4 (Cyprus, Israel); and 20 per cent
in group 22-4 (Canada, United States).
The total number of migrant workers can be
obtained by multiplying (1) and (2). The number of
migrant domestic workers is obtained by multiplying
all three, (1)*(2)*(3).
At the other end of the spectrum, migrant workers
are fewer than 1 per cent of all workers in many
groups including the following: group 11-2 including
Egypt, Morocco and Sudan; groups 21-2 and 21-3
which include Brazil, Mexico and many other countries
of Latin America and the Caribbean; groups 51-1 and
51-3 covering China, Democratic People’s Republic of
Korea and Mongolia; and group 52-2 including
Indonesia, Myanmar, Philippines, Viet Nam and other
lower-middle income countries in South-Eastern Asia.
In some groups a very large proportion of workers
are migrants. The largest value of (MW/W), 67 per
cent, is for cell 41-4 (i.e. broad subregion 41 (Arab
States), income level 4) which is composed of Saudi
Arabia and Gulf countries (Bahrain, Kuwait, Oman,
Qatar, United Arab Emirates), where two out of every
three workers in the group are migrants. Other high
figures include: around 40 per cent in groups 52-4
A high proportion (22-25 per cent) of migrant
workers are domestic workers in high income
countries in the broad subregions 21 (Argentina,
Chile, Uruguay, Bolivarian Republic of Venezuela and
richer Caribbean countries), 51 and 52 which include
Australia, Japan, New Zealand, Republic of Korea,
Singapore and some smaller countries. In group 52-2,
26 per cent of migrant workers are domestic workers;
ANNEXES
ANNEX B
TABLE B.3
Size of the male labour force, migrant workers and migrant domestic workers, by broad subregion
and income group, 2013
MALE
W (total number of workers, MW/W (Migrant workers as %
millions)
of all workers)
Income group
Subregion
1
11
2
3
38
15
4
MD/MW (Migrant domestic
workers
as % of all migrant workers)
Income group
All
1
53
2
3
0.5
2.4
4
Income group
All
1
1.0
2
3
3.8
3.3
4
All
3.5
12
109
65
17
0
192
1.4
3.0
6.7
8.3
2.4
13.2
1.1
4.1
2.1
5.8
21
2
12
132
28
174
0.3
0.9
0.7
4.8
1.4
2.2
2.3
2.2
3.0
2.6
98
98
0.3
0.3
5
115
119
6.9
1.8
1.9
22
31
20.0 20.0
4.5
15.6 15.2
32
12
10
56
78
18.0
4.9
6.4
8.0
0.0
0.0
0.6
0.3
33
13
28
2
43
4.0
6.9
29.3
7.1
10.8
0.8
0.7
2.5
41
11
10
19
41
3.9
14.0
68.0 36.8
0.0
4.1
11.4
10.4
475
55
537
0.2
0.1
3.9
0.5
0.0
1.5
5.0
4.5
10
192
0.8
0.2
8.1
39.1
3.4
0.0
15.0
1.2
3.6
3.2
1.0
26.0
7.7
0.4
4.1
14.1
4.4
2.0
51
8
52
4
148
30
53
14
472
22
508
1.3
0.8
5.3
138
772
743
382 2 035 1.3
1.2
1.4
Total
16.3
6.7
3.5
3.7
See notes to table B.2.
but as noted, in this group migrant workers form a
very small proportion (0.2 per cent) of all workers.
Statistics on migrant workers and migrant domestic
workers are shown separately for males and females in
tables B.3 and B.4. There is little gender difference in
the patterns of variation in the proportion of migrant
workers to all workers (MW/W). However, the pattern
of variation in the proportion of migrant domestic
workers to all migrant workers (DW/MW) differs
markedly for males and females.
For males, there are no cells with very high values
for the proportion of domestic workers among
migrant workers. The ratio (DW/MW) is below 10 per
cent in all cells, except for four with values in the
range 10-15 per cent, and a higher value in a very
small cell. The last-mentioned is probably an outlier; it
is cell 53-1 (Afghanistan, Nepal), where the total
workforce W is small, as is the proportion of migrants
in the workforce (small MW/W, and hence even
smaller MW).
The cells with high values for the proportion of
domestic workers among migrant workers (DW/MW)
noted in table B.2 for the total (male+female)
population therefore arise primarily from the even
more sharp differences for female migrant workers.
Two-thirds of female migrant workers are domestic
workers in the high income group in broad subregion
41 (Saudi Arabia and the Gulf countries Bahrain,
Kuwait, Oman, Qatar, United Arab Emirates); while
one in two female migrant workers are domestic
workers in the high income group in broad subregions
21 (Argentina, Chile, Uruguay, Bolivarian Republic of
Venezuela and richer Caribbean countries) and 52
(Australia, Brunei Darussalam, New Zealand,
Singapore). Similar figures are also found in the uppermiddle income group of region 41 (Iraq, Jordan,
Lebanon), while over a third (36 per cent) of female
migrant workers are domestic workers in the high
income group in broad subregion 51 (Japan, Republic
of Korea, and also Hong Kong (China) and Macau
(China)).
79
ILO Global estimates on migrant workers: Results and methodology
TABLE B.4
Size of the female labour force, migrant workers and migrant domestic workers, by broad
subregion and income group, 2013
FEMALE
W (total number of workers, MW/W (Migrant workers as %
millions)
of all workers)
Income group
Subregion
1
11
2
3
14
4
4
Income group
All
1
18
2
3
0
0
4
Income group
All
1
1.2
2
3
0
0
4
All
23.0
12
100
51
14
0
165
1.2
2.7
4.5
9.0
2.0
18.4
1.7
9.7
2.2
9.4
21
2
8
96
19
125
0.2
1.0
0.8
5.8
1.6
8.2
14.4
19.7
47.8
35.3
85
85
3.3
3.3
4
95
99
23.3
10.3
10.6
22
31
20.6 20.6
9.8
18.2 17.9
32
12
8
51
72
15.9
4.8
10.3 10.6
0.8
0.8
0.8
0.8
33
9
16
2
27
7.0
14.9
54.8 14.8
10.2
3.2
3.7
4.5
41
3
2
3
9
3.5
23.2
58.0 30.0
11.2
47.6
67.0
60.8
378
40
426
0.2
0.1
6.4
0.7
3.7
16.5
36.1
33.9
8
144
0.6
0.1
7.3
40.8
3.6
3.7
52.9
16.3
50.6
39.2
2.0
2.8
2.8
2.8
4.9
13.5
4.0
51
7
52
4
108
23
53
9
173
5
187
5.7
1.8
1.1
123
378
550
304 1 356 1.5
2.0
1.3
Total
See notes to table B.2.
Many more issues may be examined from the main
results presented above. The commentary in this
annex has aimed to highlight the main patterns
observed concerning the number and characteristics of
migrant workers and migrant domestic workers across
the world.
80
MD/MW (Migrant domestic
workers
16.5
2.8
13.7 13.8 12.7
Annex C
Countries covered, by domain (cross-classification of detailed subregion and income group)
With 20 detailed subregions and four income
groups, there are 20x4=80 cells in table C.1. Only 49
of those cells contain at least one country. Counting
separately for total, male and female, this gives a
maximum of 49x3=147 non-empty cells. These cells
form the basic units for imputation of missing values
on the variables. For certain suitably defined statistics,
the average value is computed and then assigned to
all countries in the cell with data missing on the
variable concerned. For some variables, the cell may
contain no countries with data available. In that case
the mean value is taken from the “nearest” cell for
which it is available.
The major regions, broad subregions and detailed
subregions are as shown in Annex A.
TABLE C.1
Cross-classification of countries, by region, subregion and income group
Subregion
Broad
Detailed
Countries 11
111
12
121
122
Northern
Africa
Central
Africa
Income group
No. of
countries
1
Low income
Countries
2
Lower-middle
income
Countries
3
Upper-middle
income
Countries
4
High income
Countries
6
3
Egypt
Sudan
3
Algeria
Tunisia
Morocco
Libya
8
3
Chad
2
Cameroon Congo
2
Angola
Gabon
1
Equatorial Guinea
16
12
Central
African
Rep.
Congo
(DR)
Burundi
2
Kenya
Zambia
1
Mauritius 1
Réunion
Comoros Rwanda
Eritrea
Ethiopia
Malawi
Zimbabwe
123
Southern
Africa
5
2
Lesotho
Swaziland
3
Botswana South Africa
Namibia
Western
Africa
16
10
Benin
Liberia
6
Mauritania
Mali
Nigeria
Burkina
Faso
Gambia
Niger
Cabo
Verde
Côte
d’Ivoire
Ghana
Senegal
Guinea
Sierra Leone
Togo
10
1
GuineaBissau
Haiti
4
Cuba
Guadeloupe
5
Dominican Jamaica
Rep.
8
4
El Salvador Honduras
Guatemala Nicaragua
Costa Rica Panama
12
2
Bolivia
6
Brazil
124
21
211
212
213
Eastern
Africa
Caribbean
Central
America
South
America
Mozambique
Somalia
Tanzania,
United
Rep.
Madagascar Uganda
Guyana
4
Belize
Bahamas Puerto
Rico
Barbados Trinidad
and
Tobago
Martinique
Mexico
4
Argentina Uruguay
Paraguay
Colombia Peru
Ecuador
Suriname
Chile
Venezuela,
Bolivarian
Rep.
81
ILO Global estimates on migrant workers: Results and methodology
Subregion
Broad
Detailed
Countries 22
221
31
311
212
213
Northern
America
Northern
Europe
Income group
No. of
countries
1
Low income
Countries
2
Lower-middle
income
Countries
3
Upper-middle
income
Countries
4
2
10
10
8
South
America
12
4
El Salvador Honduras
4
Guatemala Nicaragua
2
Bolivia
Guyana
Belize
High income
Countries
2
Central
America
Canada
United
States
Denmark Latvia
Estonia
Lithuania
Finland
Norway
Mexico
Costa Rica Panama
6
Brazil
Paraguay
4
Bosnia and Serbia
Herzegovina
Argentina Uruguay
Greece
Slovenia
Italy
Spain
Malta
313
Western
Europe
7
7
Austria
Luxembourg
Belgium Netherlands
France
Switzerland
Germany
32
321
Eastern
Europe
10
2
Moldova, Ukraine
Rep. of
3
Belarus
Romania
5
Czech
Republic
Hungary
Russian
Federation
Poland
Slovakia
2
Cyprus
Israel
6
Bahrain
Qatar
Kuwait
Saudi
Arabia
United
Arab
Emirates
Korea,
Rep. of
Bulgaria
33
41
331
411
Central
and
Western
Asia
11
Arab
States
12
5
Armenia
Tajikistan
Georgia
Uzbekistan
4
Azerbaijan Turkey
KazakhstanTurkmenistan
Kyrgyzstan
3
Palestine
Yemen
3
Syrian
Arab Rep.
Iraq
Lebanon
Jordan
Oman
51
52
511
521
522
523
53
531
Eastern
Asia
7
SouthEastern
Asia
11
Australia
and New
Zealand
Pacific
Islands
2
Southern
Asia
9
1
1
2
Korea,
DPR
Cambodia
6
Indonesia Philippines
2
China
Malaysia
Mongolia
Thailand
4
Hong
Kong,
China
Japan
Macau,
China
Singapore
2
Brunei
Darussalaam
2
Australia
New
Zealand
Lao PDR
TimorLeste
Myanmar Viet Nam
3
2
2
Afghanistan Nepal
5
Papua
Solomon
New
Islands
Guinea
Bangladesh Pakistan
Bhutan
1
2
Fiji
Maldives
Iran,
Islamic Rep.
Sri Lanka
India
Total
82
176
30
44
44
58
Annex D
Data availability for different variables, by country and sex
Table D.1 shows whether (=1) or not (blank) input
data on a particular variable were available.
Information is provided for each of the 176 countries
included in the present analysis, for total, male and
female separately. The following four variables are
covered.
Full information for all the 176 countries is available
from standard international sources on the three base
variables:
Migrant workers
MW
Total domestic
workers
D
Migrant domestic
workers
MD
Migrant workers by
main sector
MW (sector). Sectors include
agriculture, industry and services
Total population aged 15+
P
Migrant population aged 15+
M
Total workers
W
For each of the variables included, information is
also provided on whether at least one data point is
available, on total (T), or male (M), or female (F).
TABLE D.1
Data availabilty status for different variables, by country and sex
Domain Serial
code
No.
Country
T
M
W
M
F
Any T,
M or F
T
D
M
F
Any T,
M or F
T
M
D
M
F
Any T, Sector
M or F
T
M
F
Any T,
M or F
Total
data
points
1112
1
Egypt
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1112
2
Morocco
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1112
3
Sudan
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1113
4
Algeria
1
1
1
1
1113
5
Libya
0
1113
6
Tunisia
0
1211
7
Central
African
Rep.
0
1211
8
Chad
0
1211
9
Congo,
Dem. Rep.
0
1212
10
Cameroon
1212
11
Congo
0
1213
12
Angola
0
1213
13
Gabon
0
1214
14
Equatorial
Guinea
0
1221
15
Burundi
0
1
1
1
1
1
1
1
1
3
1
1
1
1
1
1
1
1
12
83
ILO Global estimates on migrant workers: Results and methodology
Domain Serial
code
No.
84
Country
T
M
W
M
F
Any T,
M or F
T
D
M
F
Any T,
M or F
T
M
D
M
F
Any T, Sector
M or F
T
M
F
Any T,
M or F
Total
data
points
1221
16
Comoros
0
1221
17
Eritrea
0
1221
18
Ethiopia
1221
19
Madagascar
1221
20
Malawi
1221
21
Mozambique
1221
22
Rwanda
1221
23
Somalia
1221
24
Tanzania,
United Rep.
1
1
1
1
1
1
1
1
1221
25
Uganda
1
1
1
1
1
1
1
1
1221
26
Zimbabwe
1
1
1
1
3
1222
27
Kenya
1
1
1
1
3
1222
28
Zambia
1
1
1
1
1223
29
Mauritius
1
1
1
1
1224
30
Réunion
1232
31
Lesotho
1232
32
Swaziland
1233
33
Botswana
1233
34
Namibia
1233
35
South Africa
1241
36
Benin
1241
37
Burkina
Faso
1241
38
Gambia
1241
39
Guinea
1241
40
GuineaBissau
1241
41
Liberia
1241
42
Mali
1241
43
Niger
1241
44
Sierra Leone
1241
45
Togo
0
1242
46
Cabo Verde
0
1242
47
Côte d’Ivoire
0
1242
48
Ghana
1242
49
Mauritania
1
1
1
1
3
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
9
1
1
1
1
12
1
1
1
1
12
3
0
1
1
1
1
3
0
1
1
1
1
1
1
1
1
1
1
1
5
1
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
12
0
1
1
1
1
1
1
1
1
6
0
1
1
1
1
1
1
4
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
7
1
1
1
1
12
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
12
0
ANNEXES
ANNEX D
Domain Serial
code
No.
Country
T
M
W
M
F
Any T,
M or F
T
D
M
F
Any T,
M or F
T
M
D
M
F
Any T, Sector
M or F
T
M
F
Any T,
M or F
Total
data
points
1242
50
Nigeria
1
1
1
1
1
1
4
1242
51
Senegal
1
1
1
1
1
1
4
2111
52
Haiti
1
1
1
1
1
1
2113
53
Cuba
2113
54
Dominican
Rep.
2113
55
Guadeloupe
2113
56
Jamaica
2114
57
Bahamas
2114
58
Barbados
0
2114
59
Martinique
0
2114
60
Puerto Rico
2114
61
Trinidad and
Tobago
2122
62
El Salvador
2122
63
Guatemala
2122
64
Honduras
2122
65
Nicaragua
1
2123
66
Belize
1
2123
67
Costa Rica
1
1
2123
68
Mexico
1
2123
69
Panama
2132
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1
1
1
1
3
1
1
1
1
12
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
3
1
1
1
1
1
1
1
1
1
12
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1
1
1
1
3
4
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
70
Bolivia,
Plurinational 1
State of
1
1
1
1
1
1
1
1
1
1
1
2132
71
Guyana
1
1
1
1
2133
72
Brazil
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
2133
73
Colombia
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
2133
74
Ecuador
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
2133
75
Paraguay
1
1
1
1
2133
76
Peru
1
1
1
1
2133
77
Suriname
2134
78
Argentina
1
1
1
1
1
1
1
1
1
1
1
1
2134
79
Chile
1
1
1
1
1
1
1
1
1
1
1
1
2134
80
Uruguay
1
1
1
1
1
1
1
1
1
1
1
1
9
2134
81
Venezuela,
Bolivarian
Rep.
1
1
1
1
1
1
1
1
1
1
1
1
9
1
1
1
1
1
1
1
1
1
1
1
1
1
12
4
9
3
3
1
1
1
1
1
1
1
1
12
0
9
1
1
1
1
12
85
ILO Global estimates on migrant workers: Results and methodology
Domain Serial
code
No.
86
Country
T
M
W
M
F
Any T,
M or F
T
D
M
F
Any T,
M or F
T
M
D
M
F
Any T, Sector
M or F
T
M
F
Any T,
M or F
Total
data
points
1
1
1
1
9
1
1
1
1
12
2214
82
Canada
1
1
1
1
1
1
1
1
2214
83
United
States
1
1
1
1
1
1
1
1
1
1
1
1
3114
84
Denmark
1
1
1
1
1
1
1
1
1
1
1
1
3114
85
Estonia
1
1
1
1
3114
86
Finland
1
1
1
1
3114
87
Iceland
1
1
1
1
3114
88
Ireland
1
1
1
1
1
1
1
1
3114
89
Latvia
1
1
1
1
1
1
3114
90
Lithuania
1
1
1
1
1
1
3114
91
Norway
1
1
1
1
1
1
1
1
3114
92
Sweden
1
1
1
1
3114
93
United
Kingdom
1
1
1
1
3123
94
Albania
0
3123
95
Bosnia and
Herzegovina
0
3123
96
Macedonia,
The Former
Yugoslav
Rep.
3123
97
Serbia
1
3124
98
Croatia
3124
99
Greece
3124
100 Italy
3124
101 Malta
3124
102 Portugal
1
1
1
3124
103 Slovenia
1
1
3124
104 Spain
1
3134
105 Austria
3134
9
3
1
1
1
1
1
1
1
1
9
3
1
1
1
1
1
1
1
1
12
4
4
1
1
1
1
9
3
1
1
1
1
1
1
1
1
1
1
1
1
12
1
1
1
1
3
1
1
1
1
1
4
1
1
1
1
1
1
4
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
106 Belgium
1
1
1
1
1
1
1
1
1
1
1
1
3134
107 France
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
3134
108 Germany
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
3134
109 Luxembourg
1
1
1
1
1
1
1
1
1
1
1
1
3134
110 Netherlands
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
3134
111 Switzerland
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
3212
112
1
1
1
1
3212
113 Ukraine
3213
114 Belarus
3213
115 Bulgaria
1
3213
116 Romania
1
Moldova,
Rep.
1
3
9
9
3
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
5
1
1
1
1
1
1
1
1
12
ANNEXES
ANNEX D
Domain Serial
code
No.
Country
T
M
W
M
F
Any T,
M or F
T
D
M
F
Any T,
M or F
T
M
D
M
F
Any T, Sector
M or F
T
M
F
Any T,
M or F
Total
data
points
3214
117
Czech
Republic
1
1
1
1
1
1
1
1
1
1
1
1
3214
118 Hungary
1
1
1
1
1
1
1
1
1
1
1
1
3214
119 Poland
1
1
1
1
1
1
1
1
1
1
1
1
3214
120
1
1
1
1
1
3214
121 Slovakia
1
1
1
1
1
3312
122 Armenia
1
1
1
1
1
4
3312
123 Georgia
1
1
1
1
1
1
1
5
3312
124 Kyrgyzstan
1
1
1
1
1
1
1
3312
125 Tajikistan
1
1
1
1
3312
126 Uzbekistan
3313
127 Azerbaijan
3313
128 Kazakhstan
3313
129 Turkey
3313
130 Turkmenistan
3314
131 Cyprus
3314
132 Israel
1
1
1
4112
Occupied
133 Palestinian
Territory
1
1
1
4112
134
4112
135 Yemen
1
4113
136 Iraq
1
1
4113
137 Jordan
1
1
4113
138 Lebanon
1
4114
139 Bahrain
1
1
1
1
1
1
1
1
1
1
1
1
9
4114
140 Kuwait
1
1
1
1
1
1
1
1
1
1
1
1
9
4114
141 Oman
1
1
1
1
1
1
1
1
1
1
1
1
9
4114
142 Qatar
1
1
1
1
1
1
1
1
1
1
1
1
9
4114
143 Saudi Arabia 1
1
1
1
1
1
1
1
1
1
1
1
9
4114
144
1
1
1
1
1
1
1
5111
145 Korea DPR
5113
146 China
5113
147 Mongolia
5114
148
5114
149 Japan
5114
150 Korea, Rep.
Russian
Federation
1
1
1
1
1
1
9
1
1
1
1
12
9
4
1
1
1
1
1
1
1
1
9
1
1
1
1
12
3
0
1
1
1
1
1
1
1
1
1
1
4
1
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
12
0
1
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
9
Syrian Arab
Rep.
0
United Arab
Emirates
1
1
1
1
1
1
1
1
1
7
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
1
1
5
0
1
1
1
1
1
1
1
1
1
1
7
1
1
1
1
1
1
1
1
6
1
1
1
1
3
1
1
1
1
1
4
1
1
1
1
1
4
Hong Kong,
China
1
1
87
ILO Global estimates on migrant workers: Results and methodology
Domain Serial
code
No.
Country
T
M
W
M
F
Any T,
M or F
Macau,
China
T
D
M
F
Any T,
M or F
1
1
1
1
T
M
D
M
F
Any T, Sector
M or F
T
M
F
Any T,
M or F
Total
data
points
5114
151
5211
152 Cambodia
1
1
1
1
1
1
1
1
5212
153 Indonesia
1
1
1
1
1
1
1
1
5212
154 Lao PDR
0
5212
155 Myanmar
0
5212
156 Philippines
5212
157 Timor-Leste
5212
158 Viet Nam
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
5213
159 Malaysia
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
5213
160 Thailand
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
5214
161
Brunei
1
Darussalaam
1
1
1
1
1
1
1
1
1
1
1
5214
162 Singapore
1
5224
163 Australia
1
5224
164
5232
1
1
1
1
1
1
1
1
3
1
1
1
1
1
1
1
1
1
1
1
1
12
1
1
1
1
9
1
1
1
1
12
0
9
1
1
1
1
1
1
1
6
New
Zealand
1
1
1
1
1
1
4
165
Papua New
Guinea
1
1
1
1
1
1
4
5232
166
Solomon
Islands
6
5233
167 Fiji
5311
168 Afghanistan
5311
169 Nepal
1
1
1
1
1
1
4
5312
170 Bangladesh
1
1
1
1
1
1
4
5312
171 Bhutan
5312
172 India
1
1
1
1
1
1
4
5312
173 Pakistan
1
1
1
1
1
1
4
5312
174 Sri Lanka
1
1
1
1
1
1
4
5313
175
1
1
1
1
1
5313
176 Maldives
Iran, Islamic
Rep.
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
12
0
0
1
1
1
1
1
1
1
1
1
1
1
12
0
Number of countries with some data
Total
134
97
85
96
112 126 127 126
127
73
73
73
73
60
60
60
60 1 056
Notes:
Number of countries (including countries with no data) 176.
Shaded cells: 7 cells in which data were deleted during subsequent editing because of inconsistency/implausibility.
The last column gives for each country the total
number of data points available. The maximum
number is 12 = 3x4, three items (T, M, F) for each of
the four variables MW, D, MD and MW (sector). The
value in this column exceeds 0 for 134 of the
countries, these being the countries for which at least
88
one data point was available. The sum of the column
gives the total number of data points (1,056) in the
whole database.39
39 As noted in section 4.3, seven of these data points were deleted during
subsequent editing, resulting in the exclusion of one country.
Annex E
Data quality: Alternative imputation methods
In this annex the preliminary global and regional
estimates of migrant workers are evaluated using
alternative imputation procedures for the statistical
treatment of countries with missing data.
To evaluate the extent to which the global and
regional estimates of migrant workers depend on the
particular method of imputation adopted for treating
countries with missing values, two alternative
imputation methods have also been applied to the
datasets, one based on regressions and the other based
on cross-product ratios. The two methods are described
in detail below, and the results are then compared.
Given this notation, the starting point of the
methodology is to assume a simple linear relationship
between the labour force participation rate for
migrants and the corresponding rate for non-migrants
as follows:
where a and b are the unknown parameters of the
assumed linear relationship and p is the share of
working-age migrants in the total working-age
population of the country, i.e.:
E.1 Imputation using regressions
The imputation method is based on an assumed
relationship between the labour force participation
rate of migrant workers and the national labour force
participation rate. After fitting the data, the
parameters of the relationship are estimated and used
to derive estimates of the labour force participation of
migrants from the information on the national labour
force participation of the country.
The relationship assumes that the difference
between the labour force participation rates of the
two populations varies linearly with the share of
working-age migrants in the country. The linear
relationship may be re-expressed in terms of the total
labour force participation rate as follows:
Let MLFPR represent the labour force participation
rate of migrants and NLFPR the labour force
participation rate of non-migrants in a given country.
In terms of the notations introduced earlier:
and
–
where MW is the number of migrant workers, M the
number of working-age migrants, W the total labour
force and P the total size of the working-age population
of the country. Similarly, let LFPR represent the total
labour force participation rate of the country, i.e.:
where q =1-p. Substituting the expression in the
linear relationship between MLFPR and NLFPR one
obtains, after rearranging terms and simplification:
89
ILO Global estimates on migrant workers: Results and methodology
TABLE E.1
Estimated regression parameters and regression fit of relationship between labour force
participation rate of migrants and the national labour force participation rate, by sex and broad
region
BOTH SEXES
Broad region*
Number of
countries
a
b
R2
Arab States
8
-0.0833
1.0865
0.7588
Eastern Europe and Central Asia
8
0.0798
0.3048
0.3026
Latin America and the Caribbean
18
-0.1470
2.4569
0.3823
Northern Africa
3
-0.0902
12.0164
0.2784
Northern America
2
0.2373
-0.6612
1.0000
Northern, Southern and Western Europe
20
0.0952
0.4858
0.8709
9+9
-0.0029
0.6931
0.2702
11
-0.1229
-1.7847
0.5239
Number of
countries
a
b
R2
Arab States
8
0.1295
0.1594
0.2740
Eastern Europe and Central Asia
8
-0.1387
0.3232
0.4640
Latin America and the Caribbean
18
-0.2552
3.7544
0.7376
Northern Africa
3
-0.2339
15.7899
0.6087
Northern America
2
2.1684
-11.726
1.0000
Northern, Southern and Western Europe
20
-0.0619
0.7298
0.0825
9+9
-0.1376
1.1676
0.4625
11
-0.1294
0.2268
0.3241
Number of
countries
a
b
R2
Arab States
8
0.1085
0.9231
0.6837
Eastern Europe and Central Asia
8
0.0943
1.0147
0.2540
Latin America and the Caribbean
18
-0.0655
2.2220
0.0823
Northern Africa
3
0.0021
11.6334
0.7886
Northern America
2
-0.7667
4.8686
1.0000
Northern, Southern and Western Europe
20
0.0731
0.7690
0.5505
9+9 = 18
0.0076
0.8125
0.1593
11
-0.1018
-3.0117
0.5779
South-Eastern Asia and the Pacific, + Southern
Asia
Sub-Saharan Africa
MALE
Broad region*
South-Eastern Asia and the Pacific, + Southern
Asia
Sub-Saharan Africa
FEMALE
Broad region*
South-Eastern Asia and the Pacific, + Southern
Asia
Sub-Saharan Africa
Note: * For this analysis two pairs of regions were merged: regions 32 and 33, together forming Eastern Europe and Central Asia; and South-Eastern Asia and
the Pacific, merged with Southern Asia for the purpose of estimation. Eastern Asia was not included.
90
ANNEXES
ANNEX E
TABLE E.2
Cross-classification of the working-age
population by migrant status and labour force
status
Migrant status
Labour force
status
Total
It can be observed that the resulting expression is
the relationship between the labour force participation
rate of migrant workers and the corresponding rate
for the total working-age population of the country.
This relationship is parabolic in terms of p. This means
that the difference between the labour force
participation of migrants and the national labour force
participation rate increases or decreases with the share
of working-age migrants in the total working-age
population at low values of p. The value of the
difference between the two rates reverses its direction
after reaching a threshold.40
The parabolic regression was fitted to the available
data on migrant workers for each broad regional
grouping and for men and women, as well as for both
sexes, separately. The results are shown table E.1. The
corresponding tables for men and women separately
are also shown. It can be observed that except for Northern
America, Northern, Southern and Western Europe,
and the Arab States (regions generally without much
missing data), the regression fits are not close in other
regions. The values R2 are mostly around 0.30.41
Based on the estimated regression parameters, the
number of migrant workers in countries with no
available data is imputed as follows:
where a and b are the estimated regression
parameters of the region in which the country belongs,
Mj is the migrant working-age population in the
40 The threshold may be calculated as the point where the parabolic
relationship (aq+pqb) reaches its maximum, in other words, when the
derivative of the function is zero, p=(b-a)/2.
41 The standard deviations of the estimated parameters by region, as well as
the datasets used and calculations, are stored in an Excel file available
from the ILO.
Total
1
0
1
a
b
W
0
c
d
P-W
M
P-M
P
country, LFPRj is the total labour participation of the
country (Wj/Pj), pj is the share of working-age migrants
in the total working-age population (Mj/Pj) and qj=1-pj.
All the necessary data are available from the benchmark
UN and ILO datasets for 2013 on population, labour
force and international stock of migrants.
As in the other imputations described earlier, the
regression imputations were carried out for total
population and also for male and female separately.
The resulting estimates were then proportionally
adjusted to ensure that the male and female estimates
add up to the estimate for both sexes.
E.2 Imputation using cross-product ratios
The other method used for the statistical treatment
of countries with missing data on migrant workers
was based on the calculation of cross-product ratios
describing the relationship between migrant status
and labour force status of the working-age
population. Consider the cross-tabulation of the
working-age population (P) by migrant status and
labour force status as shown in table E.2.
In the cross-tabulation, migrant status equal to 1
means “migrant” and migrant status equal to 0
means “non-migrant”. Similarly, labour force status
equal to 1 means being in the labour force, and labour
force status equal to 0 means being outside the labour
force. There are M migrants indicated in the last row
of the column Migrant status = 1, and there are W
workers indicated in the last column of the row
Labour force status = 1. The total number of nonmigrants is therefore P-M and the total number of
persons outside the labour force is P-W.
The core elements of the cross-tabulation are the
number of migrant workers (a), the number non91
ILO Global estimates on migrant workers: Results and methodology
migrants in the labour force (b), the number of
migrants outside the labour force (c), and finally the
number of non-migrants outside the labour force (d).
These terms may be expressed as
a = MW
b = W-MW
c = M-MW
d = P-W-M+MW = (P-M) – (W-MW)
The degree of association between two
dichotomous variables such as migrant status and
labour force status specified here may be measured by
the cross-product ratio defined by
If the two variables are not associated together the
cross-product ratio is 1 (α=1). Thus, if there is no
association between migrant status and labour force
status in a particular region, α = 1 for that region. In
that case, the labour force participation rates of
migrants and non-migrants are the same and the
number of migrant workers may be derived by simply
multiplying the number of migrants of working age
(M) by the national labour force participation. In
general, the cross-product ratio may take any value
between -∞ and +∞. Table E.3 shows their values
calculated on the basis of countries with available data
by sex and for the 20 detailed subregions of the ILO
regional groupings.
The estimates show a strong association between
migrant status and labour force status (α>2) in the
Arab States, all parts of Europe (Northern Europe,
Southern Europe and Western Europe) and the Pacific
Islands. By contrast, there is little association between
the variables (α=1) in the Caribbean, Central America
and North Africa.
Consider now a country j for which no data on
migrant workers were found. An estimate of the
migrant workers in that country may be obtained
under the assumption that the association between
migrant status and labour force status in the country is
the same as that of the region to which it belongs.
Under this assumption, the estimation of migrant
workers in country j consists of finding the value a =
MW which together with data on population of
working age (Pj), migrants of working age (Mj), and
total labour force (Wj) gives the cross-product ratio of
the region to which the country belongs.
It can be shown that the desired value a is the
solution of the quadratic equation,
where A=1-α, B=Pj-(1-α)(Mj+Wj) and C=-αMjWj. The solution is given by
The procedure was applied to the datasets on
migrant workers, and the estimates of the number of
migrant workers for countries with no available data
were calculated for male, female and both sexes,
separately. As in standard practice, the estimates were
proportionally adjusted to ensure that the sum of the
male and female estimates is equal to the estimate of
total.42
42 The full datasets used and calculations are stored in an Excel file at the
ILO. The results have been compared with the corresponding estimates
obtained from the other imputation methods as part of the analysis of the
global and regional estimates.
92
ANNEXES
ANNEX E
TABLE E.3
Estimated cross-product ratio of relationship between migrant status and labour force status, by
sex and detailed subregion
Detailed subregion
Number of
countries
Cross-product ratio (α)
Both sexes
Male
Female
Arab States
10
4.8953
3.4759
4.4090
Australia and New Zealand
1
1.7928
2.4312
1.6500
Caribbean
4
0.9764
1.0366
1.8955
Central Africa
1
0.5176
0.5978
0.3766
Central America
6
0.9018
0.5649
1.1591
Central Asia
4
1.5512
0.7507
8.7113
Eastern Africa
5
0.1877
0.2157
0.2279
Eastern Asia
1
1.9318
2.1589
1.7046
Eastern Europe
7
2.7601
0.5940
2.3161
Northern Africa
3
0.9929
0.5348
1.6938
Northern America
2
1.7509
1.7027
1.5173
Northern Europe
9
3.3320
0.7592
3.7775
Pacific Islands
2
2.4411
0.2178
2.8133
South America
9
0.7376
0.6751
0.7887
South-Eastern Asia
8
1.1717
0.9224
1.4653
Southern Africa
2
1.3618
1.5931
1.4544
Southern Asia
4
1.8767
1.9214
0.7728
Southern Europe
5
2.8195
0.8628
3.0440
Western Africa
7
1.4827
0.6642
0.8060
Western Europe
7
2.1180
2.0282
0.7184
E.3 Comparison of the results
Table E.4 compares the global estimates of migrant
workers by sex obtained from the alternative imputation
methods with those derived from the simple imputation
method using subregional averages. The results show
close agreement among the global estimates. The
alternative imputation methods give slightly higher global
estimates (150.9 million using regression, 151.8 million
using cross-product ratios, against 150.6 million using
subregional averages). The discrepancies by sex are
slightly higher but do not exceed 2 per cent.
The comparison by region presented in table E.5
also shows close agreement between the regional
estimates obtained from the different methods of
imputation. The highest relative discrepancy is about
2.2 per cent and relates to the estimates for Arab
States, using regression imputations.
Finally, the comparison of the estimates by income
level of countries is shown in table E.6. The results
show close agreement in absolute numbers, but
considerable differences in relative numbers. The
highest discrepancies in absolute terms concern the
regression imputation methods for lower- and uppermiddle income countries. The results deviate by more
than 1.3 million migrant workers with the
corresponding estimates obtained from imputation
with subregional averages.
In relative terms, the highest discrepancy is for the
estimate of migrant workers for low income countries
based on the method of imputation by cross-product
93
ILO Global estimates on migrant workers: Results and methodology
TABLE E.4
Alternative imputation of countries with missing data, by sex
(‘000)
Imputation method
Subregional average*
Quadratic regression
Cross-product ratio
Total
150 631
150 866
151 821
Male
85 064
85 716
86 602
Female
65 567
65 150
65 219
Note: *Estimates in this table differ somewhat from the “final” estimates presented in the body of this report. The above were computed using an earlier version
of the data file and of details of the estimation procedure used. Nevertheless, these differences have little effect for the present purpose, which is to assess the
effect of different imputation procedures on the results.
TABLE E.5
Alternative imputation of countries with missing data, by major region
(‘000)
Imputation method
Subregional average Quadratic regression
Cross-product ratio
Total
150 631
150 866
151 821
Africa
8 400
8 258
8 499
Americas
41 286
41 333
41 132
Arab States
18 046
18 460
18 203
Asia and the Pacific
25 017
24 839
24 865
Europe and Central Asia
57 882
57 976
59 122
Note: The standard 11 broad subregions used for presentation of the results in the body of this report have been collapsed for the purpose of this table. See also
note to table E.4.
TABLE E.6
Alternative imputation of countries with missing data, by income group
(‘000)
Imputation method
Subregional average* Quadratic regression
Total
150 631
150 866
151 821
Low income countries
3 426
3 612
3 974
Lower-middle income countries
17 373
16 069
17 248
Upper-middle income countries
15 637
16 975
16 759
High income countries
114 195
114 210
113 840
Note: *See notes to the preceding tables.
94
Cross-product ratio
ANNEXES
ANNEX E
ratios. The difference is about 16 per cent, but it may
be explained by the relatively small size of the
aggregate itself (about 3 million), that transforms a
small difference in absolute terms into a large
difference in relative terms.
variable MW, the number of migrant workers. Data
are not missing on this variable to the same extent as
data on variables concerning migrant domestic labour
(MD). Whether the conclusions here apply also to
variables with greater proportions of missing data
needs to be verified.
E.4 Concluding remarks
In conclusion, a qualification should be noted. The
above analysis shows on the whole close agreement in
the results coming from quite different methods of
imputation. However, the analysis has dealt with
All results presented in this report have used the
method based on cell averages of the cross-tabulation
of detailed subregions and income groups to impute
missing country values in the cell, separately for total,
male and female populations, as described in section 6.
95
Annex F
Data quality: Comparison with ILO 2010 global and regional estimates of the number
of domestic workers
The focus of this report is on global and regional
estimates of migrant workers and migrant domestic
workers. An estimation of the number of all domestic
workers is in this sense not the primary objective.
Nevertheless, the number of all domestic workers is a
parameter in the estimation of the number of migrant
domestic workers and is therefore produced as a
byproduct of application of the present procedure.
In 2013, the ILO published global and regional
estimates of domestic workers for 2010. The estimates
referred to 177 countries and territories, all included in
the present study except Netherlands Antilles. The
underlying data were obtained from national census and
survey sources and in a few cases from administrative
records. A great part of the country data, but not all, has
also been used in the present study. The estimation
methodology was however rather different. It involved
weight adjustments for countries with missing data as
opposed to explicit imputations. Also, there were
different approaches to standardization of the national
datasets. The detailed methodology is described in
Appendix I of the publication (ILO, 2013c, pp. 108−115).
The definition of domestic worker was similar to
that adopted in the present study, namely, branch of
economic activity codes 95 or 97 of the International
Standard Industrial Classification of All Economic
Activities (ISIC Rev 3, Rev 3.1 or ISIC Rev 4) or its
national equivalent. There is however an important
difference. The 2010 global estimate covered currently
employed domestic workers as opposed to the present
study, which in principle includes both currently
employed and unemployed domestic workers.
Table F.1 compares the global and regional
estimates of domestic workers for 2013 obtained from
the present study with the corresponding ILO
estimates for 2010. The countries and territories have
been regrouped to match the regional grouping of the
2010 estimates. The grouping in the 2010 estimates
was into six major regions; the countries comprising
each region are listed in the notes to the table.
The results show a considerably higher estimate of
the number of domestic workers in 2013 relative to
96
the 2010 estimate. The global number of domestic
workers in the present exercise is estimated at 67
million for 2013, compared to a little under 53 million
in 2010, an increase of over 25 per cent.
The differences at the global level may be the result
of a number of general factors:
(i) Population growth between 2010 and 2013 is
a factor contributing to the difference.
(ii) Additional contribution to increase over time
may also come from socio-economic factors
such as economic development, increased
inequality, and urbanization.
(iii)In addition, a part of the difference is due to
the additional component of unemployed
domestic workers included in principle in the
2013 estimate but not in the 2010 estimate.
(iv)We believe that the present methodology is
more precise and subject to less bias of
underestimation.
(v) Perhaps the most important contributing
factor is the availability of more and possibly
better data for the 2013 study, not available
for the 2010 study.
In any case, there are measurement errors in any
estimation process and a degree of discrepancy should
be expected in results using somewhat different
databases and methodologies.
It is instructive to compare the distribution of
domestic workers across regions in the 2010 and 2013
estimates. These are shown in column (2) of the
respective panels in the table. Their difference in
percentage points is shown in column (6). The most
significant differences in the two distributions are the
following.
(i) For Industrialized Countries and for Africa, the
share in each case is larger by around 4
percentage points in the 2013 estimates
compared to the 2010 estimates.
(ii) For Latin America and the Caribbean, the
share is reduced by over 10 percentage
points.
ANNEXES
ANNEX F
TABLE F.1
Comparison of global and regional estimates of domestic workers, 20101 and 2013
ILO 2010 estimates1
Total (male+female)
(1)
(2)
New 2013 estimates
(3)
(1)
(2)
(4)
(6)
1 Industrialized countries2
3 555
6.8
0.8
7 212
10.7
1.4
4.0
2 Eastern Europe and CIS3
595
1.1
0.3
1 221
1.8
0.5
0.7
12 077
23.0
1.2
14 466
21.5
1.4
-1.4
9 390
17.9
1.2
13 217
19.7
1.6
1.8
19 593
37.3
7.6
17 903
26.7
6.0
-10.6
5 Africa6
5 236
10.0
1.4
9 297
13.8
2.2
3.9
6 Arab States7
2 107
4.0
5.6
3 823
5.7
7.7
1.7
52 553 100.0
1.7
67 139
100.0
2.0
0.0
3
3.1
4
Asia and the Pacific
excluding China4
China
Latin America and
Caribbean5
Total
ILO 2010 estimates1
Female
(1)
(2)
(3)
New 2013 estimates
(5)
(1)
(2)
(4)
(5)
(6)
1 Industrialized countries2
2 597
6.0
1.3
0.73
5 736
10.7
2.5
0.80
4.7
2 Eastern Europe and CIS3
396
0.9
0.4
0.67
863
1.6
0.8
0.71
0.7
9 013
20.7
2.5
0.75 10 713
19.9
3.2
0.74
-0.7
8 451
19.4
2.6
0.90 11 728
21.8
3.1
0.89
2.4
18 005
41.3
17.4
0.92 15 677
29.2
12.5
0.88
-12.1
5 Africa6
3 835
8.8
2.5
0.73
6 843
12.7
3.7
0.74
3.9
6 Arab States7
1 329
3.0
20.5
0.63
2 195
4.1
24.9
0.57
1.0
43 626 100.0
3.5
0.83 53 753
100.0
4.0
0.80
0.0
3
3.1
4
Asia and the Pacific
excluding China4
China
Latin America and
Caribbean5
Total
Column headings:
(1)
Domestic workers (‘000)
(2)
% share of total
(3)
Share in total employment
(4)
Share in total labour force
(5)
Proportion of females among domestic workers: ratio (1)female/(1)total
(6)
Change in the % distribution: % in 2013 - % in 2010
Notes:
1 ILO, 2013c, p. 20, table 3.1.
2 Australia, Austria, Belgium, Canada, Cyprus, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, Republic of Korea, Luxembourg,
Malta, Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, United Kingdom, United States.
3 Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, Kyrgyzstan, Latvia,
Lithuania, Republic of Moldova, Poland, Romania, Russian Federation, Serbia, Slovakia, Slovenia, Tajikistan, The former Yugoslav Republic of Macedonia, Turkey,
Turkmenistan, Ukraine, Uzbekistan.
4 Afghanistan, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Fiji, Hong Kong (China), India, Indonesia, Islamic Republic of Iran, Democratic Republic
of Korea, Lao People’s Democratic Republic, Macau (China), Malaysia, Maldives, Mongolia, Myanmar, Nepal, Pakistan, Papua New Guinea, Philippines, Solomon
Islands, Sri Lanka, Thailand, Timor-Leste, Viet Nam.
5 Argentina, Bahamas, Barbados, Belize, Plurinational State of Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador,
Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Suriname, Trinidad and
Tobago, Uruguay, Bolivarian Republic of Venezuela.
6 Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Congo, Democratic Republic of
Congo, Côte d’Ivoire, Egypt, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar,
Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Reunion, Rwanda, Senegal, Sierra Leone, Somalia, South Africa, Sudan,
Swaziland, United Republic of Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe.
7 Bahrain, Iraq, Jordan, Kuwait, Lebanon, Occupied Palestinian Territory, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, United Arab Emirates, Yemen.
97
ILO Global estimates on migrant workers: Results and methodology
It should be noted that in the case of industrialized
countries, the 2010 report specifies the category as
“Industrialized Countries (selected)”, presumably
implying that the coverage of countries in that region
was less than complete. If so, this would have resulted
in underestimation.
It has been suggested that the 2010 estimate for
Latin America and the Caribbean is rather high, and
out of line with estimates from other regions. For
instance, in that region domestic workers are
reported to form 7.6 per cent of total employment,
a figure very much higher than those in other
regions, which fall in the range 0.3-1.4 per cent with
the (expected) exception of Arab States (5.6 per
cent).
Columns (3) and (4) of the table show domestic
workers as a proportion of total employment and of
the total workforce in the respective panels for the
2010 and 2013 estimates. The two measures are not
exactly the same. Since the 2010 estimates are in
terms of employment, column (3) shows the share of
domestic work in total employment. Since the 2013
estimates are in terms of labour force (including
employment and unemployment), column (4) shows
the share in total labour force.
98
In any case, the figures for the two estimates close,
at least in terms of variation across regions. The overall
average ratio for 2013 (2.0 per cent) is higher than the
average for 2010 (1.7 per cent).
The second part of the table shows the same results
for female domestic workers separately. The overall
pattern is very close to that already discussed for the total
(male+female) domestic workers. This is expected since
80 per cent or more of domestic workers are female.
The new information in the table for females
concerns the variation across regions of the share of
women among domestic workers. This is compared in
column (5) of the respective panels for 2010 and
2013. The results for the two estimates are quite
similar in structure. The main differences observed are
higher in Industrialized Countries in 2013 than in 2010
(80 versus 73 per cent), and a lower percentage
female among domestic workers in Arab States
(57 versus 63 per cent). We may also note that the
proportion of females is lower by a smaller margin
(around 4 percentage points) in the 2013 estimates.
Overall, the percentage of females among domestic
workers is 80 per cent according to the 2013 estimates,
compared to 83 per cent in the earlier estimates.
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This report provides information on the order of magnitude of labour
migration and migrant domestic workers. It begins with a presentation of the
main results obtained and description of what is being estimated. It then
provides a detailed description and analysis of the global and regional
estimates of migrant workers and migrant domestic workers for 2013 with
breakdown by sex and broad branch of economic activity. The report also
describes the nature and quality of the data used, and the sources and
methodology used as well as their limitations. Six annexes complement the
material presented in the main body of the report.
The report intends to help draw attention to the economic and social issues
of labour migration and facilitate the development of sound international
statistical standards in the future.
ISBN 97-8-922130479-1
9 789221 304791
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