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WORLD
EMPLOYMENT
OUTLOOK
SOCIAL

TRENDS FOR WOMEN 2017

International Labour Office • Geneva

Acknowledgements

The World Employment and Social Outlook: Trends for Women 2017 was prepared by the Labour Market Trends and Policy Evaluation Unit (led by Steven Tobin) of the ILO Research Department. The report was produced by Stefan Kühn, Richard Horne and Sheena Yoon. Judy Rafferty provided valuable research assistance.

The forecast data underlining this report are derived from the ILO’s Trends Econometric Models, managed by Stefan Kühn and Steven Kapsos. The report would not have been possible without the feedback and baseline labour market information provided by the team led by Steven Kapsos, notably David Bescond, Rosina Gammarano, Roger Gomis, Yves Perardel and Marie-Claire Sodergren of the ILO Department of Statistics.

The report benefited from the guidance of Sangheon Lee, Director a.i. of the Research Department and comments by L. Jeff Johnson, Deputy Director of the Research Department. The team wishes to acknowledge the excellent suggestions provided by Deborah Greenfield, Deputy Director-General for Policy and James Howard, Senior Adviser to the Director-General. The authors are also very grateful to the Gender, Equality and Diversity Branch at the ILO and other colleagues for their input and feedback, notably Laura Addati, Antonia Asenjo, Florence Bonnet, Marva Corley-Coulibaly, Fernanda Dutra, Elizabeth Echeverria Manrique, Sara Elder, Ekkehard Ernst, Michela Esposito, Valeria Esquivel, Andre Gama, Carla Henry, Phu Huynh, Steven Kapsos, Takaaki Kizu, Santo Milasi, Guillermo Montt, Louise Nennen, Shauna Olney, Ira Postolachi, Uma Rani, Pelin Sekerler-Richiardi, Claudia Ruiz, Isabel Valarino, Christian Viegelahn and Zheng Wang.

Table of contents

Acknowledgements

Executive summary

1.Gender gaps in the labour market: Trends and impacts of improving outcomes for women

Introduction

1.1Labour force participation

1.2Unemployment

1.3Gender gaps in sectoral and occupational opportunities

1.4Status in employment

1.5Gaps in income

1.6Economic benefits of reducing gender gaps

2.Assessing the factors driving gender gaps in the labour market

Introduction

2.1Understanding the gender gaps: Descriptive evidence and analytical framework

2.2Preferences, gender role conformity and socio-economic constraints: An empirical assessment

2.3Decomposing gender gaps in the workplace

2.4Concluding remarks

3.Policy considerations

3.1Reshaping gender role conformity and personal preferences

3.2Addressing socio-economic constraints

3.3Raising equality in labour market conditions

3.4Moving forward

Appendices

Appendix A.Regional, country and income groupings

Appendix B.Labour market estimates, projections and scenarios

Appendix C.Estimating female labour force participation

Appendix D.Decomposing gender gaps in employment relationships

Appendix E.Gender breakdown of key labour market indicators

References

Boxes

1.Measuring gender segregation in employment distribution

2.Involuntary part-time employment in EU-28

3.Gaps in working time and social protection coverage

4.Gender dimensions of working poverty in developed countries

5.Estimating the economic impact of “25 by 25”

6.The value of invisible work

7.Transforming social norms and personal preferences

8.Relieving socio-economic constraints on women

9.Empowering women to secure improved labour market conditions

Figures

1.Composition of total sectoral segregation by region, 1997 and 2017

2.Relative concentration of gender in occupation, by income group, latest year

3.Employment status by gender and region, 2017

4.Average female labour force participation rates and income per capita, 2016

5.Analytical framework of labour market outcomes

6.Estimating cumulative effects of fundamental drivers

7.Decomposition of gender gaps in type of employment relationship

8.Visual framework of policy responses to gender gaps in the labour market

Tables

1.Labour force participation rate by sex (per cent) and gender gap (percentage points), 1997–2021

2.Unemployment rate by sex (per cent) and gender gap (percentage points), 1997–2021

3.Employment by economic class and sex, 2017

4.Effects of reducing gender gaps in the labour market by 2025

5.Preferences of women and constraints on their participation in the labour market, 2016 (per cent of respondents)

6.Marginal impact of drivers on the probability to participate in the labour market

Executive summary

Gaps between men and women in the world of work remain widespread and begin with women’s limited access to the labour market…

Gender gaps are one of the most pressing labour market challenges facing the global community. In fact, women are substantially less likely than men to participate in the labour market, i.e. to either be in employment or looking for a job: the current global labour force participation rate for women at just over 49 per cent is nearly 27 percentage points lower than the rate for men (with no improvements anticipated in 2018). Worryingly, underlying this gap is a downward trend in participation rates for both men and women: between 1997 and 2017, the participation rates for both sexes have fallen by roughly 3 percentage points. In some countries, where the gap has narrowed it has been as a result of male rates falling more sharply than those of their female counterparts.

In 2017, the largest gender gap in participation rates, at nearly 31 percentage points, is faced by women in emerging countries, followed by those in developed countries, at just over 16 percentage points, and in developing countries, with a gap of 12 percentage points. In terms of regions, the gaps are widest in the Arab States, Northern Africa and Southern Asia, where they exceed 50 percentage points. These three regions also have the lowest levels of female participation rates (less than 30 per cent, compared to the global average of 49 per cent).

Looking ahead to 2021, out of the 11 subregions with available information, the gap is expected to narrow in only six. In other instances it is anticipated that the gap will widen or remain stable.

In light of the continuing lack of adequate progress in improving the situation of women in the labour market, the ILO Director-General has launched the Women at Work Centenary Initiative. This initiative intends to identify innovative action that could give new impetus to the ILO’s work on gender equality and non-discrimination in the run-up to and following the ILO’s centenary in 2019. The information provided in this report constitutes a key building block in moving that agenda forward.

… and, once in the job market, women have restricted access to quality employment opportunities.

When women do participate in the labour market, they are less likely than their male counterparts to find a job. Globally, the unemployment rate for women stands at 6.2 per cent in 2017, representing a gap of 0.7 percentage points from the male unemployment rate of 5.5 per cent. In 2018, both rates of unemployment are expected to remain relatively unchanged, meaning that the gap will persist at its current level (with no anticipated improvement before 2021, based on current trends). At the global level, this gap has remained relatively unchanged in recent years, albeit with considerable variation according to income group. For instance, in emerging countries the gap has widened: from 0.5 percentage points in 2007 to 0.7 percentage points in 2017. In contrast, the gaps in both developing and developed countries have narrowed to reach 1.8 and 0.5 percentage points, respectively.

In the Arab States and Northern Africa, unemployment rates among women exceed 20 per cent, more than twice the rate of their male counterparts, and consequently the gap stands at over 10 percentage points in 2017 (in both regions the gap has widened in the past decade).

Once in employment, nearly 15 per cent of women are contributing family workers (i.e. self-employed persons working in an establishment operated by a relative) whereas, among men, this status accounts for just 5.5 per cent. In developing countries where nearly 36.6 per cent of women, compared to only 17.2 per cent of men, are engaged as contributing family workers, this gap – standing at over 19 percentage points – is the widest. Moreover, the gap has widened by over 2 percentage points in the past decade.

Women are also more likely to undertake a greater number of hours of unpaid work due to time spent on household chores and care provision. Overall, they are more likely to work longer hours than men when both paid and unpaid work are taken into account. Moreover, when in paid employment, on average, women work fewer hours for pay or profit either because they opt to work part time or because part-time work is the only option available to them. In developing countries with available information, time-related underemployment among women, measured as persons working fewer hours than they would like to, can be as high as 40–50 per cent. Similarly, involuntary part-time work, of less than 30 hours per week, as a share of total employment in the EU-28 stood at 8.2 per cent in 2015 for women, compared with 2.6 per cent for men.

In addition, a comparison of the sectoral distribution of employment by sex reveals strong evidence of gender segregation, with education, health and social work being the sector with the highest relative concentration of women, followed by wholesale and retail trade. Moreover, the extent of gender segregation along sectoral lines has increased by one-third over the past two decades. There is also occupational segregation in both emerging and developed countries, where women are relatively concentrated in the services and sales sector. Women are also concentrated in clerical support (in developed countries) and as skilled agricultural, forestry and fishery workers (in emerging countries).

Closing these gaps would yield significant economic benefits and improve individual welfare

In 2014, G20 leaders committed to the “25 by 25” target, i.e. to reduce the gap in participation rates between men and women by 25 per cent by the year 2025. Estimates for this report indicate that, under certain assumptions, if such a goal were to be realized across all countries, it has the potential to boost global employment by 189 million, or 5.3 per cent. The vast majority of job gains (162 million) would be in emerging countries due to their relative size, combined with the fact they also have the widest gender gaps. The impact in developing and developed countries would be smaller, both in absolute terms and as a percentage of current employment levels (due primarily to the presence of comparably narrower gender gaps in labour market participation). Nevertheless, in both instances employment would grow, by 2 per cent in developing and by 3.3 per cent in developed countries.

Such an outcome would yield significant economic gains, raising global GDP in 2025 by 3.9 per cent, or US$5.8 trillion (equivalent to raising average global GDP growth over the next eight years by almost half a percentage point). The regions with the largest gender gaps, namely Northern Africa, the Arab States and Southern Asia, would see the greatest benefits. However, even in North America and parts of Europe, average annual GDP growth would be lifted by a quarter of a percentage point, an important contribution during times of weakened economic growth. The achievement of such a goal would also unlock large potential tax revenues, which could be used to finance measures to address the gender gaps in the labour market discussed below. Indeed, global tax revenue could increase by US$1.5 trillion.

While there are clear economic benefits to be gained by engaging more women in the labour force, there are also other significant positive impacts, such as the improvement in the welfare of women and the opportunity that it would afford them to realize their goals. Indeed, irrespective of their employment status, 70 per cent of women prefer to work at paid jobs. Considering that more than half of all women globally are out of the labour force, this suggests that there are significant challenges restricting their capacity and freedom to participate.

Moving forward, closing gender gaps will require concerted efforts across a range of policy dimensions. In this respect, the most immediate concern for policy-makers should be to alleviate the constraints on women’s freedom to choose whether or not to enter the labour market and the barriers they face once they are in the workplace.

Women who would like to improve their labour market situation are being held back by social norms and a range of socio-economic constraints

A precondition to entering the labour market is, of course, the preference and desire to do so. However, while aspirations are necessary, they are not sufficient. Indeed, a range of other factors are at play which can deter women from participating. Moreover, a woman’s preference and decision (even the freedom to choose) to participate in the labour market or improve her job quality (e.g. in terms of pay, occupation or status in employment) can be affected by a number of constraints.

In fact, with respect to deciding whether to participate in the first instance, socio-economic constraints have the largest impact on women’s probability of participating across nearly all country groupings (up to 30 percentage points). The most prevalent among these socio-economic constraints are work–life balance, marital status and lack of transportation.

Gender role conformity – the preferences and expectations of both women and men in the labour market – also plays a major role in constraining female labour market participation. This is true across all country groupings, albeit to a lesser degree in emerging countries. The most important factors in this regard, though to varying degrees, are the extent to which family members approve of women undertaking paid work and the degree of acceptance within a given society of women in the workplace.

The analysis of workplace gender gaps, i.e. gaps between men and women with respect to employment type (e.g. contributing family workers, part-time employment, etc.), reveals that the most important driver of these differences is neither the occupation nor the sector in which women are working, but other factors (such as discrimination or education). This is a strong indicator that social norms have a significant direct impact on gender gaps in employment.

A comprehensive suite of measures is needed that, first, reshapes gender role conformity and improves equality in labour market conditions…

Given the importance of social norms and gender role conformity in explaining gender gaps in the world of work, appropriate policy responses must address the root causes of segregation and diversify traditional employment opportunities for women and men. Only then can the constraints on women’s roles in the workplace be broken down. This means combating discrimination both within and outside the workplace. In particular, appropriate policy responses, with a view to achieving the targets of the Sustainable Development Goals (notably Goal 5, Gender Equality), need to principally address the differential treatment and perception of women relating to their place both in the world of work and in society more broadly, including, but not limited to:

•Promoting equal remuneration for work of equal value: The principle of equal remuneration for work of equal value needs to be integrated into law and included in collective bargaining processes. This requirement comes with prerequisites, such as improved wage transparency and gender-neutral job evaluation, and should also leverage existing tools, such as minimum wage setting systems, as well as strengthening collective bargaining. Achieving this aim will also promote equal treatment of an extended range of different types of workers, including informal workers.

•Tackling the root causes of occupational and sectoral segregation: Challenging social norms and placing greater value on skills related to care provision will help to break down gender stereotypes. This process begins in childhood, through education and outreach, e.g. by ensuring equal access to educational opportunities and addressing gender differences in the field of study selected by individuals. Rewarding paid care work with adequate remuneration is also an important step. Supporting women’s representation, participation and leadership in decision-making will also help to challenge occupational and sectoral segregation. This applies to all areas of government and within employers’ and workers’ groups, as well as in firms.

•Transforming institutions to prevent and eliminate discrimination, violence and harassment against both women and men: Legislation to prevent and eliminate discrimination based on gender is an important first step. However, laws alone, even with strict implementation and monitoring, are insufficient to prevent and eliminate discrimination, violence and harassment in the world of work. Other measures, such as dissuasive sanctions, specialized equality bodies and public awareness campaigns are important complements in any effort to change attitudes and social norms.

…and, second, addresses the socio-economic factors, including care provision, that influence participation

Women make a disproportionate contribution to society and households in providing work that is largely invisible and often undervalued. Work of this nature – which includes, among other things, caring for family members – is necessary for household survival and makes a significant contribution to the multidimensional welfare of societies, the development of communities and the advancement of the capabilities of all human lives. Hence, recognizing and giving value to the disproportionate contributions that women make in these areas, often while also holding a paid job, will play a critical and complementary role in unlocking the potential and value of women’s work. Measures in this regard could include:

•Introducing improved policies to promote work–family balance: Millions of women and men worldwide are without the fundamental right to adequate parental protection and other basic social protection measures. Improved efforts need to be made to lay the groundwork for achieving harmonized work–family balances, and thus providing quality, family-friendly working conditions for women and men. At the same time, atypical hours and low-quality part-time work disproportionately penalize women in the labour force. Further, guaranteeing adequate social protection must include the incorporation of a gender dimension into the design, implementation and evaluation of social protection systems, in a way that is responsive to the characteristics and needs of both men and women, including maternity and women’s unequal share of what is commonly regarded as the female labour market, namely unpaid family care provision and household work, while simultaneously including gender-neutral provisions to avoid perpetuating segregation.

•Creating and protecting quality jobs in the care economy: Women are over-represented in certain areas of work, including the care professions – areas that often have a history of inadequate regulation and protection. Accordingly, there is a need to promote decent work for care professionals, including domestic and migrant workers, to address decent work deficits in the care professions. At the same time, recognizing, reducing and redistributing unpaid care work through public care services and changes in social infrastructure is imperative.

•Targeting the macroeconomic environment and informal economy: Macroeconomic policies can be important enablers of gender equality; however, due to their increased likelihood of being engaged in informal employment or other vulnerable categories of employment, women can be disproportionately impacted by downturns and reversals in fiscal commitments to important social provisions. Therefore, the prerequisite of more inclusive macroeconomic policies needs to be complemented by support for gender-responsive policies, including the formalization of jobs in the informal economy.

1

Gender gaps in the labour market
Trends and impacts of improving outcomes for women

Introduction

Gender gaps in the world of work are one of the most pressing labour market and social challenges facing the global community. Women are less likely than men to participate in the labour market, and those who do participate are less likely to find a job. If they do manage to find employment, women are often subject to various inequalities in the workplace. With this in mind, this chapter quantifies the magnitudes of gender gaps in the world of work and how they compare across regions.

The chapter first presents a detailed account of the female labour force participation gaps, outlining important regional differences in women’s access to the labour market. This is followed by an examination of the gaps between unemployment rates for women and men. The chapter also looks at select gender gaps in the workplace, covering status in employment, wages and income, and sectoral and occupational opportunities. Finally, the chapter attempts to quantify the potential economic impact from closing gender gaps in the labour market.

1.1Labour force participation

Global labour force participation gaps remain large, driven by emerging countries

Globally, the labour force participation rate for women – at 49.4 per cent – is 26.7 percentage points lower than the rate for men in 2017 and likely to remain unchanged in 2018 (table 1). Underlying this gap is a long-term downward trend in participation rates for both men and women, with the combined participation rate decreasing from 65.7 per cent in 1997 to 62.9 per cent in 2017. Between 1997 and 2007, the male participation rate declined much more than the female one, narrowing the gap between the two, but in the past decade both have remained somewhat stable, along with the gap.

The largest gender gap in participation rates, at 30.6 percentage points, is faced by women in emerging countries.[1] The second largest occurs in developed countries, at 16.1 percentage points; however, this has narrowed by more than 5 percentage points over the past two decades and is projected to continue to close. Developing countries have the smallest participation gap; however, the high participation rate for women in developing countries is often driven by economic necessity.

Table 1

Labour force participation rate by sex (per cent) and gender gap (percentage points), 1997–2021

Country/region

1997–2017

2017

2018

2018–21

Gap

Gap

Gap

Gap

World

76.1

49.4

26.7

76.0

49.3

26.7

Developing countries

82.6

70.3

12.3

82.6

70.3

12.3

Emerging countries

77.5

46.9

30.6

77.4

46.7

30.7

Developed countries

68.0

51.9

16.1

67.9

51.8

16.1

Northern Africa

74.1

22.9

51.2

74.1

22.9

51.2

Sub-Saharan Africa

76.3

64.6

11.7

76.4

64.7

11.7

Latin America and the Caribbean

78.3

52.7

25.6

78.3

52.7

25.6

Northern America

68.3

56.2

12.1

68.1

56.1

12.0

Arab States

76.4

21.2

55.2

76.3

21.3

55.0

Eastern Asia

76.8

61.3

15.5

76.6

60.9

15.7

South-Eastern Asia and the Pacific

81.2

58.8

22.4

81.1

58.8

22.3

Southern Asia

79.4

28.6

50.8

79.5

28.7

50.8

Northern, Southern and Western Europe

63.8

51.3

12.5

63.6

51.2

12.4

Eastern Europe

68.1

53.0

15.1

67.9

52.9

15.0

Central and Western Asia

73.5

44.1

29.4

73.5

44.1

29.4

Notes: Throughout this report, figures for 2017 and beyond are projections. Developments for the period 2018–21 are marked with a red upward arrow if the gap is projected to widen by more than 0.1 percentage points, by a green downward arrow if it is projected to narrow by more than 0.1 percentage points, and a black horizontal arrow for developments in between. Numbers in the “Gap” columns refer to the percentage point difference between the male and female labour force participation rates or the change over time but may not correspond precisely due to rounding.

Source: ILO’s Trends Econometric Models, November 2016.

Participation gaps are expected to worsen or remain unchanged in most regions

With the exception of Eastern Asia and Southern Asia, the gender gap in labour market participation has narrowed in every region over the past two decades, albeit to varying degrees. This narrowing is largely attributable to improvements in female participations rates. The one exception is Northern America, where the participation rate for men is falling faster than the rate for women, thus leading to an apparent “improvement” in the gap.

Africa

•Northern Africa has one of the widest gender gaps in labour force participation rates, at 51.2 percentage points (behind the Arab States at 55.2 percentage points). A declining male participation rate and a relatively stable female rate helped narrow the gap by 2.2 percentage points between 1997 and 2007. During the past decade, however, progress has slowly reversed, with a widening of the gap by 0.2 percentage points albeit as participation rates increased for both genders (though at different rates). The region still faces one of the lowest rates of female labour force participation, at 22.9 per cent in 2017, but a continued narrowing in the gender gap is expected through 2021.

•The labour force participation gap in sub-Saharan Africa remains almost unchanged from a decade earlier, at 11.7 percentage points. Furthermore, little change is anticipated through 2021. With a participation rate of 64.6 per cent, a higher share of women in sub-Saharan Africa are active in the labour force than in any other region. However, this reflects both a prevalence of poverty and a lack of access to social protection, leaving both men and women little choice but to work out of necessity. This effect – in addition to limited access to education and vocational opportunities – is also responsible for more women working in vulnerable forms of employment, namely as own-account workers[2] or contributing family workers[3] (see section 1.4).

The Americas

•Over the past two decades, Latin America and the Caribbean has recorded the largest percentage-point reduction in the labour participation gap of all regions. The gap narrowed by 9.5 percentage points over the period, to 25.6 percentage points in 2017, most of which took place between 1997 and 2007 (7 percentage points). The overall trend was driven by a steady decline in the male participation rate combined with an increase in the share of women entering the labour force. Between 1997 and 2007, the female participation rate rose by 5.3 percentage points, but since then it has increased by a more modest 0.8 percentage points, reaching 52.7 per cent in 2017. A further modest narrowing in the gap is anticipated from 2018 to 2021.

•In Northern America, the participation gender gap has narrowed by 3.4 percentage points over the past 20 years, to reach 12.1 per cent in 2017. Since 2007, the participation rate for men has declined by 3.8 percentage points and that for women by 2.5 percentage points, bringing the female rate to 56.2 per cent in 2017. Both the female and male rates are anticipated to decrease marginally in 2018, but at similar paces, and so the gap should remain unchanged through 2021.

Arab States

•The widest gender gap in labour force participation – at 55.2 percentage points – continues to persist in the Arab States. The participation rate for women is still the lowest globally, but it has been rising steadily – reaching 21.2 per cent in 2017. However, progress has been too slow to bridge the gap and catch up with the male counterpart rate of 76.4 per cent. Moreover, progress has slowed over the past 20 years, as the gap has narrowed by only 0.2 percentage points since 2007, following a reduction of 2.6 percentage points between 1997 and 2007. The gender gap is expected to continue to close through 2021.

Asia and the Pacific

•Eastern Asia is one of only two regions (with Southern Asia) where the female labour force participation rate has declined markedly since 1997. For Eastern Asia, this has resulted in a widening of the labour force participation gap to 15.5 percentage points in 2017. In fact, the participation rates for both men and women declined significantly between 1997 and 2007, but over the past decade the male participation rate has declined by 1.3 percentage points, while the female rate has declined by 2 percentage points, resulting in a widening of the gap. Despite this, the participation rate for women in the region remains the second highest globally at 61.3 per cent. A further decline in female participation through 2021 is expected to widen the gap further.

•In South-Eastern Asia and the Pacific, the gender gap in participation rates has narrowed slightly – by 1.6 percentage points – over the past two decades, to bring the gap to 22.4 percentage points in 2017. The narrowing of the gap has primarily been driven by a slight increase in the female labour force participation rate, while the male rate declined. The region has a relatively high rate of female participation, at 58.8 per cent, while the rate for men, at 81.2 per cent, is the highest globally. These high participation rates for both women and men reflect the limited access to social protection in the region, particularly given that incomes are relatively low. The gap in participation is expected to remain unchanged from 2018 to 2021.

•Over the past decade, Southern Asia has experienced the largest widening of the gap of all regions. The gap increased by around 2.1 percentage points from 2007 to 2017, resulting in a gap of 50.8 percentage points in 2017. From 2007, the rising participation rate of women observed during 1997–2007 began to reverse, resulting in a decline of 4.5 percentage points in the rate of female participation, to 28.6 per cent in 2017. A slight increase in the female rate and narrowing in the participation gap are anticipated from 2018 to 2021.

Europe and Central Asia

•In Northern, Southern and Western Europe, the gap in the labour force participation rate has narrowed by 8.3 percentage points in the past 20 years, to reach 12.5 percentage points in 2017. This trend has been driven by a declining male participation rate, while the rate for women has increased, reaching 51.3 per cent in 2017. This trend was amplified in the wake of the global financial crisis. A modest narrowing in the gap is anticipated between 2018 and 2021, as participation rates are expected to decrease for both women and men.

•Eastern Europe experienced the strongest trend reversal in the labour force participation rate gap between 1997–2007 and 2007–17. Declining participation rates for both women and men between 1997 and 2007 caused the regional gender gap to narrow by 1.5 percentage points over this period. The gap then widened between 2007 and 2017, by 1.5 percentage points, to 15.1 percentage points in 2017. The female rate, at 53.0 per cent in 2017, and the male rate, at 68.1 per cent, are both expected to decrease slightly in 2018, helping to narrow the gap further by 2021.

•In Central and Western Asia, the labour participation gender gap has declined steadily over the past 20 years, by 2.3 percentage points, as more women than men have entered the labour force. As a result, the labour force participation rates in 2017 stand at around 44.1 per cent for women and 73.5 per cent for men, giving a gender gap of 29.4 percentage points. Little change in the participation rates for women and men, and thus the gap, is expected through 2021.

1.2Unemployment

Women in the labour force are less likely than men to find jobs…

While women are less likely to participate in the labour force, when they do participate, they are more likely than their male counterparts to be unemployed. Globally, the unemployment rate for women stands at 6.2 per cent in 2017, representing a gap of 0.7 percentage points from the male unemployment rate of 5.5 per cent (table 2). This is projected to remain relatively unchanged going into 2018 and through 2021.

Since 1997, the global gender gap in unemployment has stayed around 0.8 percentage points. In emerging countries, however, the gap has widened in the past decade: from 0.5 percentage points in 2007 to 0.7 percentage points in 2017. In contrast, since 1997 the gaps in both developing and developed countries have narrowed, by 0.2 and 0.8 percentage points respectively. Accordingly, as of 2017, developed countries have the least difference between male and female unemployment rates, with a gap of 0.5 percentage points.

…a challenge that is particularly marked in certain regions

Africa

•In Northern Africa, women who participate in the labour force face the second highest unemployment rate globally, at 20 per cent, more than twice the rate for men. Significant narrowing in the unemployment gap was achieved between 1997 and 2007, with the gap being reduced by 2.5 percentage points. However, progress has since reversed; the gap widened by 0.7 percentage points between 2007 and 2017 and is expected to continue to widen, albeit marginally, through 2021. This reversal is largely attributed to an increase in the female unemployment rate, which far outstrips increases in the rate for men.

•Over the past decade, Sub-Saharan Africa has experienced declining unemployment rates for both men and women, although there has been little change in the gender gap. At 2.1 percentage points, this is the fourth largest gap in unemployment rates across all regions. Modest increases in both male and female rates are expected in 2018, but the percentage point gap is projected to remain the same.

The Americas

•The gender gap in unemployment in Latin America and the Caribbean has improved substantially since 1997, narrowing by 1.1 percentage points to 2017. Nevertheless, at 3.4 percentage points in 2017, it remains sizeable, behind only the Arab States and Northern Africa. During 1997–2007, progress was driven by the decline in the unemployment rate for women being bigger than that for men. In contrast, over the past decade, unemployment rates have increased, with the male rate increasing faster than the female rate. A modest increase in both female and male rates is anticipated in 2018, maintaining the same gap.

•Northern America is one of three regions in the world where women in the labour force have a higher likelihood of being employed than men. The unemployment rate for women, at 4.9 per cent in 2017, is 0.4 percentage points lower than the rate for men, at 5.3 per cent. Over the past decade the gap has widened, partly as a result of the financial crisis having a disproportional impact on male-dominated sectors, thus leading to an increase in the male unemployment rate in excess of that experienced by women. In 2017, however, both female and male rates are below their pre-crisis levels of 5.0 and 5.4 per cent, respectively. A small increase in both rates is expected in 2018.

Table 2

Unemployment rate by sex (per cent) and gender gap (percentage points), 1997–2021

Country/region

1997–2017

2017

2018

2018–21

Gap

Gap

Gap

Gap

World

5.5

6.2

0.7

5.5

6.2

0.7

Developing countries

4.7

6.5

1.8

4.7

6.5

1.8

Emerging countries

5.4

6.1

0.7

5.5

6.1

0.6

Developed countries

5.9

6.4

0.5

5.9

6.4

0.5

Northern Africa

9.5

20.0

10.5

9.3

20.0

10.7

Sub-Saharan Africa

6.2

8.3

2.1

6.3

8.4

2.1

Latin America and the Caribbean

7.0

10.4

3.4

7.1

10.5

3.4

Northern America

5.3

4.9

–0.4

5.4

5.1

–0.3

Arab States

8.3

21.2

12.9

8.2

20.7

12.5

Eastern Asia

5.1

3.7

–1.4

5.2

3.7

–1.5

South-Eastern Asia and the Pacific

3.8

3.9

0.1

3.8

3.9

0.1

Southern Asia

3.8

5.0

1.2

3.8

4.9

1.1

Northern, Southern and Western Europe

8.8

9.3

0.5

8.7

9.2

0.5

Eastern Europe

6.4

5.8

–0.6

6.2

5.7

–0.5

Central and Western Asia

8.9

9.6

0.7

9.1

9.6

0.5

Notes: Developments for the period 2017–21 are marked with a red upward arrow if the gap is projected to widen by more than 0.1 percentage points, by a green downward arrow if it is projected to narrow by more than 0.1 percentage points, and a black horizontal arrow for developments in between. Numbers in the “Gap” columns refer to percentage point differences and may not correspond precisely to calculations using the female and male columns due to rounding.

Source: ILO’s Trends Econometric Models, November 2016.

Arab States

•Women in the Arab States experience the highest rate of unemployment across all regions, at 21.2 per cent in 2017. This is more than twice the rate for their male counterparts, at 8.3 per cent, resulting in the largest regional unemployment gender gap, at 12.9 percentage points. Since 2007, the region has experienced a substantial widening of the gap, by 1.8 percentage points, mostly due to an increase in the unemployment rate for women. The gap is expected to narrow somewhat between 2018 and 2021.

Asia and the Pacific

•In Eastern Asia, the unemployment rate for women, at 3.7 per cent in 2017, is 1.4 percentage points lower than the rate for men, at 5.1 per cent. Since 1997, the unemployment rate for women has remained around 3.7 per cent, the lowest rate among women globally, while the unemployment rate for men has steadily risen. As a result, the unemployment gender gap has been widening since 1997, when the male rate was 0.9 percentage points higher than the female rate.

•In South-Eastern Asia and the Pacific, the narrow gender gap in unemployment rates – at 0.1 percentage points – is driven by low unemployment rates for both men and women, at 3.8 and 3.9 per cent, respectively. The gap has remained relatively narrow over the past two decades, with the male rate occasionally surpassing the female rate. Little change from the 2017 rates is anticipated through 2021.

•In Southern Asia, unemployment rates are relatively low, below the world average, at 5.0 per cent for women and 3.8 per cent for men in 2017, resulting in a gap of 1.2 percentage points. Between 1997 and 2007, the regional gap narrowed by 0.5 percentage points; however, the trend has since reversed, driven by increases in the female unemployment rate and reductions in the male rate. Little change is expected through 2021.

Europe and Central Asia

•In Northern, Southern and Western Europe, the female unemployment rate of 9.3 per cent in 2017 is 0.5 percentage points higher than the male rate, at 8.8 per cent. This represents a narrowing of the gender gap from 1.3 percentage points in 2007, largely because the male unemployment rate has increased at a faster pace than the female rate. Since 2010, however, the gap has been widening again, and this trend is anticipated to continue through 2021.

•In Eastern Europe, over the past two decades, unemployment rates for women have consistently been lower than those for men. In 2017, the rates are 5.8 per cent for women and 6.4 per cent for men, representing a gap of 0.6 percentage points. Despite modest decreases in both female and male rates being anticipated in 2018, little change in the gender gap is expected.

•In Central and Western Asia, the unemployment rate for women, at 9.6 per cent in 2017, is 0.7 percentage points higher than the rate for men, at 8.9 per cent. The gap narrowed significantly between 1997 and 2007, when it closed from 1.8 percentage points to 0.2 percentage points. Over the past decade, however, the unemployment rate for women has increased steadily, while the rate for men has remained unchanged. Little change in the 2018 rates and gap is anticipated through 2021.

1.3Gender gaps in sectoral and occupational opportunities

As detailed in the previous sections, women are less likely than men to participate in the labour force, and when they are active in the labour force they are more likely to be unemployed. This section shows that in addition, when women have jobs, there are also distinct differences or gaps by sector and occupation. Segregation by gender in these dimensions remains commonplace and is often a symptom of important underlying differences in opportunities for women and men, particularly in terms of access to different types of jobs.

Accordingly, this section presents global and regional estimates on gender segregation along sectoral and occupational dimensions (see box 1 on how gender segregation is measured).

Box 1

Measuring gender segregation in employment distribution

In this report, gender segregation is measured by summing the concentration of women relative to men in a sector or occupation. This is one of the measures of segregation proposed by the European Commission (EC, 2009), where it is labelled the “Index of dissimilarity” (see also ILO, 2012). Women (or men) are relatively more concentrated in sectors or occupations when the female (male) share out of total female (male) employment in that sector or occupation is higher than the male (female) share. It is worth noting that relative concentration of women, for example, in one particular sector, does not necessarily mean that more women than men work in that sector, since overall female employment may be smaller than male employment. Nonetheless, the index shows the contribution of each sector or occupation to the cumulative gender difference in the distribution, which is reflected by the height of the bars in figure 1.

Figure 1

Composition of total sectoral segregation by region, 1997 and 2017

Notes: See Appendix E for details regarding sectoral employment data. The figure shows the difference between women and men with respect to the share of employment in a sector relative to total employment for the respective gender. Hence, it shows in which sectors women are relatively over-represented compared with men. Positives and negatives need to be of equal height because for any over-represented sector there needs to be an under-represented sector. The total height of a bar indicates the overall sectoral gender segregation. The world aggregate is lower than most regional aggregates since opposing over-representations across regions offset one another. The computation is based on 14 sectors.

Source: ILO Trends Econometric Models, November 2016.

Increasing gender gap in sectoral employment

A comparison of the sectoral distribution of employment by sex reveals strong evidence of gender segregation. Figure 1 shows that the global average segregation across all sectors has increased between 1997 and 2017, from 15.0 percentage points to 20.5 percentage points. In other words, to achieve matched allocation of men and women in every sector would require a shift of one in every five men or women to different sectors. At the global level, education, health and social work is the sector with the highest relative concentration of women, followed by wholesale and retail trade. In contrast, the sectors of construction and transport, storage and communication tend to have the highest relative concentration of male workers.

The high relative concentration of women in wholesale and retail trade in the global average is driven largely by Eastern Asia and South-Eastern Asia and the Pacific (and, to a lesser extent, sub-Saharan Africa). Similarly, these two Asian regions, alongside Northern Africa, also tend to have over-representation of women in manufacturing, which is reflective of female-dominated sectors such as apparel manufacturing. The high relative concentration of women in agriculture is a prominent feature in Southern Asia, and to a lesser extent in the Arab States, Central and Western Asia, Eastern Asia and sub-Saharan Africa. This is reflective of the traditional gender roles held by lower-income households in rural areas. In all regions, construction, transport, storage and communication, and public administration and defence have relatively high concentrations of men (this last sector is driven by the inclusion of armed forces in the classification).

In terms of total sectoral segregation (as denoted by the cumulative heights of the columns in figure 1), in 2017 the Arab States region has the largest sectoral segregation, at 49.6 percentage points. This is a slight reduction from the 53.4 percentage points recorded in 1997, driven by a reduction of female over-representation in agriculture. Northern Africa has the second highest total sectoral segregation, at 35.4 percentage points, up from 29.2 percentage points in 1997, in part driven by increased over-representation in education, health and social work. In Latin America and the Caribbean, total sectoral segregation is at 35.1 percentage points, up slightly from 33.0 percentage points in 1997.

Sub-Saharan Africa, on the other hand, has the lowest aggregate sectoral segregation, at 14.3 percentage points, although this has increased slightly from 11.5 percentage points in 1997. The increase occurred despite a reduction in relative female concentration in agriculture, as this was offset by an increase in relative female concentration in wholesale and retail trade. Northern America and Northern, Southern and Western Europe have fairly similar sectors and levels of segregation per sector, specifically predominant female concentration in education, health and social work, and relative male concentration in manufacturing, construction and transport, storage and communication.

Figure 2

Relative concentration of gender in occupation, by income group, latest year

Notes: The figure shows the difference between women and men with respect to the share of employment in an occupational group relative to total employment for the respective gender. Hence, it shows in which occupational groups women are relatively over-represented compared with men. The sample includes 68 emerging countries and 54 developed countries. Latest year is at least 2004, with 67 per cent of observations being 2014 or later. The computation is based on nine occupations.

Source: ILO calculations based on ILOSTAT.

Significant occupational segregation

In addition to gender segregation by sector, there is also gender segregation by occupational group. In 2017, total occupational segregation in developed countries is, at 30.4 percentage points, almost twice as large as in emerging countries, at 17.0 percentage points.

As shown in figure 2, segregation patterns are relatively similar between emerging countries and developed countries, albeit to different magnitudes. In both country income groups, men are more highly concentrated in the following occupational groups: craft and related trades workers, plant and machine operators and assemblers, and managers. In contrast, in both country groups, women are more highly concentrated in the services and sales workers and professionals groups.

In developed countries, women are also relatively more concentrated among clerical support workers and, to some degree, in elementary occupations. While men tend to be relatively more concentrated among skilled agricultural, forestry and fishery workers in developed countries, women are relatively concentrated in these occupations in emerging countries.[4]

Segregation is a symptom, as well as a cause, of further gender gaps on the labour market

Labour market segregation at the sectoral and occupational levels is potentially self-reinforcing because some occupations are more common in some sectors than in others. Furthermore, such segregation also affects gender gaps related to incomes and working conditions, and could even affect gaps in labour force participation and unemployment. In other words, if women are segregated into certain occupations and those occupations are not growing, this phenomenon can have a severe impact on labour market outcomes.[5]

Figure 3

Employment status by gender and region, 2017

Source: ILO’s Trends Econometric Models, November 2016.

1.4Status in employment

This section shows that while women are more likely to work in certain occupations and sectors, they are also less likely to be in wage and salaried employment than their male counterparts and are more likely to be in vulnerable forms of employment.

With economic development, more women than men transition to wage and salaried employment

Globally, the share of wage and salaried employment in total employment has increased from 48.4 per cent in 1997 to 54.8 per cent in 2017 (figure 3). Over this period, the share for women increased by 8.9 percentage points, from 46.5 to 55.4 per cent, while for men the share increased by 6 percentage points, from 48.4 to 54.4 per cent. As a result, the gender gap was reversed, so that in 2017 the share of workers in wage and salaried employment is higher for women than for men.

The share of women in wage and salaried employment tends to increase with a country’s level of economic development.[6] In developing countries, for example, only 13.6 per cent of women enjoy wage and salaried employment, compared with 24.3 per cent of men. At the other end of the spectrum, the share of women in wage and salaried employment in developed countries is 89.1 per cent, compared with 83.7 per cent of men. The difference is marginal for emerging countries (around 51 per cent for both).

Looking only at the past decade, the share of workers in wage and salaried employment has been increasing for both sexes worldwide: by 3 percentage points for males and 3.7 percentage points for females, between 2007 and 2017. The most marked improvement for both men and women is observed in emerging countries, where the share increased by 4.3 percentage points for males and 5.7 percentage points for females, which corresponds to a reduction in the gender gap of 1.4 percentage points over the period. Developing countries also experienced a notable overall improvement in the share for both genders – 3.6 percentage points for men and 2.6 percentage points for women – but this resulted in a widened percentage point gap.

Box 2

Involuntary part-time employment in EU-28

Northern, Southern and Western Europe has relatively narrow gender gaps in participation and unemployment rates. However, this measure masks the fact that the share of women in part-time work, at 32.1 per cent in 2015, is considerably higher than that of men, at 8.9 per cent. Of course, part-time employment can offer work flexibility and act as a potential entry point to full-time employment for both men and women, but may also reflect constraints on entering full-time employment, particularly for women. For instance, the traditional role of women as caregivers is found to increase the gender gap in part-time employment. In 2015, 42.4 per cent of female part-time workers cited “family or personal responsibilities” or “looking after children or incapacitated adults” as the reason for their part-time employment, compared with 11.8 per cent of male part-time workers. This underscores how women’s greater care responsibilities disproportionately impact the time intensity of their employment.

Moreover, gender gaps in involuntary part-time employment are more prevalent. In fact, involuntary part-time work as a share of total employment for women was recorded as 8.2 per cent in 2015, compared with 2.6 per cent for men, giving a gender gap of 5.6 percentage points. This represented a widening of the gender gap since 2007, when it was 4.0 percentage points (6.1 per cent for women compared with 2.1 per cent for men).

Source: ILO calculations based on Eurostat.

By region, the greatest disparities are observed in sub-Saharan Africa, where the male share in wage and salaried employment, at 36.3 per cent, is 13.7 percentage points higher than the female share, at 22.6 per cent. The gap has also widened over the past decade, from 12.9 to 13.7 percentage points. Southern Asia also has a large gender gap in the wage and salaried employment share, in which the male rate, at 26.8 per cent, is 8.6 percentage points higher than the female share at 18.2 per cent in 2017. This gap has also widened, by 0.5 percentage points, since 2007.

In contrast, the share for women is higher than that for men in four out of 11 regions in 2017 – the biggest difference is in Northern, Southern and Western Europe, where the share for women is 88.6 per cent, 7.7 percentage points higher than that for men, at 80.9 per cent (see box 2 for a discussion on involuntary part-time employment in the EU-28). The gains since 2007 have been fairly proportionate, with only a marginal narrowing in the gap. Similarly, Northern America’s female share, at 92.4 per cent, is 4.8 percentage points higher than the male share, at 87.6 per cent in 2017. However, in this region there has been a more noticeable closing of the gap between the male and female rates, of 0.8 percentage points.

Reductions in the vulnerable employment gender gap represent improvements in the gaps for both own-account workers and unpaid family workers

More than 42 per cent of all workers worldwide are categorized as being in vulnerable employment – that is, own-account workers or contributing family workers. These workers are more likely to be poorly paid and under weak or no contracts, have little or no labour protection or support, be employed in the informal sector, have reduced access to social protection systems and be living in poverty (box 3). The female rate of vulnerable employment, at 43.3 per cent, is only marginally higher than the male rate, at 42.5 per cent, corresponding to a gap of 0.8 percentage points in 2017 (which has declined by 3.4 percentage points since 1997).

However, some prominent gender disparities can be observed within the subcategories of vulnerable employment. Own-account work represents around 37 per cent of male employment, compared with 28.4 per cent of female employment. This gives a gap of 8.6 percentage points, down from 11.2 percentage points a decade earlier. In contrast, women are more likely than men to be contributing family workers.

Globally, 14.9 per cent of employed women are contributing family workers, compared with 5.5 per cent of employed men. In terms of own-account work, the share of male employment is greater than that of female employment in all country income groups. Between 2007 and 2017, this gender gap narrowed in emerging and developed countries, by 3.7 and 0.6 percentage points, respectively, but in developing countries it widened slightly, by 1.2 percentage points.

Some progress in reducing the vulnerable employment rate in emerging and developing countries may be undermined by female transitions from contributing family work to own-account work, rather than by transitions to wage and salaried employment. This means that these female workers may be shifting from one form of vulnerable employment to another.

Box 3

Gaps in working time and social protection coverage

In general, women are more likely to work shorter hours for pay or profit, and longer hours for unpaid work. Time dimensions of employment reflect a range of additional gender gaps beneath the surface that correspond to factors such as time-related underemployment (working hours below a country specific threshold for workers willing and available to work additional hours), and hours spent doing household and care work.

•Time-related underemployment: Women are more likely to be working fewer hours than men, but in addition women are also more likely to work short hours against their choice. In fact, for countries with available data, time-related underemployment can be as high as 40–50 per cent in developing countries (see also box 2).

•Hours of unpaid work: On average, women tend to spend around 2.5 times more time on unpaid household and care work than men. Women are, however, spending less time in such work as a result of more women entering the labour market, shrinking family sizes as well as improvements in time-saving technologies and infrastructures.

Part of the reduction in hours spent by women in unpaid family work has been due to improvements in access to social protection, particularly maternity protection. Nonetheless, women are still less likely to have access to social protection and when they do, they are often only entitled to lower benefits, partly as a reflection of lower pay and a shorter contribution period.

•Informal employment: With respect to informal employment as a share of total employment, the direction of the gap depends very much on the income group considered. For instance, among 107 countries with available information, women are under-represented in informal employment in emerging countries (regardless of whether agriculture is included). In contrast, among developing countries women are over-represented by 4.9 percentage points and 8.7 percentage points (depending on whether agriculture is included or excluded, respectively).

•Access to pensions: Women tend to be less well-covered by pensions as a result of lower contributions attributed in part to lower pay, as well as a greater incidence of informal and non-standard forms of work. The proportion of women above retirement age receiving a pension is on average 10.6 percentage points lower than that of men.

•Maternity coverage: While most countries provide some maternity protection for employed women, nearly 60 per cent of women do not have a statutory right to maternity leave and 65.9 per cent do not benefit from mandatory coverage by law for income replacement during maternity leave. Even when women are covered, there is often a lack of awareness, problems with implementation as well as insufficient contributory capacity.

Source: ILO, 2016a.

Progress in reducing the gap in contributing family work may be undermined by transitions to own-account work

Globally, the proportion of women workers engaged as contributing family workers declined from 20.6 per cent in 2007 to 14.9 per cent in 2017. In relative terms, the global gender gap for contributing family work narrowed from 13 percentage points in 1997 to 9.4 percentage points in 2017. As mentioned, it is likely that this has, in part, been due to women transitioning to own-account work, who thus often do not obtain any of the rights and protections more commonly offered to wage and salaried workers.

The gender gap for contributing family work is widest in developing countries, where 36.6 per cent of women and 17.2 per cent of men are engaged as contributing family workers. This equates to a gap of 19.4 percentage points, which represents a deterioration relative to the 17.0 percentage point gap recorded in 2007. In emerging countries, 15.6 per cent of employed women are in this category, compared with 5.4 per cent of men, which represents a narrowing of the gap of 5.4 percentage points since 2007. For developed countries, where the female share of contributing family work is 1.6 per cent, compared with 0.4 per cent for males, there has also been a marginal narrowing of the gap, by 0.7 percentage points.

Since 2007, gender gaps in contributing family work have decreased most significantly (by almost twice the global reduction of 5.6 percentage points) in Eastern Asia (10.8 percentage points), South-Eastern Asia and the Pacific (9.4 percentage points) and Southern Asia (9.2 percentage points). In contrast, Northern Africa and sub-Saharan Africa have both experienced a widening of the gender gap over the past decade, due to reductions in the female rates of employment as contributing family workers being smaller than the reductions in the male rates. As a result, the gender gaps for both regions in 2017 are at their widest since 1997.

1.5Gaps in income

Unequal remuneration between women and men persists virtually everywhere

As outlined in the ILO Global Wage Report 2016/17 (ILO, 2016b), gender wage gaps vary widely by country, but they tend to persist virtually everywhere. Estimates from the report reveal that gender gaps in hourly wage rates can reach 40 per cent in some countries. That is, female hourly earnings can be as little as 60 per cent of male hourly earnings. Part of this is explained by occupational and sectoral segregation between men and women (see section 1.3).

Differences in the remuneration of men and women can be partially explained by individual characteristics, such as education, skill and experience. Others argue that the gap is a result of the non-linearity of hours worked, i.e. shorter hours worked by women (preferring a more flexible schedule), which yields a disproportionate reduction in earnings, especially in certain sectors (e.g. legal and financial) (see for instance Goldin, 2014). In other cases, however, a recent study analysing micro-data for 2010 from the United States shows that after controlling for observable factors, such as education, job experience and occupation, 38 per cent of the gender wage gap remained unexplained (Blau and Kahn, 2016). In fact, various studies, decomposing the gender wage gap into determinable structural components and the unexplained gap, have attributed the unexplained gap to discrimination (Weichselbaumer and Winter-Ebmer, 2003).

In developed countries, the gender gap widens at higher wage levels

Overall for developed countries, the higher the average wage, the wider the gap in wages. In these countries, the gender wage gap is particularly magnified among the highest paid occupations, such as managers and CEOs: this is known as vertical segregation, or the “glass ceiling”. For instance, in Europe, the overall gender pay gap reaches close to 20 per cent; however, among CEOs the gap is twice as large, at nearly 40 per cent, and it continues to widen to 50 per cent among top 1 per cent of earners (ILO, 2016b).

Similarly, horizontal segregation, or “glass walls”, driven by social norms, also contributes to the wage gap by segregating occupations by female or male characteristics (e.g. nursing vs engineering). As a result, the preferences and choices of women and men in the labour market are shaped by prevailing societal norms, which confine women and men to certain occupations. In consequence, women are often concentrated in lower-paid occupations and sectors, further driving the gap in wages (see figure 1, in section 1.3). Moreover, women also face the motherhood wage penalty when they re-enter employment after having a child. This penalty is driven by one or a combination of the following: lower wages arising from working shorter hours, forgone job experience, the exchange of higher-wage jobs for more flexible but lower-wage jobs, and discrimination by employers (Budig and England, 2001).

Box 4

Gender dimensions of working poverty in developed countries

In-work poverty in developed countries is related to a host of factors, including age group, education, household size, household type, working time and type of contract (ILO, 2016a), all of which have underlying gender elements. For instance, persons with dependent children are more likely to be working and below the risk-of-poverty threshold – defined in the EU-28 as those living on less than 60 per cent of median income after social transfers – than those without. When this is narrowed down to single parents with dependent children – who are more likely to be women – the risk-of-poverty rate for the EU-28, at 19.8 per cent is nearly double the rate for two or more adults with dependent children, at 10.5 per cent in 2015 (Eurostat, 2017). In the US (where the definition is an absolute measure of income below the official poverty threshold), working women are still more likely than working men to be in poverty, at 6.3 per cent and 5 per cent, respectively in 2015 (BLS, 2017).

In emerging and developing countries households with working women experience less poverty

Almost one-third of working women and men in emerging and developing countries do not earn enough to lift themselves and their households out of poverty (ILO, 2017a). However, a higher share of working men, at 29.1 per cent, live below the poverty threshold than working women, at 27 per cent (table 3).[7] This does not necessarily mean that women earn higher labour incomes; instead, it is rooted in the fact that working women are more likely to live in households with multiple earners. Sub-Saharan Africa and Southern Asia represent exceptions to this point, as working women face higher rates of poverty than men. In both these regions, women face exceptionally high rates of contributing family work; as a result, they are unlikely to be engaged in quality forms of employment. Furthermore, labour force participation in these regions may be driven by necessity, so that women in poor households are very likely to work.

Conversely, working women in emerging and developing countries are more likely than working men to live in a household with a per capita income of above US$13 per day, at 19.7 per cent for women compared with 16.3 per cent for men. This gap is especially pronounced in the Arab States and Latin America and the Caribbean, at more than 5 percentage points. This shows the importance of female labour force participation in reducing poverty, although it does rely on women having access to decent jobs.

For a brief discussion of gender dimension and working poverty in developed countries, see box 4.

Table 3

Employment by economic class and sex, 2017

Country grouping

Share of employment (%)

Working poverty (<US$ PPP 3.10/day)

Developing middle class (US$ PPP 5–13/day)

Developed middle class (> US$ PPP 13/day)

Emerging and developing countries

29.1

27.0

35.7

36.9

16.3

19.7

Developing countries

67.1

68.8

10.3

9.3

2.2

1.7

Emerging countries

26.0

21.5

38.5

41.0

16.4

21.3

Source: ILO’s Trends Econometric Models, November 2016.

1.6Economic benefits of reducing gender gaps

Despite being only one dimension of welfare, the possibility of quantifying the economic gains generated by pursuing equality between men and women in the labour market merits further discussion. The complete eradication of the gap in labour market participation (equivalent to adding 742 million women to the labour force) is a desirable goal over the long term, whereas the G20 target “25 by 25”, which aims to reduce the gap in participation rates between men and women by 25 per cent by the year 2025, is a more attainable benchmark over the medium term (G20, 2014).

Applying this target to all countries, rather than just G20 nations, reveals that reducing gender gaps in the labour market could increase the global labour force by 204 million by 2025 (an increase of 5.4 per cent) (table 4). Holding unemployment rates and male participation rates constant, this would boost global employment by 189 million (or 5.3 per cent), which could in turn increase global GDP by 3.9 per cent or US$5.8 trillion (equivalent to raising average global GDP growth over the next eight years by almost half a percentage point) (box 5).

The bulk of the additional employment, i.e. 162.4 million (6.2 per cent), would be generated in emerging countries (due to their relative size, combined with the fact they also have the widest gender gaps). The impact in developing and developed countries in terms of the anticipated percentage increase in employment is comparatively small (due to the fact that their gender gaps are narrower). Thus, the analysis suggests that while much larger efforts in all policy dimensions will be required in developing countries to reduce poverty, closing gender gaps can contribute significantly to reducing poverty in emerging countries.

Perhaps not surprisingly, the regions with the largest gender gaps, namely Northern Africa, the Arab States and Southern Asia, would see the greatest benefits in terms of growth. Nevertheless, even Northern America and Northern, Southern and Western Europe could increase their average annual GDP growth by a quarter of a percentage point, an important contribution during times of near-zero economic growth.

The achievement of such a goal unlocks large potential tax revenues. For example, global tax revenue could increase by US$1.5 trillion given currently projected government revenue shares in GDP, most of it in emerging (US$990 billion) and developed countries (US$530 billion). Consequently, policies promoting gender equality could be self-financing (see Chapter 3). Of course, larger female participation might shift some home production, which is unaccounted for in GDP, to market production, so that the actual increase in global output will be smaller.

Closing gender gaps in the workplace could unlock additional economic benefits. For instance, the fact that the largest part of the gender wage gap is explainable by observable factors, notably sectoral and occupational segregation (see above), suggests that women tend to be employed in lower productivity sectors and occupations.[8] Consequently, facilitating women’s access to higher productivity sectors and occupations once in the labour market, as well as valuing female-dominated sectors and occupations, would provide an additional boost to global growth.

There are also other significant positive impacts, notably those that affect a woman’s freedom to choose and, in turn, her welfare (Kabeer, 2005).[9] In particular, the discussion of closing gaps must be extended towards achieving the objective of substantive equality that allows women the substantial freedom to choose and act (Sen, 1990 and Nussbaum, 2011).[10] Moreover, substantive equality aims to reach not “sameness” but the equality of results, outcomes, and human dignity that lie at the heart of the notion of decent work by recognizing the different constraints that men and women face in the world of work (Hepple, 2001).

In light of this, and given the potential economic, social and individual benefits to be gained by closing gender gaps in the labour market, it is important to understand their underlying drivers. The following chapter will therefore investigate the root causes of gender gaps in the world of work by focusing on labour force participation, in an effort to understand why so many women are outside the labour force despite expressing a preference to work. Of course, increasing labour supply also requires a corresponding rise in labour demand, both in absolute quantity and for women in particular.[11] Chapter 3 of this report specifically details, among others, policies to address gender discrimination in both the supply of and demand for labour.

Table 4

Effects of reducing gender gaps in the labour market by 2025

Country/region

Labour force

Employment

GDP

Millions

Per cent

Millions

Per cent

Per cent

World

203.9

5.4

188.6

5.3

3.9

Developing countries

7.8

2.1

7.1

2.0

2.0

Emerging countries

175.5

6.3

162.4

6.2

4.8

Developed countries

20.6

3.3

19.0

3.3

2.6

Northern Africa

11.4

13.0

9.1

11.8

9.5

Sub-Saharan Africa

11.1

2.2

10.1

2.1

2.2

Latin America and the Caribbean

17.4

5.0

15.8

4.9

4.0

Northern America

4.8

2.5

4.5

2.5

2.0

Arab States

7.8

11.8

6.0

10.2

7.1

Eastern Asia

27.3

3.0

26.2

3.0

2.5

South-Eastern Asia and the Pacific

15.9

4.1

15.0

4.0

3.5

Southern Asia

92.7

11.1

87.7

11.0

9.2

Northern, Southern and Western Europe

5.7

2.6

5.1

2.6

2.0

Eastern Europe

4.5

3.3

4.2

3.3

2.6

Central and Western Asia

5.3

6.6

4.8

6.5

5.7

Note: The assumptions of the scenario are described in box 5. GDP shows the percentage difference in projected additional GDP in 2025, using PPP exchange rates.

Source: ILO estimations based on ILO’s Trends Econometric Models, November 2016.

Box 5

Estimating the economic impact of “25 by 25”

In order to estimate the economic impact of the “25 by 25” scenario, a number of assumptions were necessary. The first is obviously that the female participation rate increases such that the gap between the male and the female participation rate, by country, decreases by 25 per cent by 2025. The second is that each additional woman in the labour market succeeds in finding a job at the same rate as those currently in the labour market, i.e. the female unemployment rate remains unchanged. Finally, it is assumed that these additional women attain 80 per cent of the average labour productivity in the respective year and country so as to take into consideration prevailing sectoral and occupational segregation, i.e. the fact that women are currently employed in lower productivity sectors and occupations.

1. For details regarding the list of regional, country and income groupings, see Appendix A.

2. Working on their own account or with one or more partners, hold the type of job defined as a self-employed job, and have not engaged on a continuous basis with any employees to work for them during the reference period.

3. Workers who are self-employed in a market-oriented establishment operated by a related person living in the same household, but with a limited degree of involvement in its operation to be considered a partner.

4. According to the ISCO 08 classification, the skilled agricultural occupations category includes subsistence farming, which has a high female share.

5. For instance, Kapsos, Silberman and Bourmpoula (2014) find that severe occupational segregation resulted in 20.7 million fewer women employed in India over the period from 1994 to 2010.

6. The gap between the male and female shares of wage and salaried employment in total employment decreases by 0.6 percentage points for each 10 per cent increase in GDP per capita. At the global average, the gap is zero at around US$20,700 (in PPP).

7. Defined here as being in households living on less than US$3.10 per day per capita, also known as the moderate poverty threshold.

8. The underlying assumption is that wage levels and productivity are related.

9. The term “agency” refers to a person’s ability to realize the goals and values that they have reason to pursue (Sen, 1995).

10. Substantive equality considers the specific needs of both women and men to achieve true equality. For instance, despite the fact that a policy is non-discriminatory, it may be still be indirectly discriminatory by not acknowledging the different needs of women (e.g. maternity health). Substantive freedoms extend beyond agency to “[e]xpanding the freedoms we have reason to value” enabling “fuller social persons, exercising our own volitions and interacting with – and influencing – the world in which we live” (Sen, 2001).

11. ILO (2017a) outlines a comprehensive set of policies necessary to raise overall labour demand, which includes well-designed and coordinated fiscal loosening against a backdrop of measures that ensure an equitable distribution of gains.

2

Assessing the factors driving gender gaps in the labour market

Introduction

The previous chapter showed that large gender gaps exist across a range of labour market dimensions. Women are less likely to participate, tend to face higher unemployment rates and suffer segregation in terms of job quality and access to certain occupations and sectors. This situation generates far-reaching consequences for the multidimensional welfare of people, extending well beyond the obvious economic loss, due to the underutilization of human capabilities and development potential.[12] These gender gaps are present despite the preference of many women to participate in the labour market, underscoring the fact that women’s choices are constrained by a number of factors. Tackling gender gaps therefore requires a deeper understanding of their underlying drivers, which this chapter aims to provide.

This chapter is structured as follows: section 2.1 discusses the descriptive findings from the Gallup–ILO 2017 study and develops a framework for analysing the drivers and mechanisms underlying gender gaps in the labour market. section 2.2 then applies this framework so as to gain a better understanding of the Gallup–ILO descriptive findings and quantify the drivers of lower female labour force participation. This is followed by an assessment of gender gaps in the workplace, i.e. once a female participates (section 2.3), and the final section summarizes the findings stemming from this analysis.

2.1Understanding the gender gaps: Descriptive evidence and analytical framework

Factors underlying gender gaps in the labour market

As detailed in Chapter 1, the gap in labour market participation rates is connected to the level of economic development, i.e. total participation rate levels were highest on average in developing countries, followed by emerging and developed, respectively. A closer examination at the country level, however, shows that the female labour force participation rate is higher in developing and developed countries than in emerging countries, a phenomenon labelled the “feminization U” (figure 4) (see also Goldin, 1995; Mammen and Paxon, 2000; Tansel, 2001; Attanasio, Low and Sánchez-Marcos, 2005; Bhalotra and Umaña-Aponte, 2012). It is probable that the participation rates of women in developing countries are higher due to the economic necessity to work. Yet, the transition from emerging to developed country does not automatically lead to a higher female participation rate. Instead, the wide spread of female participation rates across countries at all income levels suggests the presence of factors beyond economic growth at work.

Figure 4

Average female labour force participation rates and income per capita, 2016

Source: ILO calculations based on ILOSTAT and World Bank.

Table 5

Preferences of women and constraints on their participation in the labour market, 2016 (per cent of respondents)

Prefer to work

Challenges reported in the labour market

Work and family balance

Lack of affordable care

Family members don’t approve

Abuse/harassment/discrimination

Lack of good-paying jobs

Unequal pay

World

79.2

24.6

9.6

4.3

10.3

9.5

6.5

Northern Africa

69.9

30.5

6.5

5.6

24.5

7.9

3.1

Sub-Saharan Africa

85.4

19.2

11.9

5.8

14.9

7.9

3.4

Latin America and the Caribbean

74.9

22.5

14.2

3.6

9.6

7.7

6.1

Northern America

79.1

23.0

7.5

0.8

10.0

2.6

31.8

Arab States

70.5

29.4

4.5

7.5

9.8

9.1

3.8

Eastern Asia

79.8

21.7

22.4

1.2

6.7

7.4

3.6

South-Eastern Asia and the Pacific

81.2

26.6

7.3

1.2

8.8

6.6

5.8

Southern Asia

55.1

20.0

8.3

11.3

11.3

4.5

2.3

Northern, Southern and Western Europe

84.8

33.9

5.1

1.4

8.0

6.5

14.0

Eastern Europe

75.4

25.0

4.4

0.7

6.1

23.8

7.0

Central and Western Asia

79.8

23.2

8.7

8.6

6.9

16.7

3.5

Notes: This table accounts for the survey sample of a total of 70,561 observations representing the survey responses of women in 127 countries. Population weights are applied accordingly to achieve equal country representation. Results may vary slightly to those published in Gallup–ILO, 2017 due to country coverage. Darker shading indicates the top two challenges in the region. See Appendix C on weighting methods.

Source: ILO calculations based on Gallup World Poll 2016.

In fact, the 2016 Gallup World Poll, covering 142 countries, illustrates that the constraints women face are a function of a range of factors which extend beyond economic development (table 5). And while there is considerable scope in recognizing the value of work beyond the labour market (see box 6), the survey results demonstrate that globally a majority of women (just over 79 per cent) prefer to work at paid jobs despite their employment status (table 5) (70 per cent among full sample). Considering that approximately half of women globally are out of the labour force, this suggests that there are significant challenges restricting their capabilities and freedom to participate.[13] At the global level, the most important constraint reported by women is the struggle to balance work and family, followed by the presence of abuse, harassment or discrimination. This is broadly consistent across regions, although there are some important regional variations. For instance, in Northern America, the most important challenge to accessing the labour market is equal pay, in Eastern Asia it is access to affordable childcare, while in parts of Europe and Central Asia the absence of well-paying jobs is an important consideration. These findings underline the importance of investigating the nature and scope of the drivers that cause gender gaps in labour force participation as many of these factors are interrelated. For example, the expectation that a woman is the principal caregiver may explain, in part, women’s perceptions regarding work–family balance.

Box 6

The value of invisible work

Efforts to close gender gaps in the labour market and the resulting individual and economic benefit that would be derived from such efforts must be put in the broader context. Notably, these efforts should not undermine or fail to recognize valuable contributions to society made outside of the labour market. Instead, they reveal that women are disproportionately contributing to society and households with work that is largely invisible and often undervalued. Work of this nature, including for example caring for children and the elderly, as well as cleaning, cooking, fetching water and firewood is necessary for the household’s survival. While often invisible, this work is also necessary for the multidimensional welfare of societies and advancing the capabilities of all human lives.

Consequently recognizing and giving value to invisible work is central to the future of societal welfare and human development and must play a critical and complementary role in unlocking the potential and value of women’s work. The revised classification of employment and work stemming from the 2013 International Conference of Labour Statisticians should go a long way in helping to better capture some of the many elements of invisible work.

Source: See, for instance, England and Folbre, 1999.

As table 5 suggests, a woman’s decision whether to participate or not in the labour market involves a complex set of fundamental drivers in conjunction with life-cycle circumstances, which can be broadly classified as (i) personal preferences, (ii) socio-economic constraints, and (iii) gender role conformity (figure 5). These three fundamental drivers are determined by the prevailing social norms in the society that a woman inhabits.

•Personal preferences are an important driver of an individual’s labour market outcome, within the constraints set by prevailing social norms and socio-economic factors (in conjunction with life-cycle circumstances) (figure 5). Indeed, a woman’s preference for engaging in work is an expression of both what is perceived to be legitimate in her personal circumstances and her socialized identity (Sen, 1990). In fact, many women who prefer to work are able to choose to do so and, as a result, derive pleasure and economic and social well-being from decent work opportunities. On the other hand, some women may prefer to stay at home due to a lack of jobs that are “socially acceptable” for women, because childcare costs may exceed their potential incomes or because their role within the household may be perceived as more valuable (economically and socially).

•Socio-economic constraints represent the institutional, economic and physical constraints faced by both men and women. As discussed above, economic conditions can dictate the availability and quality of jobs, both within and across countries. For instance, a downturn in the business cycle can temporarily provoke economic necessity within a household, in which case women often move into paid employment, so that, in effect, their labour supply acts as an insurance mechanism (Attanasio, Low and Sánchez-Marcos, 2005; Bhalotra and Umaña-Aponte, 2010). Among institutional constraints, the legal and policy framework of tax systems can often impose high marginal tax rates on secondary earners with lower income, who are more likely to be women, hence disincentivizing their participation in the labour force (Stotsky, 1996; Jaumotte, 2003; EC, 2015). Alternatively, the lack of care facilities poses a problem for the entire household, but in reality its effect is disproportionally felt by women due to their assigned gender role as caregivers (Badgett and Folbre, 1999). In addition, discrimination directly creates gender gaps through the disadvantageous treatment of women in terms of payment, hiring or promotion. Finally, since policies are influenced by social norms, they can shape socio-economic constraints while simultaneously reinforcing prevailing norms (Sjöberg, 2004).

•Gender role conformity: Class, patriarchy and social hierarchy, often determined by caste, locality, ethnicity and religion, interact with one another to shape society’s norms around gender roles (Bardhan, 1984; Klasen and Pieters, 2012). As a result, women are often compelled to conform to the gender roles deemed to be acceptable by their family, community or society in order to avoid the consequences of social exclusion, insecurity or other conflicts. These attitudes towards gender roles construct a social hierarchy that not only defines women’s roles in the labour market, but also adds to the disproportionate burden of household responsibilities that they must bear (Badgett and Folbre, 1999).

Figure 5

Analytical framework of labour market outcomes

Gender gaps in the labour market can reinforce one another and affect even the decision to participate

The three fundamental drivers as described above help determine the various labour market outcomes that were detailed in Chapter 1, notably (i) labour market participation, (ii) occupation or sector of employment, (iii) type of employment relationship, and (iv) income.

This relationship, however, is recursive. The labour market outcomes themselves can influence the decision to participate in the labour market in the first instance. Put simply, unequal labour market outcomes (e.g. if women are paid less or have limited occupational opportunities) can shape the decision to participate.

In addition, these labour market outcomes are interdependent. For example, if as a result of social norms women can only find employment in certain types of occupations that are characterized by part-time employment relationships, it may lead, in turn, to a wage penalty. In this context, occupation, employment type and income reinforce one another.

Education also plays an important role. Educational opportunities (including access and quality) are affected by social norms. Indeed, gender differences in most developed countries can manifest themselves in the choice of study, and in developing and emerging countries in the level of education that is achievable (EC, 2009). In this respect, education, including vocational training, is a primary determinant of labour market outcomes. Moreover, educational attainment and the field of study determine not only labour market entry but also career trajectories (which then affect other aspects of the labour market such as occupational choice and income).

As evidenced by the discussion, there is considerable interconnectivity and interdependence among the drivers of labour market outcomes and the outcomes themselves. With this in mind, the next section identifies and isolates – to the extent possible – the impact of these individual drivers on female labour force participation rates. The approach therefore links to the “capabilities approach” discussed earlier (see section 1.6) by capturing the voices of women expressing the challenges they face within the labour market, their preferences in terms of work, their perception of their own futures and the normative constraints on their ability to make decisions about work, both within the home and in the labour market.

2.2Preferences, gender role conformity and socio-economic constraints: An empirical assessment

Using micro data from the Gallup World Poll, this section examines in more detail the relationships among the fundamental drivers of female labour force participation and attempts to quantify their impact, including assessing the role of education. The analysis is conducted separately according to the level of economic development, as this indicator captures potentially significant differences in how drivers interact and influence labour force participation rates. The region of the Arab States and Northern Africa (ASNA) is treated separately, given that – as highlighted in Chapter 1 – these two regions have the lowest levels of female participation rates.

Personal preferences

Estimates indicate that a woman’s preference to work, all other factors being equal, positively affects her probability of participating in the labour market. On average, the marginal effect of preferring to work on participation rates is relatively consistent across country-income groups (table 6), ranging from 12 percentage points in developing countries to 15 percentage points in emerging countries and to over 18 percentage points in developed countries. However, at just over 23 percentage points, the largest effect is found in the ASNA region. These findings provide strong evidence that a woman’s preference to work has a significant positive effect on participation. However, preferences are a function of a range of factors, as discussed above, including women’s life-cycle circumstances, their socio-economic conditions, their gender roles and the conditions of the local labour market.

Table 6

Marginal impact of drivers on the probability to participate in the labour market

Developing countries

Emerging countries

Developed countries

ASNA countries

Personal preference

Prefer paid work

12.0

14.4

18.4

23.7

Socio-economic constraints

Mild poverty

5.0

4.1

0.4

6.7

Severe poverty

7.8

6.4

1.3

12.9

Married/partner

3.1

–2.2

–3.6

–6.6

Children

0.7

–1.1

–0.9

–0.3

Challenge: Work and family balance

–2.6

–3.8

–0.3

–2.8

Challenge: Lack of affordable childcare

–4.8

–2.1

–4.0

–6.2

Challenge: Abuse, harassment or discrimination

–4.2

–2.0

–1.9

–2.6

Challenge: Lack of transportation

–16.5

–5.7

4.3

–1.5

Gender role conformity

Urban

0.1

–2.9

1.3

1.2

Acceptability

2.7

3.2

7.1

3.3

Challenge: Family non-approval

–5.1

–7.3

–2.9

–1.2

Religion

–10.9

–2.3

0.3

not available

Education

Secondary education

–5.2

3.1

7.6

6.8

Tertiary education

10.5

15.3

15.1

27.8

Notes: The table shows the unconditional marginal impact of fundamental drivers on the probability of women to participate in the labour market. The unconditional marginal impact shows the impact of a marginal change of only the indicator, holding all other characteristics at their actual value. Estimates are based on a probability model of individual female labour force participation, see Appendix C for details. The “Challenge”-related indicators assess the marginal impact of that challenge mentioned as the main challenge as opposed to “lack of flexible hours” mentioned as the main challenge. Religion indicates the marginal impact of a religion with respect to non-religion. The sample of non-religious women in the ASNA region is too small to allow an estimation of the impact of religion. “Children” presents the marginal effect of the first child. Green (red) indicates positive (negative) estimates. Bold indicates significance at the 10 per cent level.

Source: ILO estimates based on Gallup World Poll.

Socio-economic constraints

The labour supply status of a woman has inevitable economic consequences. Non-participation implies forgone earnings, while participation may incur costs in terms of forgone home production (including provision of care), transportation and other factors. In fact, severe poverty – i.e. a shortage of money for food and shelter – is a very strong driver of labour force participation, independent of preferences and gender roles. Table 6 indicates that among women suffering from severe poverty in developing and emerging countries, the probability to participate in the labour market increases by 7.8 and 6.4 percentage points, respectively. For ASNA countries, the effect of severe poverty on the probability of labour force participation is nearly double in comparison, emphasizing the strength of economic necessity as a driver of women’s labour market participation in this region, irrespective of social norms.[14] Living in mild poverty yields similar results, albeit to lesser degrees.

Having a spouse or domestic partner reduces the probability of women participating in the labour market in emerging, developed and ASNA countries by between 2.6 and 6.5 percentage points (relative to single women). For developing countries, however, the effect is reversed, with partnerships having a positive effect on participation, by 3.3 percentage points.

In general, lower probabilities of participating among partnered women are likely due to two main reasons. The first is the economic stability that arises from a partner’s income, reinforced by the “male breadwinner” bias: the fact that the (male) “breadwinner” is providing the household income reduces the economic imperative for a woman’s labour market participation (in the case of developing countries, this effect is outweighed by economic necessity). The second and related reason is that economic stability enables social norms to confine women to more traditional roles within the household, i.e. shouldering a disproportionate burden of household responsibilities, which limits their choice and availability for paid work.

The presence of children as a factor by itself does not make a significant difference to the probability of a woman’s labour market participation. The effect in developing countries is positive, albeit small (0.7 percentage points). In emerging and developed countries, in contrast, the presence of children marginally reduces participation probabilities. This suggests that other factors, such as socio-economic constraints related to childcare, rather than the presence of children per se, are weighing down women’s participation rates. Indeed, table 6 shows that women facing the challenge of “lack of affordable care for children or family members” have a lower probability to participate in the labour market, with a slightly larger effect observable in developing countries. Even in developed countries, this challenge is an important constraint (a 4 percentage point lower probability to participate). More generally, the relatively small impact of children on female participation rates suggests a significant double burden of both paid and unpaid work that many women are conditioned to bear. In fact, in emerging countries, the challenge of achieving “balance between work and family or home/no time to spend with family” negatively affects participation probability by 3.8 percentage points.

Limited access to and safety of transportation is estimated to be the greatest obstacle to women’s participation in the labour market in developing countries, reducing their participation probability by 16.5 percentage points. This challenge also has a noticeable negative effect in emerging countries. Unfair treatment at work due to “Abuse, harassment or discrimination” discourages participation by 4.2 percentage points in developing countries, indicating the inequitable weight of social norms which prevail in these countries despite the comparably high levels of labour market participation of women.

Gender role conformity

The previous section showed that a woman’s participation is, among other things, very much a function of her ability to balance her role as a caregiver in the household with the demands of the workplace. A woman’s role in the household and thus her preference and decision (even the freedom to choose) to participate in the labour market, are determined by social norms. The gender roles that arise from social norms vary across regions, but also vary according to the different traditional values held within different localities (urban vs rural), religions and even individual households.

Table 6 shows that living in an urban environment has only a limited impact in most instances on the probability of women participating in the labour market. It does, however, lower the probability for women in emerging countries to participate by 2.9 percentage points. Women living in households whose members accept female engagement in paid work are more likely to participate in the labour market (the effect is positive regardless of country grouping), and for developed countries it increases the probability to participate by as much as 7.1 percentage points).

Religions embody a complex system of values that extends to gender roles. Consequently, table 6 shows the average results of religion across groups. For example, in developing countries, the probability to participate is substantially reduced due to religion (although not always significant). In developed and emerging countries, the results are mixed: in some cases the effect is positive, in others negative (Pastore and Tenaglia, 2013).

Education

The effect of education on the probability to participate in the labour market is a function of the different weights given to the income and substitution effects. The substitution effect reflects the larger opportunity cost of staying at home as potential labour income rises with higher education, thereby encouraging participation. The income effect, in contrast, occurs when the economic needs of the family can be met with a lower rate of participation, so that more of a woman’s time can be devoted to fulfilling traditional gender roles.

In general, higher levels of education, specifically tertiary education, increase the probability of labour market participation (substitution effect dominates). Secondary education has a similar but smaller effect across the regions, with the exception of developing countries, where secondary education decreases participation by 5.2 percentage points (only in this exception does the income effect dominate).

Figure 6

Estimating cumulative effects of fundamental drivers

Notes: The figure shows the cumulative marginal change in a hypothetical woman’s probability to participate in the labour market from changing identified fundamental drivers from most constraining to least constraining. First, the marginal impact of a non-poor woman preferring to work as opposed to staying at home is shown, keeping all other drivers constrained. Next, socio-economic constraints are relaxed. Third, gender role constraints are relaxed. Finally, the woman’s education level is assumed to be tertiary instead of primary.

Source: ILO calculations based on Gallup World Poll.

In addition, the impact of education on participation might capture more than just the balance between the income and substitution effects. Indeed, the level of education a woman can attain is, to a certain degree, influenced by other factors. A higher level of education could actually be an indicator that a woman’s gender role and socio-economic conditions are generally more conducive to participation. In that case, the direct impact of higher education is likely to be overestimated. This indicates that policies aimed at raising female education levels also need to address gender role and socio-economic constraints.

Taking into consideration the interaction of drivers, the desire to participate is outweighed by socio-economic and gender role factors

The analysis of unconditional marginal effects of fundamental drivers revealed – to varying degrees and depending on the level of economic development – how each one affects the probability of women participating in the labour market. However, the framework introduced above (figure 5) shows that drivers are inter-related and interdependent. With that in mind, this subsection analyses the marginal change of a woman’s[15] probability of participating in the labour market, with the constraints of each of the fundamental drivers relaxed one at a time (figure 6).

The main result is that a change in a woman’s preference has a comparably lower effect – ranging from approximately 3 to 10 percentage points – on her probability to participate when socio-economic factors and gender role conformity are taken into consideration. This demonstrates that personal preferences are a necessary, but not sufficient condition to raising female labour force participation. In fact, the consecutive loosening of some socio-economic constraints[16] has a more sizeable impact on women’s probability of labour force participation in developed, emerging and developing countries (up to 30 percentage points). However, in ASNA countries the impact is considerably smaller (at 13.1 percentage points).

Addressing gender role conformity also has an important effect across all regions, albeit less so in emerging countries. More conducive gender roles[17] would, in fact, make the largest contribution to raising a woman’s probability of participating in ASNA countries, with 28.0 percentage points, but would also make a sizeable contribution in developing and developed countries (at around 20 percentage points (figure 6). Finally, the impact of tertiary education in each region, unconstrained by socio-economic conditions and gender roles, is fairly similar to the unconditional marginal effect of education presented in table 6. In sum, the personal preference to pursue paid work is a very important driver of female labour force participation. However, unless accompanied by improvements to socio-economic conditions and less gender role conformity, the impact will be limited.

2.3Decomposing gender gaps in the workplace

The analysis so far in this chapter has focused on the factors underlying participation in the labour market. However, Chapter 1 also identified existing gender gaps in the type of employment relationship, showing for instance that a larger share of women than men are employed as contributing family workers.

A report by the European Commission’s expert group on gender and employment (EC, 2009) lists a number of key factors identified by the literature as causing gender segregation in the workplace. These include comparative biological advantages, differences in human capital investment, differential roles within the household, preferences and prejudices, socialization and stereotypes, entry barriers and organizational practices. ILO (2016a) emphasizes the fact that these factors are self-reinforcing. Women and men are, respectively, more likely to pursue careers in sectors and occupations identified as “feminine” or “masculine” and are discouraged to do otherwise.

Knowledge of the underlying reason for gender gaps within the workplace is relevant for policy-making. For instance, knowing that women are more affected by a particular type of employment relationship (e.g. part-time) because they are more likely to have a certain occupation (e.g. health care) could focus policy-makers’ investigations on why women are found in that occupation, and why that type of employment relationship is so prevalent within that occupation. Similarly, as section 1.6 revealed, closing gender gaps in the workplace could yield substantial economic benefits. Consequently, this section decomposes gender gaps in the workplace into three specific components: sectoral segregation, occupational segregation and residual factors. The latter category contains all remaining factors, such as education, experience and discrimination.

Segregation only partially explains gender gaps

Figure 7 presents the decomposition of selected gender gaps into these three components. Each component contributes, positively or negatively, to the over-representation of men or women by type of employment relationship (e.g. contributing family worker, informal employment, in household employment). The sum of the three components determines the overall gender gap in employment shares by type of employment (see Appendix D for details of the methodology used for the decomposition). The results vary considerably by group of countries.

In emerging and developing countries, residual factors play a significant role in all types of employment where women are over-represented (i.e. as in contributing family workers, in household employment, part-time employment and time-related underemployment), with the exception of household employment, where sectoral segregation is driving female over-representation (although the residual component is still reinforcing the over-representation of women).

Figure 7

Decomposition of gender gaps in type of employment relationship

Notes: The figure decomposes the gender gap in types of employment relationship into three components. Based on labour force surveys for 29 emerging and developing countries: Argentina, Armenia, Bangladesh, Plurinational State of Bolivia, Brazil, Brunei Darussalam, Cambodia, Chile, Dominican Republic, Ecuador, The Gambia, Guatemala, India, Indonesia, Lao People’s Democratic Republic, Liberia, Madagascar, Malawi, Mexico, Myanmar, Namibia, Nepal, Peru, Samoa, South Africa, United Republic of Tanzania, Thailand, Uganda, Viet Nam, where available years differ by country, with earliest available 2002 and latest 2015. Eurostat labour force surveys for EU-28 countries, except Bulgaria and Germany, but including Iceland, Norway and Switzerland, for the years 2009–2013. Appendix B details the methodology.

Source: ILO calculations based on ILOSTAT and EUROSTAT.

For a selection of European countries with available data, where women are over-represented (all cases with the exception of multiple job holders), the residual factor is once again an important determinant in the case of wage and salaried employment and part-time employment. However, in the case of temporary employment and time-related underemployment, occupation segregation is driving the gap between men and women.

The analysis highlights the fact that the residual component is the largest driver of the majority of the workplace gender gaps assessed. This indicates that social norms – a key component of the residual factors – can have a large direct impact on gender differences in types of employment relationships. In addition, occupational segregation is a strong explanatory factor for gender gaps in European countries.[18] Moreover, the indirect influence of segregation is significant, and potentially underestimated due to the partial lack of sufficiently detailed information; it therefore merits further investigation.

2.4Concluding remarks

The findings of this chapter highlight the driving forces of women’s labour market participation and, hence, the most significant barriers that women face in the labour market today. Most notable is that the preference to participate is necessary but insufficient, and encouraging female labour market engagement will need to be accompanied by measures and efforts to alleviate the socio-economic and gender role constraints so as to enable their agency to freely participate and overcome gender gaps in their employment endeavours.

Moving forward, policy-making should be centred on expanding legitimate opportunities for women in the labour market, both in terms of quantity and in terms of quality, allowing them broad access to the occupation and employment type they want, unconstrained by gender stereotypes. The next chapter details how these measures can be specifically targeted and achieved through recommendations for policy initiatives.

12. The term “multidimensional” defines welfare in a sense that it accounts for a plurality of conditions beyond income and basic health but additionally the central capabilities related to longevity, knowledge and education, social relations, access to representation, subjective feelings and more that are central to the dignity of human lives (Nussbaum, 2001).

13. Capabilities in the context of this report adopts Sen’s (1990) and Nussbaum’s (2001) approach that recognizes that the full empowerment of women requires the ability and agency to realize their goals in all areas of life including financial, economic, political, social, cultural, and in and outside the home.

14. This does not imply that women face fewer social norm constraints in developing countries, but that they are manifested in other dimensions of the labour market, such as employment quality, as outlined in Chapter 1.

15. Each driver (with the exception of age, poverty, number of household members and marital status) are changed to their opposite (e.g. from not preferring to work to preferring to work) in order to obtain the difference in the predicted probability to participate.

16. This includes women having access to phone and internet, an optimistic rather than pessimistic attitude about their general future, believing that they have a good chance compared to men in the labour market, believing that they would not be constrained by children, and assuming that they only face the least constraining challenge instead of the most constraining one.

17. This assumes that a woman is living in an urban instead of rural location, that her family accepts that a woman should have the ability to pursue paid work, that the woman holds the belief that society respects women, and that the woman is non-religious.

18. The available European labour force surveys contain sectoral information at the one-digit level, but occupational information at the two-digit level, which a priori allows occupational segregation to explain more of the gender gap than sectoral segregation.

3

Policy considerations

Chapter 1 of this report looked at trends with respect to women’s engagement with the world of work. These can be categorized into broad gender gaps, namely those in: (i) labour force participation, (ii) unemployment, (iii) sectoral and occupational opportunities, (iv) status in employment and (v) income. Addressing these gaps will be vital to improving women’s access to the labour market, promoting female economic empowerment and ensuring improved conditions at work for both men and women. Narrowing the various gender gaps will also bring about improvements in individual welfare and economic growth. Yet, as set out in Chapter 2, a number of overlapping and inter-connected drivers exist that are shaping these gender gaps.

Accordingly, a multifaceted and comprehensive framework is required to address the root causes of gender gaps that are in line with international labour standards (ILO, 2016a). With this in mind, this chapter outlines a series of policy recommendations in an effort to improve labour market outcomes among women (figure 8).

3.1Reshaping gender role conformity and personal preferences

As discussed in Chapters 1 and 2, while more women are in work than ever before, their labour market participation is consistently lower than that of men and they are often segregated in sectors and occupations which reflect gender stereotypes at work. And while gender issues should be mainstreamed in the overall policy framework of a given country, a range of appropriate policy actions can nevertheless break down the constraints of gender role conformity and the personal preferences that are often shaped by it.

Increasing labour force participation rates

Advancing flexible working arrangements and reintegration measures: Such initiatives are important and effective means of raising women’s participation in the labour market. Flexible working arrangements can help to prevent women from leaving the labour market, either permanently or temporarily, due to leave and childcare restrictions. Such policies also need to address social norms and attitudes to encourage greater male uptake of flexible working arrangements. Equally, reintegration measures can help to overcome the difficulties associated with returning to the labour market and should include vocational training, retraining and skills upgrading for those who have taken a period of leave to, among other things, provide care for relatives or children.

Promoting women’s entrepreneurship: New start-up rates are lower among female entrepreneurs in comparison to their male counterparts and tend to be concentrated in firms with lower productivity and profitability. Accordingly, policies should be put in place to reduce any barriers that prevent more women from starting and developing businesses; these policies include legal frameworks for female business development, entrepreneurship training and skills development, as well as improved access to finance and ownership of capital (see box 7 on ILO’s Women’s Entrepreneurship Development Programme).

Figure 8

Visual framework of policy responses to gender gaps in the labour market

Reshaping gender role conformity and personal preferences

Increasing labour force participation rates

Promoting women’s entrepreneurship

Advancing flexible work arrangements and reintegration measures

Tackling root causes of occupational and sectoral segregation

Challenging gender stereotypes through education and valuing care-related skills

Supporting women’s representation, participation and leadership in decision-making

Addressing socio-economic constraints

Introducing improved policies for work–family balance

Ensuring sufficient parental protection extended to all men and women

Guaranteeing adequate social protection

Improving approaches to atypical working hours and part-time work

Creating and protecting quality jobs in the care economy

Promoting decent work for care professionals, including domestic and migrant workers

Recognizing, reducing and redistributing unpaid care work through public care services and social infrastructure

Targeting the macroeconomic environment and informal economy

Adopting gender-responsive macroeconomic policies

Supporting the formalization of jobs in the informal economy

Raising equality in labour market conditions

Promoting equal conditions for work of equal value

Establishing a system of wage transparency and gender-neutral job evaluation

Supporting adequate and inclusive minimum wages and strengthening collective bargaining

Tackling discrimination, violence and harassment against women and men

Eliminating unequal treatment of women and men in the labour market and ending violence and harassment in the world of work

Transforming institutions to prevent and eliminate discrimination, violence and harassment

Source: Based on ILO, 2016a.

Tackling the root causes of occupational and sectoral segregation

Challenging gender stereotypes through education and placing higher value on care-related skills: Breaking gender stereotypes begins in childhood through education (both formal and informal forms) and outreach. Addressing gender differences in the field of study selected by individuals, for instance by shifting the focus to subject-specific participation, is one potential avenue. As demonstrated in the previous chapters, the field of study and, subsequently, choice of occupation are often determined by considerations such as care responsibilities and the anticipated flexibility and family-friendly nature of certain occupations, thus perpetuating occupational segregation. Accordingly, initiatives need to address both issues of study and employment in non-stereotypical fields, through recruitment, training and funding, among other measures, to help young men and women make choices that challenge social norms within education, vocational training and occupational sectors (box 7).

Box 7

Transforming social norms and personal preferences

ILO Women’s Entrepreneurship Development Programme: While globally women entrepreneurs lead only a third of all businesses in the formal economy today, a large majority of them in developing and emerging countries run micro- and small enterprises in the informal economy. This reflects the systemic barriers that women face in starting, developing and growing businesses successfully. This includes limited access to networks, difficulties in accessing property/land (due to a lack of property and inheritance rights), lack of access to titled assets which restricts their loan acquisition capacity, lack of access to formal finance, sectoral segregation of entrepreneurial opportunities, limited access to business development services and harassment in the process of registering and operating their enterprises. The ILO’s Women’s Entrepreneurship Development Programme aims to create an enabling environment through an integrated framework for growing women’s enterprises by removing barriers to business start-up and growth, improving access to markets, facilitating access to and control over resources, enhancing social inclusion and protection and fostering a supportive culture and context.

Transforming social norms through policy: Policy design and implementation can either shape existing norms or promote new ones, directly or indirectly. However, reshaping social norms requires not only equality and family policies but also policies that directly address such issues through public awareness initiatives. For instance, public awareness campaigns, such as HeforShe, Programme H, or MenCare, have made significant advances in changing norms regarding masculinity by engaging men in gender equality initiatives. These initiatives range from promoting equal sharing of household tasks and caregiving among men (MenCare) to creating a communal educational space for critical reflection on gender stereotypes among men (Programme H) to engaging male business and political leaders as agents of change to challenge perceptions of social norms in their countries and organizations (HeforShe).

Source: ILO, 2016c; ILO, 2008; UNHLP, 2016.

Supporting women’s representation, participation and leadership in decision-making: In order to challenge occupational and sectoral segregation, women must be in a position to influence and shape workplaces, not least with respect to issues that disproportionately impact women. Means of improving women’s participation and leadership include quotas, targets and goals for participation at different levels, across all areas of government and within employers’ and workers’ groups, as well as in firms. Additional measures may include leadership training and mentorship, as well as financial incentives and fast-tracked career measures. Governments need to play a leading role in implementing such measures in the public sector.

3.2Addressing socio-economic constraints

Introducing improved policies for work–family balance

Millions of women and men worldwide are without the fundamental right to adequate parental protection and other basic social protection measures. In line with the ILO Workers with Family Responsibilities Convention, 1981 (No. 156), improved efforts need to be made to lay the groundwork for achieving harmonized work–family balances, and thus promoting quality and family-friendly working conditions for women and men. At the same time, atypical hours and low-quality part-time work disproportionately penalize women in the labour force.

Ensuring sufficient parental protection extended to all women and men: Maternity protection, paternity leave and other protection measures help to encourage women to return to work and facilitate reduced career breaks as well as allowing for a more harmonized work–family balance. The Maternity Protection Convention, 2000 (No. 183), sets out the minimum requirements for maternity leave and other related entitlements. National laws and policies need to be designed to minimize the financial cost to employers, particularly small and medium-sized enterprises, as well as expanding coverage to those categories of workers who are frequently excluded, such as non-standard workers, domestic workers and homeworkers. At the same time, there is a need for more statutory provision of leave entitlement for fathers and measures to encourage uptake of leave (box 8).

Guaranteeing adequate social protection: Gender gaps in the workplace, as well as the greater propensity for employment in areas such as unpaid care work, go hand in hand with inequalities in social protection systems. This includes access, coverage and provision of social security for women. As emphasized in the ILO Social Protection Floors Recommendation, 2012 (No. 202), social security is an important tool for promoting equal opportunities and gender equality. A gender dimension must therefore be incorporated into the design, implementation and evaluation of social protection systems in a way that is sensitive to the characteristics of the female labour market, such as unpaid family and domestic work, while simultaneously including gender-neutral provisions to avoid perpetuating segregation.

Improving approaches to atypical working hours and part-time work: The ILO Workers with Family Responsibilities Recommendation, 1981 (No. 165), encourages the adoption of measures designed to improve both working conditions and the quality of working life, including those which target “the progressive reduction of daily hours of work and the reduction of overtime, and more flexible arrangements as regards working schedules, rest periods and holidays”. Such measures should include the strengthening of workers’ rights to request changes to working hours, as well as reducing the wage penalty on part-time workers compared to their full-time counterparts, as detailed in the ILO Part-Time Work Convention, 1994 (No. 175).

Creating and protecting quality jobs in the care economy

Women are over-represented in certain occupations, including the care professions, which often have a history of insufficient regulation and protection.

Promoting decent work for care professionals, including domestic and migrant workers: To address decent work deficits in the care professions, there is a need to adjust policies with regard to labour market opportunities and social protection. This includes promoting male participation in these occupations. Any measures need to be in line with the ILO Workers with Family Responsibilities Convention, 1981 (No. 156), and in the case of domestic care workers also with the ILO Domestic Workers Convention, 2011 (No. 189). For migrants, the Migrant Workers (Supplementary Provisions) Convention, 1975 (No. 143), must be applied, to ensure that policies and practices are non-discriminatory in their efforts to promote decent work.

Recognizing, reducing and redistributing unpaid care work through public care services and social infrastructure: Care is a universal right and an important component of gender equality, yet too often care workers are placed at a disadvantage in their caregiving roles and as informal workers. Consequently, recognizing and attaching appropriate value to unpaid care and domestic work is important to remove biases in national income accounting that otherwise fails to include these workers. Further, efforts are required to challenge norms around caregiving, particularly with regard to ensuring that care is an equal right and responsibility of both women and men, and thus redistribution and rebalancing of care responsibilities between women and men is required. In addition, there is a need for enhanced public provision of basic infrastructure and services to alleviate the burden of care work (box 8).

Box 8

Relieving socio-economic constraints on women

Universal access to quality childcare: Access to quality childcare is essential for enabling women’s labour force participation while advancing the positive development of children. However, such access to quality care needs to be available to all workers regardless of their employment and income status. In India, the Self Employed Women’s Association (SEWA) provides childcare services through a cooperative that accommodates the working hours of parents while taking children of a very young age and ensuring the provision of health services (including nutrition). Such services, also found in Chile (Peñalolen childcare) and Kenya (SOCFINAF’s childcare), not only enable women to work more but also contribute to alleviating food and healthcare costs at home, which is a particularly acute challenge in low-income families. As a result, universal quality childcare not only contributes to achieving gender equality but also promotes greater social equality by contributing to the reversal of intergenerational poverty.

Shaping parental leave policies for women and men: From a gender-equality perspective, encouraging the uptake of well-paid leave by both mothers and fathers is essential. For mothers it is a way to secure employment, health and economic security, and for fathers it allows for a more gender-equal division of work and helps to shift some of the responsibility for unpaid work from mothers to fathers. However, leave should not be considered in isolation, but in combination with the other existing family and employment policies in the country so as to mitigate any negative effects on participation (Hook, 2010; Gangl and Ziefle, 2015). The ILO standard on paid maternity leave duration mandates a minimum leave period of 14 weeks, set by Convention No. 183. At the same time, a number of factors influence men’s leave uptake; for instance, the policy design, in which leave should be an individual entitlement, non-transferable, well-paid and universal (Haas and Rostgaard, 2011), while norms concerning leave uptake, family and individual cases and circumstances should be taken into account. In addition to leave for parents, paid leave and supportive policies and services for workers who care for sick, disabled and elderly family members should also be promoted.

Achieving substantive equality through gender budgeting: Sound fiscal policies play an important role in influencing the sustainability of growth through their effect on labour supply, human capital growth, investments and inequality. For these reasons, gender-responsive budgeting is a key mechanism in contributing to closing gender gaps and promoting women’s empowerment and development. It considers and monitors the differential impacts of changes in fiscal policies and budgetary allocations on different groups of the population – especially those that affect women and girls disproportionately – and makes a fiscal commitment to address the different needs and circumstances of women through both public expenditure and taxes. For instance, gender budgeting acknowledges the disproportionate contribution that women make in the care economy and integrates this knowledge when assessing the benefits and costs of government programmes. Already, in many countries, gender budgeting has influenced policy changes in education, health and infrastructure with the consideration of gender-oriented goals (e.g. India and Mexico) and has led to greater accountability for more equitable public spending (e.g. Austria and Ecuador).

Source: Cassirer and Addati, 2007; ILO, 2013; UNHLP, 2017; Moussié, 2017; Hook, 2010; Gangl and Ziefle, 2015; Haas and Rostgaard, 2011; ILO, 2014; Stotsky, 2016.

Targeting the macroeconomic environment and informal economy

Macroeconomic policies are important enablers of gender equality and should be complemented by the provision of support for factors such as formalization of jobs in the informal economy.

Adopting gender-responsive macroeconomic policies: Economic growth alone is insufficient to facilitate gender equality; instead, a focus on inclusive growth, particularly employment creation and enhancement of livelihoods, is necessary (e.g. by gender budgeting (box 8)). This requires a balanced framework of legislation, regulation and policies. Adequate and appropriate resources need to be safeguarded to make sure these priorities do not give way to others, particularly in times of economic downturn. In low-income and developing countries governments’ priorities may be more concentrated on investment in time-saving infrastructures, such as water, electricity and energy, while in developed countries the focus may be more on improving the status of low-income families through measures such as tax credits and transfers.

Supporting the formalization of jobs in the informal economy: Formalization of jobs in the informal economy entails a range of both top-down and bottom-up approaches. For instance, a policy mix that mainstreams employment promotion can help to generate better quality and more formal job opportunities, while the regulatory environment can be shaped around the process of formalization through legislation, taxation, rights and other means. Meanwhile, bottom-up approaches can include initiatives and measures aimed at increasing access to finance or promoting entrepreneurship, as well as providing incentives to businesses for formalizing employees. At the same time, further efforts to recognize informal worker organizations, such as Self Employed Women’s Association (SEWA) in India, can help collectivize informal workers.

3.3Raising equality in labour market conditions

Promoting equal conditions for work of equal value

The principle of equal remuneration for work of equal value needs to be integrated into law and included in collective bargaining processes. This requirement comes with prerequisites, such as improved wage transparency, and should also leverage existing tools, such as minimum wage setting systems. It also entails equal treatment of different types of workers, including informal workers.

Establishing a system of wage transparency and gender-neutral job evaluation: According to the Equal Remuneration Convention, 1951 (No. 100), equal pay for work of equal value should be ensured through the application of national laws and regulations. However, with the relative absence of reliable and open data on wages, improved access must be complemented by information on pay and bonuses and promotion structures for governments, employers’ and workers’ organizations (box 9). In addition, gender-neutral job evaluation methods are required to determine the value of work in an objective manner, through neutral assessments of skills, working conditions, responsibilities and effort.

Supporting adequate and inclusive minimum wages and strengthening collective bargaining: Ensuring equal pay for work of equal value must be an objective and feature of minimum wage policies in accordance with ILO Convention No. 100, the Minimum Wage Fixing Convention, 1970 (No. 131), and the Wage Fixing Recommendation, 1970 (No. 135). To this end, minimum wage policies can be used to combat gender-based pay discrimination, particularly given the higher propensity for women to be in lower-paid jobs than men (box 9). Moreover, while minimum wages should be free from gender bias, they can be an effective tool for targeting specific groups of workers, such as domestic workers and those in the care professions, thus indirectly benefiting the more vulnerable groups of women workers. Strengthened collective bargaining for effective social dialogue is imperative in this regard.

Tackling discrimination, violence and harassment against women and men

Direct and indirect gender-based discriminatory practices are evident throughout the world of work and exacerbate the wage gap, reduce female labour force participation and generally place women at a disadvantage in the labour market. Accordingly, measures need to be implemented in line with the ILO fundamental Convention on Discrimination (Employment and Occupation), 1958 (No. 111), as well as the UN Convention on the Elimination of all Forms of Discrimination against Women (CEDAW).

Box 9

Empowering women to secure improved labour market conditions

Legal enforcement of equal pay through pay transparency: Despite equal pay policies existing in a number of countries worldwide, there can be structural challenges that result in limited or ineffective enforcement procedures. These stem in part from the financial and other barriers of addressing wage inequality by taking legal action. The extensive time requirements of litigation, arduous processes and insufficient understanding of individual rights or company disclosure obligations also contribute to the difficulties of achieving redress. Legislation can counter these obstacles, particularly if it results in the establishment of specialized bodies to provide technical support for both remedy and enforcement. To such ends, enforcement would require the publishing of quality data on pay gaps and the drivers of these gaps, thus increasing transparency to facilitate bargaining processes. Iceland and the United Kingdom provide useful examples in this respect, as the first countries in the world to mandate equal pay for women and men through the enforcement of pay transparency. Once implemented, the law will be mandatory for both public and private firms (above a certain size), requiring them to be audited and receive certification of their equal pay practices, or face fines.

Engaging women in collective bargaining for decent work: Gender concerns are frequently excluded in collective bargaining, often as a result of the lack of female participation and representation at the decision-making levels in trade unions. However, trade unions can play a significant role in addressing gender-related concerns, namely concerns regarding the gender wage gap, flexibility in working time, parental leave, childcare services, gender-based violence in the workplace, freedom of association and the inclusion of vulnerable categories of workers (informal, domestic, migrant, pregnant and family workers). In order to better integrate women in trade unions, the International Trade Union Confederation (ITUC) has produced specific guidelines for making trade unions gender inclusive and instigated the Labour Rights for Women Campaign to mobilize and engage women workers of all types to participate in trade unions. Including more women in trade unions allows collective agreements on gender equality to be achieved more rapidly since the concerns of women are actively recognized, represented and legitimized through the process of social dialogue.

Source: ILO, 2016d; Ministry of Welfare, Iceland (https://www.velferdarraduneyti.is/jafnlaunastadall/), Government of Equalities Office, United Kingdom (https://www.gov.uk/government/news/gender-pay-gap-reporting-goes-live); ITUC, 2007; ITUC, 2016; ILO, 2016e.

Eliminating unequal treatment of women and men in the labour market and ending violence and harassment in the world of work: The responsibility for eliminating unequal treatment and ending violence and harassment falls on all areas of society. Governments and workers’ and employers’ groups need to establish and strive to implement legislative frameworks that ensure equal access and eligibility for women and men to labour regulation, employment legislation and social protection, without discrimination or harassment. Meanwhile, violence and harassment are an occupational safety and health issue and legislation under this body can be effective at combating the issue; however, it must be sufficiently broad to include all forms of violence and harassment. Moreover, there is an ongoing obligation to implement and monitor laws that prevent and prohibit discrimination, violence and harassment (ILO, 2017b).

Transforming institutions to prevent and eliminate discrimination, violence and harassment: Laws alone are insufficient to prevent and eliminate discrimination, violence and harassment associated with work, and supplementary equal treatment mechanisms still need to be underpinned by effective remedies, dissuasive sanctions as well as effective enforcement through labour inspection, specialized equality bodies and access to courts. Ultimately, attitudes and social norms need to change, including through the agency of public awareness campaigns (see also box 7).

3.4Moving forward

The world of work is characterized by persistent and widespread gender gaps, driven by a range of factors. Closing these gaps would yield significant economic benefits and improve individual welfare. With that in mind, this chapter has outlined a number of policy recommendations, though in no way mutually exclusive, that can help to narrow the gender gaps in labour market outcomes and guide changes in the attitudes and norms that surround the role of women at work. Importantly, a rights-based approach can serve to underscore, and complement, gender dimensions in policy-making. Indeed, moving forward, success will require efforts across a range of policy dimensions.

Appendix A. Regional, country and income groupings

Africa

Northern Africa

Algeria

Egypt

Libya

Morocco

Sudan

Tunisia

Western Sahara

Sub-Saharan Africa

Angola

Benin

Botswana

Burkina Faso

Burundi

Cabo Verde

Cameroon

Central African Republic

Chad

Comoros

Congo

Congo, Democratic Republic of the

Côte d’Ivoire

Djibouti

Equatorial Guinea

Eritrea

Ethiopia

Gabon

The Gambia

Ghana

Guinea

Guinea-Bissau

Kenya

Lesotho

Liberia

Madagascar

Malawi

Mali

Mauritania

Mauritius

Mozambique

Namibia

Niger

Nigeria

Rwanda

Sao Tome and Principe

Senegal

Seychelles

Sierra Leone

Somalia

South Africa

Swaziland

Tanzania, United Republic of

Togo

Uganda

Zambia

Zimbabwe

Americas

Latin America and the Caribbean

Antigua and Barbuda

Argentina

Bahamas

Barbados

Belize

Bolivia, Plurinational State of

Brazil

Chile

Colombia

Costa Rica

Cuba

Dominica

Dominican Republic

Ecuador

El Salvador

Grenada

Guatemala

Guyana

Haiti

Honduras

Jamaica

Mexico

Netherlands Antilles

Nicaragua

Panama

Paraguay

Peru

Puerto Rico

Saint Kitts and Nevis

Saint Lucia

Saint Vincent and the Grenadines

Suriname

Trinidad and Tobago

United States Virgin Islands

Uruguay

Venezuela, Bolivarian Republic of

Northern America

Canada

United States

Arab States

Bahrain

Iraq

Jordan

Kuwait

Lebanon

Occupied Palestinian Territory

Oman

Qatar

Saudi Arabia

Syrian Arab Republic

United Arab Emirates

Yemen

Asia and the Pacific

Eastern Asia

China

Hong Kong, China

Japan

Korea, Democratic People’s Republic of

Korea, Republic of

Macau, China

Mongolia

Taiwan, China

South-Eastern Asia and the Pacific

Australia

Brunei Darussalam

Cambodia

Cook Islands

Fiji

French Polynesia

Guam

Indonesia

Kiribati

Lao People’s Democratic Republic

Malaysia

Marshall Islands

Micronesia, Federated States of

Myanmar

Nauru

New Caledonia

New Zealand

Palau

Papua New Guinea

Philippines

Samoa

Singapore

Solomon Islands

Thailand

Timor-Leste

Tonga

Tuvalu

Vanuatu

Viet Nam

Southern Asia

Afghanistan

Bangladesh

Bhutan

India

Iran, Islamic Republic of

Maldives

Nepal

Pakistan

Sri Lanka

Europe and Central Asia

Northern, Southern and Western Europe

Albania

Andorra

Austria

Belgium

Bosnia and Herzegovina

Channel Islands

Croatia

Denmark

Estonia

Finland

France

Germany

Greece

Iceland

Ireland

Italy

Latvia

Liechtenstein

Lithuania

Luxembourg

Macedonia, the former Yugoslav Republic of

Malta

Monaco

Montenegro

Netherlands

Norway

Portugal

San Marino

Serbia

Slovenia

Spain

Sweden

Switzerland

United Kingdom

Eastern Europe

Belarus

Bulgaria

Czech Republic

Hungary

Moldova, Republic of

Poland

Romania

Russian Federation

Slovakia

Ukraine

Central and Western Asia

Armenia

Azerbaijan

Cyprus

Georgia

Israel

Kazakhstan

Kyrgyzstan

Tajikistan

Turkey

Turkmenistan

Uzbekistan

Developed countries (high income)

Andorra

Antigua and Barbuda

Argentina

Australia

Austria

Bahamas

Bahrain

Barbados

Belgium

Brunei Darussalam

Canada

Channel Islands

Chile

Croatia

Cyprus

Czech Republic

Denmark

Estonia

Finland

France

French Polynesia

Germany

Greece

Guam

Hong Kong, China

Hungary

Iceland

Ireland

Israel

Italy

Japan

Korea, Republic of

Kuwait

Latvia

Liechtenstein

Lithuania

Luxembourg

Macau, China

Malta

Monaco

Netherlands

Netherlands Antilles

New Caledonia

New Zealand

Norway

Oman

Poland

Portugal

Puerto Rico

Qatar

Saint Kitts and Nevis

San Marino

Saudi Arabia

Seychelles

Singapore

Slovakia

Slovenia

Spain

Sweden

Switzerland

Taiwan, China

Trinidad and Tobago

United Arab Emirates

United Kingdom

United States

United States Virgin Islands

Uruguay

Emerging countries (middle income)

Albania

Algeria

Angola

Armenia

Azerbaijan

Bangladesh

Belarus

Belize

Bhutan

Bolivia, Plurinational State of

Bosnia and Herzegovina

Botswana

Brazil

Bulgaria

Cabo Verde

Cambodia

Cameroon

China

Colombia

Congo

Cook Islands

Costa Rica

Côte d’Ivoire

Cuba

Djibouti

Dominica

Dominican Republic

Ecuador

Egypt

El Salvador

Equatorial Guinea

Fiji

Gabon

Georgia

Ghana

Grenada

Guatemala

Guyana

Honduras

India

Indonesia

Iran, Islamic Republic of

Iraq

Jamaica

Jordan

Kazakhstan

Kenya

Kiribati

Kyrgyzstan

Lao People’s Democratic Republic

Lebanon

Lesotho

Libya

Macedonia, the former Yugoslav Republic of

Malaysia

Maldives

Marshall Islands

Mauritania

Mauritius

Mexico

Micronesia, Federated States of

Moldova, Republic of

Mongolia

Montenegro

Morocco

Myanmar

Namibia

Nauru

Nicaragua

Nigeria

Occupied Palestinian Territory

Pakistan

Palau

Panama

Papua New Guinea

Paraguay

Peru

Philippines

Romania

Russian Federation

Saint Lucia

Saint Vincent and the Grenadines

Samoa

Sao Tome and Principe

Serbia

Solomon Islands

South Africa

Sri Lanka

Sudan

Suriname

Swaziland

Syrian Arab Republic

Tajikistan

Thailand

Timor-Leste

Tonga

Tunisia

Turkey

Turkmenistan

Tuvalu

Ukraine

Uzbekistan

Vanuatu

Venezuela, Bolivarian Republic of

Viet Nam

Western Sahara

Yemen

Zambia

Developing countries
(low income)

Afghanistan

Benin

Burkina Faso

Burundi

Central African Republic

Chad

Comoros

Congo, Democratic Republic of the

Eritrea

Ethiopia

The Gambia

Guinea

Guinea-Bissau

Haiti

Korea, Democratic People’s Republic of

Liberia

Madagascar

Malawi

Mali

Mozambique

Nepal

Niger

Rwanda

Senegal

Sierra Leone

Somalia

Tanzania, United Republic of

Togo

Uganda

Zimbabwe

Appendix B. Labour market estimates, projections and scenarios

The source of all global and regional labour market estimates in this World Employment and Social Outlook report is ILO, Trends Econometric Models (TEM), November 2016. The ILO has designed and actively maintains econometric models which are used to produce estimates of labour market indicators in the countries and years for which country-reported data are unavailable. These allow the ILO to produce and analyse global and regional estimates of key labour market indicators and related trends.

The TEM is used to produce estimates and projections – disaggregated by age and sex as appropriate – of unemployment, employment and status in employment. The output of the model is a complete matrix of data for 188 countries. The country-level data can then be aggregated to produce regional and global estimates of labour market indicators, such as the unemployment rate, the employment-to-population ratio, status in employment shares and vulnerable employment rate.

Prior to running the TEM, labour market information specialists in the Research Department, in cooperation with ILOSTAT and specialists in ILO field offices, evaluate existing country-reported data and select only those observations deemed sufficiently comparable across countries using criteria including: (1) type of data source; (2) geographic coverage; and (3) age group coverage.

•With regard to the first criterion, in order for data to be included in the model, they must be derived from either a labour force survey or a population census. National labour force surveys are generally similar across countries, and the data derived from these surveys are more readily comparable than data obtained from other sources. A strict preference is therefore given to labour force survey-based data in the selection process. However, many developing countries which lack the resources to carry out a labour force survey do report labour market information based on population censuses. Consequently, due to the need to balance the competing goals of data comparability and data coverage, some population census-based data are included in the model.

•The second criterion is that only nationally representative (i.e. not prohibitively geographically limited) labour market indicators are included. Observations corresponding to only urban or only rural areas are not included, as large differences typically exist between rural and urban labour markets, and using only rural or urban data would not be consistent with benchmark data such as GDP.

•The third criterion is that the age groups covered by the observed data must be sufficiently comparable across countries. Countries report labour market information for a variety of age groups and the age group selected can have an influence on the observed value of a given labour market indicator.

Apart from country-reported labour market information, the TEM uses the following benchmark files:

•United Nations World Population Prospects, 2015 revision for population estimates and projections;

•ILO Economically Active Population, Estimates and Projections (EAPEP) for labour force estimates and projections;

•IMF/World Bank data on GDP (PPP, per capita GDP and GDP growth rates) from the World Development Indicators and the World Economic Outlook October 2016 database;

•World Bank poverty estimates from the PovcalNet database.

Estimates of labour market indicators

The TEM produces estimates of unemployment rates to fill in missing values in the countries and years for which country-reported data are unavailable. Multivariate regressions are run separately for different regions in the world in which unemployment rates, broken down by age and sex (youth male, youth female, adult male, adult female), are regressed on GDP growth rates. Weights are used in the regressions to correct for biases that may result from the fact that countries which report unemployment rates tend to differ (in statistically important respects) from countries that do not report unemployment rates.[1] For 2016, a preliminary estimate is produced, using quarterly and monthly information available up to the time of production of this World Employment and Social Outlook report (November 2016).

The model also estimates employment by status using similar techniques to impute missing values at the country level. In addition to GDP growth rate, the variables used as explanatory variables are the value added shares of the three broad sectors in GDP, per capita GDP and the share of people living in urban areas. Additional econometric models are used to produce global and regional estimates of working poverty and employment by economic class (Kapsos and Bourmpoula, 2013).

Projections of labour market indicators

Unemployment rate projections are obtained using the historical relationship between unemployment rates and GDP growth during the worst crisis/downturn period for each country between 1991 and 2005, and during the corresponding recovery period.[2] This was done through the inclusion of interaction terms of crisis and recovery dummy variables with GDP growth in fixed effects panel regressions.[3] Specifically, the logistically transformed unemployment rate was regressed on a set of covariates, including the lagged unemployment rate, the GDP growth rate, the lagged GDP growth rate and a set of covariates consisting of the interaction of the crisis dummy and the interaction of the recovery-year dummy with each of the other variables.

Separate panel regressions were run across three different groupings of countries, based on:

(1)geographic proximity and economic/institutional similarities;

(2)income levels;[4]

(3)level of export dependence (measured as exports as a percentage of GDP).[5]

The rationale behind these groupings is as follows. Countries within the same geographic area or with similar economic/institutional characteristics are likely to be similarly affected by the crisis and have similar mechanisms to attenuate the impact of the crisis on their labour markets. Furthermore, because countries within given geographic areas often have strong World Trade Organization (WTO) and financial linkages, the crisis is likely to spill over from one economy to its neighbour (e.g. Canada’s economy and labour market developments are intricately linked to developments in the United States). Countries with similar income levels are also likely to have similar labour market institutions (e.g. social protection measures) and similar capacities to implement fiscal stimulus and other policies to counter the impact of the crisis. Finally, as the decline in exports was the primary crisis transmission channel from developed to developing economies, countries were grouped according to their level of exposure to this channel, as measured by their exports as a percentage of GDP. The impact of the crisis on labour markets through the export channel also depends on the type of exports (the affected sectors of the economy) involved, the share of domestic value added in exports and the relative importance of domestic consumption (for instance, countries such as India and Indonesia, with a large domestic market, were less vulnerable than countries such as Singapore and Thailand). These characteristics are controlled for by using fixed effects in the regressions.

In addition to the panel regressions, country-level regressions were run for countries with sufficient data. The ordinary least squares country-level regressions included the same variables as the panel regressions.

To take into account the uncertainty surrounding GDP prospects, as well as the complexity of capturing the relationship between GDP and unemployment rate for all the countries, a variety of ten (similar) multilevel mixed-effects linear regressions (varying-intercept and varying-coefficient models) are utilized. The main component that changes across these ten versions is the lag structure of the independent variables. The potential superiority of these models lies in the fact that, not only is the panel structure fully exploited (e.g. increased degrees of freedom), but it is also possible to estimate the coefficients specifically for each unit (country), taking into account unobserved heterogeneity at the cluster level and correcting for the random effects approach caveat that the independent variables are not correlated with the random effects term.

Overall, the final projection was generated as a simple average of the estimates obtained from the three group panel regressions and also, for countries with sufficient data, the country-level regressions. For a selection of countries (41 out of 188), an average of another set of forecast combinations was made according to judgemental examination in order to represent more realistically the recent trends observed in each country’s economic forecast.

Short-term projection model

For 41 countries, the preliminary unemployment estimate for 2016 and the projection for 2017 are based on results from a country-specific short-term projection model. The ILO maintains a database on monthly and quarterly unemployment flows that contains information on inflow and outflow rates of unemployment, estimated on the basis of unemployment by duration, following the methodologies proposed by Shimer (2012) and Elsby, Hobijn and Sahin (2013). A multitude of models are specified that either project the unemployment rate directly or determine both inflow and outflow rates, using ARIMA, VARX and combined forecast techniques. The short-term projection model relies on several explanatory variables, including hiring uncertainty (Ernst and Viegelahn, 2014), policy uncertainty (Baker, Bloom and Davis, 2013), macroeconomic forecasts by Oxford Economics and the Manpower Employment Survey Outlook. All estimated models are evaluated on an eight-quarter ahead rolling pseudo out-of-sample forecasting evaluation starting in Q1 2009, among which five models are selected using a weighting of the mean and maximum forecast error. The top five model forecasts are then averaged.

Changes to the estimates and projections: Trends Econometric Models (TEM) 2016 vs 2015

As for the previous editions of the TEM, global and regional unemployment levels and rates have been revised to take into account new information on unemployment rates as well as revisions to labour force and economic growth historical data and projections. Sources of discrepancy between the TEM November 2016 and the TEM November 2015 unemployment figures may be summarized as follows:

New unemployment rate data entries reported in national labour force surveys: Overall, the TEM November 2016 shows 260 new reported observations, of which 103 are recent (2014 or 2015), as compared with the TEM November 2015. Three countries have information on unemployment rates that before had none, most prominently Myanmar.

Backward revisions to historical unemployment rates: Some 61.7 per cent (512 observations) of the unemployment rates observed in the TEM November 2016 between 2008 and 2014 have been subject to backward revisions. Yet, the magnitude of these changes is negligible in the large majority of cases. In only 10.7 per cent of cases (89 observations) were revisions larger than 0.1 percentage points, and in only 15 cases were they larger than 1 percentage point. Notably, these include: Costa Rica (2010–2012), Paraguay (2011), Israel (2008–2011) and Mongolia (2011).

Revisions to past and projected GDP growth rates: Between the IMF World Economic Outlook (WEO) October 2015 and the WEO October 2016 updates, the forecast for global real GDP growth rate in 2016 was revised downward by 0.5 percentage points, while it was revised downward by 0.4 percentage points for 2017. Regarding the historical series, real GDP growth was revised upward by 0.1 percentage points for 2015. These changes to GDP growth past data and projections have led to small revisions in the estimated relationship between unemployment rate and GDP growth rate.

As a result of the changes described above, the baseline projection for the global unemployment rate was revised downward by 0.1 percentage points for the year 2016 and upward by 0.1 percentage
points for the years 2017 and 2018.

1. For instance, if simple averages of unemployment rates in reporting countries in a given region were used to estimate the unemployment rate in that region, and the countries that do not report unemployment rates should happen to differ from reporting countries with respect to unemployment rates, without such a correction mechanism the resulting estimated regional unemployment rate would be biased. The “weighted least squares” approach adopted in the TEM corrects for this potential problem.

2. The crisis period comprises the span between the year in which a country experienced the largest drop in GDP growth, and the “turning point year” when growth reached its lowest level following the crisis, before starting to climb back to its pre-crisis level. The recovery period comprises the years between the “turning point year” and the year when growth returned to its pre-crisis level.

3. In order to project unemployment during the current recovery period, the crisis-year and recovery-year dummies were adjusted based on the following definition: a country was considered to be “currently in crisis” if the drop in GDP growth after 2007 was larger than 75 per cent of the absolute value of the standard deviation of GDP growth over the 1991–2008 period and/or larger than 3 percentage points.

4. The income groups correspond to the World Bank income group classification of four income categories, based on countries’ 2008 gross national income (GNI) per capita (calculated using the Atlas method): low-income countries, US$1,025 or less; lower middle-income countries, US$1,026–US$4,035; upper middle-income countries, US$4,036–US$12,475; and high-income countries, US$12,476 or more.

5. The export dependence-based groups are: highest exports (exports ≥ 70 per cent of GDP); high exports (exports < 70 per cent but ≥ 50 per cent of GDP); medium exports (exports < 50 per cent but ≥ 20 per cent of GDP); and low exports (exports < 20 per cent of GDP).

Appendix C. Estimating female labour force participation

This appendix describes the econometric approach used to estimate the determinants of female labour force participation. The underlying database is the 2016 round of the Gallup World Poll, which conducts interviews with 500 men and 500 women[1] aged 15 and over in 142 countries around the world. Participants are asked a wide range of questions, including questions related to their status in the labour market. The individual status of participation of women – yes or no – is the variable of interest to be explained by the econometric analysis.

The econometric model of choice when dealing with a binary response variable is a probit regression. The probability of women i in country j participating in the labour market (Pr (Pij = 1)) is modelled using the cumulative distribution function of the standard normal distribution (Φ()), taking explanatory variables as an argument. This function is bound between zero and 1 and is solved using maximum likelihood techniques.

Pr (Pij = 1) = Φ(αj + βAij + γBijCij + δDijE)(1)

Equation (1) represents the functional form chosen for the estimation, where α β γ and δ represent vectors of coefficients and ABCD and E represent vectors of variables. Women from multiple countries j are pooled together to estimate average effects, but also to increase the sample size. Country dummies imply country-specific intercepts αj and capture all country-specific aggregate characteristics that affect all individuals in a country, such as policy, culture or the labour market conditions. Country dummies need to be used since very limited country-specific information is available for many of the countries in the sample.

Explanatory variables are introduced in three different ways into equation (1). First, individual characteristics can directly impact participation, represented by βAij. Second, the impact of characteristic B might differ between individuals with different levels of characteristic C, which is captured by the interaction term γBijCij. Finally, the impact of characteristic D can differ between individuals, depending on whether they are in a country group of characteristic E, captured by the term δDijE.

The model is estimated separately for developing, emerging and developed countries. The explanatory variables of interest are the ones representing either individual preferences, socio-economic constraints or gender roles imposed by society. Furthermore, control variables that are well established in the literature, such as age and education, have also been included. Table C1 lists the variables used in the regression analysis and states the base level in the case of dummy variables.

The estimation takes into account sampling weights, clustering, and stratification of the survey design to most accurately compute the standard errors. The population weights of the survey are applied as the sampling weights to achieve the correct point estimates without bias. Stratification based on the sampling of each country separate from other countries provides smaller standard errors for the overall sample size. Clustering regards each individual as the primary sampling unit.

Table C2 presents the estimated coefficients of the regression of the probit model for the four country groups estimated. The marginal impact on participation of a change in a certain variable needs to be computed by applying the cumulative distribution function of the standard normal distribution to the coefficients, assuming that values for all variables are included. Chapter 2 shows only some selected computed marginal impacts.

Table C1

List of variables

Name of variable

Description

Age, number of children, number of household members

Actual numeric value

Education (secondary, tertiary)

Dummy, base level primary education

Partnered

Dummy for women that are married or partnered, base level single, widowed, separated or divorced women

Internet, phone

Dummy for having access to internet or phone (mobile or landline), base level no access to both

Urban

Dummy for living in an urban environment, base level rural

Poverty (mild and severe)

Dummies, mild poverty indicates household stating that they have insufficient money for either food or shelter, severe for households that indicate that they have insufficient money for both, base level enough money for both

Religion (other, Christianity, Islam, Hinduism or secular)

Dummy indicating a person’s religion, base level secular/atheist

Preference to work

Dummy, responding either “prefer paid job” or “both” to question “Do you prefer to work in a paid job, stay at home, or do both”, base level “stay at home”

Acceptability of engaging in paid work

Dummy, women answering that household members find it acceptable for a woman in the household to have a paid job outside the home, base level not acceptable by household

Opportunities (better, worse)

Dummy when person finds that women have better (worse) opportunities on the labour market than men given equal education, base level same opportunities

Major challenge faced in labour market

Dummies. Women could state exactly one major challenge

Base level: lack of flexible working hours/appropriate, suitable working hours

Dummy options:

•balance between work and family or home/no time to spend with family

•lack of affordable care for children or relatives

•family members don’t approve of women working

•unfair treatment at work/abuse/harassment/discrimination

•lack of well-paid jobs

•unequal pay for doing similar work as men (or work of equal value)

•lack of transportation/lack of safe transportation

•people prefer to hire or promote men

•lack of skills, experience or education

•other

Optimism index

Dummy indicating a person’s positive attitudes about the future. Respondents are asked whether certain aspects of their life are improving or getting worse

High gap

Dummy indicating if the individual lives in a country with a high labour force gender gap. Gap thresholds are set according to the economic development level of the country

Table C2

Estimated coefficients

Variables

(1)

(2)

(3)

(4)

Developing countries

Emerging countries

Developed countries

ASNA countries

Age

0.06***

0.10***

0.14***

0.11***

(0.01)

(0.00)

(0.01)

(0.01)

Age^2

–0.00***

–0.00***

–0.00***

–0.00***

(0.00)

(0.00)

(0.00)

(0.00)

Children

0.02*

–0.03***

–0.03

–0.02

(0.01)

(0.01)

(0.03)

(0.03)

Children^2

–0.00**

0.00***

–0.00

–0.00

(0.00)

(0.00)

(0.01)

(0.00)

Household number

–0.06***

0.00

–0.00

–0.09***

(0.02)

(0.02)

(0.03)

(0.03)

Household number^2

0.00***

–0.00

0.00

0.01**

(0.00)

(0.00)

(0.00)

(0.00)

Secondary education

–0.16***

0.10***

0.24***

0.25***

(0.04)

(0.02)

(0.04)

(0.07)

Tertiary education

0.31**

0.54***

0.50***

0.93***

(0.13)

(0.03)

(0.05)

(0.09)

Urban

–0.15

–0.14

–0.05

–0.18

(0.20)

(0.09)

(0.24)

(0.22)

Internet

–0.01

0.19***

0.28***

0.14**

(0.05)

(0.02)

(0.05)

(0.07)

Phone

0.25***

0.11***

0.04

0.28**

(0.04)

(0.03)

(0.14)

(0.11)

Poverty (mild)

0.14***

0.13***

0.01

0.25***

(0.04)

(0.02)

(0.05)

(0.06)

Poverty (severe)

0.22***

0.22***

0.04

0.47***

(0.04)

(0.03)

(0.06)

(0.09)

Partnered

0.07

–0.12**

–0.25***

–0.57***

(0.09)

(0.05)

(0.09)

(0.16)

Household accepting

–0.03

–0.03

–0.13

–0.23

(0.09)

(0.07)

(0.17)

(0.22)

Prefer to work, rural, and household not accepting

–0.73

–0.18

0.07

0.37

(0.64)

(0.17)

(0.23)

(0.27)

Prefer to work, rural, and household accepting

0.17

0.17**

0.54**

0.32

(0.12)

(0.08)

(0.21)

(0.28)

Prefer to work, urban, and household not accepting

0.33

0.27**

0.39

0.10

(0.24)

(0.12)

(0.32)

(0.29)

Prefer to work, urban, and household accepting

0.35

0.37**

0.69*

0.54

(0.25)

(0.15)

(0.39)

(0.42)

Stay at home, urban, and household accepting

–0.23

0.05

0.14

0.24

(0.22)

(0.10)

(0.25)

(0.27)

Prefer to work and partnered

0.02

0.14***

0.21**

0.38**

(0.09)

(0.05)

(0.09)

(0.15)

High gap (LFPR)

–0.50***

–1.51***

(0.18)

(0.14)

High gap and prefer to work

0.11

0.23***

(0.13)

(0.07)

Opportunities (better)

0.05

0.07***

0.14***

0.09

(0.04)

(0.02)

(0.05)

(0.07)

Opportunities (worse)

0.07

–0.02

–0.06*

–0.02

(0.04)

(0.02)

(0.03)

(0.06)

Opportunities (don’t know)

–0.09

–0.11**

0.03

–0.36**

(0.07)

(0.04)

(0.10)

(0.18)

Optimism index

0.00

0.00**

0.00***

0.00

(0.00)

(0.00)

(0.00)

(0.00)

Challenges

Work–family balance

–0.10

–0.11***

–0.01

–0.11

(0.08)

(0.04)

(0.06)

(0.11)

Lack of affordable childcare

–0.17**

–0.06

–0.14*

–0.24

(0.08)

(0.05)

(0.08)

(0.15)

Family disapproval

–0.16*

–0.17***

–0.10

–0.05

(0.09)

(0.06)

(0.14)

(0.14)

Abuse/harassment/discrimination at work

–0.15**

–0.06

–0.07

–0.10

(0.08)

(0.04)

(0.08)

(0.12)

Lack of well–paying jobs

–0.07

–0.01

0.05

0.02

(0.09)

(0.04)

(0.08)

(0.13)

Unequal pay

–0.08

–0.02

0.01

0.19

(0.11)

(0.06)

(0.07)

(0.16)

Lack of transportation

–0.46***

–0.16**

0.15

–0.05

(0.15)

(0.07)

(0.22)

(0.15)

Prefer to hire men

–0.11

–0.05

–0.09

0.33

(0.12)

(0.07)

(0.09)

(0.20)

Lack of skills or experience

–0.07

0.00

0.03

–0.06

(0.11)

(0.07)

(0.14)

(0.31)

Other challenge

–0.27***

0.01

–0.05

–0.00

(0.09)

(0.05)

(0.07)

(0.14)

Don’t know

–0.17**

–0.13***

–0.15*

–0.11

(0.08)

(0.04)

(0.08)

(0.13)

Religion

Other and stay at home

–1.11*

–0.10

0.03

0.75**

(0.63)

(0.16)

(0.14)

(0.30)

Other and prefer to work

–0.18

0.11

0.09

0.87***

(0.24)

(0.08)

(0.06)

(0.28)

Christianity and stay at home

–0.98

–0.20

0.20*

0.25

(0.60)

(0.15)

(0.10)

(0.28)

Christianity and prefer to work

–0.15

0.05

–0.03

0.48***

(0.22)

(0.08)

(0.04)

(0.15)

Islam and stay at home

–1.18**

–0.54***

–0.71***

(0.60)

(0.16)

(0.24)

Islam and prefer to work

–0.39*

–0.12

–0.22*

(0.23)

(0.09)

(0.12)

Hinduism and stay at home

–0.57

–0.40**

–0.81

(0.64)

(0.20)

(0.55)

Hinduism and prefer to work

–0.17

–0.07

0.45*

(0.31)

(0.12)

(0.27)

Number of observations

10 620

31 143

16 741

4 777

Countries

22

59

35

11

Note: Standard errors are shown in parentheses. Countries with not enough observations for selected variables are dropped.
*** p < 0.01, ** p < 0.05, * p < 0.1.

1. In some countries a greater number of people were surveyed. See gallup.com for further information.

Appendix D. Decomposing gender gaps in employment relationships

This appendix describes the methodology used to decompose gender gaps in types of employment relationship, such as status in employment, informality, working time or type of contract. A gender gap in, say, own-account work can be due to the fact that women are more likely than men to be employed in sectors, or to have occupations, that are more or less prone to be characterized by own-account work, or by the fact that there is a gender gap within that sector or occupation. Consequently, the gender gaps of job quality indicators will be decomposed into three components: (1) gaps due to sectoral segregation, (2) gaps due to occupational segregation, and (3) residual gender gaps.

A gender gap in an employment relationship type j is determined as male employment in j as a share of total male employment, , minus female employment in j as a share of total female employment, . The employment share in a certain employment relationship type for each sex, , is computed as the sum of employment in j over all sectors and occupations, divided by the sum of employment over all sectors and occupations. This can be expanded to show the share of employment in an employment relationship type within a certain sector s and occupation o , multiplied by the share of that sector and occupation in overall sectoral employment , multiplied by the share of that occupation in total employment (sho). Mathematically, this means

(1)

where is employment in sector s and occupation o of employment relationship type j. Differences in job quality employment shares between men and women can be decomposed into

(2)

where Δsh reflects the difference in the shares between men and women for that particular share, which is multiplied by the weight of that category in the total employment. The weights are chosen as the average between the male and the female shares in employment. The first component of the decomposition reflects the gender gap due to residual factors, the second component the gap due to sectoral segregation, and the last component the gap due to occupational segregation.

The decomposition is applied to the micro data of labour force surveys available in the ILOSTAT repository. Surveys exist for a total of 29 developing and emerging countries, for recent years but also going back as far as 2003 for some countries. For each survey available for a country and year the most detailed sectoral and occupational decomposition is used, where the level of detail varies from ISIC two-digit level to six-sector aggregates, as well as ISCO two-digit or ISCO one-digit level. The decomposition is also applied to micro data of labour force surveys from 2009 to 2013 provided by Eurostat. These include the 28 EU member countries, with the exception of Bulgaria and Germany, and the additional inclusion of Iceland, Norway and Switzerland. The sector data is available at one-digit level, while occupations have been aggregated to the two-digit level.

In a first step, the decomposition is averaged over all the available survey years for a country, in order to smooth out potential fluctuations due to survey variability, but also to obtain a single value for a country. The second step involves the contributions to the total gap being averaged by taking the simple mean of all available countries. The sum of the average contributions would then represent the average gender gap for the employment relationship type. In a third step, the absolute sum of the contributions is rescaled to 100, greatly improving the readability of a figure when presenting decompositions of multiple variables.

Appendix E. Gender breakdown of key labour market indicators

Table E1

Unemployment developments by gender, 1997, 2007 and 2017

Country/region

Rate (per cent)

1997

2007

2017

World

6.0

6.8

5.3

5.9

5.5

6.2

Developing countries

4.6

6.6

5.0

6.7

4.7

6.5

Emerging countries

5.9

6.4

5.3

5.8

5.4

6.1

Developed countries

6.9

8.2

5.4

6.1

5.9

6.4

Northern Africa

12.4

23.6

9.0

18.2

9.5

20.0

Sub-Saharan Africa

6.6

9.0

6.8

8.9

6.2

8.3

Latin America and the Caribbean

7.9

12.4

6.7

10.4

7.0

10.4

Northern America

5.3

5.3

4.9

4.6

5.3

4.9

Arab States

9.7

24.5

8.2

19.3

8.3

21.2

Eastern Asia

4.8

3.9

4.2

3.3

5.1

3.7

South-Eastern Asia and the Pacific

3.8

4.0

5.1

5.4

3.8

3.9

Southern Asia

4.6

5.5

4.0

4.6

3.8

5.0

Northern, Southern and Western Europe

9.9

12.3

6.8

8.1

8.8

9.3

Eastern Europe

10.1

9.8

6.7

6.2

6.4

5.8

Central and Western Asia

8.4

10.2

8.9

9.1

8.9

9.6

Table E2

Unemployment developments by gender, 1997, 2007 and 2017

Country/region

Number (millions)

1997

2007

2017

World

96.3

71.9

98.2

73.7

115.8

85.3

Developing countries

4.0

4.9

5.8

6.8

7.3

8.8

Emerging countries

71.4

48.8

74.7

51.5

87.9

59.1

Developed countries

20.9

18.2

17.8

15.3

20.5

17.4

Northern Africa

4.7

2.5

4.3

2.7

5.5

3.6

Sub-Saharan Africa

8.2

9.1

10.8

12.3

13.5

15.5

Latin America and the Caribbean

10.6

9.9

10.8

11.3

13.1

13.6

Northern America

4.5

3.8

4.6

3.7

5.3

4.2

Arab States

2.1

1.0

2.6

1.3

3.7

2.1

Eastern Asia

22.3

14.6

20.8

13.0

26.9

15.0

South-Eastern Asia and the Pacific

5.5

4.2

9.0

7.0

7.8

5.9

Southern Asia

17.0

7.9

18.6

8.2

20.6

9.2

Northern, Southern and Western Europe

11.1

10.2

8.0

7.7

10.4

9.3

Eastern Europe

7.7

6.7

5.2

4.4

4.9

4.1

Central and Western Asia

2.7

2.1

3.4

2.1

4.1

2.8

Table E3

Labour force participation developments by gender, 1997, 2007 and 2017

Country/region

Rate (per cent)

1997

2007

2017

World

79.3

52.2

77.1

51.1

76.1

49.4

Developing countries

84.1

69.4

83.5

70.9

82.6

70.3

Emerging countries

81.2

51.6

78.5

49.4

77.5

46.9

Developed countries

71.5

49.9

69.9

51.7

68.0

51.9

Northern Africa

74.7

21.4

73.2

22.1

74.1

22.9

Sub-Saharan Africa

77.9

61.7

75.8

63.9

76.3

64.6

Latin America and Caribbean

81.9

46.6

80.1

51.9

78.3

52.7

Northern America

74.0

58.5

72.1

58.7

68.3

56.2

Arab States

75.5

17.5

74.5

19.2

76.4

21.2

Eastern Asia

83.4

68.9

78.1

63.3

76.8

61.3

South-Eastern Asia and the Pacific

82.0

58.0

81.7

58.6

81.2

58.8

Southern Asia

83.3

34.3

81.9

33.1

79.4

28.6

Northern, Southern and Western Europe

66.9

46.1

66.0

49.9

63.8

51.3

Eastern Europe

66.6

51.5

66.6

53.0

68.1

53.0

Central and Western Asia

74.6

42.9

71.2

40.8

73.5

44.1

Table E4

Labour force participation developments by gender, 1997, 2007 and 2017

Country/region

Number (millions)

1997

2007

2017

World

1600.8

1058.7

1865.2

1239.5

2116.8

1374.2

Developing countries

87.3

75.2

115.0

101.4

154.8

135.9

Emerging countries

1210.1

761.1

1421.9

886.5

1616.5

968.2

Developed countries

303.3

222.4

328.3

251.6

345.5

270.1

Northern Africa

37.5

10.8

47.6

14.5

57.9

18.1

Sub-Saharan Africa

124.4

101.5

160.1

138.6

216.7

186.5

Latin America and Caribbean

134.1

79.7

160.1

108.5

185.4

130.4

Northern America

85.8

71.3

94.8

80.2

99.5

84.4

Arab States

21.4

4.3

31.8

6.7

45.2

10.1

Eastern Asia

462.5

371.6

501.4

395.7

526.3

408.4

South-Eastern Asia and the Pacific

143.9

104.8

175.4

129.0

204.2

151.5

Southern Asia

370.0

143.8

461.0

177.2

541.1

185.8

Northern, Southern and Western Europe

112.2

82.7

117.4

94.2

117.5

99.8

Eastern Europe

76.2

67.9

77.7

71.7

76.8

69.6

Central and Western Asia

32.8

20.3

38.0

23.3

46.1

29.5

Table E5a

Employment status by gender, 1997

Country/region

Share of employment (%)

Wage and salaried workers

Employers

Own-account workers

Contributing family workers

World

48.4

46.5

3.9

1.5

38.2

24.5

9.6

27.5

Developing countries

22.2

11.6

2.3

0.7

59.5

57.8

15.9

29.9

Emerging countries

42.3

38.9

3.3

1.2

43.1

26.0

11.2

33.9

Developed countries

80.6

85.2

6.4

2.6

12.0

7.9

1.0

4.3

Northern Africa

56.7

46.1

9.5

2.1

23.6

22.9

10.2

28.9

Sub-Saharan Africa

34.3

18.5

2.4

0.8

50.9

55.9

12.3

24.9

Latin America and the Caribbean

58.5

60.3

5.8

2.6

29.7

25.9

6.0

11.2

Northern America

84.6

90.5

6.1

2.6

9.2

6.7

0.1

0.3

Arab States

71.0

65.7

4.2

1.0

19.8

17.0

5.0

16.3

Eastern Asia

52.6

45.6

3.9

0.9

34.7

19.1

8.7

34.4

South-Eastern Asia and the Pacific

37.2

31.6

3.8

1.2

41.6

22.5

17.4

44.6

Southern Asia

22.0

11.2

1.4

0.5

62.3

43.5

14.3

44.8

Northern, Southern and Western Europe

79.3

86.2

7.5

3.1

11.8

6.9

1.4

3.9

Eastern Europe

84.6

84.6

4.0

3.9

9.8

7.9

1.6

3.6

Central and Western Asia

53.5

49.5

4.9

0.6

34.4

27.8

7.2

22.0

Table E5b

Employment status by gender, 2007

Country/region

Share of employment (%)

Wage and salaried workers

Employers

Own-account workers

Contributing family workers

World

51.4

51.7

3.6

1.4

37.5

26.3

7.6

20.6

Developing countries

20.7

11.0

2.6

0.8

56.3

50.7

20.5

37.5

Emerging countries

46.7

46.1

3.2

1.3

42.0

28.9

8.2

23.7

Developed countries

82.3

87.9

5.7

2.3

11.3

7.2

0.6

2.5

Northern Africa

59.5

49.1

9.6

2.1

22.7

18.9

8.2

29.8

Sub-Saharan Africa

32.9

20.0

2.7

0.9

47.6

47.6

16.8

31.5

Latin America and the Caribbean

62.3

64.5

5.9

2.8

27.2

23.9

4.6

8.8

Northern America

86.3

91.8

5.2

2.2

8.4

5.9

0.1

0.1

Arab States

75.7

76.2

4.6

1.4

16.3

12.7

3.4

9.7

Eastern Asia

62.4

57.4

2.9

0.9

30.3

22.8

4.3

18.9

South-Eastern Asia and the Pacific

41.6

36.7

4.1

1.5

41.8

28.6

12.5

33.2

Southern Asia

22.7

14.6

1.6

0.5

63.8

44.6

12.0

40.2

Northern, Southern and Western Europe

80.3

88.0

6.6

2.6

12.2

7.3

0.8

2.2

Eastern Europe

83.3

85.0

4.5

3.7

11.2

9.2

1.0

2.1

Central and Western Asia

63.2

59.5

4.9

1.1

27.4

25.2

4.5

14.1

Table E5c

Employment status by gender, 2017

Country/region

Share of employment (%)

Wage and salaried workers

Employers

Own-account workers

Contributing family workers

World

54.4

55.4

3.2

1.3

37.0

28.4

5.5

14.9

Developing countries

24.3

13.6

2.8

1.0

55.7

48.9

17.2

36.6

Emerging countries

51.0

51.8

2.8

1.1

40.8

31.4

5.4

15.6

Developed countries

83.7

89.1

5.1

2.2

10.7

7.2

0.4

1.6

Northern Africa

62.8

54.0

8.4

1.6

23.5

17.1

5.3

27.3

Sub-Saharan Africa

36.3

22.6

2.8

1.0

46.9

45.8

14.0

30.6

Latin America and the Caribbean

62.6

65.3

5.6

2.6

28.6

25.3

3.1

6.8

Northern America

87.6

92.4

4.9

2.0

7.5

5.5

0.0

0.1

Arab States

79.2

77.2

3.7

1.2

15.2

14.6

1.9

7.0

Eastern Asia

69.3

65.5

2.4

0.9

26.0

25.4

2.2

8.1

South-Eastern Asia and the Pacific

49.0

44.4

3.8

1.5

39.6

30.3

7.7

23.8

Southern Asia

26.8

18.2

1.4

0.6

63.5

50.1

8.2

31.0

Northern, Southern and Western Europe

80.9

88.6

5.9

2.4

12.5

7.7

0.7

1.3

Eastern Europe

85.0

88.7

2.6

1.3

11.5

8.3

0.9

1.7

Central and Western Asia

67.5

65.9

4.8

1.7

23.1

18.4

4.5

13.9

Table E6a

Employment by economic class and gender, 1997

Country/region

Share of employment (%)

Working poverty, (<US$ PPP 3.10/day)

Developing middle class, (US$ PPP 5-13/day)

Developed middle class, (>US$ PPP 14/day)

Emerging and developing countries

60.3

62.3

16.4

15.6

6.4

6.7

Northern Africa

43.6

45.8

25.2

24.1

3.8

6.3

Sub-Saharan Africa

73.9

77.2

10.5

8.9

3.5

2.6

Latin America and the Caribbean

20.1

16.5

37.2

38.3

25.2

29.9

Arab States

26.4

18.2

28.5

27.3

23.8

30.4

Eastern Asia

69.0

70.0

10.4

9.8

1.6

1.6

South-Eastern Asia and the Pacific

64.2

64.3

16.6

16.8

5.2

6.3

Southern Asia

72.8

81.1

9.6

5.6

1.3

1.1

Eastern Europe

5.3

6.8

48.9

52.1

31.9

28.4

Central and Western Asia

27.4

28.6

38.8

34.1

13.5

13.7

Table E6b

Employment by economic class and gender, 2007

Country/region

Share of employment (%)

Working poverty, (<US$ PPP 3.10/day)

Developing middle class, (US$ PPP 5-13/day)

Developed middle class, (>US$ PPP 14/day)

Emerging and Developing Countries

42.3

40.2

27.4

28.7

9.5

10.8

Northern Africa

30.1

28.1

35.8

36.7

4.9

8.1

Sub-Saharan Africa

69.4

72.8

11.7

9.9

3.9

3.0

Latin America and the Caribbean

12.6

11.4

41.3

40.6

32.8

36.8

Arab States

22.1

17.2

27.7

26.2

30.8

36.2

Eastern Asia

28.5

26.9

39.7

40.1

4.2

4.1

South-Eastern Asia and the Pacific

45.7

45.7

23.8

24.0

8.7

10.1

Southern Asia

64.2

71.0

13.7

9.4

1.7

1.6

Eastern Europe

3.8

3.5

40.4

40.3

51.4

52.8

Central and Western Asia

15.5

14.7

46.1

49.7

22.6

17.7

Table E6c

Employment by economic class and gender, 2017

Country/region

Share of employment (%)

Working poverty, (<US$ PPP 3.10/day)

Developing middle class, (US$ PPP 5-13/day)

Developed middle class, (>US$ PPP 14/day)

Emerging and Developing Countries

29.1

27.0

35.7

36.9

16.3

19.7

Northern Africa

24.2

21.8

42.3

45.0

5.1

7.7

Sub-Saharan Africa

61.2

64.6

13.6

11.6

4.7

3.7

Latin America and the Caribbean

8.0

7.8

40.9

39.0

40.4

44.9

Arab States

21.1

19.6

25.3

23.3

35.9

40.3

Eastern Asia

10.1

9.7

61.0

59.1

19.1

20.4

South-Eastern Asia and the Pacific

24.5

22.0

35.8

36.2

17.6

20.2

Southern Asia

46.8

51.8

21.2

16.4

3.1

2.6

Eastern Europe

3.1

3.1

29.0

29.7

64.4

64.6

Central and Western Asia

6.8

5.5

45.8

51.2

36.9

31.9

Table E7a

Employment share by sector and gender, 1997

Sector

Gender

Region

W

NAf

SSA

LAC

NAm

AS

EA

SEAP

SA

NSWE

EE

CWA

Agriculture, forestry, hunting and fishing

41.6

34.5

62.1

12.2

1.6

36.5

47.3

47.7

73.9

5.5

16.0

48.5

38.5

31.0

56.6

24.2

3.8

16.2

42.1

45.5

53.1

6.9

19.9

34.3

Mining and quarrying

0.4

0.3

0.5

0.2

0.2

0.4

0.3

0.4

0.5

0.1

0.7

0.3

1.2

0.9

1.1

0.6

0.9

1.3

1.8

0.8

0.7

0.7

3.3

1.3

Manufacturing

14.1

20.1

6.4

11.8

11.0

4.7

18.0

12.6

9.7

14.1

21.3

11.2

13.6

11.6

7.5

15.6

19.6

9.8

13.3

10.7

11.0

24.9

21.0

13.6

Utilities (electricity, gas, etc.)

0.3

0.4

0.1

0.3

0.5

0.2

0.2

0.1

0.1

0.4

1.5

0.7

0.9

1.3

0.7

0.9

1.7

0.9

0.6

0.5

0.6

1.3

3.0

1.4

Construction

1.2

0.5

0.3

0.5

1.3

0.5

1.2

0.9

1.4

1.5

2.1

0.9

8.0

8.9

3.4

9.5

10.3

12.2

9.3

7.3

5.3

11.9

9.2

8.4

Wholesale and retail trade; repair of motor vehicles, motorcycles and personal and household goods

15.1

4.2

15.5

20.2

14.3

2.8

18.9

16.8

3.4

16.2

13.0

10.4

13.8

13.4

13.4

18.1

15.4

17.1

13.8

13.2

12.4

13.8

11.3

15.2

Accommodation and restaurants

2.6

0.6

2.2

5.9

6.8

0.4

1.2

4.5

0.7

4.9

2.1

1.5

1.8

1.6

0.7

3.2

5.0

2.2

1.1

2.1

1.3

3.2

0.9

2.7

Transport, storage and communication

1.8

1.1

0.5

1.7

4.0

1.2

1.7

0.7

0.3

3.4

4.9

2.1

5.9

5.6

4.6

7.1

7.6

6.7

5.0

6.6

5.1

7.9

9.9

6.4

Financial activities

1.6

1.2

0.5

1.5

6.1

0.8

1.4

0.9

0.2

3.8

2.0

1.3

1.1

1.0

0.4

1.1

3.4

0.7

1.0

0.8

0.7

3.1

0.7

0.8

Education

5.3

18.7

2.8

10.2

12.0

21.9

2.0

3.6

2.8

10.6

13.1

9.6

2.7

7.1

2.3

3.1

4.6

5.9

2.1

2.1

2.1

3.9

3.4

3.3

Health and social work activities

5.1

5.5

1.3

8.1

18.7

4.4

1.9

1.7

0.9

16.9

10.9

5.3

1.6

1.9

0.9

2.6

4.1

2.2

1.4

0.6

0.7

3.8

2.1

1.6

Public administration and defence; compulsory social security

2.4

8.0

1.6

4.5

4.4

5.9

0.7

2.0

1.3

7.4

4.5

3.1

4.9

12.2

3.7

5.1

4.6

18.1

3.9

4.7

3.7

7.7

7.1

6.1

Real estate, business and administrative activities

2.2

0.7

0.7

3.7

9.7

0.7

0.8

1.1

0.2

7.6

3.0

1.9

2.3

1.0

0.7

4.6

10.5

2.0

1.1

1.4

0.7

7.0

3.1

2.0

Other services

6.4

4.3

5.5

19.5

9.3

19.5

4.2

6.9

4.6

7.6

4.9

3.2

3.8

2.5

3.9

4.3

8.5

4.6

3.5

3.7

2.7

4.0

5.2

3.1

W = World; NAf = Northern Africa; SSA = Sub-Saharan Africa; LAC = Latin America and the Caribbean; NAm = Northern America; AS = Arab States; EA = Eastern Asia; SEAP = South-Eastern Asia and the Pacific; SA = Southern Asia; NSWE = Northern, Southern and Western Europe; EE = Eastern Europe; CWA = Central and Western Asia.

Table E7b

Employment share by sector and gender, 2007

Sector

Gender

Region

W

NAf

SSA

LAC

NAm

AS

EA

SEAP

SA

NSWE

EE

CWA

Agriculture, forestry, hunting and fishing

36.5

33.0

61.7

9.3

0.8

19.4

39.3

40.7

67.6

3.4

12.5

36.8

32.5

27.9

57.0

20.2

2.1

10.8

31.3

40.1

45.2

5.1

14.4

25.3

Mining and quarrying

0.3

0.3

0.6

0.1

0.2

0.5

0.3

0.3

0.4

0.1

0.6

0.3

1.0

0.9

1.2

0.7

1.0

1.6

1.3

0.9

0.7

0.5

3.0

1.3

Manufacturing

12.4

16.3

5.0

12.5

7.1

4.8

14.9

13.5

12.7

10.9

17.5

11.5

12.9

10.2

6.4

15.1

14.5

8.9

12.6

10.9

12.2

21.8

19.8

14.5

Utilities (electricity, gas, etc.)

0.4

0.5

0.2

0.2

0.4

0.3

0.4

0.2

0.1

0.4

1.6

0.8

0.8

1.2

0.8

0.8

1.1

1.1

0.7

0.4

0.5

1.1

3.1

1.4

Construction

1.6

0.6

0.4

0.6

1.7

0.8

2.0

0.8

2.5

1.5

2.0

1.5

10.7

12.4

4.3

11.7

13.0

15.1

13.4

8.4

8.7

13.5

11.5

10.2

Wholesale and retail trade; repair of motor vehicles, motorcycles and personal and household goods

17.0

4.5

15.9

21.3

14.0

4.6

22.9

19.6

4.1

15.6

16.6

13.3

15.0

14.1

13.0

18.2

14.7

18.0

16.6

14.4

13.7

13.6

13.2

16.3

Accommodation and restaurants

2.9

0.8

1.8

6.3

7.4

0.8

1.6

3.5

1.1

5.5

3.0

2.5

2.2

2.1

0.7

3.3

5.6

3.0

2.0

1.7

1.7

3.7

1.2

3.5

Transport, storage and communication

2.1

1.3

0.6

2.2

4.1

1.7

2.4

1.1

0.5

3.5

4.7

2.5

7.0

6.7

5.2

8.1

8.4

7.4

6.0

7.8

6.7

8.1

10.9

7.6

Financial activities

1.9

1.4

0.5

1.5

6.2

1.2

2.0

1.2

0.4

3.6

2.5

1.5

1.3

0.9

0.4

1.1

3.7

1.0

1.4

1.2

0.9

2.9

1.1

0.9

Education

6.1

19.6

3.2

9.6

12.5

27.0

3.1

4.4

3.9

10.9

13.2

11.6

2.9

6.2

2.5

3.1

5.0

5.6

2.6

2.1

2.4

3.8

3.4

3.6

Health and social work activities

5.9

6.8

1.6

8.4

20.3

6.8

3.3

2.3

1.3

18.3

11.2

6.2

1.9

1.7

1.1

2.5

4.6

2.3

2.2

0.7

0.8

4.1

2.3

1.6

Public administration and defence; compulsory social security

2.6

8.5

1.5

4.4

4.5

6.8

1.1

2.4

0.9

7.3

5.1

3.8

4.6

11.3

3.4

5.2

4.7

15.0

4.2

4.9

2.6

7.1

7.3

6.6

Real estate, business and administrative activities

3.1

1.2

0.9

5.0

11.8

2.0

1.2

1.8

0.4

10.4

5.3

3.6

3.3

1.7

1.0

5.9

13.3

3.8

1.6

2.3

1.4

10.2

6.3

3.5

Other services

7.2

5.1

6.0

18.7

9.1

23.5

5.5

8.1

4.2

8.6

4.2

4.0

3.8

2.8

3.0

4.1

8.2

6.3

4.1

4.1

2.6

4.4

2.7

3.5

W = World; NAf = Northern Africa; SSA = Sub-Saharan Africa; LAC = Latin America and the Caribbean; NAm = Northern America; AS = Arab States; EA = Eastern Asia; SEAP = South-Eastern Asia and the Pacific; SA = Southern Asia; NSWE = Northern, Southern and Western Europe; EE = Eastern Europe; CWA = Central and Western Asia.

Table E7c

Employment share by sector and gender, 2017

Sector

Gender

Region

W

NAf

SSA

LAC

NAm

AS

EA

SEAP

SA

NSWE

EE

CWA

Agriculture, forestry, hunting and fishing

30.5

28.3

57.4

8.8

0.8

19.0

28.6

30.3

60.4

2.4

8.0

28.9

27.6

23.4

54.0

20.2

2.1

11.4

21.9

31.6

37.7

4.4

11.4

21.5

Mining and quarrying

0.3

0.2

0.7

0.2

0.2

0.6

0.2

0.4

0.2

0.1

0.5

0.3

1.0

0.8

1.2

0.8

0.9

1.9

1.1

1.1

0.6

0.5

2.9

1.4

Manufacturing

10.4

16.9

5.6

10.5

5.5

4.0

11.6

13.3

11.4

8.3

14.0

11.0

11.6

10.4

6.6

13.5

13.1

9.2

10.0

11.7

12.1

18.8

17.7

13.2

Utilities (electricity, gas, etc.)

0.5

0.5

0.2

0.3

0.4

0.4

0.7

0.3

0.2

0.6

1.8

1.0

1.0

1.4

0.8

1.0

1.2

1.4

0.8

0.7

0.7

2.0

3.8

1.8

Construction

2.4

0.6

0.5

0.6

1.1

0.8

3.8

1.1

4.9

1.5

1.9

1.9

13.0

14.9

5.2

12.9

11.0

16.3

18.4

11.3

12.1

10.9

12.4

12.3

Wholesale and retail trade; repair of motor vehicles, motorcycles and personal and household goods

18.5

5.4

17.2

21.5

13.4

6.3

26.9

20.2

4.8

14.4

17.7

13.2

15.1

14.5

12.6

17.0

14.9

17.4

17.9

14.3

13.6

13.7

13.5

14.6

Accommodation and restaurants

4.2

1.2

3.5

7.8

8.1

1.1

2.4

7.2

2.0

5.7

3.8

3.8

3.0

2.6

0.9

3.5

6.1

2.8

3.5

3.5

2.2

4.3

1.5

4.2

Transport, storage and communication

2.8

1.6

0.8

2.3

4.4

1.6

4.1

1.4

0.7

4.5

5.0

2.9

8.3

8.2

6.3

8.7

9.8

6.8

8.0

8.1

8.0

11.2

12.2

8.3

Financial activities

2.1

1.4

0.7

1.6

5.6

1.2

2.7

1.5

0.5

3.3

3.0

1.9

1.5

0.8

0.5

1.1

3.6

1.2

1.8

1.5

1.2

3.0

1.3

1.0

Education

7.1

20.9

3.3

9.4

13.1

23.0

4.3

6.1

5.3

11.9

14.4

13.0

3.2

5.7

2.6

3.1

5.5

5.8

2.9

2.5

2.8

4.3

3.3

3.9

Health and social work activities

7.4

8.1

2.2

9.1

23.0

7.3

6.1

3.2

2.2

20.5

12.2

7.6

2.3

1.8

1.5

2.8

5.5

2.5

3.3

0.9

1.0

4.9

2.5

1.6

Public administration and defence; compulsory social security

2.9

8.5

1.2

4.5

4.8

6.9

1.5

3.4

1.1

6.9

6.0

3.8

4.5

10.2

2.5

4.8

5.0

13.6

4.5

5.5

2.2

6.8

7.4

7.0

Real estate, business and administrative activities

3.7

1.4

0.8

6.2

12.3

1.5

1.7

2.9

0.9

11.8

6.5

6.3

4.0

2.0

1.7

6.5

14.9

3.3

1.8

3.6

2.7

11.3

7.3

5.7

Other services

7.3

5.3

5.8

17.4

7.2

26.2

5.3

8.7

5.3

8.1

5.0

4.6

3.9

3.2

3.7

4.1

6.3

6.3

4.3

3.6

3.1

3.8

2.8

3.3

W = World; NAf = Northern Africa; SSA = Sub-Saharan Africa; LAC = Latin America and the Caribbean; NAm = Northern America; AS = Arab States; EA = Eastern Asia; SEAP = South-Eastern Asia and the Pacific; SA = Southern Asia; NSWE = Northern, Southern and Western Europe; EE = Eastern Europe; CWA = Central and Western Asia.

Table E8

Occupation share by gender, 2015

Occupations

Emerging countries

Developed countries

Armed forces occupations

0.3

0.0

0.4

0.1

Managers

5.0

2.4

9.5

7.1

Professionals

5.7

9.5

14.1

19.7

Technicians and associate professionals

3.1

4.0

11.2

12.8

Clerical support workers

3.7

4.8

7.1

17.6

Services and sales workers

10.5

16.3

16.4

27.1

Skilled agricultural, forestry and fishery workers

36.3

41.9

3.0

1.4

Craft and related trades workers

17.5

9.1

20.3

3.1

Plant and machine operators and assemblers

6.7

1.9

11.4

2.8

Elementary occupations

10.4

9.7

6.1

8.2

Not elsewhere classified

0.7

0.4

0.4

0.3

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