Women’s labor force participation and gender discrimination in the labor market

Mehmet §engiir

1 Introduction

Every individual needs income to take part in economic and social life. The fact that the type and amount of income is different does not change the obligation of individuals, who are a social entity, to earn income. The most familiar/traditional way of making individual income is to be involved in the production process in any way. Simply, whatever production factor a person has, he or she will get that type of income. At the end of this sharing process, which is also called the functional distribution of income, everyone will have a certain amount of income. Although the working class is at a lower level in terms of getting a share from income, it constitutes an important group within the income group. Therefore, the income earned by individuals has an important role in terms of economic activities. When we consider that income earners constitute a large paid of the population, it is seen that they have a determining fearnre both in temis of individual expenditure and consumption and national total demand.

Wage is a compensation for the work done and it will also be the resource for the expenses to be made and the expenses one wants to make. It is clear that obtaining high wages will also mean more spending or consumption. The amount and type of spending will also vary from person to person. All we have said so far is based on the assumption that individuals have a job and are earning income. But what if individuals do not have a job to work at? Or if there is no income? In this case, it will be very difficult for them to exist in social and economic terms.

Being involved in the labor market is a desirable or must-have situation for people, as it means earning income. Particularly participation of the women in the labor force is extremely important for them and, more importantly, for the society. On average, the population is equal in temis of the number of women and men in most countries worldwide. In this case, the absence of women in the labor force means that half of the population remains idle. In addition, when women are not involved in the labor force, there is a lack of income and subsequent poverty. If a woman is poor, her children will also be a poor as a result. Another problem for women in the labor market is that they do not have equal positions and wages with men even if they enter the market in sufficient numbers.

In this chapter, initially, information is given about the concepts such as labor market, wage, social exclusion and gender inequality. Afterwards, the ratio of participation of women in the labor market and whether the wages earned in the labor market differ by gender are examined for different countries. Finally, factors such as wage and education, which have an impact on the participation of women in the labor force, were analyzed within the framework of the established model and suggestions were offered.

2 Labor market and wage

Wage is a compensation for the work done and it will also be the resource for the expenses to be made and the expenses one wants to make (Case and Fair, 2006). Factor markets are markets where production factors are bought and sold. The labor market is also a marketplace where supply and demand of labor, which is one of the factors of production, exists. Wage can simply be defined as the share that the labor which participates in the labor force takes from production.

Labor markets are also regulated by supply and demand forces like all other markets in the economy (Mankiw, 2012, p. 376). The individual labor supply curve shows that the amount of labor provided by anyone depends on the ratio of wage which that person has earned. As the wages of the workers increase, the amount of labor they offer will increase. Therefore, the relationship between wages and the amount of labor will be positive and upward (Krugman and Wells, 2010).

The labor market labor supply curve is determined based on the decisions of household individuals on labor supply (Parkin, 2012, pp. 422-424). The labor supply curve has a positive slope and will vary depending on how individuals respond to wage changes. If there is an increase in wages, individuals are expected to increase the amount of labor supply. Because people will tiy to buy more goods and services with higher wages. Individuals may prefer to increase their labor supply, i.e. to work or not to work. However, the point that should not be forgotten is that with increasing wages, leisure time becomes a more expensive commodity (Case and Fair, 2006).

It is especially important how an individual member of the labor market will make a choice regarding how many horns a week s/he will work. When we think about this preference, we need to know these three factors very well: the first of these is the selection objects, the second is the elements that restrict the selections and finally the pleasures that affect or direct the selections. You may think of working or leisure time as a choice or selection object. However, individuals will actually have chosen only one at a moment in time and will be faced with one. If a preference has been made for working, the hours to work will have been selected. In fact, the hours left for leisure will also have been determined. If individuals waive their leisure time, they get paid a wage in return as a reward. Income is gaining the ability to purchase consumer goods, but it is the result of a situation where leisure time is also waived (Estrin et al., 2008). Although the labor supply curve is an increasing function of wages and it has a positive slope, it is normal to observe changes in labor supply from time to time. According to Mankiw, the reasons for this change in the amount of labor supply are changes in trends or preferences, changes in alternative opportunities and migration (Mankiw, 2012). If the preference of individuals is to work, labor supply will increase due to the amount of labor. Similarly, increases in the amount of labor in the areas receiving migration and in the sectors the labor force is directed towards as well will be observed.

The labor demand curve shows the firm’s effort to minimize the cost of the amount of labor against changing wages (Besanko and Braeutigam, 2010, p. 266). The labor demand amount of any film is determined at the point where the marginal product value of labor equals the wage ratio. Therefore, the labor demand curve of a firm is obtained from the marginal product value curve (Parkin, 2012, p. 420). The labor market lias a different structure from other markets. Labor force demand is derived or derivative demand. In other words, labor is not an end product offered directly to consumers, but it is used for the production of goods and services. In order to make the demand for labor more understandable, we should focus on companies that want to hire labor in order to produce goods and services and to offer more sales (Mankiw, 2012, p. 376). It is understood here that labor demand is a decreasing function of wages and it also has a negative slope.

The marginal product value of any factor is equal to the value of the additional output obtained from this factor using an additional unit. Therefore, the marginal product value of labor will also be equal to the output value obtained by the use of an additional unit of labor. In this case, the marginal product value (VMPL) of labor will be equal to the multiplication of the unit output price (P) and the marginal product (MPL) of labor (Krugman and Wells, 2010, p. 514).

If the marginal product value of labor is greater than the cost of a worker, that is the wage, films will be demanding for labor. Otherwise, if the cost of labor demand is higher than its contribution to additional output, labor will not be demanded.

A number of factors affect the demand for labor. The main factors can be listed as changes in technology, changes in output prices, changes in the supply of other factors. If technological change is oriented towards increasing labor productivity, demand for labor will increase. However, if the technology changes mostly in the direction of robotic production, it will lead to a decrease in labor demand. As the price of the goods produced by the films increases, the film will want to produce more and the demand for labor will shift to the right. Similarly, if the price of the produced goods decreases, the demand for labor decreases and shifts to the left. As the supply or price of other factors involved in production changes, it will also change the demand for labor, depending on the amount of labor and the degree of substitution (Mankiw, 2012; Parkin, 2012; Krugman and Wells, 2010).

The market labor demand is derived by considering the labor demands of the companies. Market labor demand consists of the sum of all the amount of labor demanded by all films in the market at each wage level (Parkin, 2012). Wages in a competitive labor market are equal to the marginal product value of labor. It is also adjusted to balance labor supply and demand. In this case, the balance wage level in the labor market is determined at the point where labor supply and demand are equal (Mankiw, 2012, p. 384).

At the point where the market labor supply and labor demand are equal, the balance wage level has been determined. If there is an increase in the level of equilibrium wage, labor supply, that is, the number of those who want to work, will increase. However, firms will not assume a positive attitude towards demanding additional labor at high wages, and even a decrease in labor demand will occur. In this case, unemployment will occur because the labor supply will be more than the demand for labor. On the contrary, if the market balance wage decreases, the number of people who want to work at low wages will decrease and the labor supply will shift to the left. Labor demand will increase at low wage level, but there will not be enough labor to meet this demand. In such a situation, there will be a lack of labor supply or extra labor demand in the market.

3 Participation of women in the labor market, social exclusion, gender inequality and poverty'

Poverty is a situation where individuals camiot meet minimum consumption when viewed from a monetary perspective. Poverty can be defined as the deprivation of welfare in the society or it can have different definitions, such as poverty of home, food, health and education (Haughton and Khandker, 2009). In such a situation, measurement of it is commonly made on the basis of comparing the resources required for the consumption bundle. In this comparison, if the individual or household does not have enough resources to meet their basic needs, they will be defined as poor (Foster, 1998, p. 335). Poverty is basically associated with the inability to have enough income to sustain life, which in absolute terms defines poverty. Besides absolute poverty, the concept of relative poverty, which provides information about the proximity to the average welfare level in society, is also important.

Relative poverty provides information about the distribution of income for people in different income groups. On the other hand, relative poverty only shows whether an individual is poor and the numerical size of the poor (Khusro, 1999, p. 51). For us, the point here is whether women are in poverty rather than whether the society is in poverty as a whole.

The concept of women’s poverty was used for the first time in America in 1978. Pearce introduced the phenomenon of women’s impoverishment between 1950 and 1970 based on the fact that women living in America constitute 75% of the total poor (Pearce, 1978). Now, “feminization of poverty” has been added to the concepts of poverty and it has been revealed that women suffer most from poverty in the society.

It would not be conect to address women’s poverty in a single dimension. In addition to the inequality and discrimination they face in the labor markets, women are pushed to the secondary position in comparison to men in many social fields due to structural inequality in society (Bradshaw and Linneker, 2003, p. 9).

The most determining reason why women fall into poverty is inequality in the labor markets. In addition to the unequal wage in the labor markets, shouldering responsibilities such as marriage, maternity, divorce and financial weakness also impoverish women (Goldberg and Kremen, 1990, pp. 4-7).

According to Buvinic (1997), women attribute poverty to two main reasons: discrimination in the labor market and inability to access adequate educational opportunities. Another concept that needs to be addressed along with women’s poverty is social exclusion. Because it would be wrong to think that women are only unequal or devoid in terms of income. Feminization of poverty covers many economic and social fields, materially and morally.

The concept of social exclusion was used to explain the social transformation in France in the 1980s. The concept of “social exclusion,” first used by sociologists, has become widespread over time and has attracted the attention of all social sciences. Social exclusion generally refers to the weakness of a person or group in society in terms of economic and social participation (Silver, 1994). With the advent of the concept of social exclusion, it was also observed that women were the most affected by this situation. It is clear that women will feel the negative effects of social exclusion in an environment where poverty is feminized. As the concept of social exclusion has become widespread, the situation has staited to be expressed as mass deprivation and poverty instead of on an individual level.

As a result of the new forms of poverty and exclusion, it is seen that deep distinctions occur between the lowest groups, which experience mass weakness, and the highest groups. The most important negative aspect of this situation is the polarization between the living groups in the society. With technology and new types of production, the current unemployment ratio will also increase rapidly. When this situation is considered, the most important problem will be whether integration with the large excluded and weak population in the society will be ensured (Bhalla and Lapeyre, 2004, pp. 4-5). Countries tend to support the weakest segments in society to avoid social and economic crises. This support will be in terms of social and economic empowerment.

Empowerment can basically be defined as having personal power, being able to make and implement individual choices, having a dignified life atrd campaigning for fundamental rights (Narayan et al., 2002, p. 13). Empowering women will also be an important political topic, as they experience the most inequality in the social and economic spheres in society. Therefore, our study is based on the participation of women in the labor force, which is among the rrrain reasons for women to fall into poverty atrd social exclusion.

3.1 Indicators of participation of women in the labor force, wages and gender inequality’

In this section, the labor force participation ratio of women, wages and the status of various gender-based parameters for development in the largest developing countries are examined. Tables and graphs are created in line with the data obtained from World Bank (WB). These countries are Argentina, Australia, Bangladesh, Brazil, Canada, China, Egypt, France, Germany, India, Indonesia, Italy, Japan, South Korea, Mexico, Pakistan, Philippines, Russian Federation, South Africa, Turkey, United States and Vietnam. For these countries, whose data are available, the participation ratio of women; whether they are self-employed or trot; employment status in different sectors such as agriculture, industry and services; and

Argentina

Australia

Bangladesh

Brazil

Canada

2000

50.38

54.62

26.52

50.27

58.65

2001

50.00

55.19

26.77

51.28

58.95

2002

48.82

55.40

27.03

52.63

60.13

2003

49.43

56.05

27.28

52.95

61.14

2004

50.06

55.80

27.54

53.99

61.26

2005

49.42

57.05

27.80

55.12

60.93

2006

49.94

57.61

27.92

54.83

61.20

2007

49.02

58.20

28.40

54.59

61.89

2008

48.12

58.66

28.89

54.49

61.99

2009

48.82

58.82

29.38

54.97

61.87

2010

47.83

58.71

29.88

53.58

61.86

2011

48.10

58.95

30.37

52.17

61.60

2012

48.19

58.83

30.86

53.08

61.61

2013

47.79

58.71

31.36

52.98

61.68

2014

47.16

58.65

31.87

52.86

61.09

2015

47.83

59.07

32.38

53.38

60.77

2016

48.48

59.25

32.89

53.32

60.81

2017

49.12

59.69

35.85

54.17

61.02

2018

48.97

59.65

36.00

54.03

60.85

Source: Data from database: Gender Statistics (World Bank-modeled ILO estimate)

schooling ratio according to gender equality index are presented by creating tables with data obtained from the World Bank (World Bank, 2019a, 2019b).

When we look at Table 2.1, we see the labor force participation ratio of women in Argentina, Australia, Bangladesh, Brazil and Canada for the period 2000-2018. While the labor force participation ratio of women in Argentina was around 50.3% in 2000, this ratio dropped to 48.9% in 2018. In Australia, participation of women in the labor force was 54% and above in this 19-year period. While the participation ratio of women in the labor force in Brazil was 50.2% in 2000, it increased to 60.8% in 2018. In Canada, participation ratio of women is, from the year of 2000 to 2018, increased from 58.6% to 60.8%. Among these five countries, the labor force participation ratio of women in Argentina decreased by approximately 1.5% in 19 years. Unlike Argentina, there has been an increase in the labor force participation ratio of women in four other countries. For the 2010-2018 period, Bangladesh was the country that increased its participation of labor force ratio the most among the five countries - by approximately 10%. However, despite this 10% increase, the labor force participation ratio of women in Bangladesh is as low as 36% by the year of 2018 (Table 2.1).

The labor force participation ratio of women in China is extremely high. Women were involved in the labor force at 71% in 2000; however, with a decrease of almost 10%, it went down to 61% in 2018. Although labor force participation of women decreased in China in 2018, it is almost three times that of India and Egypt. Even though labor force participation of women in Egypt increased by 3% in the 19-year pexiod, it was only 22.8% in 2018. Similarly, for India, the labor force participation ratio of women are quite low, even less than in Egypt. On the contrary, the labor force paxticipation ratio of women in India decreased by around 7% from 2000 to 2018. In France and Genxxany, between 2000 and 2018, the labor force participation ratios of women were on average 50% aixd above. In Germany, the labor force participation ratio of women was 55.2%, with an iixcrease of approximately 6% in 19 years by 2018 and 50.3% in France (Table 2.2).

Table 2.3 shows the paxticipation ratio of women in the labor force in Iixdonesia, Italy, Japan and Soxxth Korea in the period 2000-2018. The labor force participation ratio of women in Indonesia is quite high conxpared to some coxxntries in Exxrope, such as Italy. Despite the occasional decreases, the labor force participation ratio of women in Indonesia in 2018 increased to 52.2% from 51.6% in 2000 (Table 2.3).

When we look at the participation of women in the labor force in Mexico, it is observed that it increased from 38.8% hr 2000 to 43.7% in 2018. This ratio, which was 47.7% in the Philippines in 2000, decreased to 45.7% at the end of 19 years. The labor force participation ratio of women in Pakistan is very' low (Table 2.4).

Table 2.2 Labor force participation rate for female, 2000-2018, (15+)

China

Egypt

France

Germany

India

2000

71.01

19.91

48.60

49.06

30.38

2001

70.20

20.08

48.35

49.36

30.72

2002

69.33

18.84

48.57

49.63

31.07

2003

68.42

19.25

49.81

49.99

31.43

2004

67.55

19.72

49.69

49.81

31.80

2005

66.77

20.23

50.12

50.70

32.17

2006

66.07

20.79

50.16

51.49

30.81

2007

65.46

22.75

50.51

52.01

29.50

2008

64.92

21.50

50.69

52.13

28.23

2009

64.38

22.69

50.97

52.53

27.01

2010

63.78

22.63

50.92

52.79

25.83

2011

63.57

22.04

50.77

53.66

24.39

2012

63.40

22.50

50.93

53.70

23.02

2013

63.18

23.39

51.08

54.31

23.19

2014

62.93

23.69

50.83

54.51

23.35

2015

62.61

22.55

50.73

54.61

23.50

2016

62.23

22.85

50.68

54.96

23.66

2017

61.84

22.73

50.44

55.21

23.80

2018

61.26

22.83

50.34

55.25

23.60

Source: Data from database: Gender Statistics (World Bank-modeled ILO estimate)

Indonesia

Italy

Japan

South Korea

2000

51.06

35.44

49.26

48.82

2001

49.24

36.02

49.21

49.29

2002

47.16

36.56

48.61

49.79

2003

47.09

37.02

48.50

49.07

2004

46.56

38.26

48.30

50.10

2005

45.51

37.86

48.37

50.39

2006

45.49

37.97

48.44

50.60

2007

49.48

37.75

48.55

50.51

2008

50.23

38.39

48.60

50.19

2009

50.36

37.90

48.81

49.24

2010

51.12

37.78

48.72

49.49

2011

51.05

37.94

48.35

49.71

2012

51.60

39.16

48.29

50.02

2013

50.88

39.03

49.03

50.29

2014

50.77

39.40

49.47

51.45

2015

50.76

39.00

49.85

51.90

2016

50.92

39.64

50.59

52.24

2017

52.17

40.10

51.41

52.74

2018

52.24

40.00

51.39

52.75

Source: Data from database: Gender Statistics (World Bank-modeled ILO estimate)

Table 2.4 Labor force participation rate for female, 2000-2018, (15+)

Mexico

Philippines

Pakistan

Russian

2000

38.88

47.74

16.06

54.69

2001

38.18

47.71

15.95

53.54

2002

38.92

47.57

16.08

54.54

2003

38.45

47.04

16.95

54.29

2004

40.65

46.72

17.87

54.75

2005

41.32

47.55

18.37

55.22

2006

42.51

47.97

18.92

55.58

2007

42.91

48.04

19.21

56.17

2008

42.78

47.53

19.67

56.09

2009

43.32

48.30

20.74

56.25

2010

43.16

48.49

21.74

55.92

2011

43.49

49.26

22.09

56.15

2012

44.55

48.84

22.35

56.08

2013

44.55

48.78

22.62

55.78

2014

43.55

49.56

22.96

55.73

Mexico

Philippines

Pakistan

Russian

2015

43.89

49.07

23.92

55.58

2016

43.95

48.62

23.81

55.72

2017

43.66

45.51

23.70

55.30

2018

43.78

45.71

23.90

54.91

Source: Data from database: Gender Statistics (World Bank-modeled ILO estimate)

Table 2.5 Labor force participation rate for female, 2000-2018, (15+)

South Africa

Turkey’

United States

Vietnam

2000

49.02

26.29

59.03

72.05

2001

49.42

26.83

58.81

72.00

2002

49.11

27.55

58.59

71.95

2003

47.20

26.21

58.46

71.88

2004

45.08

23.38

58.15

71.78

2005

46.97

23.30

58.24

71.67

2006

48.35

23.05

58.34

71.58

2007

47.38

23.11

58.26

71.48

2008

47.92

23.99

58.46

71.43

2009

46.29

25.48

58.14

71.40

2010

44.73

27.03

57.50

71.31

2011

45.20

28.32

56.93

71.46

2012

45.42

28.88

56.70

71.97

2013

46.39

30.10

56.29

72.90

2014

46.61

30.24

56.11

73.19

2015

47.80

31.48

55.83

72.95

2016

47.80

32.51

55.99

72.64

2017

48.92

33.57

56.31

72.70

2018

48.85

33.52

56.05

72.65

Source: Data from database: Gender Statistics (World Bank-modeled ILO estimate)

Table 2.5 shows the participation ratio of women in labor force in South Africa, Turkey, the USA and Vietnam between the years 2000 and 2018. Vietnam is the country with the highest labor force participation ratio of women among these four countries. Almost three-quarters of women are involved in the labor force in Vietnam. Turkey, among these four countries, has the lowest ratio of labor force participation of women. In Turkey, the labor force participation ratio of women was 33.5%, with the increase of 7% in this period. The 7% increase is a relatively positive development, but it is quite a low labor force participation ratio compared to other countries (Table 2.5). For the period of 2000-2018, the labor force paxticipation data of women from 22 countries are listed alphabetically. Country data in alphabetical order is presented in Table 2.1 to Table 2.5.

Paxticipation of women in the labor force is very crucial both individually and socially. There are many factors that affect labor force participation. These factors can affect the entire labor force. However, sonxe factors especially affect labor force participation of women. Among the factors affecting labor force participation, dozens of factors such as development level of countries, industrialization, capital accumulation of countries, marginal productivity of labor, technology-based production, education, traditions, age, gender, taxes, sxxbsidies, price of manufactured products can be listed. When the level of economic aixd social development is low in a country, women’s labor force participation rates are also decreasing. Women caxmot take part in the labor nxarket, as job opportunities will be very' limited in countries where industrialization lags behind. The stronger the capital structure of the country’s econonxies, the easier it will be to finance investments. As a result of easier investment, job opportunities for individuals will also increase. Similarly, government subsidies and tax cuts to investors accelerate women’s participation in the labor market, because investors will either increase their existing investments or make new investments with these supports. The increase in the education level of women also increases the productivity of labor, and women can get jobs easier. Apart from these, the sexist viewpoint and traditions in some societies prevent women’s participation in the labor market. Women’s labor force participation rates are decreasing as a resxxlt of the thought that put men at the first position and employs women only in household responsibilities.

However, factors such as gender, traditions, nxindset in the country, the sector which is worked in and the level of education affect women more and negatively compared to men. Some of the variables that affect the labor force participation of women are presented in the following tables, with data obtained from WB.

Employment numbers of wonxen in the agricultural sector in the period 2000- 2009 are shown in Table 2.6. In Argentina and the USA, the share of women employed in agxicxxlture remained below 1% in the ten-year period. While the employment ratio of wonxen in the agricultural sector in the USA was 0.86% in 2000, it decreased to 0.68% in 2009. In Argentina, the enxployment ratio of women in the agricultural sector was 0.24% and 0.39% in the same years (Table 2.6).

We can see the course of women employment in the agricultural sector in Table 2.7 for the period after 2009. Accordixxg to 2010 data, Pakistan is the couixtry with the highest women employment ratio in the agricultural sector.

When we look at the women employment ratio in industry, which is another important sector in the economy, we see that it is extremely low conxpared to the agricultural sector. The country with the highest share of women enxployment in the ixxdustxy sector was China, with a ratio of 26.5% in 2000. After China are countries such as Russia, Mexico, Japan and Italy, with the approxinxate ratios of 21-22%. The coxmtries with the lowest share of women enxployed in industry were Egypt aixd Bangladesh in 2000. At the end of the next nine years after 2000, the women employnxent ratio in the industrial sector decreased in countries other than Vietnam, Tiukey, India, China aixd Bangladesh. One of the reasons for these

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Argentina

0.24

0.30

0.47

0.97

0.97

0.72

0.49

0.42

0.35

0.39

Australia

3.43

3.45

3.03

2.62

2.68

2.45

2.37

2.29

2.22

2.22

Bangladesh

78.78

77.44

75.96

74.28

72.48

70.49

68.19

67.52

66.86

66.25

Brazil

14.41

15.48

15.94

15.58

15.66

15.66

14.75

13.79

13.03

12.15

Canada

1.65

1.34

1.36

1.32

1.29

1.35

1.38

1.31

1.21

1.19

China

46.94

46.94

46.94

46.02

43.82

41.73

39.54

37.77

36.59

35.13

Egypt

39.41

31.92

27.57

39.07

46.78

46.66

43.33

46.56

45.62

46.06

France

2.84

2.82

2.78

2.68

2.64

2.28

2.22

2.14

1.83

1.88

Germany

2.11

2.07

1.96

1.83

1.70

1.73

i.6i

1.61

1.32

1.25

India

74.38

73.90

73.43

72.82

72.11

71.43

70.40

69.23

68.21

66.98

Indonesia

46.74

44.52

45.40

47.54

44.62

44.38

41.13

41.40

41.36

39.94

Italy

4.35

4.37

3.93

3.69

3.16

3.26

3.32

3.06

2.83

2.66

Japan

5.55

5.28

4.96

4.90

4.71

4.57

4.44

4.30

4.25

4.11

South Korea

12.25

11.31

10.67

10.14

9.25

8.93

8.64

8.27

7.93

7.51

Mexico

6.56

5.80

5.93

5.00

5.31

4.82

4.54

4.49

4.32

3.83

Philippines

24.05

24.31

24.63

24.24

23.75

23.49

23.15

22.74

22.37

22.12

Pakistan

65.04

64.91

64.71

64.41

65.92

67.25

69.11

72.75

75.34

72.88

Russian

Federation

11.72

9.32

8.94

8.16

7.71

7.91

7.64

6.82

6.60

6.40

South Africa

8.46

7.14

8.43

7.22

7.08

5.38

5.41

5.11

4.19

3.63

Turkey

64.72

62.00

59.14

55.73

50.74

47.36

43.54

42.60

40.11

37.79

United States

0.86

0.84

0.83

0.81

0.78

0.75

0.74

0.66

0.68

0.68

Vietnam

66.28

64.97

63.13

61.71

59.99

56.96

53.78

51.48

50.82

50.02

Source: Data from database: Gender Statistics (World Bank-(World Bank-modeled ILO estimate)

Table 2.7 Employment in agriculture, female percentage (2010-2018)

2010

2011

2012

2013

2014

2015

2016

2017

2018

Argentina

0.38

0.28

0.28

0.22

0.15

0.07

0.03

0.01

0.01

Australia

2.28

1.89

1.92

1.67

1.83

1.85

1.69

1.65

1.63

Bangladesh

65.53

65.32

65.02

64.73

64.36

63.86

63.38

59.84

59.43

Brazil

11.57

11.01

5.90

5.67

5.21

5.05

4.76

4.29

4.23

Canada

1.11

1.10

1.11

1.09

1.05

1.01

1.14

0.96

0.95

China

33.75

31.91

30.75

28.64

26.83

25.97

25.12

24.44

24.24

Egypt

42.87

43.37

37.59

42.87

43.22

40.21

38.40

36.88

36.66

France

1.76

1.87

1.83

1.84

1.64

1.64

1.62

1.58

1.56

Germany

1.20

1.18

1.10

1.01

1.01

0.96

0.89

0.88

0.87

India

65.61

62.80

59.97

59.58

59.09

58.60

58.16

57.63

57.06

Indonesia

38.40

36.79

35.26

33.91

33.37

32.61

30.07

28.82

28.53

Table 2.7 (Continued)

2010

2011

2012

2013

2014

2015

2016

2017

201S

Italy

2.71

2.63

2.57

2.43

2.40

2.43

2.51

2.35

2.31

Japan

3.93

3.72

3.54

3.38

3.30

3.22

3.05

2.99

2.96

South Korea

6.88

6.75

6.32

6.11

5.63

5.10

4.72

4.47

4.41

Mexico

3.86

3.90

4.10

3.86

3.69

3.66

3.57

3.74

3.69

Philippines

21.75

21.40

20.93

20.11

20.19

18.69

17.30

15.32

15.15

Pakistan

74.08

74.45

74.82

75.15

73.19

72.06

73.14

72.96

72.72

Russian Federation

5.56

5.68

5.41

5.40

5.15

5.11

5.01

4.03

3.99

South Africa

3.78

3.45

3.60

3.49

3.20

4.24

3.89

3.72

3.66

Turkey

39.20

39.27

37.10

35.56

32.77

31.00

28.59

28.20

27.89

United States

0.74

0.77

0.76

0.67

0.70

0.74

0.77

0.77

0.75

Vietnam

51.19

50.95

49.51

48.78

48.13

45.45

43.49

41.47

41.11

Source: Data from database: Gender Statistics (World Bank-(World Bank-modeled ILO estimate)

decreases in the employment ratio of women in the industrial sector is the global financial crisis in 2008 (Table 2.8).

China, Vietnam, Russia, India and Mexico are the countries with the highest women employment share in the industry sector. The country with the lowest share of women employment in the industry sector is Egypt with 5.9%. Egypt’s women employment share in the industry sector is at a low level of 6.8%, even by 2018. As in the previous decade, China has the highest women employment ratio for this nine-year peiiod (Table 2.9).

The share of women employed in the sendees sector is quite high compared to other sectors. With a ratio of 12.5%, Bangladesh is the country with the lowest women employment ratio in the service industry in 2000. It is seen that India is in second place and Pakistan is in third place after Bangladesh (Table 2.10).

The countries with the highest share of women employed in the sendee industry in 2010 are USA, Canada, Argentina, Australia and France. On the contrary, the countries with the lowest women employment ratio in the sendee industry are Pakistan, India and Bangladesh (Table 2.11).

There are many factors that affect women taking part in labor market or economic activities. Although some of these factors are about the market and its components, others are about social structure and institutions. Women working for someone else or being self-employed are possible determinant circumstances. The ratios of self-employed women in 2000 and 2018 are presented in Table 2.12 and Table 2.13.

The countries with the highest ratio of self-employed women are India, Indonesia and Vietnam in 2000. The ratio of self-employed women is around 56.5% in India, 31.5% in Indonesia and 30% in Vietnam. Countries with the lowest ratio of self-employed women are Germany, France and the USA, with an approximate ratio of 4% in 2000. In 2009, although there is a decrease in the ratio of self- employed women, India is in first place with a ratio of 53%, as it was ten years ago (Table 2.12).

Table 2.S Employment in industry, female percentage (2000-2009)

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Argentina

10.32

10.42

9.40

11.17

10.79

10.60

10.60

10.37

10.17

9.62

Australia

10.23

9.51

9.53

9.54

9.36

9.31

9.50

9.32

9.31

8.90

Bangladesh

8.65

9.01

9.51

10.11

10.83

11.65

12.50

12.62

12.75

12.89

Brazil

13.11

12.68

12.49

12.58

12.71

13.06

12.81

12.98

13.36

12.99

Canada

11.45

11.30

11.50

11.33

11.32

10.80

10.46

10.21

9.71

8.94

China

26.56

26.17

25.78

25.85

26.53

27.15

27.77

28.09

28.22

28.37

Egypt

6.86

10.13

10.36

6.16

5.78

4.91

6.01

6.15

5.54

5.12

France

13.96

13.85

13.25

12.30

12.41

11.97

11.61

11.43

10.85

10.20

Germany

18.28

17.88

17.62

16.92

17.01

16.02

16.01

15.95

15.01

14.56

India

11.66

12.07

12.52

13.01

13.61

14.13

14.68

15.34

15.99

16.72

Indonesia

14.97

16.12

15.78

15.16

14.21

15.91

15.36

15.03

14.58

14.42

Italy

20.53

20.44

20.11

19.88

17.74

17.38

16.71

16.48

15.86

14.83

Japan

21.56

20.77

19.74

19.37

18.43

17.41

17.38

17.35

16.55

15.36

South Korea

19.09

18.50

17.99

17.89

17.76

16.58

15.97

15.57

15.18

13.75

Mexico

22.08

21.83

20.51

19.52

19.25

18.85

18.78

18.08

17.44

16.24

Philippines

13.28

12.51

11.78

11.60

11.40

11.23

11.05

10.86

10.74

10.61

Pakistan

15.71

15.69

15.66

15.65

15.37

15.06

14.87

12.47

12.02

12.58

Russian

Federation

22.30

23.26

23.01

22.19

21.60

21.05

20.54

20.13

19.14

17.90

South Africa

15.29

16.38

17.52

16.88

17.23

16.85

16.90

16.13

13.13

13.13

Turkey

11.60

12.27

13.86

14.63

16.00

16.12

16.43

16.16

15.84

15.33

United States

12.71

11.86

10.98

10.78

10.46

10.24

10.06

10.05

9.60

8.68

Vietnam

10.12

11.15

11.70

12.97

13.73

14.79

15.91

16.09

16.52

16.98

Source: Data from database: Gender Statistics (World Bank-(World Bank-modeled ILO estimate)

Table 2.9 Employment in industry, female percentage (2010-2018)

2010

2011

2012

2013

2014

2015

2016

2017

2018

Argentina

9.38

9.95

9.46

8.92

8.33

8.59

8.86

9.11

8.98

Australia

8.61

8.53

8.36

8.30

8.32

7.83

8.09

7.62

7.58

Bangladesh

13.05

13.49

13.94

14.42

14.90

15.41

15.94

16.78

16.74

Brazil

12.27

11.58

12.62

12.22

11.94

11.66

10.77

10.68

10.58

Canada

8.54

8.97

9.00

8.73

8.60

8.55

8.53

8.49

8.40

China

28.59

29.04

29.06

29.41

29.72

29.32

29.14

28.79

28.27

Egypt

5.93

5.09

5.51

5.06

5.02

5.62

6.21

6.85

6.81

France

10.06

10.44

10.16

10.03

9.57

9.30

9.17

9.79

9.65

Germany

14.27

14.19

14.16

14.01

14.17

13.87

13.81

13.87

13.63

India

17.57

18.25

18.77

18.75

18.72

18.67

18.62

18.69

18.75

Indonesia

14.94

15.06

15.77

15.73

15.42

15.76

15.99

16.66

16.49

Table 2.9 (Continued)

2010

2011

2012

2013

2014

2015

2016

2017

201S

Italy

14.10

14.23

13.80

13.69

13.34

13.23

12.90

12.76

12.60

Japan

14.94

14.68

14.71

14.68

14.71

14.77

14.55

14.15

14.01

South Korea

14.35

14.39

13.97

13.60

13.73

13.77

13.34

13.71

13.54

Mexico

16.18

16.02

15.94

16.26

16.59

16.74

17.09

17.52

17.36

Philippines

10.43

10.27

10.07

10.13

10.09

10.01

9.94

9.89

9.78

Pakistan

11.52

11.43

11.29

11.14

12.96

14.52

14.11

14.07

14.05

Russian

Federation

17.80

17.20

17.18

16.96

16.59

16.13

16.06

15.77

15.69

South Africa

13.04

12.97

12.28

12.43

12.06

11.76

11.94

12.24

12.14

Turkey

15.95

15.24

14.95

15.39

17.18

16.25

15.98

15.67

15.49

United States

8.46

8.73

8.78

8.83

8.85

8.84

8.70

8.71

8.49

Vietnam

17.26

16.56

16.84

16.96

17.50

19.32

20.77

21.81

21.66

Source: Data from database: Gender Statistics (World Bank-(World Bank-modeled ILO estimate)

Table 2.10 Employment in services, female percentage (2000-2009)

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Argentina

89.44

89.28

90.13

87.85

88.25

88.68

88.91

89.21

89.49

89.99

Australia

86.34

87.04

87.44

87.84

87.96

88.24

88.14

88.39

88.47

88.89

Bangladesh

12.57

13.55

14.53

15.61

16.69

17.86

19.31

19.87

20.39

20.86

Brazil

72.48

71.84

71.57

71.84

71.63

71.27

72.44

73.23

73.61

74.87

Canada

86.90

87.35

87.14

87.35

87.40

87.85

88.15

88.49

89.08

89.87

China

26.49

26.89

27.28

28.13

29.65

31.12

32.68

34.14

35.19

36.50

Egypt

53.73

57.95

62.08

54.77

47.44

48.43

50.66

47.29

48.84

48.81

France

83.20

83.33

83.98

85.02

84.95

85.75

86.16

86.43

87.32

87.92

Germany

79.61

80.05

80.42

81.25

81.29

82.25

82.38

82.43

83.67

84.19

India

13.96

14.02

14.05

14.17

14.28

14.44

14.92

15.42

15.80

16.30

Indonesia

38.30

39.36

38.82

37.30

41.17

39.71

43.52

43.57

44.06

45.65

Italy

75.12

75.19

75.96

76.43

79.10

79.36

79.97

80.46

81.31

82.51

Japan

72.89

73.95

75.30

75.73

76.86

78.01

78.18

78.34

79.20

80.53

South Korea

68.66

70.19

71.34

71.96

72.99

74.49

75.39

76.16

76.89

78.74

Mexico

71.36

72.37

73.56

75.47

75.44

76.33

76.67

77.42

78.24

79.93

Philippines

62.67

63.17

63.59

64.16

64.84

65.28

65.80

66.40

66.89

67.27

Pakistan

19.25

19.40

19.64

19.94

18.70

17.70

16.02

14.78

12.64

14.54

Russian

Federation

65.98

67.42

68.05

69.64

70.69

71.05

71.82

73.05

74.26

75.71

South Africa

76.25

76.49

74.06

75.90

75.69

77.76

77.69

78.76

82.68

83.24

Turkey

23.68

25.73

27.00

29.64

33.26

36.52

40.03

41.24

44.05

46.88

United States

86.43

87.30

88.19

88.41

88.76

89.00

89.21

89.29

89.72

90.65

Vietnam

23.60

23.88

25.16

25.32

26.28

28.25

30.31

32.43

32.66

33.00

2010

2011

2012

2013

2014

2015

2016

2017

201S

Argentina

90.24

89.76

90.25

90.86

91.52

91.33

91.11

90.87

91.01

Australia

89.10

89.58

89.73

90.04

89.86

90.32

90.23

90.72

90.79

Bangladesh

21.42

21.20

21.04

20.85

20.74

20.73

20.68

23.38

23.83

Brazil

76.17

77.41

81.48

82.12

82.85

83.29

84.48

85.03

85.19

Canada

90.35

89.93

89.89

90.19

90.34

90.44

90.33

90.55

90.65

China

37.66

39.05

40.19

41.95

43.46

44.71

45.74

46.77

47.50

Egypt

51.21

51.54

56.90

52.07

51.76

54.17

55.39

56.26

56.53

France

88.18

87.69

88.00

88.13

88.78

89.07

89.21

88.63

88.79

Germany

84.54

84.63

84.74

84.98

84.82

85.18

85.30

85.25

85.50

India

16.82

18.95

21.26

21.68

22.19

22.74

23.22

23.69

24.19

Indonesia

46.65

48.15

48.96

50.36

51.21

51.63

53.94

54.52

54.98

Italy

83.19

83.14

83.63

83.88

84.26

84.34

84.59

84.89

85.08

Japan

81.14

81.60

81.76

81.94

81.99

82.00

82.40

82.86

83.04

South Korea

78.77

78.86

79.70

80.30

80.64

81.13

81.94

81.82

82.04

Mexico

79.96

80.08

79.97

79.88

79.72

79.61

79.34

78.74

78.94

Philippines

67.82

68.33

69.00

69.76

69.72

71.31

72.76

74.78

75.07

Pakistan

14.41

14.12

13.89

13.72

13.85

13.42

12.75

12.97

13.22

Russian

Federation

76.64

77.11

77.42

77.64

78.25

78.76

78.92

80.19

80.32

South Africa

83.18

83.58

84.12

84.08

84.74

84.00

84.18

84.04

84.20

Turkey

44.85

45.50

47.95

49.05

50.05

52.75

55.43

56.13

56.62

United States

90.81

90.50

90.46

90.50

90.45

90.42

90.52

90.53

90.75

Vietnam

31.55

32.49

33.65

34.25

34.37

35.24

35.74

36.71

37.24

Source: Data from database: Gender Statistics (World Bank-(World Bank-modeled ILO estimate)

Table 2.12 Own-account workers, female percentage (2000-2009)

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Argentina

18.79

19.65

18.39

16.57

16.61

16.58

15.80

15.06

15.12

15.70

Australia

8.08

8.23

8.60

8.37

8.23

8.51

8.09

7.92

8.11

8.15

Bangladesh

15.47

15.52

15.63

15.74

15.74

15.73

15.94

17.48

19.11

20.79

Brazil

21.00

22.94

23.38

23.45

23.17

23.81

23.43

22.99

22.49

22.01

Canada

8.65

7.93

8.25

8.24

8.06

8.22

8.11

8.36

8.38

8.95

Chma

28.67

28.70

28.69

28.56

28.48

28.32

28.18

28.10

28.00

27.97

Egypt

12.25

11.79

6.80

13.37

13.76

13.85

10.86

14.76

10.58

12.73

France

3.87

3.87

3.77

3.95

3.68

3.76

4.18

4.01

3.78

4.09

Germany

3.83

3.73

3.76

4.01

4.23

4.86

4.90

4.94

4.83

4.89

India

56.63

55.36

53.96

52.75

51.38

50.17

51.05

52.02

52.75

53.81

Indonesia

31.49

29.83

32.12

28.34

30.37

27.84

30.53

32.33

33.10

32.41

Italy

14.11

14.17

14.19

14.24

14.31

13.80

13.70

13.47

12.95

12.59

Table 2.12 (Continued)

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Japan

6.75

6.24

5.99

5.86

5.77

5.63

5.41

5.25

5.05

5.07

South Korea

18.16

18.52

18.32

16.76

17.72

17.78

17.59

17.23

16.94

16.07

Mexico

20.76

21.81

22.93

23.89

23.93

22.41

22.30

22.33

22.31

23.43

Philippines

29.90

30.86

30.98

30.74

30.25

30.08

29.79

29.09

28.76

28.38

Pakistan

16.97

16.97

15.89

16.03

15.89

16.03

15.75

14.03

13.61

13.61

Russian

Federation

7.97

6.14

5.95

5.47

5.62

5.95

5.72

5.41

5.35

5.35

South Africa

13.77

20.60

14.15

14.61

13.95

16.30

15.33

13.90

11.44

10.55

Turkey

12.81

13.10

12.68

12.39

10.04

13.59

12.59

11.56

11.09

12.84

United States

3.91

3.86

3.77

3.82

3.80

3.71

3.72

3.63

3.46

3.53

Vietnam

30.29

28.21

29.29

30.76

31.20

34.71

38.15

41.44

44.46

46.94

Source: Data from database: Gender Statistics (World Bank-(World Bank-modeled ILO estimate)

Table 2.13 Own-account workers, female percentage (2010-2018)

2010

2011

2012

2013

2014

2015

2016

2017

2018

Argentina

15.18

14.88

15.22

15.86

16.24

16.83

17.45

18.00

18.00

Australia

8.20

8.08

8.01

7.63

8.00

7.95

8.12

8.11

8.10

Bangladesh

22.77

24.01

25.25

26.47

28.63

30.65

32.49

38.41

38.58

Brazil

21.40

20.90

17.32

17.25

17.18

17.97

18.88

19.58

19.56

Canada

8.70

8.56

8.72

9.02

8.93

9.07

8.97

9.11

9.09

China

27.82

27.72

27.70

27.69

27.66

27.71

27.69

27.59

27.50

Egypt

15.56

13.93

13.43

11.17

9.95

6.30

8.33

8.61

8.68

France

4.61

4.65

4.71

4.82

5.39

5.32

5.46

5.72

5.70

Germany

4.96

5.11

5.08

4.90

4.81

4.66

4.67

4.63

4.63

India

54.89

52.94

50.89

51.38

51.92

52.50

53.04

53.52

53.97

Indonesia

30.63

29.11

27.50

27.73

28.44

28.42

30.23

31.31

31.54

Italy

12.61

12.36

12.49

12.13

12.73

12.35

12.27

11.63

11.63

Japan

4.94

4.76

4.36

4.23

4.34

4.08

3.92

3.88

3.94

South Korea

15.33

14.83

14.55

14.18

13.78

13.49

13.26

13.53

13.70

Mexico

22.73

22.97

23.13

23.21

22.50

22.90

23.05

22.23

22.20

Philippines

28.17

27.80

27.23

27.25

27.25

27.51

27.72

28.58

28.57

Pakistan

14.06

16.08

15.79

15.55

19.23

21.06

19.84

19.96

20.17

Russian

Federation

4.63

5.04

4.80

5.13

4.96

5.03

5.12

4.23

4.22

South Africa

10.57

10.16

9.62

9.40

8.49

8.99

8.97

9.47

9.46

Turkey

12.90

11.79

10.89

10.83

9.20

8.93

8.91

9.50

9.55

United States

3.48

3.42

3.48

3.47

3.34

3.28

3.22

3.30

3.27

Vietnam

43.47

44.53

46.01

46.43

41.61

40.58

40.28

39.73

39.83

When we consider the self-employed women ratio in 2010, India, Vietnam and Indonesia are the top countries. Countries with the lowest ratio of self-employed women are the USA, Russia, Japan, France and Germany. The results here are largely similar to those between 2000 and 2009. Bangladesh reached a rate of 38.5% in 2018 and became the country with the highest increase, with a change of about 16% between 2009 and 2018. After Bangladesh, Pakistan became the second country, with a 6% increase in the proportion of self-employed women (Table 2.13).

The high number of self-employed women does not indicate that women have more say in the national economy by itself. There can be many reasons behind this. In countries such as the USA, France and Germany where the self-employed women ratio is low, it is difficult to say that they are less visible. In fact, it is likely to see self-employed women more in countries such as Pakistan and India due to reasons such as security, traditional roles and some other reasons.

The gender equality index value is obtained as a result of comparing the schooling rates of female students with those of male students at any educational level in the academic year and proportioning them. When the calculated index value is “1,” it is understood that there is no gender difference in schooling rates, which means equality is provided. If the index value is less than 1, there is inequality in favor of male students; if it is greater than 1, the inequality is in favor of female students (www.tuik.gov.tr). When we look at Table 2.14, Pakistan is the country

Table 2.14 Gender parity index of the gross enrolment ratio in primary and secondary education (2009-2017)

2009

2010

2011

2012

2013

2014

2015

2016

2017

Argentina

1.05

1.04

1.04

1.03

1.03

1.03

1.03

1.02

1.02

Brazil

1.04

1.03

1.03

1.03

1.03

1.02

1.02

1.00

Canada

0.99

0.99

0.99

1.00

1.01

1.01

1.01

1.01

1.01

China

1.00

0.99

0.99

1.00

1.01

1.01

1.01

1.01

1.01

Egypt

0.98

0.97

0.97

0.99

0.99

1.00

0.99

0.99

France

1.00

1.00

1.00

1.00

1.01

1.01

1.00

1.00

1.00

Germany

0.96

0.96

0.96

0.96

0.96

0.97

0.97

0.96

0.96

India

0.99

0.99

1.00

1.00

1.07

1.07

1.07

1.10

1.08

Indonesia

0.98

1.02

1.02

1.01

0.98

0.98

0.99

1.00

0.99

Italy

0.99

0.99

0.99

0.99

0.99

0.99

1.00

1.00

0.98

South Korea

1.00

1.00

1.00

1.00

1.00

1.00

1.00

0.99

1.00

Mexico

1.02

1.03

1.02

1.02

1.02

1.02

1.03

1.04

1.04

Philippines

1.02

1.02

1.01

1.01

1.02

Pakistan

0.83

0.82

0.82

0.82

0.81

0.83

0.83

0.84

0.85

Russian

Federation

0.99

0.99

0.99

0.99

0.99

0.99

0.99

0.98

South Africa

1.00

1.00

1.00

1.00

1.02

0.96

1.02

1.01

Turkey

0.94

0.95

0.95

0.96

0.98

0.98

0.98

0.98

0.96

United States

1.01

1.00

1.00

0.99

1.00

1.00

1.01

1.00

0.99

with the highest inequality in favor of any gender and after Pakistan, Turkey and Germany follow.

When we look at the ratio of women employment among workers with salaries and wages, the top counties are Russia, the USA, France and Germany. In these countries, the proportion of women with salaries and wages between 2010 and 2018 is 90% or more. In South Africa, Japan, Australia and Canada, it is seen that the ratio of women with salaries and wages is over 85% in the period of 2010 to 2018.

When the data are analyzed in the 2010-2018 period, the countries that had the lowest ratio of women with salaries and wages are India, Bangladesh and Pakistan. Bangladesh, which had a 16.5% ratio of women with salaries and wages in 2000, was the country that showed the highest increase by 2018, with an increase of approximately 15%. Although Bangladesh is the country with the highest rate of increase in nine years, it is still very' low compared to many other countries, with a rate of 31.8% in 2018. Turkey, showing an increase of around 13% from 2000 to 2018, is the second country with the highest increase after Bangladesh. In terms of women with salaries and wages, Vietnam is the third country, with the highest increase, a 10% increase. However, Vietnam’s situation is far behind, as in 2018 compared to other country examples, just like Bangladesh (Table 2.15).

Table 2.15 Wage and salaried workers, female percentage (2010-2019)

2010

2011

2012

2013

2014

2015

2016

2017

2018

Argentina

80.66

81.32

81.29

80.90

80.54

79.89

79.18

78.57

78.58

Australia

86.72

87.05

87.34

87.78

87.44

87.44

87.41

87.56

87.56

Bangladesh

16.56

17.98

19.47

21.03

23.39

25.74

28.09

31.17

31.89

Brazil

71.23

72.61

75.49

75.36

75.78

74.90

74.58

73.57

73.62

Canada

88.46

88.52

88.53

88.22

88.32

88.07

88.26

88.14

88.15

China

45.29

46.20

47.01

47.78

48.54

49.20

49.90

50.63

51.32

Egypt

52.04

52.82

56.25

52.01

51.11

51.47

65.76

62.96

63.42

France

92.45

92.28

92.39

92.32

91.88

91.83

91.61

91.46

91.48

Germany

91.58

91.51

91.66

91.90

92.02

92.25

92.35

92.45

92.47

India

12.46

14.49

16.75

17.10

17.51

17.94

18.35

18.77

19.22

Indonesia

34.44

36.33

38.56

39.61

39.64

41.55

41.90

41.64

42.39

Italy

81.49

81.75

81.70

81.78

81.57

81.71

81.95

83.02

83.05

Japan

88.17

88.67

89.28

89.61

89.81

90.28

90.85

91.11

91.14

South Korea

72.98

73.71

74.23

75.01

75.77

76.74

77.35

77.35

77.47

Mexico

65.60

65.25

65.50

66.26

67.23

67.05

67.37

68.34

68.42

Philippines

52.84

53.02

54.33

55.58

55.08

56.48

57.85

59.32

59.62

Pakistan

20.72

21.38

23.00

24.67

26.55

24.86

24.13

24.57

25.11

Russian

Federation

94.07

93.79

94.00

93.64

93.78

93.74

93.64

94.53

94.54

South Africa

85.35

85.86

86.92

87.04

88.18

87.93

87.66

87.15

87.14

Turkey

50.98

51.93

54.60

56.87

60.52

62.08

63.71

63.67

64.02

United States

94.42

94.50

94.41

94.43

94.64

94.74

94.84

94.72

94.78

Vietnam

28.03

28.74

29.16

29.43

30.59

34.23

35.79

37.73

38.31

  • 4 Application for the determination of factors affecting participation of women in the labor force
  • 4.1 Method and model

Panel data analysis is mostly the result of examining cross-sectional variables consisting of individuals, households, regions, countries or another group in a certain tune dimension (Baltagi, 2005). In the time series analysis, which reflects a single country' example, problems have started to occur in obtaining data in terms of time dimension. Researchers have had a tendency to work with extremely large data within a short time range. If the behavior between units is different, panel data analysis provides researchers with considerable ease in modelling. However, providing such flexibility is not available in the time series analysis. In such a case, the analyzes have now shifted to panel data analysis on the basis of cross- sectional diversification and heterogeneity (Greene, 2012). If the cross-sections of the data sets used in the analysis are of the same length, balanced panel data analysis will be applied. If the data sets have different cross-sectional lengths, unbalanced panel data analysis will be preferred (Wooldridge, 2003). The factors that affect the ratio of participation of women in the labor force were analyzed through the following model. In the model, FLFP stands for female labor force participation, WSWP for ratio of wage- and salary'-eaming women, UNFP for the ratio of unemployed women in the female labor force, О WFP for the ratio of self- employed women, GCEP for the ratio of government consumption expenditures to Gross Domestic Product, GNI for per capita purchasing power, GSP for the ratio of total savings to GDP, SGPI for the gender equality index in primary and secondary education, and GHEP represents the ratio of the state’s domestic health expenditures to GDP.

The data of 19 countries included in the study between 2000 and 2018 were obtained from the World Bank database. Since the type of data used is available for the specified years, a period limitation has been made. Period limitation has been made until the date, which can be reached for the developing countries, and the largest countries in teims of national income in the world economy.1

Using the Hausman test, it is decided which model to use in panel data analysis. The researcher will examine the differences between the coefficients of fixed effect and random effect models with the help of the Hausman test and decide which one to choose (Aim and Moon, 2001; Hsiao, 2014).

When we look at the results of the Hausman test, for the model that is formed, it is seen that there is no statistical and systematic difference between the results of fixed effect estimation method and random effect estimation

Table 2.16 Variables and data descriptions

Type

Code

Explanation

Dependent

Variable

FLFP

Labor force participation rate, female (% of female population ages 15+)

Independent

Variables

WSWP

Wage and salaried workers, female (% of female employment)

UNFP

Unemployment, female (% of female labor force)

OWFP

GCEP

Own-account workers, female (% of female employment) government final consumption expenditure (% of GDP)

GNI

GNI per capita, PPP (current international $)

GSP

Gross savings (% of GDP)

SGPI

School enrollment, primary and secondary (gross), gender parity index (GPI)

GHEP

General government health expenditure (% of GDP)

Table 2.17 Wald chi2 and Hausman Test Wald chi2 Hausman

chi2 Prob>chi2 clii2(8) = (b-B)'[(Y_b-Y_B)A(-l)](b-B) = 3 321,8

76541.45 0.000 Prob>chi2 = 0.000 (Y_b-Y_B is not positive definite)

Ho: sigmariyz = sigmaA2 for all i Test: Ho: difference in coefficients not systematic method. After obtaining such a result, in the estimation of the model, the fixed effect model was preferred with the assumption that the fixed effect models are consistent.

Changing variance problems in the models that are formed in panel data analysis should also be tested. The presence of variance problems in models is investigated by using the Wald test (Hayes and Cai, 2007; Stock and Watson, 2008).

The results of the Wald test for our model are given in Table 2.17. When we look at the results of the Wald test in the table, it is seen that there is a changing variance problem in our model. In order to see the effect of the changing variance problem and to eliminate this problem, our model was also analyzed by a robust regression method that makes predictions by using resistant errors.

In models formed in panel data analysis, whether there is a problem of autocorrelation is tested by GDW (Durbin-Watson Test; Bliargava et al., 1992) test and LBI-test (Baltagi and Wu, 1999). If the model has autocorrelation and also a changing variance problem is found in the test result, estimation is made by using a clustered method for consistent results (Wooldridge, 2003; Williams, 2000; Rogers, 1993). According to the results of the Durbin-Watson and Baltagi-Wu tests in the table, there is a problem of autocorrelation in our model. Therefore, our model will be re-estimated by a clustered standard errors method.

Women’s labor force participation 37 Table 2.IS Durbin-Watson and Baltagi-WuTest

Durbin-Watson

Baltagi-Wu(LBI)

0.393

0.706

Basic threshold value "2"

Table 2.19 Pesaran CD, Friedman and Frees Tests

Pesaran CD Test

Friedman Test

Frees Test

0.639. Pr = 0.14

2.448 Pr = 0.0014

3321

Critical values from Frees' Q distribution

alpha = 0.10 : 0.1360

alpha = 0.05 : 0.1782

alpha = 0.01 : 0.2601

Pesaran, Friedman and Frees tests can be used to determine the inter-unit correlation problem in the cross-section data. If problems such as correlation between the units, changing variance and autocorrelation are detected as a result of the tests, it is necessary to re-estimate with a different method. In such a case, the method developed by Driscoll and Kraay (1998) is used to obtain deviation-free results (Hoyos and Sarafidis, 2006; Driscoll and Kraay. 1998).

hi our model, tests for determining the correlation between units are included in Table 2.19. As a result of Pesaran CD and Frees tests performed to determine the correlation between units, a correlation was detected between the error terms in our model. Although this problem was not detected in the Friedman test, we preferred to re-estimate with the Driscoll-Rraay method to obtain more consistent results fr om our model.

4.2 Findings

Table 2.20 includes the results of four models: linear regression model prediction, robust regression model prediction using resistant errors, cluster method prediction with clustered standard errors and prediction by using the Driscoll-Kraay method.

The estimation results of the basic model with four different methods are given in Table 2.20. It was determined that some of the independent variables were statistically significant for all models and some variables were statistically significant only in some models.

The effect of women working for a salary or wage on the labor force participation of women ratio was statistically significant at 1 % in all four models. However, despite the statistically significant results, the direction of the effect was negative. A one-unit increase in employment of women with a salary or wage had a 0.20 unit reduction effect on labor force participation rates.

Table 2.20 Model Estimations

FLFP

Variables

(1)

Model

(2)

Robust

(3)

Cluster

(4)

DK

WSWP

-0.208***

[0.033]

-0.208**

[0.074]

-0.208**

[0.074]

-0.208***

[0.051]

UNFP

0.045***

[0.006]

0.045***

[0.012]

0.045***

[0.012]

0.045***

[0.006]

О WFP

-0.044**

[0.018]

-0.044

[0.031]

-0.044

[0.031]

-0.044*

[0.026]

О WFP

0.131***

[0.029]

0.131*

[0.074]

0.131*

[0.074]

0.131***

[0.024]

GNI

0.104***

[0.013]

0.104***

[0.033]

0.104***

[0.033]

0.104***

[0.011]

GSP

0.032*

[0.018]

0.032

[0.035]

0.032

[0.035]

0.032

[0.019]

SGPI

-0.179*

[0.092]

-0.179

[0.221]

-0.179

[0.221]

-0.179*

[0.117]

GHEP

-0.064***

[0.016]

-0.064

[0.040]

-0.064

[0.040]

-0.064***

[0.009]

Constant

3.124***

[0.175]

3.124***

[0.315]

3.124***

[0.315]

3.124***

[0.169]

Observations

F

  • 273
  • 18.41
  • 273
  • 4.670
  • 273
  • 4.670
  • 273
  • 46.96

Prob > F

0.000

0.00318

0.00318

R-squared

0.375

0.375

0.375

Number of countrycode

19

19

19

rho

0.992

0.992

0.992

sigma

0.313

0.313

0.313

sigma_e

0.0279

0.0279

0.0279

sigmau

0.312

0.312

0.312

r2_w

0.375

0.375

0.375

0.375

r2_b

0.367

0.367

0.367

r2_o

0.122

0.122

0.122

r2_a

0.308

0.356

0.356

corr

-0.540

-0.540

-0.540

N_clust

19

19

Number of groups

19

lag

2

Standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1

The unemployment rate of women variable is also statistically significant, at 1% in all models. The effect of the ratio of women without jobs on labor force participation of women is positive. A one-unit increase in unemployed women ratio increases labor force participation of women rates by 0.04 units.

The fact that women are self-employed is statistically significant at the level of 5% for the first model and 10% for the fourth model. The fact that women are self- employed has a negative impact on labor force participation rates. This negative effect and severity appear to be the same in almost all models. A one-unit increase in self-employment reduces labor force participation of women ratio by 0.04 units.

The effect of the increase in the total consumption expenditures of the state on the labor force participation ratio of women is statistically significant in all models. The effect of the consumption by state expenditures was found to be significant at the level of 1% in the first and fourth models and 10% in the second and third models. The direction of the effect of this variable, which is significant in all models, is also positive. A one-unit increase in the consumption expenditures of the state increases the labor force participation rates of women by 0.13 units.

Expressing the purchasing power per capita, GNI variable is also statistically significant at 1% in all models. It is observed that the increase in purchasing power per capita supports the participation of women in the labor force. A one- unit increase in purchasing power per capita increases the labor force participation rate of women by 0.1 units.

The effect of total savings on labor force participation ratio of women was statistically significant only in the first model. Although it is not statistically significant in all models, the total savings increase in the country positively affects labor force participation ratio of women.

The gender equality index in primary and secondary education is statistically significant in the first and fourth models. The effect of gender equality index on education is negative. In other words, a one-unit change in favor of male students has a decreasing effect on the labor force participation rates of women by 0.17 units.

The impact of public health spending on labor force participation of women is significant at 1% in the first and fourth models. The direction of the impact of public health spending on labor force participation ratio of women is negative in all models. A one-unit increase in public health spending reduces labor force participation ratio of women by 0.06 units.

5 Conclusion and evaluation

It is out of the question that women, who make up half a country’s population, are not involved in economic and social life. The absence of women in the labor market means a material and spiritual loss, both individually and nationally. The negative experiences of women about their social, economic and legal status in the past centuries have no place in this age. With the historical process, women have gained their economic and social rights and become more visible in the society. However, despite all these developments, women are sometimes pushed to the background compared to men in the economic field. There are many reasons why women remain in the background compared to men. Many factors, such as the country or region people live in, social structures and institutions, the responsibilities and roles women undertake, the economic and social development levels of the countries and the education level, can be listed among these reasons.

Women, who participate in economic activities as men, should be in equal conditions in the labor market. They should not be more disadvantaged than men in matters such as the nature of the jobs they work, the positions they hold, the amount of income they earn and the amount time spent working.

In our study, the place of women in the labor force market is presented by tabulating the numerical data of selected countries. Then, the factor that has an impact on the labor force participation rate of women for the same country' group, other than a few, is analyzed through the established model. After estimating the model with four different methods, four different analysis results are obtained. It was found that most of the results of the analysis had similar results for all models. When we look at the details of the analysis results, per capita purchasing power, public consumption expenditures, unemployed women rates and savings have been found to affect labor force participation of women positively. On the other hand, it was determined that working for a salary or wage, self-employment, public health expenditures and gender index in education negatively affect the labor force participation ratio of women.

The increase in the consumption expenditures of the state encourages production by increasing demand in the economy. As a result of firms or producers turning towards more labor force and women employment for increase in production, the labor force participation ratio of women also increases.

Women being self-employed decreases the labor force participation ratio. This is the result of women not being able to be workers anywhere when they choose to work for themselves. Besides, security or social structure may have an effect on women preferring to be self-employed.

The increase in purchasing power per capita increases the participation of women in the labor force. This situation can be interpreted as an increase in per capita income. This will be more reasonable when we think that women are involved in the labor force to earn individual income. The labor force participation ratio will increase again, as more work or more working time will be required for more income.

According to the results of the analysis, the effect of women employment with salary or wages has a negative effect on their involvement in the labor force. This simation increases the chance that women are mostly employed in informal and low wage jobs. The income of the women working in regular and legal jobs will also be continuous. However, the income earned by women working in informal and temporary jobs will also be periodic.

If the gender equality index in education is in favor of male students, labor force participation rates of women are negatively affected. In other words, women with low or lack of education are also unable to participate in the labor market. The relationship between education level and income has been empirically established in many studies. In general, an increase in education level and income increase act in unison. Therefore, with an increase in the education level of women, the ratio of participation in the labor force and their incomes will increase.

Of course, it is not possible to limit the factors that affect women’s participation in the labor force with the variables in our model. It is also likely that there are numerous factors that affect participation of women in economic life and the labor force. The selected country examples and the size of the data obtained are the limitations of our study. This study has revealed a number of factors that affect the labor force participation ratio of women within the frame of selected countries and established models. However, it is evident that this is not enough, and that there are many factors and variables to be discussed.

The role of women in society and the responsibilities of women are always heavier than those of men. Women should no longer experience problems such as gender inequality, physical and sexual violence, social exclusion, lack of security, and social and economic weakness. For this to happen, it is important for girls to access educational opportunities as easily as boys. Women must receive equal pay with men when they do the same job. Women should always be able to choose whatever role they want, not only a secondary role compared to men. Childcare and housework should not be seen as the single and basic duty of women, and it should be accepted that women exist in economic and social fields. In short, women should not be exposed to any economic or social inequality only because they are women. It should also be remembered that individuals in societies and states should increase their positive efforts even more rapidly.

Note

1 Argentina, Bangladesh, Brazil, Canada, China, Egypt, France, Germany, India, Indonesia, Italy, South Korea, Mexico, Pakistan, Philippines, Russian Federation, South Africa, Turkey, USA.

References

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Women's labor force participation 43

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3 The gender-responsive budgeting

 
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