a study of the factors affecting female ......2020/09/01 · census and statistics in sri lanka....

Sri Lankan Journal of Business Economics, 2020 9 (1)

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A STUDY OF THE FACTORS AFFECTING FEMALE LABOUR FORCE

PARTICIPATION IN SRI LANKA

L.U. Mallawarachchi 1 and T.S.G. Peiris2

Abstract

In the Sri Lankan context, the lower participation of females in the

labour force has been a common problem since 1960s. The purpose

of this paper is to study the factors affecting the female labour force

participation in Sri Lanka. Data has been gathered from the Annual

Labour Force Survey in 2016 conducted by the Department of

Census and Statistics in Sri Lanka. This analysis is based on a

sample of 45,210 female individuals and the selected seven

variables are in relation to the head of the household, race, religion,

marital status, education attainment, literacy in English and age

category of the individuals. Person Chi square test and logistic

regression have been used to identify the most influential factors

and the results indicated that six variables have a significant

relationship with the female labour force participation. When all the

seven variables were considered simultaneously using Binary

logistic model, only the race, marital status, relation to the Head of

the Household (HHH), education attainment and literacy in English

were found significant on Female Labour Force Participation

(FLFP). The overall correct classification rate of the Binary

Logistic model is 73.9%. Therefore, the government and policy

makers can focus on these factors when introducing new policies

and schemes in order to increase the level of active participation of

females in the labour force as female contribution is essential for

the economic growth of the country.

Keywords: Female Labour Force Participation, Labour Force,

Logistic Regression, Pearson Chi Square test

1 Department of Mathematics, Faculty of Engineering, University of Moratuwa, Sri Lanka.

*Corresponding author. E-mail: [email protected] 2 Department of Mathematics, Faculty of Engineering, University of Moratuwa, Sri Lanka.

mailto:[email protected]


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1. Introduction

In the global context, unfair treatment of individuals on the basis of gender has been

a deeply rooted problem. Women are most likely to expose to the issue of gender

inequality, which reduces their active participation in labour force. This has

negatively influenced the overall productivity and the economic development of the

country.

Sri Lanka is considered as a Lower Middle-Income country with a GDP per

capita of USD 3,835 in 2016 with an entire population of 21.2 million people. At

the end of the 30 years of civil war in 2009, Sri Lankan economy has been grown

with an average of 6.2% over the period of 2010 to 2016 and the services sector has

contributed for 60% of the GDP in 2016 (Central Bank Report, 2017). However,

over the past two decades, females indicate a lesser participation in labour force

(30%-35%) on these sectors (ILO, 2017).

Sri Lankan labour force

Labour force participation rate has been defined as the economically active

population percentage of 15 years of age and above or labour force to the total

working age population. The working age population is in two groups as

economically active and economically inactive. Labour force is the current

economically active population and it is defined as the sum of the number of

employed and unemployed population in an area or country during the current

reference period (DCS, 2016). Table 1 indicates the Sri Lankan labour force

participation and unemployment rates over the period of 2000 to 2017.

Table 1: Labour force participation rates and unemployment rates (2000-2017) Labour Force

Participation

Rate

Year

2000 2001 2002 2003 2004 2005 2006 2007 2008

Female 33.9 31.9 33.6 31.4 31.5 30.9 35.7 33.4 33.2

Total 50.3 48.8 50.3 48.9 48.6 48.3 51.2 49.8 49.5

Unemployment Rate

Female 11.1 11.5 12.9 13.2 12.8 11.9 9.7 9.0 8.4

Total 7.6 7.9 8.8 8.4 8.3 7.7 6.5 6.0 5.4

Labour Force

Participation

Rate

Year

2009 2010 2011 2012 2013 2014 2015 2016 2017

Female 33.0 31.2 34.0 32.9 35.4 34.6 36.0 35.9 36.6

Total 49.0 48.1 53.0 52.5 53.7 53.2 54.0 53.8 54.1

Unemployment Rate

Female 8.6 7.7 7.1 6.3 6.6 6.5 7.6 7.0 6.5

Total 5.8 4.9 4.2 4.0 4.4 4.3 4.7 4.4 4.2

Source: Department of Census and Statistics, 2000- 2017

On one hand, as shown in the above Table 1, female labour force

participation rate (FFPR) ranges from 33.9% to 36.6% from 2000 to 2017 and when

compared with the total percentage of labour force participation rates, female

proportion is comparatively lesser than males. On the other hand, the percentages of


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female unemployment rates (FUR) are comparatively higher with respect to the

males throughout the period of 2000-2017.

However, this was different when compared with the achievements of

human development outcomes, as the percentage of educated females which has

increased. The enrollment and retention rates in secondary schools and the higher

performance levels at the public examinations, females are more in comparison to

males. Further the percentage of female students is significantly higher in

universities and non-vocational tertiary educational institutes compared to male

students (World Bank Report, 2017) (Refer Table 2).

Table 2: Percentage of candidates admitted for the government universities

(2006– 2017)

Year 2006 2007 2008 2009 2010 2011

Female 13.03 15.03 14.50 15.62 14.31 19.44

Total 14.34 16.53 16.01 17.20 15.45 20.44

Year 2012 2013 2014 2015 2016 2017

Female 16.42 16.77 16.43 18.22 18.52 18.70

Total 16.71 17.53 17.14 18.68 19.10 19.25

Source: University Grants Commission, 2006-2017

According to Table 2, it’s proved that, more females are admitted to the

government universities throughout the period 2006 to 2017. Following Table 3

illustrates the unemployment rates of females by the level of education over the

period 2002 to 2017.

Table 3: Female unemployment rates by the level of education (2002-2017)

Education Level Year

2002 2003 2004 2005 2006 2007 2008 2009

Grade 5 & Below - - 2 - 1.4 1.4 1.6 1.8

Grade 6 - 10 - 11.4 10.6 - 8.7 7.5 6.4 7

G.C.E. O/L - 19.4 18.9 - 14.3 11.3 12.4 11.5

G.C.E. A/L and Above - 23.3 23.8 - 16.8 17.5 15.3 15.5

Education Level Year

2010 2011 2012 2013 2014 2015 2016 2017

Grade 5 & Below - - 3.5 3.8 3.7 4.3 3.7 3.5

Grade 6 - 10 5.8 5.2

G.C.E. O/L 10.1 8.9 8.8 8.3 8.9 9 9.1 7.9

G.C.E. A/L and Above 15.8 13.1 10.8 11.6 11 13.5 11.9 11.3

Source: Department of Census and Statistics, 2002- 2017

As shown in Table 3, it’s clear that there is an increasing trend in the female

unemployment rates with the increase of educational qualifications. Those females,

who obtained G.C.E.A/L and above qualifications, shows an unemployment rate of

23.3% in 2003 and it has reduced to 11.3% in 2017. This indicates that qualified

young people have lesser opportunities in the job market and it is further proved by

Table 4 which includes data related to the unemployment with respect to the age

category of people in Sri Lanka.


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Table 4: Unemployment rates by the age groups (2016)

Age Group Unemployment Rates %

Total Female Male

15 - 24 21.6 29.2 17.1

25 - 29 9.2 15.9 5.1

30 - 39 2.4 5.1 0.9

Over 40 0.8 1.3 0.5

Source: Department of Census and Statistics, 2016

According to Table 4, the unemployment rates females (7%) higher than

that of males (2.9%) irrespective of the age group. Further, youth unemployment

rate (15-24) of females is comparatively higher than that of males (17.1%). Similar

pattern is followed by all the other age categories with respect to the level of

unemployment. Table 5 illustrates the distribution pattern of employment in year

2016.

Table 5: Distribution of employment status (2016) Employment Status Male Female

Employee 66 34

- Public 13.9 11.3

- Private 52.1 22.7

Employer 87.9 12.1

Own account worker 71.1 28.9

Contributing family worker 22.7 77.3


According to Table 5, out of the employed population, only 34% of the

employees are females and from them 11.3% are public servants and 22.7% are

private employees. Similarly, out of the employers, 12.1% of the minority

representing females. Further, 77.3% of the females are contributing family

workers.

Table 6: Unemployment rates and percentage distribution of employment

status for each province (2016)

Province Unemployment

Rate

Employment Status

Paid

Employee Employer

Own

Account

Worker

Contributing

family

Worker

Western 3.2 67.7 3.8 24.9 3.5

Central 5.1 57.3 2.2 31.0 9.5

Southern 5.6 56.5 2.5 33.4 7.6

Northern 6.3 61.6 3.2 31.3 3.9

Eastern 5.5 60.3 1.1 35.2 3.4

North Western 3.2 51.2 3.7 34.6 10.4

North Central 3.4 40.3 1.0 41.7 17.0

Uva 4.6 42.5 1.1 38.5 17.9

Sabaragamuwa 5.5 58.9 2.3 32.0 6.8



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As shown in Table 6, in 2016, highest unemployment rate is indicated in

Northern Province (6.3%) followed by Southern province (5.6%). According to

employment status, highest paid employees are in the Western province. Similarly,

the highest number of own account workers are in North Central province (41.7%).

Following Table 7 illustrates the employed population in 2016 with respect to the

occupations.

Table 7: Employed population by the occupation (2016)

Occupation Total

Gender % Contribution of

females to the total

population Male Female

Managers, Senior Officials and

Legislators 6.0 6.7 4.9 28.4

Professionals 6.5 3.7 11.8 63.8

Technical & Associate Professionals 6.1 6.0 6.2 35.9

Clerks and Clerical support workers 4.0 3.0 5.9 51.4

Services and Sales workers 11.2 11.8 10.2 32.0

Skilled Agricultural, Forestry and

Fishery workers 18.1 18.1 18.0 35.1

Craft and Related Trade workers 16.0 16.1 15.9 34.8

Plant and Machine operators and

Assemblers 8.8 11.8 3.4 13.5

Elementary Occupations 22.6 22.1 23.4 36.5

Armed Forces Occupations 0.6 0.7 0.3 16.7


Table 7 clearly indicates that females are heavily concentrated on certain

occupations. In the category of ‘professionals’ females are accounted for 11.8%

while males are representing 3.7%. Similarly, in the elementary occupations,

clerical workers, technical professionals’ females are dominating with the rates of

23.4%, 5.9% and 6.2% respectively. Further, it’s important to note that in all the

other categories like managers, senior officials, legislators, skilled agricultural,

forestry and fishery workers, plant and machine operators, males are dominating

with the rates of 6.7%, 18.1%. 11.8%.

2. Literature Review

In the Sri Lankan labour force, participation of females is less in comparison to

males. As stated in the World Bank report in 2017, this was due to low LFP, high

unemployment and wage differences between the sexes. This report has further

supported three hypotheses to explain the reasons for gender gaps in the job

market.

Firstly, the household roles and responsibilities of women especially when

they get married at young ages, they are less likely to engage in the labour force.

According to 2015 statistics, marriage lower the odds of FLFP by 4.4 percentage

points, while boosting men’s odds by 11 percentage points. Further, married women

of those who have small children indicate a less chance of becoming a paid


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employee and also lower earnings with compared to the men’s odds before 2010

(World Bank, 2017).

Similarly, with respect to certain cultural beliefs and norms, females are

supposed to engage in in-house activities have been the reasons for gender gap in

LFP. According to a study of Gunatilaka (2013), nearly 70% of the married women

in Sri Lanka are having at least 1 child under the age of 5 and are less likely to

engage in work rather than spending their time at homes. This rate is comparatively

higher with women in the urban areas as i.e. around 75% and in the rural areas this

is around 71%. These women have stated that their inactive status in the labour

force is due to their household duties and responsibilities (Gunatilaka, 2013).

Further, Gunatilaka (2013) found that the married females who are the

heads of households have certain cultural constraints and different perceptions and

attitudes regarding their roles as married women and the gender division of

household and care labour within the family unit. But the problem is, with all these

constraints if they are encouraged to seek for employment, there are some

restrictions imposed by the private sector on the nature and type of work that

women are able to take up.

Third hypothesis is the gender discrimination related to the job search,

hiring and promotion processes. Gender may be discriminated based on the nature

of the job. Especially females are given less priority in the jobs related to the field

of construction and IT based on its nature. At times in the hiring and recruitment

process, less priority is given for those who do not have contacts with the existing

employees at work place. In some cases, job promotions are offered in a similar

way (World Bank, 2017).

Malhota (1997) carried out a study on ‘Entry versus success in Labour

Force,’ related to job opportunities of young women in Sri Lanka to determine the

behavioural patterns of Sri Lankan young women in developing societies at the

household level. It has been revealed that higher education levels lead to greater

labour force participation and also highly educated females are more likely to be

unemployed than employed. Further, the study showed that the lack of sustained

economic growth, high level of education, favourable female position in household

collectively made the unfavourable situation for the young females in the labour

force participation.

Another study carried out by Bombuwela et al. (2013) was based on the

effect of glass ceiling related to the career development of women. The study has

been carried out based on the conceptual model developed according to the

variables identified in the literature survey. This study helps to determine the effects

of glass ceiling on women’s career development in relation to the female executive

level employees who are working in the private sector companies. Simultaneously,

the hypotheses were developed in order to find out whether there is a significant

effect of individual factors, family factors, organizational factors and cultural

factors on women’s career development.

Further, this study was supported by an empirical survey which was

conducted using a self-administered questionnaire and a sample of 150 women

executives. Both descriptive and inferential statistics were used for data analysis.

The findings revealed that there is a moderate negative relationship between the


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glass ceiling and the women’s career development and it also proved that individual

factors, organizational factors and cultural factors have a significant effect on

women’s career development whereas family factor has an effect on the glass

ceiling (Bombuwela et al., 2013).

In the global context, several studies were carried out in relation to the

determinants of female labour force participation. As stated by Psacharopoulos and

Tzannatos (1989), factors such as age and fertility and religion affect the FLFPR

irrespective of the country that is investigated. In addition, Uwakwe (2004) found

that in the Nigerian context, factors like family responsibilities, pregnancy, and

physical factors; nutrition, water and health services affect the FLFPR. Moreover,

the State and Planning Organization of Turkey and the World Bank (2010) pointed

out that the FLFP in Turkey is affected by both socioeconomic and cultural factors

including; house responsibilities and childcare/eldercare, urbanization, marital

status. Faridi et al.(2009) further added that close relatives’ educational status,

household assets, spouse’s participation in economic activities, number of children,

age of children, husband’s salary influence and the female’s decision on whether to

participate or not participate in the labor market are factors affecting the decision

making.

According to a study undertaken in Pakistan, Khadim and Akram (2013)

identified three categories of factors that influencing the female participation in

economic activity; individual and demographic factors (age, education, marital

status), socio economic condition factors (per capita income of the household,

number of dependents, household type), geographic location factors (urban and

rural residence).

Although this is a common issue in most of the countries, few studies have

been conducted, especially in the Asian countries. Therefore, the review was useful

to explain that age and fertility, religion, physical factors like nutrition and health

conditions, cultural factors like household responsibilities, childcare, eldercare,

urbanization, marital status, number of children, age of children, husbands’ salary

influence on the labour force participation and decision of females in other

countries impact the status.

3. Methodology

Conceptual framework

Figure 3 indicates the Conceptual Framework of the proposed study.


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Figure 3: Conceptual framework of the proposed study

Source: Author constructed

Source: Constructed by authors

According to the previous literature, seven factors were selected as

independent variables in order to determine the impact on the labour force

participation of females in Sri Lanka (Figure 3).

For this study, data were obtained from Sri Lanka Annual Labour Force

Survey in 2016 conducted by the Department of Census and Statistics, Sri lanka.

The survey has been carried out from January to December in 2016, using a Two

Stage Stratified sampling and a sample of 25,750 housing units which includes

85,082 individuals have been selected. Here, the census blocks prepared for the

Census of Population and Housing in 2012 have been selected as the primary

sampling units and the secondary sampling units are the housing units selected from

the 2575 primary sampling units. By using the method of systematic random

sampling, from each of the selected primary sampling unit, 10 housing units (SSU)

were selected for the survey. Out of the entire population, 85,082 of individuals

were selected for the Annual Labour Force Survey in 2016 and this analysis was

carried out based on the 45,210 female individuals.

Statistical techniques used

In typical two factors (A & B) having 2 levels can be illustrated as shown below.

Table 8: Two way frequency table

A B

Total B1 B2

A1 f11 f12 f1.

A2 f21 f22 f2.

Total f.1 f.2 f..


Let {fij} = Observed frequency of the row category = i and column category = j

Independent Variables

− Relationship to HHH (X1)

− Race (X2)

− Religion (X3)

− Marital status (X4)

− Education Level (X5)

− Literacy in English (X6)

− Age Category (X7)

Dependent Variable

Female Labour Force

Participation


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Hypotheses

H0: Factor A is independent of factor B or there is no significant association

between the two factors A and B.

H1: There is a significant association between the two variables (i.e. dependent and

independent variable).

In order to test the above hypothesis based on 2 way frequency table,

Pearson Chi Square Test statistic has been used.

Pearson’s Chi-Square Test (exact) = ∑(Observed-Expected)

Observed

2

[1]

Test statistic is distributed x2 (r-1) (c-1) where r and c are the number of rows

and columns. Source: Peiris (2018)

Binary logistic regression is used to develop a model using the most

significant factors identified. There are four main methods that can be used to select

the variables for the model such as Forward Selection (LR), Forward Selection

(Wald), Backward Elimination (LR) and Backward Elimination (Wald) (Peiris,

2018).

Assume X1i, X2i…….. Xki are the explanatory variables for the ith individual. When

the response variable is dichotomous,

1 If a female is in the labour force

Female Labour force

participation

0 If a female is not in the labour force

Binary logistic model gives the relationship between the response and explanatory

variables as follows.

Log (pi

1-pi

) = β0 + β1x1i + β2x2i + …………. + βkxki [2]

Where i = 1,2,…..n Xi0 = 1 for all i = 1,2,…..n

βi indicates the parameters of the model.

In order to test the significance of logistic regression model, following test has been

used.

Hosmer and Lemeshow Test

The test statistic H is given by,

H = ∑(𝑂𝑔−𝐸𝑔)

2

𝑁𝑔𝑝𝑔(1−𝑝𝑔)𝑛𝑔=1 [3]

𝑂𝑔 – Number of observed cases in gth group

𝐸𝑔 – Number of expected cases gth group under the fitted model

𝑔 – Number of groups


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Hypotheses

H0: Model is significant vs H1: Model is not significant

Under H0, the test statistic is asymptotically follows Chi-square g-2 df (Peiris,

2018).

4. Results and Discussions

By using the 2-Way frequency table, the influence of each selected explanatory

variable on FLFP has been tested.

Influence of age category on FLFP

Table 9: Influence of age category on FLFP

Labour Force

Total Yes No

Age

Category

15 - 19 Count 178 3150 3328

% within X7 5.30% 94.70% 100.00%

20 - 24 Count 793 2152 2945

% within X7 26.90% 73.10% 100.00%

25 - 29 Count 1028 1914 2942

% within X7 34.90% 65.10% 100.00%

30 - 34 Count 1236 2097 3333

% within X7 37.10% 62.90% 100.00%

35 - 39 Count 1400 1893 3293

% within X7 42.50% 57.50% 100.00%

40 and above Count 273 337 610

% within X7 44.80% 55.20% 100.00%


Chi Square test statistic - χ2 = 1403.204 (p=0.000)

According to Table 9, within the age category of 15-19, 20-24, 25-29, 30-34, 35-39

and 40 years above, there were 94.7%, 73.1%, 65.1%, 62.9%, 57.5%, 55.2%

females who were not in the labour force. As the chi square test statistic (1403.204)

is highly significant (p=0.000), it can be concluded that there is a significant

influence of age category on the female labour force participation in Sri Lanka.


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Influence of marital status on FLFP

Table 10: Influence of marital status on FLFP

Labour Force

Total Yes No

Marital

Status

Single Count 1764 5367 7131

% within X4 24.70% 75.30% 100.00%

Married Count 7632 14363 21995

% within X4 34.70% 65.30% 100.00%

Widowed Count 1037 3579 4616

% within X4 22.50% 77.50% 100.00%

Divorced Count 92 88 180

% within X4 51.10% 48.90% 100.00%

Separated Count 313 223 536

% within X4 58.40% 41.60% 100.00%



The results of the chi square statistics (642.333, p=0.000) confirms that there is a

significant influence of the marital status on FLFP. Among the females who are

single, 75.3% were not in the labour force and among the married females 65.3%

were not representing the labour force. This may be because they have more

responsibilities to look after their loved ones at homes (Table 10).

Influence of race on FLFP

Table 11: Influence of race on FLFP

Labour Force

Total Yes No

Race

Sinhala Count 8465 16201 24666

% within X2 34.30% 65.70% 100.00%

Sri Lankan

Tamil

Count 1293 4002 5295

% within X2 24.40% 75.60% 100.00%

Indian Tamil Count 604 696 1300

% within X2 46.50% 53.50% 100.00%

Sri Lankan

Moor

Count 449 2601 3050

% within X2 14.70% 85.30% 100.00%

Malay Count 13 65 78

% within X2 16.70% 83.30% 100.00%

Burger Count 13 31 44

% within X2 29.50% 70.50% 100.00%

Other Count 1 24 25

% within X2 4.00% 96.00% 100.00%



As the results of the Chi Square statistic (764.021) is not significant (p>0.05), it can

be concluded that there is no significant influence of race on EUY. Thus, it can be

concluded that the percentage of females who are not in labour force is significantly


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different among races. The lowest percentage (53.5%) can be seen among Indian

Tamils while the highest percentage (96.0%) is among different races other than

Sinhala, Tamil, Malay or Burger.

Influence of religion on FLFP

Table 12: Influence of religion on FLFP

Labour Force

Total Yes No

Religion

Buddhist Count 8008 15237 23245

% within X3 34.50% 65.50% 100.00%

Hindu Count 1579 3802 5381

% within X3 29.30% 70.70% 100.00%

Muslim Count 460 2665 3125

% within X3 14.70% 85.30% 100.00%

Roman Catholic

and Christians

Count 789 1914 2703

% within X3 29.20% 70.80% 100.00%

Other Count 2 2 4

% within X3 50.00% 50.00% 100.00%



The result of the Chi Square statistics (520.862, p=0.000) confirms that there is a

significant influence of religion on EUY. Thus, it can be concluded that the

percentage of females who are not in the labour force is significantly different

among religions. The corresponding percentages among Buddhists, Hindus,

Muslims, and others are 65.5%, 70.7%, 85.3% and 50.0% respectively (Table 12).

Influence of the relationship to head of household on FLFP

Table 13: Influence of relationship to HHH on FLFP

Labour Force

Total Yes No

Relationship

to HHH

Head of

Household

Count 2018 3800 5818

% within X1 34.70% 65.30% 100.00%

Wife/Husband Count 5349 9683 15032

% within X1 35.60% 64.40% 100.00%

Son/Daughter Count 2170 5660 7830

% within X1 27.70% 72.30% 100.00%

Parents Count 119 1345 1464

% within X1 8.10% 91.90% 100.00%

Other Relative Count 1072 3088 4160

% within X1 25.80% 74.20% 100.00%

Domestic

Servant

Count 89 5 94

% within X1 94.70% 5.30% 100.00%

Boarder Count 16 20 36

% within X1 44.40% 55.60% 100.00%

Other Count 5 19 24

% within X1 20.80% 79.20% 100.00%



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Among the females who are the heads of the households, 65.3% were not in the

labour force. Similarly, among the female wives, 64.4% and among the females

who are parents 91.9% did not represent the labour force. As the chi square test

statistic (808.082) is highly significant (p=0.000), it confirms that, there is a

significant influence of the relationship to Head of Household on the female labour

force participation in Sri Lanka.

Influence of the level of education on FLFP

Table 14: Influence of education attainment on FLFP

Labour Force

Total Yes No

Education

Attainment

Passed G.C.E(O/L) Count 936 2898 3834

% within X5 24.40% 75.60% 100.00%

Passed G.C.E(A/L) Count 2055 2961 5016

% within X5 41.00% 59.00% 100.00%

Degree Count 654 282 936

% within X5 69.90% 30.10% 100.00%

Post Graduate

Degree / Diploma

Count 148 62 210

% within X5 70.50% 29.50% 100.00%

Special Educational

Institutions

Count 9 20 29

% within X5 31.00% 69.00% 100.00%

No Schooling Count 362 1009 1371

% within X5 26.40% 73.60% 100.00%



As the Chi Square test statistic is significant (p=0.000), it can be concluded that

there is a significant influence of educational attainment on FLFP. Among the

females who passed G. C. E. (O/L) examination, (75.6%) were not in the labour

force and among the females who passed G.C.E. (A/L) examination, (59.0%) were

not in the labour force.

Influence of the literacy level of English on FLFP

Labour force survey data were collected for the literacy levels of three languages

(Sinhala, Tamil and English). As English is considered to be an international

language, the analysis is performed to study the impact of English Literacy level on

the female labour force participation.

Table 15: Influence of the literacy level in English on FLFP

Labour Force

Total Yes No

English Literacy

Level

Able to read

and write

Count 2241 3475 5716

% within X6 39.20% 60.80% 100.00%

Unable to

read and write

Count 8597 20145 28742

% within X6 29.90% 70.10% 100.00%



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The results of the chi square test statistics (191.052, p=0.000) indicate that there is a

significant influence of the level of English literacy on FLFP. According to Table

15, among the females who have the ability to read and write, 60.8% are not in the

labour force and among the females who are unable to read and write, 70.1% are not

in the labour force. As chi square is significant, it can be concluded that among the

English literate females, the percentage of those who are not in the labour force is

significantly lower than the percentage of unemployed females among non-English

literate persons.

The results of the Chi Square analysis confirmed that out of the selected

variables, religion, marital status, age category, education attainment, relation to

HHH and literacy level in English do have a significant influence, while race is not

significantly influencing on the female labour force participation.

In order to find out the most significant variables for the model, there are

four approaches that can be used (discussed in methodology).

Table 16: Reference categories used for the model

Variables Reference Category

Relationship to HHH Other

Race

Religion

Marital Status

Education Attainment

Literacy in English

Age Category

Other

Other

Other

Other

Unable to read and write

40 and above


As similar results were obtained in all four methods, only the results

obtained in Forward LR is illustrated below.

Table 17: Model summary for Binary Logistic Regression

Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square

1 18352.824a .098 .139

2 17856.424a .125 .177

3 17483.236a .145 .205

4 17151.096a .162 .230

5 17089.133a .165 .234

6 17049.205a .167 .237


These indicators sometimes known as Pseudo R2. It indicates the explained

variation in the dependent variable based on the final model varies from 16.7% to

23.7% depending on Cox & Snell R2 and Nagelkerke R2 respectively.


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Table 18: Results of Hosmer and Lemeshow Test Step Chi-square df Sig.

1 .000 4 1.000

2 11.985 8 .152

3 15.708 8 .047

4 15.275 8 .054

5 19.867 8 .011

6 16.374 8 .037


According to Table 18, the value given in the fourth column is the

probability of the chi square statistic used to test the null hypothesis (H0: Model is

significant). In other words, this is the probability of obtaining this chi-square

statistic (16.374) for goodness of fit of the model. In this case, the models in each

step are not significant as the corresponding p values are less than 0.05. This

implies that the model can be further improved by including the interaction terms.

As it is practically impossible to get an output for a large data set, two-way

interactions have not been considered.

According to Table 19, the fitted model can be written as follows;

Log (P

1-P) = 2.230 – 0.699 (x1=1) – 0.676 (x1=2) – 0.719 (x1=3) – 1.224 (x1=4) – 0.551

(x1=5) – 4.286 (x1=6) – 1.245 (x1=7) – 2.368 (x2=1) – 1.928 (x2=2) – 3.088 (x2=3) – 1.213

(x2=4) – 1.931 (x2=5) – 2.473 (x2=6) + 0.799 (x4=1) + 1.453 (x4=2) + 0.390 (x4=3) – 0.050

(x4=4) – 0.321 (x5=1) – 0.183 (x5=2) – 0.494 (x5=3) – 1.636 (x5=4) – 1.931 (x5=5) + 0.765

(x5=6) – 0.323 (x6=1) + 3.168 (x7=1) + 1.159 (x7=2) + 0.630 (x7=3) + 0.370 (x7=4) + 0.097

(x7=5) + 2.230 (x7=6)

The results in Table 19 proves that variables such as the relationship to

HHH, race, marital status, education attainment, literacy level in English and age

category do have a significant influence on FLFP.


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Table 19: Final results of the logistic regression model via forward Selection

(LR) method

B S.E. Wald df Sig. Exp(B)

Step

6

Relationship_to_HHH (X1) 45.665 7 .000

HHH -.699 .840 .693 1 .405 .497

Wife/Husband -.676 .838 .651 1 .420 .509

Son/Daughter -.719 .838 .736 1 .391 .487

Parents -1.224 .914 1.793 1 .181 .294

Other Relative -.551 .838 .433 1 .511 .576

Domestic Servant -4.286 1.043 16.884 1 .000 .014

Boader -1.245 .948 1.726 1 .189 .288

Race (X2) 355.408 6 .000

Sinhala -2.368 1.065 4.938 1 .026 .094

Sri Lankan Tamil -1.928 1.067 3.267 1 .071 .145

Indian Tamil -3.088 1.069 8.344 1 .004 .046

Sri Lankan Moor -1.213 1.068 1.289 1 .256 .297

Malay -1.931 1.146 2.839 1 .092 .145

Burger -2.473 1.193 4.298 1 .038 .084

Marital_Status (X4) 206.694 4 .000

Single .799 .161 24.549 1 .000 2.224

Married 1.453 .156 86.488 1 .000 4.277

Widowed .390 .227 2.951 1 .086 1.477

Divorced -.050 .307 .026 1 .871 .951

Education_Attainment (X5) 268.238 18 .000

Passed G.C.E. (O/L) -.321 .207 2.417 1 .120 .725

Passed G.C.E. (A/L) -.183 .214 .733 1 .392 .832

Degree -.494 .283 3.051 1 .081 .610

Postgraduate

Degree/Diploma

-1.636 .227 52.091 1 .000 .195

Special Educational

Institutions

-1.931 .330 34.155 1 .000 .145

No Schooling .765 .670 1.306 1 .253 2.150

Literacy Level (X6)

Able to read and write

-.323

.051

40.255

1

.000

.724

Age_Category (X7) 917.460 5 .000

15-19 3.168 .129 599.680 1 .000 23.765

20-24 1.159 .105 122.835 1 .000 3.186

25-29 .630 .098 41.709 1 .000 1.877

30-34 .370 .094 15.498 1 .000 1.448

35-39 .097 .093 1.081 1 .298 1.102

Constant 2.230 1.382 2.604 1 .107 9.298


In order to check the goodness of fit of the model, predicted values were

obtained at the critical level probability of 0.5 (Table 20).


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Table 20: Observed and predicted results of the EUY

Predicted group Total

Yes No

Labour

Force

Yes Count 1310 3598 4908

% within Labour_Force 26.7% 73.3% 100.0%

No Count 701 10842 11543


Total Count 2011 14440 16451



According to Table 20, 10842 are predicted correctly as unemployed

(93.9%) and from 4908 females are employed, 1310 are predicted correctly as

employed (26.7%). The overall correct classification by the model is 73.9% ((1,310

+ 10,842)/16,451).

Based on the results obtained, it can be concluded that out of the selected

seven variables, six variables significantly influence the female labour force

participation in Sri Lanka.

5. Conclusion

This research paper is aimed to analyze the factors affecting the female labour force

participation in Sri Lanka based on the Annual Labour Force survey data in 2016

gathered from the Department of Census and Statistics in Sri Lanka. The analysis is

carried out using seven factors and the study proves that those females who were in

the age category of 40 years and above shows the highest contribution on the labour

force participation and also most of those women were separated. Separated women

have lots of responsibilities such as to look after their kids, parents other than their

household responsibilities and these may be few of the reasons for their higher

participation on labour force. With respect to their race, majority in the female

labour force represent the Indian Tamils and in addition most were in different other

categories of religion. According to the relationship to the heads of households,

most of these women work as domestic servants.

The study further confirms that as the level of the educational qualifications

increases there is an increasing trend in the participation of females in the labour

force. Furthermore, it’s always better to have a qualified workforce, which will be

useful for the economic development of the country. In the aspect of Literacy level

of English, most of the females who were not in the labour force did not have the

ability to either read or write in English which is a crucial factor to be concerned

about. It is noted that lack of proper knowledge in English was a major issue for the

less participation of females in the labour force.

Based on the above conclusions, following recommendations can be given.

As the age group of 40 and above shows the highest rate of female participation in

the labour force, it is necessary to establish welfare centers such as child day care

centers, adult day care centers, where working women can keep their dependents

safely while they are at work. Further, in order to increase the women involvement

in labour force, it’s essential to initiate flexible working hours in companies either


70

on shift basis or as part time work, so that it would be a support for the females in

balancing their personal life and work life simultaneously. With the use of

technological advancements, it is high time to create job opportunities for women to

work from home which will increase the involvement of females in the labour force

as in developed countries. According to the job market requirements, the secondary

level and the tertiary level education system needs to be updated and the skills need

to be developed so that it would not be difficult for the females to find suitable jobs

and contribute to the labour force.

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