ECONOMETRICS REPORT

Participation of Women in the Labour Market Group 6

Ajay Kumar 1411211

Archana Valsan 1411215

M. Krubakar 1411237

EXECUTIVE SUMMARY

INTRODUCTIONIn the current scenario, female labor force participation is an important driver and an outcome

of growth and development of a country. Women’s labor force participation tends to increase

with economic development of the country and this relationship is not uniform at a country

level. Most of the OECD countries have experienced an increase in female labour

participation during the last few decades to the order of 54% in 1980 to 71% in 2010. i The

rate of growth has varied across countries and despite an overall increase in female labour

participation rates, these differences were significant in the early 2000s. This difference in

labor force participation across countries is driven by various economic factors like economic

growth, education and social norms and initiatives taken by the respective governments.

Apart from economic advancement at a country level, another salient factor that drives

female labour participation is the changes in labour demand- for instance, with the emergence

of service sector and new production activities, the demand for female labour force is on the

rise. In 2008, nearly one third of the female working population was involved in the service

sector.ii Another major determinant to female labour participation is the government policies

that enable parents to achieve work-life balance and since 1980, there has a rising focus by

the government of OECD to countries to expand these expenditure. The major policy

instruments are leave from work provided after child birth, extent of childcare services and

tax benefits provided for female workers.

MOTIVATIONThis empirical study aims to determine the participation of women in the labour force in nine

OECD countries having similar GDP per capita values namely, Australia, Canada, Spain,

UK, Italy, The Netherlands, France, Germany and Norway. This study aims to highlight the

key trends and factors that affect the female labor force participation and explain the

underlying reason for such an observed causality. Gender wage gap exists in most of the

OECD countries despite having a legislation to ensure equal pay for equal work, irrespective

of gender. Historically, there is a significant wage gap between women and men with women

being paid lesser wages than men. In all OECD countries, women on an average earned 16%

less than men in 2010iii compared to 2000, when the difference in wages was 4 percentage

points higher. In many OECD countries, the wage gap at the top of the earnings distribution

is much higher than at the median indicating the presence of glass ceiling- a phenomena that

prevents women from moving up the career ladder to top notch salaries. In certain countries,

notably in in Germany, Austria, Spain and Italy wage gap is significant among the male and

female low earners as well. Gender wage gap also increases with age and child bearing as

indicated by OECD studies that in 2010, the gender wage gap for 25-29 years was 9%

compared to 24% for 55-59 year olds.iv There is a growing need to focus the attention of

CEOs and senior managers on improving gender balance backed by the following reasons

namely, a) To attract and retain the best talent, b) To enhance diversity c) To serve consumer

markets having women as the major customers.v Hence, with increasing competitive pressure,

firms are in need of the best talent and in this regard, women account for a growing share of

talent emerging from the education system and firms risk losing out if they fail to leverage

this resourceful pool.vi Despite the potential benefits that firms can obtain from providing a

more prominent role to women, they are under-represented in the business sector. viiHence,

the topic of female labor force participation makes for an interesting study.

DATA AND VARIABLES USED

The data source used was OECD website (http://www.oecd.org/).

The variables used are listed in the table with explanations

Variables Notation Explanation(As per OECD)viii

Dependent variable

Female labour participation flpr The labour force participation rates is calculated as

the labour force divided by the total working-age

population. This indicator is broken down by age

group and it is measured as a percentage of each

age group.

Independent variables

Labour market characteristics

Part time female employment pef Ratio of females employed as part time (who

usually work less than 30 hours per week in their

main job)ix to the total females employed.

Percentage of services in

employment

empinser

Unemployment rate une Unemployment rate is the number of unemployed

people as a percentage of the labour force, where

the latter consists of the unemployed plus those in

paid or self-employment.

Employees, services, female femser Ratio of females employed in services to the total

number of females who are employed expressed as

a percentage.

Strictness of employment

protection

strict The OECD indicators of employment protection

legislation measure the procedures and costs

involved in dismissing individuals or groups of

workers and the procedures involved in hiring

workers on fixed-term or temporary work agency

contracts.x

Gender Wage Gap gendwagg

ap

The gender wage gap is unadjusted and is defined

as the difference between median earnings of men

and women relative to median earnings of men.

Data refer to full-time employeesxi

Demographic characteristics

GDP per capita gdpc A measure of the total output of a country where

the gross domestic product (GDP) is divided by the

number of people in the country.xii

Agriculture/Fishing/Forestry empagri Percentage of labour force engaged in agriculture,

fishing and forestry.

Services Employment empser Percentage of labour force engaged in services

industry.

Manufacturing empman Percentage of labour force engaged in

manufacturing.

Value added services gdpser

Log of population lpoptot Logarithm All nationals present in, or temporarily

absent from a country, and foreigners settled in a

country. This indicator shows the total number of

people living in an areaxiii

Population ages >=65 pop65 The elderly population is defined as people aged 65

and over. It is calculated as ratio of people aged 65

years and above to the total population.xiv

Age dependency ratio, old agedep Ratio of the elderly population and the working age

(15-64 years) population.xv

Employment to population

ratio

empl Ratio of total people who are employed to the total

population expressed as a percentage.

Female enrolled-secondary-

education

rawsec Total number of females enrolled in secondary

educationxvi

Log number of female

enrolled-tertiary education

femter Logarithm of Ratio of number of females enrolled

for tertiary education- (highest level of education,

by age group. This includes theoretical

programmes for advanced research or high skill

professions such as medicine and vocational

programmes leading to the labour market.)xvii to the

total number of people enrolled for tertiary

education expressed as a percentage.

Log number of female

enrolled -secondary education

femsec Ratio of number of females enrolled for secondary

education to the total number of students enrolled

for secondary education expressed as a

percentage.xviii

Fertility rate The total fertility rate in a specific year is defined

as the total number of children that would be born

to each woman if she were to live to the end of her

child-bearing years and give birth to children in

alignment with the prevailing age-specific fertility

rates.xix

Mobile access Measured as the number of mobile subscriptions

per 100 inhabitants.xx

Internet access Internet access is defined as the percentage of

households who reported that they had access to the

Internet. In almost all cases this access is via a

personal computer either using a dial-up, ADSL or

cable broadband access. This indicator is measured

in percentage of all householdsxxi

Policies promoting work life balance

Government expenditure on

education

gexp This includes direct expenditure on educational

institutions and educational-related public subsidies

provided to households and administered by

educational institutions.xxii It is expressed as a

percentage of GDP and calculated as an index with

a base year of 2000.xxiii

METHODOLOGY

We started collecting data from 1980 onwards till 2010. As we kept on adding explanatory

variables the gap in the data available started becoming more prominent. So finally we

truncated the data set from 1998 to 2009 as data gaps was less sparse for this range. In case

data for one of the years was missing in between two years, we extrapolated the value by

recognising the trend in the explanatory variable.

Since we have panel data for 12 years, we have to make a selection between either of the two

Panel Data methods.

Step1: Creating ‘lag’ and ‘log’ variables

Lag variables were created for the following Explanatory Variables-

a) Tertiary Education - We expect the female enrolment in yesteryear’s Tertiary

Education to differently impact the job proportion of female in the two years.

Intuitively, if the enrolment of female is higher this year we expect female

participation in labour to be higher in future when they graduate.

Log variables is created for the following Explanatory Variables-

a) GDP per Capita

b) Total Population

c) Number of female enrolled for Tertiary Education

Step 2: Deciding between Fixed Effect or Random Effect Method of Estimation

We expect the data to have country fixed effects. We are dealing with 9 different countries,

many of which have been involved in World War and inter-country fights in the 19th Century.

Difference of culture, political ideology and financial upbringing gives us sufficient reason to

believe the existence of heterogeneity bias. The ‘unobserved effects’ are negatively co-related

with the explanatory variables and we try out Hausman test to statistically test the preference

of ‘Random Effect’ Estimation over ‘Fixed Effect’ Estimation.

Results are attached in Exhibit 1.

RESULTS AND DISCUSSION

Relationship between Fertility and Female Labour Force Participation

Rationale- A lot has been focussed, discussed and debated on the correlation between

Fertility and Labour Force Participation. However, Benjamin Cheng1 in his vast study has

concentrated on causality of these two factors in the Japanese and US labour force market. He

has shown that Fertility Rate Granger Causes Female Labour Participation. This pristinely

indicates the number of children (past fertility) exerts a prominent negative effect on female

labour participation discouraging them from seeking impactful and full time employment

outside from house. As birth rate climbs up, new mother choose to withdraw from employee

workforce and concentrate on childcare. This result has been further corroborated by works

of Paresh Kumar Narayana and Russel Smyth2 that Fertility negatively Granger causes

Female Employment for Australia.

In the nine OECD countries which are the focus of our study, year on year increase in female

labour force participation over 1998-2009 has been positive, with an average CAGR of

0.8107 percent. The population during the same time saw an average CAGR of 0.7241

percent. The proportion of female joining the labour force outpaced the increase in

population. The most striking example was Germany, where population has remained

stagnant from 1998-2009, but the women who came out to be a part of labour force increased

from 62.54% in 1998 to 70.37% in 2009. Surprisingly, during 1998-2009, there has been a

positive correlation between fertility rates and female labour supply in these 9 countries. This

is contrary to the microeconomic predictions and seminal work done by Becker and Lewis

(1973), and empirical work done by Butz and Ward (1979) for U.S. and Mincer in which

Economic models of fertility relate higher women education level with an increase in labour

supply and a reduction in fertility.3

1 The causality between fertility and female labour force participation in Japan, pp1142 Female labour force participation, fertility and infant mortality in Australia: some empirical evidence from Granger causality tests, Applied Economics, February 2006, pp 5703 Labour Market Participation of Women and Fertility: the Effect of Social Policies, Daniela Del Boca, Rolf Aaberge, Ugo Colombino, John Ermisch, Marco Francesconi, Silvia Pasqua and Steinar Strøm, pp3

The rationale behind the microeconomic prediction is logical and intuitive. Women face a

disproportionate trade-off between raising children and having a career. When we regress

‘female labour proportion’ with ‘fertility’ we ought to expect a negative relation. However,

the situation is complicated by endogeneity. For instance women with high career ambition

will be negatively correlated with fertility, and the regression between ‘fertility’ and ‘labour

force participation’ will be spurious. To a larger extent panel data takes care of the

endogeneity by averaging out the “unobservable” and eliminating it when making a “Fixed

Effect Method” estimation. The ‘Fixed Effect Method’ output between Female Labour Force

Participation and Fertility is mentioned below for the 9 OECD countries, and it is positive.

Flpr Coef.

Std.

Err. t P>t P>t [95% Conf. Interval]

           

Fert

0.24213

9

0.02768

8 8.75 0.00 .1871918 .2970854

_cons

0.26130

7

0.04493

9 5.81 0.00 .1721272 .3504857

Reasons for positive correlation

‘Societal Response Hypothesis’ puts forth the changing attitude towards working mothers,

increased support from the government in form of social expenditure, maternity leaves,

extended child-care services from the society have mellowed down the trade-off faced by

women while being a mother and an employee at the same time. Vinod Mishra, Ingrid

Nielsen and Russell Smyth in their discussion paper4 conclude that if causation runs with a

positive correlation from fertility rates to female labour force participation then this would be

in tandem with the ‘Societal Response Hypothesis’ and would make the return of women to

workforce more easier. However, much criticism from economists has prevented this

hypothesis from being concrete.

Another explanation that has been put forward has been that the positive correlation is due to

increasing part time employment, increasing educational attainment of female, and enrolment

of females in primary and secondary education. We ran a regression between female labour

force participation, and fertility while controlling for education, part time employment and

4 The relationship between female labour force participation and fertility in G7 countries: evidence from panel cointegration and granger causality, pp6

country fixed effects to find a negative relation between fertility and female labour force

participation.

Flpr Coef. Std. Err. t P>t

         

Pef 0.476024 0.052595 9.05 0

Lpoptot 1.425502 0.158437 9 0

Fert -0.00254 0.030887 -0.08 0.935

primsec 0.401591 0.280239 1.43 0.155

_cons -10.4058 1.136695 -9.15 0

Government Expenditure

The question of causality between Fertility and Female Employment leads to how should

government direct policies towards the population. If Female Labour Force Participation

negatively causes fertility then the government needs to improve “paid maternity leaves” to

encourage working women to have more children in OECD countries. On the contrary if

higher Fertility Rates, negatively causes Female Labour Force participation then the

government policies should be directed towards reducing the cost of mothers from re-entering

the labour force and increasing child care facilities thus reducing the opportunity cost for

women to tend to their children.

Thus either ways government expenditure on social causes have a role in improving the

Female Labour Force Participation. The only point worth debating can be the causality of

Government Expenditure enhancing Fertility Rates and Fertility Rates hence increasing

Female Labour Force Participation or government Expenditure directly impacting Female

Participation Rate which in turn causally impacts fertility. Which in anyways is beyond the

scope of this work.

From our regression analysis we found Government Expenditure positively impacts Female

Labour Force Employment. Since social expenditure, rather than infrastructure building was

regressed we expect the lag of government expenditure not to make much of a difference, and

was proved with our result. The lag of government expenditure wasn’t significant while

controlling for other factors.

Florence Jaumotte5 in her work has shown that inadequate childcare childcare is a constraint

for full time employees than for part time employees, and even his result are in line with our

results about the positive significance of Social Expenditure on Female Labour Force

Participation.

  Co-eff Std. Error t p>t

         

gexp 0.3623326 0.1532204 2.36 0.02

laggex

p 0.1138038 0.1329684 0.86 0.395

_cons -11.11923 2.065181 -5.38 0

Part Time Female Employment

Various published works of econometricians on Female Labour Force Participation such as

Jaumotte (2003)5 and Olivier Thévenon6 have categorised the determinants of female labour

participation between “labour market” characteristics and “family friendly policies”. Labour

Market Characteristics have ‘Flexibility of Working-time” as an explanatory variable. The

reasons put forth by them revolve around two buckets- “flexi work timings to accommodate

work and family” and “employers trying to avoid restrictive employment contracts”.

According to 2001 European Labour Force Survey7, the proportion of female part time

workers on account of household activities was more than 40 percent. From our data we

found the correlation between Female Labour Force Proportion to be positively correlated to

Part Time Employment, and countries, save Netherlands, with higher Female Labour Force

Proportion had higher proportion for Part Time Female Employees. Thus policies that tend to

remove distortions against part time working favour women contribution to work force.

5 FEMALE LABOUR FORCE PARTICIPATION: PAST TRENDS AND MAIN DETERMINANTS IN OECD COUNTRIES ECONOMICS DEPARTMENT WORKING PAPERS NO.376, pp196 Thévenon, O. (2013), “Drivers of Female Labour Force Participation in the OECD”, OECD Social, Employment and Migration Working Papers, No. 145, OECD Publishing; pp 217 http://ec.europa.eu/eurostat/statistics-explained/index.php/Statistics_in_focus

A Fixed Estimate Regression is given between Female Labour Force Participation and Part

Time Female Employment while controlling for other variables. The relationship is

significant and the coefficient is positive.

Flpr Coef. Std. Err. T P>t [95% Conf. Interval

           

Pef

0.282544

2

0.051724

4 5.46 0

.1796846 .38540

38

Proportion of Dependent Population

The proportion of older people in labour force has been on a constant decline on a worldwide

basis. In 1950, 33.33 percent of people above 65 participated in labour force, today it is just

20 percent8. In European countries the same set of figures were 22 percent and 5 percent

respectively8. This change increases the pressure on government expenditure for pensions and

healthcare for the elderly. As the proportion increases further (none of the 9 countries in the

1998-2009 range have a fertility rate above 2- the self-sustaining level) three changes are

simultaneously happening9 which is improving the female labour proportion in the labour

force. First is a social change, as the generation prior to 1970 moves out of the workforce the

social change is integrating women faster into the formal labour force because of break of

social traditions. Second is, the economic dimension that female contribution leads to higher

economic growth. Third is, more female participation leads to financial sustainability of the

welfare situation of these countries. With respect to the third point governments will be

further propelled to promote female labour participation to reduce the fiscal gap due to rising

welfare expenditures.10

Fixed Effect regression while controlling for the rest of the variables gives the following

output-

flpr Coef. Std. Err. t

P>t

[95% Conf. Interval]

8 World Population Ageing 1950-2050, Population Division, DESA, United Nations; pp 19 The Trend in Female Labour Force Participation: What Can Be Expected for the Future? Marike Knoef, Rob Euwals, Daniel van Vuuren; pp 2610 The Trend in Female Labour Force Participation: What Can Be Expected for the Future? Marike Knoef, Rob Euwals, Daniel van Vuuren; pp 2

           

pop65

0.468244

9

0.250205

7 1.87 0.065

-0.029876

0.966366

Proportion of female enrolled for secondary education:

Educational attainment helps us to explain the differences in labour force participation among

men and womenxxiv Studies have shown that a key factor driving women’s aspiration to join

the labour force is the sharp increase in girl’s educational attainment in the recent decades.xxv

Logarithm of females employed for tertiary education

The relation between female labour force participation and logarithm of tertiary education is

negative which is counter intuitive. One of the possible explanation for this anomaly is the

concept of discouraged worker effect theory according to which well educated women do not

prefer re-joining the work force once they decide to drop out of work force (Sabarwal et al.,

2010). Another possible explanation could be the “income effect” according to which, higher

levels of education leads to jobs with higher hourly wage rates that enables families to afford

having one parent who works part time.xxvi

flpr Coefficient Standard

Error

t p>t 95% Conf. Interval]

Lower Upper

ltertorg -0.0824 0.0308 -2.68 0.009 -0.1435 -0.0211

Unemployment rate:

As expected, unemployment rate and female labour participation are negatively correlated

with each other. Family related and labour market related constraints can curb women’s

ability to help stabilise family incomes. The perceived need for them to seek employment and

join the labour force may not be so severe if earnings losses of men in their households are

temporary or if out-of-work benefits provided by the government is high. In addition, means-

tested unemployment benefits that reduces once one partner in the household starts earning

also acts like a significant barrier to boosting female unemployment. With the onset of

recession in 2009, gender employment gaps reduced across all OECD countries with the

exception of Israel, Sweden, Poland and Korea. Female employment suffered only marginally

compared to male’s since the impact of recession was predominant in the manufacturing,

trade and construction sectors compared to the services sector where most of the females are

employed (In 2008, one-third of the female working population was employed in service

sector in OECD countries)xxvii and the services sector showed a modest decline during the

same period. Evidence from previous studies on recessions have shown that while men are

more likely to lose jobs at the onset of recession, they are more likely to find jobs once the

economy recovers (Maier 2011). However, the same cannot be said for women since the

phenomena of discouraged worker effect is more predominant and they may not try re-

joining the labour force once the economy recovers (Sabarwal et al., 2010).

flpr Coefficient Standard

Error

t p>t 95% Conf. Interval]

Lower Upper

pop65 -0.2545 0.0946 -2.69 0.009 -0.4429 -0.0661

Gender Wage Gap

A whole gamut of historical, cultural, and social reasons have contributed to gender wage

gap. The key factors put forth by Australian Justice Department arexxviii-

a) Men and Women still work in different areas of workforce, workspace and industries.

Jobs of hospitality, catering, nursing are female dominated and have been traditionally

undervalued.

b) Casual and non-career part time jobs, which provide negligible opportunities for

training, development and career progression find healthy proportion of women

employees.

c) Lack of permanent flexible work timings impact women with dependent children

more than man who shrug off child’s responsibility to the mother.

Gender wage gap a) negatively impacts the morale of female b) decreases the opportunity

cost of rearing children c) reduces the income effect. Thus gender wage gap negatively

impact female labour force participation.

flpr Coef. Std. Err. t P>t [95% Conf. Interval]gendwagga

p-

0.14435 0.067606 -2.14 0.036 -0.27882-

0.00988Impact of various sectors on female labour force participation:

What drives women’s labour force participation?

The growth/stagnancy in female labour force participation can be examined by analyzing the labour supply as well as labour demand effects.

Labour supply effects:

Rising male education and income act as catalysts in lowering the female labour force participation due to the popular income effect; house hold income rises and there is no/less necessity for the women to work to sustain family life

Social stigmas and restrictions can lead a woman to not work in certain blue-collar and menial jobs

However the factors mentioned above applies only to the mean and higher level of education distribution whereas these concerns are overcome by economic distress and the woman has to work

Fertility decline in women leads to higher female participation The growing education attainment level among individuals might not be inclined

towards labour market expansion. There is a possibility that the rising female education might be associated with declining labour market orientation (unobserved)

As an epilogue to the previous point, the focus of female education might be to improve the marriage rather than her labour market prospects

Labour demand effects:

Even if the above mentioned labour supply factors are improved upon labour demand is what sets the lower benchmark for female participation

In particular, say the number of educated women needed in a particular white-collar job (service industries, healthcare and education) declines which will naturally propagate to labour supply as well

Therefore the relative difference in labour supply and demand of these type of jobs at the local level will impact the participation rates

Apart from the supply and demand related effects, the following 3 sectors have an important part in determining the female labour force participation.

Service sector Manufacturing sector Agricultural sector

Hence we would look into these factors in detail

Employment in Services

Service Sector in a particular country and female employment are correlated. The correlation

in our data for 9 OECD countries for 12 years is .7871. Historically, it has been shown by

Rogersonxxix that the correlation for OECD countries is 0.82. Rogerson, in his 2007 paper,

finds that the female employment differential between America and Europe can be attributed

to the prevalence of service industry. A large service sector provides larger opportunities for

women for better employment opportunities both in terms of wages and new job opening.

This effect is a demand driven effect, which attracts more women to be a part of labour force.

The supply driven effect rises from the fact that women having better jobs and higher pay

leads to a bigger demand for services and higher consumption for services.

Hence focusing on expansion in service sector demand and supply would be one way to

address the stagnant FLFP economies. One way to go about it would be to decrease the entry

regulations and increase the financial and social incentives for women.

In our analysis we found Proportion of Employment provided by Services to have a positive

effect on Female Labour Force Participation while controlling for other factors.

flpr Coef. Std. Err. t P>t[95% Conf. Interval]

empser 1.359142 0.265847 5.11 0 0.830382 1.887902

Employment in Agriculture

Agriculture being the biggest employer is a characteristic of African and Asian economies.

They are low income countries, mostly backward and with inadequate access to good quality

education. They do employ large number of women in agriculture sector but it is more on

account of ‘push’ factors rather than the ‘pull’ factors. As the employee proportion of

Agriculture increases it can be hypothesised that women engagement in labour force will

increase. However, what becomes more troublesome is the skewness of amount of work put

in by women via men, and the elongating gender wage gap. In agrarian economies women

tend to children, the elderly, the cattle and other household activities. In addition to this

women are also involved in working at agricultural farms for minimal to no wages. Women

are more likely to be involved in part time and seasonal employment and earn lower wages

than menxxx, thus increasing the gender wage gap. The decreasing dependence on agriculture

to fuel the GDP growth is one of the causes why female participation across GDP per capita

is the famous ‘U Curve’. As share of agriculture decreases and manufacturing picks up GDP

per capita increases which dither women from participating in Agriculture, thus leading to a

reduction in Female Labour Participation. However, once Service sector starts picking up

which has better pay and lesser physical demands women once again re-enter the labour

force.

The Fixed Effect Estimators are given below for employment proportion of agriculture and

how it impacts Female Labour Force Participation.

flpr Coef.Std. Err. t P>t [95% Conf.

Interval]

empagri0.52080

50.6670

9 0.78 0.437 -0.806011.84761

9

Exhibit1

hausman remod femod

(b) (B) (b-B)sqrt(diag(V_b-

V_B))

Remod femodDifferenc

e S.E.

pef -0.04409560.227225

8

-0.271321

4 0.026384

lgdpc -0.0301120.009260

8

-0.039372

8 0.0422746

strict -0.0208073 -0.006465

-0.014342

3 .

pop65 0.24811950.547054

4

-0.298934

9 0.3824955lpoptot 0.00952 1.379112 -1.369592 .

une 0.6986535

-0.339093

6 1.037747 0.1725852

primsec -0.62047670.253005

4 -0.873482 0.3260118

empagri -5.8885840.520804

5 -6.409388 0.5608185empser -1.052758 1.359142 -2.4119 0.1157595

empman -1.967496 1.370422 -3.337918 .

gendwaggap 0.3052527

-0.144349

70.449602

4 0.108446

ltertorg -0.208462

-0.067491

5

-0.140970

6 0.0803217

lagtert1 1.28E-08 8.41E-09 4.43E-09 2.00E-08

fert -0.0167517

-0.015662

8

-0.001088

9 0.0209693

govexp 0.4443363 0.1422340.302102

3 0.1792156

chi2(14) = (b-B)'[(V_b-V_B)^(-1)](b-B) 436.15 Prob>chi2 = 0.0000

REFERENCES

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