The research reported in this thesis was on “Female participation in the labor force of Pakistan”. The purpose of research was to study the issue for the survival of female in Pakistan’s economy which causes female to participate and not to participate in the labor force of Pakistan. The secondary data was collected by consultation of literature in the libraries and Internet and also from the material printed by different hospitals of Lahore. Stat Graphic software was applied to analyze the time series data for regression analysis and the results were interpreted by usual principles of statistics. The findings suggested that the female education and female human capital have a positive significant impact on the female participation in the labor market and female fertility have a negative significant impact on the participation. Thus, female schooling and gender gap in education has become narrowed over the year, it is important for the government policy to ensure sustainability of female education to encourage females to participate for the economy of Pakistan.
Today’s world economic status stands at a very recessionary stage, which has compelled all the households to participate in some form of economic activity, so that these households could make both end of their family meet. In such a scenario, the labor market globally has witnessed a major influx of female labor force, for the past decade or two. Labor market has become a serious aspect in the eyes of strategists and economists. The pivotal aspect to be highlighted here is the female participation in the labor force that has somehow affected the structure and mode of labor market. Developed countries indeed provide platform to their female labor force. The labor force is the set of non-military people are officially looking for a job or employed. Each individual age 16 or older is classified as employed, unemployed or not in the workforce, according to current guidelines and recent. The most common reasons for a child not be classified, not in the labor force are retired, student, or institutionalized. The size of the workforce changes over time. Workforce changes occur due to a combination of demographic, social, and seasonal as well as macroeconomic conditions. In general, the majority of the population is part of the workforce. However the developing countries have recently seen a rising trend in the participation of females into the labor force. This paper aims to identify and analyze the reasons & causes of female labor force participation and the variables that affect this phenomenon. In order to carry out this research time series analysis of the data has to be taken into account. This also aims to analyze the female labor force participation in the Pakistan and the basic constraint while conducting this research, however, is the lack of recorded data available to analyze the trend of the female labor force participation. Pakistan is a developing country that faces many sociological aspects that effect the female labor force participation. Prominent of these factor are; Cultural aspects, religion, government policies, female health, gender discrimination and education. This paper has a mounting managerial value for the policy makers and employers, to establish the concerned aspects of female participation in labor market.
One of the most striking phenomena of recent times has been the extent to which women have increased their share of the labor force; the increasing participation of women in paid work has been driving employment trends and the gender gaps in labor force participation rates have been shrinking. There are three important stages of women’s participation in the workforce. During the late 19th century in the 1920s, very few women worked. They were young single women who generally left the workforce to marriage, unless the family needed two incomes. These women work in the textile industry or as servants. Between 1930 and 1950, labor force participation of women has increased primarily due to increased demand for office workers, women’s participation in the movement of secondary and due to electrification, which has reduced the time spent doing housework .During 1940’s, 35-45 year olds were cater the highest portion of labor force than any other age groupIn the 1950s to the 1970s, most women were secondary earners who work primarily as secretaries, teachers, nurses, and the librarian. By the mid-1970s, there was a period of revolution of women in the workforce caused by a source of various factors. Women are specifically designed for their future in the labor market, invest more in the majors apply to the university that prepared them to enter and compete in the labor market. Over the last 25 years, however, the young generation has jumped into the work force, considerably increases from a percentage of 40% to 75% from 1970 to 1990. Especially in the 1980s and early 1990s, labor force growth was substantially higher for women than for men for every region of the world except Africa. As we peep in the past only males were considered to be the bread earners of the family but now in this era of globalization female gender is actively participating in earning activities. Pakistan is a developing country that has been suffering serious fiscal deficits and low GDP growth rate for more than two decades. Under these circumstances, active female participation in labor market could help alternate many negative elements related to the economic crisis that the country faces. Share of female in total labor force has increased from 2.8 million in 1994-95 to 4.5 million in 1999-00. There are a number of factors faced by Pakistan that discourage female participation in the labor force. In order to establish these problematic factors uniformly, a thorough study of the history of this issue has been done. It has been found that over the past decade female participation in labor force has increases from 14.4% to 37.7% in 2001-02. Women face serious issues at their workplace. Lack of proper education, frail and non – favorable government policies, rigid norms of the society and sheer gender discrimination with man dominated mindsets are the core problems face by the female labor force of Pakistan.
During the last century, the issue of women in the workplace has been tumultuous. In the early 20th century, few women participated in the workforce. Woman’s place was at home taking care of the family and management of the domestic world. It was considered unsuitable for women in certain professions, and most women did not work, other than going about their daily business at home. The Great Depression exacerbated the fact that unemployment has reached its highest level in history, but women, more than ever, staying home to care for their husbands who now found themselves without work. Second World War was a complete reversal of this trend. Booming productivity and men left their homes, some to work, most to join the war effort. Women, in large masses for the first time, also affected the labor market. Since then they have not looked back, as the employment of women in the workforce has increased steadily over the four decades after the Second World War. It was not until very recently that women in the rate of employment growth has stabilized. Define what “participation in the labor market” is an essential starting point for any investigation. At what age is it considered fit for work? What constitutes a person “actively seeking” employment? Economists often try to explain the rate of participation by age, sex, race and income groups and use this information to cite trends. The income-leisure model theorizes that the choice of working or not women’s work is based mainly Wages for work against non-wage labor. This theory considers the non-labor income has a negative influence. Empirical evidence, however, suggests that women choose to work if wages are good regardless of the benefits non-work. Because most men are permanently in the labor force, estimates of reserves and manpower projections concentrate supply mainly on women. International Generalizations are often misleading, since trends vary considerably across countries.
In most countries, women work less as compared to men toward the value of production. The social environment, inconsistencies and statistical methods to save the work contribute to this inequality. In Pakistan, young single women who generally left the workforce for marriage, unless the family needed more income. The pivotal aspect of the Pakistani women is to be highlighted here is the female participation in the labor force that has somehow affected the structure and mode of labor market. These women worked primarily in the textile manufacturing industry or as domestic workers. As mentioned above in productivity boomed the Women of Pakistan, masses for the first time, hit the labor market. Since then they have not looked back, as the employment of women in the workforce has increased steadily over the four decades after the Second World War. It was not until very recently that women in the rate of employment growth has stabilized. According to a news reporter survey in early 2008, the Pakistani women and labor force indicators were very weak although Pakistan was doing well on the Political Empowerment of Women, where it ranked at 43th among 128 countries.
In the past ten years, mothers were the largest contributors to rising rates of women in the overall labor force in Pakistan. For mothers with children aged between 6 and 17, a surprising 77% are in the workforce. With children under 6, this percentage includes 62%, but both much higher than ten years ago. For mothers of infants under one year, the percentage in the labor force grew by nearly 20% over the last decade. So, the general trend is a strong reflection of social norms of today: paid work is an integral part of the lives of many women, as opposed to the early 20th century when work was the norm.
The most common reasons for a non-child to be classified not in the labor forces are to be retired, a student, or institutionalized. The size of the labor force changes over time. During the last century, few women participated in the workforce. Woman’s place was at home taking care of the family and management of the domestic world. It was considered unsuitable for women in certain professions, and most women did not work, other than going about their daily business at home. While over all the discoveries of the world is that women’s participation in the labor force decreases at first and then is takes a boom in the later years when a critical level of development was reached. Education is seen as a potential booster in developing countries which is registered as an official bench mark for women. The economic and social values of their work have frequently been under-recorded and underestimated. Therefore, in order to understand the factors, which determine when and where women are employed, one has to study the changing pattern of employment of men as well as women in our rural economy. Now we analyze that what has been the position and participation rate of women in labor market over the years in Pakistan.
The Pakistani female education, female labor participation and health care continue to interval behind men in most of the fields of life. Women who comprise almost half of the population are enormously vulnerable, especially in developing countries. Pakistani women like in many developing countries insulate behind men because of social, cultural and traditional norms. Low literacy rate high birth rate, poor health, low life anticipation and non-recognition of their work within the family are some of the common personality of women in Pakistan as well as in other developing countries. It is regrettable that individuals apparently similar with respect to productivity receive widely different earnings on the basis of non-economic criteria like sex, which raises serious questions of equity, efficiency and human rights.
Schultz (1990) assessed the patterns in women’s labor force participation and composition of their participation among wage earner, self-employed and un-paid family worker. He used the data of labor force for 75 countries by the sector of economic activity and wage/self-employed/unpaid workers and sex. The job type was classified into four categories i.e. (1) wage and salary worker, (2) employers, self-employed, or own-account workers, (3) unpaid family workers, and (4) others. The data was collected for the period of 10-30 years span from 1950-1982. He used “only income per adult” as an approximation and it was treated as the determinant of different labor force sectors and job composition changes. The statistical methods used were Logistic Regression Model and Decomposition of Changes over Time by Fractional Model-the two sector model considering the inter/intrasectoral changes in job types are responsible for the current change in the labor market position of women. He concluded that women’s are the only one who loses ground due to labor market regulations and distortions in low-income countries with consensus on the rate and structure of economic growth because it slow down the women’s participation from non-market to family market work and from family to firm employment.
Marcel Fafchamps(1999) conducted an extensive investigation that whether in specific four districts of Pakistan the human capital affects the productivity and the labor allocation of rural households. During the study he found two factors that households with better educated males are earning higher income from outside farm and it further divert labor resources away from farm activities toward non-farm work. The writer also stipulates on the variable of education he says that, there has no significant effect on crop productivity and production of livestock. The article has separately disturbed the 30 percentage of average income of household as compared to the crops, livestock and with the activities outside farm and more over rental income and remittances amount to another 30 percent. Agricultural wage income is trifling among sample households. During the research the researcher has come to a result that the effect of human capital on the household incomes is moderately realized through the disinclined of the labor from low productivity activities to nonfarm work. There is no importance for the Female education and nutrition in the farming so it does not affect productivity and labor distribution in any methodical convention, the effect remains consistent with the insignificant role that the women play in market leaning activities in Pakistan. The hypothesis results provides strong evidence against the two factors which are perfect labor and factor market as the education is effecting the male farmers where as on the other hand the females are still kept uneducated and on farm activities.
Vlasblom and Schippers(2004) established a quantitative research on the increase in female labor force participation in Europe that causes differential changes in female labor market. They illustrated that low education level and the effects of children are known as the most significant factors for female participation rate. They used data from Labor Force Survey of Eurostat for the analysis of year 1992 and 1999. The samples they used for data analysis, they restricted themselves to the female age ranges from 25-45year and 25-35years. They used the same method of analysis used by Henkens(2002) i.e. Labor Supply Model and Decomposition Analysis to compute the observed changes in female participation due to different factors which are related to different characteristics. The key variables used for explanation are age, education level, number of children present in the household, the age of the youngest and the difference between the youngest and oldest child in the household. They concluded that the rate of married women has increased in the female participation in the labor force of Europe. It reveals that the participation behavior of women has not only changed but also some characteristics of the population changed as well and participation rate have increased with or without children. It also shows a shift in fertility pattern in all over the Europe resulted in a higher supply of female labor force.
Guy Standing(1977) is talking about the historical data collected in a 1973. He uses this data for the survey to examine some of the determinants of labor force participation specifically in Sri Lanka. The whole research he conducted was based on the regression technique, using both tabular and ordinary least squares which had already examined through regression analysis. The article consulted on women that they have the most prominent and positive result on the way through high level of participation in estate areas. Furthermore one important result is the optimistic relationship between education and participation of the women, whereas on the other hand the fertility and urbanization have a negative impact on participation. The article also say about the variable of participation rates which refer to the men, who accounts to be rated as less variable supportive than those of women. In Sir Lanka the women participation increases as they get married. In addition it has often assumed that the woman labor force participation give rise to her domestic productivity because of their children. The level of income is co related with the opportunity cost and the household income. The factor of less rated male participation has deducted significant sectored differences. The higher participation rates of married men are also noteworthy. In the article other variables have an effect, even though a weak one. The result shows that the male participation is included through education, migration, family income, and family size.
Sundaram and Vanneman(2007) conducted a research on the multidimensionality of gender inequality with respect to the female labor force participation. They examined the relationship of literacy and education among 409 districts of India and come up with a result that males are more educated in those areas where women are more in the labor force and this is due to the higher number of proportion among males. They used general models of gender inequality which were used by Chafetz(1984) to determine the role women from household to state governance. They also demonstrated the cultural attribute related to women’s labor force participation in a higher number due to the withdrawing of females from school and to put them at work which create a gender gap in basic education. They used district level data from 1991, Indian Consensus and to analyze data they used Spatial Autocorrelation. The samples were divided into different independent variables i.e. Female share of Labor Force, Child Labor, Patrol local Exogamy, Educational Development and Economic Development. They concluded that the correlation between girls and adult women’s labor force participation is responsible for the negative association of adult women’s work and girl’s literacy and they also concluded that girls have better chance to go to school in those areas where the women do not work.
D. Narasimha Reddy(1979) in this paper the researcher is analyzing the aspects of female work participation in India using the qualitative and quantitative research. The reading is divided into four sections. The first division deals with an aggregate analysis of the relationship between female participation rates and definite demographic and some of the socio-cultural factors. The age factor specific productiveness rates and the female participation rates in India do not show any inverse relationship. As the India rural female activity participation all over the world is touching the peak. The second section is committed to an econometric model of rural female participation. The writer on the second part, outline the factor of education which is also a variable. He says that a gradual decline in the participation rates of the women specially is with increase in education up to middle school level, matric and above the participation rates reduces at large. Then the third section in which reference is made to certain qualitative aspects of female work, and with a view of providing a complete summary of female work participation. The last section endeavor to emphasize the policy implications of the results of the article. The research is conducted on the 1971 sample data. While viewing a cross section studies of several countries show that there is a strong inverse relationship between female participation rates and fertility in economically developed countries and while such a relationship is either weak. In the result it was found out that women’s direct contribution to specific agriculture was not less than 50 per cent of all agricultural work and if the female role in animal husbandry and farm support activities at home were taken into account their contribution would be much higher like 30 percent.
Harry A. Sackey(2005) study is on the Ghanaian economy where there is an issue of survival for women to participate in the labor market or not to participate. As the research was further conducted the evaluation showed a decline in fertility as Parallel to the rising trend in female
participation rates. The study indicated the decline to be the factor of schooling factor. The data for this study was taken from the Ghana so that the clear picture of the living standards surveys with demographically enriched information is taken accurately to estimate female labor force participation and fertility models. The variable which is used in this study was the female schooling matters. In the study conducted the economic theory suggests that a concurrent consideration of the effects of schooling on fertility and labor supply and as well as on wages and investments in children could be more revealing than focusing on a single outcome. The writer is saying that the female schooling in both urban and rural localities and in both primary and post primary schooling levels apply significant positive impact on women’s labor market participation, and have an reverse effect on fertility. On the step ahead the study is then including that although the gender gap in education has become narrower over the years but it is extremely important for government policy to ensure the sustainability of the female educational accomplish obtained. On the ending note the key mechanism of this article which is for enhance female human capital and the productive employment with sympathetic impacts on perceptions of ideal family size and more over fertility preferences.
The research type is quantitative in nature as the historical data is being used for checking the female labor force participation with the help of my independent variables which are female education and training, female fertility (female health), and human capital.
The research is secondary in nature because of usage of historical data for evaluation. Research being carrying out is time series. My data is available in the series of years so I would take a data for 10 years to check the historical trend.
Ministry of education, World Bank, Asian Development Bank, World Health Organization and Government of Pakistan Statistics are some sources from which I collected the reference data.
Female Education & Training: The education of a female. (Primary, Secondary, Tertiary and Health Education)
Fertility (Female Health): The capability of a female to produce as much number of children as she can.
Human Capital: Human skills which increase the market value of a job’s person.
Labor Force: Total number of people who are employed or looking for a job in the country.
Gender Wage Gap: The difference between male and female with respect to intellectual, cultural, political, social, economic and physical attributes.
Women Empowerment: The decision making power of female with regard to its education, participation, public speaking and related to herself.
The ratio of female population in Pakistan is 52% and the research will be comprising on the sample size of those females whose age ranges from 15-64 years.
H0: Female Education and Training does not affect females to participate in the labor force
H1: Female Education and Training does affect females to participate in the labor force
H0: Female Fertility does not affect females to participate in the labor force
H1: Female Fertility does affect females to participate in the labor force
Ho: Human Capital does not affect females to participate in the labor force
H1: Human Capital does affect females to participate in the labor force
The techniques for carrying out the research will be Regression Analysis. The technique has been very useful according to the literature reviewed. It will help in analyzing the data in order to know whether to accept or reject the null hypothesis.
Charts, graphs and tables would be used to represent the results after the data will be computed through different statistical software’s like Minitab and Stat graphics.
The results generated from the above software’s would be compiled and would be analyzed using the tables, graphs and regression results.
Parameter Estimate Error Statistic P-Value
CONSTANT 11.5662 5.7346 2.01691 0.0903
Female Education -0.0558931 0.0222006 -2.51764 0.0454
Fertility Rate -0.239874 1.00939 -0.237643 0.8201
GNI per capita 0.012258 0.00161781 7.57692 0.0003
Analysis of Variance
Source Sum of Squares Df Mean Square F-Ratio P-Value
Model 26.8871 3 8.96236 209.47 0.0000
Residual 0.256721 6 0.0427869
Total (Corr.) 27.1438 9
R-squared = 99.0542 percent
R-squared (adjusted for d.f.) = 98.5813 percent
Standard Error of Est. = 0.20685
Mean absolute error = 0.122481
Durbin-Watson statistic = 2.51518
The output shows the results of fitting a multiple linear regression model to describe the relationship between Female Labor Force and 3 independent variables. The equation of the fitted model is
Since the P-value in the ANOVA table is less than 0.01, there is a statistically significant relationship between the variables at the 99% confidence level.
The R-Squared statistic indicates that the model as fitted explains 99.0542% of the variability in Female Labor Force. The adjusted R-squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 98.5813%.The standard error of the estimate shows the standard deviation of the residuals to be 0.20685. This value can be used to construct prediction limits for new observations by selecting the Reports option from the text menu. The mean absolute error (MAE) of 0.122481 is the average value of the residuals. The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file.
Since the DW value is greater than 1.4, there is probably not any serious autocorrelation in the residuals.
In determining whether the model can be simplified, notice that the highest P-value on the independent variables is 0.8201, belonging to Fertility Rate. Since the P-value is greater or equal to 0.10, that term is not statistically significant at the 90% or higher confidence level. Consequently, you should consider removing Fertility Rate from the model.
After getting the p-values we can conclude that
As the probability is less than 0.10 we reject Ho. So, Female Education and Training does affect females to participate in the labor force.
As the probability is less than 0.10 we reject Ho. So, Human Capital does affect females to participate in the labor force.
As the probability is not less than 0.10 we reject Ho. So, Female Fertility does not affect females to participate in the labor force.
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