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Abstract:
The present study tried to explore the impact of socioeconomic and demographic variables on female labour force participation (FLFP). For this purpose, a well-structured questionnaire was used to collect data from 150 people drawn from three different tehsils. The data were analyzed through frequency distribution, cross-tabulation, and a logistic model. The results indicated that in Tehsils of Dera Ghazi Khan, Taunsa, and Kot Chuttah, most of the females were not contributing to commercial activities since they were facing restraints like being illiterate, having no family permission, inadequate employment opportunities, and extensive household which made their lives extremely busy. Furthermore, the logistic model results indicated that the education, experience, parents' education, and earnings of the respondent were statistically significant. However, the study concluded that female education was the most crucial factor in the FLFP in economic activities and the improvement of employment opportunities
Key Words:
Female Labor Force Participation, Logistic Regression, Socioeconomic and Demographic variables, Survey Data
Introduction
Various nations worldwide achieve economic growth because of the proper use of available resources. There are many factors, like the availability of land, labour, capital, technology, etc. Economic growth requires physical capital and human capital of equal value. Nevertheless, both men and women play a vital role in human capital. As a result, female participation is critical for economic development and growth.
According to the Labor Force Survey 2015-16 (GoP, Statistics Division, Pakistan Bureau of Statistics), female participation was 15.8% in 2014–15 and 5.8% in 2013–14. The natural participation rate dropped by 0.57% from 32.27% in 2014–15 to 31.70% in 2017–18. Moreover, 65.50 million people made up the total civilian labor force in 2017–18, 50.74 million of them were men and 14.76 million of whom were women.
It is observed that, mostly in developed countries, women have equal opportunities for education and FLFP. In almost all developed economies, women work side by side with men. Females in developed countries are self-sufficient in decision-making. That’s why the FLFP rate is significantly high in developed countries. According to the statistical database source (Nation Master, 2019), the FLFP rate is 70% in the U.S., 73.2% in Canada, 60.6% in Japan, 58% in Europe, and more than 60% in Sweden, Norway, and Switzerland. It ranges between 50 and 60 percent in Germany, France, Austria, and Australia.
Nevertheless, the phenomenon is the opposite; in developing countries, females have great education opportunities compared to their mothers, but they still have minimal opportunities to do jobs and participate in paid labour force activities, as they are not independent in decision-making. In developing countries, females must face many constraints like society's behavior towards working women, patriarchal ideology, security issues, religious beliefs, etc.
According to Kazi and Raza (1986), most females are pushed into work only when their families are poor. The primary reason for working in these economies is that more females are in the home compared to their male counterparts. In developing countries, women have a minimal role in paid labour force activities.
According to Sorsa, P., et al (2015) in most Asian countries, females do not participate in labour force activities due to patriarchal ideology and the harsh mindset of society in relation to working women. So, primarily, females engaged in unpaid labour force activities spend lots of time taking care of one's children. According to Etzaz and Amtal, (2010), women's labour force participation is curial for the living standards, saving rates, and dependency burden among households. Education is an extremely crucial factor that affects female labour force participation. If females have a higher level of education, they have more chances to enter labour force activities. They may have more liberty to do jobs because they feel independent with a high education. As a result, education is the most important factor in encouraging females to engage in wage-earning activities that benefit the economy.
Due to social, historic, and cultural conventions, Pakistani women, like those in many other emerging nations, trail males in many areas. Women in Pakistan generally have low literacy rates, large birth rates, and a lack of appreciation for their domestic labor. The current study's goal is to investigate the various social, economic, and demographic aspects that significantly affect women's decisions on whether to enter the workforce.
Numerous studies are available on the subject. However, issues including the respondent's mother's education, how society treats working women, and job limitations imposed by the family's head of household have been disregarded. Therefore, the main goal of this study is to ascertain how the aforementioned elements affect women's decisions to join the paid workforce. Two factors or levels typically influence a woman's decision to engage in economic activity. To begin with, considerations like employment availability, her educational background, and any skills she may have influenced her decision to work. The second is at the collective level; in this case, her choice is influenced by the socioeconomic and demographic characteristics of the place or region in which she resides.
According to neoclassical economists, education is a key factor in determining whether women will enter the workforce. Women's participation in the job force is more noteworthy the better educated they are (Becker 1965; Mincer 1980). It is also true that there are more jobs accessible to them as their education increases. Women's production will increase with increased investment in human resources, including education, training workshops, and skills.
The present study is being conducted in the provisionally administrative tribal areas of southern Punjab (PATA). The tribal areas cover a large area. So, for our concern, we take only D.G. Khan from the provisionally administrative tribal areas of southern Punjab. Further, D.G. Khan has four tehsils, including D.G. Khan, Taunsa, Kot chuttah, and Koh-e-Sulaiman. Due to the unavailability of relevant data, Tehsil Koh-e-Suleman is excluded from the study area. Our research study identified the significant determinants of female labour force participation (FLFP) and the constraints females face in the study area. The binary logistic model determines the factors that affect the FLFP. This model is widely used in literature, not only at the national level but also in international studies. Some of them are Faridi et al. (2009), Umbreen and Kokab (2017), Che and Sundjo (2018), Sorsa et al. (2015), and Ayferam (2015).
Sharma, A., Saha, S., (2015) investigated female employment trends in India and concluded that the share of rural women in the workforce was much higher than urban women. However, women in rural India were inferior in the labour market vis-à-vis their urban counterparts. Moreover, most rural women were predominantly engaged in agriculture, with low-earning opportunities. The wage differential between rural and urban women was also striking. Therefore, the results suggested appropriate government intervention and policy formulation for addressing the issue.
The studies mentioned above demonstrated that determinants of FLFP exist in various countries. These studies also define the different socioeconomic and demographic factors and constraints females face during their labour force participation. These studies also discovered how females' socioeconomic and demographic factors and constraints affected the FLFP. In most studies, the researchers explore different factors such as the education of the female, marital status, number of females in the household, age of the respondent, income level of the family, and constraints faced by women like patriarchal ideology, purdah, religious beliefs, society's behavior towards working women, security issues, etc. The above studies also described how higher education for women leads to higher labour force participation. In the literature mentioned above, most studies used probit-logit or logistic regression to explain the determinants of FLFP. In the present study, we have attempted to investigate the role of education, location, education of the father, mother, and husband, parent's income, husband's income, family size, number of children, number of earners, and earnings of the respondent in detail
Methodology
The study was based on cross-sectional data collected through a field survey. The study used a questionnaire as a tool for data collection. A well-structured questionnaire was organized for collecting data. The survey data were collected during the year 2019. Random samples of 150 respondents were taken from district Dera Ghazi Khan, and then150 samples were equally divided between the three tehsils of district Dera Ghazi Khan (D.G. Khan): Tehsil D.G. Khan, Taunsa, and Kot Chuttah, respectively. The empirical study used multistage and purposive sampling techniques as sampling designs.
The recent study was conducted in tribal areas of southern Punjab. The area of tribal lands is very vast. As a result, the study focuses solely on District Dera Ghazi Khan (D.G. Khan) in the tribal areas of southern Punjab. Further, D.G. Khan has four tehsils, including D.G. Khan, Taunsa, Kot Chuttah, and Koh-e-Suleman.
Model Specification
The theoretical model of FLFP contains so many variables, like location, marital status, age, education experience, parents’ education, husband’s education, parent’s income, husband’s income, number of children, family status, nature of work, number of earners in a family, earning, nature of the work, monthly income of a family, family size, and monthly expenditure of respondent’s family.
The dependent variable is categorical, such as whether the female participates in the labour force. Therefore, the study used the logit model to determine the impact of independent variables on the dependent variable, female labour force participation (FLFP).
Following the Hausman, J., & McFadden, D. (1984), Cramer, J. S. (2003), Demaris, A. (1992) and Memon, M. H., et al. (2015) Khan, K, et al (2016) and (2017) The logit model is
Logit (P)=ln??(P?(1-P)?)
Whereas the value of “P” shows an actual number lies between 0 and 1, so the model as desired below:
ln??(P?(1-P)?)= ???0?+?_1 x_1i+?_2 x_2i………………..+?_k x_ki
If we write this model according to our female labor force participation model, then this model looks like that,
ln??(P?(1-P)?)=?_0+?_1 x_1i+?_2 x_2i+?_3 x_3i+?_4 x_4i+?_5 x_5i+?_6 x_6i+?_7 x_7i+?_8 x_8i+?_9 x_9i+?_10 x_10i+?_11 x_11i+?_12 x_12i+?_13 x_13i+?_14 x_14i+?_15 x_15i+?_16 x_16i+?_17 x_17i+?_t
Whereas: ln = natural logarithm ? = constant, P= probability that female participation in labour force, 1-P=probability that female does not participate in labour force, x_1i= education, x_2i= experience, x_3i=parents education, x_4i=husband education, x_5i=parents income,x_6i=husband income,x_7i=earning, x_8i = monthly income, x_9i=number of kids, x_10i= family size, x_11i=monthly expenditure, x_12i= no sources of income,x_13i= nature of work, x_14i = constraint from family, x_15i=unmarried, x_16i = widow, x_17i=diverse, ?_0 = intercept of the estimated regression line or constant, ?_i = co-efficient of the estimated regression line.
Mincer Earning Function
Named after Jacob Mincer, the Mincer
earnings function is a single equation that describes pay income as a function of education and experience. Thomas Lemieux claims that the equation is "one of the most extensively utilized models in empirical economics" after studying it on numerous datasets. The logarithm of earnings is typically represented mathematically as the product of the years spent in school and a quadratic function of the "years of prospective experience."
lnW=f(S,W)=ln w_0+?s+?_1 x+?_2 x^2
Whereas: w is earnings, w_0 is the earnings of those without schooling, s represents years of schooling; x shows experience in the potential labour market.
Results and Discussion
The data were analyzed using the following techniques following the study's goals: frequency distribution, descriptive statistics, cross-tabulation, and a binary logistic model. Since our dependent variable was not continuous, we cannot employ a simple regression model with the ordinary least squares method. The maximum likelihood estimation method is the most effective and applicable method for discrete variables. The determinants influencing female labour force participation were estimated using a binary logistic model and the MLE (maximum likelihood estimation) method. Hence, to calculate the FLFP's parameters, we employed a binary logistic model (female labour force participation).
Table 1. Frequency Distribution of the status of the work of the respondent
| width="84"> f width="107">Percent width="107">Valid Percent width="106">CP | > Housewife width="84">40 width="107">26.7 width="107">26.7 width="106">26.7 | > Working Women width="84">110 width="107">73.3 width="107">73.3 width="106">100.0 | > Total width="84">150 width="107">100.0 width="107">100.0 width="106">
| > Note: Frequency (f), Cumulative Percent (CP), Source: Authors' own calculation from primary data | |||||||||||||||||||||||
| width="103"> f width="107">Percent width="107">Valid Percent width="106">CP | > width="103"> 110 width="107">73.3 width="107">73.3 width="106">73.3 | > Illiterate width="103">7 width="107">4.7 width="107">4.7 width="106">78.0 | > FP width="103">17 width="107">11.3 width="107">11.3 width="106">89.3 | > NEO width="103">4 width="107">2.7 width="107">2.7 width="106">92.0 | > due to busy life width="103">12 width="107">8.0 width="107">8.0 width="106">100.0 | > Total width="103">150 width="107">100.0 width="107">100.0 width="106">
| > Note Frequency (f), Cumulative Percent (CP), No Family Permission (NFP), No Employment Opportunities (NEO). Source: Authors' own calculation from primary data | ||||||||||||||||||||
| Constraints width="52">0 width="52">T width="78">D/LHV width="60">B width="43">SE width="60">Total | > No Constraints width="52">0 width="52">38 width="78">11 width="60">16 width="43">45 width="60">110 | > Illiterate width="52">7 width="52">0 width="78">0 width="60">0 width="43">0 width="60">7 | > No Family Permission width="52">17 width="52">0 width="78">0 width="60">0 width="43">0 width="60">17 | > No Employment Opportunities width="52">4 width="52">0 width="78">0 width="60">0 width="43">0 width="60">4 | > due to busy life width="52">12 width="52">0 width="78">0 width="60">0 width="43">0 width="60">12 | > Total width="52">40 width="52">38 width="78">11 width="60">16 width="43">45 width="60">150 | > Whereas: Teacher (T), Doctor or Lady Health visitors (D/LHVs), Banking (B), and Self-employed (SE) Source: Authors' own calculation from primary data | ||||||||||||||||||||
| Variables width="180" nowrap="">Coefficient width="191" nowrap="">T-statistics | > Intercept width="180" nowrap="">-18330.113 width="191" nowrap="">-2.986*** | > taunsa Sharif width="180" nowrap="">-3272.685 width="191" nowrap="">-3.340*** | > Marital Status width="180" nowrap="">6340.790 width="191" nowrap="">5.118*** | > Age width="180" nowrap="">490.962 width="191" nowrap="">5.464*** | > Education width="180" nowrap="">940.279 width="191" nowrap="">4.410*** | > Experience width="180" nowrap="">1466.323 width="191" nowrap="">2.697*** | > Type of Family width="180" nowrap="">-3302.465 width="191" nowrap="">-2.197** | > Work Nature width="180" nowrap="">1710.436 width="191" nowrap="">3.760*** | > R- Squared width="371" nowrap="" colspan="2">.600 | > Adjusted R- Squared width="371" nowrap="" colspan="2">.580 | > Dependent variable: Earning of the respondent. F-value 30.451, ***, **, and * represent the significant level at zero, five, and ten percent, respectively. | ||||||||||||||||
| Variables width="177">Coefficient width="171">P-Value | > Step 0 variables age width="177">1.466 width="171">.226 | > width="177"> 21.508 width="171">.000 | >
26.333 width="171">.000 | >
12.308 width="171">.000 | >
5.190 width="171">.023 | >
.346 width="171">.556 | >
.013 width="171">.911 | >
.470 width="171">.493 | >
81.089 width="171">.000 | >
5.850 width="171">.016 | >
.991 width="171">.320 | >
2.927 width="171">.087 | >
43.462 width="171">.000 | >
1.589 width="171">.207 | >
4.534 width="171">.033 | >
76.739 width="171">.000 | >
119.377 width="171">.000 | >
.142 width="171">.706 | >
4.317 width="171">.038 | >
.426 width="171">.514 | >
6.818 width="171">.009 | >
5.176 width="171">.023 | >
1.704 width="171">.192 | > joint width="177">30.362 width="171">.000 | >
| ||
