UserMicrosoft Word - 5J. Appl. Environ. Biol. Sci., 6(8)117-128, 2016 |
© 2016, TextRoad Publication |
ISSN: 2090-4274 |
Journal of Applied Environmental and Biological Sciences www.textroad.com |
The Role of Working Women in Poverty Reduction in District Peshawar |
Dr. Zilakat Khan Malik1, Dr. Muhammad Tariq2, Uzma Begum3 and Dr. Shahid Jan4 |
1Professor, Department of Economics, University of Peshawar, Pakistan |
2Assistant Professor, Department of Economics, Abdul Wali Khan University Mardan Pakistan 3M.Phil Scholar, Department of Economics, University of Peshawar. |
4Associate Professor, Department of Management Sciences, Abdul Wali Khan University Mardan. |
Received: March 19, 2016 |
Accepted: June 24, 2016 |
ABSTRACT |
Women constitute more than half of the total population of the country. The developing countries of the world are |
facing severe problem of gender inequality which results in absolute poverty, and slow economic growth. The objectives of this study are to examine the status of the working women and their share to the family income and their contribution in the reduction of family poverty. Both primary and secondary data are used to achieve the objectives. A sample of 150 is selected from district Peshawar which is the targeted area of this research. The data is collected through questionnaire. The data is analyzed using statistical techniques and SPSS. The results of the study are tabulated. The results show that there is positive relation (0.09) between the income of the household and the share of women in the family income. The relation is significant at 0.01 levels of significance. Women share is negatively related (-0.111) with the women marital status. The relation is significant at 0.01 levels of significance. Problems in work is negatively related (-0.011) with the women share in the family income. Nature of job is positively related (0.014) with the women share. This coefficient is significant at 0.01 levels of significance. Educational status of the women is positively related (0.010) with the share of women in the family income. The DW value in the model is 1.828, which lies in the no auto-correlation region. Poverty is negatively affected (-2.527) by women share in the family income. This coefficient is significant at 0.01 levels of significance. The availability of credit is negatively related (-0.130) to poverty of the family. Number of children in the family is positively related (0.066) to the poverty of the family. Number of earning family members is negatively related (-0.073) to the poverty of the family. The study is useful for further researches in the same field and helpful for policy makers. The results can be utilized by the NGOs working in the field of women empowerment and women working status. |
KEYWORDS: Poverty, Working Women, Regression |
INTRODUCTION |
1.1 Background of the Study |
The living standard, dependency burden and saving pattern of the households can be determined by women decision about participation in economic activities. Most women in Pakistan work in non-market activities in the home or the informal sector. With the passage of time there is an increase in women’s participation in modern sector activities. Women employment is determined by extremely complex factors both at individual level and aggregate level. At the individual level factors such factors as the availability of jobs, education and skills affect women’s decision about work. At the aggregate level factors such as economic, social and demographic circumstances of the locality determine women’s decision about work. Women are induced to participate in the labor market by the push and pull factors. The push factors mainly represent financial pressure. The push factors include severe economic necessities due to which women are pushed from poorest families into the market. (Kazi & Raza, 1986). The push factors are common in a society characterized by income groups, low income women are more likely to participate in economic activities. A high family income might greatly reduce the necessity of augmenting income by involving women in the labor force. The pull factors are determined by those factors that create demand for labor and include the level of education, training and experience. |
Education is one of the key determinants in women’s economic participation. The higher the level of education, the greater is women’s economic participation. Furthermore; socio-economic characteristics like education and skills proves helpful in the availability of certain jobs to them. Investment in human capital such as job training work experience increase productivity and results in high earning of the women. Demographic factors |
* Corresponding Author: Dr. Zilakat Khan Malik, Professor, Department of Economics, University of Peshawar, Pakistan. |
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like age and family size are also important in determining women’s participation in economic fields. Large family |
size increase dependency burden as a result mothers are pushed in economic streams for the uplift of the family. Other potential factors which affect women participation are family structure and education level of the family and husband. Availability of jobs, occupation and the distance from the possible place of work are some other factors that affect women economic participation. The present study is an attempt mainly to analyze the share of working women in household incomes. The present study focused mainly on women active role in poverty reduction. |
The extent of poverty is closely related to the extent of inequalities. Poverty is the result of inequalities in multiple spheres. Gender inequality is one among them. Gender equality is a key factor in achieving anti-poverty and development goals. The United Nations regarded women as a catalyst for change to achieve the goal of poverty reduction. Poverty reduction is the process of shifting people from below poverty line situation to above poverty line situation. The developing countries of the world are facing severe problem of gender inequality which results in absolute poverty, and slow economic growth. Pakistan is one among them facing the same problem. The socio- economic factors e.g. high population growth rate, poor economic conditions, social and cultural constraints and political uncertainty greatly deteriorates the state of poverty in Pakistan. Since creation Pakistan has inherited the menace of poverty. Female constitute fifty two percent of the total population of the country which is the main reason for poverty because of their low share in economic activities. By ensuring women economic participation the menace of poverty would certainly be minimized. |
Sustainable development, pro-poor growth and poverty can be reduced by increasing participation of women in economic activities. Gender equality and women participation are essential for poverty reduction efforts. Women usually invest more than men in their families. Child’s survival increase by 20% due to livelihood of mother and when the household income is controlled by women. When in household income women share is high there is less discrimination against women and girls. The living condition of poorest families can be improved by drawing women into the economic stream. Women participation in economic field can be increased by providing them proper educational facilities, professional training programs, access to employment opportunities both in formal and informal sector. Rural women participation can be made sure by launching agriculture extension programs, providing microcredit to run small scale business at home. From the comparison of the lives of poor inhabitants across the developing world it is cleared that women and children are most deprived of basic necessities. The economic condition of women is a best indicator of the living standard of herself and her family. (Todaro 2003) |
The public status of women in Pakistan is uncertain they are facing social, cultural and economic constraints which are main obstacles in women’s participation in economic activities as a result they are among the poorest of poor. Women play an important role in the field of Education, Business, Science and innovation. Women and girls make two-third of one billion people in the world’s who are dependent on others for their survival due to lack of education and non-economic participation. The living conditions of poor household can be improved by bringing women to the economic stream. Women’s contribution in household income has positive impact on poverty reduction. The greater the share of working women to household income the better will be the economic condition of the family. |
This study analyzed women’s access to markets (labor, land, and credit) and women’s decision making power within household and its relation with poverty reduction and productivity at the individual and household level. |
1.2 Objectives of the Study |
The dissertation has the following main objectives. |
1. To examine the determinants of women share in households income in district Peshawar. |
2. To analyze the effect of income earning activities on decision making process and poverty reduction in district Peshawar. |
2. Literature Review |
The current literature deals with the review of pioneering and recent studies related to the role of working women in poverty reduction. |
Mohammad et al. (2012) focused on the phenomenon of women’s economic activity and employment is the main issues in every economy after the industrial revolution. Women’s contribution in formal sectors can be measured but in informal sectors it is not measurable. The survey was conducted in urban areas of Iran using the household data of income and expenditure for the purpose of assessing the role of women in economic activities. Results have shown that women are more efficient than men in managing household expenditures. The logistic model suggested that women are more employed in those household which were headed by men rather than by women. But education of women matters a lot for being employed rather than being headed by male or female. |
Awan (2012) established a linkage between the problems of women and the contribution of the civil society, to achieve a respectable status for the women within various sectors of the Pakistani society. Civil society in Pakistan has been working for social, economic and political empowerment of women. It is important to state that the concept |
of women empowerment is deeply linked with an over-all change in the structure of male dominated society, on the |
one side and the truthful understanding of religion (Islam) on the other. She said that this reality cannot be neglected that providing equal opportunities of education and employment to women has contributed not only to their welfare, but also to their over-all well-being. In spite of the fact that more representation is given to the women in legislatures, neither have they suggested remedies for the grievances and dismal plight of women, nor they could suggest a broad framework to improve the role of female in the socio-economic and political life in Pakistan. |
Faridi et al. (2011) found that in Bahawalpur women’s age and experience determine her employment status. Results showed that women with low educational status are highly inclined towards self employment rather than women with high educational status tend towards government jobs. Factors such as husband’s salary, number of children and condition of the household increases chances of women’s self employment whereas location, dependent family members and per capita income create hurdles in women’s self employment |
Kousar (2010) measured the contribution of working women in informal sector, which was the agenda for national and international organizations but no steps were taken to measure it. The study is concerned with women working in informal sector and their contribution to household budget. The categorization of work in informal sector is different from regions to regions. She concluded that women involved in tailoring contribute more to their family budget as this is the most suitable work for women both literate and illiterate. OLS method was applied to primary data collected from both urban and rural areas of Bahawalpur to analyze the determinants of women contribution to household budget. Results have shown that married women being the head of the household, belonging to nuclear family, having assets and good health contribute much to their household budget. The positive factors which determine women’s contribution are unemployed husbands, loan burden and presence of infants at home and residency in urban area. Women contribution is negatively affected by educational and employment status of husband, presence of school going children and presence of adults (male and female) in family. The contributing determinants are different for rural and urban areas. It was suggested government must take steps in favor of education of women, availability of credit, health facilities and minimum wage legislation in informal sector. |
Susan (2010) showed an inverse relationship between women employment and income inequality. The study is conducted by using micro-data for seventeen countries of OECD. Women earning have a positive impact on equalizing income distribution despite of large differences in employment between educated and uneducated women. These countries have similarities among them; Nordic with high employment rate, women contribute only a small proportion of their income to household income. U.S has greater earning equality among couples. Raising women employment and reducing inequality in employment would have a positive impact on promoting household income equality, and would have a substantial impact on reducing poverty. Data was taken from Luxemburg Income Survey (U.S) seventeen industrialized countries by grouping them into a group of three. Results have shown that women income have a positive impact on equalizing household income in all countries. Demographic differences exist among them but these counts less to equality than to work pattern. |
Chaudhry and Nosheen (2009) considered multidimensional nature of women empowerment in southern Punjab. They used four indices for estimating the cumulative index i.e. personal autonomy, family decision making, domestic economic decisions and political autonomy. The results showed that women empowerment is positively affected by education, working status of women, rate of household participation, socio cultural norms and access to media. The main focus of the study was on women participation in terms economic activities. |
Saikou and Wen-Chi Huang (2008) conducted study in rural Gambia to assess the role of working women in poverty reduction through microfinance. Donor agencies and NGO’s in Gambia are promoting microfinance through women’s groups. Microfinance proved to be helpful in poverty alleviation as it provide opportunity to rural women of Gambia to start business at micro level. Majority of women in Gambia are involved in self employment in agriculture and non-agriculture activities. |
Ejaz (2007) found the positive factors that affect female labor force participation by using Pakistan Social and Living Standards Measurement Survey, 2004-05. He concluded from his study that age, educational attainment and marital status significantly and positively affect female labor force participation. Results showed that women are more likely are they to participate in economic activities who belong to nuclear family and have access to vehicles, whereas females with large number of children and the availability of home appliances reduces the probability of female labor force participation. |
Grameen Bank (2006) is an institution strongly advocating the provision of micro credit for the poor women of villages in Bangladesh to improve their economic condition. Para Tearl from gender watch feels that GB finds it safe to help poor women in its dealings because they are not mobile. They cannot run away if they are unable to repay their loans. They can easily be located and intimidate because they will always be limited to home with their families. According to Tearl, the Grameen Bank has two million clients and is an internationally acknowledged success but its emphasis on giving women their share of poverty and its attempts to make a virtue out of the |
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symptoms of inferiority fits in with a world that has given up on the goal of economic development as we have |
known it in the past. Tearl believes that “the key to Grameen Bank’s popularity in the powerful capitals around the globe is that it is a Southern ‘success’ story which helps to excuse the failures of the North. |
Khan et al. (2005) found the affect of home-based work of women and children on family income. Further they investigated the effect of that increased income on the nutrition, health and education of the household. Results showed that household size is positively affecting the decision to participate in home-based work while living condition is negatively affected. |
3. Data and Methodology |
3.1 Data, Sample Size and Sampling Method |
Primary data has been collected through a detailed and well structured questionnaire. The area of research for this purpose is district Peshawar. The questionnaire included all the relevant questions about all the aspects of working women. A sample size of 150 is taken from the population of 130,000 working women in district Peshawar to assess the share of working women in household’s income and its positive impact on poverty reduction. Sample has been drawn through stratified random sampling by dividing the population into different groups on the basis of the employment status of the women. The reason for the selection of 150 sample size is the time and monetary constraints. To obtain secondary data different sources were used like websites, government reports, population census reports etc. |
3.2 Empirical Models |
Model 1 |
The following model has been used for the estimation of determinants of women share in households income. |
WS lnHHI+ MS + PW + NJ + WEdu + μ |
Whereas, |
lnHHI = Log of household income MS = Marital status of women PW = Problems in work |
NJ = Nature of job |
WEdu = Women own educational background |
Model 2 |
To estimate the impact of women share of income on poverty level the following model has been used. |
Poverty WS+ ACr + NC + NEFM + μ |
Whereas, |
Poverty = Poverty is determined by household whole level of income. If household income is low, household is poor and vice versa. |
WS = Women share to household income is measured by the increase in overall income of the household ACr = Availability of credit. Its value is 1 if credit is easily available and 0 if vice versa. |
NC = Number of children |
NEFM = Number earning family members |
4. Analysis of Data |
This part of the thesis includes data analysis. The data analyzed here is collected through questionnaire from the respondents. The analysis is carried out in two steps; in the first step, descriptive analysis is carried out to analyze those factors which determine the working status of the women and their share in the family income. In the second step, regression analysis is carried out to know the factors affecting women share in the family income and the impact of the women share on poverty reduction of the family. |
4.1 Descriptive Analysis |
Descriptive analysis is carried out to analyze those factors which determine the working status of the women and their share in the family income. The results of the analysis are tabulated with frequencies and percentages. Question-wise descriptive analysis is given below: |
4.1.1 Age of the Respondents |
To get data regarding age of respondents, this question is added in the questionnaire. The responses of the women regarding age are listed in the table 4.1 below: |
Table 4.1: Age of the Respondents |
Age (years) | Frequency | Percent | Statistics | Age |
21-25 | 24 | 16.0 | Mean | 2.5667 |
26-30 | 49 | 32.7 | Std. Deviation | 0.99944 |
31-35 | 45 | 30.0 | Variance | 0.999 |
36-40 | 32 | 21.3 | | |
Total | 150 | 100.0 | |
Source: Field Survey 2014 |
Table 4.1 above shows that majority of the respondents (33%) are within the age-group of 26-30, followed by those |
within the age-group of 31-35. The results show that most of the households are in middle age groups and having more years of work and earning. Age is an important factor in determining the women share in the family income. The higher the age means the more years of earning and hence greater contribution to the family income. The same is depicted in the figure 4.1. The mean value is 2.5667 which means that majority of the respondents are in the middle age groups. Standard deviation and variance show that variations in the data are very low. |
4.1.2 Marital Status of the Respondents |
An important aspect impacting the choices of ladies in economic contribution is the situation whether they are married or not. The ratio of married women that is least likely to join economic actions. And vice versa for those women who are either divorced or widow. The table and figure below show the marital status of the respondents. |
Table 4.2: Marital Status of the Respondent |
Marital Status | Frequency | Percent | Statistics | Marital Status |
Unmarried | 77 | 51.3 | Mean | 1.6067 |
Married | 61 | 40.7 | Std. Deviation | 0.74991 |
Widow | 6 | 4.0 | Variance | 0.562 |
Divorced | 6 | 4.0 | | |
Total | 150 | 100.0 | | |
Source: Field Survey 2014 |
The table and figure 4.2 shows that majority of the working women (51%) are unmarried, while 40% of them are |
married. Few of them are widows (4%) and divorced (4%). This shows that majority of the unmarried respondents are supporting their families with their income. Divorcee position of a woman is another important aspect that favorably improves the probability of female's economic contribution to the economy. This factor is very important in determining the women’s share in the family income and reduction in the poverty level of the family. The mean value of the data is 1.6067 which shows that majority are unmarried. The standard deviation and variance are very low which shows small variations in the data. |
4.1.3 Literacy Level of the Respondents |
Literacy level is one of the most important variables in the determination of the women’s share in the family income and their role in improving the family living standards and reduction of poverty. The question regarding literacy level is included. The data is shown in table 4.3. |
Table 4.3: Literacy Level of the Respondents |
Level of education | Frequency | Percent | Statistics Level of education |
Illiterate | 36 | 24 | Mean 2.7733 |
Matriculation | 9 | 6.0 | Std. Deviation 1.86866 |
Intermediate | 8 | 5.3 | Variance 3.492 |
B.A/B.Sc | 36 | 24 | | |
MA/M.Sc | 25 | 16.7 | | |
Above | 36 | 24.0 | | |
Total | 150 | 100.0 | | |
Source: Field Survey 2014 |
The table and graph above show that majority of the respondents are having high qualification. Almost 65% of the |
respondents are graduate and above. However, there are a large number of illiterate respondents as well. These illiterate working women also contribute to the family income and reduction in poverty. Education is an important factor in determining the share of working women in family income. The high the education level, the higher is the |
contribution of females in the household income. The variance and standard deviation of this question are very high |
which shows that there are very large variations in the data set. |
4.1.4 Employment Status of the Respondents |
The responses regarding employment status are provided below in table |
Table 4.4: Employment Status of the Respondents |
Employment status | Frequency | Percent | Statistics | Employment status |
Self employed | 54 | 36.0 | Mean | 2.0933 |
Government Servant | 37 | 24.7 | Std. Deviation | 0.96479 |
Private employee | 50 | 33.3 | Variance | 0.931 |
Agriculture | 9 | 6.0 | | |
Total | 150 | 100.0 | | |
Source: Field Survey 2014 |
The table 4.4 above shows that majority of the respondents (36%) are self employed followed by (33.3%) who are |
employed in private organizations. About 25% of the respondents are the government servants. Employment status is important in determining the women’s share in family income. Some professions pay more while others pay less. A woman working in private organizations earn more but having low job security as compare to women employed in the government job. Mean value of data is given as 2.0933 and standard deviation 0.96499 which shows very small variation in the data set. |
4.1.5 Type of Family |
The type of family affects women’s decision about work. The greater the family, the more will be the needs of family and vice versa. To collect information regarding the type of family, this question is included in questionnaire. The responses are provided in table 4.5. |
Table 4.5: Type of Family |
Type of Family | Frequency | Percent | Statistics | Type of Family |
Nuclear | 96 | 64.0 | Mean | 1.3600 |
Joint | 54 | 36.0 | Std. Deviation | 0.48161 |
Total | 150 | 100.0 | Variance | 0.232 |
|
Source: Field Survey 2014 |
4.1.6 Family Educational Background |
Women’s decision about work is greatly determined by her family educational background. A question about family educational background is included in the questionnaire. The responses of respondents are listed in table 4.6 below: |
Table 4.6: Family Educational Background |
Family Educational Background | Frequency | Percent | Statistics | Family Educational Background |
Uneducated | 51 | 34.0 | Mean | 1.6600 |
Educated | 99 | 66.0 | Std. Deviation | 0.47530 |
Total | 150 | 100.0 | Variance | 0.226 |
Source: Field Survey 2014 |
Table 4.6 shows family educational background of the respondents. Majority (66%) of respondents belongs to |
educated families and (34%) of respondents belong to uneducated families. This result shows that the more educated the families the more will be women contribution in economic activities. The mean of the data is 1.6600 which means that majority of the respondents belong to educated family background. Standard deviation is 0.47530 and variance is 0.226 which shows that there is small variance in the data. |
4.1.7 Number of Earning Family Members |
Living standard of a family depends on number of earning family members. The greater the number of earning family members, the better will be the living standards of the family. Details about number of earning family members are given below: |
Figure 4.7 Number of Earning Family members |
Source: Field survey, 2014 |
Table 4.7 shows that majority of families (34.7%) have only two earning members followed by families (23.3%) |
having three earning members. About 12% families have only one earning member. 17 % families have 4 earning members. 8% families have 5 earning members. 4% have 6 earning members and only a single case of (0.7%) has seven earning members. The families having greater number of earning members have better living standard than families where earning members are less than dependent members. Mean value is 2.8933. Standard deviation and variance of this question show that there is little variation in the data set. |
4.1.8 Nature of Job |
The status of working women is determined by the nature of job. The share of women in household income depends on her level of earning. Level of earning in turn is determined by nature of job. This question shows in what kind of earning activities women are engaged. Table and figure given below shows respondents’ response to this question: |
Table 4.8: Nature of Job of the Respondent |
Nature of Job | Frequency | Percent | Statistics | Nature of Job |
Ministerial | 2 | 1.3 | Mean | 5.56000 |
Agriculture | 10 | 6.7 | Std. Deviation | 2.014977 |
Shop keeping | 18 | 12.0 | Variance | 4.060 |
Business | 9 | 6.0 | | |
Teaching | 44 | 29.3 | | |
Health Technician | 8 | 5.3 | | |
Knitting, embroidery, sewing | 18 | 12.0 | | |
Any other | 41 | 27.3 | | |
Total | 150 | 100.0 | | |
Source: Field Survey 2014 |
Table 4.8 shows that (29%) of respondents are engaged in teaching at different levels i.e. schools, colleges and |
universities. (27%) of respondents are involved in different income earning activities which is specified as “any other” in the questionnaire. (12%) of respondents earn their income from shop keeping and knitting embroidery stitching etc at home which is most suitable job for those women who are not educated. (6%) of women are involved in business at small scale and agriculture. Also some health technicians are interviewed whose share in the sample is (5%). (1%) of women are also involved in ministerial job but their share is very small in the sample. From the above table it is cleared that in the sample area women preferred teaching the most. |
4.1.9 Total Income of the Family |
The total earning of the family is given in the table below: |
Table 4.9: Total Income of the Family |
Income of the Household Head (Rs per month) | Frequency | Percent | Statistics | Income of the Household Head |
1000-20000 | 46 | 30.7 | Mean | 2.2067 |
21000-40000 | 56 | 37.3 | Std. Deviation | 1.16029 |
41000-60000 | 29 | 19.3 | Variance | 1.346 |
61000-80000 | 12 | 8.0 | | |
81000-100000 | 4 | 2.7 | | |
Above 100000 | 3 | 2.0 | | |
Total | 150 | 100.0 | | |
Source: Field Survey 2014 |
Table 4.9 shows different income combination of the household head and their respective frequencies. Majority |
(37%) of families’ income is Rs.21000-40000 per month. Few families’ (2%) monthly income is very high i.e. above Rs.100000. From the table it is clear that there is high variation in the data, which is also depicted by the high variance and standard deviation. Mean of the data is 2.2067. The same is depicted in the figure given below. |
4.2.0 Monthly Income from Primary Occupation |
Women’s share in household income is determined by their income. Here income is divided in two; income from primary occupation and income from secondary occupation. Income of respondents from primary occupation is shown in table below: |
Table 4.10: Monthly Income from Primary Occupation |
Monthly Income from Primary Occupation (Rs per month) | Frequency | Percent | Statistics | Monthly Income from Primary Occupation |
1000-20000 | 82 | 54.7 | Mean | 1.8667 |
21000-40000 | 31 | 20.7 | Std. Deviation | 1.17962 |
41000-60000 | 20 | 13.3 | Variance | 1.391 |
61000-80000 | 9 | 6.0 | | |
Above 80000 | 8 | 5.3 | | |
Total | 150 | 100.0 | | |
Source: Field Survey 2014 |
Table 4.10 shows different combination of income, the women earn from their primary occupations. Income of the |
women is dependent on their level of education and nature of job. A large number of women (55%) earn Rs 20000 and below per month, whereas (45%) women earn more than Rs.20000 per month. The same is depicted in the figure. Variance and standard deviation are high showing large variation in the data. |
4.2.1 Monthly Income from Secondary Occupation |
In addition to income from primary occupation women also earn from secondary occupation. Details of women earning from secondary occupations is given below: |
Table 4.11: Monthly Income from Secondary Occupation |
Monthly Income from Secondary Occupation
(Rs per month) | Frequency | Percent | Statistics | Monthly Income from Secondary Occupation |
00 | 82 | 54.7 | Mean | 1.6400 |
1000-10000 | 50 | 33.3 | Std. Deviation | 0.89202 |
11000-20000 | 12 | 8.0 | Variance | 0.796 |
21000-30000 | 2 | 1.3 | | |
31000-40000 | 4 | 2.7 | | |
Total | 150 | 100.0 | | |
Source: Field Survey 2014 |
The above table shows that majority of women 55% are not involved in earning from secondary occupation, because |
of their involvement in domestic chores. The remaining 45% earn different amounts from their secondary occupation. The same is depicted in the pie chart. From the field survey it is cleared that those women are involved in secondary occupation whose income from primary occupation is low. Mean value is 1.6400. the value of standard deviation and variance is low showing low variation in the data set. |
4.2.2 Participation in Decisions of Family Matters |
Table 4.12 shows women’s response to participation in decision of family matters. |
Table 4.12: Participation in Decisions of Family Matters |
Participation in decisions of family matters | Frequency | Percent | Statistics | Participation in decisions of family matters |
No | 40 | 26.7 | Mean | 1.7333 |
Yes | 110 | 73.3 | Std. Deviation | 0.44370 |
Total | 150 | 100.0 | Variance | 0.197 |
Source: Field Survey 2014 |
From the table, it is cleared that majority of working women 73% participate in decision of family matters. However |
27% do not participate in decision of family matters. The same is depicted in the chart 6.19. Mean value is 1.7333. Standard deviation and variance is low for this question which means that there is little variation in the data set. |
4.2.3 Family’s Response on Women Participation |
Family’s response on women participation is important in women’s decision about work. Family response on women participation is given in table and figure below: |
Table 4.13: Family Response on Women Participation |
Family response on women participation | Frequency | Percent | Statistics | Family response on women participation |
Negative | 27 | 18.0 | Mean | 1.8200 |
Positive | 123 | 82.0 | Std. Deviation | 0.38547 |
Total | 150 | 100.0 | Variance | 0.149 |
Source: Field Survey 2014 |
Table 4.13 shows family’s response on women participation. Family’s negative response is a constraint on women |
participation in income earning activities. Looking to the table 4.13, it can be said that most of the families’ (82%) show positive response on women participation however some families (18%) have objection on women participation at work. The same is depicted by the pie chart. Mean value is 1.8200. Least values of standard deviation and variance show that there is low variation in the data set. |
4.3 Regression Analysis |
Regression analysis is carried out to know the factors affecting women share in the family income and the impact of the women share on poverty reduction of the family. Two models are estimated in this part. To estimate the models, primary data was used which was collected through questionnaire from the respondents in the research area. The estimation of the models and results are given below. |
4.3.1 Estimation of Determinants of women share |
This model is estimated to analyze the determinants or different variables affecting women share. The results of the model are given in the table below: |
Table 4.14: Determinant of women share in the family income |
Variable | Coefficient | | Std. Error | T Statistic | | Sig. |
Constant | -0.045 | 0.079 | -0.569 | 0.570 |
Lnhhincome | 0.019 | 0.007 | 2.559 | 0.012 |
Marital status | -0.111 | 0.011 | -10.362 | 0.000 |
Problems in work | -0.011 | 0.010 | -1.169 | 0.244 |
Nature of Job | 0.014 | 0.002 | 5.806 | 0.000 |
Educational status | 0.010 | 0.015 | 0.677 | 0.499 |
R-Square:0.55 Adj. R-Square:0.54
Durbin Watson Statistic:1.82 |
Looking to the table 4.14, it can be concluded that there exist positive relationship (0.09) between income of the |
household and the women’s share in total household earning. Greater the earning of the household means that greater will be the women’s share in total household earning. According to t-statistics the coefficient of this variable is significant at 5% significance level. Women share is negatively related (-0.111) with the women marital status. A married woman has less contribution to the family income as compared to an unmarried woman. The coefficient of the marital status is significant at 1% significance level. Problems at work is negatively related (-0.011) with the women share in the family income. The greater the problems in the workplace, the more women feel hesitation for wok and hence less women share in the family income. However, this relation is insignificant. Nature of job is positively related (0.014) with the women share. Nature of job is significant factor in affecting the women share. The coefficient of this variable is significant at 1% significance level. Finally, educational status of the women is positively related (0.010) with women share in household earnings. The more a woman is educated, the more she will be contributing to the family income and vice versa. However, the coefficient of the estimate is not significant. |
The value of R2 is (0.558). This R2 and adjusted R2 shows that the overall model is good fit and it can be concluded |
that there exist a strong relationship between women share in household income and all the exogenous determinants. |
4.3.2 Estimation of Women Share Affecting Poverty Level of the Family |
This model is used to estimate the impact of women share on the poverty level of the family. The model was estimated using primary data which was collected through questionnaire from the respondents. That household whose monthly income is less than Rs.35000 is regarded as poor and vice versa. The results of estimated models are given in the table below: |
Table 4.15: Women Share Affecting Poverty Level of the Family |
Variable | Coefficient | Std. Error | T Statistic | Sig. |
Constant | 10.125 | 0.141 | 71.795 | 0.000 |
Women Share | -2.527 | 0.436 | -5.791 | 0.000 |
Availability of Credit | -0.130 | 0.050 | -2.574 | 0.014 |
Number of children | 0.066 | 0.015 | 4.480 | 0.000 |
Number of Earning Family members | -0.073 | 0.019 | -3.801 | 0.000 |
R-Square:0.60
Adj. R-Square:0.56
Durbin Watson Statistic:1.84 |
Table 4.15 shows that poverty is negatively affected (-2.527) by women’s share in the family income. Greater the |
share of the women in the family income, lower will be poverty of the family. This means that women participation in the workforce of the family, their earning, and share to household earnings improves the living standards of the family and hence reducing the poverty level. The coefficient of the estimate is significant at 1% significant level. The easy access to credit is negatively related (-0.130) to poverty of the family. When credit facilities are easily available, women can start their own small business, improving their earning and hence contributing more to the family income. This way poverty of the family can be easily reduced. The total number of children within household is positively related (0.066) to poverty of the family. The more the children in a family, more dependent family members are there, hence increasing the level of poverty. The coefficient of the number of children in the family is significant at 1% significant level. Finally, number of earning family members is negatively related (-0.073) to the poverty of the family. The more the members of a family earning means high level of household income, so low will be the poverty. The coefficient of the number of earning family members is significant at 0.01 levels of significance. The value of R2 is (0.601). This R2 and adjusted R2 show that the overall model is a good fit and it can be concluded that there exists a strong relationship between women share in household income and all the exogenous determinants. Looking to the table it can be seen that F-statistic is 15.057 which means the whole model is significant at 1% significance level. Now to check the model whether auto-correlation exists or not, Durbin-Watson test is used which tells that if the D.W. i.e. d*> (4-dL) where dL = lower value, there exists auto-correlation in model. The DW value in the model is 1.846, which lies in the no auto-correlation region. It is concluded that the model has no auto- correlation for all areas of research. |
Conclusion |
The objectives of this study are to examine the status of the working women and their share to the family income and their contribution in the reduction of family poverty.. A sample of 150 is selected from district Peshawar which is the targeted area of this research. The main findings of the study are that there is positive relation (0.09) between the income of the household and the share of women in the family income. The relation is significant at 1% significance level. Women share is negatively related (-0.111) with the women marital status. The relation is significant at 0.01 levels of significance. Problems in work is negatively related (-0.011) with the women share in the family income. Nature of job is positively related (0.014) with the women share. This coefficient is significant at 1% significance level. Educational status of the women is positively related (0.010) with the share of women in the family income. Poverty is negatively affected (-2.527) by women share in the family income. This coefficient is significant at 1% significance level. The easy access to credit is negatively related (-0.130) to poverty of the family. Total number of children in the household is positively related (0.066) to the poverty of the family. Number of earning family members is negatively related (-0.073) to the poverty of the family. It has been suggested that women role cannot be underestimated in the our society and they should be given equal opportunities of education, employment etc. |
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