Year-end Sale % OFF 
Abstract
The current study examines the role of education on earnings in informal sector. By using stratified random sampling technique sample of 382 individuals is collected through questionnaire and interviews. Ordinary least square method is applied for statistical estimation on data. Education of the respondent in years and categories of education variable from primary to M. Phil. level, age of the respondent, experience, gender, area of living, spouse education, mother’s education, household size, size of land, livestock, availability of healthy diet, investment, and rental property are positively associated with earnings of farmers and businessmen, whereas age square, experience square, schooling of respondent, technical education, working hours, father’s education, marital status and spouse job are negatively associated with earnings of farmers and businessmen.
Highlights
According to human capital theory, there exists a positive and significant impact of education and work experience on earnings of the individuals
We find the positive impact of area of living on earnings {individuals belong to urban area have higher earnings} when we regress it with experience variable whereas it has negative relationship with earnings {individuals belong to rural area have higher earnings} when we replace the variable experience with age of the individuals
When we replace the experience variable with age in our model, the negative relationship of area of living is because of that individuals belong to urban areas having rental earnings which are low and they have no practical exposure that is why they have same earnings with increasing age whereas individuals belong to rural areas having own businesses and farm production so as their practical exposure increases, their earnings are increasing with age
Summary
According to human capital theory, there exists a positive and significant impact of education and work experience on earnings of the individuals. The research concluded that education, age, experience, occupation, gender, working hour, spouse education, family background and family status were positively and significantly contributed to earnings of all respondents. We compare two more models in which education variable is categorized in different levels and these categories are used in model instead of education variable measured in years and regress on log of earnings with all other variables used in previous models including experience first and replace it with age of the respondent. SPED shows the education of spouse of the individual measured in years It should have positive impact on earnings of individuals, as educated spouse will be responsible for good health of individuals which will be raised the productivity and earnings of them (Todaro and Smith, 10th Edition). According to the IS curve model, expenditures will lower the savings and investment and actual productivity and income level will be decreased (Mankiv, G.)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Pakistan Journal of Humanities and Social Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
