Male-female of labor force, non-form enterprise ownership, level

Male-female
earning differentials have been analyzed by the economists over a very long
period of time.

Human
capital approach states that number of years in formal education is proportional
to an individual’s productive capacity and skills, which in turn is responsible
for increased wage rates. This effect was captured by Mincer (1962) who said that
years of schooling is responsible for approximately one third of inequality in
earnings. Mincer and Polachek (1974) emphasized that women’s human capital
declines during stages of intermittency mainly because of child bearing and upbringing.
 Weiss (1995) concluded that wages have a
direct connection with higher education levels and experience.

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Aikaeli
(2010) conducted a study using the data from 2005 of Rural Investment Climate Survey.
Using generalized least squares, he concluded that size of labor force,
non-form enterprise ownership, level of education of the head of household and
the land usage (acres) plays vital role in determining the average income of
rural household. He also noted that a male headed household earns more than a
female one.   

While doing
research for this paper I noticed that most studies conducted in Pakistan used
dummy variable approach for different level of education and for other factors
included in the regression model mainly because limited data available.

Hamdani
(1977) used education dummies for different levels of education and age to
represent experience. He calculated returns for 1541 Rawalpindi male workers,
and stated that differentials in income emerge within the early years of work
and carry on in the future for all education levels. He also concluded that
education levels plays an important role in decreasing income inequality. Khan
and Irfan (1985) also conducted a similar study using Population, Labor force
and Migration (PLM) Survey for 2593 employees.

Ashraf and
Ashraf (1993, 1996) also used same kind of dummies and variables to study gender
earnings gap in Pakistan and found that there was a sharp drop between 1975 and
1985-86 in male-female earnings differentials

Siddiqui and
Siddiqui (1998), however used a different approach. They used age, industry,
schooling and employment status as independent variables for their regression
to estimate the wage functions differently for both males and females. With the
methodology used by Oaxaca (1973) and Cotton (1988) they blamed discrimination
in labor markets and differences in productivity for gender wage differentials.

Another study
I found was done by Nasir (1999). He used data from Pakistan Integrated
Household Survey (PIHS) 1995-96 which consists of 4916 public school graduates
and 338 of private schools, and came to a conclusion that graduates from private
schools gain higher earnings in labor market.

Nasir (2002)
used data of completed school years to estimate education returns, came to a
conclusion that only matric and B.A/BSc degrees are responsible for male-female
wage gap in labor market. On the other hand, using data from Labor Force Survey
of Pakistan, Reilly and Hyder (2005) examined the pay gap in public and private
sectors. With the help of dummies and age as a proxy for experience, their
results concluded that private sector workers earn more at all educational
levels with the exception for matric levels.  

Keeping
Mincerian approach in mind Ali (2007) used data from PSLM and PIHS surveys to
discern gender wage gaps. He determined that returns rises with the education
levels regardless of nature of enterprise, employment status, school kinds,
region and sex. His other findings included ’employers and self-employed individuals earn more than paid employees’
and ‘urban employees earn more than the
rural employees’. Also he stated that private school graduates earn more
than public school graduates and that female workers earns more than male
workers at the same level of educations. 

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