education and earnings

Written by Hassan Nasir and Hamza Butt 10:32 pm Current Affairs, Pakistan, Published Content, Research Papers

The Relationship Between Education and Earnings in Pakistan

This paper examines the effect education has on the earnings of salaried people in Pakistan. The analysis confirms that education has a positive role in determining an individual’s earnings – i.e. every extra additional level of education increases the earnings of an individual.
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About the Author(s)
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Hassan Nasir works at British Council Pakistan. He completed his MS in Economics from Queen Mary University in London.

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Hamza graduated from NUST Business School in 2015 where he completed his Bachelor in Economics.

Written in 2012


This paper examines the effect education has on the earnings of salaried people in Pakistan. To conduct this study, data was used from the Labor Force Survey (LFS) 2010-11. The analysis confirms that education has a positive role in determining an individual’s earnings – i.e. every extra additional level of education increases the earnings of an individual. The analysis also revealed that females earned less than males. Furthermore, it was confirmed that people in Pakistan’s urban centers (cities) make more money compared to the people in rural areas. The paper ends with providing recommendations such as the need to enhance Pakistan’s educational sector. More educational institutions need to be developed in Pakistan as well as the quality of education improved. Government and private educational institutions should promote a market-oriented approach rather than the traditional rote learning techniques – this would require an overhauling of the current curriculum and teaching methods.


Education is a very important aspect of our life. Its benefits are not only limited to making a person a more civilized human being but it also massively affects a person’s earnings. Education not only prepares individuals for entering into the labor force but also equips them with skills that will be useful for them throughout their lives. There are, however, numerous dimensions of education. Education and training are viewed as the major sources of human capital accumulation that have a direct and positive effect on an individual’s lifetime earnings. The objective of our study is to examine the effects of educational attainment on the earnings made by workers in Pakistan.

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Problem Statement

One of the major problems in Pakistan is poverty and this is usually linked to the lack of education of individuals. An increase in earnings plays an important role in reducing poverty by raising income levels and the standard of life. In this paper, we will examine the role of education in determining earnings in Pakistan.

Research Question

This paper tries to answer the following questions:

  • Does educational attainment increase workers’ earnings? Is there any relationship between them?
  • Are the earnings also affected by age?
  • What is the relationship between the age of the household head and their earnings?
  • Is there any wage difference between males and females?
  • What is the effect of each additional level of educational attainment on earnings?           

Government Policy

The government of Pakistan has tried to support education by injecting subsidies in the education sector and establishing public (government) schools all over Pakistan. However, the quality of education that is offered in public schools is not up to the mark. Unfortunately, the quantity of educational institutions is also nothing remarkable. The number of public schools and universities in Pakistan are not sufficient to cater to the needs of the ever-increasing population of the country. Pakistan has a massive youth bulge (with around 60% of the population being below 30) which adds further stress on the education sector.

Pakistan spends only 2.5% of its GDP on education in the form of a subsidized public school system. The major educational expenditures in the public school system are paid by the government, which includes the salaries of teachers and staff members and the funds for building the educational infrastructure. To encourage people, especially the lower-income families, to send their children to receive an education, only a nominal school fee is charged. 

The previous government of Pakistan recently added a new scheme of Waseela-e-Taleem under the Benazir Income Support Program, which provides grants and scholarships to students from underprivileged families.

Objective of the Study

The objective of our study is to analyze the impact of an increase in the level of education on the earnings of Pakistan’s working population. This will help highlight the importance of attaining education on a micro (individual) and macro (societal) level. Furthermore, it will capture the attention of different public and private sector organizations on this issue.  

Organization of Study

This paper is divided into 6 sections. The preceding section, Section 1, focuses on the introduction and includes the problem statement, research question, government policy and the objective of this study. Section 2 comprises of the literature review. Section 3 will discuss the sources of data and the methodology used to conduct the analysis. It also features an exploratory analysis of data, model development, and an estimation procedure. Section 4 contains the results and analysis of our research while section 5 includes the conclusion and discussion. Finally, Section 6 provides recommendations for future action.

Literature Review

A myriad of human capital research conducted found a direct relationship between educational attainment and the earnings of individuals. The research indicates that education remains the major determinant of good labor market outcomes for individuals – however, despite being necessary, it is not sufficient alone.

Psacharopoulos and Layard (1979) found that experience-earnings profiles are steeper for individuals that have a higher level of education. Using a human capital approach, they show that education and training are complementary. Training increases workers’ productivity and individuals who receive higher education are more likely to invest in additional training. Therefore, the experience-earnings profiles are steeper for the more educated. Similar findings have also been obtained by Altonji and Dunn (1995) and Altonji and Pierret (1997).

Studies conducted by Haque (1977), Hamdani (1977), Guisinger et al. (1984), Khan and Irfan (1985), Ahmad et al. (1991), and Ashraf and Ashraf (1993 and 1996) have estimated earnings functions and found that compared to other developing countries, the rate of return to different levels of education in Pakistan are low. Their findings indicate that there exists an inverse relationship between educational attainment and the degree of income inequality and a positive association between earnings and level of education.

Shabbir and Khan (1991) have estimated the Mincerian earning function between provinces by using a nationally representative sample of literate wage earners and salaried males to study wage differentials arising due to educational attainment levels. Their study’s findings displayed that an additional year of schooling increased earnings by 7-8%. Weiss (1970) also found that education attainment significantly reduces wage differentials.

Many studies that aim to look at the correlation between education and labor market outcome in developing countries overlook the fact that the existence of different employment segments, especially in the rural and informal sectors, could also have major implications for the role of education in labor market integration. Vijverberg (1995) observes that some types of employment, such as self-employed work, cannot be linked to the individuals’ credentials, or to a pay scale of any sort, meaning that education can only play a minor role in explaining an individual’s earnings levels.

Employees with secondary or lower education have flatter experience–earnings profiles than employees with tertiary education (Brunello & Comi 2004). Hence, we can conclude that returns from education are not temporary or present at initial stages only, but increase with time spent in labor markets.

Countries that experienced faster labor productivity growth seem to have higher wage differentials due to different levels of education attained by different workers. This can explain the phenomenon observed in the labor markets of developing countries. 

Data and Methodology

The data from the Labor Force Survey 2010-11 has been utilized throughout the paper to conduct this research. The household education and wage data have been used to analyze the effect on earnings with each additional level of education attained.

Descriptions of Data

The Labor Force Survey (LFS) is periodically conducted to collect wage, education, and experience data from a large nation-wide probability sample of households. The data is collected via interviews. The survey covers the entire area of the country. A detailed description of the sampling methodology is given in the Labor Force Survey report 2010-11. The Labor Force Survey collects, complies, and analyses data related to Pakistan’s labor force from different sectors of the economy.

Exploratory Data Analysis

In this section, the paper has grouped those variables that have an impact on education in distinct ways. These variables have been categorized under two main headings, economic and demographic.

Economic variables: Earnings (wages).

Demographic variables: Age (squared), Gender, Education, Rural/urban location, number of household members.

Method and Methodology

To carry out our analysis of the LFS data 2010-11, we have designed a simple regression model. The equation can be expressed in mathematical form as:

Y (Earnings) = β12 (Education) + β3 (Gender) + β4 (Agesq) + β5 (Region) + β6 (Provinces) + µ (standard error)

Dependent Variable:Earnings

Independent Variables: Education, region, gender, four provinces, and age (squared)

After running the regression analysis, we conducted the bivariate analysis in which the dependent variable was tabulated one by one against each independent variable.

It shows the link between education and earnings.


Education:                           Province:                 Gender:            Region:

1 = Primary   
6 = Matriculation                1 = Punjab               1 = Male            1 = Urban
7 = Inter (Fsc)                      2 = Sindh                  2 = Female        2 = Rural 
8 = Bachelors                       3 = KPK
13 = Masters                        4 = Baluchistan
14 = MPhil/PhD

In the first step, the dependent variable i.e. wages per month were regressed on independent variables. This model considers the wage-earning of a household as a dependent variable and others as the independent variables. The regression analysis states that as the level of education increases, the overall income of households increases. It shows a direct positive relationship between education and earnings. The results show that people with MPhil/PhD (14) earn 35378.28 more units of income as compared to those who have primary education. These results are in line with the economic theory, which states that a person who has more education earns more income.

Another important factor, which plays a role in the monthly earnings, is the gender of the person working. The male earning is more as compared to females. The result clearly shows that females earn 4546 units of income less than males.

The province and region also affect the monthly wages of workers. The results show that people in Sindh and Baluchistan earn more monthly income than people in Punjab by 1178 and 1616 units respectively, while people living in KPK earn 695 units of income less than those living in Punjab. People working in rural areas earn 1518 units less than those who work in urban areas. These results are also in line with the theory having all the variables being statistically significant.

In the analysis, the positive sign of age squared of the household head indicates that age has a positive impact on monthly wages. It tells us that as the age rises, the wage also proliferates at an increasing rate. The value of r-square is 0.389, which means that around 39% of the variation in the dependent variable is explained by the independent variable. The low value of r-square might be because of the cross-sectional data set used in our study.

The following tables show the effect of matriculation, intermediate, graduation, masters, and MPhil/PhD on the earnings of both males and females separately.


It shows the link between wage and males.

The above table shows the effect of educational attainment on wages of males only from the data. The result shows that a male with a PhD earns 39435 units of more monthly income than those who just have primary education. The results for males also show a positive direct relationship between their education level and monthly incomes.


It shows the link between wage and females.

The above table shows the results of the relationship between the educational level and the monthly wages of females in Pakistan. Akin to the males, the results show a direct positive relationship between the educational level and monthly wages of females. Females who have a PhD earn 30934 more units of monthly wage compared to those with primary education.

Comparing the analysis of males and females, it can be discerned that increase in monthly wages due to an increase in the education level for males is more than that of females. We can see that at almost every level of education males earn more than females except at the matriculation level. At the matric level, we can see that males earn 2912 more units of monthly income than males at the primary level. Conversely, females earn 4045 more units of monthly income than those at the primary level of education, which is more than that of males.

Descriptive Statistics of Variables Used

Wage, gender, education, age

The mean wage of salaried people, which were included in our data set, is 12206 with a minimum wage of 0 and a maximum wage of 99899 as shown in the table above. The mean age of the sample is 22 with a minimum age of 0 and a maximum age of 99. About 51% population of Pakistan are males and 49% are females and the mean level of educational attainment of the sample is 2 years – with the minimum level being 0 and the maximum being 14. 

Bivariate Analysis

As the data set was quite large, we had to make ranges of income in order to carry out a bivariate analysis. The following table shows the codes and the range of incomes that they represent: 

CodeWage range

Wage and Region

It shows the link between wage and region.

The bivariate analysis of monthly wages and regions shows that more than 90% of the people living in urban areas earn a monthly income between 50,000 and 75000. The table shows us that people living in urban areas have higher salaries. This is not surprising as urban centers usually have higher income sources such as multinationals, industries, corporations and so on. People living in rural areas, however, depend on low paying jobs like farming, labor, and other blue-collar jobs.

 Wage and Age

It shows the link between wage and age.

The above table is the bivariate analysis of monthly wages and age groups. The result shows us that as the age of a person increases he/she starts earning more money. The results indicate that more than 80% of people who lie in the age group of 35-59 earn more than 50000 units of monthly income.

This analysis highlights that as the age of a person increases their income increases as well, although incomes begin to decrease as a person nears his/her retirement age.

Wage and Gender

It shows the link between wage and gender.

1= Male   2= Female 

Females earn much less than males for each range of wages. Therefore, a severe wage differential exists between males and females in Pakistan – one of the primary reasons being that females are less educated in Pakistan compared to males.  

Wage and Province  

It shows the link between wage and province.

The above table shows us the bivariate analysis of wages and provinces. The results tell us that the highest-paid workers reside in Sindh. More than 50% of people earning more than 50,000 units of monthly income reside in Sindh. While the majority of people earning less than 20,000 units of monthly income reside in Punjab. This is probably because the income/economy of Punjab depends a lot on agrarian means which is low paying.

Wage and Education

The following table shows the different levels of education along with their respective codes.

CodeLevel of Education
It shows the link between wage and education.

According to the above bivariate analysis between wage and education levels, about 50% of people earning less than 20,000 units of monthly income have primary level education. On the other hand, people earning more than 50,000 units of monthly income have a higher level of education. The table illustrates that 40% of people earning more than 50,000 are those who hold a bachelor’s degree, while 45% of people have a master’s level education.

Furthermore, 46% of the people earning more than 75000 units are those who have attained a masters level education, and 8% of people who earn 75000 have attained a PhD level of education.

Discussion and Conclusion

This paper investigated the effect of education on the earnings of wage earners in Pakistan. It examines the increase in earnings that take place at each additional level of education i.e. primary, matric, intermediate, bachelors, masters, and MPhil. The results of the research conclude that there is a direct relationship between education and earnings. Males in Pakistan earn much more than females. While people in Punjab and Sindh earn more than people in KPK and Baluchistan. The age of the household head is also positively related to earnings.

Education positively affects the process of economic development by raising the productivity of workers. Both private and social returns of education are high so there is an immense need for improvement in the education sector – in quantitative as well as qualitative terms.


Pakistan’s government seriously needs to increase its budget for the education sector and provide greater subsidies in order to encourage poor parents to send their children to schools. 

The policy recommendations that can be suggested after looking at the results of our research is that the government, NGOs, as well as the private sector should take serious action in establishing more schools across Pakistan. However, their focus should not only be on the number of schools and universities but also the quality of education. A goal-oriented education program needs to be implemented in the country along with overhauling of the public schools by introducing modern teaching methods that discourage rote learning and encourage critical thinking among students. Furthermore, a change in curriculum is also required. In order to achieve these goals, teacher-training programs should be introduced. Unfortunately, in Pakistan, low educational requirements for teaching positions and extremely low salaries offered to teachers, especially at the primary level, reflect the low level of priority accorded to basic and elementary education.

Other than improvement in the quality of education, Pakistan’s government should start awareness programs for females to attain more education. As we have seen from the study, females earn much less than males – this is because the majority of women in Pakistan do not attain higher levels of education due to a prevailing patriarchal mindset in the country. The government should focus on prioritizing education for females, as low literacy in females causes a great loss in labor.

Job opportunities should be created all around the country in all the provinces and regions. As the study shows the majority of highly paid people reside in urban centers of the country as rural areas only offer low paying jobs. The government should introduce policies, which would provide relatively higher-paying jobs in rural areas as well. The government should improve the infrastructure in rural areas and also create special economic zones (SEZ), the latter of which would greatly attract companies to set up in rural areas due to lower or no taxes. This in return would increase employment opportunities and raise the wages of people.


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