Abstract

The research report also includes officials' recommendations, such as universities taking the lead in engaging with industry to reconcile education and training in a mutually beneficial manner in order to avoid the grim prospect of unemployment. This would also serve as a two-way information channel, with students learning about possible employers and their needs and companies learning about their future employees' attributes. It's also possible that the value of social and political connections in acquiring work will be reduced. Across all age groups, the father's educational level is favourably and significantly related to the children's educational success. Except in the case of students, where the low EDUCATIONAL group showed the predicted sign, the mother's educational level was less essential. Employees and self-employed people are surprised by the indicators in the medium educational category. The research report also offers policymakers options to avoid the grim future of unemployment, such as colleges taking the lead in collaborating with industry to reconcile education and training in a mutually beneficial manner. This would also serve as a two-way information channel, with students learning about potential employers and their requirements and employers learning about the characteristics of their future employees. It's also possible that the value of social and political connections in acquiring work will decline.

Key Words

Job Market Demand, Higher Education, Punjab, Pakistan

Introduction

Universities are Pakistan's main source of higher education, and their growth has been impressive in the past. Recognizing the critical role of higher education in quality research for long-term education and economic development, Pakistan's government dissolved the University Grants Commission (UGC) in 2002 and established the Higher Education Commission (HEC), with the goal of strengthening higher education in the country, with a special focus on applied science and technology research. Higher education is a significant kind of human capital investment. It can be thought of as a high-level or specialized form of human capital that makes a significant contribution to economic progress. Pakistan, like other developing countries, is suffering from severe socioeconomic problems such as poverty, unemployment, inflation, and bad health. Education is thought to be the most effective strategy for overcoming such issues, with ramifications in all parts of human existence. In endogenous growth theories, it is one of the key factors of economic growth. Given the importance of education, emerging countries are placing a high priority on the creation of educational policy and tying their educational sectors to market demand. Educational policy, economic level, cost of education, access to educational institutions, educational structure, parents' educational backgrounds, and teaching quality are thought to influence demand for higher education (Aina & Casalone 2020). Higher education demand is inversely related to the price (Cost) of higher education and is positively related to income (Almeida, 2021).

High admissions requirements and the cost of education are also predicted to have an impact on higher education enrolment and attendance (Bagwasi, 2019). Broek & Hake (2012) discovered the effects of changes in the armed forces on higher education, implying that a country's employment level can have a reasonable impact on higher education. Similarly, socioeconomic status is a significant factor of higher education attainment (Davis & Weinstein 2002). An instrumental view of education has grown ingrained in societies all over the world. Its importance in guaranteeing the economic success of individuals and nations has been emphasized. Instrumentalism has grown in tandem with the tremendous development of higher education (HE). However, many of policymakers' and economists' assumptions about the relationship between higher education and the labour market are showing to be incorrect (Fasih, 2008).

There has been a boom in studies into whether or not people are well-matched to their jobs in recent years, with the primary goal of determining whether or not university graduates' skills are underutilized by their employers. This literature is riddled with terminological ambiguity. Overqualified, overeducated, and over-skilled are all terms that are frequently used interchangeably. The key distinction is whether someone requires their greatest qualification to secure their job and whether they require the competencies and skills connected with that qualification to perform the job (Haigh & Clifford 2011). We use the terms under- or over-educated for the former and under-or over-skilled for the latter. The subjective method, the statistical technique, and the empirical method are all possible ways to measure both of these things.

Higher levels of education are generally correlated with greater job market performance, boosting individuals' ability to enter the labour market in a better position and shielding them from unemployment. When highly educated people are unemployed, it may indicate their unwillingness to accept positions of lower quality than they believe are appropriate for their skill level (Ismail & Khan 2021). The impact of educational attainment on people's job market outcomes includes not only increased employment access but also improved job quality in terms of working conditions. Higher levels of educational achievement are linked to higher pay. Even overeducated people (those in positions that demand a lower skill level than they have) earn more money in the long run than those doing the same job with only the abilities required (Kolasa, 2021).

Thousands of pupils with high qualifications have remained jobless for one reason or another. Higher education is changing its goals to better prepare students for careers, opportunities, progressive occupations, workplace training (Pages & Stampini, 2007), and a better understanding of their abilities. Higher education currently assists students in developing employability skills (Nasir & Nazli, 2000), which means that allowing students to obtain and retain employment allows them to make changes in the workplace. Higher education allows students to study job options by building understandings of shifting patterns in the workplace. With this goal in mind, every learner expects their high qualification to help them attain more life success (Pages & Stampini, 2007). They make plans for their long and short-term professional objectives.

Higher education prepares postgraduates to be well-informed and attentive to opportunities, but it also places constraints on their professional options. In general, the reduction in work prospects postpones the fulfillment of nations' aspirations for postgraduates. The pressure on higher education to equip students with the changing trends and needs of the labour market has increased as a result of a smaller group of corporations demanding more graduates. Higher education is a major pillar in ensuring the quality and quantity of developmental governance (Lee, et al, 2019). Various studies have shown that every additional year of higher education can improve pay job production by 10%, even when controlled for other characteristics. Higher education, as well as skill development, is critical for export efficiency.

Cities with higher education rates in Pakistan exhibit a high level of development (Osiobe, 2019). Students must grapple with job options, particularly in Pakistan, where the economy is shaky, and there is insecurity among production students upon graduation and employment. Harmony between higher education and the workplace is a context-specific strategy to creating a viable educational interface for the workplace. A relationship between a learner and learning, a learner and a teacher, a learner and the working environment, work and employers, a learner to another learner, and education with a job is crucial in this process (Tynjala, Valimaa, & Sarja, 2003). Higher education is now widely regarded as the most important deciding instrument for the labour market, having a significant impact on the higher status and fascinating work innovations. It is a dynamic, growing system for occupation with a variety of training options.

For students and educational institutions alike, the employment market has been a major source of anxiety. One of the most serious issues in higher education is the disconnect between the academic training students receive at colleges and the job they are expected to do when they enter the workforce. As a result, the question arises as to whether employment market demands have an impact on higher education. As a result, the overall goal of this thesis is to gain a better knowledge of the impact of job market demand on higher education. More precisely, the thesis will look into whether higher education in various university subjects evolves in response to job market need. As a result, the study's proposed title is "Effect of job market demand on higher education in Pakistan."

Previous research on both factors have been undertaken, as stated in the study's introduction ( i-e job market demand and higher education productivity). The world is changing all the time, and education, particularly higher education, is becoming increasingly crucial. Every noble institute is charged with figuring out how to help learners become the educated citizens that our society requires while also meeting the demands of the learners (Warrick et al., 2010). As education and employment are perennial topics of debate, there is a large body of literature on the subject. The link between education and employment is regarded as faulty. It's the same as higher education presenting study plans just for the purpose of creating graduates rather than educating them for jobs (Harvey, 2000).

The study's major goal was to determine how advertised employment influenced people's decisions to pursue higher education at the university level in Punjab. In addition, the opinions of alumni and chairpersons will be sought regarding work demands. Students who are well educated are the backbone of the country's development, and job market demands direct students' choices of fields and occupations. As a result, the study's findings will benefit students' educational careers by assisting them in making decisions about their career and job options. The findings of the study will also be useful to Pakistani policymakers and human resource managers in terms of expanding the disciplines available and developing additional departments in response to market demands. The study's findings will also aid teachers in advising and career counseling their students. Teachers will educate their students in accordance with societal needs. This research will also provide a more detailed ground reality of education preparation for the workplace, allowing strategists to prepare for a check and balance between the supply of high education, the quality of work in the workplace, and the demand for high education. This study also serves as a blueprint for future researchers who want to delve deeper into the topic of job market demand and higher education.

Literature Review

There is a large body of literature on education and employment since it is a perennial topic of human resource and educational policy debate. For defenders, the link between education and employment is anti-intellectualism. It's the same as higher education giving training therapies for graduates solely to help them acquire jobs, rather than strengthening intellectual elements of personality, developing general morals, and building a nation with innovation and humanity (Harvey, 2000). On the contrary, higher education is today confronted with new global competency issues. Furthermore, we have differing viewpoints about the relationship between education and employment, as seen by disparities in study findings (Tynjala et. al., 2003). However, heated discussions have erupted regarding the dimensions of education and how to reshape education to better serve the world of labour. We hold opposing viewpoints on the effects of schooling on the future labour market. According to Lettmayr (2012), education has altered in various domains during the last few decades, including technical education, globalization of education, demographic demands, and trends. These factors have a significant impact on the number and quality of highly qualified members of society.

The world is changing all the time, and education, particularly higher education, is becoming increasingly crucial. Every noble institute is charged with figuring out how to help learners become the educated citizens that our society requires while also meeting the demands of the learners. The nature of derived goals is determined by the collaborative interplay of education and work (Warrick, Daniels, & Scott, 2010). However, there are some implications in Pakistan for attaining goals, educational approaches, employment with the elimination of unemployment, developing professions, organizational structure, and many more (Lee et al, 2019). There is a need for time to connect with pupils in order to prepare them for the workplace. It is exciting and hard, and more study is needed to expand this area both within and internationally. This study was done in this context to investigate job market needs and their impact on higher education. The results of this study will address the vacuum in research on Pakistan's link between higher education and labour market demand by providing deeper evidence of education as a predicting object of job in Pakistan. The findings and conclusions of this study contributed to a better understanding of how current job market demand influences students' higher education.

According to Halai and Durrani (2020), Pakistan's current educational system is a relic of partitioned British India. After the 18th Amendment to the constitution recognised education as a core human right in 2010, an examination of Pakistan's education system finds that nothing has changed in the country's schools. Access, quality, infrastructure, and opportunity inequalities continue. According to the Pakistani Constitution, the Federal Government is responsible for the policy, planning, and development of educational institutions in the federating units (1973). This was in addition to wide policymaking, coordination, and advising powers; education was otherwise treated as a separate topic (Ali et al., 2021).

The Federal Ministry of Education is also in charge of educational institutions in the Federal Capital Territory. Local governments manage universities in the provinces, while the federal government funds them wholly through the University Grants Commission (formerly known as the Higher Education Commission, situated in Islamabad) (Amir et al., 2020). The Minister of Education is in charge of the Federal Ministry of Education. The Minister of Education is the ministry's highest-ranking civil servant, with joint secretaries and joint education advisers from several departments assisting him. Six departments make up the federal education component, each overseen by a joint education advisor. The parallel issue was deleted from Schedule 11 of the Islamic Republic of Pakistan's modified Constitution of 1973. Pakistani provinces are now able to make policy decisions based on their own interests and those of their communities (Zafar & Ali 2018).

Ramsey's report from 2014 demonstrates that Pakistani educational institutions are afflicted by social and political unrest. Six educational policy documents are supplied, all of which reflect political concentrations, and no consistent transfer will arise for another policy. The findings of the study revealed a low-cost, illiterate education (Fontanella et al., 2020). Pakistani educational costs account for around 3.4 percent of the country's gross domestic product

(GDP) outside of India. 

Illiteracy was, however, roughly 65 percent lower than in Nepal (Akbarari et al., 2000).

De Lange et al., (2014) research the relationship between preparation and job access and exit rates of joblessness with various instructive stages in the Netherlands in the context of cutting-edge monetary framework. In comparison to the least well educated, the results suggest that a highly-trained employee has a better chance of being hired. In another example of Spain's cutting-edge financial arrangement, Mora, Garcia, and Garcia (2006) investigate the relationship between higher education and joblessness in Spain, finding that the most youthful group of higher education had the worst job prospects. Moreau and Leathwood (2006) investigated the impact of higher education on unemployment in global locations and discovered that awful affiliation is introduced in the most extreme global areas. Yun focused on the effect of higher education on joblessness in each other view of 15 countries in 2021, and discovered a couple of unmistakable conclusions from the current investigation.

Data and Methodology

Data Collection

The data was gathered by the researcher himself. The data was collected using a survey method and content analysis. First and foremost, the researcher obtains written permission from the directors of institutes and university alumni. The interviewees then decided on the time and location. The researcher conducted interviews at the agreed-upon time and location. The researcher captured the audio of the interviews. The researcher also took written field notes to record the interviewees' facial expressions. The researcher conducted a content analysis on the prepared checklists. The researcher organised the data collected in a systematic manner. The respondents' responses were written on sheets in a variety of ways. Separate sheets were created based on the codes that had been created. In addition, a summary of these interviews was generated. The SPSS programme was used to examine the data acquired by checklists (Statistical Package for Social Sciences). The collected data provided the frequencies and percentages.




Population

The population is a huge group from which the sample

was taken. Only universities in the public sector are considered for this research study in Pakistan's higher education system. PHEC's mission covers 42 public and 26 private universities, as well as 773 colleges and 1632 private colleges, all of which provide higher education in Pakistan. For the job demand, 840 individuals were interviewed, with 240 of them being female.

The goal of the study was to first understand the nature and needs of graduates' job, and then to use that knowledge to assess the skills-based concepts of education and work that support the employment-focused higher education model. Whereas higher education policy is increasingly based on an abstract, overly simplified conception of graduates' work (see chapter two), this study sought to improve understanding of its nature and demands by creating narratives that accounted for the complexity and variation of participants' experience and practise. This is known as the "searching for complications" research strategy, since it aims to "enhance generalisations by identifying new things to add to the grid of variable components that helps us to understand any example of that kind," according to Becker (2014). 

Questionnaires were created for each of the six sample groups, and early versions were sent to small local samples taken from the Islamabad and Rawalpindi areas in each case. The questionnaires were improved as a result of the feedback. Individual history, institutional experiences, educational career and when employment experience were all covered in the questionnaires for self-employed, student, employees, and unemployed. Validity and dependability are required in research instruments. To validate the research tool's face and content validity, a group of five experts in both language and the relevant issue evaluated these items. On the survey, the item that received 84 percent of the experts' support was kept. In a pilot study, the research tools were tested on two employees and data from public universities. A total of 100 employees were initially gathered, and replies were recorded after SPSS processing. A coding method from 1 to 5 was devised for the extremely agree to strongly disagree range of the scale. Following the data analysis from the pilot testing, some items were changed and others were tweaked to provide the best research results.

Analysis of Data

In recent years, a number of growing countries have expressed worry about educated unemployment. Urban particularly, where substantial increases in the population's educational level have been coupled by high open unemployment rates among educated persons In the Philippines, Sri Lanka, and Malaysia, the situation has been particularly dire. The problem in these countries is typically perceived as a mismatch between the school system's career aspirations and the work prospects supplied by the labour market.

 

Overall Employment Situation

The prevailing opinion of emerging countries is that, rather than addressing the problem of underutilization of labour resources, the problem has been compounded by planned growth over the last two decades. Population growth rates are high, while capital accumulation is modest, and some of the more generally suggested factors include an increased prevalence of capital expensive approaches because labour absorption isn't keeping up with output growth.




 

Table 1. Pakistan Offers Higher Education and Job Chances

S. No	Sector	1961/62	1971/72	1981/82	1991/92	2011/12	2020/21
1	Agriculture	65.4	59.6	57.32	54.8	52.65	52.73
2	Mining & Quarrying	0.1	0.2	0.45	0.15	0.14	0.10
3	Manufacturing	9.7	13.5	12.47	13.63	14.52	13.44
4	Construction	1.8	2.1	3.41	4.20	4.92	4.80
5	Electricity & Gas	-	0.2	0.37	0.49	0.74	1.13
6	Transport etc.	1.7	2.7	4.84	4.87	4.73	4.59
7	Wholesale/Retail Restaurants	-	7.4	9.89	11.09	11.08	11.94
8	Financing & Insurance	-	0.8	0.86	0.67	0.86	0.82
9	Community and Personal services	-	11.2	11.0	7.27	9.78	10.10
10	Activity not defined	0.7	0.7	3.12	0.33	0.27	0.27

Source: Labour force surveys/various issues

 

Table 2. Occupational Distribution of Employed Labour Force

S. No	Sector	1961/62	1971/72	1981/82	1991/92	2011/12	2020/21
1	Professional & Managerial	1.08	1.81	2.09	3.01	3.05	3.08
2	Managerial workers and Administrative	0.35	0.68	0.57	0.67	0.86	0.79
3	Clerical staff	2.53	3.24	2.14	2.47	2.85	2.14
4	Workers related sale	5.71	6.29	12.52	9.25	10.23	10.21
5	Workers for services	5.63	5.10	3.01	4.02	4.20	4.14
6	Agriculture & Animals	65.3	59.7	57.22	54.7	52.64	52.82
7	Works in Production	18.15	21.19	22.17	24.18	25.13	25.16
8	Not classified	0.01	1.82	1.14	.12	.15	.10

Source: Labour Force Surveys, Various Issues

 




Since its inception, the Central Statistical Office has performed Labour Force Surveys 1961/62 to provide data on different areas of employment and unemployment in Pakistan. They were conducted at irregular intervals, despite the fact that they were based on nationally representative samples and standard questionnaires. Consequently, their utility for time series comparison is limited. When compared to the census, the LFS uses distinct concepts for measuring employment and unemployment, restricting the two sets of data's comparability. Despite this, these surveys are the principal source of national-level employment/unemployment data that is cross-classified by age, education, occupation, and other variables. This study's estimates are almost entirely based on data from these surveys.

 

Sectoral Distribution of Labour Force

The information on the occupational distribution of the labour force does not support the notion of a qualitative fall in employment patterns in the 1970s. As indicated in the table, the time was marked by an increasing proportion of white-collar and factory employees.




 

Table 3. Unemployment Rates by Level of Education, in Percentages

	Total	No Education	Incomplete Primary	Primary But less than matric	Matric but less than a degree	Degree but less than postgraduate	Postgraduate
1990/91	2.0	1.7	2.8	2.8	4.2	5.8
2000/01	1.74	1.13	1.07	3.01	5.74	4.05	1.61
2010/11	3.6	3.0	4.8	4.5	5.7	2.7	3.6
2020/21	4.9	3.4	6.2	4.5	5.5	3.0	3.0

Source: Labour Force Survey, Various Issues




When the educational makeup of the unemployed is compared to that derived from employment exchange statistics, it becomes clear that different educational groups have different registration behaviors.




 

Table 4. Distribution of Labour Force by the level of Education, 2020/21

	Total	No Education	Incomplete Primary	Primary But less than matric	Matric but less than a degree	Degree but less than postgraduate	Postgraduate	Others
Population aged ten years and above	60.57	39.79	3.16	12.21	4.45	.62	.22	.13
Labour Force	26.90	17.63	.871	5.31	2.45	.40	.17	0.07
a. Employed	25.85	17.03	.82	5.07	2.31	.39	.16	.07
b. Unemployed	1.05	.60	.054	.238	.135	.012	.005	.005
Not in Labour Force	33.67	22.16	2.289	6.90	2.0	.218	0.052	.059
Unemployment rate	3.9	3.4	6.2	4.5	5.5	3.0	3.0

Source: Labour Force Survey, Various Issues

 




Educated Unemployment

Based on LFS data, unemployment rates for Pakistan by degree of education have been calculated. The table shows these estimates, as well as those produced from this study for subsequent years. In general, the statistics reveal that educated people have greater unemployment rates than illiterates. This is consistent with data from other developing countries, where illiterate groups had lower unemployment rates than their more educated peers. The higher unemployment rate among educated people is attributable to the fact that educated people from wealthier families can afford to wait for the ideal job in the high-wage modern industry rather than accepting low-wage positions.




 

Table 5. Comparison of Labour Force Survey (LFS) with Employment Exchange Data (EE) % Age Distribution of Jobless by Educational Level

Literacy and educational level	2015		2020
	LFS (a)	EE	LFS (b)	EE
Level of Illiterate	41.7	27.0	54.43	21.5
People below matric	22.93	32.0	23.38	36.6
Matric with less  degree	23.79	44.0	15.58	44.3
Diploma	-	1.5	-	1.5
No. of Graduates	2.9	4.1	1.9	4.0
No. of Post Graduate	1.6	1.4	1.6	1.2

(a). The figures in the LFS are for the year 2014/15, (b). The figures in the LFS are for the year 2020/21

Source: Various Issues in the Labour Force Survey

 




Despite the lack of accurate information on the migrants' educational status, the Bureau of Emigration and the International Migration Project (PIPO/PIDE) both have data on their skill composition. The Bureau of Emigration collects data on all migrants who have traveled through official procedures from all countries, the latter research, on the other hand, focuses entirely on Middle Eastern migrants and is based on a poll of 12,500 departing passengers from the country's three main airports.

 

Employment and Manpower Projections

Manpower planning is crucial in terms of overall

economic planning. It is also necessary for the improvement of living conditions and the eradication of extreme poverty. The combination of physical and human capital resources, technology, and a nation's institutional structure results in participation in society's production processes and the subsequent distribution of benefits. In this context, manpower planning aids in establishing a viable balance between a nation's resources and objectives. It not only examines the feasibility of reaching production targets that are dependent on the availability of the required skill mix, but also the relevance of these goals in terms of maximizing human resource usage. Manpower planning, in this way, contributes to the convergence of human resource and economic planning.




 

Table 6. Province-wide Unemployment Rates by Level of Education

	Total	No Education	Incomplete Primary	Primary But less than matric	Matric but less than a degree	Degree but less than postgraduate	Postgraduate	Others
2000/01
Sindh	0.8	0.6	0.6	1.5	1.8	.8	1.1	-
Punjab	1.7	1.3	1.0	3.2	8.0	2.2	1.9	-
Baluchistan	0.2	0.2	0.3	-	-	-	-	-
KPK	2.0	1.0	1.2	5.9	9.4	2.6	1.7	-
2010/11
Sindh	1.9	1.5	2.9	2.6	3.5	1.5	1.7	-
Punjab	4.5	3.9	5.7	5.5	6.7	4.2	6.5	-
Baluchistan	1.6	1.05	-	7.8	1.2	-	-	-
KPK	3.6	2.9	5.9	3.5	9.3	3.3	4.3	-
2020/21
Sindh	1.8	.2	1.4	1.7	2.9	1.7	2.5	-
Punjab	4.3	3.5	8.1	5.4	6.2	6.0	3.3	-
Baluchistan	4.8	4.2	-	2.8	1.0	2.0	1.7	-
KPK	8.0	8.0	10.0	12.0	-	4.0	-	-

Source: Labour Force Survey, Various Issues

 




Despite its importance, Pakistan's planning system has unable to properly incorporate manpower planning. Part of the reason for this failure is the lack of a comprehensive employment plan and a restricted database. Various Plans did, however, include personnel predictions of differing degrees of depth, and research endeavors to attain the same have been made in the past. First, a brief review of previous attempts at such exercises is provided, along with a discussion of some of their fundamental drawbacks. The imbalances are then determined by estimating manpower requirements for the Plan period and comparing them to predicted labour availability.

Except for the last two, the results of the various investigations are presented in a table. The range of estimates for both availability and requirements is large. The labour supply differs by three million people. It's worth noting that both supply forecasts were based on the same demographic projections. If other population estimates are used, the results will be drastically different. On the demand side, studies based on overseas experience (such as Ruud) tend to grossly overestimate productivity gains. IACP and Irfan's research take Pakistan's experience into consideration, implying a moderate rate of economic structural development and, as a result, a slow rate of workforce structure transition. The unemployment estimates indicate a lot of variation, which is consistent with the supply forecasts. According to these projections, unemployment as a percentage of the labour force in 2018 might vary from 0.4 to 42 percent.

 

Labour Requirements

A closer study at Table, which shows the employment elasticities for various time periods, can indicate the type and nature of inconsistencies that result from extrapolating previous data into the future. These elasticity indicators show a lot of variation between sub-periods. A manpower projection based on a single sub-period is unlikely to be accurate for the entire Plan period. According to the latest sub-requirements, period's Pakistan would end up importing rather than exporting labour in 2014/15.




 

Table 7. Employment Forecast for 2018-19, in Millions

	Employment in 2014-15	Target growth of output	Elasticity of employment	The growth rate of employment	Employment in 2018-19
Agriculture	24.16	14.90	10.51	12.05	26.25
Mining and Quarrying	10.03	170.2	10.32	12.02	10.02
Manufacturing	13.34	19.30	10.32	13.25	14.125
Electricity	10.30	18.02	10.23	11.25	10.33
Construction	11.29	19.20	10.68	16.25	11.75
Trade	13.21	17.90	10.41	13.01	13.52
Transport	11.23	17.24	10.41	12.91	11.24
Financial institutions	10.22	16.20	10.15	11.02	10.23
Services	12.45	14.71	10.20	11.20	12.28
Unallocated	10.13	-	-	-	12.21

Source: Labour Force Survey, Various Issues

 




Cohen's estimates are the lowest, while ARTEP's are the highest for the terminal year labour requirements shown in Table 7. The disparity is primarily due to differences in benchmarks, as all of the studies end up with a 3% annual growth rate in total employment. However, the implications for employment sectoral composition vary. Agriculture accounts for 53.9 percent of overall employment in the ARTEP study, Cohen's and this study's yields, respectively, 51.9 and 52.1 percent. Agriculture employs 52.7 percent of the workforce, according to the 2014/1 Labour Force Survey. The projections in this exercise indicate a minor movement away from agriculture, whereas the findings of ARTEP's analysis indicate an increase in agriculture. Such a structural transition appears hardly plausible in the aftermath of rapid development, as the Plan envisioned. The inter-sectoral production correlations emphasize this even further.




 

Table 8. Alternative Workforce Predictions in Millions for the Benchmark Year (2008-09)

	Actual (a)	ARTEP Study	Cohen’s Study	Manpower Division
Agricultural Force	24.25	24.25	23.25	23.14
Mining and Quarrying	10.02	-	10.02	10.02
Manufacturing	13.25	13.36	13.76	13.75
Electricity	10.30	10.12	10.14	10.19
Construction	11.24	11.12	10.24	11.25
Trade	13.21	12.54	12.68	12.48
Transport	11.23	11.28	11.45	11.24
Financial institutions	10.23	-	-	10.24
Services	12.85	12.45	12.65	12.62
Unallocated	10.04	10.24	10.22	10.09

Source: Labour Force Survey, various issues. The numbers serve as the foundation for study estimates in the future.

 




In light of historical economic growth, the forecasts' implications for sectoral productivity are assessed. It's worth noting that the inter-sectoral productivity links discussed here are only in the single digits, which hides a lot of diversity within the sectors. Intra-sectoral productivity differentials may have been substantially bigger than cross-sectoral productivity differentials in the presence of the technological dualism that pervades the entire economy. Furthermore, the pricing and cost structures of economic activity have been distorted by numerous government initiatives, making it difficult to analyze the productivity link between sectors. For the year 1998/99, Kemal discovered manufacturing productivity to be one-third of what it was anticipated to be at domestic prices.

 

Students', Graduates', and Employers' Perspectives

The primary purpose of this study was to look into the gap between higher education and the job. This could be looked into using either a quantitative approach to manpower planning or a qualitative approach to determine if higher education training meets the demands of businesses. We've gone with the more conservative approach. This emphasises the scale and nature of the relationship between higher education and job opportunities in related sectors.




 

Table 9. National Budget as a %age of GNP & Public Expenditure as a %age of GNP

	% of GNP		% of National Budget
Year	Developed Countries	Under Developed Countries	Developed Countries	Under Developed Countries
2000	4.2	2.4	12.3	11.5
2005	5.6	3.2	16.2	12.6
2010	5.8	3.7	18.8	13.2
2015	6.4	3.9	18.6	15.1
2020	7.4	4.2	22.3	16.1

Source: World Bank

 




Subject Choice

People who were unemployed or self-employed were also asked to rank reasons for adopting various fields from a list of possibilities. In their topic selection, the unemployed place the least emphasis on the possibility of satisfying future professional needs, which is consistent with their low respect for this reason as a motivator for pursuing higher education in general. Unfortunately, because the many explanations presented are not mutually exclusive, the response ranking is inconsistent. As a result, despite the fact that future career needs are minimal, the course's market value is second highest. As seen in Table, the unemployed place a greater emphasis on personal preferences than on career needs.




 

Table 10. Reasons for Choosing a Certain Subject are ranked in Order of Importance

	Unemployed		Self-Employed
	Average Ranking	No.	Average Ranking	No.
Possibility of meeting professional requirements	1.74	220	1.88	108
Possibility of achieving a personal goal	1.98	241	1.87	95
Course content	2.28	98	2.9	12
Market Value	2.21	208	2.8	58
Prestige	-	-	2.6	110

Note: The greatest possible rank is 3, while the lowest possible rank is 1.

 




When comparing employee and student samples, the number of scientific students recruited from a rural domicile grows in lockstep with the overall number of students recruited into higher education from a rural domicile. As a result, it appears that regional background has little bearing on the choice of science

topics.

The reactions of self-employed people are unexpected. All of the listed arguments are given the most weight feasible. This could be due to what is commonly referred to as the self-general employed's excitement. These weights, on the other hand, are significantly higher and the response rates are significantly lower than on a similar question, the results of which are presented in Table. Aside from the immediate reactions mentioned above, sex, regional background, performance, and financial status could all be linked to undergraduate subject choice. Given Pakistan's growing emphasis on science, it's critical to study the characteristics of those who select science and whether they've changed over time.




 

Table 11. Science Subjects Chosen by Men and Women, in Percentages

Sex	No of Students	No of Employees
Female	22 (28)	12 (18)
Male	82 (71)	85 (80)
No.	392	752

Note: The percentage distribution by sex in the sample is shown in parentheses.

 




Women make up a substantially higher percentage of science specialisations among students than among employees, as shown in the table above. However, the percentage (20%) represents less than a quarter of the total number of female students in the sample. A comparable table is provided for the student's regional background (Table below).




 

Table 12. Science Disciplines Chosen by Area, in Percentages

Region-wise distribution	No of Students	No of Employees
Urban	65 (68)	72 (68)
Rural	40 (35)	35 (32)
No.	380	689

Note: The percentage distribution by domicile in the sample is shown in parentheses.

 




When comparing employee and student samples, the number of scientific students recruited from a rural domicile grows in lockstep with the overall number of students recruited into higher education from a rural domicile. As a result, regional background appears to have minimal influence on science topic choice.




 

Table 13. Subject Preference based on Socioeconomic Position (in Percentages)

Status	Students	Employees	Self-Employed	Un-employed
Low	17.5 (20.1)	17.5 (14.2)	3.8 (3.5)	19.9 (14.5)
Middle	81.0 (75.1)	79.6 (73.2)	91.2 (85.6)	77.2 (84.6)
High	4.5 (6.8)	3.2 (13.5)	3.8 (8.9)	1.5 (3.5)
No.	1022	1564	190	261

Note: The percentage ages in parentheses show the socioeconomic status distribution.

 




The socioeconomic background of the interviewees may have influenced their subject selection. The cross-tabulations, on the other hand, refuted this assertion. Except for students, socioeconomic status had little influence on topic choice when parents' income and education were combined. Even there, however, the connection was shaky. With the exception of the unemployed, parents had a major, if weak, influence. Despite the lacklustre and inconsequential results, the accompanying table depicting subject choice by socioeconomic background is offered because of the intriguing patterns it exposes.




 

Table 14.  The Findings of Regression Analysis Revealed the Factors

Variables	Students	Employees	Self-Employed	Un-employed
Provincial Distribution (with dummies)
Punjab		.321*** (1.25)	-.625** (1.65)	-1.245* (3.24)
Sindh		.431** (1.34)	-.423*** (2.14)	-2.524** (1.98)
KPK		.120** (2.31)	.321*** (2.14)	-0.352* (1.62)
Balochistan		.254** (1.91)	.215*** (2.31)	0.111** (1.01)
Mother’s Educational Level
Low	-.214* (1.02)	-	-	-
Medium	-	.321* (1.21)	.751** (1.31)	-
Father’s Educational Level
Low	-	.785** (2.14)	.157*** (2.35)	.123** (2.14)
Middle	.452** (1.21)	.241** (2.14)	-	-
Science Specialization	.258* (1.23)	.129*** (1.91)	-	-
Male Sex	-	.369* (1.69)	.852** (1.31)	.654* (2.27)
Rural Domicile	-	.357* (1.36)	-	-
Constant	12.1204***	13.5421***	13.2151**	11.3245***
R2	.86	.82	.79	.83
Obs.	890	2005	1140	252

***, **, * represents significance of 1%, 5% & 10% level, respectively 

Regression Results and Determinants of Performance

By putting the performance variable on an interval scale, regression analysis might be used to identify the importance and size of the impact of multiple backgrounds and other variables in explaining performance. The above table shows the results of a series of step-wise regressions. The first group of variables that explain performance discrepancies is provinces. It appears that there are systematic disparities in examination board standards among provinces, which is a valid interpretation. The educational backgrounds of the parents had an impact on the performance of children. The dummy variables for the father's educational level have negative signals, indicating that the father's educational level is positively correlated, and as expected, strong correlations exist between the educational performance of youngsters in all groups.

With the exception of students, who exhibited

the predicted sign in the low educational group, the mother's educational level was less essential. The indicators in the medium educational category appear shocking to employees and self-employed people. The mother's intermediate educational level, particularly for self-employed mothers, In explaining the children's performance, the lower level was determined to be more important than the higher level.

Recommendations

Other sources of aid should be sought because higher education would get relatively little new funds under the development plan. The private sector, especially businesses and other organizations, might be encouraged to contribute to education and research funding, especially applied research. Another option is to only charge tuition fees to families that can afford them. A small proportion of students, especially qualified students from economically disadvantaged homes - girls and rural students - should get subsidized or free education.

Career counseling was not available in three-quarters of the higher education institutions evaluated. In reality, by the time they started university, 80% of all students/employees had chosen a career path. Only half of the unemployed chose to attend college, and the self-employed decided to go it alone late in their academic careers. The latter two categories mostly consisted of Humanities students who had a broader choice of job options and hence needed coaching. Science is a magnet for the brightest students. High matriculation results are required for the Science option, and these students had all received counseling and discussed career options with their teachers. However, while in university, all of the students reported a collective need for career counseling, literature, job agencies, and company connections.

More labour market data is required, which the competent central ministry may distribute to organisations. Each faculty member might be appointed a resource person to provide such information to students, connect with local businesses and promote visits from potential employers, and market vacation job and training opportunities. The section about moving to the working world delves deeper into this topic.

The relevance of merit in the higher education process and market has been discovered: the majority of respondents believe that poor grades are the most common reason for rejection from various levels of higher education. Merit appears to be getting more essential in higher education with time since it was perceived by a larger proportion of students than employees. In actuality, independent confirmation for such a position was provisionally gained using more objective statistical approaches. As a result, success in the matriculation exam was a good predictor of future performance.

Universities should take the lead in interacting with industry to reconcile education and training in a mutually beneficial manner in order to avert the dreadful prospect of unemployment. This would also function as a two-way information channel, with students learning about possible employers and their needs and companies learning about their future employees' attributes. It is also possible that the role of social and political connections in gaining a job may be reduced.

Attempts to combine labour demands and education have been undertaken in the past. Manpower planning has yet to be connected with overall planning efforts due to a lack of data and a complete employment strategy. In the last year of 2005, the available labour force was estimated to be 311.42 million people. However, there is no way to know if supply will keep up with demand. BA graduates currently work in fields unrelated to their subjects of study. This isn't unique, and it demonstrates a level of professional flexibility that's desirable. However, they must adapt and learn on the job for longer periods of time, and there must be greater uncertainty while applying for jobs, as evidenced by the fact that the job hunt takes 10-11 months.

Conclusion

Because of their lack of contacts with recruiting employers, educational institutions have had a limited role in career planning. Moreover, and probably for the same reason, Employees and employers both thought they had little impact on the respondent's job search. The desire for such connections was scored very high by the unemployed. Surprisingly, both employees and employers ranked newspaper advertisements as the most essential source of job leads. The lack of suitable channels to smooth the transition from college to work resulted in a fairly lengthy waiting period. Employees had to wait an average of eleven months before finding work. As a means of alleviating this problem, the majority of students suggested arranging a temporary work before starting a post-education permanent job. Given that two-fifths of self-employed people want to provide summer jobs and one-third of them have job placement programmes, this may be looked into more. It was also thought that hiring based on merit and a qualification assessment was vital.

Around 28% of employees thought that contacts helped them find work. Only 4% of the unemployed, on the other hand, said they had any such ties; the most important reason for their unemployment was a lack of contacts. Similarly, a lack of contacts was recognised as the most major cause for self-employed people's failure to find a job. While academic competence was a crucial determinant of successful admission, high mean earnings, and a short job search time, other less desirable variables such as 'contacts' also played a role in the labour market. Despite some bitterness on the part of the jobless as a result of their situation, an increasing share of the unemployed have a medium or good academic achievement. As a result, this issue merits some consideration. This is also true since the unemployed were more likely than any other category to come from low-status families.

The unemployed blamed their plight on a lack of labour market information, a lack of correlation between the labour market and higher education, and the absence of a national employment policy. A closer match between topic expertise and occupational category could possibly shorten the waiting period. There was little association in this area in general, while it was stronger for postgraduates than graduates. Despite this, employers were generally pleased with their employees' work performance. Only 4% of self-employed people found their educational training to be irrelevant to their employment, whereas 86% of self-employed people were satisfied with their jobs. Employees expressed moderate satisfaction with their job's professional features and the application of their skills. Employers appeared to be aware that this was an important component in job satisfaction.

Moreover half of employees require on-the-job training, confirming the findings that there is little correlation between speciality and job type. More people in the non-science category did so than in the scientific group. This is to be expected, given that they lack the specialised abilities that scientific students possess. In response to this demand, only 14% of educational institutions and half of employers give such training. Employees emphasised the necessity for a personnel planning strategy, while employers stated that better teaching, refresher courses, and on-the-job training were the answers to increased job performance. Their concern about the monetary side of the positions was previously noted.

In general, discontent with pay should result in a high percentage of job movement. Surprisingly, just a small percentage of workers changed occupations. This could be explained in part by the employees in the sample having a young average age, as well as the large amount of vertical and horizontal mobility that employers believe exists inside their organisations.

As a result of this research, a number of conclusions on a variety of topics have been reached. Some are of scientific interest to the general public, while others may be of importance to policymakers. The study's stated goals, academic consensus, researcher bias, or a mix of these factors could all point to a conclusion the most serious topic out of numerous in social science research. The main goal in this instance was to gather qualitative data about the disconnect between higher education and the workplace. 

Because their condition is one that needs special consideration from a human and social aspect, the unemployed should be the focal point of this mismatch among the groups under investigation. In part, the evidence on the reasons of unemployment blamed the unemployed, and in part, it blamed social concerns. As a group, the unemployed demonstrated a lack of skill diversification as a result of their topic choice. They primarily focused on non-science areas, whereas individuals who specialised in science-based subjects fared well in the employment market. While a large number of the unemployed expressed dissatisfaction with scientific programmes, career needs were not considered a factor for seeking additional education or choosing subjects.

Pursuing studies out of curiosity could explain why the unemployed (both sexes) were largely middle or high performers, with a disproportionately large percentage in the latter category. Although academic performance has been established as a key driver of job market success, this has not been the case for the unemployed. Instead, they claimed that their failure to get work was due to a lack of contacts. This is likely due to the fact that the unemployed were disproportionately from lower-income families.

As a result, it appears that unemployed people from low-income families are doubly disadvantaged. The findings backed with the popular belief that a student's socioeconomic background can help them succeed in school. Even if students perform well while coming from a low-income family, they may face prejudice on the job market. Despite the fact that this is not a conscious policy of exclusion, a lack of links achieves the same goal. Even among those who did find work, socioeconomic background was found to be a significant factor of earnings. 

To level the playing field, the hiring process must be devoid of any type of discrimination based on family history, as well as compensating for gaps in job market information and connections between the workplace and academic institutions. Open advertising and evaluation based on externally evaluated written tests and academic performance is one recommended cure, at least for the public sector. Education standardization would be a more dramatic, but presumably more beneficial.

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