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Research Questions

The research attempts to examine the relationship between wage rates and education, particularly understanding the US Census data of 2008. It is essential to take research gaps and the existing body of current literature into account when formulating a complex research issue.

Research pioneered fifty years ago such as Becker and Chiswick (1966) the recent studies like Coady and Dizioli (2018) highlight the importance of human capital in determining earning potential and support the idea that education and salaries are positively correlated. However, Hoffmann et al. (2020) highlight subtleties in this connection by pointing out that returns vary depending on the kind of schooling and subject studied. Nolan et al. (2019) also support a more thorough investigation of evolving trends in pay disparities. Considering these observations, our research question aims to explore the complexities of the relationship between education and wages in 2008. It is important to understand empirically to what extent education type and level affect hourly wage rates, and what the influence of contextual factors like gender, race, and regional disparities shape the relationships. This is the primary question that captures the complexity of the relationship between education and wages. The study tries to contribute to the current discussion on income inequality and the influence of education on economic outcomes by offering a thorough knowledge of the mechanisms at work by putting the following research enquiry.

RQ1:   What is the impact of education on the wage rate in the US in 2008?

RQ2:   In light of the effect of contextual variables including gender, ethnicity, and regional differences, how did education affect hourly wage rates in 2008?

Project Aim & Objectives

This study’s primary aim is to thoroughly examine the relationship between educational level and wage in the USA during 2008 and to identify the complex factors that lead to income inequalities; several other demographic variables are also interlinked in this relationship. Therefore, the objectives that are framed to be accomplished to meet the primary aim are:

  1. To conduct a thorough Literature Review to compile the existing body of knowledge about the relationship between pay rates and education and understand the gap.
  2. To estimate the education level and its effect on hourly salary or wage rates in the USA to understand the education-based income inequalities in 2008
  3. To develop insightful suggestions that shall help reduce pay gaps by recommending various stakeholders such as businesses, educational institutions and governmental policy makers towards a more informed and fair approach to education.

Literature Review

Wage Disparities

The issue of wage inequality has been a persistent and complex difficulty in labour markets worldwide (Schroder, 2019). Kunze (2018) highlights the intricacy of pay differences, contending that a wide range of characteristics, such as gender, experience, and education, have an impact on them. According to the Human Capital Theory, which was promoted by Becker in 1964, pay disparities are mostly a reflection of how differently people have invested in their education and skill sets, connecting salaries to individual productivity (Abbott and Gallipoli, 2018). The theories have drawn criticism for failing to recognise structural disparities (Marginson, 2019). The persistence of gender-based pay discrepancies despite educational achievements highlights the complex interaction between discrimination and sociocultural systems. Institutional factors which labour market institutions influence in shaping workers' negotiating strength and pay-setting mechanisms also contribute to wage discrepancies (Zwysen, 2022). Another level of analysis is provided by the temporal patterns in pay discrepancies. According to Cohen and Ladaique (2018), there has been a historical change in the gender pay gap, which they attribute to shifting social standards and more women pursuing higher education. The dynamic aspect of pay differences is highlighted by this study, necessitating a sophisticated understanding that goes beyond individual factors. Regional differences add to the story's complexity. Kuznets (2019) shed light on how regional economic structures affect pay inequalities, showing that different economic environments in different areas have a major role in the differences in wage rates. Though there is a clear causal relationship between education and salaries, the relationship is not straightforward. The investigation of mandatory schooling legislation by Dosi et al. (2018) shows that the causal relationship between education and earnings depends on several variables, such as labour market circumstances and educational quality. This subtlety casts doubt on the notion that there is a clear-cut correlation between income and education.

Education and Wage Disparities

Examining the complex link between education and salary differences is a central topic in the labour economics debate. Understanding this relationship has been made possible by Becker's (1964) Human Capital Theory, which holds that investing in education increases productivity (Abbott and Gallipoli, 2018). This highlights the importance of human capital accumulation but subtleties in this connection show up, casting doubt on the theory's simplicity. The relationship between pay disparities and education goes beyond personal characteristics. Arshed et al. (2018) provide a time dimension to the conversation by highlighting evolving trends in college enrolment and completion. The intricacy is increased by the intersections between schooling and other demographic characteristics. The investigation of the gender pay gap reveals enduring differences, especially among highly educated people, emphasising the need to identify and resolve societal injustices. The complex web of education and pay inequities is further exacerbated by regional differences. According to Cohen and Ladaique (2018), there is a significant geographical difference in intergenerational mobility and income outcomes, indicating that the influence of education on salaries is shaped in large part by regional economic circumstances. This regional lens offers a sophisticated perspective on the relationship between educational achievement and geographic location. Although the theoretical foundation is provided by human capital theory, a thorough understanding that takes into account contextual variables, historical patterns, and demographic intersections reveals the complex relationship (Kunze, 2018).

Experience in Wage Disparities

One important area of labour economics that sheds light on the complex mechanisms that lead to income inequalities is the investigation of the role that experience plays in pay disparities (Costa Dias et al., 2020). According to Becker's Human Capital Theory (1964), experience is a kind of human capital that has a big impact on productivity (Canavati et al., 2021). The core tenet of the theory is supported by empirical research by Stinebrickner et al. (2019), which consistently confirms the positive correlation between job experience and wages. However, several reasons make this seemingly simple relationship more difficult. Although early job experience is linked to significant pay growth, the additive influence gradually decreases with time. The idea that experience and pay have a linear and continuous connection is called into question by this occurrence, which forces a deeper look at the earnings trajectory throughout a career. Moreover, the idea of job matching which Haltiwanger et al. (2018) emphasized also adds another level of complication. According to this people learn about their productivity and the requirements of certain employment, which influences future salary negotiations and prospects for professional progression. The way that experience interacts with demographic variables gives the conversation a new angle. Rattso and Stokke (2018) emphasise that variables like gender and education have an impact on the experience-wage link, suggesting that not all demographic groups gain equally from experience. This intersectionality draws attention to the need for a sophisticated view of the labour market that takes into consideration the varied experiences of various demographic groups. As labour markets become more globalised, factors like foreign experience and its effect on salaries become more important. Machado et al. (2023) highlight the significance of taking the global environment into account while analysing the link between experience and pay showing the influence of having foreign experience can be both a useful asset and a possible cause of wage discrepancies.

Demographic Factors & Wage Rates

The gender pay gap, which still exists despite improvements in law and education, is a fundamental component of this conversation. According to Roshchin and Yemelina (2022), discrimination, occupational segregation, and caregiving obligations all have an impact on gender wage discrepancies. Blau and Kahn (2020) say that compounding pay penalties for minority women, the interconnectedness of gender with race and ethnicity exacerbates wage discrepancies. Tran (2023) challenges the idea of a universal education-wage premium by revealing the differing effects of education on wages across various racial and ethnic groups, the importance of education as a demographic determinant also merits consideration. In addition to education and gender, marital status is an important demographic factor that influences salary differences. According to Polachek (2019), married people often get higher wages because of things like more stability and dedication at work. However, as the marital pay premium is higher for males than for women, this result interacts with gender dynamics. Racial and ethnic background also adds to the intricacy of wage discrepancies. This is further layered by the regional component, which highlights the differences in pay rates that exist across different geographic locations. Firebaugh (2018) emphasises the significant regional differences in income outcomes and intergenerational mobility, highlighting the regional economic circumstances that influence pay differentials. This highlights different patterns in wage growth and occupational structures between urban and rural regions, urban-rural divisions also play a part.

Regional and Urban/Rural Variances

It has long been acknowledged that regional differences play a significant role in determining salary differences. Significant regional differences in intergenerational mobility and income highlight the significant influence of local economic circumstances on wage rates (Firebaugh, 2018). The unequal allocation of resources and economic possibilities between areas creates complications that go beyond personal characteristics and affect earning potential. Disparities between rural and urban areas give the conversation more depth (Manduca, 2019). The differences in wage growth and occupational patterns between rural and urban locations highlight the distinct economic environments that influence employment and income prospects. Rural communities may struggle with fewer job opportunities and lower salary levels, whereas urban centres often draw higher-skilled and specialised jobs, adding to wage premiums. Mastronardi and Cavallo (2020) discuss how the availability of infrastructure and the spatial concentration of sectors affect pay rates. Agglomeration economies have a significant influence in promoting higher salaries in urban areas. However, there is more than one dimension to the interaction between regional, urban, and rural characteristics and wage rates. Firebaugh (2018) refutes the popular narrative by demonstrating that regional economic structures also have an impact on wage differences, in addition to the urban-rural gap. Geographic determinants of income are complex and are influenced by a variety of factors, including industry specialisation, innovation ecosystems, and economic composition. These factors greatly affect wage rate variances.

Proposed Conceptual Map

The literature review reveals that the relationship of pay rate education and other demographic variables have complex relationships. Based on this assumption, a proposed conceptual mode for this study has been established. (Please see Figure 1).

Figure 1: Proposed Conceptual Model

Source: Own Development

This aids in testing the hypotheses and establishing the relationship between the variables. The model provides a framework for methodically analysing and measuring the complex relationships influencing salary differences The study aims to explain the factors that influence wage rates, therefore the conceptual model outlines the connections between independent and dependent variables. The independent variables (education, experience, marital status, gender, urban/rural status, area, and colour/ethnicity) are proposed to have differing degrees of impact on the dependent variable, wage rate. It is expected that experience and education will have a favourable impact, in line with the Human Capital Theory (Abbott and Gallipoli, 2018). Urban/rural status and regional variances capture geographical impacts, whereas demographics, gender, and marital status contribute to social dynamics. These variables are defined below (Please see Table 1).

Table 1: Definitions of Variables of the Study

S.No.

Variables

Definition

Independent Variable

1

Education

The amount of formal education a person has received is often expressed in terms of years of education finished, degrees earned, or certificates obtained.

2

Experience

The total amount of time and expertise earned by actively participating in the workforce reflects the knowledge and skills one has acquired in the real world over one career.

3

Marital Status

The social and legal status of an individual concerning marriage, denoting whether they are widowed, divorced, single, or married.

4

Gender

The characteristics, roles, and behaviours that are socially and culturally linked to being male or female, shape expectations in society and often affect opportunities and treatment.

5

Region

the division of a person's place of residence into urban (highly populated, metropolitan regions) and rural (sparsely inhabited, non-metropolitan) areas according to topographical features.

6

Race and Ethnicity

Colour is the way that one's skin tone or race is viewed, while ethnicity is the collection of common cultural characteristics, ancestry, nationality, or background. Each of these elements influences a person's social identity.

Dependent Variable

7

Wage Rate

The money that someone is paid for working; is usually represented as an hourly, monthly, or annual rate that shows how much money someone gets paid for working.

Source: Own Development

 

 

 

 

 

 

 

References

Abbott, B. and Gallipoli, G., 2018. Human Capital Inequality: Empirical Evidence (No. 2018-085).

Arshed, N., Anwar, A., Kousar, N. and Bukhari, S., 2018. Education enrollment level and income inequality: A case of SAARC economies. Social Indicators Research140, pp.1211-1224.

Becker, G.S. and Chiswick, B.R., 1966. Education and the Distribution of Earnings. The American Economic Review56(1/2), pp.358-369.

Blau, F.D. and Kahn, L.M., 2020. The gender pay gap: Have women gone as far as they can?. In Inequality in the United States (pp. 345-362). Routledge.

Canavati, S., Libaers, D., Wang, T., Hooshangi, S. and Sarooghi, H., 2021. Relationship between human capital, new venture ideas, and opportunity beliefs: A meta‐analysis. Strategic Entrepreneurship Journal15(3), pp.454-477.

Coady, D. and Dizioli, A., 2018. Income inequality and education revisited: persistence, endogeneity and heterogeneity. Applied Economics50(25), pp.2747-2761.

Cohen, G. and Ladaique, M., 2018. Drivers of growing income inequalities in OECD and European countries. Reducing inequalities: a challenge for the European Union?, pp.31-43.

Costa Dias, M., Joyce, R. and Parodi, F., 2020. The gender pay gap in the UK: children and experience in work. Oxford Review of Economic Policy36(4), pp.855-881.

Dosi, G., Pereira, M.C., Roventini, A. and Virgillito, M.E., 2018. The effects of labour market reforms upon unemployment and income inequalities: an agent-based model. Socio-Economic Review16(4), pp.687-720.

Firebaugh, G., 2018. The new geography of global income inequality. In The Inequality Reader (pp. 681-694). Routledge.

Haltiwanger, J., Hyatt, H. and McEntarfer, E., 2018. Who moves up the job ladder?. Journal of Labor Economics36(S1), pp.S301-S336.

Hoffmann, F., Lee, D.S. and Lemieux, T., 2020. Growing income inequality in the United States and other advanced economies. Journal of Economic Perspectives34(4), pp.52-78.

Kunze, A., 2018. The gender wage gap in developed countries. The Oxford handbook of women and the economy, pp.369-394.

Kuznets, S., 2019. Economic growth and income inequality. In The gap between rich and poor (pp. 25-37). Routledge.

Machado, V.N., Sonza, I.B., Nakamura, W.T. and Mendes, J.S., 2023. Does Foreign Experience Influence Executive Compensation in Emerging Markets?. Emerging Markets Finance and Trade, pp.1-15.

Manduca, R.A., 2019. The contribution of national income inequality to regional economic divergence. Social Forces98(2), pp.622-648.

Marginson, S., 2019. Limitations of human capital theory. Studies in higher education44(2), pp.287-301.

Mastronardi, L. and Cavallo, A., 2020. The spatial dimension of income inequality: An analysis at municipal level. Sustainability12(4), p.1622.

Nolan, B., Richiardi, M.G. and Valenzuela, L., 2019. The drivers of income inequality in rich countries. Journal of Economic Surveys33(4), pp.1285-1324.

Polachek, S.W., 2019. Equal pay legislation and the gender wage gap. IZA World of Labor.

Rattso, J. and Stokke, H.E., 2018. Dynamic private-public wage gap: Return to experience, education level and city effect (No. 17518).

Roshchin, S. and Yemelina, N., 2022. Meta-analysis of the gender pay gap in Russia. HSE Economic Journal26(2), pp.213-239.

Schroder, S., 2019. Wage inequality in the global economy.

Stinebrickner, R., Stinebrickner, T. and Sullivan, P., 2019. Job tasks, time allocation, and wages. Journal of Labor Economics37(2), pp.399-433.

Tran, D.B., 2023. Returns to education revisited: Evidence from rural Vietnam. Cogent Education10(1), p.2184019.

Zwysen, W., 2022. Wage inequality within and between firms: Macroeconomic and institutional drivers in Europe. ETUI Research Paper-Working Paper.

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