There is a popular saying that all Yalies earn at least $110 after graduation. One can wonder whether a high level of education necessarily guarantees a high income in the future. In this paper, I will argue that the higher the level of education attained, the higher will be the earned income. In order to validate such causal relationship, I will bring three control variables in my testing hypothesis: the spurious variable of gender, the conditional variable of racial background, and socio-economic status—being in the middle or working class. As a spurious variable, gender affects people’s level of education attained and income. Being a woman or man predetermines people’s level of education and income in the future. Indeed, women have less access to education than their male counterparts, and thus they will earn a smaller income. A possible factor that can explain this is the dominance of paternalistic family in most Western societies that encourage men to pursue higher levels of education.
Table of Contents
Summary Statistics
Re-coding my Variables
Table 1.1.: Analysis of the Original Relationship between Education and Income
Table 1.2.: Income by Education, Controlling for Gender (Male)
Table 1.3.: Income by Education, Controlling for Gender (Female)
Table 2.1..: Income by Education, Controlling for Social Class (Middle Class)
Table 2.2.: Income by Education, Controlling for Social Class (Working Class)
Table 3.1.: Income by Education, Controlling for Race (Whites)
Table 3.2.: Income by Education, Controlling for Race (Blacks)
Table 3.3.: Income by Education, Controlling for Race (Hispanics)
Research Objectives and Core Themes
The primary objective of this research is to investigate the causal relationship between the level of education attained and earned income, testing whether higher education consistently leads to higher future earnings while accounting for control variables such as gender, race, and socio-economic status.
- The influence of gender as a potential spurious variable on income and education levels.
- The impact of racial background on educational access and subsequent income.
- The role of socio-economic status (working vs. middle class) in shaping educational value and economic outcomes.
- Statistical validation using cross-tabulations, Kendall’s tau, and chi-square significance testing.
Excerpt from the Book
Table 1.1.: Analysis of the Original Relationship between Education and Income
The initial cross-tabulation illustrates a linear relationship for people who earn less than $15K and $30K. Yet, there is a curvilinear relationship across the tabulation as the table 1.1 illustrates. For people who earn between $30K and $75K, there is a negative gap of 9.4% between people who have some college education or an advanced degree. There is also a threshold effect in the category of people who earn between $75K and more than $110K. The percentage gap between people who have less than a high school education or a high school diploma and their counterparts who have some college education or an associate degree differs greatly from the percentage gap between people who have some college education and those who have an advanced degree.
The can be justified by the following: more people who have a college degree or an advanced degree earn between $75K and more than $110K. The threshold effect limits researchers’ access to information by limiting their ability to make predictions for those who fall under the category of earning between $75K and more than $110K. There is overall a strong relationship between the level of education and income as confirmed by the p-value (0.000) and Pearson chi-square of 317.69. The relationship between the level of education and income is thus significant.
Summary of Chapters
Summary Statistics: This chapter outlines the statistical methodology, including the use of Kendall’s tau for ordinal variables and the assessment of statistical significance via p-values and chi-square tests.
Re-coding my Variables: This section details the data transformation process, collapsing educational and income categories into manageable groups for comparative analysis.
Table 1.1.: Analysis of the Original Relationship between Education and Income: This chapter presents the foundational cross-tabulation, demonstrating a significant linear and curvilinear relationship between education levels and income.
Table 1.2.: Income by Education, Controlling for Gender (Male): This chapter isolates the male subgroup to test if gender acts as a spurious variable, finding the relationship between education and income remains significant.
Table 1.3.: Income by Education, Controlling for Gender (Female): This chapter analyzes female data, observing how educational attainment impacts income for women and comparing these findings to the initial baseline.
Table 2.1..: Income by Education, Controlling for Social Class (Middle Class): This chapter examines the middle-class cohort, highlighting specific income trends and the impact of educational levels on earnings within this demographic.
Table 2.2.: Income by Education, Controlling for Social Class (Working Class): This chapter evaluates the working-class demographic, finding that social class serves as a conditional variable that weakens the relationship between education and income.
Table 3.1.: Income by Education, Controlling for Race (Whites): This chapter performs a controlled analysis for White subjects, confirming the continued statistical significance of the education-income link.
Table 3.2.: Income by Education, Controlling for Race (Blacks): This chapter explores the relationship for Black subjects, noting differences in how educational attainment correlates with income compared to the broader population.
Table 3.3.: Income by Education, Controlling for Race (Hispanics): This final analytical chapter investigates Hispanic subjects, concluding that race acts as a weak conditional variable in the overall economic model.
Keywords
Education, Income, Causal Relationship, Gender, Racial Background, Socio-economic Status, Cross-tabulation, Kendall’s tau, Chi-square, Statistical Significance, Ex post facto, Threshold Effect, Income Inequality, Labor Market, Empirical Research.
Frequently Asked Questions
What is the fundamental focus of this research paper?
The paper examines the causal link between educational attainment and earned income to determine if a higher level of education consistently guarantees a higher financial return for individuals.
Which demographic variables are identified as central to the study?
The study centers on gender, racial background, and socio-economic status as primary control variables to determine how they influence the relationship between education and income.
What is the primary goal of the author?
The goal is to validate the hypothesis that higher education leads to higher income, while testing whether variables like gender and class act as spurious or conditional factors.
Which specific scientific methods are utilized for the analysis?
The author uses quantitative empirical methods, specifically cross-tabulation analysis, supported by statistical measures like Kendall’s tau, Pearson chi-square, and p-value testing.
What topics are covered in the main body of the work?
The body contains a series of controlled statistical analyses comparing income distribution across different educational brackets, segmented by gender, race, and social class.
Which keywords best describe the core of this work?
Key terms include Education, Income, Causal Relationship, Gender, Socio-economic Status, Cross-tabulation, and Statistical Significance.
How does the author define the "spurious variable" in the context of gender?
The author hypothesizes that gender might predetermine both income and education, but the data analysis leads to the conclusion that gender is not a spurious variable as it does not negate the relationship.
What does the "threshold effect" imply in the data analysis?
The threshold effect refers to the specific income categories (e.g., $75K to over $110K) where researchers observe limitations in predictive accuracy due to the distribution of subjects with high levels of education.
Why did the author choose to re-code the initial variables?
Re-coding was necessary to collapse six initial categories of education and income into three distinct groups, which allowed for a more robust and statistically meaningful comparative analysis.
What is the final conclusion regarding the role of race as a conditional variable?
The author concludes that race is a weak conditional variable, as it slightly weakens the relationship for Whites and Hispanics, while strengthening it for Black subjects.
- Quote paper
- De Zhong Gao (Author), 2011, Education and Income-A Case Study, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/188464
