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Essay Available:
Pages:
6 pages/≈1650 words
Sources:
2 Sources
Level:
MLA
Subject:
Mathematics & Economics
Type:
Term Paper
Language:
English (U.S.)
Document:
MS Word
Date:
Total cost:
$ 31.1
Topic:

Income Relation To Education: Employment (Term Paper Sample)

Instructions:

intro: why is it interesting( 1page) , data(.25page) empincal results (2 pages) why model how ,interprest result ,specification tests (1page) conclusion(.75 pages)
It's a term paper of econometrics, you will be using ipums-cps, make sure you using ipums - cps and pleases read the attachment, my topic is income relate to education. feel free to create one that makes you conformable to do.

source..
Content:

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Income relation to education

Introduction

A country’s competitiveness and policies influence employment and the more people are educated, the better that a country improves her human resources. Those with transferable skills are more likely to find work in different industries, and mostly these are those educated. The share of work for people with only a high school diploma has declined and this is especially with the decline in manufacturing in the U.S. and the coal mining industry. Educated people can re-enter employment easily than those without higher education and find work in different industries.

According to the Bureau of Labor Statistics, the median weekly wage for those with less than a high school diploma was $ 520 and the unemployment rate was 6.35%, higher than the national average at 3.6%. On the other hand, unemployment is for those with professional and doctoral degrees were 1.5% and the weekly wages were higher at $1836 and $ 1743 respectively (BLS). Nonetheless, BLS statistics on education attainment mainly reflected the highest education attainment and n job training, different training programs and apprenticeship Nonetheless, BLS and Census break down information on educational attainment based on race for those above 25 years of age.

There has been a clamor to expand education opportunities and attract more students to gain valuable skills, but not all people can be absorbed into the job market, but those who attain the highest proficiency and competency in marketable skills are more likely to be employed. Similarly, the job prospects, of graduates partly depends on their majors as Business, Economics and STEM have more openings than the arts and social sciences, and this is further compounded by the majority of students being in the liberal arts programs.

Data

The dependent variable is the total personal income, which depends on wage annd salary income as well as educational attainment (recode). The data for the three variables were for the years 2015 to 2017, and the year 2018 was not considered as there is insufficient data annd information, but the data included monthly information. The data is drawn from the IPUMS-CPS (Current Population Survey) which is an integrated set of data covering data from 1962 annd includes information from the monthly (Flood et al).

Empirical results

Even though, the data collected was not restricted to workers in the 18-64 age bracket, a majority of the workers fall into these age brackets. The individual-level information provides insights on how education influences income levels. Weighted percentages increase the number of variables and in the years is recent, these were not considered in the analysis. Income levels change between the 2015 and 2017 years, but the estimates show that the results of the relationship between the income levels and education are significant. The coefficient estimates (standard errors) are contained in the model summary annd sample statistics.

In the one-sample test total test, the value of P-Value is < .00001, and the results were significant at p < .05 for all the three variables. The results imply that there is a significant relationship between income and education. The results further show that the personal income depends on the salaries, but the relation with education is possibly negative. However, I used the educational attainment recode variable and this may affect the relationship between the variables
The F changes were 1654262177.637 in the model summary, which is also the same value for the ANOVA (Flood et al). The latter divides the variations observed into different categories based on the cause of factor. The case for this is that it helps to evaluate the differences between the ...

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