Examining the Relationship between Education Levels and Experience on Income Levels - Research Proposal Example

EXAMINING THE RELATIONSHIP BETWEEN EDUCATION LEVELS AND EXPERIENCE ON INCOME LEVELS Objective: To test whether there is a relation betweeneducational level and experience on income levels found in large urban center of US states; to determine if it is relatively important to pursue higher education as success indicator. Design: Correlation Coefficient and Cross sectional analysis Setting: Any urban setting in U.S. (i.e. New York), one to two establishment with at least 500 manpower. Data sources: US census statistics and vital statistics for the years 2005 and 2006. Survey between income group and level educational attainment in one/two company (i.e. Microsoft). Rationale Several studies shown that there is a significant relationship between education and income and the questions are “Is it worth completing university?” and how this relation should be interpreted. If these questions are supported with solid data as a result of an extensive analysis, we can therefore provide an argument to students and parents who are in fact deciding if they would go to universities or send their children to higher level of schooling. Studying the level of education and it’s relation to income level affects several factors. Income in general is a social status predictor. As Treiman1 found from his studies, the high correlation between prestige, income, and education are interrelated to a highly significant degree, and they quite possibly reflect the widely known fact that in all countries today schooling has become the accepted route to positions that provide higher status, prestige, and income. This belief has been carried on by our parents and thus many encourage their children to enter universities to pursue higher education. Also, the significance of income to life expectancy of an individual has been explored by many. A study conducted by Muller2 showed that physical and social conditions associated with low levels of education may be sufficient for interpreting the relation between income inequality and mortality. Accordingly, income level could be a predictor of how long a person would live. These are due to the capability of the individuals to afford risk measures like hospitalization, health cards and insurances. Another aspect could be the psychological impact to low income majority who experiences higher level of stress that affects physiological health of the individual. An expanded regression analysis by Lynch3 indicated that lack of high school education was related to lack of health insurance, belonging to economically depressed minority groups, working in jobs with high risk of injury, and smoking. This finding suggests that lack of material resources, occupational exposure to risk, and certain learnt health risk behavior might be reflected in the large education-mortality effect. In the research paper presented by Mackenback7, it showed that there are some studies which generated negative correlation between income inequality and life expectancy. In 1992, the BMJ8 published a now famous paper showing a strong negative correlation between income inequality and life expectancy. Among nine Western industrialized countries those which had less income inequality seemed to have higher life expectancy. A few years later this study was replicated by looking at income inequality and mortality in states within the United States analysis which seemed more secure because of having more and better quality data. These findings, which suggested that income inequality is bad for the health of the whole population and not only for those with the lowest incomes, were seen to have important implications.9 As a supporting data, the difference in the result of two similar studies raises concerns to researches and the scientific society because it still posses the same questions… the significance of income to life. And as we see it, one factor that must be dealt with is the significance of education to securing a higher income profession. Before we dwell further with the effect of income inequality to the life expectancy, we should first determine the validity of assuming that education impacts on the income level of an individual, as well as the life expectation or mortality. As manifested in the US census, higher educational degrees are typical prerequisites for highly compensated work in the United States and other industrialized nations. According to US census data for the year 1998, the median earnings of adult, year round workers with professional degrees are about four times higher than those of adults who had not completed high school.4 Thus, the level of education ought to be correlated with cumulative income, which is the basis for measuring income inequality. The problem The related studies cited above posses the following problem: Is level of education a valid factor in determining the level of income an individual may be able to secure? Is there a significant correlation between levels of education to level of income? Purpose of study This study aims to validate the correlation between level of educational attainment and the level of income an individual may be able to get. Also, it will specifically identify the impact of education to the types of job and average income bracket each educational level may be able to secure. For example, this may be able to answer the question “what average income will a highschool graduate will be able to get?” It is common in our culture that parents send their children to schooling because securing a graduation certificate entitles their children to a brighter future. But how are we sure that it can indeed pave the individuals way to attaining success which we sometimes correlate with the income level or the kind of profession one were able to get? With the high cost of tuition fees and the amount of time spent on schools, can we be sure that we made the right investment? The significance of this study is to show the value we spent in going to school, gaining a degree in relation to the type of job we gained after graduation. As well as, to determine if what we spent on school, in terms of monetary investment, and time investment, is well compensated. This could also be used as basis if there’s a need to improve the current standard of education being given by the academic community in the country. Knowing that current standard does achieve its goal of giving a better future to its students will provide a baseline for our educators to maintain the present content of its syllabus or if it is not being met, then, is there a need to improve it? For example, we can assess if competency-based education will be more effective. The finding of this study can also serve as motivator to our schools administrator or education experts to review the extent of education each student must take. Research questions a. Is it worth completing university? b. What are the minimum and maximum values for average employment income by census tract within the selected state or urban area? c. What average bracket of educational level gains highest average level of employment income? d. How will education help our citizens improve their economic status? e. How might education level affect average employment income? Hypothesis If level of education is related to level of income, then higher educational level may be a significant predictor of getting higher income. Then people who have attained higher education i.e. masteral degree, are most likely to get the highest paying jobs. Data Gathering and analysis This study will use survey as means of gathering information. The research team will identify one (1) or two (2) establishments where they can run the survey, getting the job requirements of the identified establishment, including the educational attainment for specific positions – from rank & file position to the executive position. The income value for each specific job will be determined as well. They will conduct interview to Human Resource staff, specifically in the recruitment section. Then data gathered will be tallied using frequency distribution method, wherein we will determine the range of one educational level for one specific department or job type. The percentage method is a useful indicator for determining which items in a group were favored most and favored least by the respondents of a research study % = [F (100)]/ n where: % means percentage, F means frequency and n means number of cases To measure the relationship between level of education and level of income, the research team would be using the Pearson correlation coefficient5 for each position. Using the formula below, where X stands for educational level and Y as average income: Assuming that the relationship between level of education and level of income as linear:   Where Cov(y,x) = the covariance of y and x Var(x)= the variance of x Var(y)= the variance of y Another research method that may be used to process gathered data is the cross-sectional data analysis. Cross-sectional data6 in statistics and econometrics is a type of one-dimensional data set. Cross-sectional data refers to data collected by observing many subjects (such as individuals, firms or countries/regions) at the same point of time, or without regard to differences in time. Analysis of cross-sectional data usually consists of comparing the differences among the subjects. Conclusion If through extensive research using two (2) different approaches would validate finding of previous studies conducted in this field, then we can strongly conclude that level of education has significant impact on the income level of an individual. Knowing this, the value of securing higher education will be validated and be well supported. Thus, entering universities would be a good investment for students, as well as the parents. This will strengthen the trust given to education or academic society as success indicator for our children or our own personal growth. Also, we can be more confident in our exploration of life expectancy in relation to income inequality. Sources 1Treiman (177) found in Lindgren, Henry Clay & Harvey, J. H. (1981) , An Introduction to Social Psychology, 3rd Ed.C.V. Mosby Company, St. Louis, Missouri, USA. 2 Muller, Andreas (2002), Education, income inequality and mortality: a multi-regression analysis. USA. Retrieved on March 11, 2007 from http://www.bmj.com/cgi/content/full/324/7328/23 3 Lynch JW, Kaplan GA, Pamuk ER, Cohen RD, Heck KE, Balfour JL, et al. Income inequality and mortality in metropolitan areas of the United States. Am J Public Health 1998; 88: 1074-1080 4 US Census Bureau. Table B. Median earnings of full-time, year-round workers by selected characteristics: 1998. www.census.gov/hhes/income/income98/in98ern.html (accessed 23 Mar 2001). 5 The Correlation Coefficien (Pearson r). Retrieved on March 9, 2007 from http://weber.ucsd.edu/~aronatas/corr.html. 6 Wikipedia, the free encyclopedia. Retrieved on March 10, 2007 from http://en.wikipedia.org/wiki/Cross-sectional_data. 7 Mackenbach, Johan P. (2005). Individual Income level vs inequality of income mediates health effects? Retrieved on March 11, 2007 from http://www.ureader.co.uk/message/331722.aspx 8 Wilkinson RG. Income distribution and life expectancy. BMJ 1992; 304: 165-168[ISI][Medline]. 9Kennedy BP, Kawachi I, Prothrow-Stith D. Income distribution and mortality: cross-sectional ecological study of the Robin Hood index in the United States. BMJ 1996; 312: 1004-1007 Read More