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presentation_1.pptxHow does Age Influence Income?
ECO490
Danni Song
Nancy Haskell () - How Does Age Influence Income? This is a more grammatically correct title. You should remove my name and remove the last line, which merely repeats the title.
Introduction
The aim of this study is to find out if an individual age has any influence on income
How do age influence income?
As workers gains more experience there should be a premium to compesate their skills
Knowing the relationship between the age and income is important in determining salaries and also individuals can use it to project their future earnings
In the current generation the young population has outnumbered old people
Nancy Haskell () - You can remove the 2nd bullet point because it is already stated on the previous slide.
Literature review
According to Cloninger (2016), age is a natural cause of income disparity and it can’t be easily affected by government policies.
Bares (2016), increase in age leads to increase in income up to the age of 55 years and after this age the level of income begin to decline.
Organizations reported that older workers are more expensive than younger ones.
Parramore (2016), Human capital theory argues that experience can increase income
Knowledge of how age affects income is useful in explaining income disparity among workers
The past studies have not clearly explained the type of relationship between age and income.
Age-earning relationship is important in explaining income levels in employment life-cycle
Model
Regression equation is represented below
𝑤= 𝛽0 +𝛽1w+𝛽2b+𝛽3as+𝛽4a1539+ 𝛽4a4069+𝜀
where; income =w, age=𝛽0, black=𝛽1w,Asian =𝛽2b, age15_39= 𝛽3as, age40_69=𝛽4a4069, and 𝜀 =error component.
Income is the independent variable, age is a dependent variable.
Control variables are white, black, Asian, age15_39, age40_69
The prediction is that age affects income
Nancy Haskell () - Try to make sure your text does not run into the purple area, where we cannot read it. You can remove the first bullet point. The regresison equation needs the numbers after the betas to be subscripts. Also, the last betas should be a 5 (you have 4 listed twice). Income is your dependent variable. Age15-39 and Age40-69 are your key independent variables. Black, White, and Asian are your control variables. You should remove the part that says age = B0.... that is wrong. You want to predict how age affect income in the last bullet. Meaning, what is the sign on the coefficient estimates?
Data
The data was collected from US Bureau of Labor Statistics (2017).
The data is a time series for a period of 15 years.
Sample size is made up of 350 observations
Data collected consist earnings from employees of different age.
The data contain all the required variables
Data
Summary statistics of the study
| Data Mean | Standard dev. | Min | 25thpercentile | Median | 75thpercentile | Max | |
| Income | 52847.16 | 8646.483 | 36796 | 46693 | 50836 | 58252 | 74551 |
| Age1539 | .34 | .02 | .291 | .328 | .336 | .345 | .396 |
| Age4069 | .37 | .02 | .279 | .363 | .375 | .384 | .43 |
| White | .80 | .12 | .269 | .719 | .8175 | .888 | .972 |
| Black | .11 | .09 | .006 | .035 | .083 | .161 | .38 |
| Asian | .05 | .08 | .006 | .017 | .027 | .047 | .567 |
| Race | .96 | .05 | .678 | .942 | .973 | .991 | 1.026 |
Nancy Haskell () - You need to increase the size of the font in this table to be more readable.
Empirical results
The fixed effect model has three equations
The first equation uses age and constant as the only variables and the R2=0.994
The second regression equation use black, Asian and constant variables, the value of is lower at R2=0.991
The last equation tests all the variables and the R2=0.994
This shows that age affects income more than other variables in the model
Nancy Haskell () - You need a table of regression results, instead of words on this slide.
Empirical results
From hypothesis testing, the Hausman test and the F-test both gives values of zero meaning that fixed effects should be included
According to the OLS age affects income
Control variables have little effect on income
Robustness check shows that all variable results are within the range
Variables age4069 and black have a high relationship with income
Nancy Haskell () - Do not discuss the Hausman and F-test in the presentation. As in the prior slide, you need a table of regression results rather than words.
Conclusion
The main objective of the study was to find out if age affects income
This means that experienced workers are compensated more
According to the results age and race (black) have a high relationship with the income.
Asian variable and white have less relationship with income
Nancy Haskell () - Try to add one more bullet point that offers some thoughts on future research, or ways to expand your study.
Works cited
Bares, A. (2016). Cafe Classic: The Age-Earnings Relationship Is Not What You Think. Compensation cafe, 2-3.
Cloninger, D. O. (2016). What factors influence income inequality? Conversation, 2-3.
Parramore, L. S. (2016). ECONOMY. 50 Is the New 65: Older Americans Are Getting Booted from Their Jobs and Denied New Opportunities:, 1-2.
Statistics, U. B. (2017). Current Employment Statistics - CES (National). washinhton DC: U.S. Bureau of Labor Statistics.
