for michael smith

ECO490

Danni Song

Dr. Nancy

How do Age Influence Income?

Introduction

As people age their level of income might be expected to increase or remain at the same level until retirement. As individual ages, their working energy decrease leading to a decrease in their level of income. However, they gain more job experience as compared with employees who have worked for few years. The relationship between age and income explains why experienced workers have difficult time trying to adjust to job loss because their earnings reflect special skills required in the industry. The aim of this study is to find out if there exist any premiums to compensate for the job experience acquired after many years of work. The population data will be collected from a sample divided into age groups, the first group will comprise of workers aged between 15 to 39 years and the second group will be made up of individuals aged between 40 to 69. The data will be analyzed using a regression model to find out if there is any linear relationship between age and income. Finally the papers will use the statistical results to explain the relationship between the variables.

Literature review

Age-earning relationship is used to explain income growth in an employment life cycle. Many studies have been done in the past. According to Cloninger (2016), it is important to understand the factors that bring income inequality in our society. He argues that understanding the causes of income inequality can assist in reducing the gap. However, he states that age is a natural cause of income disparity and it can’t be easily affected by government policies. According to 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. Bares (2016) explained from a research carried out between 2000 and 2009 by collecting data from individuals from different age groups. As an individual goes up through the employment ladder they gain more experience and become more resourceful to the organization they are working with.

A research conducted by Towers Perrin found out that as employees become old, the organization views them as more expensive and less productive as compared to young workers according to Bares (2016). The study explains only increase in salary due to works experience and also reduction in earnings for old people because they become less productive to the company. The aim of this study is to found out if there exists any linear relationship between the variables, an existing relationship between the variables is important in decision making.

There are different explanations of the relationship between age and income; they include human capital theory, aging labor markets and organizational dynamics. Income rises as a worker ages and goes up the ladder until mid-50s when he or she reaches the peak of his or her earning and then the level of earnings start to decrease. The decrease in income from mid 50s is attributed to decrease in productivity due to advancement in age. Parramore (2016) explains that workers over 50 years are perceived to have less energy and this lead to decrease in income as a worker gets closer to retirement. This shows that age affects individual income in the labor markets and there should be a special relationship between the variables. This study will expound on the past studies by developing a regression analysis to explain the relationship between age and income.

Model

A model is a simplified reality description; an economic model is designed to yield hypotheses about a testable economic behavior .A model represents an economic process by use of quantitative relationship between the variables (Howitt, 2014). In this study a regression model will be used to represent the relationship between the independent and the dependent variable. The regression model is made up of one dependent variable, one independent variable and four control variables.

Control variables in the equation include white, black, Asian, age15_39, age 40_69. The econometric model will be represented as follows; (𝑤= 𝛽0 +𝛽1w+𝛽2b+𝛽3as+𝛽4a1539+ 𝛽4a4069+𝜀)(include equation on its own line), where income (w), income, age (𝛽0), black(𝛽1w), Asian (𝛽2b), age15_39(𝛽3as), age40_69(𝛽4a4069), and 𝜀 (error component). Individual’s age affects the level of income and the control variables will be monitored to protect the results from any internal interference and the equation will be computed using the ordinary least square method. Control variables are constants meaning that only age that will have impact on the income. (This is not correct; control variable also need to vary and be represented by data in the regression)

This section also needs to discuss economic theory/ reasoning behind the coefficient predictions in table 1.

Variables

Table 1A

Dependent variable	Income
Key Independent Variables	Age
Other Control Variables	White Black Asian Age15_39 Age40_69

Hypothesis testing

Table1

Econometric model:	𝑤= 𝛽0 +𝛽1w+𝛽2b+𝛽3as+𝛽4a1539+ 𝛽4a4069+𝜀
Predictions:		dy/dx	d2y/dx2	d2y/dx2
Dependent Variable:	Income (I)
Key independent variables:	Age(a)	β1+ 2β2 Ē>0 β1>0	β2<0	n.a
Other control variables:	White(w)	β3	n.a	n.a
	Black (b)	β4	n.a	n.a
	Asian (as)	β5	n.a	n.a
	Age15_39(age1539)	β6	n.a	n.a
	Age40_69(age1539)	β7	n.a	n.a

Data

The data is from be collected from the US Bureau of Labour Statistics website. According to the US Bureau of Labour Statistics records, the data is collected over a long period of time and this provides detailed information about the income of individuals in dfferent age groups. Data fron the US Bureau of Labour Statistics is a time series covering many years, this will assist in making a more reliable conclusion. The data is corresponding to all the theoritical variables without any biasness. There is information about all the variables including the control variables. For this study, data for 350 workers was collected from 2000 to 2015, the study will cover 15 years.

Empirical results

Table 2

	Data Mean	Standard dev	Minimum	25th percentile	Median	75th percentile	Maximum
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

Regression analysis

Table 3

VARIABLES	FE	FE	FE
age1539	87747.242		108811.951*
	[52,664.765]		[61,705.641]
age4069	-129830.041***		-120056.791***
	[38,832.259]		[39,283.405]
black		-38421.505	-56706.969
		[46,937.956]	[41,974.303]
Asian		2166.779	7741.906**
		[5,134.188]	[3,609.254]
Constant	81842.353***	68790.468***	72194.257**
	[25,643.207]	[1,996.386]	[29,642.889]
State FE	Y	Y	Y
Year FE	Y	Y	Y
Observations	350	350	350
R-squared	0.994	0.991	0.994

Need to explain what is different across all these regressions and appropriately label the top roll of the table.

R squared measures how close variables are fitted in the regression equation; a high value of Squared shows a close relationship between the variables. The R squared of 0.994 shows that age affects individual income. Change in age will lead to a change in the level of income. This means that others factors held constant a worker age have an impact on his or her income due to their additional skills. The error component and the autocorrelations are low because there is only one independent variable. The results may be affected by heteroskedacity because as workers gets near their retirement age their level of income will reduce. But the results cannot be concluded until hypothesis test is carried out.

Need to interpret the magnitude, economic & statistical significance of your regression result.

Hypothesis testing

Table 4

Hausman test	Ho: 𝛽re= 𝛽fe	x2 stat= 47.25 p value=0.0000	Conclusion Reject ho, therefore include fixed effects
F-test	Ho: all state fixed effects statistically insignificant	F-state=110.19 P value=0.0000	Conclusion Reject ho, therefore include fixed effects

After testing for p values the null hypothesis is rejected, meaning that fixed effects are needed in the regressions.

Robustness section

Robustness check

Table 5

VARIABLES	FE	FE	FE
age1539	1.912		1.912
	[1.184]		[1.184]
age4069	-2.274***		-2.274***
	[0.755]		[0.755]
black	-1.581**	-1.26	-1.581**
	[0.747]	[0.836]	[0.747]
Asian	0.01	-0.093	0.01
	[0.096]	[0.123]	[0.096]
Constant	11.282***	11.163***	11.282***
	[0.545]	[0.035]	[0.545]
State FE	Y	Y	Y
Year FE	Y	Y	Y
Observations	350	350	350
R-squared	0.994	0.991	0.994

You then need to discuss how these results in table5 compare to the results in table3.

Error component cannot be estimated except by carrying out estimation and then making residuals. To estimate the error component we require non statistical information. There is need to incorporate panel data to get the clear relationship between the variables.

Abstract

The aim of this study is to establish if individual’s age has any impact on their level of income. The data was collected through observation from the US Bureau of Labour Statistics, analyzed and tested to determine the level of relationship. Information on the linear relationship between age and income is important to explain the premium paid to additional experience in labor market. Skilled workers have an advantage at work place as compared to younger workers. The results of the study did not provide a clear relationship and there is need to include panel data to assist in predicting variables that vary over time. The p values from f-test and Hausman test are zero meaning that further research should be carried out to determine other variable that may assist in getting the correct relationship.

Reference

Bares, A. (2016). Cafe Classic: The Age-Earnings Relationship Is Not What You Think. Compesation cafe, 2-3.

Cloninger, D. O. (2016). What factors influence income inequality? Conversation, 2-3.

Howitt, D. (2014). The sage Dictionary of Statistics. London: Sage.

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.