690 Final

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The Cost of Independence: An Argument on Not Leaving Home Just Yet

By: Gil Puyat

Abstract

This paper will examine the role of parental wealth in generating a strong positive effect on

earnings. It compares the benefits of staying at home versus being independent by running a regression on

collected population survey data from the IPUMS-CPS database. Parental aid can possibly reduce the

burden on an individual, and allow them to be more productive in education; thus having a positive effect

on future earnings. The results suggest that parental aid in the form of staying at home have a significant

positive effect on an individual’s future income.

Introduction

Most of us have faced the decision of leaving home and being independent, and in one way or

another we have also all weighed the costs and benefits of our decisions. Here an argument is made and

proven that staying at home has significant benefits to the quality of education received, be it through the

aid of resources from parents, or through the relief from the burden of being on your own. The increase in

quality of education is in turn affected by a direct positive relation to the amount of income an individual

may earn. This paper tries to develop some evidence that may help argue against not leaving home just yet.

Literature Review

There have been many studies on the relationship between education and earnings, and the role of

parents in transferring wealth through generations. Maurer-Fazio & Dinh (2004) describe the role of

education in determining a worker’s income in urban China’s labor markets. They find that returns to

education for workers who have found employment by means of a competitive method to be higher than

those whose jobs were assigned. The authors decompose earnings differentials based on worker data from

1999-2000 by surveying 4873 individuals from 118 enterprises in six cities. They find that workers who

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have labored for their positions tend to be more productive. Through random sampling of census data

collected from a wide spread area, and applying regression analysis, they were able to accurately describe

the population.

In, Blanden, Gregg & Macmillan (2007) they claim, those born to poor parents have restricted

mobility and often do not achieve their economic potential. Their study proposes that education is the most

obvious and efficient means of transmission explaining intergenerational mobility. A strong association

between incomes across generations indicates weak intergenerational mobility. Cognitive ability offers a

substantive contribution to mobility, but only if given ample opportunity to be nurtured. This study differs

in such a way that it shall be looking at the simple relation of two variables to explain their causal

relationships.

In Liu and Zeng (2007), they examine the role of genetic ability in generating strong positive role

on intergenerational earnings in the U.S. The study finds the differences between adopted and non-adopted

children’s ability and its importance in transmitting earnings ability from their parents. The authors

measure IQ level and compare them to kinship correlations. Regression results from kinship correlations

converge on the conclusion that differences in ability can explain a substantial fraction of the variation in

IQ. They summarize that about half of the variation in IQ among individuals in the population can be

explained by genetic ability.

In Behrman and Rosenzweig (2005), the authors find that parental resources are important in

determining such factors as children’s human capital, returns to schooling, and future earnings. Newly

available data on parent and in-laws indicate that parental resources of marital partners may affect resource

distribution within marriage. Regression results from these data sets show the effects of parent and in-law

characteristics such as transfers, bequests, and visits by parents and in-laws, have a large effect on

children’s human capital, returns to schooling, and earnings. They find that parental wealth has economic

advantages for children even as they become adults, as they tend to have more education, higher quality

education, better connections for jobs, and transfers from gifts or bequests. Thus, in addition to direct

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income advantages which are facilitated by greater human capital investment, those with wealthier parents

enjoy additional consumption benefits.

Theoretical Argument

By the human capital theory, investment in human capital, such as education, and training,

increases the productivity of an individual by cultivating skills and adding to knowledge. A well educated

individual is more productive and capable, which makes them more profitable for employers. High

profitability increases competition in the job market, thus possibly increasing wages as demand for jobs

increase. An educated worker has acquired the basic skills in problem solving and analysis. Achieved

through the academic process, training received during education makes workers more useful by increasing

their cognitive ability. Another reason for profitability is that since employers have to spend less time and

effort training workers for a position, workers can work independently with little or no guidance. Wages

may also increase with the level of training due to higher competition in the job market. Employers prefer

to hire, and may pay a premium for highly skilled and very productive individuals, who can increase their

profits. All these factors contribute to possible explanations for the effects of education on earnings

More importantly, the effects of parental income on an individual’s ability to attend school are

considerable. By staying at home and receiving benefits from parents, an individual is less pressured and

can freely pursue their educational goals with much more efficiency and less distraction. This leads to a

higher overall quality experience that can translate into larger future financial gains by human capital

accumulation. Staying at home alleviates the burden, which would otherwise be placed upon an individual.

This can include a number of things like increased responsibility, or the incurring of expenses such as, but

not limited to, rent, utilities, and meals. Free from excess load, physical and cognitive resources can be

concentrated solely on the task of education.

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Empirical testing

Methodology

Using Multiple Linear Regression techniques on IPUMS-CPS population survey data for the U.S.

in 2008, the natural log of income wage was calculated as the dependent variable and factors such as age,

sex, race, education level, region, hours worked, and a relation variable were used as independent variables,

a regression line was estimated, and the relative probabilities were calculated for each variable. The main

variable in question used to support my theory is a dummy variable named Independent, which is

calculated from a set of relation variables in the IPUMS-CPS website that is equal to 0 if the individual

lives at home and is equal to 1 if he is self-sufficient.

Hypothesized Results

For the main variable in question, which is the dummy variable named Independent; I hypothesize

a pretty large significant positive number for its coefficient. I predict that the coefficient’s value will be

considerably large enough to warrant a conclusion that parental income has a major effect on and

individual’s future income.

Empirical Testing

Data Description

The data collected is based on a 2008 nationwide population survey from the IPUMS-CPS data

website. Below is a description of the data:

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Descriptive Statistics

A. Continuous Variables

Standard

Min Max Mean Deviation

Wage and Salary Income $1 $706,117 $40,818 $47,739

Age 15 85 40.27 13.68

Weeks worked last year 1 52 46.56 12.06

Usual hours worked per week (last yr) 1 99 38.89 11.89

B. Dummy Variables

Percent Percent

Educational Attainment Race/Ethnicity

Dropout 12.44 White 65.48

Highsch 28.37 Latino 15.34

Some College 19.55 Afamer 10.75

College 29.30 Asianam 5.31

Postgrad 10.34 Otherace 3.12

Region Sex

West 22.94 Female 48.74

Northeast 18.49 Male 51.26

Midwest 22.84 Parental Aid

South 35.73 Independent 12.39

The average salary in the data set is $40,818, the average working age is 40 years old, and the

average work week was about 52 hours. Under the variables for education the highest percentage of people

falls under the category of having a college degree with 29.30% of the people falling under that category.

The regional variables had the most people coming from the south with 35.73%, and the least from the

northeast with 18.49 percent. The largest ethnic group in the race category is the whites with 65.48% and

other races being 3.12 %. The sex variables are almost evenly split with the males just a bit over at 51.26%

of the sample.

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Regression Results

Regression Results Table

Model 1 Model 2 Model 3

Coefficient Coefficient Coefficient

(t-stat) (t-stat) (t-stat)

Constant 6.169 6.741 7.005

17372.213 18415.795 17708.854

Age 0.011 0.009 0.006

2434.840 2154.586 1373.125

Weeks Worked 0.048 0.046 0.045

8610.547 8740.324 8536.137

Hours worked 0.038 0.035 0.033

6556.100 6326.909 6029.476

Race Ethnicity

African American -0.142 -0.074 -0.078

-734.686 -405.479 -433.786

Hispanic -0.277 -0.092 -0.118

-1528.596 -515.870 -668.805

Asian American 0.053 -0.039 -0.047

184.613 -145.539 -174.677Other Racial/Ethnic -0.126 -0.066 -0.056

-294.725 -164.524 -141.503

Gender

Female -0.173 -0.214 -0.227

-1385.170 -1825.769 -1949.036

Region

Northeast -0.032 -0.045 -0.039

-165.212 -251.194 -221.271

Midwest -0.134 -0.103 -0.107

-730.199 -598.366 -626.382

South -0.120 -0.103 -0.106

-725.414 -668.327 -693.405

Educational Attainment

Less Than Highs School -0.745 -0.699

-3291.686 -3092.194High School -0.461 -0.446

-2801.361 -2735.953

Four Year College Degree -0.354 -0.335

-2153.462 -2049.656

Post Graduate Degree 0.203 0.204

941.145 956.388

Parental Aid

Independent -0.339

-1672.199

Adjusted R Squared 0.585 0.636 0.643

F Statistic 18,863,536.706 17,152,886.348 16,561,095.283

Sample Size 101,536 101,536 101,536

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Interpretation

To ascertain the effects of each of the added variables three separate regression were calculated.

In the first model we estimated a model considered to be the baseline in order to measure the effects of our

other hypothesized variables. In the first model age, races, gender, region, hours and weeks worked were

used as control variables to estimate a regression line with an adjusted R-squared value of 0.585 on a

sample of 101,536 individuals. A fairly high value of R-squared means that about 58.5% of the variability

can be explained by the model, giving us a baseline as we add in the hypothesized variables. The extremely

large value of the F-statistic of 18,863,536.706 indicates that the factors in this model are highly significant

in determining the dependent variable.

In the second model the education variable has been added in to explain the effect of education on

earnings. We have come up with a more statistically significant result as the value of R-square his

increased to a 0.636, meaning that education creates a much better explanation for the increase in earning,

as 63.6% of the variability in the data can be explained by the model. For those who have less than a high

school degree, they make around -74% less than then their cohorts with at least some college. Having a

high school degree decreases that negative effect on earnings, with the members of this group earning -46%

less than their counterparts. Having a college degree is better by roughly 11 percentage points than having a

high school diploma. Post Graduate degree holders make on average 20.3% more than their counterparts

with some college.

In the third model with the included hypothesized variable estimating the amount of parental aid

through a relation variable, the adjusted R-square value raised up to 0.643, meaning that an increase of .

7%in the variability can be explained by the data. The F-statistic of 16,561,095.283 shows an improvement

over model two, so the estimated line is a better fit of the data. The coefficient for the variable named

independent is a -0.339, meaning that being independent has a -33.9% effect on earnings. The addition of

this variable shows omitted variable bias, but its most significant effect is on those with less than a high

school degree.

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Conclusion

This article has explored the role of parental aid in determining the transfer of wealth along

generations by alleviating the burden of being independent and making use of the parent’s resources

through staying at home. Human capital theory explains the increase in productivity of the worker, making

them more valuable to employers. While the amount of parental aid received increases the gains of the

effects of education by providing individuals the opportunity to concentrate in education. Though it is

possible that there are many other factors involved in making this determination, the rise in income through

education, supplemented by parental aid can explain a majority of these cases. The results are a positive

insight into this particular phenomenon.

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References:

Blanden, J., Gregg P., Macmillan L. 2007. Accounting for Intergenerational Income Persistence: Non-cognitive Skills, Ability and Education. The Economic Journal, 117 (March), C43-C60. The RoyalEconomic Society 2007. Blackwell Synergy Publisihing.

Maurer-Fazio, M., Dinh N. 2004. Differential Rewards To, And Contributions Of, Education in UrbanChina’s Segmented Labor Markets. Pacific Economic Review, 9: 3 (2004), pp.173-189. Blackwell SynergyPublishing.

Behrman, Jere R., and Rosenzweig, Mark R. 2005. Parental Wealth and Adult Children’s welfare inMarriage. The Revue of Economics and Statistics, August 2006, 88(3):496-509.

Liu, Haoming, and Zeng Jinli. 2007. Genetic Ability and Intergenerational Earnings Ability. Journal of Popular Economics (2009) 22:75-79.

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