TITLE: Student Mean Income based on College Tiers and Household Income Hierarchy DATE: 04/22/2022 Kwame Darko-MensahINTRODUCTION The degree to which an individual's position in the income distribution continues or changes from one generation to the next is referred to as intergenerational income mobility (Stuhler, 2018). Policymakers are increasingly worried about intergenerational economic mobility since income inequality has risen in many nations in recent decades (Deutscher, et. al (2021). This study aims predict a child’s percentile using their parents’ income percentile and other factors. To do this analysis, a cross-sectional data obtained from the data repository https://opportunityinsights.org/data/ was used. The dataset contains information of 1515 and has 21 variables. Our most interesting variables are k_mean and par_pctile whereby we need to check if there exists any relationship between k_means and par_pctile. In the research, our dependent variable is child’s percentile income while our independent variable is parent’s percentile income. Other variables such as age and sex which are likely to have some impacts on our analysis will be included in the analysis. Literature Review Introduction A variety of techniques are used in conceptualizing and assessing intergenerational mobility. This section focuses on illustrating several models for predicting children’s status based on their parents’ earnings and other related factors through case studies. Case Studies Intergenerational mobility, according to a new study by the National Bureau of Economic Research, is the relationship between parents' and children's socioeconomic level (Cholli & Durlauf, 2022). The study also utilized the term "socioeconomic status" to refer to a person's income. A variety of mechanisms have been proposed to explain the relationship between parental and child status, and they can be split into two categories, including family and social influences. According to Cholli and Durlauf (2022), wealth, education, and the composition of the family can all have a role in determining intergenerational mobility. Social models, on the other hand, are concerned with the social environment, specifically schools and neighborhoods. Recent social science research has highlighted the importance of a broad set of cognitive skills and personality traits in determining labor market success. Parental attributes, as well as investments, have an impact on these abilities (Mogstad et al., 2021). Traditional family investment structures are used to enable for parental investment and education to be complimentary inputs, which means that each dollar of investment has a marginal impact. Parental education improves when parents invest in their children. When this is the case, parental education heterogeneity can increase intergenerational transmission. Persistence is important because parental education has an impact on the amount of money invested since parental education affec ...

1. TITLE: Student Mean Income based on College Tiers and
Household Income Hierarchy
DATE: 04/22/2022
Kwame Darko-MensahINTRODUCTION
The degree to which an individual's position in the income
distribution continues or changes from one generation to the
next is referred to as intergenerational income mobility
(Stuhler, 2018). Policymakers are increasingly worried about
intergenerational economic mobility since income inequality has
risen in many nations in recent decades (Deutscher, et. al
(2021). This study aims predict a child’s percentile using their
parents’ income percentile and other factors. To do this
analysis, a cross-sectional data obtained from the data
repository https://opportunityinsights.org/data/ was used. The
dataset contains information of 1515 and has 21 variables. Our
most interesting variables are k_mean and par_pctile whereby
we need to check if there exists any relationship between
k_means and par_pctile. In the research, our dependent variable
is child’s percentile income while our independent variable is
parent’s percentile income. Other variables such as age and sex
which are likely to have some impacts on our analysis will be
included in the analysis. Literature Review
Introduction
A variety of techniques are used in conceptualizing and
assessing intergenerational mobility. This section focuses on
illustrating several models for predicting children’s status based
on their parents’ earnings and other related factors through case
studies.
Case Studies
Intergenerational mobility, according to a new study by the

2. National Bureau of Economic Research, is the relationship
between parents' and children's socioeconomic level (Cholli &
Durlauf, 2022). The study also utilized the term "socioeconomic
status" to refer to a person's income. A variety of mechanisms
have been proposed to explain the relationship between parental
and child status, and they can be split into two categories,
including family and social influences. According to Cholli and
Durlauf (2022), wealth, education, and the composition of the
family can all have a role in determining intergenerational
mobility. Social models, on the other hand, are concerned with
the social environment, specifically schools and neighborhoods.
Recent social science research has highlighted the importance
of a broad set of cognitive skills and personality traits in
determining labor market success. Parental attributes, as well as
investments, have an impact on these abilities (Mogstad et al.,
2021). Traditional family investment structures are used to
enable for parental investment and education to be
complimentary inputs, which means that each dollar of
investment has a marginal impact. Parental education improves
when parents invest in their children. When this is the case,
parental education heterogeneity can increase intergenerational
transmission. Persistence is important because parental
education has an impact on the amount of money invested since
parental education affects the amount of money invested and
their efficacy (Torche, 2018). Studies on skills has also stressed
the importance of investments made during childhood and
adolescence, as well as the ways in which they are made.
Intergenerational mobility is defined as children's predicted
wages conditional on parents' incomes in their study on the
impact of neighborhoods on intergenerational mobility. Chetty
et al. (2018) define intergenerational mobility as the correlation
between a parent's and a child's income percentile ranks,
characterizing the possibility of moving up the income
distribution relative to parents as the correlation between a

3. parent's and a child's income percentile ranks. Deutscher, 2020)
used the rank definition to quantify intergenerational mobility
in the Australian setting, and the results were consistent with
Chetty's previous work. Another study on the decline of
intergenerational mobility in the United States employs the
Duncan socioeconomic index score, a ranking indicator for jobs,
to assess the relative status of occupations through time
(Mogstad et al., 2021). Duncan's SEI, on the other hand, has
been criticized for its calculations, which are based on male
census data and may not accurately reflect the female
population. As a result, we have decided to employ the
percentile rank definition of intergenerational mobility rather
than the occupational definition. Many of these studies are
limited when extrapolated to civilizations and cultures that
emphasize attributes other than economic success because they
were conducted in the United States as part of the Western
world. Such research has been conducted in nations with market
economy and nuclear families as the norm.
The parent's income and other characteristics are the predictor
variables in most intergenerational mobility studies, which
employ linear regression to predict the child's income (Torche,
2018). One of the numerous drawbacks of linear regression is
that it cannot establish poverty or affluence traps, the idea that
it is more difficult for a poor family to go up the social ladder
than it is for a wealthy family to move down. This is
compensated for using nonlinear models. Intergenerational
mobility varies across geographical locations, demographics,
and time, according to studies. The Great Gatsby curve was
created by Krueger (2012), an economic adviser to the US
President, to highlight an apparent direct relationship between
income inequality and intergenerational mobility.
The causal effect of childhood neighborhood exposure on
intergenerational mobility is evaluated by the difference
between the mean rank outcome of a kid who moved to a new
area and the mean rank outcome of a child who had always lived

4. in the area moved to, according to Chetty et al. (2014). They
also utilize linear regression to assess the effects of factors
including parental marital status, displacement shocks, a change
in parent income, natural disasters, and sibling comparisons.
Feigenbaum (2018) utilized a fixed-effects regression model to
find the characteristics of counties whose children have higher
income than their parents, such as racial and income
segregation, economic disparity, and educational quality.
Chetty et al. (2018) present two related models for their rank
definition in their descriptive examination of the spatial aspects
of intergenerational mobility in the United States. They rank
children based on their incomes in comparison to other children
in the same birth cohort, and parents based on their incomes in
comparison to other parents with children in the same birth
cohort. The authors calculate absolute mobility, the predicted
rank of children from households at any given percentile, by
adding the intercept and the product of the slope and parent's
percentile after regressing the parents' and children's rank
distributions (Corak, 2020). Relative mobility, on the other
hand, is merely the regression model's slope, implying that
mobility is aggregated rather than dependent on the parents of
the child. This rank-rank method has also been used to quantify
mobility in some other noteworthy research on mobility. A
study of recent trends in intergenerational mobility (Chetty et
al., 2018) and Deutscher's study on the impact of childhood
exposure on mobility are two examples.
Chetty and Hendren's seminal study (2018) offered new
evidence that where a child grows up affects their later life
results. In this study, I look at when and why place matters,
which are important considerations for anyone trying to address
inequities caused by causal place effects. Place seems to matter
the most throughout adolescence, with a strong role for local
labor markets and evidence for peer effects on a smaller scale.
The effects of exposure to location are greatest in adolescence,

5. and they are often minor and non-statistically significant in
early life. This is in line with age-atmigration studies, which
show that the benefits of moving a year earlier on language
acquisition are greatest in adolescence (Chetty et al., 2018) This
finding does not rule out the importance of early infancy.
Rather, factors such as family or more localized environmental
influences may important in early life, where most variance is
seen within rather than between the big communities studied
here.
Conclusion
While apparent or structural mobility may differ between
industrialized nations, circulatory mobility does not, according
to the Featherman-Jones-Hauser theory. In essence, circulation
mobility considers the independent relationship between an
individual's status and that of their parents, and is unaffected by
technological advancements, changes in the demand and supply
of specific occupations, or family size. If this is correct, it
could indicate that findings from US studies can be applied to
the rest of the industrialized world.
METHODSParticipants
The study used a cross-sectional data obtained from the data
repository https://opportunityinsights.org/data/ . The dataset
contains information of 1515students and has 21
variables.Materials
R-studio to run some analysis on the data to determine the best
variables to be used in the model then proceed to perform the
statistical analysis. According to Rachel et al. (2018), R-studio
is among the best data analysis tools to obtain results from raw
data collected for research.Procedure
The mean, standard deviation, median, skewness, kurtosis,
range, minimum, and maximum of the independent variables
were calculated using the psych package, which displayed the

6. mean, standard deviation, median, skewness, kurtosis, range,
minimum, and maximum of the independent variables.
The data distribution for the parent's income and the child's
income was shown using a histogram.
A correlation test was used to determine whether there is a
significant correlation between our dependent and independent
variables, as well as the sort of correlation that exists. Mishra et
al. (2019) illustrated that correlation tests are essential in
descriptive statistics and in normative tests for statistical
data.RESULTS
In the summary of our data there were no missing data. All
entries had a value.
(a) Descriptive statistics
(b) Output of summary statistics
Output of summary statistics
The table of summary statistics shown abovegave a summary of
the average, maximum, minimum, standard deviation, skewness
and kurtosis. It indicates that some of the independent variables
were negatively skewed while all others were positively
skewed. The table also shows some variables had a positive
kurtosis indicating that their distribution had heavier tails
(leptokurtic distribution) while other variables had light tails.
They had a negative kurtosis thus a platykurtic distribution.
A histogram of Childs earning
The plot indicates that the distribution of child’s income is
skewed to the right with most earnings ranging between 0 and

7. 100000.
Scatter plot of Child’s earnings and parent’s earnings
The scatter plot indicates the trend in the data. As parent’s
income increases , child’s income also increases.
(c) Scatter plot of child’s income and childs rank
The plot shows that as child’s rank increases, the child’s income
also increases.
Correlation
A correlation test was run to evaluate if there was a significant
correlation and the type of correlation that exists between the
dependent variable and independent variables. Child’s earnings
and parent’s earnings show a weak positive correlation. with a
value of 0.1867327 and a p-value of 2.535e-13 which is less
than 0.05 an indication that they had a significant correlation.
The following variables were also significantly correlated to the
dependent variable (child’s earnings) and might be considered
in the regression model; k_rank, par_mean, k_median, k_top,
tier and k_q. These variables were used as control variables in
our study since they were significantly correlated to the
dependent variable k_means. Regression analysis
The regression summary show that the overall p-value of the
model is < 2.2e-16 which is less than 0.05 implying the study is
significant.
Also, also all the variables except par_pctile are significant
with p-values less than 0.05. The variable par_pctile is not
significant in the study of factors that affect mean child’s

8. earning with a p-value of 0.4105 which is less than 0.05.
The adjusted r-squared is 0.9321 which means that 93.21% of
the dependent variable is explained by the independent
variables.
References
Chetty, R., & Hendren, N. (2018). The impacts of
neighborhoods on intergenerational mobility II: County-level
estimates. The Quarterly Journal of Economics, 133(3), 1163-
1228. https://academic.oup.com/qje/article-
abstract/133/3/1163/4850659
Chetty, R., & Hendren, N. (2018). The impacts of
neighborhoods on intergenerational mobility I: Childhood
exposure effects. The Quarterly Journal of Economics, 133(3),
1107-1162. https://academic.oup.com/qje/article-
abstract/133/3/1107/4850660
Cholli, N. A., & Durlauf, S. N. (2022). Intergenerational
Mobility. https://www.nber.org/papers/w29760
Corak, M. (2020). Intergenerational mobility: what do we care
about? What should we care about?. Australian Economic
Review, 53(2), 230-240.
https://onlinelibrary.wiley.com/doi/abs/10.1111/1467-
8462.12372
Deutscher, N. (2020). Place, peers, and the teenage years: long-
run neighborhood effects in Australia. American Economic
Journal: Applied Economics, 12(2), 220-49.
https://www.aeaweb.org/articles?id=10.1257/app.20180329
Deutscher, N., & Mazumder, B. (2021). Measuring
Intergenerational Income Mobility: A Synthesis of Approaches.
Feigenbaum, J. J. (2018). Multiple measures of historical
intergenerational mobility: Iowa 1915 to 1940. The Economic
Journal, 128(612), F446-F481.
https://academic.oup.com/ej/article-
abstract/128/612/F446/5089529

9. Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., &
Keshri, A. (2019). Descriptive statistics and normality tests for
statistical data. Annals of cardiac anaesthesia, 22(1), 67.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350423/
Mogstad, M., & Torsvik, G. (2021). Family background,
neighborhoods and intergenerational mobility.
https://www.nber.org/papers/w28874
Rachel, V., Sudhamathy, G., & Parthasarathy, M. (2018).
Analytics on moodle data using R package for enhanced
learning management. International Journal of Applied
Engineering Research, 13(22), 15580-15610.
https://www.ripublication.com/ijaer18/ijaerv13n22_19.pdf
Stuhler, J. (2018). A review of intergenerational mobility and
its drivers. Publications Office of the European Union,
Luxembourg. https://core.ac.uk/download/pdf/162257020.pdf
Torche, F. (2018). Intergenerational mobility at the top of the
educational distribution. Sociology of Education, 91(4), 266-
289.
https://journals.sagepub.com/doi/abs/10.1177/003804071880181
2
As a leader in retail, Recreational Equipment Incorporated
(REI) maintains a small store
feel with an experienced staff that provides excellent customer
service. Founded in
1938 as an outdoor equipment shop, REI has grown to over
10,000 employees with
revenues of $38 billion. Originally an equipment shops for
experienced climbers, REI
now markets to less experienced customers in the family
camping segment. Despite its
continued innovation and growth, REI is consistently ranked as
a top company to work
for.

10. Analysis
To provide excellent customer service, REI hires and trains
employees to be experienced
with whatever they sell. To create the best employees, REI
believes that experienced
employees are best qualified to recommend products to
customers, and they create the
best employees by hiring the most loyal customers. To maintain
a positive culture,
employees receive excellent benefits, are encouraged to share
their thoughts, and
participate in debates, and REI chooses to close their stores on
Black Friday.
Case Questions
1. Why does REI need employees who are experienced with
their equipment just to
sell products?
1. Why doesn’t REI take part in Black Friday even though it is
many stores’ most
successful day?