Service Learning I have experienced helping people in society developing sector. The committee called P.A.C, and it stands for Pittsburgh Islamic Center. P.A.C. was created and developed by Saudi Cultural Mission. The committee essentials were set to help poor people in Pittsburgh community. Its tendency is to advantage homeless and poor people with simple batch. This event took place on Sunday April 25, 2016, therefore we worked with divers organizations that supported our community to meet up with homeless individuals in order to fully understand the needs of a homeless person, and from that point we could start building our committee operations. P.A.C. organization is the only one that helps poor people throughout their cooperation with active members to gather food and clothes, then arrange it and get it ready for distribution. Consequently, ready made goods will be distributed on Mosques and Churches: and that’s for some of the goods. However, some of the stuff gets distributed from the major branch, which is the Islamic Center of Pittsburgh. So I have participated as an active member and I was accountable to gather then deliver the collected stuff from people and deliver it to different locations. Eventually, hand the aid to the poor individuals and families. This experience had me think of how is it possible to find very poor families among obscene wealth community. However, that was unexpected and that what made me think more of helping these poor. The benefits of engaging service work are uncountable, because it makes you invent ideas for how to help those poor. For example, free rides to specific places in the town, free bus tickets. Anything could benefit the poor to facilitate some of their daily activities. Such committees or programs could and should be supported by MDG’S, “Millennium Development Goals” because these organizations considered as part of the global developments. With that being said, this organization supports every individuals, regardless their religion or nationality, and that’s what we need to raise up the poor communities, by avoiding and ignoring races and ethnicity diversity. In essence, these types of organizations always come back with huge benefits on the society no matter how much efforts it takes to be created. And usually it doesn’t require that much effort, however, to find the individual takes time, even though it’s the easiest step to start such program. Semester Project The purpose of this project is for you to gain experience in applying methods taught in this class to a real data set of interest to you. The project will simulate the real world practices of defining research questions, identifying variables, producing data, analyzing data, writing a report, and evaluating peers. Each of these skills is critical to any career path you may choose. For the final project, you should work alone. The purpose of the project is for you to gain experience in applying the methods taught in the c.

1. Service Learning
I have experienced helping people in society developing sector.
The committee called P.A.C, and it stands for Pittsburgh
Islamic Center. P.A.C. was created and developed by Saudi
Cultural Mission. The committee essentials were set to help
poor people in Pittsburgh community. Its tendency is to
advantage homeless and poor people with simple batch. This
event took place on Sunday April 25, 2016, therefore we worked
with divers organizations that supported our community to meet
up with homeless individuals in order to fully understand the
needs of a homeless person, and from that point we could start
building our committee operations.
P.A.C. organization is the only one that helps poor people
throughout their cooperation with active members to gather food
and clothes, then arrange it and get it ready for distribution.
Consequently, ready made goods will be distributed on Mosques
and Churches: and that’s for some of the goods. However, some
of the stuff gets distributed from the major branch, which is the
Islamic Center of Pittsburgh. So I have participated as an active
member and I was accountable to gather then deliver the
collected stuff from people and deliver it to different locations.
Eventually, hand the aid to the poor individuals and families.
This experience had me think of how is it possible to find very
poor families among obscene wealth community. However, that
was unexpected and that what made me think more of helping
these poor. The benefits of engaging service work are
uncountable, because it makes you invent ideas for how to help
those poor. For example, free rides to specific places in the
town, free bus tickets. Anything could benefit the poor to
facilitate some of their daily activities.
Such committees or programs could and should be supported by

2. MDG’S, “Millennium Development Goals” because these
organizations considered as part of the global developments.
With that being said, this organization supports every
individuals, regardless their religion or nationality, and that’s
what we need to raise up the poor communities, by avoiding and
ignoring races and ethnicity diversity.
In essence, these types of organizations always come back with
huge benefits on the society no matter how much efforts it takes
to be created. And usually it doesn’t require that much effort,
however, to find the individual takes time, even though it’s the
easiest step to start such program.
Semester Project
The purpose of this project is for you to gain experience in
applying methods taught in this class to a real data set of
interest to you. The project will simulate the real world
practices of defining research questions, identifying variables,
producing data, analyzing data, writing a report, and evaluating
peers. Each of these skills is critical to any career path you may
choose.
For the final project, you should work alone. The purpose of the
project is for you to gain experience in applying the methods
taught in the class to a real data set of interest to you.
Objectives
At the end of this project, you will be able to:
1. define a research question and define appropriate variables to
measure on a topic that you care about
2. decide on an experimental design
3. gather and analyze original data (rather than data prepared
for you) on an issue of interest to you
4. prepare an appropriate report
5. provide constructive, thoughtful feedback to peers on their
projects
Project Suggestions
Conducting an empirical analysis of economic data can be
rewarding and informative. The first step in conducting an

3. empirical analysis is choosing the topic you want to study and,
within that topic, the specific question or questions you will
investigate. Although there is not a single best way to choose a
topic, the following suggestions might be useful.
1. Pick a topic that you find personally interesting, ideally one
about which you already have some knowledge. The topic might
be related to your career interests, summer work you did,
employment experience of a family member, or something of
intellectual interest to you. Often, a specific policy problem, a
personal decision, or a business issue raises questions that can
be addressed by an empirical study.
2. Make the question that will be the main focus of your study
as specific as possible. The more narrowly the question relates
to a measurable causal effect, the easier it will be to answer.
3. Check the related literature. You might find published studies
on topics closely related to yours. Use previous work to give
you ideas about data sources and about what questions have not
yet been answered.
4. Choose a question that can be answered using the available
data. Although the question you originally pose might not be
answerable using available data, the data might support the
analysis of a related and equally interesting question.
5. Share your topic on the discussion board. If you find your
topic interesting, then the odds are that others will too, and an
instructor or classmate might suggest an angle that you have not
thought of.
As shown in the table below, the course project consists of 6
checkpoints that lead up to, and include, the final report.
Activity
Deadline*
Submission
Project Checkpoint 1: Topic Selection
Monday, February 8 by 3am
Moodle Discussion board
Project Checkpoint 2: Hypothesis & Research Question
Monday, February 22 by 3am

4. Moodle Discussion board
Project Checkpoint 3: Identify Variables for Study
Monday, March 14 by 3am
Moodle Discussion Board
Project Checkpoint 4: Data Sets
Monday, April 4
NA
Project Checkpoint 5: Regression Analysis
Monday, April 25
NA
Project Checkpoint 6: Final Report
Monday, May 2 by 3am
Submit to be peer reviewed
Peer Evaluation for Project Checkpoint 6
Thursday, May 5 at 3am
Submit your evaluations of your peers' projects
* Unless otherwise noted, all deadlines are Central Time (time
zone conversion)
Project Format
This project relies on multiple regression analysis to analyze a
data set that is of interest to you. The final report for the project
should be a 5-10 page single-spaced paper that describes the
questions of interest, how you used your data set to analyze
these questions with details on the steps you used in your
analysis, your findings about your question of interest and the
limitations of your study. Specifically, your report should
contain the following:
1. Introduction. The introduction succinctly states the problem
you are interested in, briefly describes your data and the method
of analysis, and summarizes your main conclusions. A summary
of what you set out to learn, and what you ended up finding. It
should summarize the entire report.
2. Data Description. This section provides the details of the data
sources, any transformations you have done to the data (for
example, changing the units of some variables), gives a table of
summary statistics (means and standard deviations) of the

5. variables, and provides scatterplots and/or other relevant plots
of the data. If there are outliers other than those arising from
corrected typographical or computer errors, this is the place to
point them out.
3. Regression Analysis. Describe how you used multiple
regression to analyze the data set. Specifically, you should
discuss how you carried out the steps in analysis discussed in
class, i.e., exploration of data to find an initial reasonable
model, checking the model and changes to the model based on
your checking of the model.
4. Empirical Results. This section provides the main empirical
results in the paper. Conventionally, regression results are
presented in tabular form, with footnotes clearly explaining the
entries. The initial table of results should present the main
results; sensitivity analysis using alternative specifications can
be presented in additional columns in that table or in subsequent
tables. For organizational purposes and clarity, you may chose
to have some tables at the end of the paper, with appropriate
references in the body of the paper as needed. The text should
provide a careful discussion of the results, including
assessments both of statistical significance and of economic
significance, that is, the magnitude of the estimated relations in
a real-world sense.
5. Summary and Discussion. This section summarizes your main
empirical findings and discusses their implications for the
original question of interest. Describe any limitations of your
study and how they might be overcome in future research and
provide brief conclusions about the results of your study.
Criteria
Grade Levels
Introduction
Absent: the criteria is completely absent.
Poor: The introductions does not state the problem of interest or

6. describe the data and method of analysis. It does not summarize
main conclusions, and does not provide a summary of what we
set out to learn. There is essentially no introduction.
Fair: There is a short introduction that may or may not state the
problem of interest. It mentions the data, but does not describe
it. No major conclusions are drawn.
Good: The introduction is a bit lengthy or short but does state
the problem of interest, describes data and methods of analysis,
although not clearly. Main conclusions are presented are not
summarized.
Excellent: The introduction succinctly states the problem of
interest, briefly describes the data and method of analysis, and
summarizes main conclusions, a summary of what you set our to
learn, and what you ended up finding.
Data Description
Absent: the criteria is completely absent.
Poor: This section does not detail data sources, transformations,
nor does it give any summary statistics. It does not provide any
graphically relevant information.
Fair: This section provides very little information in regards to
data sources, transformations, and summary statistics. There are
few or no scatter plots or other relevant graphical
representations.
Good: This section covers data sources, transformations,
summary statistics, and scatter plots, and outliers if there are
any. However, the order, structure, and presentation of the data
does not flow well.
Excellent: Provides details of data sources, transformations of
the data, gives a table of summary statistics, and provides
scatter plots, and other relevant plots of the data. If there are
outliers, they are made obvious.
Regression Analysis
Absent: the criteria is completely absent.
Poor: The use of multiple regression is not clear nor is the
regression model explained. There are no references made to
class material.

7. Fair: A multiple regression model is evident but is not clearly
explained. Few, if any, references to the course material is
made.
Good: The use of multiple regression is made clear, but there is
not strong evidence supporting the use of a particular model.
There are references made to supporting course material to
justify decisions.
Excellent: How multiple regression was used is described. The
steps of analysis that were covered in readings/lectures is
discussed. The process for deciding on a particular regression
model is well discussed and justified.
Empirical Results
Absent: the criteria is completely absent.
Poor: This section has no appropriate empirical results
presented. There may be regression results that do not refer to
the regression model that was outlined in the regression analysis
section. There is no discussion surrounding the results.
Fair: Multiple regression results are presented. However, there
is little to no discussion of the results, and if subsequent tables
are needed, they are not presented.
Good: All results of analysis are presented. However, the
discussion of the results is unclear or inaccurate.
Excellent: Regression results are presented in tabular form with
footnotes explaining entries. The table presents main results
while sensitivity analysis or alternative specifications are
outlined in subsequent tables. The text provides a careful
discussion of the results, including assessments of both
statistical and economical significance.
Summary and Discussion
Absent: the criteria is completely absent.
Poor: There is no summary or discussion of the empirical
results. No limitations for the study are presented, and there is
no conclusion.
Fair: There is little understanding or description of the
empirical results and their implications for the original question
of interest. There are little to no limitations presented and an

8. unclear conclusion.
Good: Empirical findings are summarized and implications refer
to the original question are limited. Limitations of the study are
very briefly addressed, and the conclusion of the study could be
clearer.
Excellent: This section summarizes the main empirical findings
and discusses implications. Limitations of the study are
addressed so that they may be overcome in the future. A very
brief conclusion of the results is presented.
ECON
203
Project
[SAMPLE]
1
The
effect
of
family
income
on
labor
supply
of
employed

9. youth
in
Argentina
1.
Introduction
In
basic
economic
theory,
individuals
maximize
a
utility
function
that
depends
on
consumption
of
goods
and
leisure,
subject
to
a
budget
constraint.

10. Family
income
can
be
an
important
factor
of
the
budget
constraint,
where
higher
family
income
would
increase
the
consumption
possibilities
of
the
individual
and
decrease
their
need
to
work
in
order
to
earn

11. income
to
maintain
a
similar
level
of
consumption.
At
the
same
time,
the
individual
would
be
better
off
by
working
less
since
they
value
leisure.
In
this
study
I
want

12. to
examine
the
main
determinants
of
labor
supply
of
working
youths
in
Argentina,
measured
by
hours
worked.
In
particular,
I
am
interested
in
testing
whether
the
effect
of
family
income
satisfies
the
predictions
given

13. by
traditional
economic
theory.
The
main
prediction
would
be
that
higher
family
income
would
decrease
the
number
of
hours
work
by
the
individual.
In order to evaluate the determinants of hours of labor supplied,
I use individual level
data for 2005 from the Permanent Survey of Households of the
National Institute of
Statistics (INDEC) from Argentina. I restrict the sample to
youths between the ages of
18 and 25 that work a positive number of hours in order to
identify how family income

14. affects the intensive margin of hours of work supplied. By using
multiple regressions
model, I am able to examine the main determinants of youth
labor supply and also
examine how family income affects different populations.
I show that my final model satisfies all the underlying
assumptions. The data has no
serious outliers, nor are the independent variables highly
correlated among each other. In
addition, by examining the histograms of standardized residuals
and scatter plot of
residuals against predicted values of the dependent variable, I
am able to conclude that
the error term is normally distributed and the variance appears
to be constant. This
implies that it is not necessary to transform the dependent
variable.
Results suggest that family income plays a central role in
determining number of hours
worked by youths in Argentina. Increasing family income by
$1,000 pesos (around 16%
of the mean income) is associated to a decrease of 9 hours of
work per week (22% of the
average hours worked). This evidence supports the theoretical
predictions of the effect of
family income on labor supply. I also find that age, gender,
years of education and
experience are important determinants of labor supply. On the
other hand, family size,
number of children and being married do not appear to be
statistically related to hours
worked. Finally, I test whether males respond differently to
changes in family income
compared to females by including the interaction between a

15. dummy for male and family
income. I find that males respond less to changes in family
income than women.
ECON
203
Project
[SAMPLE]
2
2. Data
In this study I use data for Buenos Aires, Argentina for the year
2005, based on the
Permanent Survey of Households compiled by the National
Institute of Statistics
(INDEC) and processed by CEDLAS of the University of La
Plata. Since the main
objective of this study is to analyze the effect of family income
on labor supply, the
dependent variable used will be total hours worked in a week
(hrswrk) and the main
independent variable is family monthly income (faminc). Family
income does not include
income earned by the individual and is measured in Argentine
pesos. Other independent
variables included are: age (age), a dummy variable that equals
1 if the individual is male

16. (male), years of education (educ), number of children (nchild),
a dummy variable that
equals 1 if married (married), number of family members
(nfamily), and a measure of
potential experience (exp).
Table 1 presents descriptive statistics for my sample. The
sample is comprised of 685
working individuals, where around 66% of them are male. On
average, they work 41
hours a week, with a maximum of 98 hours and a minimum of 1
(individuals not working
were excluded). The average family monthly income is $6,200
pesos. In Figure 1, I show
the scatter plots of hours worked against each of the
independent variables in order to
identify severe outliers. From the plots it appears that there are
no severe outliers. In
addition, the plot of hours work against family income suggests
that there may be a
curve-linear relation between them.
Before performing regression analysis, I examined the
correlation between all my
variables in order to identify possible serious multicollinearity
between independent
variables. The correlation table is presented in Table 2. No two
independent variables
have a correlation higher than 0.8, therefore serious
multicollinearity should not be a
concern in the regression analysis.
3.
Regression
Analysis

17. In
order
to
examine
the
determinants
of
labor
supply
I
estimate
the
following
linear
regression
as
the
initial
model:
�������
= � + ��������� + ��������� + ����������
+ ������� + ���������
+ ���������� + ������� + ������ + ������
+ ��
As

18. discussed
earlier,
family
income
(faminc)
is
likely
an
important
determinant
of
labor
supply
since
it
enters
the
budget
constraint
of
the
utility
maximization
problem
of
the
individual.
I
include
family
income
squared
since
the

19. scatter
plot
from
Figure
1
suggested
that
there
may
be
a
curve-­‐linear
relation
between
family
income
and
hours
worked.
There
are
other
independent
variables
that
are
potentially
important
factors
in
determining
labor
supply

20. and
typically
used
in
labor
economics.
For
example,
older
individuals
may
receive
less
support
from
their
parents
and
may
be
forced
to
work.
Being
married
or
having
children
may
also

21. ECON
203
Project
[SAMPLE]
3
influence
the
decision
to
work.
At
the
same
time,
individuals
with
more
education
may
receive
more
attractive
job
offers,
increasing
the
opportunity
cost
of
leisure.

22. The
results
for
the
initial
model
are
presented
in
Table
3.
When
considering
the
overall
goodness
of
fit
of
the
model,
the
R-­‐squared
indicates
that
the
initial
model
explains
62%

23. of
the
variation
of
hours
of
work.
In
addition,
the
independent
variables
are
relevant
as
a
whole
since
the
null
hypothesis
of
the
F-­‐test
for
overall
significance
of
the
model
is
rejected
at
the

24. 5%
level.
Even
though
the
model
is
good
as
a
whole,
there
are
many
individual
slope
coefficients
that
are
not
statistically
different
from
zero
even
at
the
10%
level.
These
variables
are:
number
of

25. family
members,
number
of
children
and
marital
status.
Additionally,
the
quadratic
term
for
family
income
has
a
p-­‐value
of
over
20%,
therefore
I
can
discard
a
curve-­‐linear
relation
between
family
income
and
hours

26. worked.
In
the
previous
section
we
already
discarded
the
problem
of
serious
multicolinearity
between
independent
variables.
One
reason
that
some
of
these
variables
may
not
be
significant
is
the
possibility
of

27. the
variance
not
being
constant
(heteroskedasticity)
or
non-­‐normality
of
the
error
term.
In
both
of
these
cases,
the
standard
errors
obtained
would
not
be
valid.
In
Figure
2
I
present
the
histogram
of

28. standardized
residuals
to
visually
inspect
for
non-­‐normality
of
the
error
term.
The
figure
clearly
shows
that
the
distribution
follows
a
normal
distribution.
To
test
for
the
existence
of
heteroskedasticity,
I
plot
the
residuals
against

29. the
predicted
hours
worked.
This
is
presented
in
Figure
3.
It
appears
that
the
variance
is
quite
constant
and
across
predicted
values
of
hours
work.
In
order
to
obtain
a
final

30. model
on
which
to
make
inference
of
the
results,
I
drop
the
variables
that
are
not
significant
and
re-­‐estimate
the
model.
Regression
results
are
presented
in
Table
4.
As
a
result
of
dropping
these

31. insignificant
variables,
the
R-­‐
squared
does
not
change
and
the
Adjusted
R-­‐squared
increases
slightly.
This
suggests
that
including
those
three
extra
variables
was
not
contributing
anything
to
explaining
the
variation
in
the
dependent
variable

32. and
including
them
was
actually
penalizing
more
than
contributing
to
the
Adjusted
R-­‐squared.
In
addition,
I
performed
a
partial
F-­‐test
to
make
sure
that
the
variables
excluded
from
the
model
do
not
significantly

33. contribute
to
explaining
the
variation
in
hours
worked.
The
partial
F-­‐test
statistic
is
0.0313
with
a
p-­‐value
of
0.9925.
Therefore
we
cannot
reject
the
null
hypothesis
of
the
three
variables
having
coefficients
equal
to

34. zero
(i.e.
being
irrelevant
in
explaining
hours
of
work).
All
remaining
independent
variables
are
statistically
different
from
zero
at
the
10%
level.
The
reduced
model
satisfies
the
assumption
of
normality
of
the
error

35. term
and
constant
variance
as
presented
in
the
histogram
and
scatter
plots
of
Figures
4
and
5.
Since
the
assumptions
appear
to
be
satisfied,
the
reduced
model
will
be
my
final

36. model
that
will
be
used
to
make
inference.
This
model
is
defined
as:
������� = � + ��������� + ������� +
������� + ������ + ������ + ��
ECON
203
Project
[SAMPLE]
4

37. 4.
Empirical
Results
The
coefficients
obtained
in
Table
4
suggest
that
if
family
income
were
to
increase
by
$1,000
pesos,
an
individual
from
the
sample
would
decrease
labor
supply
by
9

38. hours
per
week
on
average
(all
else
constant).
Considering
that
the
average
number
of
hours
worked
is
41,
this
would
represent
a
22%
decrease
in
hours
worked
per
week
(9/41=0.219),
a
sizeable
effect
of

39. family
income
on
labor
supply.
The
older
individuals
are,
the
more
hours
they
are
likely
to
work.
Increasing
age
by
one
year
relates
to
an
increase
of
8
hours
of
work

40. per
week.
The
dummy
variable
for
male
suggests
that
males
in
my
sample
on
average
work
2
hours
more
than
females.
Quite
surprisingly,
both
years
of
education
and
years
of
potential
experience
are

41. negatively
related
to
labor
supply
and
are
similar
in
magnitude
to
the
effect
of
age.
I
found
that
family
income
has
sizeable
effects
on
labor
supply.
An
interesting
question
is
whether

42. family
income
affects
different
populations
differently.
For
example,
the
relation
could
be
different
for
males
or
females.
In
order
to
test
whether
family
income
affects
males
differently
I
estimate
the
reduced
model
including

43. an
interaction
term
between
family
income
and
the
dummy
variable
for
male.
The
results
are
presented
in
Table
5.
For
the
interaction
term
I
can
reject
the
null
hypothesis
of
the
slope
equal
to

44. zero
allowing
a
5%
chance
of
Type
I
error.
The
interaction
term
suggests
that
women
reduce
their
labor
supply
more
than
men
if
family
income
increases.
5.
Summary
and
Discussion

45. This
study
explored
the
determinants
of
youth
labor
supply
for
workers
in
Argentina.
In
particular,
I
was
interested
in
analyzing
the
role
of
family
income
and
testing
whether
the
theoretical
predictions

46. are
satisfied
empirically.
I
found
that
family
income
is
one
of
the
most
important
factors
in
explaining
the
variation
in
hours
of
work.
Holding
all
else
constant,
an
increase
of
$1,000
pesos
in
family

47. income
relates
to
a
decrease
of
9
hours
of
work
per
week
(an
effect
of
around
22%
of
the
mean).
Other
factors
I
found
to
be
important
determinants
of
hours
worked
are
age,

48. education,
experience
and
gender.
One
significant
shortfall
of
this
study
is
that
I
am
only
considering
the
intensive
margin
of
labor
supply.
That
is,
only
focusing
on
individuals
who
are
working.

49. Family
income
could
potentially
have
different
effects
on
the
extensive
margin:
deciding
whether
to
work
or
not.
Since
I
do
not
include
individuals
with
zero
hours
of
work,
I
cannot
extrapolate
my
results

50. to
this
population.
Future
research
should
address
this
issue
carefully.
A
second
shortfall
is
that
I
am
using
a
very
selected
sample
for
Argentina,
which
may
not
necessarily
be
representative

51. of
other
populations.
Youths
living
in
the
United
Sates
or
in
European
countries
may
respond
differently
to
changes
in
family
income
since
the
labor
market
conditions
are
very
different
between
countries.

52. ECON
203
Project
[SAMPLE]
5
Figure
1:
Scatter
plots
of
Hours
work
vs
independent
variables

53. Figure
2:
Histogram
of
standardized
residuals
–
initial
model
Figure
3:
Residuals
vs
predicted
hours
of
work
-­‐
initial
model
0
20
40
60
80
10

54. 0
hr
sw
rk
2000 4000 6000 8000 10000
faminc
0
20
40
60
80
10
0
hr
sw
rk
18 20 22 24 26
age
0
20
40
60
80
10
0

55. hr
sw
rk
0 5 10 15
nfamily
0
20
40
60
80
10
0
hr
sw
rk
0 .2 .4 .6 .8 1
male
0
20
40
60
80
10
0
hr
sw

56. rk
0 1 2 3
nchild
0
20
40
60
80
10
0
hr
sw
rk
0 .2 .4 .6 .8 1
married
0
20
40
60
80
10
0
hr
sw
rk

57. 0 5 10 15 20
educ
0
20
40
60
80
10
0
hr
sw
rk
0 5 10 15
exp
0
.2
.4
.6
D
en
si
ty
-2 -1 0 1 2 3
stdresid
-4
0

58. -2
0
0
20
40
R
es
id
ua
ls
20 40 60 80
Fitted values
ECON
203
Project
[SAMPLE]
6
Figure
4:
Histogram
of
standardized

59. residuals
–
reduced
model
Figure
5:
Residuals
vs
predicted
hours
of
work
-­‐
reduced
model
Table 1: Descriptive Statistics
Variable Observations Mean Std. Dev. Min Max
hrswrk 685 41.587 17.188 1 98.1
faminc 685 6184.595 1547.212 1831.163 9643.931
age 685 22.261 2.189 18 25
nfamily 685 4.632 2.350 1 15
male 685 0.663 0.473 0 1
nchild 685 0.257 0.615 0 3
married 685 0.309 0.463 0 1
educ 685 10.645 2.692 3 17
exp 685 5.625 3.264 0 16
Notes: Own calculations based on Permanent Survey of

60. Households for Argentina in
2005. Sample of youths of Buenos Aires that work.
Table 2: Correlations
hrswrk faminc age nfamily male nchild married educ exp
hrswrk 1
faminc -0.785 1
age 0.0429 -0.0366 1
nfamily -0.1014 0.1265 -0.1782 1
male 0.1647 -0.1451 -0.039 0.0171 1
nchild 0.0434 -0.0591 0.2269 -0.1368 0.0319 1
married 0.056 -0.0728 0.2376 -0.1385 0.0166 0.5936 1
educ -0.0053 0.0375 0.1085 -0.2388 -0.1767 -0.1638 -0.1406 1
exp 0.0322 -0.0557 0.5797 0.0767 0.1187 0.2862 0.2745 -
0.7466 1
Notes: Own calculations based on Permanent Survey of
Households for Argentina in 2005. Sample of
youths of Buenos Aires that work.
0
.2
.4
.6
D
en
si

61. ty
-2 -1 0 1 2 3
stdresid
-4
0
-2
0
0
20
40
R
es
id
ua
ls
0 20 40 60 80
Fitted values
ECON
203
Project
[SAMPLE]
7

62. Table 3: Initial model regression results
Coefficient
Std.
Err. T-stat P-value [95% Conf. Interval]
faminc -0.011 0.002 -6.5 0.000 -0.014 -0.007
faminc2 0.000 0.000 1.22 0.225 0.000 0.000
age 8.102 4.441 1.82 0.069 -0.617 16.821
nfamily 0.052 0.184 0.28 0.778 -0.310 0.414
male 2.165 0.883 2.45 0.014 0.432 3.898
nchild -0.138 0.837 -0.17 0.869 -1.782 1.505
married 0.173 1.109 0.16 0.876 -2.005 2.351
educ -7.722 4.424 -1.75 0.081 -16.407 0.964
exp -8.008 4.451 -1.8 0.072 -16.748 0.733
Constant 45.516 27.509 1.65 0.098 -8.498 99.529
Observations 685
Source SS df MS
R-squared 0.6228
Adj-R-squared 0.6178
Model 125857.77 9 13984.1967
F-stat 123.84
Residual 76219.1037 675 112.917191

63. Pval. F-stat 0.000
Notes: Own calculations based on Permanent Survey of
Households for Argentina in 2005.
Sample of youths of Buenos Aires that work. Ordinary least
squares estimates presented.
Table 4: Reduced model regression results
Coefficient
Std.
Err. T-stat P-value [95% Conf. Interval]
faminc -0.009 0.000 -32.59 0.000 -0.009 -0.008
age 8.185 4.431 1.85 0.065 -0.515 16.885
male 2.109 0.880 2.4 0.017 0.381 3.837
educ -7.844 4.414 -1.78 0.076 -16.511 0.822
exp -8.108 4.441 -1.83 0.068 -16.829 0.613
Constant 40.544 27.004 1.5 0.134 -12.478 93.565
Observations 685
SS df MS
R-squared 0.622
Adj-R-squared 0.6192
Model 125680.337 5 25136.0675
F-stat 223.41
Residual 76396.5367 679 112.513309

64. Pval. F-stat 0.000
Notes: Own calculations based on Permanent Survey of
Households for Argentina in 2005.
Sample of youths of Buenos Aires that work. Ordinary least
squares estimates presented.
ECON
203
Project
[SAMPLE]
8
Table 5: Reduced model regression results with interaction
Coefficient
Std.
Err. T-stat P-value [95% Conf. Interval]
faminc -0.010 0.000 -20.37 0.000 -0.011 -0.009
male -6.781 3.718 -1.82 0.069 -14.081 0.519
maleXfaminc 0.001 0.001 2.46 0.014 0.000 0.003
age 8.539 4.417 1.93 0.054 -0.133 17.211
educ -8.170 4.400 -1.86 0.064 -16.808 0.469
exp -8.443 4.427 -1.91 0.057 -17.136 0.249
Constant 44.258 26.946 1.64 0.101 -8.651 97.166
Observations 685

65. SS df MS
R-squared 0.6253
Adj-R-squared 0.622
Model 126356.579 6 21059.4299
F-stat 188.57
Residual 75720.2948 678 111.681851
Pval. F-stat 0.000
Notes: Own calculations based on Permanent Survey of
Households for Argentina in 2005.
Sample of youths of Buenos Aires that work. Ordinary least
squares estimates presented.