ECO301 Southern New Hampshire Crime Rate and Unemployment Analysis Your final project for this course is a detailed analysis of a specific problem statemen

ECO301 Southern New Hampshire Crime Rate and Unemployment Analysis Your final project for this course is a detailed analysis of a specific problem statement. How economic themes, such as demand, production, cost, and market
structure relate to a particular company will be a focus of this analysis. The final deliverable will include an introduction, problem
statement, listing of data sources collected, estimate and analyses of data, and a conclusion addressing how your findings can inform future real-world decision
making, at both an organizational level and an individual level. ECO 301: Final Project Milestone
United States Hourly Wages
Introduction:
A. The discussion of stagnant real wages has been going on for many years now. The social security
administration estimates that wages grew by only 1.2% from 2012 to 2013. 2014 data has yet to be
released. 52% of American workers earn less than $30,000 per year and only 8% more make between
$30,000 and $60,000. The Bureau of Labor Statistics (BLS) most recent report on January 16th had
wages increasing by only 0.1% from November to December 2014; the wages had actually decreased by
0.2% but it shows an increase only due to a net decrease in the CPI of 0.4%. With the Federal Reserve
looking to keep inflation at an acceptable level of 2% a year, a wage level that doesn’t keep up sets up
the issue of a serious wage gap between what is earned by the worker and what it takes to consume in
the American economy. This is a serious problem.
B. Hypothesis: Wages should increase with a decrease in the unemployment rate and with interest rates that
are lower should drive up yields and thus demand thereby causing wages to increase due to increased
capital spending. The unemployment rate is relative in the sense that changes in this category affect the
amount of individuals being hired. A steady or increase in GDP should positively affect wages in that
more output or total workers are being utilized thus pushing wages higher. W=f(IR, UR, GDP)
C. The sources of data for this equation data from the Bureau of Labor Statistics, The Federal Reserve, The
U.S. Department of Commerce, and Moody’s Corporation. The sample size of 30 years should get an
accurate picture of fluctuations in wages as well as the other variables. This time period should also be
able to smooth out certain business cycles that are reflective of inflated or deflated data. All data
sources are credible, accurate, and reliable.
D. The variables for the model consist of:
W= Average Wages per Hour
IR=Current Interest Rate
UR=Average Yearly Unemployment Rate
G=Total Yearly U.S. GDP
Changes in IR, UR, and G (X) can all have positive and or negative effects on the dependant variable
(Y) of average hourly wages. The relationship between the three independent variables is the key to the
growth in the dependent variable of Wages. An increase in one variable, while holding the other 2
constant has the ability the change the dependent variable W. For example, a decrease in the total
unemployment rate coupled by a constant rate of interest rates and a stable U.S. GDP should increase
wages.
E. Assumptions of Data: the data sources (DOC, BLS, and Federal Reserve) should have extremely
accurate and consistent data available for the last 30 years. For example the Fed has the quarterly
interest rates available for the last 30 years; these can be averaged to come up with that year’s interest
rate. The BLS surveys are compilation of data for the population across all 50 states. The U.S.
Department of Commerce has recorded annual GDP for the last 30 years, and Moody’s offers data
reflecting what corporations are offering on corporate bonds in order to raise capital. This last source is
relevant for interpreting the outcome of the data. All data sources are merely reporting agencies that
have no stake in the data itself, therefore it is deemed to be highly accurate and unbiased.
Weakness of Data: there should be no issues as all the data is accurate and there is no bias in the
reporting of the data. It is scientifically accurate based on the procedures in which it is collected. The
discussion is relevant to last 20 years and there is sufficient data from all sources relevant to this time
frame.
F. Estimation Procedure: Regression analysis can be used to determine the estimation target as it is a
function of the independent variables.
Regression Analysis:
SUMMARY
OUTPUT
Regression Statistics
Multiple R
0.997581463
R Square
0.995168776
Adjusted R Square
0.994611327
Standard Error
0.262730294
Observations
30
ANOVA
df
Regression
SS
MS
F
Significance
F
1785.2197
3.26144E-30
t Stat
P-value
Lower 95%
Upper
95%
Lower
95.0%
Upper 95.0%
3
369.6861893
123.2287
Residual
26
1.794707396
0.069027
Total
29
371.4808967
Coefficients
Intercept
Standard Error
0.616029679
10.80323
4.143E-11
5.388842298
7.9213766
5.3888423
7.921376555
Unemp. Rate
6.655109426
0.013007315
0.042842949
-0.3036
0.7638455
-0.101072257
0.0750576
-0.1010723
Int. Rate
-0.13956279
0.044314339
-3.14938
0.0040831
-0.230652217
-0.048473
-0.2306522
0.075057628
0.048473363
GDP
0.000791927
2.5853E-05
30.63191
6.246E-22
0.000738785
0.0008451
0.0007388
0.000845068
Computer Output and Evaluation of Results:
Data:
Year
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
$/Hr.
Percent
Wages
8.59
8.96
9.33
9.56
9.99
10.31
10.66
11.01
11.46
11.67
11.97
12.26
12.58
13.11
13.71
14.38
14.89
15.2
15.48
15.95
16.26
16.77
17.36
18.03
18.48
18.66
18.9
19.2
19.4
19.78
Unemp.
Rate
7.2
7
6.2
5.5
5.3
5.6
6.8
7.5
6.9
6.1
5.6
5.4
4.9
4.5
4.2
4
4.7
5.8
6
5.5
5.1
4.6
4.6
5.8
9.3
9.6
8.9
8.1
7.4
6.2
Percent
Int. Rates
8.42
6.45
6.77
7.65
8.53
7.89
5.86
3.89
3.43
5.32
5.94
5.52
5.63
5.05
5.08
6.11
3.49
2
1.24
1.89
3.62
4.94
4.53
1.83
0.47
0.32
0.18
0.17
0.13
0.12
Billions of Dollars
GDP
4,346.70
4,590.20
4,870.20
5,252.60
5,657.70
5,979.60
6,174.00
6,539.30
6,878.70
7,308.80
7,664.10
8,100.20
8,608.50
9,089.20
9,660.60
10,284.80
10,621.80
10,977.50
11,510.70
12,274.90
13,093.70
13,855.90
14,477.60
14,718.60
14,418.70
14,964.40
15,517.90
16,163.20
16,768.10
17,420.70
Evaluate adjusted R2
The adjusted R2 is 0.994611327. It indicates that approximately 99% of the variation in hourly wages
over the last three decades is explained by the three independent variables—interest rates, national
unemployment rates, and national GDP.
Table 1 provides the results of the t-tests for each of the independent variables.
Intercept
Unemp. Rate
Int. Rate
GDP
Standard
Coefficients
Error
t Stat
6.655109426 0.616029679 10.80323
-0.01300731 0.042842949
-0.3036
-0.13956279 0.044314339 -3.14938
0.000791927
2.5853E-05 30.63191
P-value
4.14E-11
0.763845
0.004083
6.25E-22
Table 1: T-Test Analysis
To assist use the t-Value Calculator: http://www.danielsoper.com/statcalc3/calc.aspx?id=10
The degrees of freedom are 26, and the probability is 0.05. The critical value is approximately 2. If the
absolute value of the t-statistic is greater than 2, the null hypothesis can be rejected. The P-value results
can be used to determine whether to reject each of the following hypotheses.
t-value (right-tail): 1.70561792
t-value (two-tailed): +/- 2.055529
Using the null hypothesis that each of the estimated coefficients is not significantly different from zero,
and a 5% probably level (or a 5% probability of obtaining the test statistic as large or larger as the one
obtained if the true value is in fact zero), the coefficients for GDP and unemployment are significant
(reject the null hypothesis that the true values are 0), while coefficients for the intercept and int. rates are
not significant (do not reject the null hypothesis that the true values of the coefficients are zero).
Unemp. Rate:
(a)
H0: ?p = 0; HA; ?p ? 0
Do not reject at the 5% level (P-value > 0.05)
Int. Rates:
(b)
H0: ?gdp = 0; HA; ?gdp ? 0
Reject at the 5% level (P-value < 0.05) GDP: (c) H0: ?pop = 0; HA; ?pop ? 0 Do not reject at the 5% level (P-value > 0.05)
F-test.
H0: ?p = ?gdp = ?pop = 0;
HA: at least one ? is not equal to zero
Use the analysis of variance (ANOVA) information given in the table below.
ANOVA
Regression
Residual
Total
Significance
df
SS
MS
F
F
3 369.6861893 123.2287 1785.2197 3.2614E-30
26 1.794707396 0.069027
29 371.4808967
Table 2: F-Test Analysis
F = 1785.219
Critical value
Critical F-Value Calculator:
http://www.danielsoper.com/statcalc3/calc.aspx?id=4
F (3, 26) = 2.97515399
Since the 1785.219 > 4.00, reject the null hypothesis. At least one of the ?’s is not equal to zero. You
can also use the “Significance of F” information, which indicates that the critical value would need to be
essentially zero to not reject the null hypothesis.
Evaluation/Analysis of Results: I come away with the idea that the 3 independent variables are very
highly reflective of the hourly wages over the last 3 decades. My concern is with interest rates; they
have fluctuated over the last 30 years and don’t necessarily move in the same direction as GDP and
wages. As it’s been easier to obtain funds in the free market, the rate of wage increase has slowed.
Interest rates aren’t seemingly affecting wages as economic/financial theory would suggest. The
question is why. I feel as though the wages trend as GDP does but not necessarily at the same rate. The
R-squared of 99% is strong, but what’s eye opening is how little interest rates affect the dependent
variable of wages. At a time when borrowing money is low, there seems to be little reinvestment by
corporations in growing the employment base at home; which in turn should push wages, but it’s not. It
seems that it’s entirely possible that current government laws that tax investment are keeping money
from flowing into the system for expansion. GDP is growing at a very strong rate, but wages have
declined from approximately 50% of GDP to about 43% in 2013. (Greenhouse, 2013)
The data suggests that there’s a correlation between the independent variables and the dependent
variable; but by how much. Is it just coincidence? Probably not as inflation does have its affect on
wages. Inflation percentage could have easily been a variable that could have replaced interest rates in
order to build a stronger model. If wages are slowly becoming a smaller piece of GDP, it’s also possible
that GDP should be replaced by another independent variable. The strongest model I think would take
into account inflation and a subdivision of the unemployment variable into multiple categories based on
education levels. Also, pulling out the top 10% of wage earners from the data would give us a better
prediction of the model. Wages for the top 10% is the sector that may be showing real wage increase
when it’s clearly hasn’t increased much over the last 3 decades when measured against inflations.
(Hersh, 2015)
Conclusion: I do come away with the idea that there are measurable real world variables that do affect
things such as supply, demand, prices, and wages. Ultimately, management decisions can be made
when tangible data is obtained and analyzed through simple regression analysis. In my profession we
are constantly looking to upgrade talent on the management side or front line team members. Hourly
wages have a clear effect on who applies for these openings. Regression may not necessarily need to be
in done in every case, but we do understand that we have to pay more for top talent. As the
unemployment rate is decreasing, we are finding that it’s turning more from an employer driven market
towards an employee driven market. The shift has been slow as this wasn’t the case in 2010 and 2011
but in the last two years we’ve seen openings remain unfilled for longer than we’d like. With a lot of
competition for the available talent, employers such as us are going to have to start increasing our base
rate for our front line team members (with the inclusion of benefits) and we’re going to have to bump
the management packages with more incentives and overrides.
As previously mentioned I do believe the model is strong, but given the state of current economic
conditions, I think the independent variables need to be switched up as globalization and technological
advances change the wage employment decisions are made. (Shah, 2013) Subdivision of employment
categories, inflation rates, and business tax rates on investment I do believe would reveal the strongest
possible model for wages.
References:
http://www.federalreserve.gov/releases/H15/data.htm
http://www.bls.gov/cps/cpswktabs.htm
http://www.bea.gov/National/index.htm
http://data.bls.gov/timeseries/LNU04000000?years_option=all_years&periods_option=specific_periods
&periods=Annual+Data
https://www.moodys.com/
Greenhouse, S. (2013, January 12) Our Economic Pickle. The New York Times. Retrieved on February
22, 2015 from http://www.nytimes.com/2013/01/13/sunday-review/americas-productivity-climbs-butwages-stagnate.html
Hersh, A. (2015, January 12) Why Wages Are Not Returning as Job Growth Increases. Center for
American Progress. Retrieved on February 22, 2015 from
https://www.americanprogress.org/issues/economy/news/2015/01/12/104179/why-wages-are-notreturning-as-job-growth-increases/
Lazear, E. (2015, January 25) Solving the Puzzle of Stagnant Wages. The Wall Street Journal.
Retrieved on February 22, 2015 from http://www.wsj.com/articles/ed-lazear-solving-the-puzzle-ofstagnant-wages-1422231934
Shah, N. (2013, August 25) Stagnant Wages Are Crimping Economic Growth. The Wall Street Journal.
Retrieved on February 22, 2015 from
http://www.wsj.com/articles/SB10001424127887323980604579028822725730720
ECO 301: Decision-Making Analysis Paper Guidelines and Rubric
Overview
Your final project for this course is a detailed analysis of a specific problem statement. How economic themes, such as demand, production, cost, and market
structure relate to a particular company will be a focus of this analysis. You will analyze these components with quantitative techniques, like regression
analysis and linear programming. You will select a product or service with substantial data. Some possible topics: unemployment and crime, exports and
underdeveloped countries, demand/supply of higher education, air pollution and population, etc. The final deliverable will include an introduction, problem
statement, listing of data sources collected, estimate and analyses of data, and a conclusion addressing how your findings can inform future real-world decision
making, at both an organizational level and an individual level.
The project is divided into three milestones and a final product, which will be submitted at various points throughout the course to scaffold learning and ensure
quality final submissions.
Prompt
Your final project should answer the following prompt: What economic theories and quantitative techniques are used to solve business decision problems and
how are they applicable in real-world settings?
Specifically, the following critical elements must be addressed:
I.
Introduction/Statement of Problem
A. What problem are you trying to solve? Discuss the history and key information about the problem and address why the issue/problem is
important.
B. Describe the model, hypothesis, and theoretical framework that will be used to explain and forecast variables. The model should be in the form of
functional equations. QD = f(P, Y, …)
C. What data sources do you plan to use? A minimum sample size of 15 is required. Include a complete description of the data sources and assess
their validity, accuracy, creditability, and reliability for the chosen issue. Make sure all data sources are referenced.
D. Which variables are used in the model? Why are they used? Considering the relation among measurable variables, what is the impact of an
independent variable X on a dependent variable Y? Are there additional independent variables that could influence variable Y? If so, explain.
E. What assumptions can you make about the data? In your analysis, consider the following: accuracy, consistence, sample as representative of the
population, biased/unbiased, efficient, and weakness of data (currency, not a complete data set, biased, not scientifically accurate).
F. What estimation procedure do you plan to use? If you are using time series data, be sure to account for the identification problem. Why did you
choose is this particular procedure?
II.
Regression Analysis – Perform a regression analysis on the data.
A. Compute for the following:
1. Standard errors
2. T-stats
3. R2
4. Coefficients, 5% and 1% levels
5. Possible other models to test
III.
Computer Output and Evaluation of Results – What are the results of your regression analysis? Include the data you used to estimate the demand
model, the results of your analysis, an analysis of the results, and a description of remaining issues that need to be addressed, i.e. other models to test.
Be sure to include standard errors, t-stats, R2, coefficients, 5% and 1% levels.
IV.
Statistical Analysis of Results – What are the implications of the t-stats, F test and Adjusted R2?
be contradictory, how can this be resolved?
V.
Conclusion and General Comments – What are key take-a-ways that can be applied in your own personal or professional real-world settings? Provide
concrete or hypothetical examples (if you are not in an applicable field) that support your conclusions. How might this model change in the future
given assumptions?
Are they consistent or contradictory?
Deliverables
Milestone
Deliverables
Due in Module
Grading Metric
1
Final Project Milestone One: Regression Analysis Practice
Submit your sample regression analysis and statistical test
Final Project Milestone Two: Demand Analysis
Submit your demand analysis
Final Project Milestone Three: Regression Analysis
Submit your project-based regression analysis
Decision Making Analysis Paper
Submit the final version of all portions of your final paper within
a single document.
Two
Regression Analysis
Three
Demand Analysis
Five
Regression Analysis
Seven
Final Project Rubric
2
3
Final
Submission
If they seem to
Rubrics
Requirements of submission: Written components of projects must follow these formatting guidelines when applicable: double spacing, 12-point Times New
Roman font, 1-inch margins, and discipline-appropriate citations.
Milestone One and Three: Regression Analysis
Critical Elements
Milestone
Submission:
Regression Analysis
Exemplary(100%)
Meets “Proficient” criteria and
includes detailed analysis of
why the computations were
chosen or other appropriate
models were selected
Proficient(85%)
Performs a regression analysis,
computing standard errors, tstats, R2, coefficients (5% and
1%), and other possible models
correctly
Needs Improvement(55%)
Performs a regression analysis,
computing standard errors, tstats, R2, coefficients (5% and
1%), or other possible models,
but not all models are used or
there are incorrect
computations
Not Evident (0%)
Does not perform a regression
analysis
Value
90
Articulation of
Response
Submission is free of errors
related to citations, grammar,
spelling, syntax, and
organization and is presented
in a professional and easy-toread format
Submission has no major errors
related to citations, grammar,
spelling, syntax, or organization
Submission has major errors
related to citations, grammar,
spelling, syntax, or organization
that negatively impact
readability and articulation of
main ideas
Submission has critical errors
related to citations, grammar,
spelling, syntax, or organization
that prevent understanding of
ideas
10
Total
100%
Needs Improvement(55%)
Not Evident (0%)
Describes selected problem or
Does not describe selected
service and includes background problem or service or does not
information, but misses why the provide background information
issue or problem is important
Value
20
Milestone Two: Demand Analysis
Critical Elements
Exemplary(100%)
Proficient(85%)
Introduction and
Meets “Proficient” criteria and Describes selected problem or
Statement of Problem selection of problem is
service and includes background
supported by scholarly research information on why the issue or
problem is important
Model Formation
Meets “Proficient” criteria and
description is insightful and
meticulously detailed
Accurately describes the model,
hypothesis, and theoretical
framework that will be used to…
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