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User: Zhijing Zhang
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___/ / /___/ / /___/ 13.1 Copyright 1985-2013 StataCorp
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Notes:
1. (-set maxvar-) 5000 maximum variables
2. New update available; type -update all-
1 .
2 . import excel "/Users/zhijing422/Desktop/2.xlsx",
sheet("Sheet1") firstrow
3 . des UnemploymentRate Female Bachelor
PopulationGrowthRate Native Age2534

storage display value
variable name type format label variable label
UnemploymentR~e double %14.2f Unemployment
Rate
Female double %14.2f %Female
Bachelor double %14.2f %Bachelor
PopulationGro~e double %14.2f %Population Growth
Rate
Native double %14.2f %Native
Age2534 double %10.0g %Age25-34
4 . reg UnemploymentRate Female Bachelor
PopulationGrowthRate Native Age2534
Source SS df MS Number of obs =
51
F( 5, 45) = 4.79
Model 103.366118 5 20.6732236 Prob > F =
0.0014
Residual 194.408 45 4.32017777 R-squared =
0.3471
Adj R-squared = 0.2746
Total 297.774118 50 5.95548235 Root MSE
= 2.0785
UnemploymentR~e Coef. Std. Err. t P>|t| [95%
Conf. Interval]
Female 1.206254 .527296 2.29 0.027 .1442252
2.268283
Bachelor -.2116358 .1102866 -1.92 0.061 -
.4337644 .0104929

Monday, November 30, 2015 at 4:49 PM Page 2
User: Zhijing Zhang
PopulationGro~e .1536832 .0481323 3.19 0.003
.0567398 .2506265
Native -.1034367 .1199954 -0.86 0.393 -.3451198
.1382464
Age2534 .0480302 .229291 0.21 0.835 -
.4137856 .5098461
_cons -50.48779 26.74174 -1.89 0.065 -104.3484
3.372837
5 .
Impact of Human Activities on Carbon Dioxide Emissions
Tom Lebo
Kate O’Brien

Applied Econometrics I
Irina Murtazashvili
December 8, 2010
1
ABSTRACT
This paper attempts to examine the relationships between
carbon dioxide emissions and
urban population, motor vehicles, forest area, and energy use.
Because carbon dioxide is the
principal greenhouse gas in the atmosphere, it has the largest
effect on the current global
warming crisis and will be the focus of our study. Using data on

74 countries from 2007, we
regress the log of carbon dioxide emissions on the log of urban
population, the log of motor
vehicles, the log of forest area, and the log of energy use to
determine the effect of human
activities on carbon dioxide emissions. The empirical results
suggest that urban population,
motor vehicles, forest area, and energy use are highly
significant in explaining carbon dioxide
emissions. We will also discuss existing environmental
technology and policy surrounding
carbon dioxide emissions.
2
1 Introduction

Human impact on the environment is an issue of growing
importance in the world today.
Climate change, a major environmental concern, is a direct
result of rising quantities of
greenhouse gases in the atmosphere, which have worsened the
“greenhouse effect,” the process
by which atmospheric gases absorb energy from the sun and trap
it in the atmosphere, warming
the Earth’s surface (United States Environmental Protection
Agency [U.S. EPA], n.d.). Global
warming is linked to a variety of serious environmental issues
occurring over the last century,
including more frequent heat waves and droughts, heavier
precipitation, more intense tropical
storms, rising sea level, ocean acidity, glacial melting, and an
increase in the global land and sea
temperature (Intergovernmental Panel on Climate Change
[IPCC] “Observed” 2007). These
climate change indicators have a widespread impact on animal
and plant life and are slowly
destroying the world’s ecosystems.
Are humans really to blame for carbon dioxide emissions and
global warming? After all,
the greenhouse effect occurs naturally and allows life on our

planet to exist, and carbon dioxide
is emitted by humans through the biological process of
respiration and used by plants during
photosynthesis (U.S. EPA, n.d.). However, the primary causes
of carbon dioxide emissions are
the combustion of fossil fuels (coal, oil, and natural gas) in
power plants and motor vehicles and
deforestation, both of which are induced by human progress and
have grown steadily since the
Industrial Revolution (IPCC “Causes,” 2007). This study will
concentrate on factors that affect
carbon dioxide emissions, namely urban population, motor
vehicles, forest area, and energy use
and examine these variables statistically to determine their
collective influence. We will look at
data from 2007 for 74 countries: carbon dioxide emissions in
kilotons, urban population, motor
3
vehicles per 1,000 people, forest area in square kilometers, and
energy use in kilotons of oil

equivalent, which includes the primary forms of oil, coal, and
natural gas.
2 Literature Review
Although the effect of greenhouse gases on the environment and
the Earth’s changing
climate are receiving increasing attention in both scientific and
political circles, research into
these topics not a new development. The Intergovernmental
Panel on Climate Change (IPCC)
was established in 1988 to provide the governments of the world
with a scientific explanation of
current climate conditions (United Nations, n.d.) Observations
made by the IPCC indicate that
increases in temperature, sea level, precipitation, drought, and
tropical storm activity and
decreases in snow and ice extent are consistent with global
warming (Intergovernmental Panel
on Climate Change [IPCC] “Observed,” 2007). Based on
observational evidence, the IPCC also
links global warming to changes in terrestrial and marine
biological systems. Atmospheric
carbon dioxide has increased globally by 36% over the past 250
years, and the increases are
mainly due to emissions caused by changes in energy use and

land use as a result of
industrialization: in other words, due to human processes such
as the combustion of fossils fuels
and deforestation (Forster et al. 2007).
A review of existing literature on environmental economics
contributed little to our
understanding of emissions as the explained variable. Most of
the literature investigates the
economic impact of emissions, such as the effect of emissions
on a country’s GDP. Of the few
empirical studies we could find concerning carbon dioxide
emissions, one examining emissions
along with energy consumption, income and foreign trade in
Turkey found that income is
actually the most significant variable in explaining emissions,
followed by energy consumption
and foreign trade (Halicioglu 2008, 2). Another study focusing
on carbon dioxide emissions in
4
major metropolitan areas of the United States found that higher

urban population density in
certain regions of the country is associated with lower
greenhouse gas production, and there is a
strong, negative correlation between emissions and land use
regulations (Glaeser and Kahn 2010,
29; Glaeser and Kahn 2010, 1).
Because economists view the climate change problem from a
cost-benefit standpoint,
they are concerned with increasing greenhouse gas emissions
not because emissions are
detrimental to the environment, but because they hinder
economic growth. Although “politicians
are proposing to spend hundreds of billions of dollars on
greenhouse gas emission reduction …
at present, economists cannot say with confidence whether this
investment is too much or too
little” (Tol 2009, 18). Additionally, carbon dioxide emissions
have a disproportionate effect on
the world’s poorest people: developing countries are more
vulnerable to the increased impact of
extreme weather events and as a result are affected more by
human-induced climate change (The
World Bank Group “News”, n.d.). Because “pre-existing
poverty is one of the main causes for

vulnerability to climate change,” economists cannot agree
“whether stimulating economic
growth or emission abatement is the better way to reduce the
effects of climate change” (Tol
2000, 36). Thus, the papers investigating factors that affect
carbon dioxide emissions are more
meaningful to our study than papers on environmental economic
policy.
Based on the scientific findings by the IPCC, we would expect
energy use to have a
significant positive effect on carbon dioxide emissions and
amount of forest area to have a
negative effect on carbon dioxide emissions. While the use of
energy produces carbon dioxide,
trees actually absorb carbon dioxide from the atmosphere, so
countries with less forest area
likely emit more carbon dioxide into the atmosphere. Since the
combustion of fossil fuels
contributes to atmospheric carbon dioxide, we would expect
motor vehicles to have a positive
5

effect on emissions. Furthermore, we would expect urban
population to have a positive effect on
carbon dioxide emissions because homes, businesses, and motor
vehicles are likely more
concentrated in urban areas.
3 Model and econometric methodology
We will use the following econometric model to determine the
effect urban population,
motor vehicles, forest area, and energy use on carbon dioxide
emissions:
log(carbon dioxide emissions) = β1 + β2 log(urban population)
+ β3 log(motor vehicles) +
β4 log(forest area) + β5 log(energy use).
We considered a few other potential models which included
more variables in both level-level
and log-level functional forms (results of those regressions can
be found in the tables following
this report). However, the log-log model above is the most
parsimonious choice, yet still exhibits
a high R-squared value. Also, all the variables were individually
statistically significant at the
lowest levels. The functional form provides an easier

interpretation of parameters in terms of
percentage changes as well. Data is from The World Bank’s
World Data Indicators, specifically
under the following indicators: Energy & Mining, Environment,
and Urban Development.
The reader should be aware of a few problems with the
econometric model. One minor
issue is that the data does not come from a random sample;
rather, the observations comprise the
broadest collection of countries for which we could find
information on all indicators we chose
to analyze. Though this violates one of our assumptions for
estimating ordinary least squares
(OLS) estimators of a multiple linear regression, the data was
not selected based on any common
characteristic that could skew results, so we kept the sample as
close to random as possible given
the data we were provided. In addition, the model violates the
homoskedasticity assumption
based on the results of the Breusch-Pagan test: when we regress
the squared sample residuals on
6

the regressors from the model, the F-statistic is statistically
significant, an indication of
heteroskedasticity. The heteroskedasticity-robust standard
errors and significance levels for our
chosen model are reported in the tables following this report.
4 Empirical results
As the tables at the end of this report suggest, the log-log model
is a very good fit for the
data. In fact, urban population, motor vehicles, forest area, and
energy use together explain about
92.9% of the variation in carbon dioxide emissions, based on
the R-squared value, which is quite
high considering only four explanatory variables are included in
the regression. The intercept,
-3.389 kilotons of carbon dioxide, is not meaningful to our
study because we would never expect
the urban population, number of motor vehicles per 1,000
people, forest area, and energy use for
a given country to all equal zero at the same time, and because
carbon dioxide is emitted through
natural processes in addition to the human activities we are
concerned with, the amount of

emissions would never be negative.
The coefficient on log(urban population) indicates that, holding
the other explanatory
variables in the model constant, for every one percent increase
in urban population, carbon
dioxide emissions are predicted to increase by about 0.342%, a
positive effect which is in line
with our expectations since we would associate higher
population density with a higher level of
emissions. Holding all other explanatory variables constant, the
coefficient on log(motor
vehicles) implies that for every one percent increase in the
number of motor vehicles per 1,000
people, carbon dioxide emissions are predicted to increase by
0.335%. Again, this result agrees
with our original hypotheses and makes sense based on the fact
that a significant amount of fossil
fuel energy use can be attributed to motor vehicles.
7
The coefficient on log(forest area) suggests that holding all

other factors constant, a one
percent increase in forest area is predicted to decrease carbon
dioxide emissions by 0.087%.
Interpreted in a more meaningful way (since we more often
think of deforestation as a cause of
the increase in atmospheric carbon dioxide, not the addition of
forest area as a way to decrease
atmospheric carbon dioxide), a one percent decrease in forest
area is predicted to increase carbon
dioxide emissions by 0.087%, as we hypothesized earlier. This
negative relationship is intuitive
because we know deforestation has been observed to lead to the
emission of carbon dioxide.
Holding all other factors constant, the coefficient on log(energy
use) indicates that a one percent
increase in energy use is predicted to increase carbon dioxide
emissions by 0.788%; in other
words, the elasticity of carbon dioxide emissions with respect to
energy use is 0.788%. This
result is in line with our expectations, since there is scientific
evidence that the combustion of
fossil fuels is a major source of atmospheric carbon dioxide.
Based on their p-values (using the heteroskedasticity-robust
standard errors), the log of

each of the explanatory variables is significantly different from
zero at the 0.05 level, so we
reject the null hypothesis that each variable by itself has no
effect on carbon dioxide emissions.
In addition to being individually statistically significant, the
explanatory variables are jointly
significant, based on the large F-statistic, and therefore
collectively explain carbon dioxide
emissions very well.
5 Conclusions
Clearly, the impact of human activities on carbon dioxide
emissions cannot be ignored.
Observational evidence already points to increased atmospheric
carbon dioxide as a direct result
of fossil fuel use and deforestation, and with the added effects
of urban population and motor
vehicles, the statistical evidence strongly supports the
connection between human activities and
8

carbon dioxide emission. Urban population, motor vehicles, and
energy use have increased
steadily as a result of worldwide industrialization while forest
area has decreased, and these
trends are forecasted to continue into the future, making the
problems of global warming and
climate change even worse (IPCC “Projected” 2007). As a
result, many world organizations have
developed and begun to implement plans to reduce greenhouse
gas emissions. Through carbon
capture and sequestration, carbon dioxide is “isolated from the
emissions stream, compressed,
and transported to an injection site where it is stored
underground permanently” (U.S.
Department of Energy, n.d.). In addition, governments
worldwide fund research and
development in renewable energy, such as solar and wind
power, to reduce dependence on fossil
fuels as the main source of energy.
International policy has also played a role in helping to solve
the problem of global
warming. The Kyoto Protocol sets standards committing
industrialized countries to meet
emissions targets through monitoring and mechanisms such as

reforestation and clean
development projects (United Nations Framework Convention
on Climate Change [UNFCCC]
“Kyoto,” n.d.). Emissions reductions and removals allow carbon
dioxide to be traded like any
other commodity in a “carbon market” similar to the stock
market (UNFCCC “Emissions,” n.d.).
Debate continues over whether the appropriate environmental
technology and policy tools
regarding carbon dioxide emissions are in place, and long-term
benefits of emissions reduction
remain to be seen. Although global policy initiatives are costly,
the responsibility taken by world
organizations to deal with the critical issues of global warming
and climate change are the first
steps in creating a more sustainable world.
9
Tables

Table 1: Regression Results of Log(Carbon Dioxide Emissions)
on
Log(Urban Population), Log(Motor Vehicles), Log(Forest Area)
and Log(Energy Use)
Dependent Variable: lCO2
Independent Variables Model 1: Log-log model (Best choice)
lurbanpop 0.342** (0.107) [0.150]
lmotor 0.335*** (0.064) [0.097]
lforest -0.087*** (0.031) [0.026]
leneruse 0.788*** (0.097) [0.132]
intercept -3.389** (0.975) [1.385]
Observations 74
R-squared 0.9288
Adj. R-squared 0.9247
Table 2: Regression Results of Carbon Dioxide Emissions on
Urban Population (in millions),
Total Population (in millions), Gross National Income (in
billions), Motor Vehicles, Forest Area
and Energy Use
Dependent Variable: CO2
Independent Variables Model 2: Level-level model
urbanpop_mill 2478.459* (749.654) [1420.035]
totalpop_mill -767.965* (216.640) [420.528]
GNI_bill -159.124*** (13.443) ]27.176]
motor 73.958** (36.564) [34.544]
forest -0.096*** (0.018) [0.031]
eneruse 3.451*** (0.105) [0.199]
intercept -25517.79** (12428.57) [9611.853]
Observations 76
R-squared 0.9961

Urban Population,
Total Population, Gross National Income, Motor Vehicles,
Forest Area and Energy Use
Dependent Variable: lCO2
Independent Variables Model 3: Log-level model
urbanpop_mill 0.069*** (0.015) [0.014]
totalpop_mill -0.014*** (0.004) [0.005]
GNI_bill 0.0007*** (0.0003) [0.0003]
motor 0.002** (0.0007) [0.0009]
forest_k -0.0009** (0.0004) [0.0004]
eneruse_k -0.007*** (0.002) [0.002]
intercept 9.476*** (0.247) [0.271]
Observations 76
R-squared 0.5873
10
Urban Population, Motor Vehicles, Forest Area and Energy Use
Dependent Variable: CO2
Independent Variables Model 3: Level-level model
urbanpop 0.0022* (0.0003) [0.0013]
motor -49.925 (59.480) [42.348]

forest -0.106*** (0.024) [0.037]
eneruse 2.518*** (0.076) [0.186]
intercept -12161.03 (20861.38) [11394.68]
Observations 78
R-squared 0.9876
Notes:
Data Sources: The World Bank World Data Indicators
Standard errors in parentheses
Heteroskedasticity-robust standard errors in square brackets
Statistical Significance: *** = 0.01 level, ** = 0.05 level, * =
0.10 level
(significance is based on heteroskedasticity-robust standard
errors)
Description of variables:
CO2 – carbon dioxide emissions in kilotons
lCO2 – log(CO2)
totalpop – total population
totalpop_mill – totalpop/1,000,000
urbanpop – urban population
lurbanpop – log(urbanpop)
urbanpop_mill – urbanpop/1,000,000
GNI – gross national income in current US dollars
GNI_bill – GNI/1,000,000,000
motor – motor vehicles per 1,000 people
lmotor – log(motor)
motor_k – motor/1,000
forest – forest area in square kilometers
lforest – log(forest)
forest_k – forest/1,000
eneruse – energy use in kilotons of CO2 equivalent

leneruse – log(eneruse)
11
Bibliography
Forster, Piers, Venkatachalam Ramaswamy, Paulo Artaxo, Terje
Berntsen, Richard Betts, David
W. Fahey, James Haywood, Judith Lean, David C. Lowe,
Gunner Myhre, and John
Nganga. “Changes in Atmospheric Constituents and in
Radioactive Forcing.”
Intergovernmental Panel on Climate Change.
http://www.ipcc.ch/ pdf/assessment-
report/ar4/wg1/ar4-wg1-chapter2.pdf (accessed November 26,
2010).
Glaeser, Edward L., and Matthew E. Kahn. "The Greenness of
Cities: Carbon Dioxide Emissions
and Urban Development." Journal of Urban Economics 67, no. 3
(May 2010): 404-418.
http://www.nber.org/papers/w14238.pdf (accessed December 2,
2010).
Halicioglu, Ferda. "An Econometric Study of CO2 Emissions,
Energy Consumption, Income and

Foreign Trade in Turkey." Unpublished essay. Munich Personal
RePEc Archive. Munich
University Library, 2008. Munich Personal RePEc Archive.
http://mpra.ub.uni-
muenchen.de/ 11457/1/MPRA_paper_11457.pdf (accessed
December 2, 2010).
Intergovernmental Panel on Climate Change. “Causes of
Change.” IPCC Fourth Assessment
Report: Climate Change 2007.
http://www.ipcc.ch/ publications_and_data/ ar4/syr/en/
spms2.html (accessed November
26, 2010).
———. “Observed Changes in Climate and Their Effects.”
IPCC Fourth Assessment Report:
Climate Change 2007. http://www.ipcc.ch/
publications_and_data/ ar4/syr/en/spms1.html
(accessed November 26, 2010).
———. “Projected Climate Change and Its Impacts.” IPCC
Fourth Assessment Report: Climate
Change 2007. http://www.ipcc.ch/ publications_and_data/ ar4/
syr/en/spms3.html
Tol, Richard S.J. "The Economic Effects of Climate Change."
Journal of Economic Perspectives
23, no. 2 (Spring 2009): 29-51. doi:10.1257/jep.23.2.29.

http://pubs.aeaweb.org/doi/
pdfplus/10.1257/ jep.23.2.29 (accessed December 2, 2010).
United Nations. “Climate Change.” United Nations Global
Issues.
http://www.un.org/en/globalissues/climatechange/ index.shtml
(accessed November 27,
2010).
United Nations Framework Convention on Climate
Change.“Emissions Trading.” United
Nations Framework Convention on Climate Change.
http://unfccc.int/ kyoto_protocol/mechanisms/
emissions_trading/items/ 2731.php
12
———. “Kyoto Protocol.” United Nations Framework
Convention on Climate Change.
http://unfccc.int/ kyoto_protocol/items/ 2830.php (accessed
November 26, 2010).
United States Environmental Protection Agency. “Greenhouse

Gases.” Climate Change
Indicators in the United States. http://www.epa.gov/
climatechange/ indicators/pdfs/CI-
greenhouse-gases.pdf (accessed November 26, 2010).
U.S. Department of Energy. “Carbon Capture and R&D
Overview.” Fossil Energy – Carbon
Sequestration. http://www.fossil.energy.gov/programs/
sequestration/ overview.html
The World Bank Group. “Indicators – Data.” The World Bank.
http://data.worldbank.org/ indicator (accessed November 26,
2010).
———. “News & Broadcast – Climate Change.” The World
Bank.
http://go.worldbank.org/ BPY7QIRNA0 (accessed November 26,
2010).
APPLIED
ECONOMETRICS

I
–
ECONOMICS
1150
From
Poorhouse
to
Jailhouse:
A
Study
of
Violence
in
Society
Anne
Davis,
Andrea
Kostura
5/10/2010

This
paper
analyzes
the
effect
of
different
socioeconomic
variables
on
violent
crime.
Using
data
on
2,106
cities
in
the
United
States,
we
regress
violent
crime
rates
per
100,000
people
on
different
measures
of

poverty,
education,
and
diversity.
The
results
indicate
that
an
increase
in
poverty
and
diversity
is
positively
correlated
with
rising
violent
crime
rates.
Education
is
also
significant
in
explaining
the
level
of
violent

crime;
we
find
that
as
the
number
of
people
with
a
high
school
degree
or
less
increases,
violent
crime
is
expected
to
increase.
Therefore,
we
believe
that
policy
that
encourages
further
education
should

be
effective
in
reducing
violent
crime.
In
this
paper,
we
state
that
something
is
in
the
public
interest
if
the
benefits
of
an
activity
are
greater
than
the
cost.
Since
there
are

considerable
costs
associated
with
violent
crime,
we
believe
it
would
be
in
the
public
interest
to
support
education
because
it
would
benefit
society
as
a
whole.
1

Introduction
The United States of America has one of the highest violent
crime rates of the
industrialized world; according to the 2008 Crime Clock of the
Federal Bureau of
Investigation one violent crime is committed every 22.8
seconds. The murder rate in
America is more than three times higher than in most of Europe.
Not all of this can be
explained by guns, as the murder rate in New York City has
consistently been at least 10
times higher than that of London for the past 200 years and
private handgun ownership
was not fully prohibited in the United Kingdom until the
passage of the Firearms Act of
1997 (Wilson and Petersilia 200?, 540-541). Crime is clearly a
significant problem in the
United States that drains the resources of the government at all
levels and plagues many
Americans. As such, it is an issue that demands national
attention on a variety of levels.
However, in order to present solutions to this pressing problem,
a more thorough
understanding of the causes and impacts of crime is needed. By

researching the effects
that different variables have on violent crime rates, policy
makers will be better equipped to
formulate effective programs to deal with and prevent violent
crime. Previous studies,
which will be discussed in the literature review, have shown
that there is a significant
relationship between violent crime and socioeconomic factors
such as education level,
poverty, and diversity. Although we include other variables, in
this study we are primarily
interested in the effect of poverty and education on violent
crime. Messner and Tardiff find
that the percentage of poor people in a city is highly positively
correlated with crime rates
(1986, 297; 306-308). Lochner finds that more educated males
commit fewer crimes, on
average (2004, 1-2). These, like other previous studies, look at
the issues of education
and poverty separately. We believe that education and poverty
are jointly significant in
explaining violent crime, and therefore, include both in our
model. Our study seeks to

2
address a gap in the literature that treats education and poverty
as two separate issues
when explaining crime. The impact of education and increased
opportunities are of
fundamental importance in order to break the cycle of poverty,
and help reduce crime.
Encarta broadly defines public interest as something that is to
“the general benefit
of the public.” Of course there will be monetary costs
associated with any policy, but
following traditional economic theory, if the benefits are greater
than the costs, then we
believe the policy would be in the public interest. The costs of
violent crime are
substantial. Miller, Cohen, and Rossman estimate the costs of
violent crime in 1987 as
follows: $10 billion in potential health-related costs, $23 billion
in lost productivity, and
almost $145 billion in reduced quality of life. These are
monumental costs so it should be
in the public’s interest to decrease violent crime (1996, 186).
Other costs that cannot be

quantified such as feelings of security, and a sense of
community are of utmost importance
as well.
Review of Literature and Economic Theory
Most economic theory looks at crime through a lens of rational
cost-benefit
decision-making. This idea posits that the propensity to commit
crime is a matter of utility,
how much satisfaction an individual derives from criminal
activity as compared with other
things, like leisure time, and time spent in productive activities
(Witte and Witt 2000, 4).
This has spurred a large body of literature related to the
interaction between incentives
and crime. The goal of policy should be to increase incentives,
or following economic
theory, increase the costs of crime and increase the benefits of
work and leisure activities.
The socioeconomics theory of crime plays a critical role in
estimating the likelihood of
committing crime by studying what factors contribute to a
susceptibility to commit crime.
Many theorists, such as Lochner, Short, Wilson, and Witt and
Witte, agree that levels of

3
education and economic status are two of the most important
determinants of crime.
When the economy is doing poorly, crime rates increase. There
is a large body of
literature that studies the relationship between criminal
behavior and poverty. It has been
shown that crime rates are higher for those in the lowest
socioeconomic groups of society;
this is known as a sub-culture of poverty (Forst 1993, 90; 103).
A study by Messner and
Tardiff found that the percent of poor people in a given society
is highly correlated with
crime rates (1986, 297; 306-308). Likewise, many other studies
attest to the significance
of poverty in determining crime rates (Williams, 1984; Wilson,
1987; Kirk, 2008). At the
same time, poverty is correlated with other socioeconomic
variables, such as race and
family structure (Short 1997, 51). Poverty can be measured in
many ways; our study
includes several different measures of poverty including median

home value, percentage
of households on public assistance, and percentage of families
living below the poverty
line. This leads to hypothesis one: as median home value
increases, violent crime is
predicted to decrease. Hypothesis two: as the percentage of
households on public
assistance and the percentage of the population living below the
poverty line increase,
violent crime is expected to increase. A related measure of the
performance of the
economy would be the number of homes that are vacant. In a
study done by the Federal
Reserve Bank of Cleveland, it is shown that home abandonment
and foreclosure
contribute to a rise in crime rates (The Federal Reserve Bank of
Cleveland 2008, 2-4).
Vacant properties contribute a potential location for crime, in
addition to being linked to
poverty or job loss. Hypothesis three: as the ratio of vacant
housing units to occupied
housing units increases, it is expected that violent crime will
increase. This variable was
used to assess the potential impact that vacant houses have in a

community by looking at
4
the density of vacancy as compared with owner occupied homes.
The ratio was converted
to a percent for easier measurement and interpretation.
The final area of importance for this study that theorists link
with crime is both social
and economic: education. More poorly educated people are
often lower skilled workers,
and tend to be poorer; in data from 1993, it can be seen that
more than 67 percent of men
serving time in jail or prison did not have a high school
education (Lochner 2004, 2). This
relates back to economic cost-benefit theory, where time spent
in other activities yields
lower utility for a less educated person than for a more educated
person. Education
increases human capital, which increases the opportunity cost of
crime and the potential
market wages (Lochner and Moretti 2004, 155-189; Witte and
Witt 2001, 4). Lochner finds
that older, more educated males commit fewer crimes, on

average (2004, 1-2). So,
hypothesis four: an increase in people with a high school degree
or less, results in an
increase in violent crime rates.
In an effort to avoid omitted variable bias, several other
variables were included that
were not of primary interest. The following variables, such as
population size, and ethnic
diversity have been shown to be important in many studies, but
are hard to change
effectively with policy. People in rural areas and small towns
are, on average, poorer than
those residing in the cities (Forst 1993, 32; Short 1997, 104-
105). However, as population
increases, it is expected that violent crime will increase also
even though poverty is
positively correlated with crime. In addition, stability of
society is another common variable
included in crime research. Shihadeh and Steffensmeier find
that the percentage of
families headed by single mothers is positively correlated with
an increase in violent crime
(1994, 732-734; 737-740). This same result is found by Land,
McCall, and Cohen (1990).

5
Undoubtedly, this variable is tied to poverty, which explains
some of its significance within
the model.
Another subgroup that has been identified in work by Forst, and
Short, is racial
groups, specifically blacks. Part of this is most certainly
related to the reality that minority
groups tend to be economically marginalized and members of
the poorest strata of society
(Short 1997, 51; Land, McCall and Cohen 1990, 922-963).
However, it can be seen that
other minority groups, such as Native Americans, Mexicans,
Vietnamese and Puerto
Ricans are all, on average, poorer than blacks, but have
substantially lower violent crime
rates (Forst 1993, 99; 105-110). Many theorize that this is
related to a “subculture of
violence,” that is seen more in black culture and is translated
into racial patterns of violent
crime (Forst 1993, 104-110). As the percentage of blacks in a
society increases, it is

predicted that violent crime will also increase. Studies show
that ethnic diversity within
society is also predicted to increase crime rates because of the
different ethnic
subcultures, lack of assimilation, and ethnic friction (Short
1997, 50). So, as the level of
diversity in a society increases, violent crime is expected to
increase.
Description of Econometric Model
This study uses data on socioeconomic indicators from 2,106
cities to estimate their
effect on violent crime rates in 2000. Violent crime is defined
as murder, rape, assault and
robbery. The data came from the Offenses Known to the Police
Dataset from the Uniform
Crime Reporting Database of the Federal Bureau of
Investigation and the 2000 Decennial
United States Census. It is from the year 2000, and includes
2,106 cities and towns with
populations over 10,000 in the United States. We chose the
year 2000 due to the
availability of data and the fact that it is a relatively recent
year. All 50 states and the

District of Columbia are represented. However, there was only
one city included from
6
Hawaii whereas, there were many cities included from some
states, such as California and
Texas, meaning that some states are overrepresented in
comparison with others.
We also created a diversity index of ethnicity. The Dindex is
measure of the
heterogeneity of a population. The value lies between 0 and 1;
a value close to 0 indicates
a highly homogenous population and a value close to 1 indicates
a highly heterogeneous
population. It is based on the proportion of people who are
white, black, native, Asian,
Pacific Islander, other, and multi-ethnic. The index used was
the index of diversity as
created by Gibbs and Martin (1962, 670-672) and specifically
used for ethnic diversity of a
population by Blau (2000, 401).1

Our model is:
ViolentCrime = β0 + β1lCensus + β2%HS + β 3%Black +
β4DIndex + β5%SingleMom +
β6lMHV + β7%HHPubAssist + β8%Poor + β9%Vacant + u,
where:
1(1−�=1��2) = The Index of Diversity, where p is the
proportion of individuals or objects in a category, and n is the
number of categories. Latinos are not identified as a separate
category because the census does not classify them
separately.
7
Table 1: Variable Definitions and Descriptive Statistics
Symbol Variable Definition Expecte
d Sign
Mean Std Dev. Min Max
ViolentCrime The proportion of violent
crimes per 100,000
people

------ 443.7093 419.8388 0 2883.842
lCensus The log of the population
of all 2106 cities in 2000
Positive 60798.01 229008.7 709 8008278
%HS Percent of the population
with a high school
degree or less
Positive .4574802 .1529567 .058005 .9073921
%Black Percent of the population
that is black
Positive .1022931 .1530621 0 .9585475
Dindex Measure of ethnic
diversity within a society
Positive .3152891 .1873605 .0172984 .7830377
%SingleMom Percent of families with
children that are headed
by single mothers
Positive .1143783 .050946 0 .4272276
lMHV The log of the median
home value, where home
value is the amount for
which the owner believes
the house would sell on
the open market.

Negative 11.66655 .5463506 10.1105 13.81551
%HHPubAssist Percent of all households
on public assistance
Positive .034875 .0258552 0 .1804097
%Poor Percent of the population
living below the poverty
line
Positive .124542 .0803762 .0037031 .5428688
Vacant Ratio of vacant housing
units to occupied housing
units
Positive .0762289 .0804158 0 1.719453
Sources: Data adapted from the 2000 Uniform Crime Report, the
Federal Bureau of Investigation; the 2000 Census, the
United States Census Bureau.
There are a few problems to be aware of when considering this
model. First, testing
in Stata provides evidence for multicollinearity between most of
the variables, such as
poor, public assistance, single mother, black, and high school.
There is nothing to be
done about multicollinearity, but it is important for the reader
to be aware that it is present.

It also means that the p-values are higher than they probably
should be. This correlation
among variables is to be expected since many of most of these
variables are part of the
cycle of poverty. This makes it hard to control for things such
as poverty across race, or
8
education level, because poorer people tend to be more poorly
educated and of a minority
ethnic group. Omitted variable bias presents another problem.
Undoubtedly, there are
other variables that affect the level of violent crime in society
that were not included in this
model. Some of these, such as psychological states,
personality, or crimes of passion are
difficult to quantify and thus are part of the error term.
A final, important problem is one of heteroskedasticity. We
tested for
heteroskedasticity using the Breush-Pagan test and the White
test and found evidence to
reject the null hypothesis of homoskedasticity. For the Breush-
Pagan test, the LM statistic

is 3276.208 with a p-value of 0. Using the White test, the
general test statistic is 111.1506
and the p-value is 7.7e-06. Both of these results provide
evidence of heteroskedasticity.
This was corrected for by running a regression to calculate the
robust standard errors; this
regression will be referred to as the robust regression. This
regression has the accurate
standard errors and t statistics.
Results
Our model contains only nine explanatory variables. This
is because in previous
regressions several of the variables considered were very
statistically insignificant, such as
median household income, percent foreign born, and homes
rented. Thus, these variables
were dropped from the regression. We have also experimented
with different functional
forms and finally settled on using the log of population and the
log of median home value.
Using different specifications in the model increased the R-
squared and the significance of
the variables. The R-squared is 0.5663 and the adjusted R-
squared is 0.5644. This

means that this model explains about 56 percent of the variance
in violent crimes.
9
Table 2: Regression Results of Violent Crime on Education and
Poverty
Dependent Variable: Proportion of Violent Crimes
Model
________________________________________
Intercept
lPopulation
High School
Black
Diversity Index

Single Moms
lMedian Home Value
Public Assistance
Poor
Vacancy
R-squared
Number of Observations
________________________________________
OLS
________________________________________
-1650.811***
(243.5532)
[247.8654]
70.26156***
(6.988612)

[7.739343]
417.8408***
(66.01002)
[60.10737]
964.5477***
(63.93466)
[105.7798]
237.9881***
(47.35428)
[59.12994]
1180.249***
(241.7778)
[291.8789]
62.92453***
(17.9631)
[17.64425]
1424.586 ***
(416.1382)
[503.4474]
422.0795***
(132.4789)
[156.4959]
471.6322***
(79.42515)
[134.3619]
0.5663

2106
________________________________________
Notes:
Sources: the 2000 Uniform Crime Report, the Federal Bureau of
Investigation; the 2000 Census, the United States
Census Bureau
Standard error in parentheses. Robust standard error in square
brackets.
Statistical Significance: *** = 0.01 level based on the robust
regression.
The results proved to be very interesting. This analysis will
focus on the variables
high school, public assistance, poor, vacancy and log of median
home value. The
coefficient on high school indicates that on average, a one
percentage point increase in
the number of people that have a high school degree or less is
expected to increase the
number of violent crimes by 417.84 per 100,000 people. This is
very significant in the
robust model with a p-value of 0. This is expected because
other studies, mentioned in
the literature review, have found that increases in years of
education are expected to lead

to decreases in violent crime. This means that policymakers
should focus on programs to
keep students in high school and encourage opportunities to
seek further education, such
as college, or vocational training. This will increase the income
generating ability of
individuals and help break the cycle of poverty. Education is a
means to move beyond
poverty, but unfortunately, poorer people often receive an
inferior education. Because
poverty and education are self-reinforcing, it is important to
improve the quality of
education as well.
The coefficient on public assistance indicates that, on average,
a one percentage
point increase in the number of households on public assistance
is expected to increase
the number of violent crimes by 1424.59 per 100,000 people.
This variable is very
significant in the robust regression with a p-value of 0.005.
Similarly, the coefficient on
poor indicates that, on average, a one percentage point increase
in the percent of people
living below the poverty line is expected to increase the number

of violent crimes by
422.08 per 100,000 people. This variable is significant in the
robust model with a p-value
of 0.007. Due to multicollinearity, this p-value may be
misleadingly high. Also, the
coefficient on vacancy indicates that, on average, a one
percentage point increase in the
ratio of vacant houses to occupied houses is expected to
increase the number of violent
crimes by 471.63. This result is very significant in the robust
model with a p-value of 0.
The maximum ratio observed was 1.71, which means that almost
twice as many houses
were vacant as were occupied. This without doubt has serious
repercussions for the
community at large, decreasing home values, and contributing
many potential locations for
crime among others effects. These results for these variables
are expected because the
positive correlation between poverty and violent crime has been
found in other studies, as
mentioned in the literature review. These three variables are

indicators of the overall
performance of the greater macro-level economy. Thus, it is
important for policy to focus
on increased jobs and community programs, such as computer
instructional sessions, in
order to reduce the number of poor people and the number of
people who need
government assistance. It cannot be forgotten that education is
still critical in order to help
raise people out of poverty and create long-term effects.
Surprisingly, the coefficient on log of median home value
means that on average, a
one percent increase in the median value of a home is predicted
to increase the number of
violent crimes by .6292 per 100,000 people. This result is very
statistically significant with
a p-value of 0, but since the number is so small it is not very
economically significant. In
addition, it is not the sign that was expected. The hypothesis
was that as median home
value increased, violent crime should decrease, but this is not
what is observed. This
could be because homes in cities are typically more expensive
and thus, have a higher

value than in smaller towns and rural areas and violent crime is
higher in cities. The
maximum median home value observed was $1,000,001 which is
in Hillsborough,
California in the San Francisco Bay area. The minimum median
home value is $24,600
which is in Pecos City, Texas, a town with a population of
around 10,000, one of the
smallest populations in our sample and Pecos City is the largest
city in its county. So, it
would appear that areas with higher home values, such as larger
cities, would be
correlated with a rise in crime because of other factors that are
more prevalent in cities,
such as ethnic diversity, and population density, not necessarily
because wealthier people
commit more crime. A way to correct for this in the future
would be to break down the
cities by neighborhoods.
Conclusion
Numerous previous researchers, such as Messner and Tardiff,

Forst, Short,
Lochner, and Wilson, have examined the impact of an
assortment of socioeconomic
variables on violent crime. We chose to focus on poverty and
education because we
believe those are the variables most likely to be manipulated by
policy. Other variables,
such as population size and diversity level are generally beyond
the reach of policy
makers. In our model, poverty was measured by poor, public
assistance, log of median
home value, and vacancy, and education was measured by high
school. We hypothesized
that median home value would be negatively correlated with
violent crime, while the
percentage of households on public assistance, the percentage of
the population living
below the poverty line, and the number of homes vacant would
be positively correlated.
As anticipated, these variables were all positively correlated
with violent crime as indicated
by the positive values of the coefficients in the basic model.
They were all statistically and
economically significant. Unexpectedly, the coefficient for log

of median home value was
also positively correlated with violent crime. As discussed
previously, we believe that this
may be because home values are higher in large cities, where
violent crime is more
prevalent. It is also important to note that although this
coefficient was statistically
significant, it was not economically significant.
Since education and poverty are both statistically and
economically significant in
explaining violent crime, policy that favorably influences these
variables ought to decrease
violent crime. Thus, in order to properly address violent crime,
education and poverty must
be understood in terms of one another. Ideally, policy should
encourage people to finish
high school and then continue on to further education.
Logically, the focus should be on
people living in poverty because they are the ones who would
benefit the most from
additional education. Higher education yields a greater income
generating ability and

more employment opportunities which in turn decreases the
incentives to commit a violent
crime. This would reduce the number of violent crimes and the
costs connected to them.
Therefore, this solution would be in the public’s interest
because society as a whole would
benefit from the increased productivity of the workforce and
from the decrease in costs
directly associated with violent crime.
Bibliography
Blau, Judith. “Group Enmity and Accord: The Commercial
Press in Three American
Cities.” Social Science History 24.2 (2000): 395-413.

Federal Bureau of Investigation Uniform Crime Reports.
http://www.fbi.gov/ucr/00cius.htm
Forst, Brian, ed. The Socio-Economics of Crime and Justice.
Armonk, New York: M.E.
Sharpe, 1993.
Gibbs, Jack P. and William T. Martin. “Urbanization,
Technology, and the Division of
Labor.” American Sociological Review 27 (October 1962): 667-
677.
Kirk, David. “The Neighborhood Context of Racial and Ethnic
Disparities in Arrests.”
Demography 45 (February 2008): 57-77.
Land, Kenneth, Patricia McCall, and Lawrence Cohen.
“Structural Covariates of Homicide
Rates: Are there any Invariances Across Time and Space?”
American Journal of
Sociology 95 (1990): 922-963.
Lochner, Lance. “Education, Work, and Crime: A Human
Capital Approach.” International
Economic Review 45 (August 2004).
Lochner, Lance and Enrico Moretti. “The Effect of Education
on Crime: Evidence from
Prison Inmates, Arrests, and Self-Reports.” The American
Economic Review 94
(March 2004): 159-189.
Messner, Steven F. and Kenneth Tardiff. “Economic Inequality
and Levels of Homicide: An
Analysis of Urban Neighborhoods.” Criminology 24 (1986):

297-316.
Miller, Ted R., Mark A. Cohen, and Shelli B. Rossman.
“Victim Costs of Violent Crime and
Resulting Injuries.” Health Affairs (Winter 1993): 186.
MSN Encarta, “Public Interest,”
http://encarta.msn.com/encnet/features/dictionary/dictionaryho
me.aspx (accessed
May, 6 2010).
Shihadeh, Edward and Darrell Steffensmeier. “Economic
Inequality, Family Disruption, and
Urban Black Violence: Cities as Units of Stratification and
Social Control.” Social
Forces 73 (December 1994): 729-751
Short, James F. Poverty, Ethnicity, and Violent Crime. Boulder,
Colorado: Westview Press,
1997.
The Federal Reserve Bank of Cleveland. “Vacancy &
Abandonment: Tackling the
Problem.” Community Reinvestment Report (Winter 2008).
http://www.clevelandfed.org/Our_Region/Community_Develop
ment/Publications/CR
Report/CRReport_Winter2008.pdf?WT.oss=crime%20and%20va
cant%20homes&
WT.oss_r=54 (accessed March 3, 2010).
United States Census Bureau 2000 Census.
http://factfinder.census.gov/servlet/DatasetMainPageServlet?_pr
ogram=DEC&_sub

menuId=datasets_1&_lang=en
Williams, Kirk. “Economic Sources of Homicide: Reestimating
the Effects of Poverty and
Inequality.” American Sociological Review 49 (April 1984):
283-289.
Wilson, William J. The Truly Disadvantaged: The Inner City,
the Underclass, and Public
Policy. Chicago: University of Chicago Press, 1987.
Witte, Ann Dryden and Robert Witt. “Crime Causation:
Economic Theories.” Encyclopedia
of Crime and Justice (2001)
http://www.econ.surrey.ac.uk/Research/WorkingPapers/econ300
.pdf (accessed
March 23, 2010).

What Influence the Unemployment Rate in the U.S?
Presented by: Zhijing Zhang, Ruohan Zhou, Yangshu Zhang,
Wei Zhou
Backgrounds
Labor Market
Backgrounds
Bureau of Labor Statistics (BLS)
Unemployment Rate
Unemployment Rate = U/LF
Labor force(LF) = Employed(E) + Unemployed(U)
Employment Rate = E/P

Unemployment Rate in Oct. 2015
Pennsylvania: 5.1% ( 27 out of 51)
Lowest: North Dakota:2.8%
Highest: West virginia: 6.9%
Our Model
Female (percentage)
Education level (bachelor)
Age (from 25-34)
Native
population growth
UnemployementR = β0 + β1Female% + β2Bachelor%+ β
3%PopulationGro+ β4 Age2534+ β5Native%+ u
Regression analysis
Female showed positive impact on the unemployment rate.
It explains that when female increases the unemployment rate is
increased too.
female always focus family works and part time jobs.

Regression analysis
Bachelor showed negative relation with the unemployment
which means that when rate of bachelor increases,
unemployment decreases as well.
Highly education is like a ticket access to the good company
with higher salary.
Regression analysis
Population also showed positive impact on the unemployment
rate. It explains that when population grows unemployment rate
is increased too.
Being a limited market, a saturation state is developed which
raises the unemployment rate.
Explanatory-Native
The coefficient on Native increases by 1 percent, the
Unemployment rate is predicted to increase by 0.103%.
Negative effect, Native-born have more advantages over
foreign-born in finding jobs.
Insignificant variable in the model.

Explanatory-Age(25-34)
A one percent increase in Age(25-34) is predicted to decrease
Unemployment rate by 0.048%
Positive relationship
Also is insignificant variable
UnemploymentR~e=unemployment rates for states annual
average
Female=the percentage of female population
Bachelor=percentage of bachelor degree education
PopulationGro~e=population growth rates
Native=percentage of native population
Age2534=percentage of population age 25 to 34
Regression analysis results:
UnemploymentR~e=-50.488+1.206Female-
0.212Bachelor+0.154PopulationGro~e-
0.103Native+0.048Age2534
N=51 R2=0.347, Adj R2=0.275
Based on the their p-values, each of the explanatory variables is
significantly different from zero at the 0.10 level, so we reject

the null hypothesis that each variable by itself has no effect on
the rates of unemployment except the variables percentage of
native population and percentage of population age 25 to 34.
In addition to being individually statistically significant, the
explanatory variables are jointly significant at 0.01 level, 0.05
level, 0.10 level based on the F statistic.
Therefore, the result of regression analysis showed the
explanatory variables have certain influences on unemployment
rates.
Works Cited
"Unemployment Rates for States Annual Average Rankings."
Unemployment Rates for States Annual Average Rankings. N.p.,
n.d. Web. 01 Dec. 2015.
"U.S. Population Growth Rate State Rank." U.S. Population
Growth Rate State Rank. N.p., n.d. Web. 01 Dec. 2015.
"U.S. Female Population Percentage State Rank." Based on US
Census 2010 Data. N.p., n.d. Web. 01 Dec. 2015.
"U.S. Bachelor Degree Education Percentage State Rank."
Based on ACS 2006-2010 Data*. N.p., n.d. Web. 01 Dec. 2015.
"U.S. Native Population Percentage State Rank." Based on US
Census 2010 Data. N.p., n.d. Web. 01 Dec. 2015.
"U.S. Percentage of Population Age 25 to 34 State Rank." Based
on US Census 2010 Data. N.p., n.d. Web. 01 Dec. 2015.

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