adam sutton chia-jung liu grant volk yin chu can shen robert matarazzo andrew ratcliffe ruben bos

Adam Sutton

Chia-Jung Liu

Grant Volk

Yin Chu

Can Shen

Robert Matarazzo

Andrew Ratcliffe

Ruben Bos

Highest since Great Depression Effects well being of population Effects well being of economy Implicit effects on policy development and

reformation

To understand the correlation, if any, between unemployment rates and population characteristics

State Expenditures Percentage of population that is White, African-

American, and Hispanic Percentage of population with no health insurance Percentage of temporary jobs Percentage population over 25 with a bachelor’s

degree Income per capita Homeless per 1000 Number of citizens participating in the food stamps

program Crimes per 100,000 citizens Percentage of blue collar jobs Data taken across 51 observations including 50

states and the District of Columbia

Variable (1) Variable (2) R-squared F-Statistics PROB

Percent bachelors Income per capita 0.694573 0.000000

Percent blue collar Percent bachelors 0.747881 0.000000

Blue collar Income per capita 0.489143 0.000000

Food stamps Crime per 100,000 0.047541 0.124279

Food stamps State expenditures 0.771099 0.000000

Percent without health care Percent white 0.207299 0.000788

Percent without health care Percent black 0.043089 0.143844

Percent without health care Percent Hispanic 0.333601 0.000009

Crime per 100,000 per state Percent white 0.387852 0.000001

Crime per 100,000 per state Percent black 0.448505 0.000000

Crime per 100,000 per state Percent Hispanic 0.078482 0.046468

Percent without health care Crime rate per 100,000 people

0.111541 0.016613

Correlations between two signs of wealth Bachelors degree and income per capita

Correlations between two signs of poverty Lack of health care and food stamp

participants

Lack of significant negative correlations between a sign of wealth and a sign of poverty

Dependent Variable

Explanatory Variable

R-squared F-Statistics PROB

Unemployment Rate Percent of jobs that are blue-collar

0.040628 0.156083

Unemployment Rate Crime per 100,000 per state

0.200999 0.000969

Unemployment Rate Participation in food stamps program

0.185520 0.001604

Unemployment Rate Homeless per 1000 per state

0.146512 0.005568

Unemployment Rate Income per capita 0.007179 0.554416

Unemployment Rate Percent of adults with bachelors

0.004037 0.657805

Unemployment Rate Percent of African Americans per state

0.144186 0.005991

Unemployment Rate Percent of Hispanics per state

0.034391 0.192619

Unemployment Rate Percent of whites per state

0.094708 0.028029

Unemployment Rate Percent of pop with no health care

0.025767 0.260486

Unemployment Rate Percent of jobs that are temp

0.005882 0.592693

Unemployment Rate State expenditures 0.147193 0.005953

Percentage of population that is White and Hispanic

Percentage of population with no health insurance

Percentage of temporary jobs Percentage population over 25 with a

bachelor’s degree Income per capita Percentage of blue collar jobs

Dependent Variable

Explanatory Variable

R-squared F-Statistics PROB

Unemployment Rate Percent of jobs that are blue-collar

0.040628 0.156083

Unemployment Rate Crime per 100,000 per state

0.200999 0.000969

Unemployment Rate Participation in food stamps program

0.185520 0.001604

Unemployment Rate Homeless per 1000 per state

0.146512 0.005568

Unemployment Rate Income per capita 0.007179 0.554416

Unemployment Rate Percent of adults with bachelors

0.004037 0.657805

Unemployment Rate Percent of African Americans per state

0.144186 0.005991

Unemployment Rate Percent of Hispanics per state

0.034391 0.192619

Unemployment Rate Percent of whites per state

0.094708 0.028029

Unemployment Rate Percent of pop with no health care

0.025767 0.260486

Unemployment Rate Percent of jobs that are temp

0.005882 0.592693

Unemployment Rate State expenditures 0.147193 0.005953

State Expenditures Percentage of population that is African-

American Homeless per 1000 Number of citizens participating in the

food stamps program Crimes per 100,000 citizens

State expenditures and food stamp participation In order to study the variables related to

state spending against unemploymentDetermined that food stamp participation is

the most significant variable while state expenditures is not

Dependent Variable: UNEMP_RATE Method: Least Squares Date: 12/01/09 Time: 14:25 Sample: 1901 1951 Included observations: 50 Excluded observations: 1

Variable Coefficient Std. Error t-Statistic Prob.

STATE_EXP -7.77E-06 1.96E-05 -0.396859 0.6933 FOOD_STAMPS 1.72E-06 8.29E-07 2.073140 0.0437

C 7.832522 0.407456 19.22297 0.0000

R-squared 0.218644 Mean dependent var 8.858000 Adjusted R-squared 0.185395 S.D. dependent var 2.240635 S.E. of regression 2.022296 Akaike info criterion 4.304468 Sum squared resid 192.2150 Schwarz criterion 4.419190 Log likelihood -104.6117 F-statistic 6.575919 Durbin-Watson stat 2.404541 Prob(F-statistic) 0.003034

0

2

4

6

8

-4 -3 -2 -1 0 1 2 3 4 5

Series: ResidualsSample 1901 1951Observations 50

Mean -1.19E-15Median 0.052733Maximum 4.919962Minimum -4.123167Std. Dev. 1.980595Skewness 0.523202Kurtosis 3.576950

Jarque-Bera 2.974647Probability 0.225977

-6

-4

-2

0

2

4

6

7 8 9 10 11 12 13

FITTEDEXPFOOD

RE

SID

EX

PF

OO

DNormal residualsNo heteroskedasticity

Crime rate and income per capita In order to study the effects of poverty and

unemploymentHighly significant regression did existCrime rate is highly significant while income

is not Introduction of dummy variable for DC

DC’s data was much higher than the 50 states

R-squared value increased because the absence of DC’s data decreased the sum of residuals

Dependent Variable: UNEMP_RATE Method: Least Squares Date: 12/02/09 Time: 14:06 Sample: 1901 1951 Included observations: 51


CRIME_PER_100000 0.005314 0.001599 3.323136 0.0017 INC_PER_CAP -1.26E-05 5.35E-05 -0.235172 0.8151

DUMMYDC -2.494080 2.984445 -0.835693 0.4076 C 7.146291 2.092222 3.415647 0.0013


Introduction of dummy variable for DCDC’s data was much higher than the 50

statesR-squared value increased because the

absence of DC’s data decreased the sum of residuals

-4

-2

0

2

4

6

6 8 10 12 14

FITTEDCRIMEINC

RE

SID

CR

IME

INC

0

1

2

3

4

5

6

-3 -2 -1 0 1 2 3 4 5 6


Mean -2.56E-16Median -0.076828Maximum 5.509507Minimum -3.422640Std. Dev. 2.010469Skewness 0.547344Kurtosis 3.090094

Jarque-Bera 2.563729Probability 0.277519

Normal residualsNo heteroskedasticity

Crime rate, food stamps, and homeless per 1000

In order to test the three most highly significant variables against unemployment

Determined that a significant relationship exists with participation in the food stamps program being the most significant variable

Dependent Variable: UNEMP_RATE Method: Least Squares Date: 12/01/09 Time: 14:49 Sample: 1901 1951 Included observations: 51


HOME_PER_1000 0.303980 0.197584 1.538488 0.1306 FOOD_STAMPS 1.11E-06 3.75E-07 2.959184 0.0048

CRIME_PER_100000 0.002413 0.001352 1.785036 0.0807 C 6.483981 0.577719 11.22343 0.0000


Normal Residuals No heteroskedasticity

0

2

4

6

8

10

-3 -2 -1 0 1 2 3 4 5


Mean -2.49E-16Median -0.093738Maximum 5.403755Minimum -3.316906Std. Dev. 1.820800Skewness 0.658761Kurtosis 3.650791

Jarque-Bera 4.588710Probability 0.100826 -4

-2

0

2

4

6

6 8 10 12 14

HCSFITTED

HC

SR

ES

ID

Introduction of dummy variable for DC Increased R2 by decreasing sum of

residualsDependent Variable: UNEMP_RATE Method: Least Squares Date: 12/01/09 Time: 15:04 Sample: 1901 1951 Included observations: 51


HOME_PER_1000 0.527074 0.251541 2.095379 0.0417 FOOD_STAMPS 8.76E-07 4.06E-07 2.155693 0.0364

CRIME_PER_100000 0.003564 0.001567 2.274129 0.0277 DUMMYDC -4.754245 3.371999 -1.409919 0.1653

C 5.807296 0.746482 7.779548 0.0000


Percent of population with no health insurance does not correlate with unemployment rate

Percent of adults with bachelors does not correlate with unemployment rate

Unemployment positively correlated with:Participation in the food stamps programHomeless rate per 1000 peopleCrime rate per 100,000 people

Should be the goal of the government to decrease unemployment by a reallocation of fundsWould lead to decrease in crime, homeless

rate, and poverty

adam sutton chia-jung liu grant volk yin chu can shen robert matarazzo andrew ratcliffe ruben bos

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