Adrian Dickson
PSC 408
Professor Benson-Saxton
12/5/23
Effect of Unemployment On Violent Crime
When considering the prosperity of any kind of community, one of the first metrics
that we tend to look at is the prevalence of violent crime. This can indicate other social
ills. Looking over the possible factors that influence the rate of violent crime, one that is
often pointed to is economic hardship. A way to measure this factor would be in the
unemployment rate. Is the unemployment rate of each U.S. state related to the
prevalence of violent crime?
My theory is that unemployment does have an effect on violent crime, specifically
a positive one. I believe that unemployment can affect violent crime in a number of
ways. If someone cannot find work, they may be more likely to turn to crime for an
easier way at making money. This may also be the case for family members of those
who are unemployed, specifically their dependents (who are often minors), who may
turn to crime for the same reason of providing for themselves. Even those people who
are unemployed who are not turning to crime out of necessity may be more prone to
violent crime because of both restlessness and the social web they navigate; jobs likely
provide stability and community that is more regulated than the outside world, which is
where things like gangs form.
For this experiment the independent variable will be the unemployment rate for
each U.S. state (and D.C.) in 2022. The dependent variable will be the reported violent
crime rate in 2022, also by state. The independent variable will be in percentage, while
the dependent variable will be incidents per 100,000 of the population. The data for the
independent variable was obtained from the Bureau of Labor Statistics (BLS). The BLS
is an agency of the U.S. Department of Labor, and collects, analyzes, and publishes a
wide array of statistics on labor economics in the United States. The data for the
dependent variable was obtained from Statista, a German data gathering platform that
collects both its own and third party statistics, which used the FBI as a source. The FBI
publishes statistics on crime and other relevant topics, and Statista gathered its
information with the FBI’s crime data website.
On this scatterplot, there is a weak to moderate visual relationship between the
two variables. However we can’t just eyeball this. To determine whether the relationship
is statistically significant, we’ll need the Pearson correlation coefficient and to conduct a
t-test. Before doing that, let’s look at the linear regression line (the orange line on the
graph). It is written as Y = a + bX, where a is equal to the y-intercept, and b is equal to
the slope. The formula for the slope is:
b=
Σ(𝑥−𝑥̄)(𝑦−ȳ)
2
Σ(𝑥−𝑥̄)
The formula for the y-intercept is:
a = Ȳ - bX̄
We can use Excel to get our relevant formula components, since we have a lot of
data. The sums on the bottom will be used not only for linear regression line, but for the
later correlation coefficient.
When we divide the sums for the slope , we get b = 61.25. Here’s an advantage
of Excel: We can reduce the decimals for the sums visually in the table without actually
rounding before we calculate the slope, which produces a more accurate slope. Now,
for the y-intercept, we plug in the slope into the formula and get a = 158.88.
This gives us a line of:
Y = 158.88 + 61.25X
For Pearson’s correlation coefficient, as stated before, we can use the components
given in Table 2. The formula is:
r=
Σ(𝑥−𝑥̄)(𝑦−ȳ)
2
2
Σ(𝑥−𝑥̄) (𝑦−ȳ)
When we plug in the correct components we get:
r=
(1777.28)
(29.02)(1303619.27)
= 0.29
r2 = 0.08
Now that we have the correlation coefficient, we can conduct a significance test.
This will tell us if the relationship between the two variables is significant. The null
hypothesis, H0, is that there is no statistically significant relationship between the
unemployment rate in the states and the rate of violent crime. We’re going to test at an
alpha level of 0.05, since this is the general standard. The degrees of freedom will be
N-2, 51-2 = 49. The critical value is 2.021.
𝑡=𝑟
𝑁−2
2
1−𝑟
𝑡 = 0. 29
49
1 − 0.08
= 2.116
Since the t score lies in the critical region. We can reject the null hypothesis, and say
that there is a statistically significant relationship between the two variables.
The findings indicate a weak to moderate positive correlation between the two
variables. However, the findings also indicate statistical significance. It’s important to
note that “significant” in these type of tests does not necessarily mean “large”, but
simply that it is unlikely that the relationship arose by chance alone. My hypothesis was
ultimately supported by the analysis conducted, which strengthens my belief in the
importance of job creation by our governments. Limitations include that for the FBI’s
crime statistics, data was taken from a large majority of the law enforcement agencies in
each state, but not all of them. Also, this might have been benefited by control for
variables that might affect the study, like age. Maybe this could be done at a later date.
Works Cited:
“Unemployment Rates for States.” U.S. Bureau of Labor Statistics,
www.bls.gov/lau/lastrk22.htm.
“Reported Violent Crime Rate in the U.S. by State 2022.” Statista,
www.statista.com/statistics/200445/reported-violent-crime-rate-in-the-us-states/.