Poverty Analysis by County (United States)

Poverty is universal.

For you always have the poor with you, but you will not always have me.

Matthew 26:11

It is our duty as human persons to love our neighbor and work for the betterment of the worldwide human community. This project is an analysis of potential contributing factors to poverty in the United States and potential reactive/proactive measures we may advocate for in order to reduce poverty rates.


Data Sources

Poverty, unemployment, and education rates (2021):

https://www.ers.usda.gov/data-products/county-level-data-sets/county-level-data-sets-download-data/.

Household size (2021):

https://data.census.gov/table/ACSST1Y2021.S1101?q=2021+household+by+state.

State Minimum Wages (2024):

https://www.ncsl.org/labor-and-employment/state-minimum-wages.

Violent Crime (2019):

https://ucr.fbi.gov/crime-in-the-u.s/2019/crime-in-the-u.s.-2019/topic-pages/tables/table-5.

Housing Price Index By State (2021):

https://www.fhfa.gov/data/hpi/datasets?tab=quarterly-data

Consumer Spending by State (2021):

https://www.bea.gov/data/consumer-spending/state

Rent by State (2021):

https://www.zillow.com/research/data

Variables:

Dependent Variables
  • % Poverty Rate (2021, by county)
Independent Variables
  • % Population with a bachelor’s degree or higher (over the period 2018-2022, by county)
  • % Households with cohabitant dependent children (2021, by state)
  • Minimum Wage (2024, by state)
  • Violent Crime per Capita (2019, by state)

My first analysis of this data was in LibreOffice Calc, using a multiple linear regression model.

Table 1: sample of cleaned data

FIPS_Code Stabr Area_name PCTPOVALL_2021 EDUCATION PCT_HOUSEHOLDS_CHILDREN PCT_UNEMPLOYMENT MIN_WAGE Violent_crime_per_100000 OUTLIER
01003 AL Baldwin County 10.8 32.5615787141814 28.7934440593649 4.1 7.25 510.8 no
01005 AL Barbour County 23 11.8811881188119 28.7934440593649 4.1 7.25 510.8 no
01007 AL Bibb County 20.6 10.9199372056515 28.7934440593649 4.1 7.25 510.8 no
01009 AL Blount County 12 14.7414067667883 28.7934440593649 4.1 7.25 510.8 no
01011 AL Bullock County 32.1 9.37674678591392 28.7934440593649 4.1 7.25 510.8 no
FIPS_Code

Federal Information Processing Standard code, used to uniquely identify geographical areas (county specific)

Stabr

State Abbreviation

Area_name

Name of the county

Dependent Variables
  • PCTPOVAL_2021: % Poverty Rate (2021, by county)
Independent Variables
  • EDUCATION: % Population with a bachelor’s degree or higher (over the period 2018-2022, by county)
  • PCT_HOUSEHOLDS_CHILDREN: % Households with cohabitant dependent children (2021, by state)
  • PCT_UNEMPLOYMENT: % of population unemployed (2022, by county)
  • MIN_WAGE: Minimum Wage (2024, by state)
  • Violent_crime_per_100000: Violent Crime per Capita (2019, by state)

Table 2: sample statistics

Number of Observations = 2914

Mean Median St. Dev Minimum Maximum
Poverty (percent of population) 14.726 13.7 5.656 3.9 43.9
Percent of Households with cohabitant dependent children (percent) 29.751 28.985 3.101 24.482 38.282
Percent of Population with a Bachelor’s degree or higher (percent) 22.857 20.792 8.895 5.163 53.460
Percent Unemployed (percent) 5.203 5 1.676 2.9 11.7
Minimum Wage (dollars $) 9.97 10.3 2.976 7.25 16.28
Violent Crime per Capita (per 100000) 358.777 370.8 98.532 115.2 595.2


Washington had the highest minimum wage at $16.28. The highest violent crime rates were by far in Arizona, Louisiana, and at the top with a rate of 595.2, Tennessee. Connecticut has the lowest unemployment at 3%, whereas Nevada has an unemployment of more than double that- an average of 6.441% state-wide. An interesting observation is that every single county in Arkansas was an outlier, because violent crime was greater than 3 standard deviations above the mean (>694.136). It is interesting to note that the entire state of Arkansas has a violent crime per capita of 867.1. This is almost 25% higher than the entire U.S. average.

The dataset was cleaned using an “outliers” column. An example of the function in this column was:

=IFS(OR(E2>$E$2920,E2<$E$2921),"yes",OR(F2>$F$2920,F2<$F$2921),"yes",OR(G2>$G$2920,G2<$G$2921),"yes",OR(H2>$H$2920,H2<$H$2921),"yes",OR(I2>$I$2920,I2<$I$2921),"yes",1,"no")

Where the 2920 and 2921 rows of E, F, G, H, and I have the values for three standard deviations above and below the mean. The function simply checked whether or not any cell in a given county’s row was more than three standard deviations above or below the mean. This made it easy to clean using a simple filter for all rows where the “outlier” column was a “yes,” and then deleting those rows.

Table 2: findings

Ordinary Least Square Estimates
Dependent variable: Poverty Rate

Independent Variable Coefficient p-value

Intercept 20.105 0.000
Percent of Population with a Bachelor’s degree or higher -0.308a 0.000
Percent Unemployed (percent) -0.0396 0.478
Minimum Wage -0.072b 0.020
Violent Crime per Capita .006a 0.000
Percent of households with cohabitant dependent children .016 0.569
  1. Significant at 1%
  2. Significant at 5%
  3. Significant at 10%

The overall significance (F) came out to 0.000, and the adjusted R2 came out to 0.26166695634134. This means that the overall model is significant, but it only describes about a quarter of the data.

Interpretations of significant coefficients:
Education

As the percent of population with a bachelor’s degree or higher increases by 1 percentage point, predicted poverty rate decreases by .308 percentage points.


Minimum Wage

As minimum wage increases by one dollar, predicted poverty rate decreases by .072 percentage points.


Violent Crime

As violent crime per capita increases by one violent crime, predicted poverty rate increases by .006 percentage points.


Even though percent of population with a bachelor’s degree or higher, minimum wage, and violent crime per capita are significant, it seems as though the most important by far is education. A one percentage point increase in the percent of population with a bachelor’s or higher will decrease poverty by approximately 4.28 times that of a one dollar increase in minimum wage, and about 51.3 times that of a one crime decrease in violent crime per capita.


Although this sounds all well and good I made a fundamental error: I did not visualize my data before I even started! Data visualization is the first thing any good analyst should start with. Because I did not visualize my data, I went in the wrong direction. I assumed all my independent variables would have a linear correlation.

As is common among men, we are wrong. This is reflected in the graph shown at the top of the page:

Fig 1: Poverty vs. Education

This looks like the least linear “linear relationship” I have ever seen. In fact, it looks like a second order relationship, or perhaps even a logistic relationship with a base of <1.

If this assumption was wrong, how does the assumption of linearity for the other independent variables hold up?

Unemployment

The original data for % unemployment is by county, however it appears as though multiple counties were surveyed together, so within a certain state multiple consecutive counties will have the exact same poverty rate. This produces the graph above. Although this might smother potential trends, perhaps aggregating by state will help visually?

As with household size, there seems to be no slope but an interesting clump of points in the middle. This is a little more clustered.

Minimum Wage

There is no discernible correlation. Slope seems to be 0. It is interesting to note that the many states who have federal minimum wage as the state minimum($7.25/hr) range from near 10% (very low relative to other states) to almost 22% (almost the highest unemployment)

Violent Crime

This graph does show a definite positive slope. A question that needs to be asked is:

“Does violent crime drive poverty, or does poverty drive violent crime? If the latter, are there any surrounding variables that impact this relationship?”

I say this because I am quite hesitant to give broad generalizations of an entire socioeconomic population. For example, the most affordable areas could be those with high gang violence. Those with less means (in poverty) would move there, yet not be the cause of this violent crime. This makes sense, and would help explain the relationship between violent crime and poverty.

It seems to be clear that there are certainly missing variables. Perhaps the missing variables would be related to housing costs, or general cost of living. Let’s add in housing prices costs and single parent households to our data.

Note: At first, I tried to aggregate data by state to simplify the analysis however after running a regression all P values were insignificant. The best practice seems to be keeping all county-level data and simply adding state-wide data to those individual counties.

These are what the new graphs look like, both county level and aggregated at state level before cleaning any outliers:

There looks like there are some trends present in this data. Running a power series regression to account for our nonlinear relationships such as education and housing costs, this is the summary:

Powerseries Estimates
Dependent variable: Poverty Rate

Independent Variable Coefficient p-value

Intercept 4.196 0.000
LN(Percent of Population with a Bachelor’s degree or higher) -0.480 0.000
LN(Percent Unemployed) -0.020 0.186
LN(Minimum Wage) 0.024 0.213
LN(Violent Crime per Capita) 0.090 0.000
L N(Household size) -0.324 0.067
LN(housing_costs)
LN(%_single_fam_household)
  1. Significant at 1%
  2. Significant at 5%
  3. Significant at 10%
Coefficients P-value
Intercept 4.196 0.000
LN(EDUCATION) -0.480 0.000
LN(PCT_HOUSEHOLDS_CHILDREN) -0.324 0.067
LN(PCT_UNEMPLOYMENT) -0.020 0.186
LN(MIN_WAGE) 0.024 0.213
LN(Violent_crime_per_100000) 0.090 0.000
LN(housing_costs) -0.080 0.000
LN(%_single_fam_household) 0.318 0.000