This document analyzes county-level poverty rates in California and Oregon in 2015. It tests two hypotheses: 1) counties with higher percentages of black residents will have higher poverty rates, and 2) counties with higher percentages of bachelor's degree holders will have lower poverty rates. Simple regressions show the percentage of black residents has a weak positive correlation with poverty, while the percentage with bachelor's degrees has a strong negative correlation. A multiple regression controlling for both variables explains over 56% of the variation in poverty rates across counties. Additional socioeconomic and demographic variables are also analyzed to further explain poverty rate differences at the county level.

1. Michael Joy
ECON 3343 (001), Grodner
April 30, 2016
California and Oregon Poverty at the County Level, 2015
Introduction
According to the Census Bureau’s 2013 supplementary poverty measure, California’s
poverty rate was 23.4 percent (Cox, 2016). That is a staggering almost 9 million Californians in
poverty. This is in stark contrast to the national poverty average of 15.9% (Cox, 2016).
Meanwhile from 2000 to 2010, Oregon suffered one of the nation’s most extreme raise in people
living in high poverty areas (Hammond, 2014). Certainty there are multiple variables that affect
poverty rates. The objective of this paper is to investigate some of these variables on county
poverty rate in the states of California and Oregon in 2015. Special consideration will be given to
how the percentage of people of with bachelor’s degrees and percentage of black people affect
poverty rate at the county level.
There are two hypothesis that will be tested. First, the percentage of black people has a
positive correlation with percentage poor. In other words counties with a higher concentration of
African-Americans will increase the poverty rate. I also examined how the level of education
affects the poverty rate. The second hypothesis is the percentage of people with bachelor’s
degrees will have a negative correlation in percentage poor. The assumption being people who
hold a bachelor’s degree are more likely to have higher incomes, and this will decrease the
poverty rate in a given county.
Data

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The data for analysis of county poverty rates in California and Oregon were gathered
from the 2015 US Census data.
Descriptive Statistics
Table 2 displays the simple descriptive statistics for the 94 combined California and
Oregon counties. Of particular note is the range for median household income. Median
household income ranges from a low of $33,611 in Lake County, OR to a high of $91,702 in
Santa Clara, California. Also worth nothing is that the average percentage of black people in
California is only 6.13%. This is much lower than the national average of 13.2%. Given
California’s recent demographic change of becoming a majority minority state the role of
disadvantage minority group African-Americans traditionally held may have been supplanted by
Latinos. California’s Asian population of 14.4% is well above the national average of 5.4%.
California and Oregon’s median value of owner-occupied housing needs to be considered as
well. Both California ($371,400) and Oregon ($234,100) are well above the national average of
Table 1. Description of Variables
Variable Key
pvy020213 Persons below poverty level, percent, 2009-2013
rhi225214 Black or African American alone, percent, 2014
edu685213 Bachelor's degree or higher, percent of persons age 25+, 2009-2013
rhi725214 Hispanic or Latino, percent, 2014
pop815213 Language other than English spoken at home, pct age 5+, 2009-2013
pop645213 Foreign born persons, percent, 2009-2013
age295214 Persons under 18 years, percent, 2014
age775214 Persons 65 years and over, percent, 2014
bza115213 Private nonfarm employment, percent change, 2012-2013
inc110213 Median household income, 2009-2013
rhi425214 Asian alone, percent, 2014
hsd310213 Persons per household, 2009-2013
hsg495213 Median value of owner-occupied housing units, 2009-2013
edu635213 High school graduate or higher, percent of persons age 25+, 2009-2013

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$175,700. Wendell Cox points to the high housing costs as a major factor in driving Californians
to poverty (Cox, 2016).
Table 2. Descriptive statistics
Variable N Mean Std Dev Minimum Maximum
pvy020213 94 15.9852017 2659.47 7.6 27.4
rhi225214 94 6.1311478 2333.6 0.1 14.8
edu685213 94 30.2073848 6156.9 9.7 54.6
rhi725214 94 36.2347697 9639.93 2.8 82.3
pop815213 94 41.0150294 9270.65 2 74.5
pop645213 94 25.4128856 5996.91 0.8 37.1
age295214 94 23.4053197 1962.9 13.4 31.6
age775214 94 13.1693775 1628.61 9.1 33.2
bza115213 94 3.0822619 911.7318725 -71.5 30.2
inc110213 94 61093.45 8275700.86 33611 91702
rhi425214 94 13.3901556 5640.5 0.2 34.9
hsd310213 94 2.9138552 173.2888188 2 3.42
hsg495213 94 376021.5 97957278.2 112300 781900
edu635213 94 81.7964168 3994.7 64.5 94.6
Finally, the percentage of foreign born people in California and Oregon is at 25.4%.
Which is well above the national average of 13.1%. By county, foreign born persons is as low as
0.8% in Wheeler, OR to 37.1% in Santa Clara, California.
Methods
To test our hypothesis this paper uses simple and multivariate regressions. The dependent
variable is the percentage of people that are at or below the poverty level. The independent
variables are percentage of blacks, percentage of people with bachelor’s degrees, and the

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variables discussed above. Our baseline model only includes the percentage black and
percentage with bachelor’s degrees:
𝑌𝑖 = 𝛼 + 𝛽1 𝑋1𝑖 + 𝛽2 𝑋2𝑖 + 𝜖𝑖
Where 𝑌𝑖 is the percentage of county i’s population in poverty. 𝛼 and 𝛽 are parameters for the
variables 𝑋1(percentage black) and 𝑋2(percentage with B.A. degrees) and ∈ is the disturbance
term. Multiple regressions were ran with several other independent variables that give a deeper
understanding of causes of county poverty in California and Oregon.
Results
The first step was to process simple regressions between percentage poor and percentage
black and percentage poor and percentage of people with B.A. degrees. These initial results here
can be incredibly helpful in understanding of future regression results. Graph 1 is a scatter-plot
graph that displays the relationship between percentage poor (Y-axis) and percentage black (X-
axis) in California and Oregon’s 94 counties.
GRAPH 1
Scatter-Plot of Bivariate Relationship: Percentage Poor and Percentage Black

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It is difficult to determine a relationship between the two variables. Because the
percentage of African-American can be as low as 0.1% and percentage poor varies from7.6% to
27.4% in many of the same low African-America population counties. Mono, California with the
lowest percentage African-American population (0.7%) and low poverty (8.5%) and Malheur,
Oregon with the highest percentage of African-American population (1.7%) has an extremely
high poverty of 27.4%. So it would be foolish to judge any relationship between percentage poor
and percentage black based solely on Graph 1. So to further my hypothesis of the positive
relationship between percentage poor and percentage black I ran a bivariate regression. In this
model percentage black was the independent variable and percentage poor was the dependent
variable to locate the first equation:
𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 1: 𝑝𝑣𝑦020213 = 14.409 + 0.257(𝑟ℎ𝑖225214)
This regression shows that the percentage black equation is positive, but not statistically
significant on a 5% significance level. This is because our p-value, which measures the overall
significance of the variable is only .0288. This is below the necessary 0.05 threshold significance
level that would make it statistically significant at a 5% significance level. Our adjusted R2 was
0.0406. Given this information equation 1 has little value in explaining poverty in California and
Oregon at the county level.
Now we need to consider the relationship between percentage poor (Y-axis) and
percentage of people with B.A. degrees (X-axis). Upon immediate observation the results are
much clearer than in graph 1. The graph shown below shows a clear negative linear relationship
between percentage with B.A. degrees and percentage poor. The second hypothesis of a negative
correlation between percentage of people with B.A. degrees and percentage poor is accurate.

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Graph 2
Scatter-Plot of Bivariate Relationship: Percentage Poor and Unemployment Rate (Code
6=California, 41=Oregon)
To verify my second hypothesis, I ran a bivariate regression with percentage with B.A. degrees
(edu685213) as the independent variable and percentage poor (pvy020213) as the dependable
variable. Listed below is the equation from the results:
𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 2: 𝑝𝑣𝑦020213 = 25.673 − 0.32(𝑒𝑑𝑢685213)
The regression verifies that the percentage of people with B.A. degrees (edu685213)
coefficient is positive. However, the p-value is less than 0.0001 so it is not statistically
significant at a 5% significance level. So we must reject the null hypothesis that all of the
coefficients combined are equal to 0. The adjusted R2 is at 0.546. This is an improvement over
equation 1’s adjusted R2. So this model explains more than half, 54.6%, of the variability in the
percentage of poverty at the county level. Clearly with these factors consider, equation 2 is a
good model to be used in explaining the percentage poverty at the county level of California and
Oregon.

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The final simple regression model is equation 3. This model will use both percentage
black and percentage with a B.A. degree. This produces the following equation:
𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 3: 𝑝𝑐𝑡𝑝𝑜𝑜𝑟 = 24.369 + 0.182 ( 𝑟ℎ𝑖225214𝑘) − 0.314 (𝑒𝑑𝑢685213)
In this third regression, the percentage black (0.182) has is still positive, but has decreased
slightly. The percentage with B.A. degrees is again negative and at -0.314 is almost completely
unchanged from equation 2.
Table 3. Baseline regression models on poverty rate (1-3): only percent black and percent
with Bachelor's degree
Ind. Variables Eq. 1 Eq. 2 Eq. 3
Intercept 14.409*** 25.673*** 24.369***
(0.818) (0.953) (1.086)
[+12.78, +16.03] [+23.78, +27.57] [+22.21, +26.53]
rhi225214 0.257** ----- 0.182**
(0.116) (0.078)
[+0.03, +0.49] [+0.03, +0.34]
edu685213 ----- -0.32*** -0.314***
(0.030) (0.030)
[-0.38, -0.26] [-0.37, -0.26]
# ofObs 94 94 94
Adj. R^2 0.0406 0.546 0.567
F-Test 4.93 113.04 61.97
P-value 0.0288 <0.0001 <0.0001
Note 1: all resultsweightedbycountypopulation(pop060210)
Note 2: * P-value between.05and .10, ** P-value between.001and .05, *** P-value <.001
Note 3: standard errorsin (XXX)
Note 4: 95% confidence interval in[XXX]
In equation 1 the percentage black coefficient is not statistically significant at the 5%
significance level, but by equation 3 the p-value of the test statistic has decrease to less than
0.0001. The adjusted R2 has risen to 0.567, slightly higher than in equation 2. So our third model
accounts for more variability (56.7%) than in either equation 1 or equation 2. The p-value of the

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F-statistic is again less than 0.0001. Since it is below the 0.05 threshold for statistical
significance, we can reject the null hypothesis that all of the variables combined are statistically
insignificant and equal to zero. However, there is still more unexplained variation which may be
explained by other independent variables.
Results from Multiple Regressions
Additional independent variables are added to gauge the effect on the fit of the model.
This is achieved by simply observing the adjusted R2. This in turn reduces the probability of bias
that occurs from omitted variables.
 Hispanic or Latino, percent, 2014 (rhi725214): My assumption is counties with a
higher Latino population will have a higher poverty rate. California with several
counties along the Mexican border may have more freshly arrived immigrants.
Immigrants who have not had an opportunity to adapt to the culture and language thus
reducing job opportunities and wages.
 Language other than English spoken at home, pct age 5+, 2009-2013 (pop815213): I
included this variable because I expect counties with a higher percentage of people
who speak languages other than English at home would increase the poverty rate.
Like the Hispanic variable, my expectation is this would variable would apply to
newly arrived immigrants in the country.
 Foreign born persons, percent, 2009-2013 (pop645213): I included this variable
because foreign born persons generally work lower skill jobs. My assumption is this
variable contributes to the poverty rate of a county.

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 Persons under 18 years, percent, 2014 (age295214): I included this variable because
many of the youth are dependents which decreases the family’s primary earner
income further. Also like foreign born persons, they work lower skill jobs that
contribute to the poverty rate. I predict those counties with a higher percentage of
persons under 18 years old will have increased poverty rates.
 Persons 65 years and over, percent, 2014 (age775214): I included this variable
because similar to people under 18 years old those seniors living with family may
decrease the family’s primary earner income further. Additionally many seniors are
only living on savings, social security or both. So the counties with a higher
percentage of persons 65 or older will have higher poverty rates.
 Private nonfarm employment, percent change, 2012-2013 (bza115213): I included
this because counties with lower employment are highly likely to have lower job
opportunities which contribute to higher poverty rates.
 Median household income, 2009-2013 (inc110213): Counties with a lower median
household income will have higher poverty rates.
 Asian alone, percent, 2014 (rhi425214): I included this variable to capture
California’s sizable Asian population. Data from the U.S. Census has shown Asian
households have higher median income than any other race (DeNavas-Walt, 2010). I
suspect that counties with a higher percentage of Asians will have lower poverty
rates.
 Persons per household, 2009-2013 (hsd310213): I included this variable because
those families with youth andor seniors in the may contribute to stretching the
primary earner’s income farther. Yet if many or all of the family earn income, this

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may actually decrease poverty rates. Considering this, my prediction is that the
persons per household will have a negative impact on poverty rates.
 Median value of owner-occupied housing units, 2009-2013 (hsg495213): I included
this variable to attempt to verify the claim stated above of housing costs leading to a
higher poverty rate. With families paying more in housing they have to accept a lower
standard of living which leads to higher poverty rates. I suspect Cox’s claim is
correct.
 High school graduate or higher, percent of persons age 25+, 2009-2013
(edu635213): I included this variable because those with just a high school education
are more likely to have lower wage jobs. This contributes to a higher poverty rate. I
assume that counties with higher percent of high school graduates will have higher
poverty rates.
Equation 4
The next equation estimated the following values for the variables’ coefficients:
Equation 4: pvy020213=58.964-0.026 (rhi225214)+0.219 (edu685213)+0.059
(rhi725214)+0.0459 (pop815213)-0.429 (pop645213)+0.257 (age295214)-0.061
(age775214)+0.089 (bza115213)-0.0004(inc110213)+0.281 (rhi425214)+ 0.297
(hsd310213)+0.000006 (hsg495213)-0.399(edu635213)
With this model the adjusted R2 jumps to an amazing 0.9420. So the regression accounts
for 94.2% of the variability in percentage poor while the p-value of the F-statistic is still below
the 0.05 threshold for statistical significance at the 5% significance level. Equation 4 is far
superior to our previous equations since it explains much more variability in percentage poverty.
Furthermore, there appears to be a bias in percentage black because the confidence intervals

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Table 4. Regressionmodelson poverty rate (3-5): selectionofindependentvariables
Ind. Variables Eq. 3 Eq. 4 Eq. 5
Intercept 24.369*** 58.694*** 65.456***
rhi225214 0.182** -0.026 0.015
edu685213 -0.314*** 0.219** 0.214***
rhi725214 ----- 0.059 -----
pop815213 ----- 0.0459 -----
pop645213 ----- -0.429** -0.297***
age295214 ----- 0.257** 0.36**
age775214 ----- -0.061 -----
bza115213 ----- 0.089 -----
inc110213 ----- -0.0004*** -0.0003***
rhi425214 ----- 0.281*** 0.216***
hsd310213 ----- 0.297 -----
hsg495213 ----- 0.000006* 0.000006*
edu635213 ----- -0.399*** -0.504***
# ofObs 94 94 94
Adj. R^2 0.567 0.94 0.94
F-Test 61.97 117.26 183.74
P-value <0.0001 <0.0001 <0.0001
Note 1: all resultsweightedbycountypopulation (pop010210)
Note 2: * P-value between.05and .10, ** P-value between.001and .05, *** P-value <.001
increased. However, the standard error did decrease indicating greater efficiency. Percentage
with B.A. degrees increased from -0.314 to 0.219. This also indicates a strong bias in the first
result. The standard error for percentage with B.A. degrees also increased indicating greater
inefficiency. The p-value of the F-test remained less than 0.0001 percent indicating again that
our variables are below the 0.05 threshold for statistical significance. So we can reject the null
hypothesis that all of the variables combined are statistically insignificant and equal to zero.
Equation 5
Any variables whose p-value that had high p-values were dropped from equation 5.
These variables are percentage Latino, language other than English spoken at home, People 65
years or older, private non-farm employment, and persons per household. Percentage black was

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also had a high p-value, however, this variable was not exclude to continue to test my original
hypothesis. This model return the following results:
Equation 5: pvy020213= 65.456 + 0.015 (rhi225214) + 0.214 (edu685213) - 0.297
(pop645213) + 0.36 (age295214) - 0.003 (inc110213) + 0.216 (rhi425214) + 0.000006
(hsg495213) - 0.504 (edu635213)
Despite dropping several variables the adjusted R2 is still 0.94. This is quite good as
there was no loss in determining the variability in percentage poverty. The p-value of the F-
statistic remained at less than 0.0001. So both independent variables remain statistically
significant. The other independent variables not mentioned saw an increase in impact and a
decrease in their standard errors compared to their values in equation 4. So the model in equation
5 is superior due to increased accuracy and efficiency in the regression. This is due to removing
several independent variables from equation 5.
Preferred Model & Interpretation
Equation 5 is the best model to estimate poverty at the county level for California
and Oregon. Now it's possible to consider the variables that most affect county poverty.
 Percentage Black (rhi225214): This variable has a small positive impact on a
county's percentage poor. Given California and Oregon's low percentage of
blacks, Hispanics may have replaced blacks as the minority group that experience
discrimination. So percentage black will have a zero to small impact on poverty
rate.
 Bachelor's degree or higher, percent of persons age 25+, 2009-2013 (edu685213):
Surprisingly, an increase of 10 percent increase in this variable will increase

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percentage poor by 2.14%. This result seems counterintuitive, but may be
explained because of inclusion of the other variable, percentage high school
graduate or higher (edu635213).
 Foreign born persons, percent, 2009-2013 (pop645213): This coefficient actually
has a negative impact on percentage poor. A 10 percent increase in percentage
foreign born will decrease poverty by almost 3%! This may be due to highly
skilled foreign workers in certain sectors of California’s job market. An
interesting follow up to this paper would be to examine this relationship more
closely.
 Persons under 18 years, percent, 2014 (age295214): This variable actually had the
second largest impact on determining county poverty in California and Oregon.
An increase of 10% in percent persons under 18 years old will increase poverty by
3.6%.
 Median household income, 2009-2013 (inc110213): This coefficient has an
incredibly small negative impact on county poverty (-0.0003). While statistically
significant other included variables had much bigger impact on county poverty.
 Asian alone, percent, 2014 (rhi425214): This was another important coefficient
in determining poverty at the county level. A 10% increase in the Asian
population increased poverty by almost 2.2%. This may be explained by
California’s higher average than the national average of immigrants.
 Median value of owner-occupied housing units, 2009-2013 (hsg495213): Counter
to Wendell Cox’s belief that housing costs is a determinant on poverty I found
that it had very little (0.000006) impact on poverty at the county level.

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 High school graduate or higher, percent of persons age 25+, 2009-2013
(edu635213): this coefficient had the largest impact on poverty at the county
level. A 10% percent increase in percentage people who were a high school
graduate or higher decreased poverty 5%. As mentioned previously percentage
with a B.A. degree may influenced this variable as well.
Conclusion and Policy Implications
By using regression analysis the original that an increase in percentage of blacks in a
county raises the county poverty rate is not consistent with the data. My second hypothesis that
percentage of people with B.A. would reduce poverty at the county level was also not consistent
with the data. Equation 3 showed a negative correlation with percentage poor, but switched signs
only we included more variables included percentage of people who are high school graduates.
In future studies it may be helpful to include one or the other variable.
However, there are several variables that help the model explain poverty in California
and Oregon. People age 18 and under and Asian both increased poverty at the county level.
However, percent of foreign born persons actually decrease poverty at a county level. Median
household income and median value of owner-occupied housing units had very little impact on
determining poverty at the county level. Which is a very surprising result.
The data suggests that government officials could reduce poverty by increasing
immigration from certain countries and limiting it in other areas such as Asia. Future study
should be considered to determine the relationship in immigration, demographics, and the labor
market have on the poverty rate. California as only the third status to have majority minority

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status would lead the way in this research. Furthermore, it would benefit government officials to
invest in family planning services and job opportunities for teenagers to decrease poverty rates.
This would decrease the impact that counties with a greater number of people 18 and under have
on poverty rates.

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Works Cited
Cox, Wendell. August 21, 2015. "California: ‘Land of Poverty’" Newgeography.com. Accessed
April 26, 2016. http://www.newgeography.com/content/005026-california-land-poverty.
DeNavas-Walt, Carmen. 2010. Income, poverty, and health insurance coverage in the United
States (2005). DIANE Publishing.
Hammond, Betsy Hammond. July 16, 2014. "Oregon's Huge Increase in People Living in High-
poverty Areas One of Nation's Most Extreme, Study Finds." The Oregonian. Accessed
April 26, 2016.
http://www.oregonlive.com/education/index.ssf/2014/07/oregons_huge_increase_in_peo
pl.html.