Michael JoyECON 3343 (001), GrodnerApril 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

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

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Table 1. Description of VariablesVariable Keypvy020213 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+, 2009-2013rhi725214 Hispanic or Latino, percent, 2014pop815213

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, 2014age775214 Persons 65 years and over, percent, 2014bza115213 Private nonfarm employment, percent change, 2012-2013inc110213 Median household income, 2009-2013rhi425214 Asian alone, percent, 2014hsd310213 Persons per household, 2009-2013hsg495213 Median value of owner-occupied housing units, 2009-2013edu635213 High school graduate or higher, percent of persons age 25+, 2009-2013

well. Both California ($371,400) and Oregon ($234,100) are well above the national average of

$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

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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

variables discussed above. Our baseline model only includes the percentage black and

percentage with bachelor’s degrees: Y i=α+β1 X1 i+β2 X2 i+ϵ i

Where Y i 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

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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 1Scatter-Plot of Bivariate Relationship: Percentage Poor and Percentage Black

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:

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Equation1 : 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.

Graph 2

Scatter-Plot of Bivariate Relationship: Percentage Poor and Unemployment Rate (Code 6=California, 41=Oregon)

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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:

Equation2 : pvy020213=25.673−0.32(edu 685213)

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.

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:J o y 7 | 17

Equation3 : pctpoor=24.369+0.182 (rhi225214 k )−0.314 (edu 685213)

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]

# of Obs 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 results weighted by county population (pop060210)

Note 2: * P-value between .05 and .10, ** P-value between .001 and .05, *** P-value < .001

Note 3: standard errors in (XXX)

Note 4: 95% confidence interval in [XXX]

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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

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.

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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.

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

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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 and\or seniors in the may contribute to stretching the

primary earner’s income farther. Yet if many or all of the family earn income, this

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

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(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

Table 4. Regression models on poverty rate (3-5): selection of independent variablesInd. Variables Eq. 3 Eq. 4 Eq. 5Intercept 24.369*** 58.694*** 65.456***rhi225214 0.182** -0.026 0.015edu685213 -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***# of Obs 94 94 94Adj. R^2 0.567 0.94 0.94F-Test 61.97 117.26 183.74

P-value <0.0001 <0.0001 <0.0001

Note 1: all results weighted by county population (pop010210)Note 2: * P-value between .05 and .10, ** P-value between .001 and .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

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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

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.

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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

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.

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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.

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 J o y 15 | 17

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

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.

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