Mapping Poverty in California

Hello everyone, and welcome back to my 'random tangent blog of stuff'. This time, with more 'stuff' and less 'random tangent'! This post is actually for completion of a project in my Geographic Information Systems (GIS) class.  I conducted an analysis of poverty levels in my home state of California, specifically examining poverty by race and family type. So, before I go any further, lemme switch my writing style over from the casual regular blogging Jason to formal academic stick-up-my-ass Jason (-:

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Analysis of Poverty by Ethnicity and Family Type in California

In 2012 California had over 6 million people in poverty, and was also one of the top states for rate of poverty (17%), as well as poverty rate growth (0.4 percentile growth from 2011 to 2012) [1]. Despite this, California still shows itself as the economic powerhouse of the nation, consisting of 13% of the US's GDP [2]. When examining California's GDP per capita, however, it comes out only moderately above the national average ($52,666 state to $49,587 national) [3]. As the 2012 federal poverty line is only $19,090 for a family of three, this means that 17% of the population of California is making about a third, or less, of the state's GDP per Capita. With California's notoriously high cost of living, this data hints at high levels of inequality within the state.

Methodology:

This analysis examines some of the characteristics of poverty in California, using the US Census Bureau's American Community Survey, 3-year (2010-2012) data at the county level [4]. Poverty is cross-sectioned within each county by ethnicity and family type.

Because of the high proportion of Hispanic's [5] in California (38.2% of the population) [6] data was broken out by Hispanic/Latino status. Unfortunately due to how the US Census Bureau's tables cross-tabulating poverty, ethnicity and family type incorporate race, only three ethnicity categories are available: White non-Hispanic, Hispanic, and Other. Other would include all other non-Hispanic/Latino races, such as Asian, African American, Pacific Islander and Native American. This does not allow analysis on African Americans separately from these groups.

Also due to how the Census Bureau collects data, family type can only be examined on three dimensions: gender of head-of-household, marital status, and presence of children. These three factors create the family type variable, with categories such as single female with kids, married couple without kids, etc.

The map data used is from the US Census Bureau's TIGER Shapefiles [7] for 2012. The counties within California were color-coded based on overall levle of poverty, and an icon was placed in the center of each county representing the race/family type combination with the highest poverty rate.

Because of the low populations in 13 of California's counties (Inyo, Mono, Alpine, Tuolumne, Mariposa, Calaveras, Amador, Sierra, Plumas, Lassen, Modoc, Trinity, Del Norte), they have no data associated with them.

Results:

The poverty rate runs from 5% in in San Mateo county to 22.0% in Tulare county, with the average poverty rate being 12.6%. Five counties had ethnicity/family type groups with 100% poverty rates, however in all cases the sample size for this group was under 35. Excluding those counties with highest poverty rate groups at 100%, 30 counties had Single Hispanic Mothers as the ethnicity/family type with the highest level of poverty, five had Single Other Mothers, three had Single Other Fathers, and two had Single Other Male w/o Kids.

County-level poverty was highest in the southern counties and those in the south central valley of California. The Single Hispanic Mother highest poverty counties are also concentrated in the southern parts of the state, however don't always match with the highest poverty rate counties. For example, the concentration of high poverty counties in the central valley (Tulare, Kings, Fresno, Madera, and Merced counties) have a mixture of Hispanic and Other race single mothers as their highest poverty group, compared to the shoreline counties which, below San Francisco, are all Single Hispanic Mothers as the highest poverty group.

Limitations:

Because the poverty rate is based on a fixed dollar amount, and county costs of living vary across the state, counties are not perfectly comparable to each other. The more rural areas of California, in the central valley, will likely show a higher rate of poverty due to the lower cost of living. This means that poverty in one county is not comparable to poverty in another county.

Also, the number of people relative to the county's population for the highest poverty group is not shown in the map, just the group with the highest poverty rate. This analysis also did not examine the distribution of ethnicity or family type within the state. This means that there could be (and in a few cases are) groups with very high poverty rates that consist of only a handful of people.

Lastly, because only the group with the highest poverty rate is shown, the poverty levels of the remaining groups is ignored. This could potentially exclude groups that have poverty rates that are very close to the highest group.

Conclusions:

Though the limitations of this analysis do hamper the ability to speak to California's poverty issue as a whole, because governance is divided by county (and city within county), this analysis does provide a perspective as to the political scope of poverty in California. With the large majority of counties facing poverty among Single Hispanic Mothers, this would be a very easy group to gain political traction in addressing.

Further study in this area could attempt to control for population distribution within the state, as well as including poverty rates for all groups of race and family type. This could be done with existing US Census data. This would give a clearer picture of what groups in California are in poverty. The unequal costs of living through California is a more challenging problem, and would require a county-by-county assessment to allow for poverty impacts to be comparable across counties.

References:

[1] US Census Bureau, Poverty: 2000 to 2012, http://www.census.gov/prod/2013pubs/acsbr12-01.pdf

[2] US Bureau of Economic Analysis, GDP By State, http://www.bea.gov/newsreleases/regional/gdp_state/gsp_newsrelease.htmState, http://www.bea.gov/newsreleases/regional/gdp_state/gsp_newsrelease.htm

[3] Author calculations from a combination of data from US Census Bureau, American Community Survey, Demographic and Housing Estimates 2012, http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml; and US Bureau of Economic Analysis, GDP By State, http://www.bea.gov/newsreleases/regional/gdp_state/gsp_newsrelease.htm

[4] http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml

[5] Please note that the Latino population is also included under the label 'Hispanic' for this analysis. As there is little consistency is how these terms are used in current literature, Hispanic was chosen as the Census Bureau puts that term first in its labels. This population includes people from Latin America and Spanish descent, such as Mexicans, Colombians, Brazilians, etc.

[6] US Census Bureau, Demographic and Housing Estimates 2012, http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml

[7] http://www.census.gov/geo/maps-data/data/tiger.html