Segregation Impacts: Unemployment

Analyzing US Census Data in Python

Lee Hachadoorian

Asst. Professor of Instruction, Temple University

Deciphering ACS Subject Table IDs

[B|C]ssnnn[A-I]

B or C = "Base Table" or "Collapsed Table"

B15002	C15002[A-I]
No schooling	Less than high school diploma
Nursery to 4th grade	High school grad, GED, or alt.
5th and 6th grade	Some college or associate's
7th and 8th grade	Bachelor's degree or higher
9th grade
etc...

[B|C]ssnnn[A-I]

A = White alone
B = Black or African American Alone
C = American Indian and Alaska Native Alone
D = Asian Alone
E = Native Hawaiian and Other Pacific Islander Alone
F = Some Other Race Alone
G = Two or More Races
H = White Alone, Not Hispanic or Latino
I = Hispanic or Latino

[B|C]ssnnn[A-I]

01 = Age and Sex
02 = Race
03 = Hispanic or Latino Origin
05 = Foreign Born; Citizenship; Year of Entry; Nativity
15 = Educational Attainment
19 = Income (Households and Families)
23 = Employment Status; Work Experience; Labor Force

Source: https://www.census.gov/programs-surveys/acs/guidance/which-data-tool/table-ids-explained.html

Comparing Segregation Impacts

A regression plot of African-American unemployment vs. segregation, with male and female observations shown in different colors. The regression line for male unemployment rises much more sharply than the regression line for female unemployment.

Tidy Data

Wide DataFrame: msa_labor_force

     msa  male_lf  female_lf
0  12060   400843     481425
1  25540    30656      35046
2  26420   231346     268923
3  26900    55943      71036
...

msa_labor_force.columns = 
    ["msa", "male", "female"]

Tidy DataFrame: tidy_msa_labor_force

      msa     sex  labor_force
0   12060    male       400843
1   25540    male        30656
2   26420    male       231346
3   26900    male        55943
...
49  12060  female       481425
50  25540  female        35046
51  26420  female       268923
52  26900  female        71036
...

pandas.melt

tidy_msa_labor_force = msa_labor_force.melt(

    id_vars = ["msa"],

    value_vars = ["male", "female"],

    var_name = "sex",

    value_name = "labor_force"
    )

pandas.melt

tidy_msa_labor_force

      msa     sex  labor_force
0   12060    male       400843
1   25540    male        30656
2   26420    male       231346
3   26900    male        55943
...
49  12060  female       481425
50  25540  female        35046
51  26420  female       268923
52  26900  female        71036
...

Let's Practice

Analyzing US Census Data in Python