Introduction

The Pacific Division is defined by the U.S. Census Bureau as a subdivision of the West Region comprising of five states: Alaska, California, Hawaii, Oregon, and Washington. These states have an estimated total population of 53,191,898 (26,489,419 male and 26,702,479 female.)

This project hopes to identify the trends between social vulnerability in census tracts and the amount of tax credit dollars they receive through two popular tax credit programs, the New Market Tax Credit (NMTC) and Low Income Housing Tax Credit (LIHTC). To identify social vulnerability, we will be using the U.S. Centers for Disease Control and Prevention (CDC)’s Social Vulnerability Index. We will also utilize data from the U.S. Census Bureau to identify census tracts and changes from the years 2010 to 2020. Data related to Median Income, Median Home Values, and House Price Indexes will be from the U.S. Census Bureau and Federal Housing Finance Agency to aid in the analysis of economic inequalities across regions. The CDC’s Social Vulnerability Index aims to approximate a communities vulnerability to disaster through the following four categories:

  • Socioeconomic Status (living below the 150% poverty line, unemployment, housing cost burden, no high school diploma, no health insurance)
  • Household Characteristics (aged 65+, aged 17 & younger, civilian with a disability, single-parent households, English language proficiency)
  • Racial & Ethnic Minority Status
  • Housing Type & Transportation (multi-unit structures, mobile homes, crowding, no vehicle, group quarters)

Once we identify particularly vulnerable communities within the division, we can examine patterns of participation in the NMTC and LIHTC programs. These national programs encourage investors to initiate building projects in at-risk communities by offering tax credits. The hope of these incentives is to reduce social vulnerability and increase economic outcomes.

For the Pacific Division, we will evaluate the impact of the NMTC and LIHTC at reducing social vulnerability (measured by flags) and increasing economic outcomes (measure by median incomes, home values, and housing price index) from 2010 to 2020. Our hypothesis is that census tracts whom receive investment from these programs will experience a decrease in social vulnerability over time as well as increases in median incomes and home values greater than the genereal trends of the division.

Library

library( here )
library( tidyverse )
library( kableExtra )
library( tidycensus )

Load Data

#Load US Census region data
census_regions <- readxl::read_excel(here::here("data/raw/Census_Data_SVI/census_regions.xlsx"))

# View divisions
census_regions %>% select(Division) %>% distinct()

## # A tibble: 9 × 1
##   Division                   
##   <chr>                      
## 1 New England Division       
## 2 Middle Atlantic Division   
## 3 East North Central Division
## 4 West North Central Division
## 5 South Atlantic Division    
## 6 East South Central Division
## 7 West South Central Division
## 8 Mountain Division          
## 9 Pacific Division

import::here( "census_division",
             # notice the use of here::here() that points to the .R file
             # where all these R objects are created
             .from = here::here("analysis/project_data_steps_radovich.R"),
             .character_only = TRUE)

census_division

## [1] "Pacific Division"

# Load API key, assign to TidyCensus Package, remember do not print output
source(here::here("password.R"))
tidycensus::census_api_key(census_api_key)

Census Variable Data Dictionary

# Preview Data
census_variables <- load_variables(2020, "acs5/subject", cache = TRUE)
census_variables %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
name label concept
S0101_C01_001 Estimate!!Total!!Total population AGE AND SEX
S0101_C01_002 Estimate!!Total!!Total population!!AGE!!Under 5 years AGE AND SEX
S0101_C01_003 Estimate!!Total!!Total population!!AGE!!5 to 9 years AGE AND SEX
S0101_C01_004 Estimate!!Total!!Total population!!AGE!!10 to 14 years AGE AND SEX
S0101_C01_005 Estimate!!Total!!Total population!!AGE!!15 to 19 years AGE AND SEX
S0101_C01_006 Estimate!!Total!!Total population!!AGE!!20 to 24 years AGE AND SEX

Division Population

# Query Census API via tidyverse
acs_pull <- get_acs(geography = "division", 
              variables = c("S0101_C01_001", "S0101_C03_001", "S0101_C05_001"), 
              year = 2020) %>% filter(NAME == census_division)

# Join data set with census_variable df
left_join(acs_pull, census_variables, join_by("variable" == "name")) %>% mutate( "year" = "2020") %>% kbl(format.args = list(big.mark = ",")) %>% kable_styling() %>% scroll_box(width = "100%")
GEOID NAME variable estimate moe label concept year
9 Pacific Division S0101_C01_001 53,191,898 NA Estimate!!Total!!Total population AGE AND SEX 2020
9 Pacific Division S0101_C03_001 26,489,419 1,875 Estimate!!Male!!Total population AGE AND SEX 2020
9 Pacific Division S0101_C05_001 26,702,479 1,876 Estimate!!Female!!Total population AGE AND SEX 2020