Introduction

The US Census Bureau has categorized the 50 states and District of Columbia into 4 distinct regions. Those regions are further divided into 9 districts. For the purposes of this report, we are focusing on the Mountain District, which is part of the West Region. There are 8 states in the Mountain District: Arizona, Colorado, Idaho, New Mexico, Montana, Utah, Nevada, and Wyoming.

According to the 2020 Census data, the Mountain division has a total population of 24,534,951, consisting of 12,282,456 males and 12,252,495 females.

For this portion of the project, we will be examining the impact of two federally funded tax programs, the New Markets Tax Program and the Low Income Housing Tax Credit program on qualifying neighborhoods. In order to evaluate the change in neighborhoods,we will be looking at Social Variability Index (SVI) Census variables which are defined by U.S. Centers for Disease Control and Prevention (CDC)’s to measure neighborhood vulnerability. In addition, we will also be looking at economic outcomes variables to include: median home values and median income from Census data, and the Federal Housing Finance Agency’s house price index.

The purpose of the federally funded New Markets Tax Credits and Low Income Housing Tax credits is to provide low-income neighborhoods investment funding to improve vulnerable neighborhoods.

The CDC’s SVI looks at 4 categories of vulnerability. All variables within these categories can be pulled from Census Data API. They include:

  • Socioeconomic Status Variables (below 50% poverty, unemployed, housing cost burden, no high school diploma, no health insurance)
  • Household Characteristics Variables (aged 65 and up, ages 17 and younger, civilian w/a disability, single-parent household, english language proficiency)
  • Racial & Ethnic Minority Variables
  • Housing Type/Transportation Variables (multi-unit structures, mobile homes, crowding, no vehicle, group quarters)

We will also be including the following economic variables in our analysis:

-Median Home Values -Median Income -House Price Index

We will look at these variables in vulnerable neighborhoods who received NMTC and LIHTC funding and compare them with similar neighborhoods who did not receive funding from these programs. Specifically we will be looking at 2010 and 2020 Census data for these neighborhoods. It is our hypothesis that those neighborhoods who received NMTC and LIHTC funding will have decreased SVI vulnerability flags and improved economic outcomes.

Library

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

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_knopp.R"),
             .character_only = TRUE)

census_division

## [1] "Mountain Division"

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

Census Variable Data Dictionary

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
8 Mountain Division S0101_C01_001 24,534,951 NA Estimate!!Total!!Total population AGE AND SEX 2020
8 Mountain Division S0101_C03_001 12,282,456 1,948 Estimate!!Male!!Total population AGE AND SEX 2020
8 Mountain Division S0101_C05_001 12,252,495 1,947 Estimate!!Female!!Total population AGE AND SEX 2020