Introduction

The US Census Bureau defines the Middle Atlantic Division as a sub-region of the Northeast United States consisting of three states: New York, New Jersey, and Pennsylvania. Across these states there is a total population of 41,195,152 (20,084,449 males and 21,110,703 females).

Throughout the Middle Atlantic Division section of this evaluation report we will examine indicators of social vulnerability utilizing data from the U.S. Census Bureau and following methodologies from the U.S. Centers for Disease Control and Prevention (CDC)’s Social Vulnerability Index and measures of economic inequalities related to Median Income, Median Home Values, and House Price Index utilizing data from the US Census Bureau and Federal Housing Finance Agency.

While there is not a uniform method for measuring social vulnerability, the CDC’s SVI has four main categories to determine vulnerability levels:

  • Socioeconomic Status (living below 150% poverty line, unemployment, housing cost burden, no high school diploma, no health Insurance)
  • Household Characteristics (aged 65 & older, 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)

All of these measures are useful to identify communities that are particular vulnerable to disasters and in need of additional support. After identifying these specific areas within the division, we will examine patterns of participation in the New Markets Tax Credit (NMTC) and Low Income Housing Tax Credit (NMTC) programs. Both of these programs are national policies that utilize tax credits to encourage investors to initiate building projects for community facilities and housing within at-risk census tracts.

Thus, we will evaluate whether these federal polices have had a measurable impact on reducing social vulnerability and improving economic outcomes from 2010 to 2020. It is our hypothesis that census tracts that receive investment from the NMTC and LIHTC programs will experience a decrease in social vulnerability and increase in median incomes and home values that is greater than the general trends in the division.

Library

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

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_courtney.R"),
             .character_only = TRUE)

census_division

## [1] "Middle Atlantic 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
2 Middle Atlantic Division S0101_C01_001 41,195,152 NA Estimate!!Total!!Total population AGE AND SEX 2020
2 Middle Atlantic Division S0101_C03_001 20,084,449 1,631 Estimate!!Male!!Total population AGE AND SEX 2020
2 Middle Atlantic Division S0101_C05_001 21,110,703 1,632 Estimate!!Female!!Total population AGE AND SEX 2020