Introduction

The U.S. Census Bureau defines the West North Central Division as a sub-region of the Midwest United States consisting of seven states: Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota. Across these states there is a total population of 21 625 690 (as of the 2020 Census).

Throughout the West North Central Division section of this evaluation report, we will examine indicators of social vulnerability using data from the U.S. Census Bureau and following methodologies from the Centers for Disease Control and Prevention’s (CDC) Social Vulnerability Index (SVI). We will also assess measures of economic inequality—Median Income, Median Home Values, and the House Price Index—drawing on data from the U.S. Census Bureau and the Federal Housing Finance Agency (FHFA).

While there is no single “best” way to measure social vulnerability, the CDC’s SVI aggregates sixteen census-derived variables into four themes to capture different dimensions of community vulnerability:

  • Socioeconomic Status (e.g., living below 150 % of the poverty line, unemployment, housing cost burden, educational attainment, health insurance coverage)
  • Household Composition & Disability (e.g., proportions of elderly and children, civilian disability status, single-parent households, English language proficiency)
  • Minority Status & Language (e.g., racial and ethnic minority populations, limited English proficiency)
  • Housing Type & Transportation (e.g., multi-unit structures, mobile homes, crowding, vehicle access)

After mapping these vulnerability themes within the West North Central Division, we will analyze patterns of participation in two federal investment programs: the New Markets Tax Credit (NMTC) and the Low-Income Housing Tax Credit (LIHTC). Both programs use tax incentives to spur private investment in community facilities and affordable housing within high-need census tracts.

Finally, we will evaluate whether these federal policies have produced measurable impacts on social vulnerability and economic outcomes between 2010 and 2020. Our working hypothesis is that census tracts receiving NMTC and LIHTC investments will experience larger declines in social vulnerability and greater increases in median incomes and home values than the division-wide trends alone would predict.

Library

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

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_gregorio.R"),
             .character_only = TRUE)




census_division

## [1] "West North Central 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%")

## Warning in attr(x, "align"): 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
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%")

## Warning in attr(x, "align"): 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
GEOID NAME variable estimate moe label concept year
4 West North Central Division S0101_C01_001 21,350,512 NA Estimate!!Total!!Total population AGE AND SEX 2020
4 West North Central Division S0101_C03_001 10,601,282 2,551 Estimate!!Male!!Total population AGE AND SEX 2020
4 West North Central Division S0101_C05_001 10,749,230 2,567 Estimate!!Female!!Total population AGE AND SEX 2020