Introduction

For this project, we examined the impact of two federally funded tax programs, the New Markets Tax Program (NMTC) and the Low Income Housing Tax Credit program (LIHTC) on qualifying neighborhoods. In order to evaluate the change in neighborhoods,we will be looked 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 looked 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.

We included 2010 and 2020 data from the census bureau, utilized funding and eligibility data for the NMTC and LITC, and included data from the Federal Housing Finance Agency for the housing price index. The data was wrangled to include eligible tracts for the Mountain Division.

To analyze our data, we used both visual and statistical tools to include; waffle charts, chloropleth maps, bivariate mapping, k-means clustering, Pearson’s R correlation, and diff-in-diff regression.

Data

In order to flag social vulnerabilities for our census tracts within the Mountain Division, we pulled US Census Data. The US Centers for Disease Control and Prevention (CDC) defined appropriate variables to measure social vulnerability indices (SVI). The CDC’s SVI measures how vulnerable a neighborhood is in the event of an emergency situation, such as a natural disaster or health outbreak. These variables are organized into four categories, and include the following variables:

  • Socioeconomic Status: percent living below 150% poverty, percent unemployed, population housing cost-burned, percent adults without high school diploma, percent without health insurance.
  • Household Characteristics: percent age 17 and under, percent age 65 and over, percent disabled civilians, percent single parent families, percent limited English speakers. -Racial & Ethnic Minority Status: percent minority race/ethnicity. -Housing Type & Transportation: percent in multi-unit housing, percent in mobile housing, percent in crowded living spaces, percent with no vehicle access, percent living in group quarters.

We obtained data for all these categories from the US Census API for 2010 and 2020, and only used variables that were available in both decades. To account for geographic changes between the decades, we pulled census tract data from 2010 and then crosswalked it with the census block group data from 2020.

Our National data included 73,057 tracts for 2010 and 2020. The Mountain Division had a 2020 population of 24, 534, 951 living in 5,250 tracts. In 2010, the tracts with the most vulnerable (Total SVI) flags were in order, Laramie County, WY, Utah County, UT, Tooele County, UT, and 2 tracts in Salt Lake County, UT. In 2020, the most vulnerable tracts (SVI flags) included the same tracts, and did not change. The least vulnerable tracts in the Mountain Division experienced change. In 2010 the least vulnerable tracts were Summit County, UT, Glacier County, MT, and Douglas County, CO in 2010. They switched completely in 2020 to Weld County, CO, Clark County, NV, and Salt Lake County, UT. It was worth noting that Salt Lake County included tracts that were the most vulnerable and tracts that were the least vulnerable in the Mountain Division.

Next, we identified tracts that were eligible for our two tax programs; the New Markets Tax Credit (NMTC), and the Low Income Housing Tax Credit (LIHTC). Markets Tax Credits are awarded to community development entities for the purpose of investing in low income communities and recipients must meet strict criteria to be eligible, but the credits are intended to for areas with low median income and high poverty rates. Low Income Housing Tax Credits are awarded to investors with the purpose of investing in affordable housing for renters. Again, this program is designed to improve neighborhood with low gross incomes and high poverty rates. We wrangled our data to determine tracts that were deemed eligible for these tax credits, but excluded tracts that received funding prior to 2010, to insure that we are measuring the impact of the programs.

For the Mountain Division, there were 2075 tracts eligible for the NMTC program, with 56 counties receiving funding. There is a large spread within tracts receiving NMTC in the Mountain division with SVI flag counts ranging from 5 - 2558, and the NMTC dollars ranging from $800,000 to $225,215,967. Montana, Idaho, and Wyoming received high dollars compared to low flag counts. Nevada received the most dollars in the highest need counties.

For the Mountain Division, there were 57 tracts eligible for the LIHTC program, with 14 counties receiving funding. The Mountain division counties receiving LIHTC project funding between 2011-2020, had a range of 9 to 487 SVI flags in 2010. The funding ranged from $270,127 and $15,437,500. There is a large range between counties in the number of SVI flags in 2010 and funding dollars spent between 2011 and 2020. Arizona had the tracts with the highest need and the highest amount of LIHTC funding. Again, Montana and Colorado received high funding dollars for a low amount of need.

In addition to the Social Vulnerability variables, we also included 3 additional economic variables: house price index (HPI), median home value, and median income. HPI is determined by analyzing mortgage transactions. The HPI was pulled from the Federal Housing Finance Agency API. The median home value and median incomes were collected using census data.

Analysis

We used several tools to analyze our data, both visually and statistically.

First, we used waffle charts, pictorial infographics, to look at our SVI variable categories. For each category we used 100 icons to visually show proportions of the percent of population who were vulnerable for each SVI variable, and included overall SVI for each category of variable.

We also looked at Chloropleth maps, to look at SVI Flags to Population Ratios. In order to accomplish this, shapefiles were downloaded and SVI flags were mapped to their respective geographical county location. We included interactive tooltips so the user can get SVI information by hovering over a county.The Chloropleth maps were colored coded to visually see areas of least vulnerability (lighter color) and high vulnerablility (darker color).

BiVariate Mapping was also another visual tool to analyze the data. For our bivariate maps, we included SVI flags and the amount of funding each county received. This provided a visualization of the areas of high versus low need, and those areas who received little or high funding from our tax programs.

We also examined the correlations between funding received (tax credits) and need (SVI flags), calculating the Pearson’s R. correlation coefficient. A Pearson’s R calculation of 0.62 suggests that there is a strong positive association between the dollar amount spent in NMTC from 2011-2020 and the flag count within the Mountain Division counties in

  1. Overall, within all the counties in the Mountain Division, there is a moderate positive correlation between SVI flags from 2010 and dollars spent on LIHTC in 2011-2020, with a Pearson’s score of 0.57.

To check to see if our correlations were not reflecting any major outliers, we used K-Means Clustering to separate our data points into meaningful groups. We then recalculated our Pearson’s R for each group.

We utilized diff-in-diff models to analyze the impacts of the New Markets Tax Credit (NMTC) and the Low Income Housing Tax Credit (LIHTC) programs as they relate to social vulnerability and economic changes in Mountain Division tracts.Diff-In-Diff models are useful as a statistical tool to analyze the effects of a program when there is a treatment group and a group who did not receive the treatment (control group). It can be used when there are two periods, before intervention and after intervention. Since we have tracts who were eligible and did receive NMTC and LIHTC dollars and tracts who did not, we analyzed the impact of these programs before and after intervention.

Results

NMTC Diff-In-Diff Models

Socioeconomic SVI

We fitted a linear model (estimated using OLS) to predict SVI_FLAG_COUNT_SES with treat, post and cbsa (formula: SVI_FLAG_COUNT_SES ~ treat + post + treat * post + cbsa) where treat represents NMTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and moderate proportion of variance (R2 = 0.18, F(75, 3360) = 10.16, p < .001, adj. R2 = 0.17)

The effect of treat × post is statistically non-significant and positive (beta = 0.02, 95% CI [-0.35, 0.39], t(3360) = 0.11, p = 0.914; Std. beta = 1.68e-03, 95% CI [-0.03, 0.03])

Since the effect of treat x post is not statistically significant, we cannot conclude that the NMTC program had a measurable impact on socioeconomic status-related social vulnerability and economic outcomes.

Household Characteristics

Show in New Window We fitted a linear model (estimated using OLS) to predict SVI_FLAG_COUNT_HHCHAR with treat, post and cbsa (formula: SVI_FLAG_COUNT_HHCHAR ~ treat + post + treat * post + cbsa) where treat represents NMTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and moderate proportion of variance (R2 = 0.19, F(75, 3360) = 10.28, p < .001, adj. R2 = 0.17)

The effect of treat × post is statistically non-significant and positive (beta = 4.92e-03, 95% CI [-0.25, 0.26], t(3360) = 0.04, p = 0.970; Std. beta = 5.86e-04, 95% CI [-0.03, 0.03])

Since the effect of treat x post is not statistically significant, we cannot conclude that the NMTC program had a measurable impact on household characteristics-related social vulnerability and economic outcomes.

Racial & Ethnic Minority SVI

We fitted a linear model (estimated using OLS) to predict SVI_FLAG_COUNT_REM with treat, post and cbsa (formula: SVI_FLAG_COUNT_REM ~ treat + post + treat * post + cbsa) where treat represents NMTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and substantial proportion of variance (R2 = 0.34, F(75, 3360) = 23.24, p < .001, adj. R2 = 0.33)

The effect of treat × post is statistically non-significant and negative (beta = -4.57e-03, 95% CI [-0.11, 0.10], t(3360) = -0.09, p = 0.932; Std. beta = -1.19e-03, 95% CI [-0.03, 0.03])

Since the effect of treat x post is not statistically significant, we cannot conclude that the NMTC program had a measurable impact on racial and ethnic minority status-related social vulnerability and economic outcomes.

Housing & Transportation SVI

We fitted a linear model (estimated using OLS) to predict SVI_FLAG_COUNT_HOUSETRANSPT with treat, post and cbsa (formula: SVI_FLAG_COUNT_HOUSETRANSPT ~ treat + post + treat * post + cbsa) where treat represents NMTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and weak proportion of variance (R2 = 0.11, F(75, 3360) = 5.71, p < .001, adj. R2 = 0.09)

The effect of treat × post is statistically non-significant and positive (beta = 0.09, 95% CI [-0.19, 0.36], t(3360) = 0.62, p = 0.537; Std. beta = 0.01, 95% CI [-0.02, 0.04])

Since the effect of treat x post is not statistically significant, we cannot conclude that the NMTC program had a measurable impact on housing and transportation access-related social vulnerability and economic outcomes.

Overall SVI

We fitted a linear model (estimated using OLS) to predict SVI_FLAG_COUNT_OVERALL with treat, post and cbsa (formula: SVI_FLAG_COUNT_OVERALL ~ treat + post + treat * post + cbsa) where treat represents NMTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and moderate proportion of variance (R2 = 0.24, F(75, 3360) = 13.76, p < .001, adj. R2 = 0.22)

The effect of treat × post is statistically non-significant and positive (beta = 0.11, 95% CI [-0.64, 0.85], t(3360) = 0.28, p = 0.779; Std. beta = 4.23e-03, 95% CI [-0.03, 0.03])

Since the effect of treat x post is not statistically significant, we cannot conclude that the NMTC program had a measurable impact on socioeconomic, household characteristics, racial and ethnic minority status, and housing and transportation access-related social vulnerability and economic outcomes.

Median Income Economic Outcomes

We fitted a linear model (estimated using OLS) to predict MEDIAN_INCOME with treat, post and cbsa (formula: MEDIAN_INCOME ~ treat + post + treat * post + cbsa) where treat represents NMTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and moderate proportion of variance (R2 = 0.17, F(75, 3360) = 9.26, p < .001, adj. R2 = 0.15)

The effect of treat × post is statistically non-significant and positive (beta = 0.03, 95% CI [-0.04, 0.10], t(3360) = 0.89, p = 0.373; Std. beta = 0.01, 95% CI [-0.02, 0.04])

Since the effect of treat x post is not statistically significant, we cannot conclude that the NMTC program had a measurable impact on Median Income-related social vulnerability and economic outcomes.

Median Home Value Economic Outcomes

We fitted a linear model (estimated using OLS) to predict MEDIAN_HOME_VALUE with treat, post and cbsa (formula: MEDIAN_HOME_VALUE ~ treat + post + treat * post + cbsa) where treat represents NMTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and moderate proportion of variance (R2 = 0.26, F(75, 3248) = 14.99, p < .001, adj. R2 = 0.24)

The effect of treat × post is statistically non-significant and positive (beta = 0.05, 95% CI [-0.07, 0.17], t(3248) = 0.88, p = 0.381; Std. beta = 0.01, 95% CI [-0.02, 0.04])

Since the effect of treat x post is not statistically significant, we cannot conclude that the NMTC program had a measurable impact on Median Home Value-related social vulnerability and economic outcomes.

House Price Index Economic Outcomes

We fitted a linear model (estimated using OLS) to predict HOUSE_PRICE_INDEX with treat, post and cbsa (formula: HOUSE_PRICE_INDEX ~ treat + post + treat * post + cbsa) where treat represents NMTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and substantial proportion of variance (R2 = 0.61, F(71, 1902) = 41.96, p < .001, adj. R2 = 0.60)

The effect of treat × post is statistically non-significant and negative (beta = -5.21e-04, 95% CI [-0.11, 0.11], t(1902) = -9.32e-03, p = 0.993; Std. beta = -1.33e-04, 95% CI [-0.03, 0.03])

Since the effect of treat x post is not statistically significant, we cannot conclude that the NMTC program had a measurable impact on House Price Index-related social vulnerability and economic outcomes.

LIHTC Diff-In-Diff Models

Socioeconomic SVI

We fitted a linear model (estimated using OLS) to predict SVI_FLAG_COUNT_SES with treat, post and cbsa (formula: SVI_FLAG_COUNT_SES ~ treat + post + treat * post + cbsa) where treat represents LIHTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and moderate proportion of variance (R2 = 0.26, F(39, 332) = 2.91, p < .001, adj. R2 = 0.17)

The effect of treat × post is statistically non-significant and negative (beta = -0.26, 95% CI [-0.87, 0.35], t(332) = -0.83, p = 0.406; Std. beta = -0.04, 95% CI [-0.13, 0.05])

Since the effect of treat x post is not statistically significant, we cannot conclude that the LIHTC program had a measurable impact on socioeconomic status-related social vulnerability and economic outcomes.

Household Characteristics SVI

We fitted a linear model (estimated using OLS) to predict SVI_FLAG_COUNT_HHCHAR with treat, post and cbsa (formula: SVI_FLAG_COUNT_HHCHAR ~ treat + post + treat * post + cbsa) where treat represents LIHTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and substantial proportion of variance (R2 = 0.43, F(39, 332) = 6.52, p < .001, adj. R2 = 0.37)

The effect of treat × post is statistically non-significant and negative (beta = -0.06, 95% CI [-0.64, 0.53], t(332) = -0.19, p = 0.847; Std. beta = -7.97e-03, 95% CI [-0.09, 0.07])

Since the effect of treat x post is not statistically significant, we cannot conclude that the LIHTC program had a measurable impact on household characteristics-related social vulnerability and economic outcomes.

Racial and Ethnic Minority SVI

We fitted a linear model (estimated using OLS) to predict SVI_FLAG_COUNT_REM with treat, post and cbsa (formula: SVI_FLAG_COUNT_REM ~ treat + post + treat * post + cbsa) where treat represents LIHTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and substantial proportion of variance (R2 = 0.59, F(39, 332) = 12.22, p < .001, adj. R2 = 0.54)

The effect of treat × post is statistically non-significant and negative (beta = -6.17e-03, 95% CI [-0.20, 0.19], t(332) = -0.06, p = 0.950; Std. beta = -2.23e-03, 95% CI [-0.07, 0.07])

Since the effect of treat x post is not statistically significant, we cannot conclude that the LIHTC program had a measurable impact on racial and ethnic minority status-related social vulnerability and economic outcomes.

Housing and Transportation SVI

We fitted a linear model (estimated using OLS) to predict SVI_FLAG_COUNT_HOUSETRANSPT with treat, post and cbsa (formula: SVI_FLAG_COUNT_HOUSETRANSPT ~ treat + post + treat * post + cbsa) where treat represents LIHTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and moderate proportion of variance (R2 = 0.20, F(39, 332) = 2.08, p < .001, adj. R2 = 0.10)

The effect of treat × post is statistically non-significant and positive (beta = 0.19, 95% CI [-0.37, 0.75], t(332) = 0.67, p = 0.503; Std. beta = 0.03, 95% CI [-0.06, 0.13])

Since the effect of treat x post is not statistically significant, we cannot conclude that the LIHTC program had a measurable impact on housing and transportation access-related social vulnerability and economic outcomes.

Overall SVI

We fitted a linear model (estimated using OLS) to predict SVI_FLAG_COUNT_OVERALL with treat, post and cbsa (formula: SVI_FLAG_COUNT_OVERALL ~ treat + post + treat * post + cbsa) where treat represents LIHTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and substantial proportion of variance (R2 = 0.42, F(39, 332) = 6.20, p < .001, adj. R2 = 0.35)

The effect of treat × post is statistically non-significant and negative (beta = -0.13, 95% CI [-1.39, 1.12], t(332) = -0.21, p = 0.837; Std. beta = -8.60e-03, 95% CI [-0.09, 0.07])

Since the effect of treat x post is not statistically significant, we cannot conclude that the LIHTC program had a measurable impact on socioeconomic, household characteristics, racial and ethnic minority status, and housing and transportation access-related social vulnerability and economic outcomes.

Median Income Economic Outcomes

We fitted a linear model (estimated using OLS) to predict MEDIAN_INCOME with treat, post and cbsa (formula: MEDIAN_INCOME ~ treat + post + treat * post + cbsa) where treat represents LIHTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and substantial proportion of variance (R2 = 0.32, F(39, 332) = 4.10, p < .001, adj. R2 = 0.25)

The effect of treat × post is statistically non-significant and positive (beta = 0.04, 95% CI [-0.20, 0.27], t(332) = 0.33, p = 0.745; Std. beta = 0.01, 95% CI [-0.07, 0.10])

Since the effect of treat x post is not statistically significant, we cannot conclude that the LIHTC program had a measurable impact on Median Income-related social vulnerability and economic outcomes.

Median Home Value Economic Outcomes

We fitted a linear model (estimated using OLS) to predict MEDIAN_HOME_VALUE with treat, post and cbsa (formula: MEDIAN_HOME_VALUE ~ treat + post + treat * post + cbsa) where treat represents LIHTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and substantial proportion of variance (R2 = 0.48, F(39, 302) = 7.11, p < .001, adj. R2 = 0.41)

The effect of treat × post is statistically non-significant and positive (beta = 0.04, 95% CI [-0.25, 0.32], t(302) = 0.26, p = 0.798; Std. beta = 0.01, 95% CI [-0.07, 0.09])

Since the effect of treat x post is not statistically significant, we cannot conclude that the LIHTC program had a measurable impact on Median Home Value-related social vulnerability and economic outcomes.

House Price Index Economic Outcomes

We fitted a linear model (estimated using OLS) to predict HOUSE_PRICE_INDEX with treat, post and cbsa (formula: HOUSE_PRICE_INDEX ~ treat + post + treat * post + cbsa) where treat represents LIHTC program participation, post is the year of 2020 after starting period of 2010, and cbsa controls for metro-level effects.

The model explains a statistically significant and substantial proportion of variance (R2 = 0.65, F(23, 102) = 8.41, p < .001, adj. R2 = 0.58)

The effect of treat × post is statistically non-significant and positive (beta = 0.03, 95% CI [-0.37, 0.44], t(102) = 0.17, p = 0.865; Std. beta = 9.99e-03, 95% CI [-0.11, 0.13])

Since the effect of treat x post is not statistically significant, we cannot conclude that the LIHTC program had a measurable impact on House Price Index-related social vulnerability and economic outcomes.

Discussion and Recommendations

In conclusion, for the Mountain Region, there was a a moderate to high correlation between tracts that received funding and those in need. However, there was no statistically significant impact of these programs on decreasing social vulnerability in any category.

Our analysis would indicate that the areas of need received funding. However, the amount of funding greatly varied across tracts. The amount of money should be included in our analysis to determine if the funding amount affected neighborhood vulnerability. Additionally, it may be beneficial to track gentrification, and check variables that measure housing affordability and population mobility.

References

R and Package Libraries

R Version

Analyses were conducted using the R Statistical language (version 4.4.1; R Core Team, 2024) on Windows 11 x64 (build 26100)

R Packages

Data

Readings