Introduction

For this project, we are evaluating the effect of two national tax credit programs, the New Market Tax Credits (NMTC) and the Low Income Housing Tax Credits (LIHTC), on improving social vulnerability and economic outcomes for eligible census tracts. To measure change, we will first be evaluating the perceived social vulnerability of each neighborhood using the CDC’s Social Vulnerability Index, which defines four main categories: socioeconomic status, household characteristics, racial & ethnic minority status, and housing type & transportation. In addition, we will measure economic outcomes using data from the U.S. Census Bureau and the Federal Housing Finance Agency (FHFA).

We will be using data from 2010 and 2020 to evaluate longitudinal change in neighborhoods. Geographical and economic data was brought in by the U.S. Census Bureau and American Community Survey (ACS), where the FHFA provided data on economic outcomes, namely the housing price index.

To accurately evaluate change, the data was wrangled to match tracts from 2010 to 2020 and determine eligibility for each tax credit program as defined below. We then utilized visual and statistical analysis tools such as waffle plots, bivariate and choropleth mapping, k-means analysis, and diff-in-diff regression analysis.

Data

The data we are using is being sourced from the U.S. Census Bureau American Community Survey (ACS) from 2010 and 2020. In 2010, the data pulled was at a tract-level, whereas in 2020 the data was pulled at the Census Block Group-level. This is due to the changes in tract boundaries over time to help ensure that we are making an equal comparison. Census Block Groups help us determine which 2020 blocks belong to which cenus tracts from 2010. These were then assigned using FIPS crosswalk data from the IPUMS National Historical Geographic Information System (NHGIS).

For our national census tract data there were 73057 rows of raw data in both 2010 and 2020. The Pacific division had 10867 rows of data for both time periods. The most vulnerable tracts, as determined by number of SVI flags, in the division for 2010 were Fresno County, CA, Kern County, CA, and Los Angeles County, CA.

Since our primary interest is in the effectiveness of the NMTC and LIHTC programs, only tracts deemed as eligible to receive benefits were analysed. Tract eligibility was determined by not having previously received tax credit dollars and the NMTC and LIHTC conditions for eligibility. In addition, we looked at areas that experienced high levels of migration which were not previously flagged as eligible. More information on eligibility can be found here: - NMTC[https://www.cdfifund.gov/programs-training/programs/new-markets-tax-credit] - LIHTC[https://www.urban.org/sites/default/files/publication/98758/lithc_how_it_works_and_who_it_serves_final_0.pdf] Of the 73057 national census-tracts, 29068 were eligible for the NMTC and 3723 were eligible for the LIHTC. In the Pacific division, 4237 tracts were eligible for the NMTC and 584 for the LIHTC.

This project was also interested in the economic outcomes of these tracts as measured by changes in median income, median home value, and the housing price index. This data was pulled from the U.S. Census Bureau’s estimates on median income and home value and used the Federal Housing Finance Agency (FHFA)’s House Price Index data.

Data was grouped for analysis on the divisional level and then again by census-tracts using adjusted FIPS crosswalk data for the year 2020. We also pulled in shapefiles from the ACS to visualize changes in SVI flag counts using choropleth mapping. Individual factors for social vulnerability were mapped using waffle charts to compare specific areas of vulnerability (recall that the CDC has four categories of vulnerability).

Analysis

To analyze the data we implored various methods including waffle charts, spatial analysis (choropleth and bivariate mapping), correlation analyses, k-means clustering, and diff-in-diff regression analysis. - Waffle Charts were used to visualize the amounts of social vulnerability across the four categories defined by the CDC: socioeconomic status, household characteristics, racial & ethnic minority status, and housing type & transportation. - Spatial Analysis using choropleth maps and bivariate color grading was used to visualize the flag-to-population ratio for census tracts. A flag-to-population ratio was used to create an equitable comparison across county tracts since larger counties will likely have larger amounts of flags for social vulnerability. - Correlation Analyses was used to determine the correlation between the amount of SVI flags and the amount of tax credit dollars recieved - k-means clustering is a machine learning technique that evaluates data points and clusters them based on similarities, helping to limit the effect of outliers. For most of our variables, this resulted in two distinct groups for the Pacific division. - A diff-in-diff regression model was chosen to evaluate effectiveness since we have data from two separate time points, before and after intervention of the tax credit programs. This model was then applied to each of our four dependent variables (the social vulnerability categories)

Each analysis method was applied to both the NMTC and LIHTC program.

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 substantial proportion of variance (R2 = 0.27, F(69, 8170) = 43.83, p < .001, adj. R2 = 0.26)

The effect of treat × post is statistically non-significant and negative (beta = -0.05, 95% CI [-0.32, 0.22], t(8170) = -0.35, p = 0.724; Std. beta = -3.34e-03, 95% CI [-0.02, 0.02])

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 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 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(69, 8170) = 14.88, p < .001, adj. R2 = 0.10)

The effect of treat × post is statistically non-significant and negative (beta = -0.05, 95% CI [-0.25, 0.15], t(8170) = -0.48, p = 0.634; Std. beta = -4.97e-03, 95% CI [-0.03, 0.02])

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 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 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 = 0.30, F(69, 8170) = 51.08, p < .001, adj. R2 = 0.30)

The effect of treat × post is statistically non-significant and negative (beta = -0.01, 95% CI [-0.09, 0.07], t(8170) = -0.32, p = 0.749; Std. beta = -2.96e-03, 95% CI [-0.02, 0.02])

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 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 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 = 0.06, F(69, 8170) = 7.56, p < .001, adj. R2 = 0.05)

The effect of treat × post is statistically non-significant and negative (beta = -0.01, 95% CI [-0.22, 0.19], t(8170) = -0.12, p = 0.901; Std. beta = -1.33e-03, 95% CI [-0.02, 0.02])

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 = 0.23, F(69, 8170) = 36.00, p < .001, adj. R2 = 0.23)

The effect of treat × post is statistically non-significant and negative (beta = -0.12, 95% CI [-0.67, 0.42], t(8170) = -0.44, p = 0.659; Std. beta = -4.27e-03, 95% CI [-0.02, 0.01])

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 = 0.18, F(69, 8166) = 26.31, p < .001, adj. R2 = 0.17)

The effect of treat × post is statistically significant and positive (beta = 0.06, 95% CI [4.76e-03, 0.11], t(8166) = 2.14, p = 0.033; Std. beta = 0.02, 95% CI [1.77e-03, 0.04])

Since the effect of treat x post is statistically significant, we can 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 substantial proportion of variance (R2 = 0.42, F(68, 7847) = 82.65, p < .001, adj. R2 = 0.41)

The effect of treat × post is statistically non-significant and positive (beta = 2.91e-03, 95% CI [-0.08, 0.08], t(7847) = 0.07, p = 0.943; Std. beta = 6.13e-04, 95% CI [-0.02, 0.02])

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 = 0.49, F(67, 5458) = 77.52, p < .001, adj. R2 = 0.48)

The effect of treat × post is statistically non-significant and negative (beta = -0.02, 95% CI [-0.12, 0.09], t(5458) = -0.36, p = 0.716; Std. beta = -3.52e-03, 95% CI [-0.02, 0.02])

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 = 0.24, F(35, 1110) = 10.06, p < .001, adj. R2 = 0.22)

The effect of treat × post is statistically non-significant and positive (beta = 7.39e-03, 95% CI [-0.32, 0.34], t(1110) = 0.04, p = 0.965; Std. beta = 1.15e-03, 95% CI [-0.05, 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 moderate proportion of variance (R2 = 0.21, F(35, 1110) = 8.59, p < .001, adj. R2 = 0.19)

The effect of treat × post is statistically non-significant and negative (beta = -0.05, 95% CI [-0.33, 0.23], t(1110) = -0.36, p = 0.720; Std. beta = -9.57e-03, 95% CI [-0.06, 0.04])

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 = 0.33, F(35, 1110) = 15.92, p < .001, adj. R2 = 0.31)

The effect of treat × post is statistically non-significant and positive (beta = 8.81e-03, 95% CI [-0.10, 0.12], t(1110) = 0.16, p = 0.877; Std. beta = 3.80e-03, 95% CI [-0.04, 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 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 weak proportion of variance (R2 = 0.06, F(35, 1110) = 2.11, p < .001, adj. R2 = 0.03)

The effect of treat × post is statistically non-significant and positive (beta = 0.10, 95% CI [-0.17, 0.38], t(1110) = 0.73, p = 0.463; Std. beta = 0.02, 95% CI [-0.04, 0.08])

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 = 0.27, F(35, 1110) = 11.51, p < .001, adj. R2 = 0.24)

The effect of treat × post is statistically non-significant and positive (beta = 0.07, 95% CI [-0.60, 0.73], t(1110) = 0.20, p = 0.842; Std. beta = 5.14e-03, 95% CI [-0.05, 0.06])

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 moderate proportion of variance (R2 = 0.23, F(35, 1110) = 9.35, p < .001, adj. R2 = 0.20)

The effect of treat × post is statistically non-significant and positive (beta = 0.02, 95% CI [-0.07, 0.10], t(1110) = 0.40, p = 0.688; Std. beta = 0.01, 95% CI [-0.04, 0.06])

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 = 0.35, F(34, 1031) = 16.30, p < .001, adj. R2 = 0.33)

The effect of treat × post is statistically non-significant and positive (beta = 2.73e-03, 95% CI [-0.14, 0.15], t(1031) = 0.04, p = 0.970; Std. beta = 9.33e-04, 95% CI [-0.05, 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 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 = 0.55, F(31, 586) = 22.81, p < .001, adj. R2 = 0.52)

The effect of treat × post is statistically non-significant and negative (beta = -0.05, 95% CI [-0.20, 0.09], t(586) = -0.70, p = 0.483; Std. beta = -0.02, 95% CI [-0.07, 0.04])

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

Our choropleth maps show that the highest amounts of SVI flags that also recieve the most tax credit dollars are located near large metropolitan areas such as Los Angeles, CA and Seattle, WA. As a whole, Alaska has high amounts of social vulnerability, most likely due to lower populations than the rest of the division. Hawaii also has high amounts of social vulnerability, but received a lower amount of tax credit dollars compared to tracts with similar SVI flags.

In the Pacific Division, the Los Angeles County, CA tract has the highest amount of flags at 9210 and also received the highest amount of tax credits. In the overall correlation analysis, there was a 0.98 correlation between SVI flags and tax credits received. However, after k-means analyses and placing Los Angeles County in a separate group, ur correlation analysis shows a moderate positive correlation between SVI flags and tax credits received.

The diff-in-diff modeling did not reveal any statistically significant coefficients except for on the outcome of Median Income under the NMTC program. This suggests a correlation between receiving credits under the NMTC program and an increase in median income from 2010 to 2020.

References

R

Analyses were conducted using the R Statistical language (version 4.3.1; R Core Team, 2023) on Windows 11 x64 (build 26100)

Packages

Data