Results and Conclusion

Introduction

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Data

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Analysis

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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(50, 7187) = 32.37, p < .001, adj. R2 = 0.18)

The effect of treat × post is statistically significant and negative (beta = -0.42, 95% CI [-0.73, -0.12], t(7187) = -2.72, p = 0.007; Std. beta = -0.03, 95% CI [-0.05, -8.08e-03])

Since the effect of treat x post is statistically significant, we can 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 = 0.06, F(50, 7187) = 9.16, p < .001, adj. R2 = 0.05)

The effect of treat × post is statistically non-significant and negative (beta = -0.06, 95% CI [-0.29, 0.17], t(7187) = -0.51, p = 0.608; Std. beta = -5.87e-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 = 0.34, F(50, 7187) = 74.72, p < .001, adj. R2 = 0.34)

The effect of treat × post is statistically non-significant and negative (beta = -0.05, 95% CI [-0.13, 0.04], t(7187) = -1.02, p = 0.309; Std. beta = -9.74e-03, 95% CI [-0.03, 9.01e-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 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 substantial proportion of variance (R2 = 0.31, F(50, 7187) = 65.39, p < .001, adj. R2 = 0.31)

The effect of treat × post is statistically non-significant and negative (beta = -0.03, 95% CI [-0.24, 0.19], t(7187) = -0.26, p = 0.795; Std. beta = -2.54e-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 substantial proportion of variance (R2 = 0.27, F(50, 7187) = 52.77, p < .001, adj. R2 = 0.26)

The effect of treat × post is statistically non-significant and negative (beta = -0.56, 95% CI [-1.17, 0.05], t(7187) = -1.80, p = 0.072; Std. beta = -0.02, 95% CI [-0.04, 1.62e-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 weak proportion of variance (R2 = 0.09, F(50, 7185) = 13.83, p < .001, adj. R2 = 0.08)

The effect of treat × post is statistically non-significant and positive (beta = 0.06, 95% CI [-7.16e-03, 0.14], t(7185) = 1.76, p = 0.078; Std. beta = 0.02, 95% CI [-2.22e-03, 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 substantial proportion of variance (R2 = 0.73, F(50, 6737) = 362.45, p < .001, adj. R2 = 0.73)

The effect of treat × post is statistically significant and positive (beta = 0.11, 95% CI [5.76e-03, 0.22], t(6737) = 2.07, p = 0.039; Std. beta = 0.01, 95% CI [6.80e-04, 0.03])

Since the effect of treat x post is statistically significant, we can 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 = 0.32, F(45, 1974) = 20.40, p < .001, adj. R2 = 0.30)

The effect of treat × post is statistically non-significant and positive (beta = 0.05, 95% CI [-0.13, 0.23], t(1974) = 0.55, p = 0.583; Std. beta = 0.01, 95% CI [-0.03, 0.05])

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 = 0.18, F(50, 7187) = 32.37, p < .001, adj. R2 = 0.18)

The effect of treat × post is statistically significant and negative (beta = -0.42, 95% CI [-0.73, -0.12], t(7187) = -2.72, p = 0.007; Std. beta = -0.03, 95% CI [-0.05, -8.08e-03])

Since the effect of treat x post is statistically significant, we can 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 weak proportion of variance (R2 = 0.06, F(50, 7187) = 9.16, p < .001, adj. R2 = 0.05)

The effect of treat × post is statistically non-significant and negative (beta = -0.06, 95% CI [-0.29, 0.17], t(7187) = -0.51, p = 0.608; Std. beta = -5.87e-03, 95% CI [-0.03, 0.02])

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 = 0.34, F(50, 7187) = 74.72, p < .001, adj. R2 = 0.34)

The effect of treat × post is statistically non-significant and negative (beta = -0.05, 95% CI [-0.13, 0.04], t(7187) = -1.02, p = 0.309; Std. beta = -9.74e-03, 95% CI [-0.03, 9.01e-03])

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 substantial proportion of variance (R2 = 0.31, F(50, 7187) = 65.39, p < .001, adj. R2 = 0.31)

The effect of treat × post is statistically non-significant and negative (beta = -0.03, 95% CI [-0.24, 0.19], t(7187) = -0.26, p = 0.795; Std. beta = -2.54e-03, 95% CI [-0.02, 0.02])

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(50, 7187) = 52.77, p < .001, adj. R2 = 0.26)

The effect of treat × post is statistically non-significant and negative (beta = -0.56, 95% CI [-1.17, 0.05], t(7187) = -1.80, p = 0.072; Std. beta = -0.02, 95% CI [-0.04, 1.62e-03])

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 weak proportion of variance (R2 = 0.09, F(50, 7185) = 13.83, p < .001, adj. R2 = 0.08)

The effect of treat × post is statistically non-significant and positive (beta = 0.06, 95% CI [-7.16e-03, 0.14], t(7185) = 1.76, p = 0.078; Std. beta = 0.02, 95% CI [-2.22e-03, 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 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 = 0.73, F(50, 6737) = 362.45, p < .001, adj. R2 = 0.73)

The effect of treat × post is statistically significant and positive (beta = 0.11, 95% CI [5.76e-03, 0.22], t(6737) = 2.07, p = 0.039; Std. beta = 0.01, 95% CI [6.80e-04, 0.03])

Since the effect of treat x post is statistically significant, we can 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 = 0.32, F(45, 1974) = 20.40, p < .001, adj. R2 = 0.30)

The effect of treat × post is statistically non-significant and positive (beta = 0.05, 95% CI [-0.13, 0.23], t(1974) = 0.55, p = 0.583; Std. beta = 0.01, 95% CI [-0.03, 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 House Price Index-related social vulnerability and economic outcomes.

Discussion and Recommendations

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References

R Version

Analyses were conducted using the R Statistical language (version 4.3.2; R Core Team, 2023) on Windows 10 x64 (build 19045).

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Data

• CDFI Fund (2023). FY 2023 NMTC Public Data Release: 2003-2021 Data File Updated - Aug 21, 2023. https://www.cdfifund.gov/documents/data-releases

• Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry/ Geospatial Research, Analysis, and Services Program. (2022). CDC/ATSDR Social Vulnerability Index 2020 Methodology. https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html

• FHFA (n.d.). HPI® Census Tracts (Developmental Index; Not Seasonally Adjusted). https://www.fhfa.gov/DataTools/Downloads/Pages/House-Price-Index-Datasets.aspx#atvol

• HUD User (n.d.). 2010, 2011, and 2012 QCT data for all of the census tracts in the United States and Puerto Rico (qct_data_2010_2011_2012.xlsx). https://www.huduser.gov/portal/datasets/qct.html#year2010

• HUD User (2023). Low-Income Housing Tax Credit (LIHTC): Property Level Data. https://www.huduser.gov/portal/datasets/lihtc/property.html

• Novogradac New Markets Tax Credit Resource Center. (2017). New Markets Tax Credit Low-Income Community Census Tracts - American Community Survey 2011-2015. https://www.novoco.com/resource-centers/new-markets-tax-credits/data-tables

• Steven Manson, Jonathan Schroeder, David Van Riper, Katherine Knowles, Tracy Kugler, Finn Roberts, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 18.0 [2020 → 2010 Block Groups → Census Tracts Crosswalks National File]. Minneapolis, MN: IPUMS. 2023. http://doi.org/10.18128/D050.V18.0

• U.S. Bureau of Labor Statistics (n.d.). CPI Inflation Calculator. https://data.bls.gov/cgi-bin/cpicalc.pl

• U.S. Bureau of Labor Statistics (n.d.). QCEW County-MSA-CSA Crosswalk (For NAICS-Based Data). https://www.bls.gov/cew/classifications/areas/county-msa-csa-crosswalk.htm

• U.S. Census Bureau. (2011). 2006-2010 American Community Survey 5-year. https://www.census.gov/newsroom/releases/archives/american_community_survey_acs/cb11-208.html

• U.S. Census Bureau. (2013). 2008-2012 American Community Survey 5-year. https://www.census.gov/newsroom/press-kits/2013/20131217_acs_5yr.html

• U.S. Census Bureau. (2022). 2016-2020 American Community Survey 5-year. https://www.census.gov/newsroom/press-releases/2022/acs-5-year-estimates.html

Readings

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