Using restricted administrative data on the voucher program, we examine the experience of voucher holders in metropolitan areas with rising rents. While some of our models suggest that rising rents in metropolitan areas are associated with a slight increase in rent-to-income ratios among voucher holders, poor renters in general see significantly larger increases in rent-to-income ratios. We see little evidence that rising rents push voucher holders to worse neighborhoods, with voucher holders in central cities ending up in lower poverty neighborhoods as rents rise. It appears that vouchers may help low-income households remain in neighborhoods as they gentrify.
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In addition, annual data on rents at the census tract level are not available, and even five-year estimates from ACS are noisy.
In certain circumstances, households earning up to 80% of the area median income can receive a voucher.
During this time period, HUD allowed FMRs to be set at median rent in selected metropolitan areas where voucher holders were concentrated in a limited number of census tracts. Collinson and Ganong (2018) find this shift had no effect on voucher holder location.
He limits sample to households with nonzero income.
Alaska, Hawaii, Puerto Rico, and the other unincorporated territories are excluded from our sample.
We use the Core Based Statistical Area (CBSA) delineations based on the 2010 standards that were announced by the Office of Management and Budget in 2013. Although we have 238 unique CBSAs, not each of them is observed in all years. Some small CBSAs have missing information on racial composition and/or share of high-school dropouts. Among the 238 CBSAs that we consider, 118 have complete demographic information for the 9 years (2006–2014), 16 have demographic information for 8 years, 7 have demographic information for 7 years, 13 have demographic information for 6 years, 8 have demographic information for 5 years, 11 have demographic information for 4 years, 21 have demographic information for 3 years, 19 have demographic information for 2 years, and 25 have demographic information for only one year.
When we estimate CBSA-level models that compare the HCV and the IPUMS samples, the sample of CBSAs falls to 156 because the CBSA of residence is missing for some households in the IPUMS sample.
Census tracts are defined according to the boundaries from the 2010 delineations. The ACS waves for years 2006–2010, 2007–2011, 2008–2012, 2009–2013, and 2010–2015 are in 2010 boundaries. We crosswalk estimates from 2005–2009, which are in 2000 boundaries, to 2010 boundaries.
We find qualitatively the same results when we link voucher data from 2005 through 2009 to neighborhood conditions as reported in 2005–09 five-year ACS data and link voucher data from 2010 through 2014 to neighborhood conditions as reported in 2010–14 five-year ACS data.
We also run opportunity models using years 2007–2014 and obtain similar results. In this set of models, tract attributes for years 2007 and 2008 are obtained from the 2000 Census.
While the gross rents charged to voucher holders rise with CBSA median rents, they should be more strongly associated with rents in the submarkets where voucher holders tend to rent (though these two rent measures are highly correlated).
IPUMS models include the same set of household controls than the HCV models except for dummies for building type.
We cannot distinguish between moves within and across Census tracts in the IPUMS sample.
We obtain similar results when we aggregate to CBSA and estimate long-change regressions examining link between increases in CBSA rent and increases in rent burden between 2006 and 2014.
We compute exposure for all poor families because publicly available tract data from the American Community Survey do not report separate counts of poor renters.
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Ellen, I.G., Torrats-Espinosa, G. Do Vouchers Protect Low-Income Households from Rising Rents?. Eastern Econ J 46, 260–281 (2020). https://doi.org/10.1057/s41302-019-00159-y
- Housing vouchers
- Rent burden