CREAT: Census Research Exploration and Analysis Tool

Papers Containing Tag(s): 'Housing and Urban Development'

The following papers contain search terms that you selected. From the papers listed below, you can navigate to the PDF, the profile page for that working paper, or see all the working papers written by an author. You can also explore tags, keywords, and authors that occur frequently within these papers.
Click here to search again

Frequently Occurring Concepts within this Search

Department of Housing and Urban Development - 31

American Community Survey - 28

Internal Revenue Service - 23

Protected Identification Key - 22

Census Bureau Disclosure Review Board - 20

Master Address File - 17

Decennial Census - 17

Person Validation System - 16

2010 Census - 15

Social Security Administration - 14

Indian Health Service - 14

Social Security - 13

Social Security Number - 13

Disclosure Review Board - 13

Current Population Survey - 12

Metropolitan Statistical Area - 12

American Housing Survey - 10

Indian Housing Information Center - 10

Person Identification Validation System - 10

Longitudinal Employer Household Dynamics - 9

Supplemental Nutrition Assistance Program - 8

MAFID - 8

Temporary Assistance for Needy Families - 8

Computer Assisted Personal Interview - 8

MAF-ARF - 8

Federal Statistical Research Data Center - 8

W-2 - 7

Personally Identifiable Information - 7

Some Other Race - 7

Ordinary Least Squares - 7

Center for Economic Studies - 6

Census Numident - 6

Administrative Records - 6

Employer Identification Numbers - 6

Disability Insurance - 6

Social Science Research Institute - 6

Census 2000 - 6

Survey of Income and Program Participation - 5

Medicaid Services - 5

Census Bureau Person Identification Validation System - 5

Census Household Composition Key - 5

Master Beneficiary Record - 5

Postal Service - 5

Individual Taxpayer Identification Numbers - 5

National Science Foundation - 5

Federal Reserve Bank - 5

Centers for Medicare - 4

Census Bureau Business Register - 4

Alfred P Sloan Foundation - 4

Composite Person Record - 4

American Economic Association - 4

Adjusted Gross Income - 4

Standard Statistical Establishment List - 4

Bureau of Labor Statistics - 4

Service Annual Survey - 4

Supreme Court - 4

Office of Management and Budget - 4

Census Bureau Master Address File - 4

Center for Administrative Records Research and Applications - 4

Department of Agriculture - 3

Earned Income Tax Credit - 3

UC Berkeley - 3

Business Register - 3

SSA Numident - 3

Unemployment Insurance - 3

Individual Characteristics File - 3

National Bureau of Economic Research - 3

Bureau of Economic Analysis - 3

Quarterly Census of Employment and Wages - 3

1940 Census - 3

Department of Homeland Security - 3

Department of Labor - 3

NUMIDENT - 3

Census Edited File - 3

Data Management System - 3

Longitudinal Business Database - 3

Department of Health and Human Services - 3

Department of Economics - 3

PIKed - 3

Computer Assisted Telephone Interviews and Computer Assisted Personal Interviews - 3

Geographic Information Systems - 3

National Opinion Research Center - 3

population - 18

housing - 18

residential - 17

neighborhood - 17

survey - 15

poverty - 15

respondent - 14

disadvantaged - 14

ethnicity - 14

census data - 12

resident - 12

data census - 10

rent - 10

data - 10

minority - 10

ethnic - 10

hispanic - 10

racial - 9

residence - 9

datasets - 8

renter - 8

medicaid - 7

welfare - 7

socioeconomic - 7

segregation - 7

race - 7

record - 7

assessed - 6

apartment - 6

agency - 6

census bureau - 6

imputation - 6

matching - 6

urban - 6

home - 6

estimating - 6

state - 6

city - 6

household surveys - 5

census survey - 5

eligibility - 5

eligible - 5

expenditure - 5

coverage - 5

black - 5

segregated - 5

neighbor - 5

statistical - 5

enrollment - 5

reside - 5

income neighborhoods - 5

family - 5

metropolitan - 5

econometric - 5

subsidy - 4

sampling - 4

population survey - 4

homeowner - 4

citizen - 4

bias - 4

income data - 4

federal - 4

indian - 4

income households - 4

subsidized - 4

immigrant - 4

impact - 4

tax - 4

employ - 4

records census - 4

census research - 4

census records - 4

employed - 4

workforce - 4

ssa - 3

survey households - 3

prevalence - 3

intergenerational - 3

child - 3

survey income - 3

residential segregation - 3

asian - 3

native - 3

percentile - 3

1040 - 3

relocation - 3

suburb - 3

use census - 3

discrimination - 3

estimation - 3

economist - 3

recession - 3

disparity - 3

rural - 3

immigration - 3

census responses - 3

microdata - 3

poor - 3

house - 3

assessing - 3

housing survey - 3

white - 3

2010 census - 3

employee - 3

job - 3

geographically - 3

department - 3

labor - 3

enterprise - 3

Viewing papers 1 through 10 of 47


  • Working Paper

    The Design of Sampling Strata for the National Household Food Acquisition and Purchase Survey

    February 2025

    Working Paper Number:

    CES-25-13

    The National Household Food Acquisition and Purchase Survey (FoodAPS), sponsored by the United States Department of Agriculture's (USDA) Economic Research Service (ERS) and Food and Nutrition Service (FNS), examines the food purchasing behavior of various subgroups of the U.S. population. These subgroups include participants in the Supplemental Nutrition Assistance Program (SNAP) and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), as well as households who are eligible for but don't participate in these programs. Participants in these social protection programs constitute small proportions of the U.S. population; obtaining an adequate number of such participants in a survey would be challenging absent stratified sampling to target SNAP and WIC participating households. This document describes how the U.S. Census Bureau (which is planning to conduct future versions of the FoodAPS survey on behalf of USDA) created sampling strata to flag the FoodAPS targeted subpopulations using machine learning applications in linked survey and administrative data. We describe the data, modeling techniques, and how well the sampling flags target low-income households and households receiving WIC and SNAP benefits. We additionally situate these efforts in the nascent literature on the use of big data and machine learning for the improvement of survey efficiency.
    View Full Paper PDF
  • Working Paper

    From Marcy to Madison Square? The Effects of Growing Up in Public Housing on Early Adulthood Outcomes

    November 2024

    Working Paper Number:

    CES-24-67

    This paper studies the effects of growing up in public housing in New York City on children's long-run outcomes. Using linked administrative data, we exploit variation in the age children move into public housing to estimate the effects of spending an additional year of childhood in public housing on a range of economic and social outcomes in early adulthood. We find that childhood exposure to public housing improves labor market outcomes and reduces participation in federal safety net programs, particularly for children from the most disadvantaged families. Additionally, we find there is some heterogeneity in impacts across public housing developments. Developments located in neighborhoods with relatively fewer renters and higher household incomes are better for children overall. Our estimate of the marginal value of public funds suggests that for every $1 the government spends per child on public housing, children receive $1.40 in benefits, including $2.30 for children from the most disadvantaged families.
    View Full Paper PDF
  • Working Paper

    Nonresponse and Coverage Bias in the Household Pulse Survey: Evidence from Administrative Data

    October 2024

    Working Paper Number:

    CES-24-60

    The Household Pulse Survey (HPS) conducted by the U.S. Census Bureau is a unique survey that provided timely data on the effects of the COVID-19 Pandemic on American households and continues to provide data on other emergent social and economic issues. Because the survey has a response rate in the single digits and only has an online response mode, there are concerns about nonresponse and coverage bias. In this paper, we match administrative data from government agencies and third-party data to HPS respondents to examine how representative they are of the U.S. population. For comparison, we create a benchmark of American Community Survey (ACS) respondents and nonrespondents and include the ACS respondents as another point of reference. Overall, we find that the HPS is less representative of the U.S. population than the ACS. However, performance varies across administrative variables, and the existing weighting adjustments appear to greatly improve the representativeness of the HPS. Additionally, we look at household characteristics by their email domain to examine the effects on coverage from limiting email messages in 2023 to addresses from the contact frame with at least 90% deliverability rates, finding no clear change in the representativeness of the HPS afterwards.
    View Full Paper PDF
  • Working Paper

    Incorporating Administrative Data in Survey Weights for the 2018-2022 Survey of Income and Program Participation

    October 2024

    Working Paper Number:

    CES-24-58

    Response rates to the Survey of Income and Program Participation (SIPP) have declined over time, raising the potential for nonresponse bias in survey estimates. A potential solution is to leverage administrative data from government agencies and third-party data providers when constructing survey weights. In this paper, we modify various parts of the SIPP weighting algorithm to incorporate such data. We create these new weights for the 2018 through 2022 SIPP panels and examine how the new weights affect survey estimates. Our results show that before weighting adjustments, SIPP respondents in these panels have higher socioeconomic status than the general population. Existing weighting procedures reduce many of these differences. Comparing SIPP estimates between the production weights and the administrative data-based weights yields changes that are not uniform across the joint income and program participation distribution. Unlike other Census Bureau household surveys, there is no large increase in nonresponse bias in SIPP due to the COVID-19 Pandemic. In summary, the magnitude and sign of nonresponse bias in SIPP is complicated, and the existing weighting procedures may change the sign of nonresponse bias for households with certain incomes and program benefit statuses.
    View Full Paper PDF
  • Working Paper

    Separate but Not Equal: The Uneven Cost of Residential Segregation for Network-Based Hiring

    October 2024

    Authors: Tam Mai

    Working Paper Number:

    CES-24-56

    This paper studies how residential segregation by race and by education affects job search via neighbor networks. Using confidential microdata from the US Census Bureau, I measure segregation for each characteristic at both the individual level and the neighborhood level. My findings are manifold. At the individual level, future coworkership with new neighbors on the same block is less likely among segregated individuals than among integrated workers, irrespective of races and levels of schooling. The impacts are most adverse for the most socioeconomically disadvantaged demographics: Blacks and those without a high school education. At the block level, however, higher segregation along either dimension raises the likelihood of any future coworkership on the block for all racial or educational groups. My identification strategy, capitalizing on data granularity, allows a causal interpretation of these results. Together, they point to the coexistence of homophily and in-group competition for job opportunities in linking residential segregation to neighbor-based informal hiring. My subtle findings have important implications for policy-making.
    View Full Paper PDF
  • Working Paper

    Revisiting Methods to Assign Responses when Race and Hispanic Origin Reporting are Discrepant Across Administrative Records and Third Party Sources

    May 2024

    Authors: James M. Noon

    Working Paper Number:

    CES-24-26

    The Best Race and Ethnicity Administrative Records Composite file ('Best Race file') is an composite file which combines Census, federal, and Third Party Data (TPD) sources and applies business rules to assign race and ethnicity values to person records. The first version of the Best Race administrative records composite was first constructed in 2015 and subsequently updated each year to include more recent vintages, when available, of the data sources originally included in the composite file. Where updates were available for data sources, the most recent information for persons was retained, and the business rules were reapplied to assign a single race and single Hispanic origin value to each person record. The majority of person records on the Best Race file have consistent race and ethnicity information across data sources. Where there are discrepancies in responses across data sources, we apply a series of business rules to assign a single race and ethnicity to each record. To improve the quality of the Best Race administrative records composite, we have begun revising the business rules which were developed several years ago. This paper discusses the original business rules as well as the implemented changes and their impact on the composite file.
    View Full Paper PDF
  • Working Paper

    Mobility, Opportunity, and Volatility Statistics (MOVS): Infrastructure Files and Public Use Data

    April 2024

    Working Paper Number:

    CES-24-23

    Federal statistical agencies and policymakers have identified a need for integrated systems of household and personal income statistics. This interest marks a recognition that aggregated measures of income, such as GDP or average income growth, tell an incomplete story that may conceal large gaps in well-being between different types of individuals and families. Until recently, longitudinal income data that are rich enough to calculate detailed income statistics and include demographic characteristics, such as race and ethnicity, have not been available. The Mobility, Opportunity, and Volatility Statistics project (MOVS) fills this gap in comprehensive income statistics. Using linked demographic and tax records on the population of U.S. working-age adults, the MOVS project defines households and calculates household income, applying an equivalence scale to create a personal income concept, and then traces the progress of individuals' incomes over time. We then output a set of intermediate statistics by race-ethnicity group, sex, year, base-year state of residence, and base-year income decile. We select the intermediate statistics most useful in developing more complex intragenerational income mobility measures, such as transition matrices, income growth curves, and variance-based volatility statistics. We provide these intermediate statistics as part of a publicly released data tool with downloadable flat files and accompanying documentation. This paper describes the data build process and the output files, including a brief analysis highlighting the structure and content of our main statistics.
    View Full Paper PDF
  • Working Paper

    Neighborhood Revitalization and Residential Sorting

    March 2024

    Working Paper Number:

    CES-24-12

    The HOPE VI Revitalization program sought to transform high-poverty neighborhoods into mixed-income communities through the demolition of public housing projects and the construction of new housing. We use longitudinal administrative data to investigate how the program affected both neighborhoods and individual residential outcomes. In line with the stated objectives, we find that the program reduced poverty rates in targeted neighborhoods and enabled subsidized renters to live in lower-poverty neighborhoods, on average. The primary beneficiaries were not the original neighborhood residents, most of whom moved away. Instead, subsidized renters who moved into the neighborhoods after an award experienced the largest reductions in neighborhood poverty. The program reduced the stock of public housing in targeted neighborhoods but expanded access to housing vouchers in other, lower-poverty neighborhoods. Spillover effects on the poverty rates of other neighborhoods were small and dispersed throughout the city. Our estimates imply that cities that revitalized half of their public housing stock reduced the average neighborhood poverty rate among all subsidized renters by 4.1 percentage points.
    View Full Paper PDF
  • Working Paper

    Incorporating Administrative Data in Survey Weights for the Basic Monthly Current Population Survey

    January 2024

    Working Paper Number:

    CES-24-02

    Response rates to the Current Population Survey (CPS) have declined over time, raising the potential for nonresponse bias in key population statistics. A potential solution is to leverage administrative data from government agencies and third-party data providers when constructing survey weights. In this paper, we take two approaches. First, we use administrative data to build a non-parametric nonresponse adjustment step while leaving the calibration to population estimates unchanged. Second, we use administratively linked data in the calibration process, matching income data from the Internal Return Service and state agencies, demographic data from the Social Security Administration and the decennial census, and industry data from the Census Bureau's Business Register to both responding and nonresponding households. We use the matched data in the household nonresponse adjustment of the CPS weighting algorithm, which changes the weights of respondents to account for differential nonresponse rates among subpopulations. After running the experimental weighting algorithm, we compare estimates of the unemployment rate and labor force participation rate between the experimental weights and the production weights. Before March 2020, estimates of the labor force participation rates using the experimental weights are 0.2 percentage points higher than the original estimates, with minimal effect on unemployment rate. After March 2020, the new labor force participation rates are similar, but the unemployment rate is about 0.2 percentage points higher in some months during the height of COVID-related interviewing restrictions. These results are suggestive that if there is any nonresponse bias present in the CPS, the magnitude is comparable to the typical margin of error of the unemployment rate estimate. Additionally, the results are overall similar across demographic groups and states, as well as using alternative weighting methodology. Finally, we discuss how our estimates compare to those from earlier papers that calculate estimates of bias in key CPS labor force statistics. This paper is for research purposes only. No changes to production are being implemented at this time.
    View Full Paper PDF
  • Working Paper

    Where to Build Affordable Housing? Evaluating the Tradeoffs of Location

    December 2023

    Working Paper Number:

    CES-23-62R

    How does the location of affordable housing affect tenant welfare, the distribution of assistance, and broader societal objectives such as racial integration? Using administrative data on tenants of units funded by the Low-Income Housing Tax Credit (LIHTC), we first show that characteristics such as race and proxies for need vary widely across neighborhoods. Despite fixed eligibility requirements, LIHTC developments in more opportunity-rich neighborhoods house tenants who are higher income, more educated, and far less likely to be Black. To quantify the welfare implications, we build a residential choice model in which households choose from both market-rate and affordable housing options, where the latter must be rationed. While building affordable housing in higher-opportunity neighborhoods costs more, it also increases household welfare and reduces city-wide segregation. The gains in household welfare, however, accrue to more moderate-need, non-Black/Hispanic households at the expense of other households. This change in the distribution of assistance is primarily due to a 'crowding out' effect: households that only apply for assistance in higher-opportunity neighborhoods crowd out those willing to apply regardless of location. Finally, other policy levers'such as lowering the income limits used for means-testing'have only limited effects relative to the choice of location.
    View Full Paper PDF