Decennial Census Return Rates: The Role of Social Capital
January 2017
Working Paper Number:
CES-17-39
Abstract
This paper explores how useful information about social and civic engagement (social capital)
might be to the U.S. Census Bureau in their efforts to improve predictions of mail return rates for the Decennial Census (DC) at the census tract level. Through construction of Hard-to-count (HRC) scores and multivariate analysis, we find that if information about social capital were available, predictions of response rates would be marginally improved.
Document Tags and Keywords
Keywords
Keywords are automatically generated using KeyBERT, a powerful and innovative
keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant
keywords.
By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the
text, highlighting the most significant topics and trends. This approach not only enhances searchability but
provides connections that go beyond potentially domain-specific author-defined keywords.
:
estimating,
statistical,
report,
survey,
respondent,
social,
hispanic,
expenditure,
regressors,
federal,
percentile,
population,
rate,
socioeconomic,
census bureau,
use census,
resident,
census use,
census survey,
assessing,
census responses,
race census
Tags
Tags are automatically generated using a pretrained language model from spaCy, which excels at
several tasks, including entity tagging.
The model is able to label words and phrases by part-of-speech,
including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are
identified to contain references to specific institutions, datasets, and other organizations.
:
Ordinary Least Squares,
Federal Reserve Bank,
Current Population Survey,
Decennial Census,
General Accounting Office,
American Community Survey
Similar Working Papers
Similarity between working papers are determined by an unsupervised neural
network model
know as Doc2Vec.
Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the
capture of semantic meaning in a way that relates to the context of words within the document. The model learns to
associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as
document classification, clustering, and similarity detection by preserving the order and structure of words. The
document vectors are compared using cosine similarity/distance to determine the most similar working papers.
Papers identified with 🔥 are in the top 20% of similarity.
The 10 most similar working papers to the working paper 'Decennial Census Return Rates: The Role of Social Capital' are listed below in order of similarity.
-
Working PaperIndividual Social Capital and Migration🔥
March 2018
Working Paper Number:
CES-18-14
This paper determines how individual, relative to community social capital affects individual migration decisions. We make use of non-public data from the Social Capital Community Benchmark Survey to predict multi-dimensional social capital for observations in the Current Population Survey. We find evidence that individuals are much less likely to have moved to a community with average social capital levels lower than their own and that higher levels of community social capital act as positive pull-factor amenities. The importance of that amenity differs across urban/rural locations. We also confirm that higher individual social capital is a negative predictor of migration.View Full Paper PDF
-
Working PaperIn-migration and Dilution of Community Social Capital🔥
June 2018
Working Paper Number:
CES-18-32
Consistent with predictions from the literature, we find that higher levels of in-migration dilute multiple dimensions of a community's level of social capital. The analysis employs a 2SLS methodology to account for potential endogeneity of migration.View Full Paper PDF
-
Working PaperPredicting the Effect of Adding a Citizenship Question to the 2020 Census
June 2019
Working Paper Number:
CES-19-18
The addition of a citizenship question to the 2020 census could affect the self-response rate, a key driver of the cost and quality of a census. We find that citizenship question response patterns in the American Community Survey (ACS) suggest that it is a sensitive question when asked about administrative record noncitizens but not when asked about administrative record citizens. ACS respondents who were administrative record noncitizens in 2017 frequently choose to skip the question or answer that the person is a citizen. We predict the effect on self-response to the entire survey by comparing mail response rates in the 2010 ACS, which included a citizenship question, with those of the 2010 census, which did not have a citizenship question, among households in both surveys. We compare the actual ACS-census difference in response rates for households that may contain noncitizens (more sensitive to the question) with the difference for households containing only U.S. citizens. We estimate that the addition of a citizenship question will have an 8.0 percentage point larger effect on self-response rates in households that may have noncitizens relative to those with only U.S. citizens. Assuming that the citizenship question does not affect unit self-response in all-citizen households and applying the 8.0 percentage point drop to the 28.1 % of housing units potentially having at least one noncitizen would predict an overall 2.2 percentage point drop in self-response in the 2020 census, increasing costs and reducing the quality of the population count.View Full Paper PDF
-
Working PaperConnected and Uncooperative: The Effects of Homogenous and Exclusive Social Networks on Survey Response Rates and Nonresponse Bias
January 2024
Working Paper Number:
CES-24-01
Social capital, the strength of people's friendship networks and community ties, has been hypothesized as an important determinant of survey participation. Investigating this hypothesis has been difficult given data constraints. In this paper, we provide insights by investigating how response rates and nonresponse bias in the American Community Survey are correlated with county-level social network data from Facebook. We find that areas of the United States where people have more exclusive and homogenous social networks have higher nonresponse bias and lower response rates. These results provide further evidence that the effects of social capital may not be simply a matter of whether people are socially isolated or not, but also what types of social connections people have and the sociodemographic heterogeneity of their social networks.View Full Paper PDF
-
Working PaperThe Impact of Household Surveys on 2020 Census Self-Response
July 2022
Working Paper Number:
CES-22-24
Households who were sampled in 2019 for the American Community Survey (ACS) had lower self-response rates to the 2020 Census. The magnitude varied from -1.5 percentage point for household sampled in January 2019 to -15.1 percent point for households sampled in December 2019. Similar effects are found for the Current Population Survey (CPS) as well.View Full Paper PDF
-
Working PaperNeighborhood Effects on High-School Drop-Out Rates and Teenage Childbearing: Tests for Non-Linearities, Race-Specific Effects, Interactions with Family Characteristics, and Endogenous Causation using Geocoded California Census Microdata
May 2008
Working Paper Number:
CES-08-12
This paper examines the relationship between neighborhood characteristics and the likelihood that a youth will drop out of high school or have a child during the teenage years. Using a dataset that is uniquely wellsuited to the study of neighborhood effects, the impact of the neighborhood poverty rate and the percentage of professionals in the local labor force on youth outcomes in California is examined. The first section of the paper tests for non-linearities in the relationship between indicators of neighborhood distress and youth outcomes. Some evidence is found for a break-point at low levels of poverty. Suggestive but inconclusive evidence is also found for a second breakpoint, at very high levels of poverty, for African-American youth only. The second part of the paper examines interactions between family background characteristics and neighborhood effects, and finds that White youth are most sensitive to neighborhood effects, while the effect of parental education depends on the neighborhood measure in question. Among White youth, those from single-parent households are more vulnerable to neighborhood conditions. The third section of the paper finds that for White youth and Hispanic youth, the relevant neighborhood variables appear to be the own-race poverty rates and the percentage of professionals of youths' own race. The final section of the paper estimates a tract-fixed effects model, using the results from the third section to define multiple relevant poverty rates within each tract. The fixed-effects specification suggests that for White and Hispanic youth in California, neighborhood effects remain significant, even with the inclusion of controls for any unobserved family and neighborhood characteristics that are constant within tracts.View Full Paper PDF
-
Working PaperWage Determination in Social Occupations: the Role of Individual Social Capital
January 2016
Working Paper Number:
CES-16-46
We make use of predicted social and civic activities (social capital) to account for selection into "social" occupations. Individual selection accounts for more than the total difference in wages observed between social and non-social occupations. The role that individual social capital plays in selecting into these occupations and the importance of selection in explaining wage differences across occupations is similar for both men and women. We make use of restricted 2000 Decennial Census and 2000 Social Capital Community Benchmark Survey. Individual social capital is instrumented by distance weighted surrounding census tract characteristics.View Full Paper PDF
-
Working PaperCitizenship Question Effects on Household Survey Response
June 2024
Working Paper Number:
CES-24-31
Several small-sample studies have predicted that a citizenship question in the 2020 Census would cause a large drop in self-response rates. In contrast, minimal effects were found in Poehler et al.'s (2020) analysis of the 2019 Census Test randomized controlled trial (RCT). We reconcile these findings by analyzing associations between characteristics about the addresses in the 2019 Census Test and their response behavior by linking to independently constructed administrative data. We find significant heterogeneity in sensitivity to the citizenship question among households containing Hispanics, naturalized citizens, and noncitizens. Response drops the most for households containing noncitizens ineligible for a Social Security number (SSN). It falls more for households with Latin American-born immigrants than those with immigrants from other countries. Response drops less for households with U.S.-born Hispanics than households with noncitizens from Latin America. Reductions in responsiveness occur not only through lower unit self-response rates, but also by increased household roster omissions and internet break-offs. The inclusion of a citizenship question increases the undercount of households with noncitizens. Households with noncitizens also have much higher citizenship question item nonresponse rates than those only containing citizens. The use of tract-level characteristics and significant heterogeneity among Hispanics, the foreign-born, and noncitizens help explain why the effects found by Poehler et al. were so small. Linking administrative microdata with the RCT data expands what we can learn from the RCT.View Full Paper PDF
-
Working PaperWHITE-LATINO RESIDENTIAL ATTAINMENTS AND SEGREGATION IN SIX CITIES: ASSESSING THE ROLE OF MICRO-LEVEL FACTORS
January 2016
Working Paper Number:
CES-16-51
This study examines the residential outcomes of Latinos in major metropolitan areas using new methods to connect micro-level analyses of residential attainments to overall patterns of segregation in the metropolitan area. Drawing on new formulations of standard measures of evenness, we conduct micro-level multivariate analyses using the restricted-use census microdata files to predict segregation-relevant neighborhood outcomes for individuals by race. We term the dependent variables segregation-relevant neighborhood outcomes because the differences in average outcomes for each group on these variables determine the values of the aggregate measures of evenness. This approach allows me to use standardization and components analysis to quantitatively assess the separate contributions that differences in social characteristics and differences in rates of return make towards determining the overall disparity in residential outcomes ' that is, the level of segregation ' between Whites and Latinos. Based on our micro-level residential attainment analyses we find that for Latinos, acculturation and gains in socioeconomic status are associated with greater residential contact with Whites, in agreement with spatial assimilation theory, which promotes lower segregation. However, our standardization and components analyses reveals that a substantial portion of White-Latino disparities in residential contact with Whites can be attributed to differences in rates of return; that is White-Latino differences in the ability to translate acculturation and gains in socioeconomic status into more residential contact with Whites. This is further elaborated upon by assessing the changes in contact with Whites for Whites and Latinos after manipulating single variables while holding all others constant. This can be interpreted as the role of discrimination which is emphasized by place stratification theory. Therefore we conclude that while members of minority groups make gains in residential outcomes that reduce segregation by attaining parity with Whites on social characteristics as spatial assimilation theory would predict, a substantial disparity will persist as Latinos cannot translate those gains into greater contact with Whites at the rate that Whites can. At the aggregate level of analysis, this means that White-Latino segregation remains substantial even when groups are equalized on social and economic characteristics.View Full Paper PDF
-
Working PaperWhen and Why Does Nonresponse Occur? Comparing the Determinants of Initial Unit Nonresponse and Panel Attrition
September 2023
Working Paper Number:
CES-23-44
Though unit nonresponse threatens data quality in both cross-sectional and panel surveys, little is understood about how initial nonresponse and later panel attrition may be theoretically or empirically distinct phenomena. This study advances current knowledge of the determinants of both unit nonresponse and panel attrition within the context of the U.S. Census Bureau's Survey of Income and Program Participation (SIPP) panel survey, which I link with high-quality federal administrative records, paradata, and geographic data. By exploiting the SIPP's interpenetrated sampling design and relying on cross-classified random effects modeling, this study quantifies the relative effects of sample household, interviewer, and place characteristics on baseline nonresponse and later attrition, addressing a critical gap in the literature. Given the reliance on successful record linkages between survey sample households and federal administrative data in the nonresponse research, this study also undertakes an explicitly spatial analysis of the place-based characteristics associated with successful record linkages in the U.S.View Full Paper PDF
