Small-Area Analyses Using Public American Community Survey Data: A Tree-Based Spatial Microsimulation Technique

2021 ◽  
pp. 008117502110575
Author(s):  
Nick Graetz ◽  
Kevin Ummel ◽  
Daniel Aldana Cohen
Keyword(s):  
Census Tract ◽  
Small Area ◽  
Household Survey ◽  
The United States ◽  
Community Survey ◽  
Local Context ◽  
Primary Data ◽  
American Community Survey ◽  
Public Data ◽  
Spatial Microsimulation

Quantitative sociologists and social policymakers are increasingly interested in local context. Some city-specific studies have developed new primary data collection efforts to analyze inequality at the neighborhood level, but methods from spatial microsimulation have yet to be broadly used in sociology to take better advantage of existing public data sets. The American Community Survey (ACS) is the largest household survey in the United States and indispensable for detailed analysis of specific places and populations. The authors propose a technique, tree-based spatial microsimulation, to produce “small-area” (census-tract) estimates of any person- or household-level phenomenon that can be derived from ACS microdata variables. The approach is straightforward and computationally efficient, based only on publicly available data, and it provides more reliable estimates than do prevailing methods of microsimulation. The authors demonstrate the technique’s capabilities by producing tract-level estimates, stratified by race/ethnicity, of (1) the proportion of people in the census-tract population who have children and work in an essential occupation and (2) the proportion of people in the census-tract population living below the federal poverty threshold and in a household that spends greater than 50 percent of monthly income on rent or owner costs. These examples are relevant to understanding the sociospatial inequalities dramatized by the coronavirus disease 2019 pandemic. The authors discuss potential extensions of the technique to derive small-area estimates of variables observed in surveys other than the ACS.

scholarly journals Potential Data Sources for a New Study of Social Mobility in the United States

2014 ◽  
Vol 657 (1) ◽  
pp. 208-246
Author(s):  
John Robert Warren
Keyword(s):  
United States ◽  
Social Mobility ◽  
Population Survey ◽  
Data Access ◽  
The United States ◽  
Community Survey ◽  
Data Sources ◽  
American Community Survey ◽  
Costs And Benefits ◽  
Trade Offs

In this article I define the main criteria that ought to be considered in evaluating the costs and benefits of various data resources that might be used for a new study of social and economic mobility in the United States. These criteria include population definition and coverage, sample size, topical coverage, temporal issues, spatial issues, sustainability, financial expense, and privacy and data access. I use these criteria to evaluate the strengths and weakness of several possible data resources for a new study of mobility, including existing smaller-scale surveys, the Current Population Survey, the American Community Survey, linked administrative data, and a new stand-alone survey. No option is perfect, and all involve trade-offs. I conclude by recommending five possible designs that are particularly strong on the criteria listed above.

scholarly journals A New Infrastructure for Monitoring Social Mobility in the United States

2014 ◽  
Vol 657 (1) ◽  
pp. 63-82 ◽  
Author(s):  
David B. Grusky ◽  
Timothy M. Smeeding ◽  
C. Matthew Snipp
Keyword(s):  
Social Science ◽  
Labor Markets ◽  
Social Mobility ◽  
Science Inquiry ◽  
The United States ◽  
Great Accuracy ◽  
Community Survey ◽  
Evidence Based ◽  
American Community Survey ◽  
New Infrastructure

The country’s capacity to monitor trends in social mobility has languished since the last major survey on U.S. social mobility was fielded in 1973. It is accordingly difficult to evaluate recent concerns that social mobility may be declining or to develop mobility policy that is adequately informed by evidence. This article presents a new initiative, dubbed the American Opportunity Study (AOS), that would allow the country to monitor social mobility efficiently and with great accuracy. The AOS entails developing the country’s capacity to link records across decennial censuses, the American Community Survey, and administrative sources. If an AOS of this sort were assembled, it would open up new fields of social science inquiry; increase opportunities for evidence-based policy on poverty, mobility, child development, and labor markets; and otherwise constitute a new social science resource with much reach and impact.

scholarly journals Precision of Disability Estimates for Southeast Asians in the American Community Survey 2008-2010 Microdata

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Carlos Siordia ◽  
Vi Donna Le
Keyword(s):  
Independent Living ◽  
The United States ◽  
Southeast Asian ◽  
Community Survey ◽  
American Community Survey ◽  
Southeast Asians ◽  
Margin Of Error ◽  
The Us ◽  
Long Form ◽  
Hearing Difficulties

Detailed social data about the United States (US) population was collected as part of the US decennial Census up until 2000. Since then, the American Community Survey (ACS) has replaced the long form previously administered in decennial years. The ACS uses a sample rather than the entire US population and therefore, only estimates can be created from the data. This investigation computes disability estimates, standard error, margin of error, and a more comprehensive “range of uncertainty” measure for non-Latino-whites (NLW) and four Southeast Asian groups. Findings reveal that disability estimates for Southeast Asians have a much higher degree of imprecision than for NLW. Within Southeast Asian groups, Vietnamese have the highest level of certainty, followed by the Hmong. Cambodians and Laotians disability estimates contain high levels of uncertainty. Difficulties with self-care and vision contain the highest level of uncertainty relative to ambulatory, cognitive, independent living, and hearing difficulties.

scholarly journals Detecting “real” population changes with American Community Survey data: The implicit assumption of treating between-year differences as “trends”

2014 ◽  
Vol 4 (2) ◽  
pp. 494 ◽  
Author(s):  
Carlos Siordia
Keyword(s):  
Population Change ◽  
The United States ◽  
Community Survey ◽  
Population Characteristics ◽  
American Community Survey ◽  
Implicit Assumption ◽  
Point Estimates ◽  
Real Population ◽  
The Us ◽  
Real Change

<p><strong>Abstract</strong></p><p><strong>BACKGROUND:  </strong>The American Community Survey (ACS) in the United States (US) collects detailed demographic information on the US population. Pressures to use year-to-year population estimates to analyze “trends” (i.e., between-year differences on the characteristics of interest) have motivated the need to explore how single- or multi-year estimates can be used to investigate changes in US population over time. <strong>OBJECTIVE: </strong>The specific aim of this manuscript is to provide empirical evidence that between-year differences in population characteristics have difference levels of uncertainty around point-estimates. <strong>METHODS:</strong> Six ACS Public Use Microdata Sample (PUMS) single year files from 2005 through 2010 are used to empirically show the heterogeneity of uncertainty in “between-year differences” on level of education, for a birth cohort born between 1960 and 1970 of non-Latino-whites and Mexican Latinos/as. <strong>RESULTS: </strong>The data show the precision of the education estimate decreases as the specificity of the population increases. For example, Mexican’s 99% confidence intervals have wider and more time-varying bandwidths than non-Latino-whites. <strong>CONCLUSIONS: </strong>Inferring meaningful population change requires the challengeable assumption that between-year differences are not the product of data artifacts. Harvesting reputable ACS data demands further research before between-year differences can be treated as “real change.”    </p><p> </p>

scholarly journals Effects of Measurement Error in the American Community Survey on Statistical Analysis: An Example using County and Census Tract Mortality Rates.

2019 ◽  
Author(s):  
Corey Sparks ◽  
Lloyd B. Potter
Keyword(s):  
Bayesian Model ◽  
The United States ◽  
Mortality Rates ◽  
Community Survey ◽  
Standard Errors ◽  
American Community Survey ◽  
Regression Analyses ◽  
Census Tracts ◽  
Indicator Data ◽  
Naive Model

The American Community Survey (ACS) summary file data provide rolling 5-year estimates of demographic and socioeconomic indicator data for small geographiesthroughout the United States. These estimates are commonly used as indicators forregression models to measure conditions in communities. The Margins of Error (MOE) inthe ACS estimates for small geographic areas can often be very large, and without takingthem into account, regression analyses using them can be mis-specified, leading to bias inregression coefficients and model standard errors. This paper directly comparesmeasurement error model specifications to naive model specifications for a mortalityoutcome in Texas Census tracts using Bayesian model specializations. The results showthat there is bias in the naive regression model results. We urge users of the ACSsummary file data to be aware of such bias as it can potentially impact interpretation ofmodel results and hypothesis tests.

scholarly journals Abortion Access in Ohio’s Changing Legislative Context, 2010–2018

2020 ◽  
Vol 110 (8) ◽  
pp. 1228-1234
Author(s):  
Alison H. Norris ◽  
Payal Chakraborty ◽  
Kaiting Lang ◽  
Robert B. Hood ◽  
Sarah R. Hayford ◽  
...  
Keyword(s):  
Public Health ◽  
Data Warehouse ◽  
First Trimester ◽  
The United States ◽  
Vital Statistics ◽  
Health Data ◽  
Community Survey ◽  
American Community Survey ◽  
Public Health Data ◽  
Abortion Access

Objectives. To examine abortion utilization in Ohio from 2010 to 2018, a period when more than 15 abortion-related laws became effective. Methods. We evaluated changes in abortion rates and ratios examining gestation, geographic distribution, and abortion method in Ohio from 2010 to 2018. We used data from Ohio’s Office of Vital Statistics, the Centers for Disease Control and Prevention’s Abortion Surveillance Reports, the American Community Survey, and Ohio’s Public Health Data Warehouse. Results. During 2010 through 2018, abortion rates declined similarly in Ohio, the Midwest, and the United States. In Ohio, the proportion of early first trimester abortions decreased; the proportion of abortions increased in nearly every later gestation category. Abortion ratios decreased sharply in most rural counties. When clinics closed, abortion ratios dropped in nearby counties. Conclusions. More Ohioans had abortions later in the first trimester, compared with national patterns, suggesting delays to care. Steeper decreases in abortion ratios in rural versus urban counties suggest geographic inequity in abortion access. Public Health Implications. Policies restricting abortion access in Ohio co-occur with delays to care and increasing geographic inequities. Restrictive policies do not improve reproductive health.

You Are Where You Are From

2015 ◽  
Vol 5 (3) ◽  
pp. 86-89
Author(s):  
Robin Mejia
Keyword(s):  
United States ◽  
The United States ◽  
Community Survey ◽  
American Community Survey ◽  
United States Census ◽  
Using Data ◽  
The Way

Using data from the United States Census 2013 American Community Survey, Robin Mejia looks at the way geography affects a person’s health, wealth, education, and prospects in life.

A Time-Space Stream of DACA Benefits and Barriers Gleaned From the American Community Survey

2020 ◽  
Vol 42 (2) ◽  
pp. 143-164
Author(s):  
Richard C. Jones
Keyword(s):  
Mexican Americans ◽  
National Level ◽  
State Level ◽  
Work Force ◽  
The United States ◽  
Community Survey ◽  
Control Group ◽  
American Community Survey ◽  
Individual Level ◽  
Time Space

This study investigates the educational and economic attainment of Mexican Dreamers over the 4 years since DACA was implemented (2012–2016). A time-space stream of benefits and barriers is evaluated at the national, state, and individual levels. Based on assumptions linking the DACA-eligible to DACA recipients, I examine the annual American Community Survey (ACS) to glean insights not provided elsewhere. At national level, the results suggest that young Mexican Dreamers entered the workforce at higher rates, but college at lower rates, than a control group of Mexican Americans. At state level, in supportive states these Dreamers entered college at higher rates but the work force at slightly lower rates, than they did in restrictive states. At the individual level, it is revealed that DACA strongly promoted college over work for women, but just the reverse for men. These distinctions are bringing about new inequalities within the Mexican Dreamer community in the United States.

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