Statistical Modeling Methodology for the Voting Rights Act Section 203 Language Assistance Determinations

Joyce, Patrick; Malec, Donald; Little, Roderick J. A.; Gilary, Aaron; Navarro, Alfredo Menéndez; Asiala, Mark

doi:10.1080/01621459.2013.859077

Cited by 6 publications

(10 citation statements)

References 12 publications

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“…As acknowledged in the main text, the Census Bureau notes substantial estimation error when measuring the size of the language minority population within a jurisdiction (Joyce et al 2014). Table 5 lists the 42 jurisdictions where the 90% confidence interval for percent VACLEP or the VACLEP population crossed the coverage threshold of 5% or 10,000, respectively.…”

Section: Robustness Of Rd Resultsmentioning

confidence: 99%

“…In the context of this study, it would be impractical to randomly assign some jurisdictions to provide materials while leaving language minorities in other areas without the assistance they need to participate in politics. However, the Census Bureau admits that substantial estimation error remains when assessing the size of the language minority population within a jurisdiction (Joyce et al 2014). Specifically, the relatively small county- and municipality-level samples used in the ACS make it difficult to precisely measure the size and characteristics of the language minority population, especially when such a population is small enough to be near the coverage thresholds of 5% or 10,000 persons.…”

Section: Cases With Coverage Determined “At Random”mentioning

confidence: 99%

“…Standard errors are robust to heteroskedasticity, with a degrees of freedom correction due to the small sample size near the discontinuity (Long and Ervin 2000;White 1980). assessing the size of the language minority population within a jurisdiction (Joyce et al 2014). Specifically, the relatively small county-and municipality-level samples used in the ACS make it difficult to precisely measure the size and characteristics of the language minority population, especially when such a population is small enough to be near the coverage thresholds of 5% or 10,000 persons.…”

Section: Cases With Coverage Determined "At Random"mentioning

confidence: 99%

See 2 more Smart Citations

Examining the Causal Impact of the Voting Rights Act Language Minority Provisions

Fraga

Merseth²

2016

J. Race Ethnicity Politics

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The following study investigates the causal impact of the Voting Rights Act (VRA) language minority provisions, which mandate multilingual election assistance if certain population thresholds are met. While lower rates of Latino and Asian American political participation are often attributed to language barriers, scholars have yet to establish a direct impact of the provisions on electoral behavior. Building off of previous state-and county-level analyses, we leverage an individuallevel voter file database to focus on participation by Latino and Asian American citizens in 1,465 counties and municipalities nationwide. Utilizing a regression discontinuity design, we examine rates of voter registration and turnout in the 2012 election, comparing individual participation rates in jurisdictions just above and just below the threshold for coverage. Our analysis attributes a significant increase in Latino voter registration and Asian American turnout to coverage under the VRA.

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Section: Robustness Of Rd Resultsmentioning

confidence: 99%

Section: Cases With Coverage Determined “At Random”mentioning

confidence: 99%

Section: Cases With Coverage Determined "At Random"mentioning

confidence: 99%

See 1 more Smart Citation

Examining the Causal Impact of the Voting Rights Act Language Minority Provisions

Fraga

Merseth²

2016

J. Race Ethnicity Politics

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“…In 1975, Congress appended Section 203 to the Voting Rights Act, which provides voting resources for US citizens that are not proficient in English. Recently, Joyce et al () described a precise approach to classify regions that satisfy the jurisdiction rule laid out by Section 203. Their results indicated that many American Indian areas/Alaska native areas/Hawaiian home lands, met the jurisdiction rule of Section 203.…”

Section: American Community Survey Median Household Income Examplementioning

confidence: 99%

Spatio‐temporal change of support with application to American Community Survey multi‐year period estimates

Bradley

Wikle

Holan

2015

Stat

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We present hierarchical Bayesian methodology to perform spatio‐temporal change of support (COS) for survey data with Gaussian sampling errors. This methodology is motivated by the American Community Survey (ACS), which is an ongoing survey administered by the US Census Bureau that provides timely information on several key demographic variables. The ACS has published 1‐year, 3‐year, and 5‐year period estimates, and margins of errors, for demographic and socio‐economic variables recorded over predefined geographies. The spatio‐temporal COS methodology considered here provides data users with a way to estimate ACS variables on customized geographies and time periods while accounting for sampling errors. Additionally, 3‐year ACS period estimates are to be discontinued, and this methodology can provide predictions of ACS variables for 3‐year periods given the available period estimates. The methodology is based on a spatio‐temporal mixed‐effects model with a low‐dimensional spatio‐temporal basis function representation, which provides multi‐resolution estimates through basis function aggregation in space and time. This methodology includes a novel parameterization that uses a target dynamical process and recently proposed parsimonious Moran's I propagator structures. Our approach is demonstrated through two applications using public‐use ACS estimates and is shown to produce good predictions on a hold‐out set of 3‐year period estimates. Copyright © 2015 John Wiley & Sons, Ltd.

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“…The predictive mean stratification is used to reduce dependency on model assumptions, since the regression model is used to group similar jurisdictions rather than to create direct predictions. See Joyce et al [29] for more details.…”

Section: Two Applications Of the Cb Perspectivementioning

confidence: 99%

Calibrated Bayes, an inferential paradigm for official statistics in the era of big data

Little

2015

SJI

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Abstract. Official statistics is dipping its toe in the ocean of big data, and leaders are emphasizing the need for a major paradigm change. One aspect is the increased volume of data that are not collected on probability samples of the target population. Making full use of these data requires a fundamental change, not only in data collection and dissemination, but also in the methods of statistical inference. The classical "design-based" approach to survey inference, developed from the seminal work of Neyman [41], is simply not applicable to these data. Rather, statistical models are needed that potentially reflect selection bias from the lack of random sampling. I suggest that "Calibrated Bayes" is the appropriate statistical paradigm for addressing the analysis. Under this paradigm, inferences for a particular data set are Bayesian, but models are sought that yield inferences with robust repeated sampling properties. Probability sampling remains a powerful tool under this paradigm, since by ensuring that the selection mechanism is ignorable it enhances robust modeling, but it is not essential for the inference. I outline two applications of Calibrated Bayes to data collected by the U.S. Census Bureau.

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Statistical Modeling Methodology for the Voting Rights Act Section 203 Language Assistance Determinations

Cited by 6 publications

References 12 publications

Examining the Causal Impact of the Voting Rights Act Language Minority Provisions

Examining the Causal Impact of the Voting Rights Act Language Minority Provisions

Spatio‐temporal change of support with application to American Community Survey multi‐year period estimates

Calibrated Bayes, an inferential paradigm for official statistics in the era of big data

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