CREAT: Census Research Exploration and Analysis Tool

An Economist's Primer on Survey Samples

September 2000

Abstract

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The 10 most similar working papers to the working paper 'An Economist's Primer on Survey Samples' are listed below in order of similarity.

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  Authors: Joseph W. Sakshaug, Trivellore Raghunathan

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  Small area estimates provide a critical source of information used to study local populations. Statistical agencies regularly collect data from small areas but are prevented from releasing detailed geographical identifiers in public-use data sets due to disclosure concerns. Alternative data dissemination methods used in practice include releasing summary/aggregate tables, suppressing detailed geographic information in public-use data sets, and accessing restricted data via Research Data Centers. This research examines an alternative method for disseminating microdata that contains more geographical details than are currently being released in public-use data files. Specifically, the method replaces the observed survey values with imputed, or synthetic, values simulated from a hierarchical Bayesian model. Confidentiality protection is enhanced because no actual values are released. The method is demonstrated using restricted data from the 2005-2009 American Community Survey. The analytic validity of the synthetic data is assessed by comparing small area estimates obtained from the synthetic data with those obtained from the observed data.
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• Working Paper

  
  
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• Working Paper

  
  
  A METHOD OF CORRECTING FOR MISREPORTING APPLIED TO THE FOOD STAMP PROGRAM
  

  May 2013

  Authors: Nikolas Mittag

  Working Paper Number:

  CES-13-28

  Survey misreporting is known to be pervasive and bias common statistical analyses. In this paper, I first use administrative data on SNAP receipt and amounts linked to American Community Survey data from New York State to show that survey data can misrepresent the program in important ways. For example, more than 1.4 billion dollars received are not reported in New York State alone. 46 percent of dollars received by house- holds with annual income above the poverty line are not reported in the survey data, while only 19 percent are missing below the poverty line. Standard corrections for measurement error cannot remove these biases. I then develop a method to obtain consistent estimates by combining parameter estimates from the linked data with publicly available data. This conditional density method recovers the correct estimates using public use data only, which solves the problem that access to linked administrative data is usually restricted. I examine the degree to which this approach can be used to extrapolate across time and geography, in order to solve the problem that validation data is often based on a convenience sample. I present evidence from within New York State that the extent of heterogeneity is small enough to make extrapolation work well across both time and geography. Extrapolation to the entire U.S. yields substantive differences to survey data and reduces deviations from official aggregates by a factor of 4 to 9 compared to survey aggregates.
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• Working Paper

  
  
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  While commercial data sources offer promise to statistical agencies for use in production of official statistics, challenges can arise as the data are not collected for statistical purposes. This paper evaluates the use of 2008-2010 property tax data from CoreLogic, Inc. (CoreLogic), aggregated from county and township governments from around the country, to improve 2010 American Community Survey (ACS) estimates of property tax amounts for single-family homes. Particularly, the research evaluates the potential to use CoreLogic to reduce respondent burden, to study survey response error and to improve adjustments for survey nonresponse. The research found that the coverage of the CoreLogic data varies between counties as does the correspondence between ACS and CoreLogic property taxes. This geographic variation implies that different approaches toward using CoreLogic are needed in different areas of the country. Further, large differences between CoreLogic and ACS property taxes in certain counties seem to be due to conceptual differences between what is collected in the two data sources. The research examines three counties, Clark County, NV, Philadelphia County, PA and St. Louis County, MO, and compares how estimates would change with different approaches using the CoreLogic data. Mean county property tax estimates are highly sensitive to whether ACS or CoreLogic data are used to construct estimates. Using CoreLogic data in imputation modeling for nonresponse adjustment of ACS estimates modestly improves the predictive power of imputation models, although estimates of county property taxes and property taxes by mortgage status are not very sensitive to the imputation method.
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  This article introduces the twangRDC package, which contains functions to address non-linkage in US Census Bureau datasets. The Census Bureau's Person Identification Validation System facilitates data linkage by assigning unique person identifiers to federal, third party, decennial census, and survey data. Not all records in these datasets can be linked to the reference file and as such not all records will be assigned an identifier. This article is a tutorial for using the twangRDC to generate nonresponse weights to account for non-linkage of person records across US Census Bureau datasets.
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• Working Paper

  
  
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• Working Paper

  
  
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  CES-08-12

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• Working Paper

  
  
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  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.
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  The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to measure the disclosure risks and societal benefits of the published data products. Following long-established precedent in economics and statistics, we argue that any proposal for quantifying disclosure risk should be based on pre-specified, objective criteria. Such criteria should be used to compare methodologies to identify those with the most desirable properties. We illustrate this approach, using simple desiderata, to evaluate the absolute disclosure risk framework, the counterfactual framework underlying differential privacy, and prior-to-posterior comparisons. We conclude that satisfying all the desiderata is impossible, but counterfactual comparisons satisfy the most while absolute disclosure risk satisfies the fewest. Furthermore, we explain that many of the criticisms levied against differential privacy would be levied against any technology that is not equivalent to direct, unrestricted access to confidential data. Thus, more research is needed, but in the near-term, the counterfactual approach appears best-suited for privacy-utility analysis.
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• Working Paper

  
  
  Interactions, Neighborhood Selection, and Housing Demand
  

  August 2002

  Authors: Yannis M Ioannides, Jeffrey Zabel

  Working Paper Number:

  CES-02-19

  This paper contributes to the growing literature that identifies and measures the impact of social context on individual economic behavior. We develop a model of housing demand with neighborhood e'ects and neighborhood choice. Modelling neighborhood choice is of fundamental importance in estimating and understanding endogenous and exogenous neighborhood effects. That is, to obtain unbiased estimates of neighborhood effects, it is necessary to control for non-random sorting into neighborhoods. Estimation of the model exploits a unique data set of household data that has been augmented with contextual information at two di'erent levels ('scales') of aggregation. One is at the neighborhood cluster level, of about ten neighbors, with the data coming from a special sample of the American Housing Survey. A second level is the census tract to which these dwelling units belong. Tract-level data are available in the Summary Tape Files of the decennial Census data. We merge these two data sets by gaining access to confidential data of the U.S. Bureau of the Census. We overcome some limitations of these data by implementing some significant methodological advances in estimating discrete choice models. Our results for the neighborhood choice model indicate that individuals prefer to live near others like themselves. This can perpetuate income inequality since those with the best opportunities at economic success will cluster together. The results for the housing demand equation are similar to those in our earlier work [Ioannides and Zabel (2000] where we find evidence of significant endogenous and contextual neighborhood effects.
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