Background Information and Context

Challenge 1: The Survey Content, Column Names, and Coding Change Over Time

The content of the CPS VRS has changed over time, in some cases because of real world changes in the legal environment - most notably the passage of the National Voter Registration Act (NVRA) in 1993 - and other times to provide a more clear reflection of changing voting behavior. The content of the survey has remained relatively consistent since 1996. As a result, many users only use the CPS since 1996, and this package provides data from 1994 onwards.

Even though the content has remained relatively constant from 1996, the Census has sometimes changed the location, names, and even the coding for individual variables. It is very important that anyone using multiple years of the CPS VRS pay extremely close attention to the coding choices that were made in each year.

We have made (what we consider) sensible decisions about which columns from the VRS get combined across disparate years, while also leaving you the opportunity to bring in additional columns or join years differently.

The cpsvote package does most of this recoding and relabeling work for you, while still retaining the data in its original format.

Challenge 2: Survey Weights Are a Necessity When Using the Voting and Registration Supplement

Most surveys provide a sample weight that allows the survey results to be generalized to the target population. Typically, survey weights are provided because the sampling design may have included survey strata, or there may have been oversampling applied to specific groups. Survey weights can also adjust for simple deviations in the sampled population and the target population, either due to non-responses or even just as a result of randomization.

The CPS uses a particularly complex survey design. As described in the “Design and Methodology: Current Population Survey” document, “The CPS sample is a multistage stratified sample of approximately 72,000 assigned housing units from 824 sample areas designed to measure demographic and labor force characteristics of the civilian noninstitutionalized population 16 years of age and older.”

Critically, the CPS is designed to generate national and state estimates, and samples households, not individuals. These two considerations both mean that individual respondents are not sampled with equal probability, i.e. a Montanan living in a single-person household will have a much higher probability of being sampled than a Californian living in a six-person household. The sample weight provided by the CPS adjusts your estimates so as to take into account these different probabilities of being sampled, and is needed to produce statistically valid estimates.

R has not historically made using survey weights very easy, but two packages have simplified the process. Thomas Lumley’s survey package and his 2011 volume Complex Surveys: A Guide to Analysis Using R are the recommended sources for weighting survey data in R. The following code will create a weighted data object using survey:

cps_survey <- survey::svydesign(ids = ~1, # simple weights, no clusters
                                data = cps_allyears_10k, # data set
                                weights = ~turnout_weight) # weight column

A recently released package, srvyr, provides “dplyr-like syntax for summary statistics of survey data” (srvyr at CRAN). srvyr acts as a wrapper around survey and makes many of the commands easier to implement. The following code will create a weighted data object using srvyr:

cps_srvyr <- srvyr::as_survey_design(.data = cps_allyears_10k, # data set
                                     weights = turnout_weight) # weight column

In most of the examples provided in this documentation, we use the srvyr command syntax, but some survey examples are provided.

The cpsvote package provides detailed instructions to use srvyr to correctly weight your data.

Challenge 3: CPS Makes Idiosyncratic Coding Choices for Voter Turnout, and Turnout Estimates Are Biased

There are two final, and related, challenges to use the CPS VRS for estimating voter turnout. Both of these adjustments need to be made in order to produce statistically valid estimates of turnout. The cpsvote packages provides turnout measures that incorporate the correct coding, and we document a method to adjust for biases in turnout.

First, the CPS has long used an “idiosyncratic” coding rule that has been recognized over time and was carefully documented by two scholars, Aram Hur and Christopher Achen, in a 2013 article titled “Coding Voter Turnout Responses in the Current Population Survey”.

The coding rule is not at all clear from the CPS documentation, and without correct coding, any turnout estimates that are produced will not match those in official Census communications.

In the CPS codebook, the variable is reported in this way (from the 2016 documentation):

The key decision made by Census staff has been to treat three of these non-response categories as “not voted”, essentially adding these cases (all except for “Yes” and “Not in Universe”) to the denominator for a turnout estimate. Because the CPS counts non-responses as “not voted,” their coding scheme has been vulnerable to changing non-response rates over time.

Hur and Achen (2013) describe the problem in their abstract:

“The Voting and Registration Supplement to the Current Population Survey (CPS) employs a large sample size and has a very high response rate, and thus is often regarded as the gold standard among turnout surveys. In 2008, however, the CPS inaccurately estimated that presidential turnout had undergone a small decrease from 2004. We show that growing nonresponse plus a long-standing but idiosyncratic Census coding decision was responsible. We suggest that to cope with nonresponse and overreporting, users of the Voting Supplement sample should weight it to reflect actual state vote counts.”

The cpsvote package provides the original Census coding scheme in the cps_turnout variable that is produced by the default functions.

A related problem with the CPS turnout estimate, documented carefully by Professor Michael McDonald in a 2014 working paper and at the United States Elections Project’s CPS Over-Report and Non-Response Bias Correction page is that, over time, two biases have crept into the CPS - one from increasing non-response rates, the second from over-reports of turnout (Michael McDonald, 2014, “What’s Wrong with the CPS?”, paper presented at the Annual Meeting of the American Political Science Association).

Hur and Achen suggest a complex post-stratification adjustment to the data that will adjust for these biases:

We recommend dropping all categories of missing turnout response, and then poststratifying the remaining CPS sample so that the survey turnout rate in each state matches the corresponding state VEP turnout.

The Hur-Achen post-stratification weight relies on the authoritative estimates for state-level turnout as calculated by the United States Election Project, directed by Professor Michael McDonald of the University of Florida. Professor McDonald helpfully provides guidance on this complex weighting procedure. Stata code is provided at his website: Commentary, Guidelines, and Stata Code.

We have integrated the Hur and Achen recoding into the cpsvote package, contained in the variable hurachen_turnout that is produced by the default functions. The original CPS weight is provided in the variable WEIGHT, and a weight adjusted for proper turnout coding and post-stratification for over-reporting is provided in the variable turnout_weight.

The cpsvote package creates an alternative survey weight based on current scientific research.