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cepumd

The purpose of cepumd is to make working with Consumer ExpenditureSurveys (CE) Public-Use Microdata (PUMD) easier toward calculating mean,weighted, annual expenditures (henceforth “mean expenditures”). Thechallenges cepumd seeks to address deal primarily with pulling togetherthe necessary data toward this end. Some of the overarching ideasunderlying the package are as follows:

• Use a Tidyverse framework for most operations and be (hopefully)generally Tidyverse friendly

• Balance the effort to make the end user’s experience with CE PUMDeasier while being flexible enough to allow that user to perform anyanalysis with the data they wish

• Only designed to help users calculate mean expenditures on and ofthe consumer unit (CU), i.e., not income, not assets, not liabilities,not gifts.

Challenges addressed bycepumd

cepumd seeks to address challenges in three categories:data gathering/organization; managing data inconsistencies; andcalculating weighted, annual metrics.

• Data gathering/organization
  • Convert hierarchical grouping (HG) files to data tables usingce_hg()
  • Help the user identify the Universal Classification Codes (UCCs)related to their analysis using a combination ofce_hg()andce_uccs()
  • Combine all required files and variables usingce_prepdata()
• Managing data inconsistencies
  • Provide the ability to recode variable categories using the CEDictionary for Interview and Diary Surveys
  • Resolve some inconsistencies such as differences code definitionsbetween the Interview and Diary (check the definitions of the “FAM_TYPE”variable categories in 2015 for an example)
  • Provide useful errors or warnings when there are multiple categoriesof something the user is trying to access, e.g., some titles in thehierarchical grouping files (“stub” or “HG” files) repeat and requiresmore careful selection of UCCs
• Calculating weighted, annual metrics
  • Calculate a mean expenditure withce_mean() orexpenditure quantile withce_quantile()
  • Account for the factor (annual vs. quarterly expenditure)
  • Account for the “months in scope” of a given consumer unit (CU)
  • Annualize expenditures for either Diary or Interviewexpenditures
  • Integrate Interview and Diary data as necessary

Installation

Install the production version withinstall.packages("cepumd")

You can install the development version ofcepumd fromGitHub, but you’ll first need thedevtools package:

if (!"devtools"%in%installed.packages()[,"Package"]) {install.packages("devtools",dependencies =TRUE)}devtools::install_github("arcenis-r/cepumd")

Key cepumd functions

• The workhorse ofcepumd isce_prepdata(). It merges the household characteristics file(FMLI/-D) with the corresponding expenditure tabulation file (MTBI/EXPD)for a specified year, adjusts weights for months-in-scope and the numberof collection quarters, adjusts some cost values by their periodicityfactor (some cost categories are represented as annual figures andothers as quarterly). With the recent update it only requires the first3 arguments to function: the year, the survey type, and one or morevalid UCCs.ce_prepdata() now creates all of the othernecessary objects within the function if not provided.

• There are two functions for wrangling hierarchical grouping datainto more usable formats:

  • ce_hg() pulls the requested type of HG file (Interview,Diary, or Integrated) for a specified year.
  • ce_uccs() filters the HG file for the specifiedexpenditure category and returns either a data frame with only thatsection of the HG file or the Universal Classification Codes (UCCs) thatmake up that expenditure category.

• There are two functions that the user can use to calculate CEsummary statistics:

  • ce_mean() calculates a mean expenditure, standard errorof the mean, coefficient of variation, and an aggregateexpenditure.
  • ce_quantiles() calculates weighted expenditurequantiles. It is important to note that calculating medians forintegrated expenditures is not recommended because the calculationinvolves using weights from both the Diary and Survey instruments.