Welcome

Thank you for your interest in the childfree package! This vignette illustrates how to use this package to access and harmonize demographic data to study childfree individuals. Childfree individuals are people who neither have nor want children (Neal and Neal 2023). The fact that they do not want children makes them different from other types of non-parents, including not-yet-parents who want children in the future, childless individuals who cannot have children, undecided people who do not know if they want children in the future.

The childfree package can be cited as:

Neal, Z. P. and Neal, J. W. (2024). childfree: An R package to access and harmonize childfree demographic data. Comprehensive R Archive Network. https://cran.r-project.org/package=childfree

For additional resources on the childfree package, please see https://www.zacharyneal.com/childfree.

If you have questions about the childfree package or would like a childfree package hex sticker, please contact the maintainer Zachary Neal by email (zpneal@msu.edu). Please report bugs in the backbone package at https://github.com/zpneal/childfree/issues.

Loading the package

The childfree package can be loaded in the usual way:

library(childfree)
#> N   ▇─┬─O   childfree v0.0.3
#>  O    ┆     CITE: Neal, Z. P. and Neal, J. W., (2024). childfree: An R package to access and
#>   K   ▇─┬─O harmonize childfree demographic data. CRAN. https://doi.org/10.32614/CRAN.package.childfree
#>    I    ┆   HELP: type vignette("childfree"); email zpneal@msu.edu; github zpneal/childfree
#>     D   ╳   BETA: type devtools::install_github("zpneal/childfree", ref = "devel")

Upon successful loading, a startup message will display that shows the version number, citation, ways to get help, and ways to contact us.

Package overview

The primary use of the childfree package is to obtain demographic data about childfree individuals from publicly available sources. Each section of this vignette describes the data sources that are available, which include:

• Demographic and Health Surveys (DHS)
• US CDC National Survey of Family Growth (NSFG)
• Michigan State University State of the State Survey (SOSS)

Future releases will offer access to additional data sources, and will harmonize data extracted from different sources.

Terminology

The childfree package uses the theoretical framework and terminology defined by Neal and Neal (2023).

Childfree: A person who does not have children and does not want children, regardless of whether they can have children, is called “childfree”. In contrast, a person who does not have children but cannot have children for biological or non-biological reasons is called “childless”.

Family Status: The terms “childfree” and “childless” are examples of “family statuses”. The “ABC” framework describes how a person’s family status is defined by the intersection of:

• An Attitude: Do you want (more) children?
• A Behavior: Have you ever had children?
• A Circumstance: Are there barriers to you having children?

A “parent” has had children (behavior = yes). Parents may be “fulfilled” if they have had exactly the number of children they want, “unfulfilled” if they have had fewer children than they want, “reluctant” if they have had more children than they want, or “ambivalent” if they do not know how many children they want(ed).

A non-parent had not had children (behavior = no). However, attitudes and circumstances distinguish different types of non-parents. A “childfree” person does not want children (attitude = no), while a “childless” person wants children (attitude = yes) but experienced barriers (circumstance = yes), and a “not-yet-parent” wants children (attitude = yes) and has not experienced barriers (circumstance = no). Non-parent family statuses are “momentary,” which means they describe a person’s status at the moment. However, a person may transition between non-parent statuses, or from a non-parent status to a parent status, over time.

Questions: The childfree package is primarily focused on data concerning childfree individuals. Childfree individuals can be identified in survey data using a “WIDE” range of questions:

• Want: Do you want children?
• Ideal: What is your ideal number of children?
• Expect: Do you expect to have children?
• Direct: Are you childfree?

Each type of question has advantages and disadvantages, and can yield different results when determining which survey respondents are childfree. The dataframes generated by the childfree package contain one binary variable for each of the “WIDE” questions available in a given dataset (e.g., cf_want, cf_ideal), plus one categorical variable (i.e., famstat) that classifies all respondents into family statuses using all available variables.

Warnings

The childfree package provides access to data about childfree individuals, and aims to facilitate research on this population. However, care should be taken when analyzing data obtained using the package’s functions:

Operationalization: Although the functions in the childfree package aim to recode each dataset’s variables in a consistent and comparable way, there are subtle differences in how variables were originally operationalized that makes perfect harmonization and comparability impossible. Exercise caution comparing the same recoded variable across datasets.

Universes: Each dataset accessible through the childfree package was collected from a population-representative sample. However, there is variation in the universes from which these samples were drawn, and therefore variation in the populations of which they are representative. For example, while respondents for the SOSS were sampled from all adults in Michigan, the respondents for the NSFG were sampled from US women ages 15-44.

Weights: Generating population estimates from these data generally requires the use of sampling weights, which are included in the dataframes generated by the childfree package. However, use of sampling weights can be complex, particularly when combining samples from multiple waves, locations, or surveys. Exercise caution using the included sampling weights.

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Demographic and Health Surveys (DHS)

The Demographic and Health Surveys (DHS) program has regularly collected health data from population-representative samples in many countries using standardized surveys since 1984. The “individual recode” data files contain women’s responses, while the “men recode” files contain men’s responses. These files are available in SPSS, SAS, and Stata formats from https://www.dhsprogram.com/, however access requires a free application. Once one or more of these files has been downloaded, the dhs() function imports the data, extracts and recoded selected variables, and returns a ready-to-use dataframe.

Although access to DHS data requires an application, the DHS program provides [model datasets]{https://dhsprogram.com/data/Download-Model-Datasets.cfm} containing fictitious data that do not require prior application to access. The “ZZIR62FL.SAV” file is a model individual recode dataset in SPSS format, and provides an example of how the dhs() function works. Running

dat <- dhs(file = "ZZIR62FL.SAV", extra.vars = c("v201", "v602", "v613"))
#> Processing DHS data files -

imports the data, extracts and recodes variables, and returns an R dataframe called dat. If you are offline or these data are otherwise unavailable, then dat <- NULL.

Inspecting selected variables for a selected observation in dat

if (!is.null(dat)) {t(dat[2368,c(3:9,19:21)])}
#>           2368       
#> famstat   "Childfree"
#> sex       "Female"   
#> age       "18"       
#> education "12"       
#> partnered "Never"    
#> residence "Urban"    
#> employed  "0"        
#> year      "2015"     
#> month     "August"   
#> v201      "0"

we see that it contains the record of an unemployed 18 year old female, who has completed 12 years of education, and is currently single, living in an urban area. She is classified as childfree because she does not have or want children.

Specifying extra.vars = c("v201", "v602", "v613") requests that the function also include these three variables, which are not extracted and recoded by default. The three variables requested in this example are the raw source variables from which dhs() determines each respondent’s family status: v201 contains the respondent’s number of children, v602 contains a code indicating whether the respondent wants children (3 = no), and v613 contains the respondent’s ideal number of children. In practice, using the extra.vars option can be useful to retain other information collected by DHS, but that is not automatically included.

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National Survey of Family Growth (NSFG)

The National Survey of Family Growth (NSFG) is conducted by the U.S. Centers for Disease Control, andregularly collects fertility and other health information from a population-representative sample of adults in the United States. Between 1973 and 2002, the NSFG was conducted periodically. Starting in 2002, the NSFG transitioned to continuous data collection, releasing data in three-year waves (e.g., the 2013-2015, 2015-2017). The nsfg() function reads raw data directly from the CDC website, extracts and recoded selected variables, and returns a ready-to-use dataframe.

For, example, we can obtain data from the NSFG collected between 2017 and 2019 using:

dat <- nsfg(years = 2017)
#> Processing NSFG data files -
#>   |                                                                              |                                                                      |   0%  |                                                                              |===================================                                   |  50%  |                                                                              |======================================================================| 100%

which returns the data in a dataframe called dat. If you are offline or these data are otherwise unavailable, then dat <- NULL. Inspecting selected variables for a selected observation in dat

if (!is.null(dat)) {t(dat[14,2:12])}
#>           14                
#> famstat   "Childfree"       
#> sex       "Female"          
#> lgbt      "Straight"        
#> race      "White"           
#> hispanic  "0"               
#> age       "26"              
#> education "College graduate"
#> partnered "Never"           
#> residence "Urban"           
#> employed  "1"               
#> inschool  "0"

we see that it contains the record of a 26 year old non-hispanic white female. She is an employed college graduate living without a partner in the city, and does not identify as religious. She is classified as childfree because she does not have or want children.

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State of the State Survey (SOSS)

The State of the State Survey (SOSS) is regularly collected by the Institute for Public Policy and Social Research (IPPSR) at Michigan State University (MSU). Each wave is collected from a sample of 1000 adults in the US state of Michigan, and includes sampling weights to obtain a sample that is representative of the state’s population with respect to age, gender, race, and education. All waves contain the same basic demographic information, but each wave also includes questions about topics commissioned by MSU faculty and others. The soss() function provides access to the waves that contain questions that allow childfree adults to be identified. It reads raw data directly from the IPPSR website, extracts and recoded selected variables, and returns a ready-to-use dataframe.

For, example, we can obtain data from the 84th SOSS wave, which was collected in April 2022 using:

dat <- soss(waves = 84, extra.vars = (c("neal1", "neal2", "neal3")))
#> Processing SOSS data files -
#>   |                                                                              |                                                                      |   0%  |                                                                              |======================================================================| 100%

which returns the data in a dataframe called dat. If you are offline or these data are otherwise unavailable, then dat <- NULL. Inspecting selected variables for a selected observation in dat

if (!is.null(dat)) {t(dat[2,c(2:9,12:13,21:24)])}
#>           2                           
#> famstat   "Childfree"                 
#> sex       "Female"                    
#> lgbt      "1"                         
#> race      "White"                     
#> hispanic  "0"                         
#> age       "60"                        
#> education "College graduate"          
#> partnered "Currently"                 
#> inschool  "0"                         
#> ideology  "Closer to the liberal side"
#> year      "2022"                      
#> month     "April"                     
#> neal1     "2"                         
#> neal2     "2"

we see that it contains the record of a 60 year old non-hispanic white female. She is a college graduate living with her partner in the suburbs, and identifies as slightly liberal, but not as religious. She is classified as childfree because she does not have or want children.

Specifying extra.vars = c("neal1", "neal2", "neal3") requests that the function also include these three variables, which are not extracted and recoded by default. The three variables requested in this example are the raw source variables from which soss() determines each respondent’s family status: neal1 contains a code indicating whether the respondent has children (2 = no), neal2 contains a code indicating whether the respondent is planning to have children (2 = no), and neal3 contains a code indicating whether the respondent want(ed) to have children (2 = no). In practice, using the extra.vars option can be useful to retain other information collected by IPPSR, but that is not automatically included.

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