psidR (version 1.2)

build.panel: build.panel: Build PSID panel data set

Description

Builds a panel data set in wide format with id variables personID and period from individual PSID family files.

Usage

build.panel(datadir = NULL, fam.vars, ind.vars = NULL,
    SAScii = FALSE, heads.only = TRUE, core = TRUE,
    design = "balanced", verbose = FALSE)

Arguments

datadir

either NULL, in which case saves to tmpdir or path to directory containing family files ("FAMyyyy.xyz") and individual file ("IND2009ER.xyz") in admissible formats .xyz. Admissible are .dta, .csv, .RData, .rda. Please follow naming co

fam.vars

data.frame of variable to retrieve from family files. see example for required format.

ind.vars

data.frame of variables to get from individual file. In almost all cases this will be the type of survey weights you want to use. don't include id variables ER30001 and ER30002.

SAScii

logical TRUE if you want to directly download data into Rda format (no dependency on STATA/SAS/SPSS). may take a long time.

heads.only

logical TRUE if user wants household heads only. if FALSE, data contains a row with value of "relation to head" variable.

core

logical TRUE if user wants core sample only. if FALSE, data will oversample poverty sample.

design

either character "balanced" or "all" or integer. "Balanced" means only individuals who appear in each wave are considered. "All" means all are taken. An integer value stands for minimum consecutive years of participation, i.e. design=3 means prese

verbose

logical TRUE if you want verbose output.

Value

• data
  resulting data.table. the variable pid is the unique person identifier, constructed from ID1968 and pernum.
• dict
  data dictionary if stata data was supplied, NULL else

Details

takes desired variables from family files for specified years in folder datadir and merges using the id information in IND2011ER.xyz, which must be in the same directory. Note that only one IND file may be present in the directory (each PSID shipping comes with a new IND file). There is an option to directly download the data from the PSID server to folder datadir or tmpdir. The user can change subsetting criteria as well as sample designs. Merge: the variables interview number in each family file map to the interview number variable of a given year in the individual file. Run example(build.panel) for a demonstration. Accepted input data are stata format .dta, .csv files or R data formats .rda and RData. Similar in usage to stata module psiduse.

Examples

# specify variables from family files you want

myvars <- data.frame(year=c(2001,2003),
                     house.value=c("ER17044","ER21043"),
                     total.income=c("ER20456","ER24099"),
                     education=c("ER20457","ER24148"))

# specify variables from individual index file

indvars = data.frame(year=c(2001,2003),
                     longitud.wgt=c("ER33637","ER33740"))

# call builder
# mydir is a directory that contains FAM2001ER.dta,
# FAM2003ER.dta and IND2011ER.dta

# default
d <- build.panel(datadir=mydir,
                fam.vars=myvars,
                ind.vars=indvars)

# also non-heads
d <- build.panel(datadir=mydir,
                fam.vars=myvars,
                ind.vars=indvars,
                heads.only=FALSE)

# non-balanced panel design
d <- build.panel(datadir=mydir,
                fam.vars=myvars,
                ind.vars=indvars,
                heads.only=FALSE,
                design=2) # keep if stay 2+ periods

# ######################################
# reproducible example on artifical data.
# run this with example(build.panel).
# ######################################

## make reproducible family data sets for 2 years
## variables are: family income (Money) and age

# suppose there are N individuals in year 1 and year 2.
# zero attrition.

N <- 10

fam <- data.frame(int85 = 1:N,int86=sample(1:N),
                 Money85=rlnorm(n=N,10,1),
                 age85=sample(20:80,size=N,replace=TRUE))
fam$Money86 <- fam$Money85+rnorm(N,500,30)
fam$age86 <- fam$age85+1
fam

# separate into data.frames.
# you would download files like those two:
fam1985 <- subset(fam,select = c(int85,Money85,age85))
fam1986 <- subset(fam,select = c(int86,Money86,age86))

# assign correct PSID varname of "family interview 1985"
names(fam1985)[1] <- "V11102"
names(fam1986)[1] <- "V12502"


# construct an Individual index file: that would be IND2009ER
# needs to have a unique person number (ER30001)
# and an indicator for whether from core etc,
# as well as the interview number for each year
#
# for sake of illustration, suppose the PSID has a total
# of 2N people (i.e. N are neither in year1 nor year2,
# but in some other years)
IND2009ER <- data.frame(ER30001=sample((2*N):(4*N),size=2*N),
                        ER30002=sample(1:(2*N),size=2*N))

# if a person is observed, they have an interview number
# in both years. if not observed, it's zero.
# randomly allocate persons to ER30001.
tmp <- rbind(fam[,1:2],data.frame(int85=rep(0,N),int86=rep(0,N)))

IND2009ER <- cbind(IND2009ER,tmp[sample(1:(2*N)),])
names(IND2009ER)[3:4] <- c("ER30463","ER30498")

# also need relationship to head in each year in the index
# 50\% prob of being head in year1
IND2009ER$ER30465 <- sample(c(10,20),prob=c(0.5,0.5),
                            size=2*N,replace=TRUE)
IND2009ER$ER30500 <- sample(c(10,20),prob=c(0.9,0.1),
                           size=2*N,replace=TRUE)
# and a survey weight
IND2009ER$ER30497 <- runif(20)
IND2009ER$ER30534 <- runif(20)
IND2009ER

# setup the ind.vars data.frame
indvars <- data.frame(year=c(1985,1986),ind.weight=c("ER30497","ER30534"))

# create a temporary datadir
my.dir <- tempdir()
# save those in the datadir
# notice different R formats admissible
save(fam1985,file=paste0(my.dir,"/FAM1985ER.rda"))
save(fam1986,file=paste0(my.dir,"/FAM1986ER.RData"))
save(IND2009ER,file=paste0(my.dir,"/IND2009ER.RData"))

# now famvars
famvars <- data.frame(year=c(1985,1986),
                      money=c("Money85","Money86"),
                      age=c("age85","age86"))

# call the builder
# need to set core==FALSE because person numbering indicates
# that all ids<2931 are not core.
# set heads to FALSE to have a clear count.
# data will contain column "relation.head" holding the relationship code.

d <- build.panel(datadir=my.dir,fam.vars=famvars,
                 ind.vars=indvars,core=FALSE,
                 heads=FALSE,verbose=TRUE)

# notice: all 2*N individuals are present
print(d$data[order(pid)],nrow=Inf)	# check the age column

# see what happens if we drop non-heads
# only the ones who are heads in BOTH years
# are present (since design='balanced' by default)
d <- build.panel(datadir=my.dir,fam.vars=famvars,
                 ind.vars=indvars,core=FALSE,
                 heads=TRUE,verbose=FALSE)
print(d$data[order(pid)],nrow=Inf)

# change sample design to "all":
# we'll keep individuals if they are head in one year,
# and drop in the other
d <- build.panel(datadir=my.dir,fam.vars=famvars,
                 ind.vars=indvars,core=FALSE,heads=TRUE,
                 verbose=FALSE,design="all")
print(d$data[order(pid)],nrow=Inf)

file.remove(paste0(my.dir,"/FAM1985ER.rda"),
            paste0(my.dir,"/FAM1986ER.RData"),
            paste0(my.dir,"/IND2009ER.RData"))

# END psidR example

# #####################################################################
# Please go to https://github.com/floswald/psidR for more example usage
# #####################################################################