Current Population Survey Design and Methodology - Census Bureau

either does not know the answer to a question or refuses
to provide the answer. Item nonresponse in the CPS is
modest (see Chapter 16, Table 16−4).
One of three imputation methods are used to compensate
for item nonresponse in the CPS. Before the edits are
applied, the daily data files are merged and the combined
file is sorted by state and PSU within state. This sort
ensures that allocated values are from geographically
related records; that is, missing values for records in Maryland
will not receive values from records in California. This
is an important distinction since many labor force and
industry and occupation characteristics are geographically
clustered.
The edits effectively blank all entries in inappropriate
questions (e.g., followed incorrect path of questions) and
ensure that all appropriate questions have valid entries.
For the most part, illogical entries or out-of-range entries
have been eliminated with the use of electronic instruments;
however, the edits still address these possibilities,
which may arise from data transmission problems and
occasional instrument malfunctions. The main purpose of
the edits, however, is to assign values to questions where
the response was ‘‘Don’t know’’ or ‘‘Refused.’’ This is
accomplished by using 1 of the 3 imputation techniques
described below.
The edits are run in a deliberate and logical sequence.
Demographic variables are edited first because several of
those variables are used to allocate missing values in the
other modules. The labor force module is edited next
since labor force status and related items are used to
impute missing values for industry and occupation codes
and so forth.
The three imputation methods used by the CPS edits are
described below:
1. Relational imputation infers the missing value from
other characteristics on the person’s record or within
the household. For instance, if race is missing, it is
assigned based on the race of another household
member, or failing that, taken from the previous
record on the file. Similarly, if relationship data is
missing, it is assigned by looking at the age and sex
of the person in conjunction with the known relationship
of other household members. Missing occupation
codes are sometimes assigned by analyzing the industry
codes and vice versa. This technique is used as
appropriate across all edits. If missing values cannot
be assigned using this technique, they are assigned
using one of the two following methods.
2. Longitudinal edits are used in most of the labor force
edits, as appropriate. If a question is blank and the
individual is in the second or later month’s interview,
the edit procedure looks at last month’s data to determine
whether there was an entry for that item. If so,
last month’s entry is assigned; otherwise, the item is
assigned a value using the appropriate hot deck, as
described next.
3. The third imputation method is commonly referred to
as ‘‘hot deck’’ allocation. This method assigns a missing
value from a record with similar characteristics,
which is the hot deck. Hot decks are defined by variables
such as age, race, and sex. Other characteristics
used in hot decks vary depending on the nature of the
unanswered question. For instance, most labor force
questions use age, race, sex, and occasionally another
correlated labor force item such as full- or part-time
status. This means the number of cells in labor force
hot decks are relatively small, perhaps fewer than
100. On the other hand, the weekly earnings hot deck
is defined by age, race, sex, usual hours, occupation,
and educational attainment. This hot deck has several
thousand cells.
All CPS items that require imputation for missing values
have an associated hot deck . The initial values for the hot
decks are the ending values from the preceding month. As
a record passes through the editing procedures, it will
either donate a value to each hot deck in its path or
receive a value from the hot deck. For instance, in a hypothetical
case, the hot deck for question X is defined by the
characteristics Black/non-Black, male/female, and age
16−25/25+. Further assume a record has the value of
White, male, and age 64. When this record reaches question
X, the edits determine whether it has a valid entry. If
so, that record’s value for question X replaces the value in
the hot deck reserved for non-Black, male, and age 25+.
Comparably, if the record was missing a value for item X,
it would be assigned the value in the hot deck designated
for non-Black, male, and age 25+.
As stated above, the various edits are logically sequenced,
in accordance with the needs of subsequent edits. The
edits and codes, in order of sequence, are:
1. Household edits and codes. This processing step
performs edits and creates recodes for items pertaining
to the household. It classifies households as interviews
or noninterviews and edits items appropriately.
Hot deck allocations defined by geography and other
related variables are used in this edit.
2. Demographic edits and codes. This processing
step ensures consistency among all demographic variables
for all individuals within a household. It ensures
all interviewed households have one and only one reference
person and that entries stating marital status,
spouse, and parents are all consistent. It also creates
families based upon these characteristics. It uses longitudinal
editing, hot deck allocation defined by
related demographic characteristics, and relational
imputation.
9–2 Data Preparation Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau