Calculating Employment Rates
Goal
The employed population can be defined differently depending on the research purpose. In labour
market analysis, you may wish to calculate employment rates or run wage regressions. In this case,
you may want to identify persons working at a certain moment in time. Within the framework of this
strict reference period, a person may be considered as employed as soon as he/she has carried out any
work (i.e., the International Labour Organisation (ILO) definition of employment, LIS variable
pclfs). Those more interested in the primary employment status at a certain moment in time should
not use the ILO definition, but rather look at those whose main activity is employment (LIS variable
pcmas).
Those wishing to perform income distribution analyses according to the activity status of individuals
(e.g., calculation of poverty rates for the working poor), may choose to focus on employment over a
longer reference period. In this case, you need to be able to identify an individual’s primary activity
over the income reference period (which in LIS is normally one year, LIS variable pumas).
In this exercise, we identify the employed population according to these three different concepts of
employment.
Activity
Calculate employment rates in the US and Germany in 2000 using the three different employment
measures. Use the sample of individuals 18 years of age and older. Recode all of those with unclear
employment attachment (coded in 900s) to missing.
Guidelines
The employment rate is the percentage of employed persons in the adult population, which we
define here as 18 years of age or older.
There are three different LIS variables that coincide with the definitions of employment
described above:
- persons carrying out any work at present can be identified using pclfs;
- persons for whom work is the main activity at present are in pcmas;
- persons who are mainly employed during a longer reference period can be found in pumas.
By definition, those coded in the 900s may have some employment attachment, but the
information is too ambiguous to either define them either as “employed” or “not employed”.
These individuals were kept in separate detailed categories to allow users to redefine them based
on the specific research project. Always check the country-level documentation to determine
how you wish to recode those assigned to the 900 (and 400, in pclfs) category.
In order to measure employment, create dummy variables for employment status, by recoding
the original variables into indicators of dichotomous employment status (yes=1 & no=0).
- In your Stata code, you should always try to be concise as possible. This will minimize the
possibility that LISSY will reject your program for overly long output. To create dummy
variables, an efficient method of programming is to use:
recode <original varnames> (recode parameters), gen(<new varnames>)
