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Calculating Employment Rates: Defining and Measuring Employment in Labor Market Analysis, Lecture notes of Economic statistics

Guidelines for calculating employment rates using different definitions of employment in labor market analysis. It explains the international labour organisation (ilo) definition, primary employment status, and income distribution analyses. The document also includes instructions for identifying the employed population using lis variables and creating dummy variables in stata. The exercise involves calculating employment rates in the us and germany in 2000 using three different employment measures.

What you will learn

  • What are the different definitions of employment used in labor market analysis?
  • How can you identify the employed population according to different employment definitions using LIS variables?

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2021/2022

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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>)
pf3
pf4

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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 (recode parameters), gen()
  • Hint : If you name your variables 1 through 3 , the next step will be easier.

 Tabulate the 3 variables to get the percentage of employed (remember to weigh the results).

 Reminder: in order to get weighted results in a tabulation, you need to use the analytical weight “ aweight ” since the tabulate command does not accept non-integer frequency weights.  If you named your variables emp1 through emp3 , you can run the 6 tabulations in a loop: forvalues i=1/3 { tab emp`i’ [aw=pweight] }

Results

US00 DE

At present (any work) 65.49 59. 64

At present (main activity) 59. 27 45.

During the calendar year (main activity) 6 3.15 54.

Comments

 As expected, the employment rate is much higher if employment is defined as any work ( pclfs ) than if it is defined as the main activity held ( pcmas or pumas ).

 Be sure to look at the Value Labels and read the Comments/Warnings in the Lissification tables! This is where you will find information about the 900 category.

  • In most cases, the difference between including the 900s or setting them to missing in pclfs is minor or non-existent. Determining whether an individual held a job in a specified period is fairly straightforward and the 900 category is reserved for truly marginal cases.

More ambiguity arises when defining primary activity ( pcmas and pumas ). This happens most often when questions about primary activity focus on employment. In some surveys, questions about non-employment are only asked of those who were not employed in pclfs , so those who appear to have marginal attachment to the labour force (e.g., part-time or irregular workers), but who have specified no other activity are coded in the 900s.