euromod_linking.methods.lma_labour_alignment.states
===================================================

.. py:module:: euromod_linking.methods.lma_labour_alignment.states

.. autoapi-nested-parse::

   Labour-market state classification for the LMA alignment method.

   The four states partition the population by their role in the alignment:
   employed / unemployed (the two states macro targets speak about), inactive
   (the recruitment pool for participation entries — domestic tasks and "other"
   inactivity, i.e. people who *could* plausibly join the labour force), and
   'other' (students, retirees, long-term sick/disabled, conscripts — structurally
   out of the labour market and therefore shielded: alignment never moves them,
   however large a cell's gap). Without that shield, a big employment target
   could "hire" retirees or students, which would be demographically absurd and
   would leak pension/education benefit changes into the results.

   les2 (from EMSD monthly activity, PL211) is preferred over les because it
   separates domestic-tasks inactivity from retirement and disability — exactly
   the distinction the recruitment pool needs; the standard les coding is the
   fallback approximation.

   Fixed methodology constants (les/les2 code sets) — not scenario-configurable.



Attributes
----------

.. autoapisummary::

   euromod_linking.methods.lma_labour_alignment.states.LES2_CODES
   euromod_linking.methods.lma_labour_alignment.states.LES_CODES
   euromod_linking.methods.lma_labour_alignment.states.WORKING_AGE


Functions
---------

.. autoapisummary::

   euromod_linking.methods.lma_labour_alignment.states.classify_labour_status
   euromod_linking.methods.lma_labour_alignment.states.definition
   euromod_linking.methods.lma_labour_alignment.states.les_variable


Module Contents
---------------

.. py:function:: classify_labour_status(les: pandas.Series, codes: dict) -> pandas.DataFrame

   Binary indicators employed/unemployed/inactive/other/active.
   'other' is the catch-all for codes outside employed+unemployed+inactive.


.. py:function:: definition(les_var: str | None = None) -> dict

   The population definitions this methodology works from, as data.

   Published so a caller can size a shock against the same population the
   alignment will move, instead of reconstructing one from other variables.
   Built from the code sets above, so it cannot drift from the classification.


.. py:function:: les_variable(columns) -> tuple[str, dict]

   Pick the labour-status variable: les2 (more detailed) when present.


.. py:data:: LES2_CODES

.. py:data:: LES_CODES

.. py:data:: WORKING_AGE
   :value: (18, 65)