Population cells

A shock’s group says which people it applies to. It is a canonical "key=value;key=value" string, and an empty string means everyone:

""                    every row
"dgn=1"               men
"deh=3-4"             medium education
"deh=3-4;dgn=1"       men with medium education
"region=BE10"         the Brussels region, and everything under it

Keys are real EUROMOD variables

A key is either a EUROMOD input variable — deh, dgn, dag, les — or region, the one named dimension with structure of its own.

That restriction is deliberate. Recoded category labels are meaningless outside the binding that invented them. An external model’s own “low / medium / high skill” bands cannot be looked up anywhere, cannot be verified against a dataset, and silently fix an aggregation the analyst may not have wanted. deh=3-4 is self-describing, checkable against the data, and needs no registry entry to interpret. An external model that publishes its own classes translates them to variable ranges in its mapping spec, which is where model-specific semantics belong.

Category names an external model might reach for are recognised well enough to produce a pointed error rather than a generic one: skill points you at deh, gender at dgn, age_band at dag.

Value specs

deh=3         exact value
deh=3-4       inclusive range — ddi=-1-0 parses, signs are handled
dag=65+       open upper bound
deh=3,5       set of values

Non-numeric values compare as strings, so only exact matches and sets apply to them.

Groups are canonicalised on their way into the shock table — keys sorted, whitespace removed — so "region=12;deh=3-4" and "deh=3-4;region=12" are one cell with one id, not two.

Regions are NUTS-aware

region resolves to the finest regional column the dataset actually carries: drgn2 if present, otherwise drgn1, otherwise the whole country. Around that it understands the NUTS hierarchy in both directions.

A coarser shock code covers its subregions: a shock on region=BE1 applies to everyone in BE10. A finer shock code collapses to the level the data supports — if the external model publishes NUTS-2 rates but the dataset only has NUTS-1, the constituent rates are averaged rather than the shock being dropped or, worse, matched against nothing.

This is the one place where the package resolves a mismatch instead of refusing it, because the alternative — requiring every external model to publish at the survey’s regional resolution — would rule out most real pairings.

Checking a cell before you use it

validate_group_syntax checks a group is well formed; validate_group_columns checks its keys exist in a given dataset. The first runs during shock-table normalisation, the second when the data shows up. specs_overlap answers whether two cells intersect, which is how overlapping set shocks are caught.

The cheapest way to see what a cell actually selects is to ask for the plan without paying for it:

plan = apply_scenario(system, data, scenario, validate_only=True)
plan["diagnostics"]["cell_population"]

which reports, per cell, how many people it contains and what their labour states are — before any transformation runs.