Variables Reference¶

DEMOS computes lazily evaluated columns (via the orca framework) that are available as inputs to model expressions and calibration steps. This page describes the naming conventions used for these columns and provides an inventory of every computed variable by table.

Naming Conventions¶

Persons table¶

Prefix / Pattern	Meaning	Example
`age_`	Age bin for persons	`age_23_35`, `age_60plus`
`age_emp_`	Age bin used in employment (enter/exit labor-force) models	`age_emp_20_40`, `age_emp_70plus`
`age_mort_`	Age bin used in the mortality model	`age_mort_21_40`, `age_mort_90plus`
`age_km_`	Age bin used in the kids-move model	`age_km_19_20`, `age_km_30plus`
`sex_`	Sex indicator	`sex_female`
`emp_idle_`	Idle (non-working) employment status bin	`emp_idle_under60`, `emp_idle_over60`
`edu_`	Education attainment bin	`edu_hs_ged`, `edu_college_plus`
`race_`	Race/ethnicity indicator	`race_white`, `race_black`, `race_asian_pi`, `race_native_am`, `race_acs_other`, `race_hawaiian`, `race_asian`, `race_other`
`mar_`	Marital status indicator	`mar_married`, `mar_widowed`, `mar_div_or_sep`, `mar_widowed_or_never`
`emp_idle__age_km_`	Cross-product: employment status × kids-move age bin	`emp_idle_under60_age_km_19_20`
`edu__age_km_`	Cross-product: education bin × kids-move age bin	`edu_hs_ged_age_km_21_25`
`intercept`	Constant term (ones vector)	`intercept`

Households table¶

Prefix / Pattern	Meaning	Example
`hh_`	Computed household-level aggregate or indicator	`hh_n_persons`, `hh_n_children`
`hh_income_`	Household income bin	`hh_income_bin1` … `hh_income_bin5`
`hh_edu_top_`	Education bin of the most-educated head/spouse	`hh_edu_top`, `hh_edu_top_bin2`, `hh_edu_top_bin3`
`hh_fam_work`	Count of working head/spouse members	`hh_fam_work`, `hh_fam_work2`
`hh_age_avg_`	Average age of head/spouse, and bins	`hh_age_avg`, `hh_age_avg_bin2` … `hh_age_avg_bin4`
`hh_age_min_`	Minimum age of head/spouse, and bins	`hh_age_min`, `hh_age_min_bin2` … `hh_age_min_bin4`
`hh_fsize_`	Household-size bin for birth model	`hh_fsize_bin23`, `hh_fsize_bingt3`
`hh_birth_age_`	Age of relevant female member, for birth model	`hh_birth_age_lt27`, `hh_birth_age_27_35`
`hh_head_`	Attribute of the household head (relate == 0)	`hh_head_age`, `hh_head_race_id`, `hh_head_race_str`, `hh_head_race_white`
`hh_head_edu_`	Education bin of head only (used in income model)	`hh_head_edu_bin1` … `hh_head_edu_bin3`
`hh_head_race_`	Race indicator derived from the household head	`hh_head_race_black`, `hh_head_race_native_am`, `hh_head_race_asian`, `hh_head_race_hawaiian`, `hh_head_race_acs_other`
`hh_size_str`	Categorical household size string (“one”, “two”, …)	`hh_size_str`
`intercept`	Constant term (ones vector)	`intercept`

hh_edu_top_bin vs. hh_head_edu_bin
These are distinct columns. hh_edu_top_bin reflects the maximum education of the head or spouse (relate < 2) and is used in the divorce and cohabitation models. hh_head_edu_bin reflects the education of the household head only (relate == 0) and is used in the income model.

Column Inventory¶

Persons columns¶

Age bins (general)¶

Column	Definition
`age_23_35`	Age ∈ [23, 35]
`age_36_60`	Age ∈ [36, 60]
`age_60plus`	Age > 60
`intercept`	1 for every person

Age bins — employment models¶

Column	Definition
`age_emp_20_40`	Age ∈ [20, 40]
`age_emp_41_50`	Age ∈ [41, 50]
`age_emp_51_70`	Age ∈ [51, 70]
`age_emp_70plus`	Age > 70

Age bins — mortality model¶

Column	Definition
`age_mort_21_40`	Age ∈ [21, 40]
`age_mort_41_50`	Age ∈ [41, 50]
`age_mort_51_70`	Age ∈ [51, 70]
`age_mort_71_90`	Age ∈ [71, 90]
`age_mort_90plus`	Age > 90

Age bins — kids-move model¶

Column	Definition
`age_km_16_18`	Age ∈ [16, 18]
`age_km_19_20`	Age ∈ [19, 20]
`age_km_21_25`	Age ∈ [21, 25]
`age_km_26_30`	Age ∈ [26, 30]
`age_km_30plus`	Age > 30

Sex¶

Column	Definition
`sex_female`	sex == 2

Employment status¶

Column	Definition
`emp_idle_under60`	worker == 0 and age < 60
`emp_idle_over60`	worker == 0 and age ≥ 60

Education¶

Column	Definition
`edu_hs_ged`	edu ∈ [16, 17] (high school diploma or GED)
`edu_college_plus`	edu > 17 (some college or higher)

Race / ethnicity¶

Column	Definition	Notes
`race_white`	race_id == 1
`race_black`	race_id == 2
`race_native_am`	race_id ∈ {3, 4, 5}
`race_asian`	race_id == 6
`race_hawaiian`	race_id == 7
`race_asian_pi`	race_id ∈ {6, 7}	Asian or Pacific Islander combined
`race_acs_other`	race_id == 8	ACS “some other race” category; building block for head-of-household columns
`race_other`	race_id ∈ {3, 4, 5, 8, 9}	Broad “other” category used in employment models

Marital status¶

Column	Definition
`mar_married`	MAR == 1
`mar_widowed`	MAR == 2
`mar_div_or_sep`	MAR ∈ {3, 4}
`mar_widowed_or_never`	MAR ∈ {2, 5}

Cross-products (kids-move model)¶

Eight emp_idle_under60 × age_km_* and eight emp_idle_over60 × age_km_* columns, plus eight edu_hs_ged × age_km_* and eight edu_college_plus × age_km_* columns. The pattern is <base_col>_<age_km_bin>, e.g. emp_idle_under60_age_km_19_20.

Households columns¶

Head-of-household attributes¶

Column	Definition
`hh_head_age`	Age of person with relate == 0
`hh_head_race_id`	race_id of person with relate == 0
`hh_head_race_str`	String label of head’s race: `'white'`, `'black'`, `'asian'`, or `'other'`
`hh_head_race_white`	1 if head’s race_id == 1
`hh_head_race_black`	1 if head’s race_id == 2
`hh_head_race_native_am`	1 if head’s race_id ∈ {3, 4, 5}
`hh_head_race_asian`	1 if head’s race_id == 6
`hh_head_race_hawaiian`	1 if head’s race_id == 7
`hh_head_race_acs_other`	1 if head’s race_id == 8
`hh_head_edu_bin1`	head’s edu < 16 (less than high school)
`hh_head_edu_bin2`	head’s edu ∈ [16, 17] (HS/GED)
`hh_head_edu_bin3`	head’s edu > 17 (some college or more)
`hh_size_str`	Categorical size: `'one'`, `'two'`, etc.

Household composition¶

Column	Definition
`hh_n_persons`	Total number of persons in the household
`hh_n_children`	Number of persons with age ≤ 17

Income bins¶

Column	Definition
`hh_income_bin1`	income < $25,000
`hh_income_bin2`	income ∈ [$25k, $50k)
`hh_income_bin3`	income ∈ [$50k, $75k)
`hh_income_bin4`	income ∈ [$75k, $150k)
`hh_income_bin5`	income ≥ $150,000

Top education of head/spouse (relate < 2)¶

Column	Definition
`hh_edu_top`	Maximum edu value among head and spouse
`hh_edu_top_bin2`	hh_edu_top ∈ [16, 17]
`hh_edu_top_bin3`	hh_edu_top > 17

Working members (head/spouse)¶

Column	Definition
`hh_fam_work`	Number of working (worker == 1) head/spouse members
`hh_fam_work2`	1 if hh_fam_work == 2

Average age of head/spouse¶

Column	Definition
`hh_age_avg`	Mean age of head and spouse (relate < 2)
`hh_age_avg_bin2`	hh_age_avg ∈ (22, 35]
`hh_age_avg_bin3`	hh_age_avg ∈ (35, 60]
`hh_age_avg_bin4`	hh_age_avg > 60

Minimum age of head/spouse¶

Column	Definition
`hh_age_min`	Minimum age of head and spouse (relate < 2)
`hh_age_min_bin2`	hh_age_min ∈ (22, 35]
`hh_age_min_bin3`	hh_age_min ∈ (35, 60]
`hh_age_min_bin4`	hh_age_min > 60

Birth-model household columns¶

Column	Definition
`hh_birth_age_lt27`	Age of relevant female member ≤ 27
`hh_birth_age_27_35`	Age of relevant female member ∈ (27, 35]
`hh_fsize_bin23`	hh_n_persons ∈ {2, 3}
`hh_fsize_bingt3`	hh_n_persons > 3

Other¶

Column	Definition
`income_segment`	Quantile-based income segment (1–6)
`hh_type`	Categorical type code (1–8) based on tenure, size, and age of head
`county_id`	First 5 characters of block_id
`tract_id`	First 11 characters of block_id
`intercept`	1 for every household

Where variables are defined¶

Each variable is defined in the module that owns it, or in variables.py if it is shared across models.

File	Variables defined
`demos/variables.py`	Shared persons variables (age bins, sex, employment, education, race, marital status), shared household variables (intercept, county/tract IDs, income segment, hh_type), and blocks aggregations
`demos/models/aging.py`	`hh_head_age`
`demos/models/birth.py`	`hh_n_persons`, `hh_fsize_bin23`, `hh_fsize_bingt3`, `hh_birth_age_lt27`, `hh_birth_age_27_35`
`demos/models/employment.py`	`age_emp_20_40`, `age_emp_41_50`, `age_emp_51_70`, `age_emp_70plus`
`demos/models/fatality.py`	`age_mort_21_40`, `age_mort_41_50`, `age_mort_51_70`, `age_mort_71_90`, `age_mort_90plus`
`demos/models/household_reorg.py`	`hh_head_race_id`, `hh_head_race_str`, `hh_size_str`, `hh_n_children`, `hh_income_bin1–5`, `hh_edu_top`, `hh_edu_top_bin2–3`, `hh_fam_work`, `hh_fam_work2`, `hh_age_avg` + bins, `hh_age_min` + bins, `hh_head_race_white`
`demos/models/income.py`	`hh_head_edu_bin1–3`, `hh_head_race_black`, `hh_head_race_native_am`, `hh_head_race_asian`, `hh_head_race_hawaiian`, `hh_head_race_acs_other`
`demos/models/kids_moving.py`	`age_km_` bins, all `emp_idle__age_km_` and `edu__age_km_*` cross-products