The Data Space

Input data shared by every by-policy projection.

The five input CSVs are read here, once per model, and referenced from Projection as data. Projection is parameterized by point_id, so each Projection[N] is a separate ItemSpace with its own cells cache; if the readers lived there, every model point would re-read every file. Holding them in an unparameterized Space reads each file once no matter how many policies are projected.

Inputs are external files: plain CSVs in the model folder’s parent directory, products/nursing_care/, rather than data stored inside the model. The model folder therefore holds nothing but formulas — no _data/, no IOSpec, no embedded values — so a diff of the model shows logic changes only. This follows annuallife.TradLife_A; contrast basiclife.BasicTerm_S, which keeps its inputs inside the model through modelx’s IOSpec machinery.

The consequence worth knowing: the model is not portable on its own. Copying the LTC_JP_S folder without its parent’s CSVs produces a model that reads and then fails on first evaluation.

input_dir() resolves the directory from _model.path.parent at run time, so the model works wherever the repository is checked out. Each table has a filename Reference and a reader Cells:

Reference	Cells	File
model_point_file	model_point_table()	model_point_table.csv
mort_table_file	mort_table()	mort_table.csv
lapse_table_file	lapse_table()	lapse_table.csv
prevalence_file	prevalence_table()	prevalence_table.csv
grade_share_file	grade_share_table()	grade_share_table.csv

The mortality table is a construction, not a copy

第三分野標準生命表2018 (dai-san bun’ya hyōjun seimeihyō 2018, the third-sector standard table) is published by 日本アクチュアリー会 at a stable public URL, free and in full, so anyone can retrieve it and check a rate — a real contrast with the CMI tables uklib has to work around. But the publisher’s site terms prohibit reproduction, alteration and transmission to third parties, so this library must not ship a copy of it. What mort_table.csv holds is a [std] construction built on the rates the library quotes and attributes: eighteen anchor rows — nine per sex, at ages 40, 60, 65, 75, 80, 85, 90, 115 and the terminal age for males, and 40, 60, 65, 70, 75, 80, 85, 90 and the terminal age for females — each carrying a rate quoted from 第三分野標準生命表2018 [REG-R18] [REG-R20], and between adjacent anchors a piecewise log-linear (geometric) graduation,

q(x) = q(a) (q(b) / q(a)) ** ((x - a) / (b - a)) for a <= x <= b

The graduation reproduces every anchor exactly by construction, so nothing sourced is disturbed by the fitting, and it is locally the Gompertz family, which is the family the publisher’s own table follows over this age range. Above the last female anchor at 90 the female curve is continued at the male age-90-to-115 log-slope and closed on the sourced female terminal rate. The file is restricted to ages 40 and over, which is the whole of this product’s issue-age range, and q = 1 at the terminal ages 116 (male) and 118 (female).

The same construction, at the same values, is shipped by every jplib product that reads this table, so a cell carries the same rate and the same provenance string wherever it appears. Every row carries that account in its provenance column, and no conclusion about Japanese insured mortality should be drawn from the file.

Two further distinctions the provenance column keeps separate. First, even the real table is a valuation table carrying an explicit safety margin — its risk-theory adjustment is bounded 70% below and 85% above the unadjusted rate — so a best-estimate basis is a [std] adjustment of it either way; that adjustment lives in Projection.mort_be_factor, not in this file. Second, no impaired-life table for the 要介護 state exists in any retrieved source, so the care-state mortality multiple is likewise a [std] Reference on Projection rather than a shipped table.

The morbidity basis is public, which is unique in this library, and is split over two files rather than one. prevalence_table.csv carries the 認定率 (nintei-ritsu, certification rate) the government publishes by broad age band, together with the logistic that interpolates it; grade_share_table.csv carries the grade composition of certified persons. Both are prevalences — a point-in-time count of certified persons — and the conversion of a prevalence into an incidence is done in Projection, where it belongs, because it needs the mortality basis.

To swap in a company basis, replace the files with same-schema ones, or point the filename References at different names, then clear the cache. No formula changes.

Cells Descriptions

input_dir()

The directory holding the input CSVs: the model folder’s parent.

Inputs are external files, not data stored inside the model, so the model folder is pure formulas. The path is resolved at run time from where the model was read, following annuallife.TradLife_A.

model_point_table()

The model point table, read from model_point_table.csv.

Indexed by point_id. point_id = 1 is the anchor cell of the technical notes’ worked example; the other seven exercise the product’s variants, its optional modules and its edge cases.

mort_table()

The mortality rates by sex and attained age, from mort_table.csv.

A [std] construction in the shape of 第三分野標準生命表2018 — quoted anchors joined by a log-linear graduation — never a copy of the published table; see the Space docstring. The rate here is the table rate; Projection applies mort_be_factor to it to reach a best estimate. The largest age present for a sex is that sex’s terminal age, which is what Projection.omega_age() reads.

lapse_table()

The annual lapse rates by policy year, read from lapse_table.csv.

The last row is the terminal rate: Projection.lapse_rate caps the policy year at the largest year in the table, so a whole-of-life projection does not run off the end of it.

prevalence_table()

The certification prevalence parameters, read from prevalence_table.csv.

Two sourced 認定率 anchors — 4.3% at ages 65-74 and 31.1% at 75 and over — and the three parameters of the logistic [std] fitted through them, prev_ceil, prev_beta and prev_x_mid. The anchors are carried for provenance; the model reads the three fitted parameters.

grade_share_table()

The grade composition of certified persons, from grade_share_table.csv.

share_ge is the share of all certified persons at that 要介護/要支援 grade or above, so care2 is 0.508 and care3 is 0.340. The grade keys are ASCII codes for the seven-point certification scale; the model point columns grade_lump, grade_annuity and grade_waiver hold them.