The Data Space

Input data shared by every by-policy projection.

The eight 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/long_term_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_KR_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
prevalence_file	prevalence_table()	prevalence_table.csv
grade_share_file	grade_share_table()	grade_share_table.csv
incidence_file	incidence_table()	incidence_table.csv
dementia_file	dementia_table()	dementia_table.csv
lapse_table_file	lapse_table()	lapse_table.csv
av_table_file	av_table()	av_table.csv

Every decrement here is a construction, and the reason is Korean

No Korean mortality table in this library is a published table. 경험생명표 (gyeongheom saengmyeongpyo, the industry experience life table, 제10회 applied from 2024-04) is produced by 보험개발원 and is not released in full: what is published is the summary — the 평균수명 and the 기대여명 — and not the rates [REG-R33] [REG-R34]. mort_table.csv is therefore a [std] Makeham-Gompertz construction, q(x) = 1 - exp(-(A + B c^x)) with A = 0.0003 and c = 1.10, in which B is solved so that the complete expectation of life at 65 reproduces the published 경험생명표 65세 기대여명 — 23.7 years for men and 27.1 for women. The construction is not fitted to anything else, and it reproduces the second published summary statistic without being asked to: it returns a 평균수명-equivalent at issue age 40 of 86.4 (men) against the published 86.3, and 90.3 (women) against 90.7. That agreement is a cross-check on the shape, not evidence about any insurer’s experience, and no conclusion about Korean insured mortality should be drawn from the file.

The morbidity basis is public administrative data, which is what makes this product different from every other decrement in krlib, and it is split over three files because it is three different things. prevalence_table.csv carries the 연령별 인정률 of the 2024 노인장기요양보험 통계연보 by sex — a prevalence, a point-in-time count of people holding a certification — together with the logistic fitted through it. grade_share_table.csv carries the grade composition of certified lives by age band, because the severe share is U-shaped in age and a single all-ages vector mis-prices a 1~2등급 benefit by up to a factor of two. incidence_table.csv carries the one Korean long-term-care incidence rate published in any retrieved document — one carrier’s disclosed 예정위험률 at ages 40, 50 and 60 — which the model uses for its sub-65 age gradient and sex ratio and as a level cross-check, never as the level itself. dementia_table.csv carries the 2023 치매역학조사 prevalence for the optional rider.

Converting a prevalence into an incidence is done in Projection, where it belongs, because it needs the mortality of the care state.

av_table.csv is the one file whose numbers come from a carrier’s own published figures: the 해약환급금 미지급형 환급률 progression at 40세, 90세만기, 20년납, 월납, from which the model reconstructs the 계약자적립액 — the amount 감독규정 제7-63조제1항제1호 makes payable on death from a cause the contract does not cover [REG-R17].

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 — 男 만나이 40, 90세만기, 20년납, 월납, 1~2등급, 해약환급금 미지급형 — and the other eight exercise the thresholds, the terms, the surrender-value forms, the optional modules and the waiting-period variants.

mort_table()

Healthy-life annual mortality by sex and 만나이, from mort_table.csv.

A [std] Makeham-Gompertz construction whose one calibration anchor per sex is the published 제10회 경험생명표 65세 기대여명 [REG-R33]; the 경험생명표 itself is not published [REG-R34]. See the Space docstring. Projection reads it directly — there is no best-estimate adjustment factor, because the anchor is an experience statistic and not a valuation margin. The largest age present is 120, where mort_rate is 1.

prevalence_table()

The certification-prevalence parameters by sex, from prevalence_table.csv.

Five sourced 인정률 anchors per sex — the 2024 연령별 인정률 of the 노인장기요양보험 통계연보, computed as (계 − 등급외) over population — 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 lives by age band, from grade_share_table.csv.

share_ge is the share of all certified lives at that 장기요양등급 or above, so g2 is the 1~2등급 share — 0.146 at 65-69, 0.111 at 80-84 and 0.148 at 85 and over. The share is indexed by age because the severe share is U-shaped: high below 65, where only the 노인성 질병 list gets in at all, lowest around 80-84, where the marginal entrant is a lightly impaired person newly crossing the 51-point line, and rising again at 85 and over. The grade keys are ASCII codes for the six-point statutory scale and the model point column benefit_grade holds one of them.

incidence_table()

The one disclosed Korean long-term-care incidence basis, from incidence_table.csv.

One carrier’s 예정위험률 — 요양(1등급) 발생률 and 요양(2등급) 발생률 at 만나이 40, 50 and 60 by sex — the only Korean long-term-care incidence rate published in any retrieved document. It is a loaded pricing rate for a select, underwritten, 180-day-waited population and is not used as the level of this model’s basis; what is taken from it is the sub-65 age gradient and the sex ratio, and the ratio of the two is published by disclosed_inc_ratio_at() as the calibration finding.

dementia_table()

The dementia-prevalence parameters of the optional rider, from dementia_table.csv.

Five sourced band prevalences from the 2023 치매역학조사 and the three parameters of the logistic [std] fitted through them, plus the two sourced 65+ sex factors. Read only when dementia_rider is on, but read once per model regardless, as every table here is.

lapse_table()

The lapse-vector parameters, read from lapse_table.csv.

Four values: the first-year rate [std], the 0.1% convergence point at 납입완료 and the 0.8% post-완납 ultimate rate that the 2024 계리가정 guidance sets for a 무·저해지 form [REG-R27], and the level rate of the 표준형 comparison vector [std]. The durational shape between the first year and 납입완료 is the guidance’s own log-linear principle model and is applied in lapse_rate().

av_table()

The 계약자적립액 progression, read from av_table.csv.

Four anchors taken from one carrier’s published 해약환급금 미지급형 환급률 progression at 40세, 주계약 1,000만원, 90세만기, 20년납, 월납 — 48.7% at 20 years, 54.4% at 30, 50.5% at 40 and 0.0% at 50 — doubled, because that form pays 50% of the notional 기본형 value once the premiums are paid. av_ratio is therefore the 계약자적립액 as a ratio to cumulative office premiums paid, and runoff_fraction is the fraction of the way from 납입완료 to maturity, which is what lets one shipped progression serve every term and paying period.