Land wave B2' — the hansen_singleton pair and their builders (Track B)

AGENTS.md

@@ -116,7 +116,7 @@ Where one builder produces a **set** of files, name it for the set and let each
 
 `scripts/` is repo tooling — the audit dashboard and the catalog generator — and produces no dataset. Keep the two apart.
 
-**Where a builder reads its input from.** The normal case is the third-party upstream, fetched at run time: six of the seven builders here do that, and it is the fetch stage of the contract below. A builder reads from `sources/` **only when the input cannot be re-fetched** — the upstream is gone, unlocatable, or was inherited with no recoverable source. `sources/` is that exception layer, not a general input tree, and it is emphatically not "the big-file directory": the defining property is un-refetchability, not size. What it must never be is a network read from another QuantEcon repo — that is how a retired repo becomes load-bearing again.
+**Where a builder reads its input from.** The normal case is the third-party upstream, fetched at run time: eight of the nine `committed` builders here do that, and it is the fetch stage of the contract below. A builder reads from `sources/` **only when the input cannot be re-fetched** — the upstream is gone, unlocatable, or was inherited with no recoverable source. `sources/` is that exception layer, not a general input tree, and it is emphatically not "the big-file directory": the defining property is un-refetchability, not size. What it must never be is a network read from another QuantEcon repo — that is how a retired repo becomes load-bearing again.
 
 Builders follow four stages — **fetch → pre-process → validate → write** — and only write on validation pass (expected columns/dtypes, row-count floor, recency of date range, no all-NaN columns, values unchanged in the overlap window with the previous vintage). Lectures always read the last-good snapshot: an upstream outage may fail a refresh, it must never break a lecture build.
 

CATALOG.md

@@ -6,7 +6,7 @@
 
 The dataset registry, **auto-generated** from the sidecar manifests (`lectures/*.yml`). Do not edit by hand — run `python scripts/build_catalog.py`. A dataset appears here once it has a manifest, which may be before its consuming lectures are repointed — an empty **Used by** column means the file is here and documented but no lecture reads it from this repo yet. Files still to migrate are tracked in [PLAN.md](PLAN.md).
 
-**31 datasets** · 31 read by lectures today · 113.0 MB total · 26 permitted / 5 restricted redistribution
+**33 datasets** · 31 read by lectures today, 2 awaiting repoint · 113.0 MB total · 28 permitted / 5 restricted redistribution
 
 | Dataset | Class | Source | Licence | Redist. | Integrity | Builder | Size | Used by |
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |
@@ -27,6 +27,8 @@ The dataset registry, **auto-generated** from the sidecar manifests (`lectures/*
 | [**forbes-billionaires.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/forbes-billionaires.csv)<br><sub>Forbes Billionaires — individual net worth</sub> | constructed | [Forbes Billionaires](https://www.forbes.com/billionaires/) |  | ⚠️ restricted | ⚠️ unverifiable | committed-frozen | 775.7 KB | [lecture-python-intro · heavy_tails.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/heavy_tails.md)<br>[lecture-wasm · heavy_tails.md](https://github.com/QuantEcon/lecture-wasm/blob/main/lectures/heavy_tails.md)<br>[lecture-intro.zh-cn · heavy_tails.md](https://github.com/QuantEcon/lecture-intro.zh-cn/blob/main/lectures/heavy_tails.md)<br>[test-actions-lecture-intro · heavy_tails.md](https://github.com/QuantEcon/test-actions-lecture-intro/blob/main/lectures/heavy_tails.md) |
 | [**forbes-global2000.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/forbes-global2000.csv)<br><sub>Forbes Global 2000 — firm size measures</sub> | constructed | [Forbes Global 2000](https://www.forbes.com/lists/global2000/) |  | ⚠️ restricted | ⚠️ unverifiable | committed-frozen | 115.6 KB | [lecture-python-intro · heavy_tails.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/heavy_tails.md)<br>[lecture-wasm · heavy_tails.md](https://github.com/QuantEcon/lecture-wasm/blob/main/lectures/heavy_tails.md)<br>[lecture-intro.zh-cn · heavy_tails.md](https://github.com/QuantEcon/lecture-intro.zh-cn/blob/main/lectures/heavy_tails.md)<br>[test-actions-lecture-intro · heavy_tails.md](https://github.com/QuantEcon/test-actions-lecture-intro/blob/main/lectures/heavy_tails.md) |
 | [**fp.dta**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/fp.dta)<br><sub>Treisman (2016) Russia's Billionaires — country-year panel of billionaire counts and covariates</sub> | verbatim | [Replication package for Treisman (2016), "Russia's Billionaires" (AER Papers & Proceedings)](https://www.aeaweb.org/articles?id=10.1257/aer.p20161068) |  | ✅ permitted | ✅ verified | n/a (verbatim) | 1000.1 KB | [lecture-python.myst · mle.md](https://github.com/QuantEcon/lecture-python.myst/blob/main/lectures/mle.md)<br>[lecture-python.zh-cn · mle.md](https://github.com/QuantEcon/lecture-python.zh-cn/blob/main/lectures/mle.md)<br>[lecture-stats · mle.md](https://github.com/QuantEcon/lecture-stats/blob/main/lectures/mle.md) |
+| [**hansen_singleton_1982_data.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/hansen_singleton_1982_data.csv)<br><sub>Hansen-Singleton (1982) replication — monthly US gross real market return and consumption growth, 1959-1978</sub> | constructed | [FRED (BEA and BLS monthly series) and the Ken French data library (F-F_Research_Data_Factors)](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) |  | ✅ permitted | ✅ verified | ✅ committed | 11.4 KB | — |
+| [**hansen_singleton_1983_data.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/hansen_singleton_1983_data.csv)<br><sub>Hansen-Singleton (1983) replication — monthly US returns, consumption and inflation, 1959-1978</sub> | constructed | [FRED (BEA and BLS monthly series) and the Ken French data library (F-F_Research_Data_Factors)](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) |  | ✅ permitted | ✅ verified | ✅ committed | 25.5 KB | — |
 | [**japan_deaths_by_age.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/japan_deaths_by_age.csv)<br><sub>Japan — deaths by single year of age, 2023</sub> | constructed | [United Nations, Department of Economic and Social Affairs, Population Division — World Population Prospects 2024](https://population.un.org/wpp/downloads) | CC BY 3.0 IGO | ✅ permitted | ✅ verified | ✅ committed | 1.7 KB | [lecture-python-intro · observed_distributions.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/observed_distributions.md)<br>[lecture-python-intro · fitting_distributions.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/fitting_distributions.md) |
 | [**japan_earthquakes.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/japan_earthquakes.csv)<br><sub>Japan region — earthquakes of magnitude 5 and above, 2000-2024</sub> | constructed | [Advanced National Seismic System (ANSS) Comprehensive Earthquake Catalog (ComCat), US Geological Survey](https://earthquake.usgs.gov/earthquakes/search/) | US Government work — public domain | ✅ permitted | ✅ verified | ✅ committed | 172.8 KB | [lecture-python-intro · fitting_distributions.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/fitting_distributions.md) |
 | [**japan_population_by_age.csv**](https://github.com/QuantEcon/data-lectures/raw/main/lectures/japan_population_by_age.csv)<br><sub>Japan — population by single year of age, 2024</sub> | constructed | [Population Estimates, Statistics Bureau of Japan, Ministry of Internal Affairs and Communications](https://www.stat.go.jp/english/data/jinsui/index.html) | Japan Statistics Bureau terms of use | ✅ permitted | ✅ verified | ✅ committed | 1.3 KB | [lecture-python-intro · prob_dist.md](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/prob_dist.md) |

PLAN.md

@@ -214,7 +214,7 @@ The remaining work decomposes by **consuming series** rather than by hosting pat
 | Track | Datasets | Coupling | Blocked on |
 | --- | --- | --- | --- |
 | **A — `intro` + `wasm`** | 17, **all done**. The last two CSVs landed as wave A4 ([#74](https://github.com/QuantEcon/data-lectures/pull/74), flipped in [#75](https://github.com/QuantEcon/data-lectures/pull/75)); `graph.txt` was never a migration — see below | — | — |
-| **B — `python.myst`** | 7: `maketable1/2/4.dta`, `fp.dta`, `hansen_singleton_1982/1983_data.csv`, `NEWQDATA.csv` | none | nothing |
+| **B — `python.myst`** | 7, cut into two waves. **B1′ done**: the `ols` trio, `fp.dta` and `NEWQDATA.csv` landed in [#79](https://github.com/QuantEcon/data-lectures/pull/79), flipped in [#80](https://github.com/QuantEcon/data-lectures/pull/80). **B2′**: `hansen_singleton_1982/1983_data.csv` | **three consumers, not one** — `lecture-python.zh-cn` reads by URL *and* holds byte-identical copies of all 7 plus both builders (and is outside `SCAN_REPOS`, so the audit cannot see it); `lecture-python.notebooks` lags a publish tag; `lecture-stats` carried a published-site prose link to `fp.dta` behind a daily linkcheck. B2′ adds a fourth kind: the two builders migrate too, and each lecture names them twice outside its data cell | nothing |
 | **C — `advanced.myst`** | 6: `dataBHS.mat`, `acs_data_summary.csv`, `bbh` ×2, `fred_data.csv`, `hansen_jagannathan_1991_data.json` | none | nothing (builder recovery is in-wave work, not a gate) |
 | **D — `programming`** | 1: `test_pwt.csv` | none | nothing — a single-PR track |
 | **E — dynamic / live-API** | the UNRATE twin, then the 15 incidental API lectures | wasm is the forcing customer | [#14](https://github.com/QuantEcon/data-lectures/issues/14) schema decisions, [#26](https://github.com/QuantEcon/data-lectures/issues/26) fetch layer |

builders/README.md

@@ -40,6 +40,8 @@ re-fetched** — see `AGENTS.md`.
 | `japan_population_by_age.py` | `japan_population_by_age.csv` | committed |
 | `us_adult_heights.py` | `us_adult_heights.csv` | committed |
 | `NEWQDATA.py` | `NEWQDATA.csv` | committed — the **only** builder here that reads a committed input (`sources/NEWQDATA.MAT`) instead of fetching. Its upstream is published nowhere; see `sources/README.md`. Reproduces its output byte for byte |
+| `hansen_singleton_1982_data.py` | `hansen_singleton_1982_data.csv` | committed — fetches FRED and the Ken French factors live. Reproduces its output byte for byte (2026-08-13) |
+| `hansen_singleton_1983_data.py` | `hansen_singleton_1983_data.csv` | committed — the same construction plus a T-bill leg, so its output is a strict superset of the 1982 file's. Reproduces its output byte for byte (2026-08-13) |
 | `business_cycle.py` | `business_cycle_data.csv`, `business_cycle_info.md`, `business_cycle_metadata.md` | run by hand, no validate stage yet (PLAN Phase 5); its three outputs are the repo's only unmanifested files |
 | `webscrape_forbes.ipynb` | `forbes-global2000.csv`, `forbes-billionaires.csv` | **committed-frozen** — an undocumented Forbes API, a spoofed user-agent and hardcoded GDPR consent cookies. Defects recorded in the two manifests rather than fixed |
 | `generating_mini.md` | `SCF_plus_mini.csv`, `SCF_plus_mini_no_weights.csv` | **committed-frozen** — its `to_csv` calls are commented out upstream and stay that way. As written it still fetches the `high_dim_data` URL; that URL is historical, and the input is now committed at `sources/SCF_plus.dta`. See `sources/README.md` |
@@ -52,16 +54,28 @@ asserts is that the path exists. `usa-gini-nwealth-tincome-lincome.ipynb` takes
 the opposite choice deliberately: its upstream name was `data.ipynb`, which is
 meaningless in a flat directory, so it is renamed to its output stem.
 
+The two `hansen_singleton_*` builders are the case that leaves no choice at all.
+Both were called `make_data.py`, sitting beside their own output in separate
+`_static/lecture_specific/<lecture>/` directories where the parent directory
+supplied the meaning. Flattened into one tree they collide outright, so each
+takes its dataset's stem — the rule's default, arrived at by necessity rather
+than by preference.
+
 **A frozen builder is committed verbatim and not edited.** That is what makes it
 provenance rather than code, and it is why the pandas-3 defect above is recorded
 here instead of patched — fixing it would mean this file is no longer the thing
 that produced those bytes. The fix belongs in `lecture-python-intro`, which still
 serves that notebook to readers.
 
-**This listing is the coverage report.** The repo has 18 `constructed` datasets
-and 10 builders; the difference is the Phase 9 recovery backlog, carried as
-`builder_status: unrecovered` in each manifest rather than hidden by
-reclassifying the file as `verbatim`.
+**This listing is the coverage report.** The repo has 21 `constructed` datasets.
+Fourteen ship a builder (9 `committed`, 5 `committed-frozen`), carried by **12**
+distinct builder files — fewer than the datasets because `generating_mini.md`
+and `webscrape_forbes.ipynb` each produce two. The remaining **7** have none:
+they carry `builder_status: unrecovered` in their manifests, which is the Phase
+9 recovery backlog, kept visible rather than hidden by reclassifying the file as
+`verbatim`. The table above lists a **13th** builder, `business_cycle.py`, which
+no manifest references — its three outputs are the repo's only unmanifested
+files.
 
 Repo tooling — the audit dashboard and the catalog generator — lives in
 `scripts/` and is not a builder.