docs: fix stale fixture description and Direct/Total vignette figures (M1, M2, M5)
M5: README.md described the bundled fixture as a "3.6 MB two-year slice (2019 + 2020)"; it's now a 15 MB four-year slice (2011, 2012, 2019, 2020), matching the regenerated fixture and the vignette's own description. M2: total-spending.Rmd cited County 1.8% / City 0.8% intergovernmental- to-Direct and County 91.6% L/M, all roughly 2x off against the bundled fixture. Measured directly against the fixture (all 50 states, each of its four years): County IG/Direct 3.4%-5.1%, City IG/Direct 2.6%-3.1%, County L/M 43%-51% (all varying by year). State 17.2%, AL 7.6%, national 11.6%, and City L/M 188.3% were re-checked and left as-is. M1: the "Why Total = Direct + M + L" paragraph described money a local government *receives* and the state "redistributing as M" -- backwards. M and L are both the *queried* government's own payments *out*: M to other local governments, L up to its state. Rewrote the explanation; the conclusion and non-M2-flagged figures are unchanged.
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@@ -47,9 +47,10 @@ explanation with worked examples.
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### Testing
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### Testing
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The package ships a bundled fixture corpus at `inst/extdata/fixture_corpus/` —
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The package ships a bundled fixture corpus at `inst/extdata/fixture_corpus/` —
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a 3.6 MB two-year slice (2019 + 2020) of the full corpus covering all 50
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a 15 MB four-year slice (2011, 2012, 2019, 2020) of the full corpus covering
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states. `tests/testthat/setup.R` automatically points `USCOGDATA_URL` at this
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all 50 states. `tests/testthat/setup.R` automatically points `USCOGDATA_URL`
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fixture, so the full test suite runs offline with no network dependency:
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at this fixture, so the full test suite runs offline with no network
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dependency:
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```r
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```r
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devtools::test() # uses bundled fixture, no credentials required
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devtools::test() # uses bundled fixture, no credentials required
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@@ -155,14 +155,15 @@ silently overstating every multi-layer figure it produces.
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# How big is the risk in practice
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# How big is the risk in practice
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Intergovernmental transfers aren't evenly distributed by government type.
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Intergovernmental transfers aren't evenly distributed by government type.
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Measured on the full published corpus, intergovernmental spending as a
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Measured against the bundled fixture corpus (all 50 states, each of its
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four years -- 2011, 2012, 2019, 2020), intergovernmental spending as a
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share of a government's own Direct spending is:
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share of a government's own Direct spending is:
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| Government type | Intergovernmental / Direct |
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| Government type | Intergovernmental / Direct |
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|---|---|
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|---|---|
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| State | 17.2% |
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| State | 17.2% |
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| County | 1.8% |
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| County | 3.4%-5.1% (varies by year) |
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| City | 0.8% |
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| City | 2.6%-3.1% (varies by year) |
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So the Direct/Total choice matters overwhelmingly for **state** governments
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So the Direct/Total choice matters overwhelmingly for **state** governments
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-- a state's Total genuinely differs from its Direct by a meaningful margin,
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-- a state's Total genuinely differs from its Direct by a meaningful margin,
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@@ -174,17 +175,22 @@ for Alabama in FY2019, and 11.6% nationally.
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# Why Total = Direct + M + L, not Direct + M
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# Why Total = Direct + M + L, not Direct + M
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It's tempting to assume `total` only needs to add `M` (payments to local
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It's tempting to assume `total` only needs to add `M`. But `M` and `L` are
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governments). But not all of the money a county or city receives arrives
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both money the queried government itself pays **out** -- they're not two
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directly from its state as an `M` payment -- some flows through as `L`
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different accounts of a receiving government's revenue. `M` is what it
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(payments *to* the state government), which the state government then
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pays to other **local** governments (e.g. a county paying a city for a
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redistributes as `M`. On the published corpus, `L` is 0 for state
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shared paving contract); `L` is what it pays **up** to its **state**
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governments (a state has no "payments to the state government" leg of its
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government (e.g. a county's contribution to a state-administered program).
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own) but is 91.6% the size of `M` for counties and 188.3% the size of `M`
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A local government's Total genuinely includes both legs, because both are
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for cities -- so a `total` that omitted `L` would silently undercount Total
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its own spending, just routed to a different kind of recipient. On the
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specifically for local governments. `cog_spending(expenditure_concept =
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bundled fixture corpus (all 50 states, 2011/2012/2019/2020), `L` is 0 for
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"total")` includes both legs (excluding the `L--` family-total rollup row,
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state governments (a state has no "payments to the state government" leg of
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which would double-count its own components).
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its own) but is 43%-51% the size of `M` for counties (varies by year) and
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188.3% the size of `M` for cities -- so a `total` that omitted `L` would
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silently undercount Total specifically for local governments.
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`cog_spending(expenditure_concept = "total")` includes both legs (excluding
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the `L--` family-total rollup row, which would double-count its own
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components).
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# Composition rules
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# Composition rules
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