Skylark CreationsSinusoidal History

Methods · Provenance & caveats

How the numbers were chosen

Where each data series comes from, what gets transformed, and why the correlation number on the calibration panel is a diagnostic and not a test statistic.

Data sources

  • US House Polarization (DW-NOMINATE)paired with Huntington — creedal passion

    Distance between Democratic and Republican House means on the first DW-NOMINATE dimension; 46th Congress–present (1879–current)

    Huntington's creedal-passion cycle predicts ~60–70 year polarization peaks. DW-NOMINATE is the cleanest long-run roll-call proxy available.

    Source: Voteview / Lewis, Poole, Rosenthal, Boche, Rudkin, Sonnet (2025) · license: freely available; project code MIT-licensed; no explicit data license · provenance: dw_nominate.source.md

  • US TFP growth (5-yr rolling)paired with Kondratiev wave

    5-year centered rolling average of Fernald's utilization-adjusted US TFP growth, derived by this project from the annual `dtfp` column. Underlying quarterly series begins 1947Q2; the centered window pushes the earliest displayable point to ~1950 and edge-clips the latest year

    Kondratiev waves predict 50–60 year cycles of technological paradigm expansion and exhaustion. TFP growth is the most direct measurable output.

    Source: Fernald (2014), FRBSF Working Paper 2012-19 · license: freely available; © FRBSF, no explicit reuse license · provenance: us_tfp_growth.source.md

  • US Top 1% Wealth Sharepaired with Peter Turchin — secular cycles

    Share of total household wealth held by the top 1% of US adults. The modern Saez–Zucman series begins 1913; pre-1913 points (1820, 1850, 1880, 1900, 1910) are spliced from earlier historical sources via OWID/WID and have wider standard errors

    Direct proxy for Turchin's elite-overproduction driver — when wealth concentrates, elite competition intensifies and instability follows.

    Source: WID · World Inequality Database (Saez–Zucman 2016 / DINA, retrieved via Our World in Data) · license: CC BY-NC-SA 4.0 · provenance: wid_top1_wealth.source.md

  • US Share of World GDPpaired with Ray Dalio — Big Cycle

    US GDP as share of all-countries GDP in the Maddison Project Database (2011 PPP \$); trimmed to 1870+ for stable country coverage. Full source 1820–2022

    Imperial-arc proxy. The data peaks at 1945 at ~32% of world GDP (war-production driven); Dalio's composite empire-score peaks ~1950 by his own statement, so the cycle and the data deliberately differ by ~5 years.

    Source: Maddison Project Database 2023 (Bolt & van Zanden) · license: CC BY 4.0 · provenance: us_world_gdp_share.source.md

  • US Liberal Democracy Index (V-Dem)paired with Strauss-Howe — saeculum

    V-Dem liberal-democracy index for the US, 1789–2025, scale 0–1

    Generational-cycle theory predicts crisis lows that line up with stress on liberal-democratic institutions. V-Dem's recent US drop is the clearest empirical analogue to Strauss-Howe's 'Fourth Turning'.

    Source: V-Dem Institute, Country-Year Dataset v16, March 2026 (retrieved via Our World in Data) · license: CC BY-SA 4.0 · provenance: vdem_libdem.source.md

  • Deaths in conventional wars (Project Mars, log)paired with Ibn Khaldun — dynastic cycle

    Natural log of (1 + deaths per 100,000) from Project Mars — log-transformed to keep WWI/WWII from flattening the rest of the series. Coverage 1800–2011; Project Mars covers conventional interstate and civil wars between states with differentiated militaries causing ≥500 deaths

    Rough proxy for Khaldun-style state-breakdown intensity. Log-transformed because WWI/WWII spikes otherwise dominate; the transform reveals secular trend and lets the cycle pairing breathe.

    Source: Our World in Data · Project Mars v1.1 (Lyall 2020) · license: OWID chart CC BY 4.0; underlying Project Mars data subject to Harvard Dataverse terms · provenance: conflict_deaths.source.md

Normalization

Every overlaid series is rescaled to the interval [-1, 1] using its own minimum and maximum over the visible window. That is convenient for eyeballing shape against a normalized sinusoid, and it is lossy: it hides absolute magnitude and makes level differences invisible. Two points stand out:

  • A series with one enormous spike (e.g. global conflict deaths in WWII) compresses every other variation toward a thin band. The visible shape near the peaks is real; the visible shape away from them is attenuated.
  • Because normalization is per-series, you cannot compare amplitudes across series. Only across time within a single series.

Why Pearson is the wrong tool

The calibration panel reports a Pearson correlation between the data series and the cycle curve. Pearson assumes two things this context violates:

  • Linearity. Sinusoids are not linear in year. Pearson will say a perfect cosine has zero correlation with the same cosine shifted by a quarter period, which is correct numerically but misses that one is just the derivative of the other.
  • Independence of observations. Time series are autocorrelated. Classical Pearson significance tests don't apply; the number has no legitimate p-value.

The panel exposes Pearson anyway because the single most important question - "how much is the peak-year choice doing?" - is visible just from watching the correlation change as you move the slider. That diagnostic use is valid. Treating the number as a test statistic is not.

Better tools for cyclic data include cross-correlation at varying lags, Lomb-Scargle or Fourier spectra, and wavelet decomposition. These are flagged for future work.

Missing and sparse data

The curves cover 1600–2050. Every data series has shorter coverage. DW-NOMINATE: 46th Congress–present (1879–current); Fernald TFP underlying series 1947Q2–present, displayed ~1950–present after the 5-year centered window; Project Mars conflict data 1800–2011; WID top-1% wealth modern coverage 1913–most-recent (with five earlier decadal points spliced from secondary sources, see below); Maddison US/world GDP share trimmed to 1870+ (earlier years have unstable country coverage); V-Dem 1789–present. Gaps are simply absent from the chart - nothing is interpolated. If a series fails to load, its legend entry shows "data unavailable" and the rest of the viz keeps working.

Two finer caveats. The modern WID/Saez–Zucman US top-1% wealth series begins in 1913; the five pre-1913 points (1820, 1850, 1880, 1900, 1910) come from earlier historical sources spliced via OWID/ WID and have wider standard errors. The TFP 5-year centered rolling average is this project's derivation from Fernald's annual dtfpcolumn, not Fernald's own published series; the windows at the extreme ends are clipped, which is why the displayed series effectively starts ~1950 rather than 1948.

Notes on individual pairings

V-Dem with Strauss-Howe, not Huntington. An earlier draft of this project paired V-Dem with Huntington as a secondary signal alongside DW-NOMINATE. We moved it to Strauss-Howe so each cycle would have exactly one paired series. Both are arguments. The Strauss-Howe pairing reads V-Dem's recent decline as a Fourth-Turning institutional-stress signal; the Huntington pairing would have read it as the trough side of a creedal-passion cycle. The data is the same; the framing differs.

No paired series for Perez. The techno-economic paradigm story is harder to reduce to a single century-long series. TFP growth is paired with Kondratiev because the Kondratiev framing is more directly about productivity surges; Perez tells a richer story about installation and deployment phases that no single time series captures cleanly.

See each series' per-source provenance file for full retrieval and processing notes.