I'm the author of a new preprint that tries to resolve why major U.S. sexual behavior surveys appeared to report contradictory trends over the past decade.
The key move is separating never-occurrence from temporary inactivity among the experienced, rather than averaging them together. That decomposition is applied symmetrically across surveys and validated against an independent dataset.
The paper then treats the remaining discrepancy as a debugging problem:
• check distributions for digit heaping and compression
• look for stock–flow reversals consistent with under-reporting
• compare adjacent survey waves for internal consistency
• cross-validate against an external series not subject to the same reporting incentives
Once those diagnostics are applied, the conflicting results reconcile cleanly.
This is a methods/measurement paper, not a causal one. The contribution is showing how small reporting artifacts and aggregation choices can produce large apparent disagreements—even in high-quality surveys—when they aren't handled carefully.
(One note for the methodologically inclined: conditioning on "sexually experienced" looks like it could induce collider bias. Section 4 and Appendix D5 address this directly—the gender gap in sexual debut didn't change differentially, so selection into the analysis sample is symmetric.)
Preprint: https://osf.io/preprints/socarxiv/jcdbm_v2
Replication code: https://github.com/Joshfkon/ResearchPaper_PartnershipGap
Happy to discuss the diagnostics, decomposition logic, or limitations.