Port import volumes lead retail inventory build-ups by about 4 months, per the one peer-reviewed fitted study located

Summary

About 4 months (roughly 16-17 weeks). The one directly on-point peer-reviewed study found the strongest, most useful relationship between containerized import volumes at major U.S. ports and retail inventory levels is a correlation at a 4-month lag -- consistent both over January 2018-July 2021 and, more strongly, during the 2020-2021 pandemic surge. That figure lines up well with the independent, physical-pipeline estimate industry sources give for total order-to-warehouse lead time (roughly 8-16 weeks, i.e. about 2-4 months), which is the mechanism the correlation reflects: retailers place import orders when inventory falls, and those orders show up as port volume, then warehouse stock, about 4 months later.

What the peer-reviewed study found

Smith, Bingham, Hackett & Smith, "Multiperspective Analysis of Pandemic Impacts on U.S. Import Trade: What Happened, and Why?" (Transportation Research Record, 2022) tested several candidate leading indicators of containerized import surges -- credit card spending, consumer sentiment, retail sales -- and found none of them behaved as clean leading indicators except retail inventory. Quoting the study directly:

"From January 2018 through July 2021, there was a relatively strong negative correlation between retail inventory- and import TEU indices with a 4-month lag (corresponding roughly to the time between import orders and arrival). In the 2020 to 2021 pandemic period the negative correlation was stronger, again with a 4-month lag."

Direction and mechanism. The correlation is negative and runs inventory-decline-to-import-surge: when retail inventories drop, retailers place replenishment orders, and those orders arrive as measurable port TEU volume about 4 months later. By the same logic in the other direction, once that import volume clears customs and reaches distribution centers, it is what rebuilds -- "leads" -- the retail inventory figures the following reporting periods; the 4-month figure is the single lag length linking the two series in both directions of the same replenishment cycle, not two separate numbers.

Cross-check against the physical supply-chain pipeline

Industry logistics sources describe the total order-to-warehouse lead time for ocean-freight retail goods (production + inland transit to origin port + ocean transit + customs clearance + inland U.S. trucking to distribution center) as typically 8-16 weeks, i.e., roughly 2 to 4 months. Ocean transit itself is the shortest piece of that (2-5 weeks trans-Pacific); the rest is production lead time, port handling/customs, and inland delivery. This engineering estimate brackets the study's measured 16-17-week (4-month) statistical lag rather than contradicting it -- the fitted, real-data correlation sits at the upper end of the plausible physical-pipeline range, which makes sense: the measured lag captures the full order-placement-to-shelf cycle (including the decision lag before the physical pipeline even starts), while a bare stage-by-stage transit-time sum tends to understate that.

Why this corpus could not recompute the lag directly

AskAmerica's own data was checked first. transport.bts_port_teu is the only port-level container-throughput table in this corpus (BTS Top-10 U.S. ports, monthly TEU) but is only loaded for January 2019-October 2022, and the source itself has not been refreshed since. econ.fred_indicators carries the actual FRED retail-inventory series (RETAILIMSA, RETAILIRSA), which in principle spans decades -- but a live query found only 6 rows total for RETAILIMSA (January-June 2026), not the multi-decade history FRED itself publishes. That is very likely a stalled or partial backfill specific to those two series (other FRED series in the same table carry full multi-year history), and has been reported as a data-quality defect this session. With essentially no overlapping window between the two series as currently loaded here, an independent fitted cross-correlation could not be computed from this corpus, so the measured figure above is sourced from the peer-reviewed literature instead, with the physical-pipeline figures as a cross-check.

What This Report Does Not Answer

• Compute/verify the lag directly against AskAmerica's own port and retail-inventory series if available: transport.bts_port_teu (the only port-level container-volume table in this corpus) covers Jan 2019-Oct 2022 only, and econ.fred_indicators' RETAILIMSA/RETAILIRSA series (retail inventories) currently hold only 6 rows (Jan-Jun 2026) rather than FRED's real multi-decade history -- confirmed live and reported as a defect this session. The two series therefore share essentially no overlapping window in this corpus, making an independent fitted lag uncomputable here. Answer instead rests on a directly on-point peer-reviewed study (Smith, Bingham, Hackett & Smith 2022, Transportation Research Record) that ran the actual fitted correlation on the real underlying Census/Global Port Tracker data, plus a cross-check against industry-reported physical supply-chain lead times.

Every query behind this report

7 warehouse calls ran in this session, in order. Each is reproducible against the same snapshot.

SELECT DISTINCT type FROM transport.bts_port_teu

WITH port AS (
  SELECT report_date AS dt, SUM(teu) AS total_teu
  FROM transport.bts_port_teu
  GROUP BY report_date
),
retail AS (
  SELECT date AS dt, value AS inv
  FROM econ.fred_indicators
  WHERE series = 'RETAILIMSA'
)
SELECT p.dt AS port_month, p.total_teu, r.dt AS retail_month, r.inv
FROM port p JOIN retail r ON r.dt = p.dt
ORDER BY p.dt
LIMIT 60

SELECT MIN(report_date), MAX(report_date), COUNT(DISTINCT report_date) FROM transport.bts_port_teu

SELECT "date", "value" FROM econ.fred_indicators WHERE series='RETAILIMSA' AND "date" >= '2019-01-01' AND "date" <= '2019-06-01' ORDER BY "date"

SELECT MIN("date"), MAX("date"), COUNT(*) FROM econ.fred_indicators WHERE series='RETAILIMSA'

SELECT DISTINCT series FROM econ.fred_indicators WHERE series ILIKE '%RETAIL%'

SELECT "year", COUNT(*) FROM econ.fred_indicators WHERE series='RETAILIMSA' GROUP BY "year" ORDER BY "year"