No public data reports C&I lending dollars by borrower industry and state — the best available answer is a proxy from where tariff-exposed manufacturing activity itself concentrates

Summary

No public data source — including this corpus, the FDIC, or the Federal Reserve's own published statistics — reports commercial bank lending dollars broken out by both borrower industry and state. The only institution that has actually built a bank-level measure of lending exposure to import/supply-chain-dependent manufacturers is the Federal Reserve Board itself, in a January 2026 staff note — and even that used confidential supervisory data (Form Y-14Q) covering just 23 large banks, reported as a relative risk score, in dollars only at the national level, never broken out by state or by manufacturing subsector. That gap is confirmed, not a failure to search hard enough.

What I can answer, and do below: where the underlying manufacturing activity that depends most on tariff-exposed imported inputs is concentrated by state — a defensible proxy for where lending exposure sits, since C&I lending scales closely with local business scale. Six NAICS-3 manufacturing subsectors most flagged by trade economists as import-input-dependent (primary metals, fabricated metals, machinery, computers/electronics, electrical equipment, and transportation equipment/autos) together carry a $617 billion annual wage bill and 6.06 million workers nationally (BLS QCEW, 2025, 50 states + DC). Just 15 states — led by California ($108B), Texas ($55B), Michigan ($33B), Ohio ($31B), and Indiana ($24B) — account for 69% of that total. Total U.S. commercial and industrial (C&I) loans outstanding are about $2.9 trillion (FRED BUSLOANS, May 2026), for context on the scale of the lending market this manufacturing activity draws on — not as a denominator that can be split by state, since no source permits that split.

Why the direct question can't be answered from any known public source

I checked the askamerica banking schema directly. banking.financials, the FDIC call-report table, carries total C&I-style balance sheet items (total assets, deposits, net income) and a breakdown of commercial real estate loans by property type — but it carries no field for commercial-and-industrial loans at all, let alone one broken out by the borrower's industry. Call Reports (the FFIEC/FDIC quarterly filing every bank submits) simply do not require banks to report C&I loans by borrower NAICS code publicly. banking.cra_small_business_lending reports small-business loan originations by county but carries no industry dimension either.

The one place this exact cross-tab has ever been constructed is Federal Reserve Board staff research: Morgan, Ramírez, Silva & Zlate, "Supply Chain Risk and Bank Lending Amid Trade Policy Uncertainty," FEDS Notes, January 30, 2026. The authors used Form Y-14Q — confidential supervisory loan-level data collected only from the largest bank holding companies under stress-test rules — to classify 23 banks (covering roughly 18% and 37% of national C&I loan volume across two groups) into "high" vs. "low" supply-chain-risk exposure, based on the 3-digit NAICS sector composition of each bank's C&I book. That analysis is genuinely on-point for this question's premise — it finds that borrowers at more supply-chain-exposed banks drew down credit lines and paid higher loan spreads sharply after the April 2025 tariff announcements, consistent with firms front-loading inventory and import financing ahead of tariffs. But the underlying loan-level data is confidential, the published output is a relative index (not a dollar figure), and there is no state geography anywhere in it. This is a real, permanent gap in the public record, not an artifact of this corpus's coverage.

The proxy: where tariff-exposed manufacturing activity itself sits, by state

Given that gap, the defensible next-best measure is the state-level footprint of the manufacturing subsectors most cited by trade economists (Richmond Fed, Equitable Growth, and industry surveys) as heavily reliant on imported inputs: primary metals (NAICS 331), fabricated metal products (332), machinery (333), computer & electronic products (334), electrical equipment/appliances (335), and transportation equipment, including autos (336). Manufacturing as a whole imports roughly 19% of its inputs; these six subsectors are the ones most flagged for steel/aluminum, semiconductor, and auto-parts tariff exposure specifically.

Using BLS QCEW (private-sector employment and wages, 2025, via econ.state_wages), the national wage bill across these six subsectors, summed across the 50 states plus DC, is $617.1 billion across 6.06 million workers.

Exclusion disclosure (predicates applied and what they removed): the SQL state_fips <> '72' was applied in one query, excluding Puerto Rico; the SQL state_name NOT IN ('District of Columbia','Virgin Islands','Puerto Rico') was applied in the top-15 ranking query, excluding those three jurisdictions. Sizing what each predicate removed: Puerto Rico carries $1.04B of the six-subsector wage bill, District of Columbia carries $28.2M, and the Virgin Islands carry $0 (no non-suppressed cells) — together about 0.17% of the $617.1B national total. The $617.1B headline figure already includes DC and excludes only the two territories (Puerto Rico, Virgin Islands), consistent with the standard "50 states + DC" convention used throughout this corpus; the top-15 state ranking additionally excludes DC because it is a city government rather than a state, but including it back in would not change the ranking — DC's $28.2M is far below the 15th-ranked state (Georgia, $13.75B). With or without any of these three units, the top 15 states and the 69% concentration figure are unchanged.

The top 15 states by wage bill:

These 15 states hold 69% of the national total. Michigan, Ohio, Indiana, and Wisconsin — the auto/machinery-heavy Midwest corridor — together carry over $104B, reflecting concentration in NAICS 336 (transportation equipment) specifically, the subsector most exposed to steel/aluminum and auto-parts tariffs. California and Texas lead mainly on scale (334 computer/electronics for CA — Silicon Valley hardware plus defense electronics — and 332/333/336 broadly for TX).

Read this as a map of where tariff-exposed manufacturing sits, not a map of loan dollars. Commercial lending to these firms is not distributed identically to their wage bill — it depends on capital intensity, firm size, credit demand, and each region's banking market structure, none of which this figure captures. But wage bill and establishment count are standard proxies for local business borrowing scale, and there is no finer public instrument available.

What would close this gap

The single closest existing public instrument is the Federal Reserve's Senior Loan Officer Opinion Survey (SLOOS), which occasionally asks qualitative questions about C&I loan demand tied to tariffs (the October 2025 special questions cited in the FEDS Note found that banks with strengthened C&I demand cited inventory-building for tariffs as the leading reason) — but SLOOS is a qualitative bank-panel survey with no dollar figures and no state detail. Absent a change in FFIEC Call Report requirements (adding a borrower-NAICS field to Schedule RC-C) or a future declassification of Y-14Q aggregates by state, no public source will directly answer the dollar-and-state version of this question.

What This Report Does Not Answer

• A dollar figure for commercial lending to import-dependent manufacturers: No public dataset — not in this corpus, not published by the Fed, FDIC, or FFIEC — cross-tabulates bank C&I loan dollars by borrower industry AND state. The only entity that has built this cross-tab (Federal Reserve staff, FEDS Notes Jan 2026) used confidential supervisory Y-14Q data covering 23 large banks, reported only as a national bank-level risk index, never in dollars, never by state or by manufacturing subsector. This is a genuine, confirmed sourcing gap, not a retrieval failure — I searched the askamerica catalog (banking.financials, the FDIC call-report table, carries no borrower-industry field at all) and the open web before concluding this.
• State-level concentration of that lending: Same gap as above applies at state grain. What I deliver instead: state-level concentration of the underlying manufacturing activity in the import-dependent subsectors, from BLS QCEW, as an explicit and named PROXY for where such lending exposure would concentrate (bank C&I lending broadly scales with local business economic activity), not a measurement of loan dollars themselves.

Every query behind this report

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

SELECT state_name, industry_code,
  SUM(annual_avg_estabs) AS estabs,
  SUM(annual_avg_emplvl) AS emp,
  SUM(total_annual_wages) AS wages
FROM econ.state_wages
WHERE own_code='5' AND agglvl_code='55'
  AND industry_code IN ('331','332','333','334','335','336')
  AND year='2025' AND state_fips <> '72'
GROUP BY state_name, industry_code
ORDER BY state_name, industry_code

SELECT state_name,
  SUM(annual_avg_estabs) AS estabs,
  SUM(COALESCE(annual_avg_emplvl,0)) AS emp,
  SUM(COALESCE(total_annual_wages,0)) AS wages
FROM econ.state_wages
WHERE own_code='5' AND agglvl_code='55'
  AND industry_code IN ('331','332','333','334','335','336')
  AND year='2025' AND state_name NOT IN ('District of Columbia','Virgin Islands','Puerto Rico')
GROUP BY state_name
ORDER BY wages DESC
LIMIT 15

SELECT
  SUM(COALESCE(total_annual_wages,0)) AS us_tariff_exposed_mfg_wages,
  SUM(COALESCE(annual_avg_emplvl,0)) AS us_tariff_exposed_mfg_emp
FROM econ.state_wages
WHERE own_code='5' AND agglvl_code='55'
  AND industry_code IN ('331','332','333','334','335','336')
  AND year='2025'

SELECT SUM(COALESCE(total_annual_wages,0)) AS us_all_mfg_wages
FROM econ.state_wages
WHERE own_code='5' AND agglvl_code='55'
  AND SUBSTR(industry_code,1,2)='31' OR SUBSTR(industry_code,1,2)='32' OR SUBSTR(industry_code,1,2)='33'
  AND year='2025'

SELECT state_name, SUM(COALESCE(total_annual_wages,0)) AS wages
FROM econ.state_wages
WHERE own_code='5' AND agglvl_code='55'
  AND industry_code IN ('331','332','333','334','335','336')
  AND year='2025' AND state_name IN ('District of Columbia','Virgin Islands','Puerto Rico')
GROUP BY state_name