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For electronics manufacturing (NAICS 334), a weighted basket of World Bank Pink Sheet commodity prices sits at an index of 286.3 in 2026M04, with 2016M04 = 100. That is +186.3% over the last 10 years, with Copper the largest single contributor to the net move. The Holt linear-trend forecast carries the basket to 380.1 in 12 months (+32.8%), with an 80% interval of [318.7, 453.2]. Other sectors: auto and ev manufacturing · construction · food processing · apparel manufacturing ·
The weights below are taken from the published references listed in each sector block, not imputed from the BACI trade flows or fitted to any model. Where a sector uses an input that the World Bank Pink Sheet does not quote directly (for example, flat steel, engineering plastics, or polyester fibre), the row is flagged as an upstream proxy. This is a substantive limitation of using only open reference prices: a refined-material index would track the downstream input more tightly, at the cost of licensing a paid data feed (CRU for steel, ICIS for plastics, PCI Wood Mackenzie for fibre).
| Commodity | Role in sector | Weight | Proxy note |
|---|---|---|---|
| Copper | copper (PCB traces, interconnects) | 0.30 | n/a |
| Aluminum | aluminum (enclosures, heatsinks) | 0.15 | n/a |
| Gold | gold (bond wire, contacts) | 0.15 | n/a |
| Silver | silver (conductive paste, contacts) | 0.10 | n/a |
| Tin | tin (solder) | 0.10 | n/a |
| Nickel | nickel (plating, batteries) | 0.10 | n/a |
| Crude oil, Brent | plastics and packaging | 0.10 | upstream proxy for engineering plastics |
| sum | 1.00 |
Weight source: USGS Mineral Commodity Summaries 2024, end-use shares for copper, aluminum, nickel, tin, gold and silver in electronics. BLS Handbook of Methods, Ch. 14.
Figure 1 plots the weighted input-cost index for electronics manufacturing. The index uses fixed-weight geometric aggregation across the basket (a geometric Laspeyres), I(t) = exp(sum_i w_i ln(p_i(t) / p_i(t0))) times 100. This differs from the arithmetic modified Laspeyres used by BLS PPI (BLS Handbook of Methods, Chapter 14); geometric weighting is used here specifically because it dampens the arithmetic sensitivity to a single commodity spike while preserving substitution-neutral elasticity at the basket level. Weights approximate published cost-share ranges from the sources cited per sector; they are not exact reproductions of any single table.
Figure 2 decomposes the basket into one line per commodity, where each point is the commodity's weight times its price ratio to 2016M04, times 100. This is the arithmetic contribution reporting convention documented in BLS Handbook of Methods, Chapter 14 ('Contributions to change'). Reading across the lines shows which single inputs the basket's movement is most sensitive to. Summed arithmetically, these lines recover the Laspeyres level; the geometric basket in Figure 1 is the log-weighted alternative.
Figure 3 extends each basket commodity with a Holt linear-trend exponential smoother on log prices, then re-aggregates to the basket with the same weights. The method is the one documented in Hyndman and Athanasopoulos (2021), Forecasting: Principles and Practice, 3rd ed., Section 8.2 (Holt's linear trend method) and 8.7 (prediction intervals), available at otexts.com/fpp3. The original references are Holt (1957), 'Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages', ONR Memo 52, and Winters (1960), 'Forecasting Sales by Exponentially Weighted Moving Averages', Management Science 6(3): 324-342. Smoothing parameters: alpha = 0.3, beta = 0.1, the conservative default in HA Table 8.10 for commodity-style series. The basket 80% interval is computed in log space as z_{0.80} times sqrt(h * sum w_i^2 sigma_i^2), which assumes independent commodity innovations; co-movement (copper-aluminum, oil-gas) widens the true interval relative to the one shown.
Input costs landed at the factory gate are the sum of the commodity basket and the freight cost to move it. Figure 4 overlays two published freight indices onto the electronics manufacturing basket: the BLS Producer Price Index for Deep Sea Freight Transportation (series PCU4831114831115, monthly since 1988) and the Cass Freight Index (series FRGSHPUSM649NCIS, monthly since 1990), both sourced from FRED. Baltic Exchange Dry Index and Freightos Baltic Index (FBX) are the purer daily spot-freight references but are not yet ingested into the workbench; the two FRED series above are the best-in-workbench proxies for now, and both are primary-source published indices, not modelled.
Figure 5 plots the latest-month basket index level for each of the five sectors against the same 10-year window = 100 base. The ranking reflects differences in material-cost structure: a copper- and gold-heavy electronics basket has different price memory than a cotton-and-oil apparel basket. Production-network economics (Carvalho and Tahbaz-Salehi 2019, 'Production Networks: A Primer', Annual Review of Economics 11: 635-663; Baqaee and Farhi 2024, 'Networks, Barriers, and Trade', Econometrica92(2): 505-541) imply that sector-level price shocks propagate along input-output linkages with multipliers above one, so the gap between the leader and the laggard sector understates the within-firm pass-through seen by a final producer.
Carvalho and Tahbaz-Salehi (2019, 'Production Networks: A Primer', Annual Review of Economics11: 635-663) show that upstream input-price shocks propagate to downstream sectors with multipliers determined by the Leontief inverse of the input-output matrix. The full Leontief decomposition requires a BEA / OECD-ICIO style table, which is not wired into this workbench; Figure 6 instead computes a bounded empirical proxy: the pass-through elasticity beta_k of each sector-k basket to the Brent crude oil price, estimated by OLS regression of dlog(basket_k,t) on dlog(Brent_t) on monthly data over the 10-year window in Figure 1. Brent is the pivot commodity: Acharya, Berner, Engle, Jung, Stroebel, Zeng & Zhao (2023, 'Climate Stress Testing', Annual Review of Financial Economics15: 291-326) document that energy-price shocks account for the plurality of input-cost volatility across G-10 manufacturing sectors, so the Brent elasticity is the canonical first-moment pass-through channel. Amiti, Itskhoki & Konings (2019, 'International Shocks, Variable Markups, and Domestic Prices', Review of Economic Studies 86(6): 2356-2402) frame the interpretation: a pass-through beta above 1 indicates cost amplification through intermediates; below 1 indicates substitution or hedging buffers. One caveat applies throughout: Brent (or its refined products) is itself a weighted component of these baskets (see the weights tables), so part of each coefficient is mechanical co-movement of the basket with its own component, not independent network pass-through of the kind the cited papers estimate on input-output data.
The Figure 6 beta gives the total cumulative energy-price pass-through; this figure gives the speed at which it arrives. For each sector-k basket, an ordinary least-squares distributed-lag regression of dlog(basket_k,t) on dlog(Brent_{t-L}) for L=0,1,...,12 recovers the lag weights beta_L. Cumulating beta_L up to lag L and dividing by the sum gives the cumulative pass-through fraction; the smallest L at which the fraction exceeds 0.5is the pass-through half-life. For context, Gopinath, Itskhoki & Rigobon (2010, 'Currency Choice and Exchange Rate Pass-Through', American Economic Review 100(1): 304-336) find multi-month adjustment lags for a related but distinct mechanism, exchange-rate pass-through into import prices; the half-lives below measure a different object (a basket's own adjustment to Brent, part of which is mechanical since Brent is a basket component) and should not be read as replications of that literature.
Figure 8 compares two bundles of Pink Sheet commodities on a common axis, each indexed to 2005 = 100 in nominal USD terms. The ratio of the two indices gives an implicit terms-of-trade series: when the resource bundle rises faster than the manufactures-input bundle, resource exporters gain purchasing power over manufactured goods. The resource-extraction bundleis an equally-weighted geometric mean of energy and industrial metals (Brent, US natural gas, copper, aluminum, iron ore); the manufactures-input bundleis an equally-weighted geometric mean of agricultural and lighter-industry feedstocks (wheat HRW, cotton A Index, rubber RSS3, sawnwood Malaysian). A rising resource bundle relative to the manufactures bundle is a positive terms-of-trade shock for resource-rich exporters (the Prebisch-Singer reversal documented by Cuddington & Jerrett, 2008, and Erten & Ocampo, 2013); a rising manufactures-input bundle squeezes downstream manufacturing margins (Acharya et al., 2023, Annual Review of Financial Economics 15: 291-326).
Carryable across all the figures above is one practical question for a procurement team: of the 7commodities in this sector basket, which ones bring the most month-to-month price noise into the cost line, and therefore deserve the first-priority forward cover or supplier re-negotiation? Figure 9 answers it directly: for each basket commodity, we compute the standard deviation of monthly log returns over the 10-year window and annualise by sqrt(12), the convention in Mandelbrot & Hudson (2004) and the Hull (2018, Options, Futures, and Other Derivatives, 10th ed.) treatment of commodity volatility. Higher bars are noisier inputs. The ranking is independent of the weight: a small-weight but high-volatility commodity (say, natural gas in food processing) can still drive the bulk of unhedged P&L variance through Bohi-Toman (1996) and the Acharya et al. (2023) energy-shock channel. Multiplying volatility by basket weight gives the variance contribution to the basket and is the canonical hedging-priority metric (the contribution-to-risk decomposition in Litterman 1996, Goldman Sachs Risk Management Series).
Context, not computed: unlike the figures above, this section is qualitative. No carbon-price, subsidy, or tariff series is wired into this workbench; the policy instruments are named for orientation.
The five sector baskets are all exposed to the same four policy shadows. COP29 (Baku, November 2024) raised the implied carbon cost of imported energy-intensive inputs via Article 6 operationalisation; this transmits first into steel and aluminum for the auto and construction baskets. The EU Fit for 55 package (COM(2021) 550) pairs CBAM with ETS2 for road and building fuels from 2027 and layers the Methane Regulation (Reg 2024/1787) on gas imports, which raises the natural-gas line in the auto and food-processing baskets. The US Inflation Reduction Act (Public Law 117-169, 2022) subsidises domestic battery-material supply (lithium, nickel, cobalt) under section 45X, which damps the copper and nickel lines of the electronics basket with a lag. The WTO Agreement on Agriculture Article 12 and the MC12 Ministerial Decision on Food Export Prohibitions bound the cereal-exporter policy space that drives the food-processing basket's tail risk; Carvalho-Tahbaz-Salehi (2019) and Baqaee-Farhi (2024) formalise how these origin-country shocks amplify through the production-network into final goods prices.