## Supply Bottlenecks: Where, Why, How Much, and What Next?

## Source details

**Canonical URL:** [Supply Bottlenecks: Where, Why, How Much, and What Next?](https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022031-print-pdf.pdf)

## Other formats

- [Markdown version](/-/media/files/publications/wp/2022/english/wpiea2022031-print-pdf.pdf.md)
- [Structured JSON version](/-/media/files/publications/wp/2022/english/wpiea2022031-print-pdf.pdf.json)

---

### Key takeaways and scope
- Focus: manufactured goods sector; four core questions on demand vs supply contributions, role of shutdowns and labor scarcity, semiconductor shortages, and expert timing for easing of bottlenecks.
- Organization: stylized facts; conceptual framework (disruptions vs rigidities); SVAR estimates of demand and supply contributions to manufacturing output, manufacturing PPI inflation, core CPI, and GDP; drivers (shutdowns, labor shortages); semiconductor market analysis; scenarios for easing and policy implications.

### Stylized facts on demand, supply, and inflation
- Demand:
  - Household spending on goods rose above pre-pandemic trend since Spring 2020, driven largely by the United States.
  - Drivers: repressed spending on contact‑intensive services; higher demand for goods enabling work/learning/leisure at home; continued infection waves; excess household savings.
  - Specific numeric: households in the euro area accumulated about 8 percent of GDP in “excess savings” during the pandemic (as of mid-2021).
- Supply constraints observed:
  - Delivery times for goods rose to historic highs, especially in advanced economies.
  - Production lagged new orders in many countries; more than half of producers in the euro area report shortages of intermediate inputs.
  - Share of firms reporting labor shortages rose (more acute in the US than in the euro area).
  - Severe port congestion and logistical bottlenecks; industrial production lost momentum in 2021 and fell outright in some countries (e.g., Germany in most months of 2021; Japan in September and October 2021).
- Sectoral concentration:
  - Motor vehicle output especially constrained by microchip scarcity; motor vehicles sector shows the highest prevalence of reported input shortages.
- Inflationary impacts:
  - Energy prices: "Energy prices stood 50 percent above their 2019 level in 2021Q3, following a 33 percent fall in 2020."
  - Manufacturing component of PPI inflation: about 10 percentage points higher in 2021Q3 than in 2017–19; 14 percentage points higher in the United States; 6 percentage points higher in United Kingdom.
  - Large and sustained cost increases can lift consumer prices and may prompt earlier monetary tightening.

### Conceptual framework: disruptions versus rigidities
- Disruptions:
  - Examples: lockdowns, declines in labor supply due to health/family care concerns.
  - Represented as temporary inward shifts in supply; expected to be transient if from shutdowns, though some labor effects can be partly permanent.
- Rigidities:
  - Examples: fixed container/shipping fleet supply in short run, obstacles to reallocating workers, insufficient skilled workers.
  - Represented by a steeper supply curve beyond a given output level; prices remain high while demand is strong; expected to ease gradually or quickly if demand recedes.
- Mechanisms:
  - Rigidities imply persistent excess demand until capacity/flexibility improves.
  - Disruptions imply temporary loss of output and elevated prices while disruption persists.
  - Both can operate simultaneously and affect policy responses.

### Identification and empirical approach
- Method: sign-restricted Vector Auto Regression (SVAR).
  - Identification assumption: demand shocks induce output and prices to move in the same direction; supply shocks cause opposite movements.
  - Manufacturing output: manufacturing component of the Industrial Production index, log-linearly detrended.
  - Price measure: growth (annualized, in log points) of manufacturing PPI during the last 3 months up to month t relative to previous 3 months (three-month rolling window).
  - Frequency and sample: monthly, seasonally adjusted; January 2001 to September 2021.
  - Countries: euro area, Germany, France, Italy, Spain, the United Kingdom, United States, Japan, Czechia, Mexico, and Turkey.
  - PPI inflation formula preserved as presented in source.

### Key empirical findings on supply vs demand in 2021
- Pre-pandemic: estimated supply shocks predominantly negative (supporting output, holding down prices).
- 2021: supply shocks frequently positive and sizable, offsetting demand-driven boosts to output.
- Counterfactual manufacturing IP impacts (September 2021, no 2021 supply shocks):
  - Czechia: about 14 percent higher.
  - Germany: about 13 percent higher.
  - Japan: about 10 percent higher.
- Euro area example:
  - Without supply shocks: output about five percent higher than trend in Fall 2021 due to cumulative demand shocks.
  - Instead: output about a percentage point lower due to a six percent drag from supply shocks.
- Manufacturing PPI inflation:
  - Euro area manufacturing PPI inflation rose to 12.5 percent in 2021Q3 from an average of 1.8 percent in 2017–19.
  - Close to half of that upward swing estimated to come from the change in the supply shock component for the euro area.
  - Country shares of PPI inflation increase attributable to supply shocks (2021 September vs 2017–19): Euro area about half; Germany 60 percent; United States and United Kingdom 45–50 percent; France and Italy about 40 percent.
- Three-variable SVAR (adding PMI suppliers’ delivery time) robustness:
  - Supply shocks broadly comparable to bivariate SVAR.
  - Drag on manufacturing output (Jan/May/Sep 2021 three-variable SVAR): Czechia about -12 percent; Germany and Japan about -10 percent; United States and United Kingdom about -2 percent (vs about -3 percent in bivariate SVAR).
  - Three-variable SVAR fits weaker, leaving larger fraction of inflation unexplained.
- Detrending robustness: quadratic trend gives similar results overall; for some countries (e.g., Germany and Spain) decomposition aligns more with peers.

### Translation to GDP and core CPI
- Rough GDP impacts of 2021 manufacturing supply shocks (relative to counterfactual with no supply shocks in 2021):
  - United States: setback of one (percent).
  - Germany: about -2.5 percent.
  - Turkey: about -2.5 percent.
  - Czechia: about -4 percent.
- Local projection for core CPI:
  - Peak impact of a one-standard-deviation demand shock on PPI inflation: typically 1.5–2.0 percentage points.
  - Peak impact of same demand shock on core CPI: typically 0.20–0.30 percentage point.
  - Supply shocks: impacts on PPI inflation smaller than for demand shocks (large variation); pass-through to core CPI partial and muted.
  - Rationale: goods make up about 30–40 percent of CPI basket; goods prices in CPI include retailer margins.
- Average estimated impacts of Jan–Sep 2021 supply shocks on core CPI:
  - Germany: about 0.5 percentage point cumulative impact in first three quarters of 2021 (close to roughly 1.3 percentage points increase in core inflation in 2021 relative to 2017–19 after removing near 1 percentage point VAT-cut reversal).
  - United States: roughly 0.4 percentage point estimated impact from manufacturing supply shock (small fraction of actual increase in core CPI inflation of about 2.5 percentage points over same period).
- Bottom-line empirical conclusions:
  - (i) Supply shocks were a major drag on manufacturing output and GDP in many countries in 2021.
  - (ii) In many countries close to half of the rise in PPI inflation between 2017–19 and 2021Q3 came from supply shocks (higher in some countries like Germany and Japan).
  - (iii) Pass-through of supply shocks to core CPI is modest but can explain much of the rise in core CPI relative to pre-pandemic averages in some countries; demand still plays a sizable role.

### Drivers of bottlenecks, heterogeneity, and econometric results
- Candidate drivers: shutdowns at home and abroad propagating through global supply chains; labor shortages; both interdependent and driven by infection waves.
- Cross-country/sector diversity:
  - Delivery times increased more in advanced economies than in emerging markets.
  - Countries with large auto sectors experienced greater supply shocks after 2021Q2 when Delta-related shutdowns curtailed semiconductor production in Asia.
  - Disruptions larger in countries with higher shares of foreign value added in gross domestic manufacturing output and manufacturing sectors located in downstream segments of global value chains.
- Sectoral sensitivity:
  - Sectors reliant on differentiated intermediate inputs (such as semiconductors) more severely constrained after shutdowns.
  - Differentiated inputs require specialized production and are relatively rigid in short run.
- Sectoral regression findings (monthly country-sector panel for 21 European economies):
  - Shutdowns and interaction of shutdowns with differentiated-input usage statistically significant in explaining sectoral production disruptions.
  - Auto sector predicted to have suffered three times more than textile sector from shutdowns.
  - A one standard deviation increase in share of differentiated inputs usage doubles marginal impact of shutdowns, lowering shutdown coefficient from −0.11 to −0.23.
- Country-level decomposition:
  - Shutdowns and labor shortages together account for about half of variation in supply shocks between 2021–20 and 2019.
  - Shutdowns contribute 33 to 40 percent; labor shortages contribute another 6 to 10 percent.
  - Shutdowns alone explain 25 percent of variation in supply shocks (partial R‑square); labor shortages explain 3 percent (Annex results).

### Semiconductor market: disruptions, outcomes, and capacity outlook
- Demand-side drivers:
  - Geopolitical tensions prompted strategic stockpiling and renationalization efforts.
  - Pandemic shifted consumer demand toward semiconductor-intensive durable goods (IT devices boom); vehicle demand recovered in second half of 2020.
  - Firms multiplied orders beyond immediate needs to hedge scarcity.
- Supply-side drivers and shocks:
  - COVID-19 containment measures affected factory production and distribution.
  - Natural events in 2020–2021: drought in Taiwan Province of China since 2020; extremely cold weather in Texas in February 2021; fire in a Japanese semiconductor factory in March 2021; floods at Chinese ports in August 2021.
- Observed outcomes:
  - September 2021: global trade in semiconductors 19 percent above long-term trend; exports of Taiwan Province of China 27 percent above trend.
  - U.S. capacity utilization well above historical averages (similar to 2018 levels).
  - U.S. PPI subindex for manufacturing of semiconductor and other electronic components only 1 percent above its longer-term trend.
  - Possible reason: supply weakness concentrated in pockets (e.g., “legacy” chips); long-term contracts yield quantity rationing rather than price rises; automakers canceled orders early and “lost their place in line.”
- Capacity and outlook:
  - Building new foundries takes about 2 to 4 years.
  - Major capital investments announced in 2021 total about $900bn (to be invested through 2030).
  - Risk that long-term capacity expansion could produce excess supply if demand normalizes or permanence overestimated.
- Impact on vehicle production (September 2021 relative to 2019 levels):
  - Motor vehicle manufacturing lower by about 13 percent in the U.S., 33 percent in Germany, and 40 percent in Japan.
  - European and North American automakers more vulnerable than Japanese and Korean firms due to reliance on older chip technologies; Japanese firms’ higher inventories delayed but did not eliminate production cuts.

### Timing for easing bottlenecks and scenario projections
- Lead times:
  - As of January (year context in source): semiconductor lead times were 25.7 weeks, largely unchanged from previous month's peak and notably higher than 12-15 weeks range in 2018-19.
- Industry expert expectations (conversations in November 2021):
  - Semiconductors: order backlogs should decline considerably during first half 2022 absent renewed lockdowns or idiosyncratic shocks; full supply expansion for structural demand requires new factories (2–4 years).
  - Logistical bottlenecks: PMIs and shipping rates indicate some easing or stabilization but conditions remain unfavorable historically; shipping congestion may not ease until second half of 2022 or 2023.
  - News-search evidence (GDELT, early December 2021): most online news suggests microchip shortages will ease in 2022 rather than 2023; within 2022, views split about half expecting easing in first half and half in second half; broader supply shortages expected to fade in second half of 2022.
- Scenario methodology for projection (assumed path):
  - Supply shocks stay at September 2021 level through end-2021, then dissipate by September 2022.
  - Use SVAR Impulse Response Functions to project impacts on manufacturing output and core PPI; transform manufacturing impacts to GDP via historical IP–GDP relationship or input‑output method.
  - Transmission lags imply effects on manufacturing output and prices take additional quarters to ease relative to underlying supply shocks.
- Projected impacts for Germany (historical IP–GDP relationship):
  - Q4 2021: supply constraints lower German GDP by 4 to 5 percent.
  - Q4 2022: GDP drag decline to around 2.2-3 percent.
  - Manufacturing PPI inflation impact: about 7 percentage points in last quarter of 2021; about 9 percentage points in Q1 2022; decline to 6 percentage points in Q4 2022.
  - Core CPI inflation impact: 0.7 percentage point in Q1 2022; soften to 0.5 percentage point in 2022; turn negative in Q3 2022; approach zero in Q4 2022 (relative to baseline without sizable supply constraints).
- Key projection conclusions:
  - During 2021 demand recovery was largely, or in some cases more than, offset by supply shocks.
  - Euro-area manufacturing output would have been about 5 percent above trend in fall 2021 absent supply shocks; supply shocks imposed roughly 6 percent drag; euro area GDP would have been about 2 percent higher.
  - In countries reliant on highly differentiated inputs (Germany, Czechia), manufacturing output would have been higher by 13–14 percent in fall 2021 absent pandemic.

### Policy implications and recommendations
- Direct supply-side measures (first line of defense):
  - Fast-track licensing of transport and logistics workers.
  - Temporarily ease restrictions on port operating hours.
  - Streamline customs inspections.
  - Ease immigration rules to alleviate labor shortages.
  - Mandate practices to limit virus spread and protect worker health.
- Fiscal policy guidance:
  - Avoid broad-based aggregate demand support that could intensify bottlenecks and raise inflation with limited output/employment impact.
  - Deploy well-targeted fiscal measures to ease bottlenecks and avoid permanent damage to potential output.
  - Preserve viable jobs that will be viable once bottlenecks ease (e.g., skills-intensive manufacturing jobs affected by input shortages).
  - Remove obstacles and disincentives to work (promote reliable childcare and elder care); support worker retraining for needed skills.
- Monetary policy challenges:
  - Balance sustaining an incomplete recovery against ensuring output catches up without allowing wages and prices to spiral.
  - Keep medium-term inflation expectations stable amid transient inflation boosts (supply disruptions and surging energy prices).
  - Central banks should communicate reaction functions to inflation and other data, monitor inflation expectations, and preserve flexibility to respond rapidly to significant changes.
  - More successful targeted regulatory and fiscal measures to alleviate bottlenecks reduce need to dampen aggregate demand and growth to contain inflation.

### Annex highlights: ports, SVAR robustness, country‑sector regressions, and semiconductor industry overview
- Ports and logistics:
  - Between September 2020 and September 2021 global container throughput averaged 6 percent above its 2019 level.
  - Industry estimate: effective stock of containers 10–15 percent below capacity (due to waiting times at ports).
  - Shipping costs increased six- to seven-fold in some cases.
  - Panel regressions show retail sales growth and containment measures positively associated with port waiting times; coefficients for retail sales range from 0.12 to 0.31 (implying a 1 percentage point increase in retail sales volumes associated with 0.12 to 0.30 hours increase in port waiting time).
- Annex 2 SVAR results:
  - Historical decompositions and IRFs for demand and supply shocks across countries; time horizons in IRFs up to 15 monthly periods.
- Annex 3 sectoral and country regressions:
  - Sectoral IP regressions show shutdowns and differentiated-input exposure amplify output losses; country regressions decompose supply-shock variation into shutdowns, labor shortages, and other factors.
  - Shutdowns explain 25 percent of variation in supply shocks; labor shortages explain 3 percent; shutdowns and labor shortages together account for about 40–50 percent of increased supply shocks in 2020/21 vs 2019.
- Annex 4 semiconductor industry overview:
  - Global market in 2019: US$420 billion.
  - R&D in 2019: US$90 billion.
  - Capital expenditure in 2019: US$110 billion.
  - Foundry market concentration: TSMC accounted for 54 percent of global foundry market share in 2018; top 10 foundry firms 87 percent of global market in 2018.
  - East Asia holds 75 percent of global production capacity and 100 percent of capacity for most advanced chips.
  - Major foundry setup cost: in the order of US$5-20 billion.

*IMF Working Paper — Supply Bottlenecks: Where, Why, How Much, and What Next? — excerpt (wpiea2022031-print-pdf).*

### INTRODUCTION ______________________________________________________________________________________________ 6

### INTRODUCTION

### Key points from the introduction
- Supply bottlenecks have emerged as a key headwind to the economic recovery and boosted inflation.
- The policy response depends on whether bottlenecks stem from higher demand or disrupted supply, and on the persistence of those shifts.
- The paper focuses on the manufactured goods sector and sets out four core questions:
  - What are the relative contributions of demand- and supply-side factors to the weakness in manufacturing output and the increase in non-energy goods prices?
  - How much have shutdowns and labor scarcity contributed to the supply bottlenecks in manufacturing? Why are some sectors affected more by the shutdowns?
  - What explains the semiconductor shortages that have been holding back auto production?
  - When do industry experts think the bottlenecks might ease, and what are the implications for macroeconomic projections and policies?

### Organization of the paper (as presented)
- Stylized facts.
- Conceptual framework distinguishing disruptions and rigidities.
- Estimates of demand and supply contributions to manufacturing output, manufacturing PPI inflation, core CPI, and GDP in several large economies in 2021.
- Exploration of how much of estimated supply shocks are explained by shutdowns and labor shortages.
- Examination of semiconductor market backlogs that have weighed on auto production.
- Scenarios for when bottlenecks might ease and implications for GDP and inflation.
- Takeaways and policy implications.

---

### STYLIZED FACTS

### Shift in demand
- Since reopening in Spring 2020, household spending on goods (aggregated over several large economies) has risen above its pre-pandemic trend, driven to a very large extent by strong spending in the United States.
- Multiple factors behind the shift include:
  - Repressed spending on contact-intensive services.
  - Higher demand for goods that enable working, learning, and leisure at home.
  - Continued infection waves.
  - An overhang of excess household savings in many countries reflecting depressed spending on services and strong income-support measures during the pandemic.
- Specific numeric note: Households in the euro area are estimated to have accumulated about 8 percent of GDP in “excess savings” during the pandemic (as of mid-2021).

### Supply-side constraints observed
- Delivery times for goods have risen to historic highs, especially in advanced economies.
- Production has not kept up with new orders in many countries.
- High shares of producers report shortages of intermediate inputs; more than half in the euro area report such shortages.
- The share of firms reporting labor shortages has risen (more acute in the US than in the euro area).
- Logistical bottlenecks: many ports have severe congestion with shipping volumes running above pre-pandemic levels and pandemic restrictions interrupting activity.
- Industrial production (IP) lost momentum in 2021 and fell outright in some countries (e.g., Germany in most months of 2021; Japan in September and October 2021).

### Sectoral concentration and microchips
- Motor vehicle output has been especially constrained by a scarcity of microchips.
- The motor vehicles sector is the hardest hit sector and shows the highest prevalence of reported input shortages among sectors.

### Inflationary impacts
- Shipping/transportation costs soared as transport systems came under strain, with some partial easing recently.
- Energy prices: "Energy prices stood 50 percent above their 2019 level in 2021Q3, following a 33 percent fall in 2020."
- PPI inflation has risen sharply in most countries; the manufacturing component of PPI inflation was:
  - about 10 percentage points higher in the third quarter of 2021 than in 2017–19,
  - 14 percentage points higher in the United States,
  - and 6 percentage points higher in United Kingdom.
- Higher input costs and sectoral margin pressures can both raise consumer prices and reallocate productive resources into sectors where supply is falling short.
- Large and sustained cost increases due to bottlenecks can harm the recovery by lifting consumer prices, cutting into households’ purchasing power, and possibly prompting earlier monetary tightening by central banks.

---

### CONCEPTUAL FRAMEWORK — VARIETIES OF SUPPLY CONSTRAINTS

### Distinction between disruptions and rigidities
- Disruptions:
  - Examples: lockdowns that temporarily disrupt production and transportation; declines in labor supply due to health or family care concerns.
  - Represented as a temporary inward shift in the supply curve.
  - Impact: With strong demand (outward demand shift), temporarily reduced supply limits output and raises prices.
  - Expected to be transient if caused by shutdowns; some elements (e.g., reduced labor supply) can be partly permanent (early retirements, lasting departure of migrant workers, preference for reduced hours) and partly temporary (immigration normalizing, people returning to work).
- Rigidities:
  - Examples: fixed supply of containers and shipping fleets in the short run, obstacles to reallocating workers across sectors, insufficient numbers of workers with newly needed skills.
  - Represented by a steeper supply curve beyond a given output level.
  - Over time, can ease as new capital (e.g., shipping containers, expanded microchip capacity) is added or barriers to labor reallocation are overcome.
  - Prices remain high while demand is strong; these constraints would be expected to fade gradually and could fade quickly if demand recedes.

### Mechanisms and expectations
- Rigidities imply persistent excess demand at prevailing prices until supply capacity or flexibility improves.
- Disruptions imply a temporary loss of output and elevated prices while the disruption persists; once reversed, supply can recover toward prior levels unless permanent labor-market exits or capital losses occurred.
- Both mechanisms can operate simultaneously and interact with demand shifts, influencing the appropriate macroeconomic policy response.

---

*IMF Working Paper — INTRODUCTION (excerpt) — Supply Bottlenecks: Where, Why, How Much, and What Next?*

### 9. Both types of supply constraints—disruptions and rigidities—dampen the responsiveness of

### 9. Both types of supply constraints—disruptions and rigidities—dampen the responsiveness of output to demand and boost prices, but require somewhat different policy remedies

### Identification and methodology
- A sign-restricted Vector Auto Regression (SVAR) approach is used to quantify the contribution of supply and demand shocks to manufacturing production and manufacturing PPI inflation.
- Identification assumption: demand shocks induce output and prices to move in the same direction, whereas supply shocks lead them to move in opposite directions.
- Data and sample:
  - Manufacturing output: manufacturing component of the Industrial Production index, converted to a log-linearly detrended variable.
  - Price measure: growth (annualized, in log points) of the manufacturing PPI during the last 3 months up to month t relative to the previous 3 months (three-month rolling window).
  - All variables: monthly frequency, seasonally adjusted.
  - Sample period: January 2001 to September 2021.
  - Countries included: euro area, Germany, France, Italy, Spain, the United Kingdom, United States, Japan, Czechia, Mexico, and Turkey.
- PPI inflation calculation (as specified):
  - 휋
    ௧
    ௉௉ூ
    = 400 * [ ln(푃푃퐼௧ + 푃푃퐼௧+1 + 푃푃퐼௧+2) − ln(푃푃퐼௧−3 + 푃푃퐼௧−2 + 푃푃퐼௧−1) ] (formula presented in the source).

### Key empirical findings: supply vs demand in 2021
- Pre-pandemic period: estimated supply shocks were predominantly negative, meaning they supported output and held down prices.
- During the pandemic (2021) supply shocks were frequently positive and sizable, offsetting demand-driven boosts to output in many cases.
- Country-level impacts on manufacturing IP in September 2021 (counterfactual without 2021 supply shocks):
  - Czechia: manufacturing component of IP would have been about 14 percent higher.
  - Germany: about 13 percent higher.
  - Japan: about 10 percent higher.
- Euro area example:
  - In the absence of supply shocks, output would have been about five percent higher than trend in Fall 2021 due to cumulative demand shocks.
  - Instead, output was about a percentage point lower due to a six percent drag from supply shocks.
- Manufacturing PPI inflation:
  - Manufacturing PPI inflation in the euro area rose to 12.5 percent in 2021Q3 from an average of 1.8 percent in 2017–19.
  - Close to half of that upward swing is estimated to come from the change in the supply shock component for the euro area.
  - Country shares of the PPI inflation increase attributable to supply shocks (2021 September relative to 2017–19):
    - Euro area: about half.
    - Germany: 60 percent.
    - United States and United Kingdom: 45–50 percent.
    - France and Italy: about 40 percent.
- Three-variable SVAR robustness check (adding PMI suppliers’ delivery time):
  - Supply shocks during 2021 are broadly comparable to the bivariate SVAR estimates.
  - Drag on manufacturing output (three-variable SVAR, Jan/May/Sep 2021):
    - Czechia: about -12 percent.
    - Germany and Japan: about -10 percent.
    - United States and United Kingdom: about -2 percent (compared with about -3 percent in the bivariate SVAR).
  - For Italy and Spain the three-variable SVAR implies larger supply-side drags than the bivariate SVAR.
  - The three-variable SVAR achieves a weaker fit than the bivariate case, leaving a larger fraction of inflation unexplained.
- Detrending robustness:
  - Using a quadratic trend instead of a linear trend provides similar results overall; for some countries (e.g., Germany and Spain) the decomposition for 2021 appears more consistent with peer countries.

### Translation to GDP and inflation pass-through
- Rough GDP impacts of 2021 manufacturing supply shocks (relative to a counterfactual with no supply shocks in 2021):
  - United States: setback of one (percent).
  - Germany: about -2.5 percent.
  - Turkey: about -2.5 percent.
  - Czechia: about -4 percent.
- Local projection for core CPI inflation:
  - Regression specification: core CPI inflation 휋_{t+h|t} regressed on contemporaneous demand and supply shocks derived from the bivariate SVAR and controls X_t (specification presented in the source).
  - Peak impact of a one-standard-deviation demand shock on PPI inflation: typically in the range of 1.5–2.0 percentage points.
  - Peak impact of the same demand shock on core CPI: typically in the 0.20–0.30 percentage point range.
  - Supply shocks: impacts on PPI inflation are smaller than for demand shocks (with large variation); pass-through to core CPI is partial and muted.
  - Rationale: goods typically make up only a subset of the CPI basket (typically around 30–40 percent) and goods prices in the CPI include retailer margins.
- Average estimated impacts of 2021 supply shocks on PPI and core CPI (Jan–Sep 2021 average):
  - Cumulative impact on core CPI in first three quarters of 2021:
    - Germany: about 0.5 percentage point (close to the roughly 1.3 percentage points increase in core inflation in 2021 relative to 2017–19 after removing near 1 percentage point VAT-cut reversal).
    - United States: roughly 0.4 percentage point estimated impact from manufacturing supply shock (small fraction of the actual increase in core CPI inflation of about 2.5 percentage points over the same period).
  - Cross-country differences reflect variation in service-sector demand-supply imbalances as well as variation in manufacturing supply shock impacts.

### Bottom-line empirical conclusions
- (i) Supply shocks have been a major drag on manufacturing output and GDP in many countries in 2021.
- (ii) In many countries close to half of the rise in PPI inflation between 2017–19 and 2021Q3 came from supply shocks (in some countries like Germany and Japan the share is a bit higher).
- (iii) The pass through of supply shocks to core CPI inflation is modest, but can nonetheless explain much of the rise in core CPI inflation relative to pre-pandemic averages in some countries. Nonetheless, the estimates leave a sizable role for demand in pushing up inflation.

### Drivers of bottlenecks and cross-country/sector heterogeneity
- Key possible drivers of the estimated supply shocks:
  - Shutdowns at home and abroad propagating through global supply chains.
  - Labor shortages.
  - Both drivers are inter-dependent and ultimately driven by infection waves.
- Cross-country and sectoral diversity:
  - Delivery times increased more in advanced economies than in emerging markets.
  - Manufacturing production weakened in much of 2021 in Germany and in September–October in Japan, while continuing to recover in the United Kingdom and Spain.
  - Countries with large auto sectors experienced greater supply shocks after 2021Q2 when Delta-related shutdowns curtailed semiconductor production in Asia.
  - Disruptions were larger in countries with:
    - higher shares of foreign value added in gross domestic manufacturing output, and
    - manufacturing sectors located in downstream segments of global value chains (hence vulnerable to upstream shutdowns).
- Sectoral sensitivity to shutdowns:
  - Sectors relying more heavily on differentiated intermediate inputs (such as semiconductors) are likely to experience more severe constraints after shutdowns.
  - Differentiated inputs tend to require specialized production processes and are relatively rigid in the short run; replacing suppliers for differentiated goods can take time and add costs.
- Empirical test (monthly country-sector panel for 21 European economies):
  - Specification links sectoral output dynamics to shutdowns and allows the impact of shutdowns to depend on “differentiated inputs usage” (equation presented in the source).

*IMF staff calculations (as presented in the chapter).*

### 2019. To calculate 푆ℎ푎푟푒

### 2019. To calculate 푆ℎ푎푟푒

### Methodology: Differentiated-input Share and Shutdown Measures
- To measure the degree of country i sector s’ differentiated input usage (푆ℎ푎푟푒
௜௦
ௗ௜௙௙
), all sectors are divided into two groups: those producing homogeneous goods and those producing differentiated goods, based on the Rauch (1999) classification.
- The share of inputs into sector s in country i from differentiated-goods-producing sectors is calculated using input-output tables.
- To construct shutdown series (푆ℎ푢푡푑표푤푛
௜௦௧
):
  - Measures of sectors’ teleworkability (as in Dingel and Neiman, 2020) are interacted with measures of shutdown stringency in the domestic economy to create shutdown series specific to each country-sector pair.
  - 푆ℎ푢푡푑표푤푛
௜௦௧
 for sector s of country i in month t is the average of the shutdown series of all domestic and foreign input-providing sectors, weighted by the share of each input-providing country-sector pair into the output of sector s in country i.
- Regression results and variable definitions are provided in Annex 3 (estimation details referenced).

### Regression Findings: Sectoral Impacts of Shutdowns and Differentiated Inputs
- Shutdowns and the interaction of shutdowns with the degree of differentiated-input usage are statistically significant in explaining sectoral production disruptions.
- The auto sector, which relies most heavily on differentiated inputs among manufacturing subsectors, is predicted to have suffered three times more than the textile sector (the least reliant on differentiated inputs) from the shutdowns (Figure 13).
- Figure 13 summarizes the impact of domestic and foreign supplier countries’ shutdowns on sectoral production, in relation to the sector’s share of inputs from differentiated-goods-producing sectors (average from 2020 Jan to 2021 Aug, percent deviation from 2019).

### Drivers of Country-Level Supply Shocks: Specification and Results
- Country-level supply shocks are modeled (monthly data for 8 countries) with the specification:
  - 푆ℎ표푐푘
௜௧
௦௨௣௣௟௬
ൌ훽
ଵ
푆ℎ푎푟푒
௜௧
௟௔௕௢௥ ௦௛௢௥௧௔௚௘
൅훽
ଶ
푆ℎ푢푡푑표푤푛
௜௧
൅훽
ଷ
퐸푥푝표푠푢푟푒
௜
⋅푆ℎ푢푡푑표푤푛
௜௧
൅훼
௜
൅휖
௜௧
- The country-level 푆ℎ푢푡푑표푤푛
௜௧
 variable is constructed similarly to the country-sector-level, averaging home and foreign countries’ shutdowns weighted by the share of country j’s output used in the total output of country i.
- The marginal impact of shutdowns is allowed to vary based on a country’s exposure to shutdowns, proxied by:
  - the weight of the auto sector,
  - dependence on foreign value added,
  - the degree of “downstreamness” in global value chains.
- Labor shortages are measured by the share of firms in establishment surveys reporting production capacity constrained by labor scarcity.
- Results:
  - Shutdowns and labor shortages together can account for about half of the variation in supply shocks between 2021–20 and 2019.
  - Shutdowns contribute 40 percent and labor shortages contribute 10 percent of that variation (Figure 14b).
  - Remaining variation may be due to logistical infrastructure bottlenecks and natural events disrupting production in 2020-21 (e.g., semiconductor sector disruptions).
  - The limited role of labor shortages in manufacturing supply shocks may reflect that labor scarcity is more severe in service sectors.

### Semiconductor Market Disruptions: Demand, Supply, and Industry Outcomes
- The semiconductor market experienced significant disruptions since the pandemic onset, with both demand increases and supply reductions causing shortages that hit vehicle production particularly hard.
- Demand-side drivers:
  - Geopolitical tensions prior to the pandemic (notably U.S.–China) increased strategic stockpiling and renationalization efforts.
  - The pandemic shifted consumer demand toward semiconductor-intensive durable goods (IT devices boom from work/study-from-home; vehicle demand recovered in second half of 2020).
  - Firms multiplied orders beyond immediate needs to hedge against scarcity.
- Supply-side drivers:
  - COVID-19 containment measures (localized lockdowns, border closures) affected factory production and distribution networks.
  - Natural events unrelated to the pandemic further disrupted production, including:
    - drought conditions in Taiwan Province of China since 2020,
    - extremely cold weather in Texas in February 2021,
    - a fire in a Japanese semiconductor factory in March 2021,
    - floods at Chinese ports in August 2021.
- Observed outcomes:
  - In September 2021, global trade in semiconductors was 19 percent above its long-term trend; exports of Taiwan Province of China were 27 percent above trend.
  - Capacity utilization in the U.S. has been well above historical averages (but similar to levels in 2018).
  - The U.S. PPI subindex for manufacturing of semiconductor and other electronic components is only 1 percent above its longer-term trend.
  - Possible reasons for small aggregate price movements despite shortages:
    - supply weakness concentrated in pockets (e.g., “legacy” chips) with limited impact on aggregate indices;
    - chips often traded via long-term contracts with fixed prices, leading to quantity rationing rather than price increases.
  - Anecdotal evidence: automakers cancelled chip orders early in the pandemic and “lost their place in line” to consumer electronics.

### Semiconductor Capacity and Outlook
- Short-term resolution of shortages expected mainly through normalization of supply and demand; long-run capacity expansion anticipated.
- Lead times and capacity:
  - Building new foundries takes about 2 to 4 years.
  - Major capital investments announced in 2021 total about $900bn (to be invested through 2030).
  - Risk: long-term capacity expansion could lead to excess supply if demand normalizes or overestimates permanence.
- Impact on vehicle production:
  - Auto industry vulnerability due to reliance on differentiated intermediate inputs and just-in-time inventories.
  - In September 2021, motor vehicle manufacturing relative to 2019 levels was lower by about 13 percent in the U.S., 33 percent in Germany, and 40 percent in Japan.
  - European and North American automakers were generally more vulnerable than Japanese and Korean firms due to reliance on older chip technologies; Japanese firms’ higher inventories delayed but did not eliminate production cuts.

### How Soon Might Supply Bottlenecks Ease?
- Lead times:
  - As of January (year context in source), lead times for semiconductors were 25.7 weeks, largely unchanged from its peak the previous month and significantly higher than the 12-15 weeks range in 2018-19.
- Short-term expectations and risks:
  - Without new virus surges or disasters, backlogs would gradually clear.
  - The Omicron variant and mobility restrictions introduced since December 2021 make the outlook uncertain and may prolong disruptions.
  - Persistent changes in labor supply preferences could permanently lower effective labor supply if sustained.
- Industry expert expectations (conversations in November 2021):
  - Semiconductors: order backlogs should decline considerably during the first half 2022 absent renewed lockdowns or idiosyncratic shocks; full supply expansion to meet structural demand (e.g., electric vehicles) requires new factories (2–4 years).
  - Logistical bottlenecks: PMIs and shipping rates suggest some easing or stabilization, but conditions remain unfavorable historically.
    - Shipping prices expected to moderate as goods demand normalizes and shipping market capacity expands (number of containerships on order almost tripled since late-2020).
    - Long-term shipping rate contract prices rose less than spot rates, indicating expectations that spot price rise is not permanent.
    - Construction of new ships and containers is slowed by supply disruptions; shipping congestion may not ease until second half of 2022 or 2023.
    - Land transport has increased job postings, especially for drivers (Figure 16b).
- News-search evidence (GDELT, early December 2021):
  - Frequency of news terms referring to supply disruptions appears to have peaked.
  - For microchips, most online news suggests shortages will ease in 2022 rather than 2023.
  - Within 2022, views are split about half expecting easing in the first half and half in the second half.
  - Broader “supply shortage” or “supply issue” searches suggest broader bottlenecks are expected to fade in the second half of 2022.

*IMF Working Paper excerpt: Supply Bottlenecks: Where, Why, How Much, and What Next?*

### 33. Assumptions on when the bottlenecks will fade can be incorporated into growth and

### 33. Assumptions on when the bottlenecks will fade can be incorporated into growth and inflation forecasts

### Scenario and projection methodology
- Assume a path for future supply shocks: supply shocks stay at their September 2021 level through the end of 2021, and then dissipate by September 2022 (Figure 19a).
- Use estimated Impulse Response Functions from the SVAR to project the impact of the assumed 2021 supply shocks on manufacturing output and core PPI (Figures 19b and 19c).
- Transform projected impacts on manufacturing output into projected impacts on GDP using either:
  - (i) the historical relationship between IP and GDP; or
  - (ii) the input-output based method described in IMF (2021).
- Transmission lags imply effects on manufacturing output and prices take a couple of more quarters to ease than the underlying supply shocks.

### Projected impacts (Germany, based on historical IP–GDP relationship)
- In Q4 2021, supply constraints would lower German GDP by 4 to 5 percent.
- The GDP drag would decline to around 2.2-3 percent by Q4 2022.
- Manufacturing PPI inflation impact:
  - About 7 percentage points in the last quarter of 2021.
  - About 9 percentage point in Q1 2022.
  - Decline to 6 percentage points in Q4 2022.
- Core CPI inflation impact (Figure 19 panel d):
  - 0.7 percentage point in Q1 2022.
  - Soften to 0.5 percentage point in 2022.
  - Turn negative in Q3 2022.
  - Approach zero in Q4 2022.
- These impacts are measured relative to a baseline without sizable supply constraints (such as the Spring 2021 WEO forecasts).

### Key empirical findings on output and inflation
- During 2021 the recovery in demand was largely, or in some cases more than, offset by supply shocks.
- Estimated numbers for the euro area:
  - Recovery in demand would have propelled euro-area manufacturing output about 5 percent above its underlying trend in the fall of 2021.
  - Absent a roughly 6 percent drag from supply constraints, euro-area manufacturing output would have been higher.
  - Based on historical correlation between manufacturing and overall output, euro area GDP would have been about 2 percent higher.
- Country-specific extremes:
  - In countries reliant on highly differentiated intermediate inputs (downstream VCs) such as Germany and Czechia, manufacturing output would have been higher by 13–14 percent in the fall of 2021 absent the pandemic.
- On prices:
  - Producer price inflation of manufactured goods in the euro area was about 10 percentage points higher relative to pre-pandemic times in the first three quarters of 2021.
  - Supply shocks can explain about half of the increase in producer prices; the remainder largely reflects higher demand.
  - Core consumer price inflation excluding energy and food: about 0.5 percentage points higher over the same period due to manufacturing supply constraints.

### Drivers and persistence of supply shocks
- Globally, up to 40 percent of the supply shocks can be traced to shutdowns (transient effects).
- Severe weather and industrial accidents that hindered microchip output in 2021 are similarly transient.
- Labor shortages explain up to 10 percent of manufacturing supply constraints and could have more persistent effects.
- In the semiconductor market:
  - Severe shortages hit automobile production.
  - Disruptions resulted from strong demand for electronic goods and rigid/weakened supply due to shutdowns and weather effects.
  - Market adjusted via quantity rationing given long-term contracts and fixed prices; anecdotal evidence suggests automakers were not prioritized by suppliers after canceling chip orders early in the pandemic.

### Outlook and scenario risks
- Pressures on inflation could persist because:
  - Fiscal support is waning.
  - Recovery of labor markets and incomes and a large stock of forced savings may continue to bolster consumer demand.
  - The shift in demand from services to goods could persist as COVID-19 becomes endemic.
- Industry expectations (late last year referenced in text):
  - Supply shortages for autos largely dissipate by mid-2022.
  - Broader bottlenecks dissipate by end-2022.
- Omicron and renewed restrictions in Europe and China inject new uncertainty; supply disruptions could last longer, possibly into 2023.

### Policy implications and recommendations
- First line of defense: tackle supply bottlenecks directly with regulatory measures, for example:
  - Fast-track licensing of transport and logistics workers.
  - Temporarily ease restrictions on port operating hours.
  - Streamline customs inspections.
  - Ease immigration rules to alleviate labor shortages.
  - Mandate practices that limit virus spread and protect worker health.
- Fiscal policy guidance:
  - Avoid broad-based aggregate demand support that could intensify bottlenecks and raise inflation with limited impact on output and employment.
  - Deploy well-targeted fiscal measures to ease bottlenecks and avoid permanent damage to potential output.
  - Preserve viable jobs that will be viable once bottlenecks ease (e.g., skills-intensive manufacturing jobs affected by input shortages).
  - Ensure recovery in labor supply by removing obstacles and disincentives to work (promote reliable childcare and elder care) and help workers obtain training for newly needed skills.
- Monetary policy challenges:
  - Balance sustaining an incomplete recovery and ensuring output catches up with pre-pandemic trend without allowing wages and prices to spiral.
  - Key is to keep medium-term inflation expectations stable amid transient boosts to inflation (pandemic-driven supply disruptions and surging energy prices).
  - Wage growth is expected to be moderate; inflation is projected to fall slightly below the European Central Bank’s target once the pandemic fades (based on recent data and historical precedent).
  - Central banks should continue to communicate reaction functions to inflation and other data, monitor movements in inflation expectations, and preserve flexibility to respond rapidly to significant changes in the medium-term inflation outlook.
  - More successful targeted regulatory and fiscal measures to alleviate bottlenecks reduce the need to dampen aggregate demand and economic growth to contain inflation.

### Annex 1 — Determinants of waiting times in ports: key points and empirical results
- Global container throughput:
  - Between September 2020 and September 2021, global container throughput averaged 6 percent above its 2019 level (seasonally- and working-day adjusted measure).
- Industry estimate: effective stock of containers is 10–15 percent below capacity (due to waiting times at ports).
- Shipping costs increased six- to seven-fold in some cases.
- Regression setup (equation (1)):
  - Dependent variable: pwt_c,t = average number of hours container ships at anchorage have waited in country c in month t.
  - Regressors include year-to-year growth in retail sales (volumes) r_s_c,t and strength of containment measures c_m_c,t; country and time fixed effects; lagged dependent variable included in some specifications.
  - Data: 19 countries, 2015M1–2021M10.
- Empirical results:
  - Positive and significant relationship between retail sales and average port waiting time across specifications.
  - Coefficient varies from 0.12 to 0.31, implying a 1 percentage point increase in retail sales (volumes) is associated with an increase in port waiting time of 0.12 to 0.30 hours.
  - Containment measures are associated with longer port waiting time, but significance disappears when time fixed effects are included.
  - The share of variation explained is quite low across specifications, indicating domestic retail sales and containment measures explain only a small part of variation in port waiting time across countries.

*IMF WORKING PAPERS Supply Bottlenecks: Where, Why, How Much, and What Next? — Source: IMF staff calculations.*

### Annex 2. Additional SVAR results

### Annex 2. Additional SVAR results

### Determinants of port waiting time (Table 1)
- Regression outcomes for Avg. wait time (four specifications):
  - Column (1)
    - L.avg wait time: 0.365*** (0.0524)
    - Containment measures: 0.0448*** (0.0148)
    - Retail sales (yoy): 0.122*** (0.0463)
    - Observations: 1,534
    - R-squared: 0.016
    - Time FE: No
    - Country FE: Yes
    - Region: All
    - Number of countries: 19
    - Number of months: 81
  - Column (2)
    - L.avg wait time: 0.333*** (0.0518)
    - Containment measures: 0.0314** (0.0129)
    - Retail sales (yoy): 0.0792* (0.0461)
    - Observations: 1,515
    - R-squared: 0.147
    - Time FE: No
    - Country FE: Yes
    - Region: All
    - Number of countries: 19
    - Number of months: 80
  - Column (3)
    - Containment measures: 0.0313 (0.0551)
    - Retail sales (yoy): 0.310*** (0.0537)
    - Observations: 1,534
    - R-squared: 0.019
    - Time FE: Yes
    - Country FE: Yes
    - Region: All
    - Number of countries: 19
    - Number of months: 81
  - Column (4)
    - Containment measures: 0.0224 (0.0531)
    - Retail sales (yoy): 0.234*** (0.0555)
    - Observations: 1,515
    - R-squared: 0.129
    - Time FE: Yes
    - Country FE: Yes
    - Region: All
    - Number of countries: 19
    - Number of months: 80
- Driscoll‑Kraay standard errors reported in parentheses.
- Significance notation: *** p<0.01, ** p<0.05, * p<0.1

### Historical decompositions (Figures A2.1–A2.2)
- Historical decompositions for manufacturing IP and PPI inflation presented for 2016–2021 across countries including Czechia, France, Germany, Italy, Spain, UK, Japan, US, Mexico, Turkey.
- Decompositions show contributions to IP and PPI fluctuations attributed to:
  - Demand
  - Supply
  - Demand+Supply

### Impulse Response Functions (Figures A2.3–A2.4)
- Impulse Response Functions (IRFs) reported for:
  - Demand shocks: IRFs of IP, PPI inflation, and Core CPI inflation after Demand shock for Germany, France, Italy, Spain, UK, EA, Czechia, US.
  - Supply shocks: IRFs of IP, PPI inflation, and Core CPI inflation after Supply shock for Japan, Turkey, Mexico, Germany, France, Italy, Spain, UK, EA, Czechia, US.
- Time horizon in IRFs: periods 0 through 15 (monthly horizons as plotted).
- Graph axes in many panels range from negative to positive values varying by country and variable (examples: IP panels up to ±60 for some countries; PPI and Core CPI panels with finer scales).

### Methodology used to assess drivers of supply bottlenecks (Annex 3 summary)
- Key dependent variables:
  - Manufacturing (subsector) industrial production—converted to percent deviations from their 2019 levels.
  - Supply shocks identified by SVAR.
- Key independent variables:
  - Government containment stringency index (Oxford COVID-19 Government Response Tracker).
  - Labor shortages—proxied by share of firms reporting production capacity constrained by insufficient labor (CBI, European Commission, U.S. Census).
  - Global value chain information from Inter‑Country Input‑Output tables (OECD, 2015).
- Sectoral exposure measures:
  - Share differentiated input usage (Share_{i,s}): sectors classified into homogeneous vs differentiated goods (Rauch, 1999); share of inputs into sector s in country i from differentiated-goods-producing sectors computed using input‑output tables.
  - Shutdown_{i,s,t}: country-specific sectoral shutdown series constructed by interacting teleworkability (Dingel and Neiman, 2020) with domestic shutdown stringency, then averaging domestic and foreign input-providing sectors’ shutdown series weighted by input‑output shares.
- Panel and sample:
  - Monthly country‑sector panel of 21 European countries and 15 manufacturing subsectors (sample list and NACE codes provided in footnote).
- Sectoral regression specification (preserving notation as in source):
  - IP_{i,s,t} = α IP_{i,s,t−1} + β1 Shutdown_{i,s,t} + β2 Share_{i,s}^{diff} ⋅ Shutdown_{i,s,t} + Γ ⋅ 1_{2020Q1–2021Q4} + ε_{i,s,t}
- Main sectoral finding:
  - Sectors relying more on differentiated inputs are more prone to shutdowns.
  - A one standard deviation increase in the share of differentiated inputs usage would double the marginal impact of shutdowns, lowering the coefficient of shutdowns’ marginal impact from −0.11 to −0.23 (Table A2, column 1).
  - Coefficients remain significant when including country‑sector fixed effects (Table A2, column 2).

### Country‑level analysis of supply shocks
- Country‑level Shutdown_{i,t} constructed analogously by averaging home and foreign countries’ shutdowns weighted by country output shares used in total output of country i.
- Allow marginal impact of shutdowns to vary with country exposure proxied by:
  - Share of auto sector
  - Dependence on foreign value added
  - Downstreamness in global value chain
- Labor shortages:
  - Proxied by share of firms reporting production capacity constrained by insufficient workers (firm surveys).
- Country regression specification (preserving notation):
  - Shock_{i,t}^{SVAR_current_month} = β1 Share_{i}^{auto/foreign/downstream} + β2 Shutdown_{i,t} + β3 Exposure_{i} ⋅ Shutdown_{i,t} + α_{i} + lag Shutdown_{i,t} + ε_{i,t}
- Magnitude results:
  - On average, the supply shock would worsen by 2¾ and ⅓, as result of a one standard deviation increase in shutdowns and labor shortages, respectively (Table A2, column 6).
- Explained variation (partial R‑square approach):
  - Shutdowns explain 25 percent of the variation in supply shocks.
  - Labor shortages explain 3 percent of the variation in supply shocks (Figure 14a).
- Decomposition of worsening in supply shocks in 2020/21 relative to 2019:
  - On average, shutdowns and labor shortages account for about 40 to 50 percent of the supply shocks.
  - Shutdowns contribute 33 to 40 percent.
  - Labor shortages contribute another 6 to 10 percent.
  - Estimates from the regression including foreign value share (Table A2, column 8) produce the largest contributions, but decomposition results do not differ significantly across regressions.

*Annex 2 and Annex 3 content as presented in the source document.*

### Annex 4. An Overview of the Semiconductor Industry

### Annex 4. An Overview of the Semiconductor Industry

### Market size and investment
- Global market in 2019: US$420 billion.
- R&D investment in 2019: US$90 billion.
- Capital expenditure in 2019: US$110 billion.
- Production relies heavily on pre-competitive basic research carried out in academia, government institutions and the private sector.

### Production stages and characteristics
- Design (i):
  - Engineers develop specifications of electronic components that form an integrated circuit.
  - Knowledge-intensive, employs highly-skilled personnel, uses specialized design software and often reusable architectural building blocks (“IP cores”).
- Manufacturing / foundries / fabs (ii):
  - Integrated circuits from chip design are “printed” into silicon wafers.
  - Highly capital-intensive: setting up a state-of-the-art foundry can have a cost in the order of US$5-20 billion.
  - Requires specialized machinery and cleanrooms where particles are filtered out of the air.
- Assembly, testing and packaging (iii):
  - Silicon wafers are converted into finished chips ready for assembly into electronic devices.
  - Relatively labor-intensive (still R&D- and capital-intensive relative to other industries).

### Industry structure and ecosystem
- Vertical models:
  - Integrated Device Manufacturer (IDM): stages (i) and (ii) undertaken within the same firm.
  - Fabless designers contract with foundries to manufacture their chips.
  - Outsourced semiconductor assembly and testing firms (OSATs) typically undertake all or part of stage (iii).
- Supporting ecosystem: materials, machinery, software, and IP suppliers support the three production stages.
- Main downstream users:
  - Consumer electronics companies (mobile phones, PCs, TVs).
  - Automakers account for only about 10 percent of total chip sales.

### Market concentration and regional specialization
- Foundries:
  - High market concentration driven by large scale requirements and capital expenditure.
  - TSMC (Taiwan Province of China) accounted for 54 percent of the global foundry market share in 2018.
  - Top 10 foundry firms accounted for 87 percent of the global market in 2018.
  - East Asia has 75 percent of global production capacity and 100 percent of the capacity for the most advanced chips.
  - Competitive advantages in East Asia: government incentives, robust infrastructure (reliable power and water supply, transportation and logistics networks), and a skilled workforce at competitive rates.
- OSATs:
  - Highly concentrated in East Asia, particularly Taiwan Province of China and China, with an increasing role for Southeast Asia (Malaysia, Vietnam and the Philippines).
  - In 2018 the largest OSAT firm had a market share of 40 percent and the top 10 OSAT firms had a market share of 91 percent.
- Geographic participation:
  - The U.S. and Europe have relatively small participation in semiconductor manufacturing but large presence in fabless design, specialized software, IP cores and equipment used at foundries.

### Vulnerabilities and policy context
- High regional specialization creates vulnerabilities to:
  - Geopolitical tensions (examples include potential export controls between Japan and South Korea in 2019; U.S. export controls to China on semiconductors and equipment containing U.S.-developed technology).
  - Natural disasters, epidemics, and infrastructure failures.
- Policy responses and implications:
  - Desire for increased self-sufficiency, particularly in the U.S. and Europe, given limited domestic manufacturing participation.
  - Full renationalization of global value chains would be prohibitively expensive.
  - Increased grants and tax incentives could potentially lead production to become less concentrated geographically.

*Annex 4. An Overview of the Semiconductor Industry, wpiea2022031-print-pdf*

---


_Source: https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022031-print-pdf.pdf_