Global Forum on Steel Excess Capacity (GFSEC):

Impacts of global excess
capacity on the health of
GFSEC steel industries
March 2024
Se 2023

OECD Facilitator
Anthony DE CARVALHO
Rodrigo PAZOS
Willem THORBECKE, Senior Fellow at RIETI (Tokyo)

Global Forum on Steel Excess Capacity (GFSEC): Impactsof global excess capacity on the health of GFSEC steel
industries
March 2024
OECD Facilitator
Anthony DE CARVALHO
Rodrigo PAZOS
Willem THORBECKE, Senior Fellow at RIETI (Tokyo)

Summary:This document presents GFSEC analysis on the impacts of global excess capacity on the health of GFSEC steel industries. It is based on
research conducted in 2023, and reflects comments made on previous versions. An important aim of this paper is to assess the impactsof excess capacity on steel industry profitability. This is because steel firms need to be able to invest in physical capital, R&D, and advanced
know-how (e.g., artificial intelligence and blockchain) to remain competitive. They also need to be profitable to make the necessary
investments in low-carbon technologies. The paper also looks at the impacts of excess capacity on the capacity utilisation, exports andimport penetration of other countries.
Acknowledgements: The report was funded through the financial contributions of GFSEC members.

This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to thedelimitation of international frontiers and boundaries and to the name of any territory, city or area.

IMPACTS OF GLOBAL EXCESS CAPACITY

3

Table of contents
Executive summary ................................................................................................................................ 4
1. Introduction......................................................................................................................................... 5
2. The impacts of excess capacity: some lessons learned from the past.............................................. 5
3. Modelling the impacts of excess capacity .......................................................................................... 7
4. Preliminary empirical results of the impacts of excess capacity ................................................... 10
4.1. Profitability................................................................................................................................... 11
4.2. Import penetration ........................................................................................................................ 16
4.3. Steel exports from countries impacted by global excess capacity ................................................ 16
4.4. Domestic capacity utilisation rates ............................................................................................... 17
5. Conclusions and next steps............................................................................................................... 20
Annex A. Methodological Annex ......................................................................................................... 21
The impact of excess capacity over the steel market ......................................................................... 21
Panel approach .................................................................................................................................... 21
Time series approach........................................................................................................................... 23
Annex B. Descriptive statistics ............................................................................................................. 25
Descriptive statistics.............................................................................................................................. 25
Annex C. Data sources.......................................................................................................................... 28
References.............................................................................................................................................. 30

FIGURES
Figure 1. Expected impacts of excess capacity on third countries
Figure 2. Actual and Predicted Steel Stock Prices
Figure 3. Actual versus hypothetical series
Figure 4. Higher subsidies for Chinese firms

6
13
19
26

TABLES
Table 1. Impacts of excess capacity over a set of market variables
Table 2. Basic statistics. Aggregated panel (72 countries)
Table 3. Basic statistics. Firm-level panel (46 firms)
Table 4. List of variables used

11
25
26
28

BOXES
Box 1. Data collected to estimate the impacts of excess capacity on steel markets
Box 2. Does global excess capacity impact exchange rates?
Box 3. What would have profitability and utilisation rates of the steel industry looked like had Chinese excess capacity
not surged like it did? A counterfactual exercise

8
17
18

4  IMPACTS OF GLOBAL EXCESS CAPACITY

Executive summary•

Non-market excess capacity is causing significant harm on the profitability and even theviability of GFSEC steel industries. So long as the root causes of this excess capacity are
not addressed (market-distorting subsidies, other government interventions, and weakmarket-based conditions), GFSEC steel industries will suffer from lower profitability thanwould otherwise be the case. This will lead to fewer resources to invest in R&D andsustainability for a healthier future.

•

Stock prices of steel companies also shed important light on profitability. The resultsfrom stock returns reported here indicate that, as overcapacity peaked in 2015, European
firms were hit especially hard during the initial phase of the excess capacity crisis. South
Korea and Türkiye also suffered significantly. While excess capacity initially did not hurt
Chinese steelmakers as much as steelmakers in European countries, starting in early 2019
Chinese stock prices have averaged more than 43% below forecasted values. Thus, theChinese steel industry has suffered recently.

•

Excess capacity “crowds out” steel production in GFSEC member countries. GFSEC
steel producers lose significant domestic market share and their capacity utilisation rates
fall due to the trade effects of global excess capacity. With carbon intensities of steel
production lower in many GFSEC members compared to those in sources of excess
capacity, this crowding out effect can have significant negative impacts on global emissions
from the steel industry.

•

A focus on high value-added and green steel can help insulate companies from thenegative impacts of global excess capacity. A lesson from the Swedish and Japaneseexperiences is that mastering advanced technologies to reduce carbon footprints and to
produce sophisticated products can help firms survive the onslaught that arises from excess
steel capacity abroad.

•

There is a need to explore other impacts. Excess capacity can also lead to job losses andother social impacts. Bankruptcies and idling of steel plants in GFSEC member countries
are also tangible consequences of excess capacity that are worthy of further study. Future
work could focus on ways to shelter the industry from these negative impacts.

IMPACTS OF GLOBAL EXCESS CAPACITY

1. Introduction
1.
The global steel industry’s capacity to produce steel has increased rapidly since theearly 2000s. Most of the growth in steelmaking capacity has occurred in non-GFSEC
economies that have industrialised rapidly. A common observation is that countries withlarge net imports of steel are eventually inclined to establish and expand their domestic
steel industry, with the help of government. They do so in order to offset imports and toachieve self-sufficiency in steel production. Often, this is supported by a host of
government interventions, including subsidies such as below-market financing to cover theinvestment costs of steel companies.
2.
Because the capacity expansions are often non-market in nature, they do notnecessarily reflect the true risks of success or failure of the relevant investment projects,
and often lead to over-investment. When steel demand eventually slows, the overinvestment reveals itself as steel prices plunge and steel companies incur losses. A common
reaction is then to sustain unviable firms through other forms of subsidies, such as bailouts
and cash grants. This keeps inefficient capacity in the marketplace that would otherwise
shut down. When this happens, steel producers in the affected economies look to export
markets to sell their surplus steel, driving down the prices and profitability of steel
industries in all countries. These episodes of over-investment in the steel sector havesubsequently led to recurring overcapacity crises, most recently the one that emerged in
2015.
3.
This document presents GFSEC research focused on the impacts of excess capacity
on international steel markets. It provides an overview of preliminary work by the
Facilitator to assess the impacts of steel excess capacity on the health of steel industries of
GFSEC members, a description of the data collected to carry out this and future research,and thoughts about possible avenues for future analysis.
4.
An important aim of this paper is to assess the impacts of excess capacity on steel
industry profitability. This is because steel firms need to be able to invest in physical capital,
R&D, and advanced know-how (e.g., artificial intelligence and blockchain) to remaincompetitive. They also need to be profitable to make the necessary investments in lowcarbon technologies. The paper also looks at the impacts of excess capacity on the capacity
utilisation, exports and import penetration of other countries.

2. The impacts of excess capacity: some lessons learned from the past
6.
Excessive levels of steelmaking capacity have very negative impacts on the steel
industry. History has shown that excess capacity is associated with surges in steel exports
from countries that are the source of global excess capacity. This leads to over-supply ofsteel on international markets and depressed steel prices, as well as lower market sharesand capacity utilisation rates for domestic steel producers in third countries that operate
under market conditions. Moreover, the recent excess capacity crisis that emerged in 2015
also led to bankruptcies and localised job losses across the GFSEC’s membership.
Typically, no matter where the excess capacity sits, it always leads to lower profitability
for steel producers everywhere. Figure 1 presents the stylised effects of non-market excess
capacity in a given country on steel industries abroad.

56  IMPACTS OF GLOBAL EXCESS CAPACITY
7.

The direct effect on profitability occurs through many channels. Two direct channels arethrough costs and prices. When excess capacity results in low capacity utilisation rates, this
implies that economies of scale are not fully exploited and thus unit costs are higher. Excess
capacity also leads to surplus steel searching for demand, thus depressing prices and often
leading to dumping of steel products on international markets. The resulting higher costs
and lower prices directly harm profitability. At the global level, the effects of excess
capacity are transmitted through trade; as suggested by Figure 1, excess capacity can leadto export surges and market share losses for import-competing domestic producers. While
not captured in the chart, non-market excess capacity and its impact on prices can also leadto producers engaging in price arbitrage. This flow chart thus does not capture all of the
impacts of non-market excess capacity.

Figure 1. Simplified depiction of impacts of non-market excess capacity on third countries
Subsidies andother government
measuresgenerate excess
capacity in country
A

Steel demandslows andprices start
declining in cty
A; keep on
producing tolower unit costs

Importpenetrationfrom A
increases in 3rd
market cts

Steel exports
from A surge,
potential for
unfair trade(dumping andsubsidisation)

Efficient steel
production in
3rd countries
gets displaced
by A’s imports

Less productive
andeconomically
viablesteel
industry in thelong runSource: Facilitator’s depiction.

Less investmentinphysical
capital,R&D
and innovationtoremainsustainable andcompetitive

Gap between
supply anddemand grows,
steel pricedeclineaccelerates

Surplus sold ininternational
markets

Capacity
utilisation rates
of steelindustries inimportingcountries fall

Profits down forsteel producers

IMPACTS OF GLOBAL EXCESS CAPACITY

8.
The longer-term impacts on profitability are arguably more important, particularly
by limiting resources available for innovation activities, advanced/skilled jobs, and otherproductivity-enhancing investments. Moreover, excess capacity means that scarce
resources, such as labour, capital and raw materials, are being misallocated. In effect,
governments that subsidise their industries or engage in behaviors that generate excess
capacity are also encouraging resource allocation to potentially inefficient or unviable firmsthat would not otherwise be in the market. Previous OECD research from several years ago
has shown that productivity developments in the steel sector have been weak relative toother industries, which may reflect weak market-based conditions or exit barriers thatprevent a reallocation of resources to the most productive firms and hinder the growthprospects of more innovative firms1.

3. Modelling the impacts of excess capacity
9.
The GFSEC seeks to understand the impacts of excess capacity on international
steel markets. This would involve modelling that looks in detail at the capacity growth ofan exporting country which is experiencing rapid, market-distorting capacity growth. Keyvariables of interest include changes in domestic market share and export trends forimporting countries. The data and analysis should highlight the global nature and theindirect impact of excess steel capacity on world markets, particularly for Global Forum
members which are typically importing nations for steel.
10.
When developing a modelling framework to assess the impacts of excess capacity,
it is important to consider that excess capacity is inherently linked with market-distorting
government interventions and weak market-based conditions. Indeed, fostering a levelplaying field, securing open and competitive markets, and ensuring a market-driven
approach to resource allocation are crucial factors linked to excess capacity. As noted in
the other substantive paper on exports, excess capacity is not simply a comparison of acountry’s capacity and production, or defined as a low capacity utilisation rate. A
government that subsidises its steel industry heavily may see its industry enjoy a highutilisation rate, even though the plants producing that output would not be economically
viable were it not for the special conditions, subsidies or support they received from thegovernment.
11.
At the same time, measuring excess capacity by the difference between capacityand demand may overlook a country’s true comparative advantage that enables its steel
industry to specialise in the export of certain steel products to meet demand abroad2. Indeed,some countries have capacity levels persistently in excess of local demand, reflecting thiscomparative advantage and not because of excess capacity per se.

To truly understand a country’s comparative advantage, it would be necessary to analyse the costsof raw materials, energy, and other inputs to steel production in each of the GFSEC countries andto compare these costs to export data.
2
See Evaluating the Financial Health of the Steel Industry¸ available at:https://www.oecd.org/sti/ind/Evaluating-Financial-Health-Steel-Industry.pdf
1

78  IMPACTS OF GLOBAL EXCESS CAPACITY

Box 1. Data collected to estimate the impacts of excess capacity on steel markets
The Facilitator has collected a wealth of information to begin analysis of the impacts of excess capacity on the health ofsteel industries around the world. These series were taken from different sources, so additional efforts were required toachieve harmonization and matching in order to be used in empirical exercises. The details can be seen below.
International trade flows. Available information includes bilateral exports and imports flows at a
detailed level of aggregation (World Harmonized System, HS 6-digit level) for the period 2000-2023, in
tonnes and in US dollars. The main source corresponds to annual series from the United Nations
Comtrade, complemented from 2016 onwards with bilateral HS6 steel international trade series fromthe International Steel Statistics Bureau (ISSB). In cases where observations were not available for acountry-year pair, mirrored statistics from respective trading partners were used.
Exports and imports series were consolidated at steel products and subproducts levels, identified inturn through worldsteel HS-subproduct correspondence tables.
Series for bilateral export (import) unit values at HS6 were also computed when available as the ratio
between tonnes and US dollars exported (imported). This series were aggregated at the subproductlevel using as weights the corresponding total tonnage exported (imported). In order to reduce volatility
in unit values, only the bilateral flows with non-zero values for the entire time period were considered.
Detailed steel production and apparent steel use. Annual and monthly series for production andapparent steel use of crude steel and finished steel products by country were obtained from worldsteel.
Capacity. Annual estimates of crude nominal steelmaking capacity at country levels provided by theOECD (OECD Steelmaking Capacity Database, 2000-2023)
Prices. The daily record of reference prices for flat and long steel products in the main trading of theworld’s largest steel producers was collected from the Platts platform.
Firm-level data. A panel for 46 steelmaking firms from 17 of the major steel producer countries ranging
from 2005-2022 from the OECD’s MAGIC database. This dataset contains information from the annual
reports and consolidated financial statements for each of the firms as well as other variables of interest,
such as the number of employees, credit rating, government grants and below-market borrowings, andgovernment participation in the firm’s ownership, among others.2
Time-series data. Daily records of stock returns for a sample of steel industry firms starting in
September 1998, aggregated at a country level for a total of 14 countries, among them many of themain steelmaking economies. Data is obtained from the Datastream database.
Final datasets. Resulting datasets compile the different sources and perform a matching over productcategories and countries. They comprise an aggregated country level panel dataset (72 countries over
2002-2022), a time-series dataset and a firm-level panel dataset (a cross section of steel firms overtime).

2

Annexes B and C clarify the list of variables used including the subsidies (grants and below-market borrowings)
considered, comparing the levels between Chinese firms and the rest. At this time, the Facilitator does not have thedetail of the corresponding granting authority and the end-use of the subsidies. However, please refer to Mercier, F.
and L. Giua (2023), "Subsidies to the steel industry: Insights from the OECD data collection"(https://doi.org/10.1787/06e7c89b-en) for more detail on Chinese subsidies.

IMPACTS OF GLOBAL EXCESS CAPACITY

12.
In this context, it is important to distinguish between cyclical excess capacity, forexample episodes when a steel market downturn leads firms to idle their capacities while
they wait for an upturn in the future, from structural excess capacity. Structural excess
capacity is the excess that is driven by subsidisation that keeps inefficient or loss-making
capacity in the market (which would normally exit under normal market conditions), or
subsidies that lead firms to invest in new capacity that they normally would not invest inabsent those subsidies, and weak market-based conditions. Subsidisation can include
supports such as government grants to insolvent or ailing firms or lending by state banks at
below-market rates to finance investments in new capacity. In both cases, the subsidies
generate non-market excess capacity.
13.
For these reasons, the Facilitator has deemed it important to make this distinction
when analysing the impacts of excess capacity. In this preliminary work, the Facilitator
examines the impacts of excess capacity from the major source of global excess capacity
currently, i.e. China, on the health of the steel industry in other countries. To that end, theFacilitator has employed the OECD’s MAGIC database, a panel dataset of 46 steelmaking
firms (among which 20 are based in China). Data corresponds to extracts of the companies’
annual reports and consolidated financial statements and includes subsidies received byfirms in the form of direct grants and below-market financing3 over the 2005 to 2022 timeperiod. The Facilitator has matched those firms with their capacity, using its own capacity
database, for the purposes of the analysis here.
14.
It is also important to consider that excess capacity in one country negatively
impacts steel industries abroad through the trade channel. As suggested by Figure 1, excess
capacity in one country is expected to impart negative effects on steel prices, leads to importpenetration and market share losses for domestic steel industries abroad, and ultimately
harms the profitability of all steel producers. Moreover, because subsidies, among otherfactors, are a key source of non-market excess capacity, the trade that occurs is often“unfair”, in the sense that it can take the form of subsidised and/or dumped steel exports
from the country which is the source of the excess capacity. In reality, the injury thatdomestic industries suffer as a consequence of the unfair imports goes beyond mere
profitability effects, especially when it impacts steel workers and the economic fabric of
local communities where the plants operate.
15.
To model the impacts of excess capacity, it is proposed to use panel regressions.
Panel regressions incorporate both cross-sectional elements (e.g. variables for a crosssection of countries, steel industries or firms at a given point in time) as well as a time series
dimension (observations over time). Panel regressions thus provide a much larger numberof observations for enhanced robustness. Moreover, panel regressions allow for so- called“fixed effects” which control for the unobserved differences of the cross-sectional elementsthat do not vary over time. For example, when analysing the impacts of excess capacity on
profitability at the firm level, factors such as managerial efficiency could theoretically
impact profitability, yet no such data are available in the panel. To account for thischallenge, panel regressions allow for fixed effects to control for time-invariant
characteristics, such as managerial efficiency, that differ across firms but not over time. In
addition, panel regressions allow for year fixed effects that take into account temporalshocks that may affect profitability in a similar manner across firms, for example
fluctuations in the business cycle.

3

For thorough understanding of the methodology employed in calculating below-market financing,please refer to (OECD, 2021[1]).

910  IMPACTS OF GLOBAL EXCESS CAPACITY
16.
In addition to panel regressions, this paper also presents an analysis using dailystock prices of steel firms to corroborate the results obtained of the impacts of excess
capacity on steel firms’ profitability from the panel regression results. As will be shown
later, comparing the results of this times series analysis with those from the panel
regressions provides comfort because they point to the same impacts, i.e. that global excess
capacity harms the profitability of steel firms everywhere.
17.
In the panel regressions employed, the aim is to examine the effects of excess
capacity (broken down into its cyclical and structural, or non-market, elements) on several
dependent variables of interest, i.e. the profitability, capacity utilisation, import penetration(i.e., Chinese steel imports as a share of importing countries’ domestic demand for steel)and steel exports from the importing countries. Panel regressions at the country and firmlevels are used to examine the effects of Chinese excess capacity on the key variables ofinterest. These regressions also include control variables; so, for example, when measuring
the impacts of excess capacity on profitability, the prices of steel, the costs of production,and other variables that impact profitability are accounted for in the estimations.
18.
Annexes A-C provide more details of the methodological approaches used together
with a descriptive analysis of the panel datasets and a full list of the variables considered.
The next section aims to provide a summary of the key results obtained from this very
preliminary work.

4. Preliminary empirical results of the impacts of excess capacity
19.
Table 1 below provides a summary of various panel regression estimations done by
the Facilitator. The table suggests that Chinese excess capacity has significant and harmful
impacts on the health of the steel industry in other countries. In particular, it seems to boost
import penetration, lower capacity utilisation rates, and reduce the profitability of theindustry in countries that are impacted by Chinese excess capacity. It appears to have few
effects on the steel exports from the impacted countries. The latter effect is curious, as one
would expect impacted countries to see a decline in steel exports as a result of global excess
capacity. However, as steel imports from sources of excess capacity rise to meet local
demand, domestic steel producers may export the steel that is no longer needed
domestically, in order to keep production running at desired levels. Thus, the effects onexports are not certain a priori.
20.
To review the impacts, the reader can look at the dependent variables listed in the
top row (i.e., the steel industry variables of countries that are being impacted by Chinese
excess capacity), and read down to see the impacts of the explanatory variables listed on
the left. For example, the first dependent variable in Table 1 is import penetration, i.e. theshare of Chinese steel imports in the importing country’s domestic demand for steel. The
results are shown in the first column, indicating highly positive and significant effects from
Chinese structural excess capacity, as proxied by the two measures of subsidies. Thus,
structural excess capacity in China is associated with higher import penetration in othercountries with a high degree of confidence. Chinese steel demand is used to capture cyclicalexcess capacity. The parameter on this variable is negative and highly significant. Thus,
when the Chinese steel industry experiences cyclical demand slowdowns, this increases
import penetration of Chinese steel products in other markets, as one would expect.
21.
Table 1 also shows the effects of interaction terms, i.e. subsidy measures multipliedby the cyclical demand measure. The purpose of interaction terms is to capture the joint
effects of explanatory variables. The parameters on these interaction terms are negative andsignificant. In other words, when steel demand in China contracts (as it did in 2014 and2015), then the impact of subsidies on import penetration will increase. That is, Chinese
structural excess capacity leads to even more import penetration when the Chinese steelmarket goes through episodes of demand weakness, which is also a logical result. This
could be an issue in the future, in view of the risks currently facing Chinese steel demand.

 11

IMPACTS OF GLOBAL EXCESS CAPACITY

22.
Table 1 also shows that some explanatory variables have no effects on thedependent variables (denoted by “Nil”). That is because, regardless of the sign of thecoefficient, the estimated parameters were insignificant. Hence, it can be assumed that they
have no effect.
23.
The remainder of this section will comment on key results for different dependent
variables. It first looks at the effects of excess capacity on profitability, then importpenetration, followed by steel exports from countries impacted by Chinese excess capacity,and lastly on domestic capacity utilisation rates. Brief boxes listing the key empirical results
will make some interpretations of the results.

Table 1. Impacts of excess capacity over a set of market variables
Signs of the coefficients obtained and degree of significance of the estimated parameters
Aggregated panel(72 countries)

Firm-level panel(46 firms)

Chineseimports /Total
domestic
demandExports /Total
production

Utilisation
rate

Total
revenue

EBITDA /Total
revenue

Exportrevenue /TotalrevenueCash grants ratio

+

Nil

-

-

-

Nil

Below-market borrowings
ratio

+

Nil

-

-

-

Nil

Chinese steel demand

-

Nil

+

Cash grants ratio x
Chinese steel demand

-

Nil

+

Below-market borrowings
ratio x Chinese steel
demand-Nil+

Note: The cells indicate the sign of the coefficient corresponding to the impact of the variables in rows over thevariables shown in columns. The colour scale reflects the degree of significance of the results obtained in the
different specifications. For example, the darkest shade indicates that very significant results have been foundin all the different estimations (greater than 1% confidence level), the lighter shades depict generally lower
confidence levels across the model specifications, albeit very significant overall. No shading indicates that theparameters estimated were insignificant, thus are assumed not to have any effect on the dependent variable(denoted “Nil”).
Source: Facilitator calculations

4.1. Profitability
24.
This section starts by commenting on the preliminary results of two methodologicalapproaches to assess the impacts of excess capacity on the profitability of steel industries.
The first approach uses panel regressions at the firm level, as explained above, to see theimpacts on firms’ total revenues and EBITDA/total revenues ratio. To corroborate thesepanel regression results, a second and novel approach, based on time series data of stock12  IMPACTS OF GLOBAL EXCESS CAPACITY

returns, is used to assess the impacts of excess capacity on profitability.4 This latterapproach compares actual stock prices of steel companies with forecasted prices during theperiod when the steel excess capacity crisis emerged. Stock prices that significantly
underperform their predicted returns indicate a negative impact from excess capacity on
profitability (Annex A also presents a methodological description of this approach).

4.1.1. Results of the panel regressions
25.
As indicated in Figure 1, excess capacity is expected to reduce the profitability ofthe steel industry in importing countries, through its direct effects on prices and costs ofproduction. Import surges in importing countries can displace local production and reduce
utilisation rates of domestic producers operating under market conditions (these effects arereviewed later in this section). As presented in Table 1, in all model specifications, thesubsidy proxies of structural excess capacity in China have highly negative and significant
parameters (one per cent confidence level or higher). The impact of Chinese excess capacity
is therefore clearly negative for other steel industries. Controls are also employed in thevarious model specifications, including costs of goods sold, total firm assets, total numberof employees, local demand for steel, exchange rates, and steel prices.

Key empirical result 1. Chinese non-market excess capacity causes serious harm on the
profitability of other steel industries
The panel regressions at firm level are unequivocal in their results. Non-market excess capacity has strong andsignificantly negative impacts on profitability. So long as the root causes of global excess capacity are notaddressed, GFSEC steel industries will suffer from lower profitability than would otherwise be the case. This willlead to fewer resources available to invest in R&D, innovation and sustainability needed to ensure a healthier future
for the steel industry.

4.1.2. Using stock price data to assess the impact of excess capacity on
profitability
26.
The strategy here is to use steel industry stock prices to investigate how the excess
capacity crisis that emerged in 2015 has affected steel industry profitability.3 Stock prices
are useful for this task since finance theory indicates that stock prices are the expected
present value of future cash flows. Black (1987, p. 113) noted that, “The sector-by-sector
behaviour of stocks is useful in predicting sector-by-sector changes in output, profits, orinvestment. When stocks in a given sector go up, more often than not that sector will show
a rise in sales, earnings, and outlays for plant and equipment.” Croux and Reusens (2013)
employed Granger causality tests in the frequency domain over the 1991Q1 to 2010Q2
period to test if stock prices predict economic activity. They reported that the slowly
moving parts of stock prices have strong predictive power for future output. McMillan(2021) employed in-sample regression techniques in the time domain over the 1973Q1 to
2017Q4 period to test if stock prices predict activity. He found that stock prices are goodpredictors of activity in several countries. Thus, investigating the behaviour of steel stock3

In future work, we hope to also include information on profitability derived from financial statements
and annual reports. One drawback with stock prices is that they are affected by factors other thanprofitability.

IMPACTS OF GLOBAL EXCESS CAPACITY

prices in the era of excess capacity can shed light on how steel industry profitability andoutput are being impacted.
27.
The investigation of stock prices here covers not only actual steel stock prices but
also predicted stock prices (see Annex A for the model). If steel stock prices fall far below
their predicted values as excess capacity emerges, this indicates that excess capacity isharming profitability.
28.
The model performs well, with a large share of the variation of stock returns
explained by variation in the explanatory variables. Figure 2 below presents graphs for theactual and predicted stock returns between 1 January 2015 and 14 September 2023, acrosscountries. Several items are worthy of note in Figure 2. One is that, between 1 January and
31 December 2015, several European countries performed the worst relative to predictedvalues. These include France, where prices were 85% below predicted values; Sweden,
where prices were 69% below predicted values; the Netherlands, where prices were 60%
below predicted values; Finland, where prices were 45% below predicted values; and Spain,
where prices were 29% below predicted values. The other two underperformers in 2015
were South Korea, where prices were 41% below predicted values and Türkiye where
prices were 29% below forecasted values. Thus, European firms were hit especially hardduring the initial phase of the excess capacity crisis.

Figure 2. Actual and Predicted Steel Stock Prices8

Log of Stock Price

Log of Stock Price

Daily stock prices beginning on 1 January 2015 until September 14 2023
Forecasted Stock Price

7
6

Stock Price

5
16

18

a) Austria

20

22

8.0

Stock Price

7.5
7.0
6.5

Forecasted
Stock
Price

16

18

b) Belgium

20

22

 13

Forecasted
Stock
Price

5
4

Log of Stock Price

Log of Stock Price

14  IMPACTS OF GLOBAL EXCESS CAPACITY

Stock
Price

3
16

18

20

6

Stock Price

5

Forecasted
Stock
Price

4
3

22

16

c) China

18

20

22

d) Finland

Log of Stock Price

Log of Stock Price

Forecasted

10Forecasted Stock Price

8
6

Stock Price

4
16

18

20

7
6
4

22

16

7

Log of Stock Price

Log of Stock Price

Forecasted
Stock
Pri ce
Stock
Price

16

18

20

22

Log of Stock Price

Log of Stock Price

6

Stock
Price

5

16
i)

22

1.8

Forecasted Stock Price

1.6
1.4

Stock Price

16

18

20

22

h) Japan

Forecasted
Stock
Pric

7

20

2.0

g) India

818f) Germany

85

Stoc kPrice5e) France

6

Stock
Price

18
Korea

20

22

6

Stock

5
4
3

16

j) Netherlands

18

20

22

Forecasted
Stock
Price

7
6

Log of Stock Price

Log of Stock PriceIMPACTS OF GLOBAL EXCESS CAPACITY

Stock
Price

5
16

18

20

7

Stock
Price

6
Forecasted
Stock
Price

5
4

22

16

18
Stock

16

Price
Forecasted
Stock
Price

14
16

20

22

l) Sweden

Log of Stock Price

Log of Stock Pricek) Spain1818m) Türkiye

20

22

11
10

Stock
Price

9

Forecasted

8

Stock
Price

16

18

20

22

n) U.S.

Note: The blue line represents actual steel stock prices and the orange line represents forecasted steel stock
prices. Forecasted steel stock prices come from a regression of steel stock returns in a country on 1) the returnon the country’s aggregate, 2) the return on the world stock market, 3) the change in the log of the country’s
exchange rate, 4) the change in the log of the spot price of crude oil. For Japan, South Korea, Sweden andTürkiye, the real effective exchange rate is used. For India the nominal effective exchange rate is used. For
Eurozone countries, China, and the U.S. exchange rates from the other two regions are used. For instance, forEurozone countries the euro exchange rate relative to the Chinese renminbi and the U.S. dollar are used. For
crude oil, the spot price for Dubai crude is used for Asian countries, the spot price of Brent crude for European
countries, and the spot price of West Texas Intermediate crude for the U.S. The regressions are run over 14
September 1998 to 31 December 2014 period (or starting on the first data that data are available). Actual outof-sample values of the right-hand side variables are then used to forecast stock prices (the orange line) overthe 1 January 2015 to 14 September 2023 period.
Source: Author’s calculations based on Datastream.

29.
A second item to note is that steel stock prices for almost all countries rose
beginning in the second half of 2020. This was a time when consumers during the pandemic
increased their demand for goods including white goods, automobiles, bicycles, food cans,disinfectant aerosol cans, and other items that required steel. This contributed to the priceof flat steel products more than doubling between June 2020 and August 2021. Steel makers
across the world benefited.
30.
A third item to note is that Chinese steelmakers performed almost as expected until
the end of 2018. Thus, excess capacity initially did not hurt Chinese steelmakers as muchas steelmakers in European countries. However, from 1 January 2019 until 14 September
2023, Chinese stock prices have averaged more than 43% below forecasted values. Thus,
the Chinese steel industry has suffered recently. Trade measures, such as AD/CVD andsafeguards as well as declining demand for steel may have also played a role.
31.
A fourth item is that, except when the COVID-19 pandemic was depressing stock
markets, Japanese steel stock prices remained on average within 10% of predicted values.
Japanese steelmakers produce many high value-added steel products, and these make them
relatively less exposed to excess capacity than steelmakers producing basic products.
32.
A fifth item is that Swedish steel stock prices in September 2023 are outperforming
predicted values by more than 50%. This notable deviation may be linked to theannouncement by the Swedish company SSAB, indicating an advancement of their 15

16  IMPACTS OF GLOBAL EXCESS CAPACITY
decarbonisation plans by one investment cycle, shifting from 2045 to 2030. This strategic
move positioned SSAB at the forefront of initiatives aimed at mitigating carbon emissions
and acted as a revaluation factor for the company. Indeed, since early 2022 there have been
several announcements of steel purchasers placing orders for green steel from SSAB where
the higher cost of steelmaking is expected to be passed on to purchasers in the form of agreen premium.
33.
The results reported here indicate that, as overcapacity emerged in 2015, European
steel producers initially suffered. Chinese steel producers have seriously underperformed
beginning in 2019. Japanese steel producers have performed almost as expected. Swedish
steel producers that have focused on reducing their carbon footprint are doing well in 2023.

4.2. Import penetration
34.
Excess capacity can trigger significant trade disturbances, particularly duringmarket downturns. As shown in Figure 1, excess capacity in China is expected to result ina surge in Chinese steel exports to third markets, thereby increasing their share of domesticmarket demand in importing economies. The empirical results of the panel regressions
support this assumption: the excess capacity variables (cyclical and structural) are positiveand significant, after controlling for other factors that impact import penetration. That is,domestic producers (and/or exporters from third countries that ship steel products to thisdomestic market) lose market share and Chinese imports gain a larger portion of thedomestic market.

Key empirical result 2. Domestic steel producers lose market share as a result of Chinese
excess capacity
The regression results show that domestic steel producers in importing countries (or third country exporters thatcompete with Chinese steel producers) lose a share of domestic demand due to the trade implications of excess
capacity.

4.3. Steel exports from countries impacted by global excess capacity
35.
Excess capacity in China could have different effects on the steel exports of othercountries. On the one hand, as steel imports from China rise to meet local demand (Keyempirical result 3), domestic steel producers may export the steel that is no longer needed
domestically, in order to keep production running at desired levels. On the other hand, thosesteel producers may also compete with Chinese exporters in third markets. China’s excess
capacity may crowd out the exports of those competing companies, and hence a negative
effect might also be expected in these cases. However, if the affected country’s exporters
attempt to find markets with less competition from Chinese steel, this could support higher
exports if the strategy is successful. Thus, the effect on exports is uncertain a priori.
36.
The empirical results show that Chinese excess capacity has varying effects on thesteel exports of other countries, that is, sometimes positive and sometimes negative, but theparameters are not significant in all the model specifications. Hence, one cannot say with
sufficient confidence that Chinese exports have any effect on the exports of impacted
country. Further work could be conducted at the country level to examine these effects
further.

IMPACTS OF GLOBAL EXCESS CAPACITY

 17

Key empirical result 3. The impact of Chinese excess capacity on other countries’ steel exports
is still inconclusive
The regression results show varying effects of Chinese excess capacity on the steel exports of other countries.
This could reflect the export orientation of steel-producing countries, the sensitivity of domestic production toimports from China, and other factors that could be explored in further analysis.

Box 2. Does global excess capacity impact exchange rates?
Some countries have fixed exchange rates, others flexible exchange rates, but most haveintermediate exchange rate regimes (Frankel, 2019[1]). For countries with fixed rate
regimes, global steel excess capacity will not affect exchange rates. For countries withflexible exchange rates, any factor impacting demand and supply for domestic andforeign currencies will impact exchange rates.
Demand and supply are affected by both capital flows and flows of goods and services.
Capital flows exert a major impact on exchange rates, driven by differing expected
returns and risk characteristics of assets in different countries. Flows of goods andservices also matter by impacting the current account.
Demand and supply of steel products is only one factor affecting the current account.
Unless steel products weigh large in export and import baskets, their impact on exchange
rates will be second order. This will be all the more true for countries with intermediate
exchange rate regimes. The exchange rate is partly policy determined in these countries,
and policymakers may offset the impact of steel trade to pursue desired exchange ratelevels.
One indication of how global steel excess capacity may not exert first order impacts on
exchange rates comes from China. As the excess capacity crisis emerged in 2015,
China’s steel exports soared. If steel exerted first-order effects on exchange rates, wewould expect the surge in exports to cause the Chinese renminbi to appreciate. Between
January 2015 and the middle of November 2023, the renminbi has depreciated 15%
against the dollar. Thus, while steel trade is one factor impacting exchange rates, it may
not be a crucial factor.

4.4. Domestic capacity utilisation rates
37.
Chinese excess capacity appears to have displaced steel production in othercountries in the past. This was observed during the initial phase of the Covid-19 pandemic,
when production levels in China continued to increase rapidly, while GFSEC member
countries saw their steel industries pull back on production in response to the severe market
downturn.

18  IMPACTS OF GLOBAL EXCESS CAPACITY
38.
The empirical results show very negative, and highly significant, effects from
Chinese excess capacity on the domestic capacity utilisation rates of steel industries in othercountries, after controlling for other relevant factors. The model specifications include
controls for other factors that affect domestic capacity utilisation rates, including local steel
demand, steel prices and exchange rates. All the excess capacity parameters are negative,and very significant.

Key empirical result 4. Chinese excess capacity “crowds out” steel production in GFSEC
members, with possible environmental concerns
The regression results show clear negative effects of excess capacity on capacity utilisation rates of the steel
industry of other countries. When this occurs, efficient steel producers in GFSEC countries see their steel
production being crowded out by Chinese excess capacity. This can lead to localised job losses and bankruptcies.
Moreover, with carbon intensities of steel production much lower in many GFSEC members as compared to China,
this effect could lead to worse outcomes for the steel industry’s greenhouse gas emissions, and other negativeenvironmental outcomes. Further analyses of these effects could be conducted in the future.

Box 3. What would profitability and utilisation rates of the steel industry have looked like had
Chinese excess capacity not surged like it did? A counterfactual exercise
The empirical analysis highlights significant adverse consequences of Chinese excess
capacity on the global steel industry, mainly attributable to substantial government support
for steel firms in China, i.e. a structural source of excess capacity. In times of declining
demand for steel, the transfer of such funds tends to increase and cover the financial distressof the recipient companies (see Annex B for a comparison of cash grants and below-market
borrowings differentiating between Chinese-based firms and other firms).
To better gauge the implications of the above empirical exercise, this box asks the question,
at what levels would profitability and capacity utilisation rates have been, had Chinese
excess capacity not surged the way it did? In other words, this box seeks to compare actual
data on utilisation rates and profits over time (in the world excluding China) withhypothetical estimates resulting from a lower subsidy intensity to Chinese firms consistent
with lower excess capacity. That is, in the counterfactual exercise, Chinese subsidy
intensities remain at the level observed at the beginning of the period of examination(implying lower excess capacity), to see how the profitability and capacity utilisation rates
in the rest of the world would have performed. Therefore, counterfactual average series ofutilisation rates and profits were computed for a constant (lower) level of government
support to Chinese firms over the entire period of analysis. In this way, the preliminarymodel results of this paper can be understood in different dimensions.
For simplicity, the 2005-2006 average for below-market borrowings (the primary source ofgovernment assistance), which accounted for an annual figure of 1.6% of average revenues
for Chinese firms in that period, i.e. significantly lower than the 2016 peak of 5.2% (andstill below the annual average of 0.3% obtained by other firms in the panel), wasIMPACTS OF GLOBAL EXCESS CAPACITY

assumed to stay constant throughout the whole period of analysis in order to construct the
hypothetical series.
Figure 3 below shows the comparison between the actual and hypothetical values for thevariables mentioned. On the left chart, the actual capacity utilisation rate, computed as theshare of crude steel production and crude capacity (in the world excluding China) is plottedagainst the hypothetical rate that assumes a constant level of subsidy intensity to Chinese
firms at the level before China’s excess capacity surged.
It can be observed that the actual average utilisation rate series (for the world excluding
China) was above 85% in the period before the 2008/09 global financial and economiccrisis. Following the financial shock, it rapidly declined to almost 65% and never fully
recovered, remaining below 80%. Conversely, the hypothetical utilisation curve suggests
that, had subsidies intensity remained constant for Chinese companies, utilisation levels in
the rest of the world would have fully recovered by around 2013 from the drop observed
during the global financial crisis.
Similarly, the right panel compares the actual average EBITDA/ total revenue ratio for nonChinese firms with a hypothetical series that maintains a constant ratio of below-market
borrowings to revenues for Chinese firms. The negative effect of Chinese subsidisation andthe consequent impact on excess capacity is also evident by the disparity between the twoseries. In fact, profitability levels in the rest of the world would have been at much healthier,and sustainable, levels. Clearly, global excess capacity harms the steel industries ofimpacted countries.
Lastly, it is evident that, in both cases, the gap between the actual and hypothetical values
widens around 2015-2016, a period characterised by peak subsidy levels for Chinese firms.

Figure 3. Actual versus hypothetical series
Global utilisation rates (left) and average profits (EBITDA as share of total revenues)

Note: The utilisation rate was computed as the cross-country average share of world crude steel production andcrude capacity excluding China. The profits indicator (EBITDA / Revenue) corresponds to the annual average
share computed for the sample of non-Chinese firms.

 19

20  IMPACTS OF GLOBAL EXCESS CAPACITY
The predictions of the models that were found to be significant were used to construct the hypothetical curves.
The specifications of the models consider for the corresponding control variables according to the methodology
in Annex A. The calculation of hypothetical curves was done in two steps. First, the gap between the in-sample
predictions and those arising for a constant level of subsidy intensity at 1.6% for below-market borrowings was
obtained. Finally, the gap, in percentage points, was added to the actual data series.
Series end in 2020 in the charts above due to missing observations for some firms in the panel for the last two
years (2021 and 2022) that distort the computation of averages.
Source: Facilitator calculations

These results are based on estimates of coefficients associated with the variables in themodel and should be interpreted cautiously. They arise from partial equations that overlook
second-order effects in supply and price conditions resulting from reduced subsidies to theChinese steel industry.

5. Conclusions and next steps
39.
Global excess capacity continues to increase. While some GFSEC members have
employed have made use of existing tools (e.g., AD/CVD, safeguards) in recent years, theexcess capacity problem continues to worsen. This highlights the need to address the root
causes of excess capacity.
40.
This paper analyses the effects of Chinese excess capacity of interest to delegations,
including the impacts on their steel industries’ profitability, capacity utilisation, exports
and import penetration. The results of this very preliminary analysis show that global
excess capacity has considerably harmful effects on the steel industries of GFSEC
members. At the same time, this work also provides some positive takeaways; notably, theanalysis of stock prices revealed that mastering advanced technologies to reduce carbon
footprints and to produce sophisticated products can help firms survive the onslaught thatarises from excess steel capacity abroad.
41.
Avenues for future research could be to examine the impacts of excess capacity on
the sector’s employment, as well as other tangible factors such the idling or closures ofsteel plants in GFSEC member economies that are suffering from the impacts of global
excess capacity. Further avenues of research could also exploit a recently developed trade
remedy database to assess the extent to which importing countries resort to trade defense
to address unfairly-traded imports resulting from excess capacity elsewhere.
42.
In future work, we also hope to also include information on profitability derived
from financial statements and annual reports. One drawback with stock prices as employedin this paper is that they are affected by factors other than profitability.

IMPACTS OF GLOBAL EXCESS CAPACITY

Annex A. Methodological Annex

43.
This section presents the overall strategy to evaluate the impact of excess capacity
over the steel market and describes how the different steps of the process fit together. Those
steps and the methodologies underlying the different methodologies are detailed in thesubsequent sections.

The impact of excess capacity over the steel market
Panel approach
44.
In the case of the steel industry, excess capacity originated in some economies as aconsequence of direct government intervention which, in an attempt to guarantee thedomestic supply of steel at low prices for use in other industries, supported and financed
investment projects to expand rapidly the steelmaking capacity of the country. This
sometimes created a significant gap between demand requirements and productive capacity
levels.
45.
The resulting excess capacity had an impact not only on the domestic market but
also on third markets mainly through the international trade channel. This promoted anincrease in the exports of low-priced steel products, boosting, in turn, their share in foreign
markets and harming profits in firms in other countries.
46.
In this empirical exercise, the objective is to assess the impacts of excess capacity
developments by looking at its effect on countries’ trade flows and measures of the healthof firms and steel markets. In this first stage, the aim is not to identify causal effects, i.e.
provide an estimate of the actual causal direct and indirect effect of excess capacity in third
markets, given that such assessments would require addressing all the potential sources ofendogeneity in the relation of interest. Rather, the aim is to measure the aggregated total
effects (causal or not) of excess capacity on third markets.
47.
The first problem that arises when trying to estimate the effect of excess capacity
is how to get a correct estimate for this variable. This is related to two different aspects, on
the one hand, which countries to consider given that cycles of excess capacity started at
different times for different countries, and secondly, how to measure excess capacity in a
way that avoids common endogeneity problems in regressions.
48.
To address the first aspect, the Chinese steel industry will be considered as a starting
point. Given that it is the world's largest steel producer it should have a significant impact
on third markets. In addition, the cycle of expansion of steel production capacity and thesubsequent demand slowdown began in the early 2000s and continues today, which
increases the observations in the identification strategy.
49.
For the second aspect of measuring excess capacity, government subsidies to firms
in the form of grants and below market borrowings will be used to try to capture thedifferent sources of government support to firms. Indeed, Chinese government support was
directly related to the investment of new capital equipment and capacity expansions, as well
as direct cash grants used to compensate the losses and finance the high fixed costs ofChinese firms. In this way, these sources of government support are directly related to non-

 21

22  IMPACTS OF GLOBAL EXCESS CAPACITY
market government interventions and excess steelmaking capacity and can hence be used
to identify the effects on third markets.
50.
Government subsidies are therefore used as a proxy for structural excess capacity
given that a typical ratio of utilisation rates in China or the difference between crude steel
capacity and domestic production or demand would create endogenous sources ofvariability in regressions, invalidating the results. However, it is important to note that there
are other proxies for structural excess capacity, related to the extent to which the playing
field is level, how competitive markets are, and whether market-driven approaches toresource allocation are in place.
51.
This happens because excess capacity may create incentives for firms to increase
production. When a firm operates with excess capacity its production is located at a
suboptimal level in which marginal costs are below average costs. The firm can thenincrease production to decrease losses. This could potentially produce a co-movement
between a variable measuring excess capacity such as the gap between capacity anddemand and utilisation rates and other controls in the equation involving market variables
such as output or demand, creating endogeneity. Therefore, the subsidies received by firms
allow to better capture the effect of interest since they are not directly related to the firm’s
production strategy.
52.
The effects of government grants and below market borrowings on selected third
market variables will be evaluated using the aggregated and firms’ datasets. Additionally,a time-series approach will be performed to study directly the impacts on profitability using
high-frequency data.
53.
Therefore, the equation to estimate at aggregate level for c (countries) and t (years)
is the following:
Equation 1
𝑌𝑐𝑡 = γ𝐸𝐶𝑡 + μ𝐷𝑡 + 𝜎(𝐸𝐶𝑡𝑥𝐷𝑡) + 𝜑𝑋𝑐𝑡 + 𝜃𝑐 + 𝑣𝑐𝑡

54.
Where 𝑌𝑐𝑡 corresponds to the third market variables: i) import penetration,measured as Chinese steel products imports as a share of domestic apparent crude steel use,ii) export orientation, measured as the share of steel products exports as a share of total
domestic crude steel production, iii) utilisation rates, measured as firms total crude steel
production as a share of nominal crude steel capacity5.
55.
𝐸𝐶𝑡 denotes the two variables chosen as proxies of the excess capacity of China,
namely cash grants and below-market borrowings measured in terms of total firm revenues.
These variables correspond to the simple yearly aggregation of the Chinese firms’ subsidies
in the abovementioned firm panel dataset. These are 20 of the largest steel producers in
China, in which the government has a significant share of the total ownership in manycases. By evaluating the impacts of 𝐸𝐶𝑡 one can capture the influence of non-market
government interventions related to the Chinese excess capacity. The associated coefficientis expected to be positive for import penetration and exports, and negative for domestic
utilisation rates.
56.
𝐷𝑡 represents Chinese steel apparent use and the term 𝐸𝐶𝑡 𝑥 𝐷𝑡 is an interaction
effect that is used to capture the link between Chinese demand and excess capacity proxies.
It could potentially capture a differential elasticity of grants and below-market borrowings
for different levels of Chinese demand.
57.
𝑋𝑐𝑡 represents relevant covariates. In this case, different regressors are used basedon the dependent variable. They include demand market variables (domestic apparent crude
5

For the full list of variables used in the analysis please see Annex C.

IMPACTS OF GLOBAL EXCESS CAPACITY

steel use and world apparent crude steel use); bilateral exchange rates against China; andsteel prices (average flat products prices for a set of major steelmaking economies).
𝜃𝑐 corresponds to country fixed effects, 𝑣𝑐𝑡 is the error term and γ, μ and σ and 𝜑 arecoefficients. The equation is estimated in natural logarithms; therefore, the coefficients can

58.

be interpreted as elasticities.
59.

A similar equation is estimated using the firms’ panel dataset:

Equation 2
𝑌𝑓𝑐𝑡 = 𝛾𝐸𝐶𝑓𝑐𝑡 + 𝜑𝑋𝑓𝑐𝑡 + 𝜃𝑓 + 𝜃𝑡 + 𝑣𝑓𝑐𝑡

60.
Where 𝑌𝑓𝑐𝑡 in this case corresponds to steel firms’ variables in third markets: i)revenue, measured as total revenue from sales in USD millions, ii) EBITDA as a share of
total revenue from sales, iii) export orientation, measured as revenue from export sales asa share of total revenue.
61.
𝐸𝐶𝑓𝑐𝑡 is for this equation constructed so as to allow for variability at the firm andcountry levels as well through time. Corresponds to the difference between the aggregatedsubsample of Chinese firms’ cash grants and below-market borrowings as a share ofrevenue received in the year and a third country firm 𝑓 cash grants and below-market
borrowings as share of revenue received in the same year. Similarly, as before, a positive
gap between aggregated Chinese and third market subsidies will be indicating a stronger
government support from Chinese firms that could be having negative effects on third
markets. Annex B shows descriptive statistics comparing the average grants and below
market borrowings received by firms based in China and the rest of firms.
62.
𝑋𝑐𝑡, in turn, shows the relevant covariates at the firm level. Analogous to theprevious case, different regressors were used depending on the regressing variable. They
include firm level covariates: costs of the goods sold and firm assets, in USD millions, andthe total number of employees. They also include previous used covariates for market
demand, exchange rates and prices for the specifications that do not include firm or time
fixed effects.
63.
𝜃𝑓 and 𝜃𝑡 corresponds to firm and time fixed effects and 𝑣𝑓𝑐𝑡 is the error term. Thisequation was estimated in both in logs and log-differences. The last one, commonly named
first difference estimator in the literature, was used in some specifications to control forunobserved heterogeneity at the firm level when not considering firm fixed effects.

Time series approach64.
A time-series approach was performed to study directly the impacts on profits using
high-frequency stock prices data.
65.
To predict stock prices a four-factor macroeconomic model is used, where steel
stock prices in a country depend on the return on the country’s stock market, the return on
the world stock market, the exchange rate, and the price of crude oil. There is a long
tradition in finance of using the return on the country’s aggregate stock market to capture
the effect of a country’s macroeconomic environment on stock returns (see, e.g., Brown
and Warner, 1985, and Coutts et al., 1994). Analogously, the return on the world’s
aggregate stock market is used to capture the effect of the world’s macroeconomic
environment on stock returns. There is also a long tradition of capturing firms’ exposure 23

24  IMPACTS OF GLOBAL EXCESS CAPACITY
to exchange rates (see, e.g., Bodner and Gentry, 1993). Oil prices also have a significant
impact on stock prices for many sectors (see, e.g., Ready, 2018).
66.
Chen et al. (1986), examining the relationship between portfolio stock returns andmacroeconomic variables, argued that, to a first approximation, macroeconomic variables
can be viewed as exogenous relative to individual portfolios. Therefore, the assumption
made is that causality flows from the macroeconomic explanatory variables to the country
steel industry portfolios and that any causality flowing in the other direction is second order.
Equation 3∆𝑅𝑖,𝑡 = 𝛼0 + 𝛼1∆𝑅𝑚,𝑖,𝑡 + 𝛼2∆𝑅𝑚,𝑊𝑜𝑟𝑙𝑑,𝑡 + 𝛼3𝛥𝑒𝑟𝑖,𝑡 + 𝛼4𝑂𝑖𝑙𝑡
67.
Where ∆𝑅𝑖,𝑡 is the daily stock return for the steel industry in country i, ∆𝑅𝑚,𝑖,𝑡is thechange in the log of the price index for country i’s aggregate stock market, ∆𝑅𝑚,𝑊𝑜𝑟𝑙𝑑,𝑡 isthe change in the log of the price index for the world stock market, 𝑒𝑟𝑖,𝑡 represents thechange in the log of the exchange rate, and 𝑂𝑖𝑙𝑡 represents the change in the log of crude
oil prices.
68.
To determine firms in each country that are in the steel industry Datastream uses
the Refinitiv Business Classification method. This method begins with all listed companies
in a country and then employs company filings, news articles, and other information toclassify companies into their respective industries. Datastream also provides data on
country aggregate stock returns, world aggregate stock returns, exchange rates, and crude
oil prices. For exchange rates, when we can find data on the real effective exchange rate in
Datastream we use these. This is the case for Japan, Korea, Sweden and Türkiye. For India
we employ the nominal effective exchange rate. For Eurozone countries, China, and the
U.S. we use exchange rates for the other two regions. For instance, for Eurozone countries
we use the euro exchange rate relative to the Chinese renminbi and the U.S. dollar. The
results do not appear to be sensitive to changes in the exchange rates used. For crude oil, we
use spot prices for Dubai crude for Asian countries, Brent crude for European countries, andWest Texas Intermediate crude for the U.S.

 25

IMPACTS OF GLOBAL EXCESS CAPACITY

Annex B. Descriptive statistics

Descriptive statistics
69.
In examining the primary descriptive statistics of the firm panel dataset for Chinese
firms compared to those of other countries, several noteworthy elements emerge.
70.
Chinese firms within the panel dataset, predominantly state-owned enterprises6,
received a notably higher average of subsidies compared to their counterparts (Table 3
Firm-level panel statistics). The primary source of financing was below-market
borrowings, accounting for an annual average of 2.7% of total revenues (USD 465 million),significantly surpassing the 0.3% observed for other firms (USD 29 million). Conversely,
direct government grants played a comparatively minor role.
71.
The analysis of subsidy sources reveals a substantial increase in the amounts
received by Chinese firms, particularly post-2010, peaking in 2016 at 6.5% of total revenue.
72.
In addition, while various profit measures exhibit a robust co-movement between
Chinese firms and their counterparts in other countries, an intriguing observation is theinverse correlation between subsidies and profits for Chinese firms. Specifically, subsidies
increased during periods of profit decline, as depicted in Figure 4.

Table 2. Basic statistics. Aggregated panel (72 countries)
Variable
Exports / Total
production

Variable name

mean

sd

min

3rd quartile

max

share_steel_exports

0.364

0.241

0

0.565

0.998

market_share_china

0.046

0.065

0

0.06

0.402

ut_rate

0.661

0.203

0.023

0.819

0.996

Chinese imports /Total domestic
demandUtilisation rate
(production /capacity)

Note: The numbers in the table refer to the corresponding statistic computed at the year level.
Source: Facilitator’s calculations

6

Of the 19 Chinese companies in the panel in 2016, 13 had a government shareholding of more than
50% and only 4 had a government shareholding of less than 10%.

26  IMPACTS OF GLOBAL EXCESS CAPACITY

Table 3. Basic statistics. Firm-level panel (46 firms)
Variable
name

mean

sd

min

3rd quartile

max

EBITDA / Total
revenue

ebitda_ratio

0.181

0.239

-0.581

0.228

3.434

Export revenue / Total
revenueshare_exports

0.337

0.485

0

0.598

0.86

Cash grants ratio

grants_rev

0.001

0.003

0

0.000210.028

Below-market
borrowings ratio

BMB_rev

0.003

0.007

0

0.002

0.038

Revenue (USD
million)revenue

19,299.63

19,559.74

350.3822,954

124,936

Cash grants (USD
million)grants

11.06

31.48

0

4.80

213.89Below-market
borrowings (USD
million)

BMB_imp

29.07

74.07

0

25.09

800

EBITDA / Total
revenue

ebitda_ratio

0.103

0.054

-0.158

0.132

0.429

Export revenue / Total
revenueshare_exports

0.126

0.097

0.01

0.203

0.358

Variable

Other firms(26)

Chinese firms(20)Cash grants ratio

grants_rev

0.003

0.006

0

0.004

0.078

Below-market
borrowings ratio

BMB_rev

0.027

0.023

0

0.036

0.151

Revenue (USD
million)revenue

15,276.56

16,319.56

177.474

20,526.60

150,761.61

Cash grants (USD
million)grants

51.26

90.84

0

55.17

686.82Below-market
borrowings (USD
million)

BMB_imp464.47599.950611.35

3,135.09

Note: The numbers in the table refer to the corresponding statistic computed at the year level.
Source: Facilitator calculations

Figure 4. Higher subsidies for Chinese firms
Evolution of government grants and Below-market-borrowings as a share of revenue for Chinese versus other firms

IMPACTS OF GLOBAL EXCESS CAPACITY

Different profit indicators for Chinese versus other firms

Source: Facilitator calculations based on OECD MAGIC Database 27

28  IMPACTS OF GLOBAL EXCESS CAPACITY

Annex C. Data sources

Table 4. List of variables used
Variable

Unit

Variable name

Dependent or explanatory
variable

SourceDimension

DatasetImports from CHN (crude
steel equivalent)

tonnesimports_chn

Explanatory

UN Comtrade,
ISSB

Country

Aggregatedpanel

Apparent steel use (crude
steel equivalent)

tonnesasu

Explanatory

Worldsteel

Country

Aggregatedpanel

Global apparent steel use(crude steel equivalent)

tonnes

asu_world

Explanatory

Worldsteel

GlobalAggregatedpanel

Nominal crude steel
capacity

tonnescap

Explanatory

Worldsteel

Country

Aggregatedpanel

Total production of crude
steel

tonnesprod_crude

Explanatory

Worldsteel

Country

Aggregatedpanel

Total exports (crude steel
equivalent)

tonnesexports_crude

Explanatory

Worldsteel

Country

Rebar prices in China

USD / tonne

price_rebar_china

Explanatory

Platts S&P
GlobalCountry

Rebar prices (5 major
steelmaking economies
average)

USD / tonne

price_rebar_global

Explanatory

Platts S&P
GlobalCountry

Aggregatedpanel

Flat prices in China

USD / tonne

price_flat_china

Explanatory

Platts S&P
GlobalCountry

Aggregatedpanel

Flat prices (5 major
steelmaking economies
average)

USD / tonne

price_flat_global

Explanatory

Platts S&P
GlobalCountry

Aggregatedpanel

Bilateral exchange rates

NCU / USD

CCUSMA02

Explanatory

OECD Main
Economic
Indicators

Country

Aggregatedpanel

Utilisation rate:(prod_crude / cap)%

ut_rateDependent

UN Comtrade,
ISSB

Country

Aggregatedpanel

Chinese market share inother economies:(imports_chn / asu) (crude
steel equivalent)%market_share_china

Dependent

UN Comtrade,
ISSB

Country

Aggregatedpanel

Exports of steel as a shareof production:(exports_crude /prod_crude)%

share_steel_exports

Dependent

UN Comtrade,
ISSB

Country

Aggregatedpanel

Government grants, % ofrevenue%grants_rev

Explanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Government grants

USD millions

grantsExplanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Below-market borrowings

USD millions

BMB_imp

Explanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Below-market borrowings,% of revenue%

BMB_rev

Explanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Total assets

USD millions

asset

Explanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Cost of goods soldUSD millions

cogs

Explanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Total equity

USD millions

equityExplanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Net income after tax

USD millions

inc_atExplanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Research anddevelopment spending

USD millions

r_dExplanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Total number of
employees

number of people

staff

Explanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Government-ownershipcategory, from 1 (<10%govt ownership) to 4(>=50% govt o0-1

soe_catExplanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Aggregatedpanel
Aggregatedpanel

IMPACTS OF GLOBAL EXCESS CAPACITY 29% of government
ownership (D = homecountry govt; F = foreigngovt), as of 2%

soe_share

Explanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Net profit margin%

profit_margin

Explanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Net profit margin, using
profit before tax%

profit_margin_bt

Explanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Firm nominal crude steel
capacity

tonnescapacity_firm

Explanatory

OECD

Firm

Firm-level
panel

Domestic sales revenue

USD millions

dom_revenue

Explanatory

OECD (MAGIC
Database)

Firm

Firm-level
panel

Sales revenue

USD millions

revenue

DependentOECD (MAGIC
Database)

Firm

Firm-level
panel

%

share_steel_exports

DependentOECD (MAGIC
Database)

Firm

Firm-level
panel

USD millions

ebitda_ca

DependentOECD (MAGIC
Database)

Firm

Firm-level
panel

%

ebitda_ratio

DependentOECD (MAGIC
Database)

Firm

Firm-level
panel

NCU / USD

Explanatory

Datastream

Country

Time-series

USD per barrel

Explanatory

Datastream

Country

Time-series

indexExplanatory

Datastream

Country

Time-series

Index

Explanatory

Datastream

GlobalTime-series%

Dependent

Datastream

Country

Time-series

Exports revenue as ashare of total revenue:(revenue-dom_revenue) /revenue
EBITDA (re-calculated by
the OECD for consistency)
EBITDA / Revenue
Bilateral daily exchange
rates
Crude oil prices (Dubai,
Brent crude, and West
Texas Intermediate)
Aggregate stock market
price index
World aggregate stock
market price index
Daily stock return for the
steel industry

30  IMPACTS OF GLOBAL EXCESS CAPACITY

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