Monetary Policy and the Economy Q3/21
Call for applications: Klaus Liebscher Economic Research Scholarship
Please e-mail applications to email@example.com by the end of October 2021. Applicants will be notified of the jury’s decision by end-November 2021.
The Oesterreichische Nationalbank (OeNB) invites applications for the “Klaus Liebscher Economic Research Scholarship.” This scholarship program gives outstanding researchers the opportunity to contribute their expertise to the research activities of the OeNB’s Economic Analysis and Research Department. This contribution will take the form of remunerated consultancy services.
The scholarship program targets Austrian and international experts with a proven research record in economics and finance, and postdoctoral research experience. Applicants need to be in active employment and should be interested in broadening their research experience and expanding their personal research networks. Given the OeNB’s strategic research focus on Central, Eastern and Southeastern Europe, the analysis of economic developments in this region will be a key field of research in this context.
The OeNB offers a stimulating and professional research environment in close proximity to the policymaking process. The selected scholarship recipients will be expected to collaborate with the OeNB’s research staff on a prespecified topic and are invited to participate actively in the department’s internal seminars and other research activities. Their research output may be published in one of the department’s publication outlets or as an OeNB Working Paper. As a rule, the consultancy services under the scholarship will be provided over a period of two to three months. As far as possible, an adequate accommodation for the stay in Vienna will be provided. 1
Applicants must provide the following documents and information:
- a letter of motivation, including an indication of the time period envisaged for the consultancy
- a detailed consultancy proposal
- a description of current research topics and activities
- an academic curriculum vitae
- an up-to-date list of publications (or an extract therefrom)
- the names of two references that the OeNB may contact to obtain further information about the applicant
- evidence of basic income during the term of the scholarship (employment contract with the applicant’s home institution)
- written confirmation by the home institution that the provision of consultancy services by the applicant is not in violation of the applicant’s employment contract with the home institution
1 We assume that the coronavirus crisis will abate in the course of 2021. We are also exploring alternative formats to continue research cooperation under the scholarship program for as long as we cannot resume visits due to the pandemic situation.
in English and German
Nontechnical summaries in English
How effective were measures introduced in the COVID-19 crisis in supporting household incomes?
Susanne Maidorn, Lukas Reiss
We analyze how the COVID-19 crisis has affected the distribution of income in Austria using a microsimulation model developed by the Office of the Fiscal Advisory Council (FISKSIM). Starting point: In 2020, more than one-third of Austrian households were affected, at least temporarily, by unemployment, short-time work or lower self-employed income. The Austrian government introduced fiscal measures to support household incomes, which, overall, clearly cushioned the financial impact of the crisis on households. We find that, by comparison, lower-income households benefited more strongly from relief payments, mainly because of measures that were specifically targeted at low-income earners, such as payments from the family hardship fund, and additional support for the unemployed. Within the different income groups, households that suffered larger financial losses because of the pandemic, on average, benefited the most from COVID-19 relief in Austria, particularly from instruments specifically designed to compensate for such losses (e.g. the hardship fund and family hardship fund). Indirectly, this also applies to the use of short-time work, which helped avoid higher income losses caused by unemployment.
Corporate equity finance in Austria – impediments and possible improvements
Peter Breyer, Eleonora Endlich, Dieter Huber, Doris Oswald, Christoph Prenner, Lukas Reiss, Martin Schneider, Walter Waschiczek
This study gives an overview of the role equity – as opposed to debt – plays in the funding of Austrian companies. Essentially, we address the challenges companies face in raising equity in Austria and present ways forward, including international best practices. In greater detail, we first discuss the equity ratios of Austrian companies before and after the onset of the COVID-19 pandemic. Austrian businesses had been steadily increasing equity funding in the years before the pandemic, and their equity ratios even caught up with the international average. Naturally, the pandemic has since been weighing considerably on the equity levels of Austrian companies. The decrease would, however, be twice as high in the absence of the support measures taken to cushion the economic effects of the pandemic. Second, we show that ownership of Austrian corporate equity is broadly distributed across all economic sectors. The bulk of Austrian companies’ equity is sourced from the rest of the world and from the domestic private sector, i.e. households and private foundations, while the financial sector plays only a minor role in providing equity. Third, we discuss what makes equity financing difficult, drawing on the results of a structured OeNB survey conducted with businesses and other stakeholders and on talks with international institutional investors. Why would business owners hesitate to raise capital externally? For example, they may be reluctant to share control with external investors and may face information deficits and data gaps as well as differences in the tax treatment of debt and equity. On the supply side, equity is limited because investors lack information on the economic situation of capital-seeking companies and because investments in unlisted companies are less liquid. Fourth, we outline possible avenues for strengthening corporate equity in Austria. Cases in point, which were identified together with representatives of national and international institutions and market participants, include providing both tax incentives and intermediation support for equity finance and establishing public-private partnerships.
The impact of climate change on monetary policy
Andreas Breitenfellner, Wolfgang Pointner
Climate change poses risks to the stability of economic and financial systems. These risks affect the mission of central banks to maintain monetary and financial stability. Hence, the aim of this article is to clarify to what extent central banks may, can and should take climate change into account in monetary policy decision-making. Rising temperatures, extreme weather events and the political and technological responses to climate change may have significant effects on prices, output or financial markets. Monetary policymakers need to reflect these effects in their assessment of inflationary risks. These effects might also decrease the natural rate of interest, thus reducing central banks’ room for maneuver in monetary policy. At the same time, climate change increases uncertainty about future economic developments as global warming drives global temperatures to new record highs, making the resulting environmental, social and financial impacts difficult to estimate. Without prejudice to the objective of price stability, the Eurosystem’s monetary policy mandate also provides for the support of general economic policies in the European Union. These general economic policies include achieving a high level of environmental protection. However, this is not to say that central banks should steer climate policy; this responsibility lies with governments and parliaments. Through carbon taxes, emissions trading schemes, direct regulations or green industrial policies, governments and parliaments can support the transition to a carbon-neutral economy more effectively and efficiently than monetary policymakers would be able to. Especially fiscal policy can and should correct market failure in pricing emissions harmful to the climate by setting carbon prices. Cost transparency and a well-managed transition would also lower the risks to financial stability. Managing climate-related financial risks is a challenge for financial institutions and central banks, but financial markets can only function efficiently when these risks are correctly priced. Monetary policy has a range of tools at its disposal that may be used to fight climate change. The framework for credit operations, collateral policies, asset purchases or asset quality assessment and disclosure could be adjusted to reflect climate-related risks and to contribute to the decarbonization of the economy. Although monetary policy activities have until now been governed by the principle of market neutrality, it is becoming increasingly clear that it would not be meaningful to stick to strict market neutrality in view of climate-related forms of market failure. A good starting point for central banks would appear to adopt a risk-oriented approach. In this spirit, one of the goals of the ECB’s new monetary policy strategy is to incorporate climate factors in its monetary policy analyses. Moreover, the ECB will adjust its operational framework for monetary policy with regard to disclosure, risk assessment, asset purchases and the collateral framework. Climate-aware policies have also been embraced by central banks beyond the euro area, including Sveriges Riksbank (which leans toward a risk-based approach) and the Bank of England (which leans toward a proactive approach). Whatever approach central banks choose, outcomes might be similarly ambitious. In any case, financial market supervision and monetary policy will complement but never replace governments’ decarbonization efforts. Our contemplations square well with the ECB’s action plan, which is a result of its monetary policy strategy review and which was presented in July 2021. In this action plan, the Governing Council of the ECB commits to further incorporating climate change considerations into its monetary policy framework and operations. In order to achieve a climate-neutral economy by the mid-21st century, all public and private economic actors will have to contribute according to their capabilities.
Nontechnical summaries in German
Treffsicherheit der Maßnahmen zur Stützung der Haushaltseinkommen während der COVID-19-Krise in Österreich
Susanne Maidorn, Lukas Reiss
Die vorliegende Studie analysiert die Verteilungswirkungen der COVID-19-Krise auf Basis des Mikrosimulationsmodells des Büros des Fiskalrats (FISKSIM). Mehr als ein Drittel der österreichischen Haushalte war im Jahr 2020 zumindest temporär von Arbeitslosigkeit, Kurzarbeit oder von Verlusten an Selbstständigeneinkommen betroffen. Die fiskalischen Maßnahmen zur Stützung der Haushaltseinkommen federten die Effekte der Krise insgesamt deutlich ab. Zudem haben niedrige Einkommen relativ stärker von den geleisteten Auszahlungen profitiert. Dies lag vor allem an Maßnahmen, die explizit auf niedrige Einkommen ausgerichtet waren, wie der Familienhärteausgleich, oder gezielt bei Arbeitslosigkeit ausgezahlt wurden. Gleichzeitig profitierten innerhalb der verschiedenen Einkommensgruppen jene Haushalte stärker, die durch den COVID-19-Schock stärkere finanzielle Verluste erlitten hatten. Hierzu trugen vor allem jene Maßnahmen bei, die auf eine Kompensation dieser Verluste ausgerichtet waren, wie der Härtefallfonds und der Familienhärteausgleich. Indirekt trifft das auch für die Inanspruchnahme der Kurzarbeit zu, durch die höhere Einkommensverluste im Fall von Arbeitslosigkeit vermieden werden konnten.
Eigenkapitalausstattung österreichischer Unternehmen – Hindernisse und Handlungsoptionen
Peter Breyer, Eleonora Endlich, Dieter Huber, Doris Oswald, Christoph Prenner, Lukas Reiss, Martin Schneider, Walter Waschiczek
Diese Studie gibt einen Überblick über die Eigenkapitalausstattung österreichischer Unternehmen und zeigt aktuell bestehende Hemmnisse in der Eigenkapitalfinanzierung sowie Optionen zu deren Überwindung auf. Sie gliedert sich in vier Teile: Der erste Abschnitt betrachtet die Eigenkapitalausstattung der österreichischen Unternehmen vor und während der COVID-19-Pandemie. Dabei zeigt sich, dass sich die Eigenkapitalquote der Unternehmen in Österreich in den Jahren vor der Pandemie stetig verbessert hat und mittlerweile im internationalen Durchschnitt liegt. Allerdings dürfte die COVID-19-Pandemie auch unter Berücksichtigung aller derzeit bekannten Hilfsmaßnahmen die Eigenkapitalausstattung der österreichischen Unternehmen deutlich reduzieren, ohne Hilfsmaßnahmen würde sie allerdings doppelt so stark sinken. Im zweiten Teil wird gezeigt, dass die Eigenkapitalgeber der österreichischen Unternehmen breit über alle volkswirtschaftlichen Sektoren gestreut sind. Die Eigenkapitalaufbringung des österreichischen Unternehmenssektors erfolgt zu einem wesentlichen Teil aus dem Ausland und von inländischen Privaten, bestehend aus privaten Haushalten und Privatstiftungen, während der Anteil des Finanzsektors am Eigenkapitalaufkommen relativ gering ist. Im dritten Teil werden aktuelle Hemmnisse betreffend die Aufbringung von Eigenkapital diskutiert, basierend auf einer strukturierten Befragung relevanter Interessensverbände und Unternehmen sowie Gesprächen mit internationalen institutionellen Investoren. Hemmnisse betreffend die Nachfrage der Unternehmen nach Eigenkapital umfassen etwa die Ablehnung der Einflussnahme durch externe Investoren, Informations- und Datendefizite seitens der Unternehmen und die steuerliche Diskriminierung von Eigenkapital gegenüber Fremdkapital. Das Angebot von Eigenkapital wird durch Informationsdefizite bezüglich der wirtschaftlichen Lage kapitalsuchender Unternehmen, die geringe Liquidität einer Beteiligung an nicht börsennotierten Unternehmen sowie fehlendes Finanzwissen auf Investorenseite beeinträchtigt. Im vierten Teil werden Möglichkeiten zur Stärkung der Eigenkapitalbasis von Unternehmen in Österreich skizziert. In den Gesprächen mit Expertenorganisationen und Marktteilnehmern wurden als mögliche Maßnahmen zur Stärkung der Eigenkapitalausstattung vor allem steuerliche Fördermaßnahmen, Investitionen in Eigenkapital durch Intermediäre sowie staatliche Unterstützungsmaßnahmengenannt.
Die Auswirkungen des Klimawandels auf die Geldpolitik
Andreas Breitenfellner, Wolfgang Pointner
Der Klimawandel ist eine grundlegende Herausforderung für die Stabilität von Volkswirtschaften und Finanzmärkten. Notenbanken, deren Auftrag die Wahrung dieser Stabilität ist, müssen sich mit daher mit dem Klimawandel und seinen Folgen beschäftigen. In diesem Artikel wollen wir daher die Fragen beantworten: Inwieweit darf, kann und soll die Geldpolitik der Notenbanken den Klimawandel in ihre Entscheidungen einbeziehen? Steigende Temperaturen, extreme Wetterereignisse und die politischen und technologischen Reaktionen auf den Klimawandel können erhebliche Auswirkungen auf Preise, Produktion oder Finanzmärkte haben. Die Geldpolitik muss diese Auswirkungen bei ihrer Beurteilung der Risiken für die Preisstabilität berücksichtigen. Diese Auswirkungen können auch den Gleichgewichtszinssatz verringern, was den geldpolitischen Spielraum der Zentralbanken einschränken würde. Der Klimawandel erhöht auch die Unsicherheit über die künftige Entwicklung der Wirtschaft, da die globale Erwärmung Temperaturen erreichen wird, die noch nie gemessen wurden, und deren ökologische, soziale und finanzielle Effekte daher nicht gut abschätzbar sind. Soweit dies ohne Beeinträchtigung des Preisstabilitätsziels möglich ist, sieht das Mandat des Eurosystems vor, dass die Geldpolitik des Eurosystems auch die allgemeine Wirtschaftspolitik in der Union unterstützt. Zu diesen Zielen der allgemeinen Wirtschaftspolitik zählt auch ein hohes Maß an Umweltschutz und Verbesserung der Umweltqualität. Zentralbanken machen aber keine Klimapolitik, das ist die Aufgabe von Regierungen und Parlamenten. Diese können durch CO2-Steuern, Emissionshandelssysteme, direkte Regulierungen oder grüne Industriepolitik den Übergang zu einer CO2-neutralen Wirtschaft effektiver und effizienter unterstützen als die Geldpolitik. Insbesondere die Fiskalpolitik kann und soll durch CO2-Preise das Marktversagen bei der Bepreisung von klimaschädlichen Emissionen korrigieren. Kostenwahrheit und ein gut gemanagter Übergang würden auch die Risiken für die Finanzmarktstabilität verringern. Das Management dieser klimabedingten Finanzrisiken ist eine Herausforderung für Finanzinstitute und Notenbanken, denn nur wenn diese Risiken auch korrekt bepreist werden, können Finanzmärkte effizient funktionieren. Der Geldpolitik stehen mehrere Instrumente zur Verfügung, die zur Bekämpfung des Klimawandels eingesetzt werden könnten. Der Rahmen für Kreditgeschäfte, Sicherheitenpolitik, Wertpapierkäufe oder die Bewertung und Offenlegung der Qualität von Vermögenswerten könnte angepasst werden, um klimabedingten Risiken Rechnung zu tragen und damit zur Dekarbonisierung der Wirtschaft beizutragen. Während bisher die geldpolitischen Aktivitäten dem Grundsatz der Marktneutralität unterworfen waren, setzt sich die Erkenntnis durch, dass dies angesichts der klimabedingten Marktversagensformen wenig zielführend wäre. Ein risikoorientierter Ansatz der Zentralbanken scheint ein guter Ausgangspunkt zu sein. In diesem Sinne beinhaltet die neue geldpolitische Strategieerklärung der EZB das Ziel, dass Klimafaktoren in künftige geldpolitische Analysen einfließen werden. Auch in Bezug auf Offenlegung, Risikobewertung, Ankauf von Vermögenswerten und dem Sicherheitenrahmen werden Anpassungen angestrebt. Auch außerhalb des Euroraums gibt es Beispiele für unterschiedliche Ansätze klimabewusster Geldpolitik, z. B. die schwedische Riksbank oder die Bank of England. Welchen Ansatz die Zentralbanken auch immer wählen, Finanzmarktaufsicht und Geldpolitik werden die Dekarbonisierungsbemühungen der Regierungen ergänzen, aber können diese niemals ersetzen. Unsere Überlegungen passen gut zu dem kürzlich vorgelegten Aktionsplan der EZB, der ein Ergebnis der jüngsten Überprüfung der geldpolitischen Strategie ist. Der EZB-Rat zeigt sich damit entschlossen, Klimaschutzaspekte stärker in seinen geldpolitischen Handlungsrahmen einfließen zu lassen, seine Analysekapazitäten im Hinblick auf den Klimawandel auszubauen und bei geldpolitischen Geschäften Klimaschutzaspekte zu berücksichtigen. Um bis Mitte des 21. Jahrhunderts eine klimaneutrale Wirtschaft zu erreichen, werden alle öffentlichen und privaten Wirtschaftsakteure entsprechend ihren Fähigkeiten dazu beitragen müssen.
Economic situation in Austria
Austrian economy growing strongly in mid-2021
Friedrich Fritzer, Martin Schneider, Richard Sellner, Klaus Vondra 2
The Austrian economy continued to recover in mid-2021. In the second quarter, real GDP grew by 3.6% compared with the previous quarter as the easing of containment measures led to significant growth. At the same time, the construction and industry sectors experienced a slowdown. According to leading short-term indicators, strong growth is expected to continue in the third quarter. One of the reasons is that summer tourism might reach its pre-crisis levels much faster than anticipated due to the sharp increase in overnight stays of Austrian, German and Dutch guests. On the other hand, industry climate indicators as well as current export trends show first signs of cooling. Owing to supply bottlenecks and shortages in materials, manufacturing businesses are increasingly struggling to handle large amounts of orders. Compared to Austria, Germany is being hit significantly harder by these bottlenecks because of its position in the supply chain and the fact that the automotive industry plays a more important role in Germany’s economy. Current economic projections point to growth between 3½% and 4% in 2021 and a growth rate of 4% to 5% in 2022. The fourth wave of the COVID-19 pandemic, however, poses a downside risk to the outlook. Following a marked increase of HICP inflation in Austria in the first five months of the year, the inflation rate remained at 2.8% in June and July 2021 and then climbed to 3.2% in August. The rise in inflation measured in 2021 to date was mainly driven by rising energy prices, which accounted for three-fourths of the increase. Close to one-fourth of the rise is attributable to nonenergy industrial goods and food, whereas the latest inflation rate for services was somewhat below the level measured in early 2021. By August, core inflation climbed to 2.5%, 0.5 percentage points beyond the January 2021 value.
1 Revision of national accounts: domestic economy recovered at a somewhat slower pace in the second quarter
|Change on previous period in %||Contribution to GDP growth in percentage points|
|Source: Statistics Austria.|
The easing of pandemic restrictions in early 2021 led to a speedy recovery of the Austrian economy. In the second quarter of 2021, real GDP grew by 3.6% (quarter on quarter; real, seasonally and working-day adjusted). Growth was mostly driven by exports, which rose by 7.1%, followed by private (+3.2%) and government consumption (+2.9%). Gross fixed capital formation lost its momentum and only grew by 1.8%, after its steep increase (+4.7%) in the first quarter. Imports fell by 2.1%, strengthening the growth of GDP.
On the output side, there are major differences between individual sectors. In the service sector, which was severely hit by the lockdown, the loosening of restrictions triggered a strong rebound in the second quarter. In the wholesale and retail trade, transportation and storage and accommodation and food service activities (NACE G–I), value added increased by 20.4% in real terms (quarter on quarter, seasonally and working-day adjusted). Although a breakdown by sectors is not available, employment data 3 suggest that growth was almost exclusively powered by accommodation and food service activities (NACE I), which had been hit particularly hard by the lockdown. Construction (NACE F) only increased marginally (+0.3%) in the second quarter following rapid growth in the first quarter (+5.1%), while industry (B–E) even recorded a slight decline in value added (–0.1%). This points to a significant shift in growth drivers in the second quarter.
|Q2 21||Q1 21||Q4 20||Q3 20||Q2 20||Q1 20||2020||2019||2018||2017|
|Change on previous period in %|
|Gross value added||+4.0||0.0||–2.8||+10.6||–10.5||–2.2||–6.3||+1.4||+2.7||+2.6|
|Agriculture (NACE A)||+5.1||+2.9||–3.8||–2.3||–0.8||+0.7||–3.0||–1.0||+3.6||+5.6|
|Industry (NACE B–E)||–0.1||+4.3||+1.8||+11.9||–12.1||–1.1||–6.4||+1.2||+4.6||+4.1|
|Manufacturing (NACE B–E)||–0.4||+4.5||+1.6||+12.7||–12.9||–1.8||–7.2||+0.8||+5.2||+3.9|
|Construction (NACE F)||+0.3||+5.1||–0.1||+5.4||–6.9||–2.5||–4.1||+2.8||+1.8||+3.3|
|Services, total (NACE G–U)||+5.8||–2.0||–4.5||+11.1||–10.7||–2.6||–6.6||+1.4||+2.2||+2.0|
|Services, private (NACE G–N)||+7.9||–2.9||–5.8||+14.0||–13.3||–2.8||–8.0||+1.7||+2.8||+2.3|
hospitality (NACE G–I)
Information and communication
Financial and insurance services
|Real estate activities (NACE L)||+1.0||+0.1||–0.2||–0.5||–0.7||+0.9||+1.0||+1.2||+1.1||+1.4|
Scientific and technical activities
|Public services (NACE O–U)||+0.9||+0.0||–1.3||+4.4||–3.9||–2.0||–2.9||+0.7||+0.7||+1.4|
|Other services (NACE R–U)||+2.6||–6.3||–12.9||+39.3||–26.8||–10.9||–18.3||+0.6||–0.4||+2.1|
2 GDP slightly surpassed its pre-crisis levels for the first time in late July
The Austrian economy continued to grow at a moderate pace at the beginning of the third quarter, as suggested by current results of the weekly OeNB GDP indicator. 4 In week 29 (July 19–25, 2021), Austrian output marginally surpassed its pre-crisis levels for the first time since the pandemic took hold. At +0.6%, it exceeded the rate recorded in the corresponding week of 2019. Still, average GDP in weeks 25–29 (June 21 to July 25, 2021) remained 0.6% below its pre-crisis level.
Compared with the same weeks of the previous year, growth rates currently show a strong positive trend (see chart 1, green line) as a result of a pronounced base effect. In week 29, output surpassed the rate recorded in the corresponding week the year before by 4.5%.
3 August 2021 results of the OeNB’s export indicator: export growth remained high in the summer
In May 2021, Austrian goods exports exceeded their level of May 2020 by 31.5% in nominal terms, as Statistics Austria’s latest data show. Hence, exports increased a little faster than expected based on the recent OeNB’s export indicator (+27.2%).
According to current results of the OeNB’s export indicator 5 based on truck mileage data, export growth remained high in June and July at –0.9% and +0.0%, respectively (monthly change, seasonally and working-day adjusted). This translates into annual growth rates of 25.8% or 12.6% (not adjusted). But the meaningfulness of these growth rates is limited due to the deep plunges in the previous year. When compared with the corresponding months of 2019, nominal exports of goods in June and July 2021 grew by 9.4% and 7.9%, respectively (working-day adjusted).
4 Leading indicators at high levels in spite of declining foreign trade
The beginning of September saw almost unchanged positive sentiment in the Austrian economy. In July, the short-term indicator of UniCredit Bank Austria reached an all-time high at 6.0 points and stayed there in August as well. Bank Austria’s purchasing managers’ index as well as the economic sentiment indicator of the European Commission maintained their high levels, although both declined slightly in July and August.
On the other hand, leading indicators for foreign trade show first signs of a possible future downturn. The European Commission’s monthly estimate of order book levels continued to increase in July. Quarterly available estimates of exports dropped in the third quarter, having reached a historical high at 20.7 points in the second quarter, but at 8.7 points, they still remain marginally above the long-term average. By contrast, the export order index declined steeply in July (to 57.4 points after 66.4 points in June), according to Bank Austria, reflecting existing capacity issues in international trade, such as logistic problems, lack of containers and closing of container ports and cargo airports in China, as a result of strict local containment measures due to the ongoing pandemic situation.
The impact of materials shortages in Austria and Germany
Transport route blockages, production losses, misallocated containers and ports shut down due to the COVID-19 pandemic or overshooting demand for industrial metals, construction materials and semiconductors: Reports about supply chain disruptions, shortages of materials and rising commodity and transport prices have been figuring prominently in the business news in recent weeks and months. Supply-side restrictions have been an issue in Austria, too, as illustrated by anecdotal evidence from individual firms (e.g. staff working short time at the MAN Truck & Bus plant in Steyr, Upper Austria 6 ) and numerous current analytical reports (e.g. by Raiffeisen Research , Erste Bank or Bank Austria ). 7 While such limitations have been debated a lot, no estimates have been available so far regarding the impact they may have had on manufacturing output in Austria. For Germany, a number of analyses were published in recent weeks. In the following, we estimate the repercussions of supply bottlenecks and disruptions on manufacturing output in Austria, using two different frameworks, and compare the results with the outcomes for Germany.
Our first analysis (building on the analysis by Beckmann and Jannsen (2021) for Germany 8 ) is based on the assumption of a long-run equilibrium between industrial production and new orders. Chart B1 compares actual output figures (blue line), output figures projected assuming an equilibrium relationship with new order levels (red line) and the percentage deviation between the two curves (green line) for Austria and Germany. When estimating the impact of supply disruptions, we factored in the production gaps observed in 2020, which were quite large above all for Germany (–10%). This is why we interpret only the gap for the fourth quarter of 2020 as being caused by the supply disruptions. According to our estimates for the second quarter of 2021, Germany’s industrial production is likely to have been 5.1% below the level that would be aligned with an equilibrium relationship with orders. This output gap translates into a 1% drop in GDP. The corresponding figures for Austria are an estimated gap of 2.2% in the second quarter of 2020 and a GDP effect of –0.4%.
Our second analysis provides for a direct comparison of gross value added in the manufacturing industry as well as manufacturing output bottlenecks resulting from materials shortages and capacity constraints (as per the WIFO indicator). We estimate the underlying relationship with a sign-restricted VAR model, building on work by Vogt (2021) for Germany. This method allows us to simulate the direct repercussions from the recent strong increase in commodity shortages on gross value added (and hence on GDP).
Table B1 quantifies the manufacturing output bottlenecks resulting from worsening materials shortages and capacity constraints for Austria for the second and third quarters of 2021. We find that these bottlenecks accounted for a GDP decline of 0.3% in the second quarter and 0.5% in the third quarter of 2021. For Germany, Vogt (2021) 9 arrived at corresponding effects: 1.5% in the second quarter and of 0.5% in the third quarter. Considering that Vogt (2021) simulates the bottleneck shock only for the second quarter and that the share of German firms suffering from supply disruptions continued to mount in the third quarter, these effects are likely to constitute a lower bound.
|Increase in supply shortages in|
Deviation of real GDP
from scenario without
|Q2 21||Q3 21||Total|
|Deviation in Q2 21||–0.3||x||–0.3|
|Deviation in Q3 21||–0.4||–0.1||–0.5|
|Source: OeNB calculations.|
These two analyses imply that the impact of materials shortages on manufacturing output has been markedly stronger in Germany than in Austria. One possible explanation is that the automotive industry accounts for a higher share of the manufacturing sector in Germany than in Austria, and that car production has been hit particularly by the global scarcity of semiconductors, which is likely to persist until 2022. What may also matter is the relative position of manufacturing firms in the supply chain networks of the two countries. Germany’s automotive industry is closer to the downstream side of production, i.e. closer to the finished products, whereas Austrian manufacturers tend to be clustered around upstream supply chain activities. Yet, supply interruptions and disruptions in early stages of the supply chain tend to cause strong effects downstream in the chain − a phenomenon known as whiplash or bullwhip effect in supply chain management.
5 August 2021: surge in overnight stays by foreign visitors
In July 2021, the number of overnight stays stagnated compared with 2020 among both domestic visitors (–1.9%) and foreign visitors (–0.3%) (–0.9% in total). Compared with the record summer of 2019, tourist overnight stays even dropped by 17.7%. This setback was driven by the sharp decline of overnight stays by foreign tourists (–28.6%), whereas the number of overnight stays by domestic tourists (+13.7%) exceeded the numbers recorded for July 2019. The numbers of overnight stays were closely aligned with the OeNB’s projections made in late July.
For August 2021, data on tourist spending based on card payments point to a marked rise of overnight stays by foreign visitors; the OeNB expects a 27% increase compared with August 2020. This would imply that the number of overnight stays in August remained only 2% below the record levels measured in 2019. This increase is attributable above all to the much higher number of German tourists (about +15%) and Dutch tourists (about +50%) compared with the previous year, which compensated for the losses caused by overseas visitors continuing to stay away.
In contrast, the number of overnight stays by domestic tourists declined slightly compared with 2020 (–6%, July: –2%). At the same time, the figures were 16% higher in August 2021 than in 2019, when Austria’s tourism industry reported the highest number of overnight stays on record. In sum, the OeNB expects the number of overnight stays to have been 15% higher in August 2021 than in August 2020 and 2% higher than in the record summer of 2019. The combined result for July and August is a year-on-year increase by 8% compared with the summer of 2020, which corresponds to a decline of 7% against 2019.
6 Economic projections see 4% to 5% output growth in 2022
Following the pandemic-related setback in 2020, the Austrian economy has been recovering briskly in 2021. Currently, real GDP growth in Austria is expected to reach between 3½% and 4% in 2021, and even slightly higher rates in 2022 (between 4% and 5%). Compared with the OeNB’s June 2021 economic outlook, current economic indicators like the OeNB’s weekly GDP indicator signal an upward risk to growth for the third quarter of 2021, whereas downside risks to growth emanate from the fourth pandemic wave in the fourth quarter of 2021.
7 Inflation significantly on the rise since early 2021
Following a marked increase of HICP inflation in Austria in the first five months of the year, the inflation rate remained at 2.8% in June and July 2021 and then climbed to 3.2% in August. The rise in inflation measured in 2021 to date was mainly driven by rising energy prices, which accounted for two-thirds of the increase. Close to one-third was attributable to nonenergy industrial goods and food, whereas the latest inflation rate for services did not change compared to the level measured in early 2021. Core inflation, which excludes services and nonenergy industrial goods, climbed to 2.5%, 0.5 percentage points beyond the January 2021 measure.
Following the lifting of pandemic-related containment measures, Statistics Austria was able to resume the collection of prices for all goods contained in the Austrian HICP basket in July 2021. In early 2021, Statistics Austria had still carried forward 20% of the relevant prices from the previous months, as the corresponding market prices were not available due to shutdowns.
Energy price inflation had climbed to 13.5% by August 2021, well above the rate observed for January 2021 (–5.3%). This increase reflects the surge in crude oil prices in recent months as well as the base effect of the decline in crude oil prices in the corresponding period of 2020. Within the energy price component, transport fuels and heating oil registered a significant uptick in prices, whereas the prices for other energy resources (gas, solid fuels, electricity, thermal energy) increased comparatively moderately. The annual rate of services inflation stood at 2.3% in August 2021 (January 2021: 2.3%). Since January 2021, the annual inflation rate has been accelerating above all for hospitality services, air tickets, sports and recreation services as well as cultural services. At the same time, rental price inflation rate went down markedly (August 2021: 0.4%; January 2021: 5.5%).
The annual rate of inflation for nonenergy industrial goods climbed to 3.0% in August 2021 (January 2021: 1.5%). This increase was driven above all by prices for clothing and footwear, furniture and furnishings as well as other durable consumer goods, such as vehicles, glassware and electrical household appliances. More recently, inflation pressures also arose from shifts in the pattern of clothing and footwear clearance sales. In 2020, shops had started to sell off excess inventory in May. Since seasonal clearance sales have been much weaker in 2021 than in 2020, base effects caused inflation to rise in this product segment. With regard to durable consumer goods, the uptick in inflation may reflect the pass-through of high commodity prices to end-user prices.
Food price inflation (including alcohol and tobacco) dropped considerably in early 2021 (January 2021: −0.5%) and amounted to 1.1% in August 2021. In recent months, above all the prices of meat and beverages have been going up markedly, possibly reflecting the reopening of hotels and restaurants in May 2021.
2 Oesterreichische Nationalbank, Economic Analysis Division, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com.
3 In “accommodation and food service activities”, employment rose by 31.9% quarter on quarter (seasonally and working-day adjusted). In wholesale and retail trade as well as in the transportation and storage services sector, employment grew by 1.1% and 2.8%, respectively.
4 See also weekly OeNB GDP indicator at https://www.oenb.at/Publikationen/corona/bip-indikator-der-oenb.html.
5 See also the OeNB’s export indicator at https://www.oenb.at/Geldpolitik/Konjunktur/oenb-exportindikator.html.
7 https://www.raiffeisenresearch.com/servlet/NoAuthLibraryServlet?action=viewDocument&encrypt=49953909-11ca-4f7b-ac18-4b24dbe0b0af&mime=HTMLfirstname.lastname@example.org , https://produkte.erstegroup.com/Retail/de/ResearchCenter/Overview/Research_Detail/index.phtml?ID_ENTRY=230563 , https://www.bankaustria.at/files/EMI%200821.pdf .
8 Beckmann, J. and N. Jannsen. 2021. Bedeutung von Lieferengpässen für die laufende Produktion in Deutschland. IfW-Box 2021.09. In: Ademmer et al. 2021. Kieler Konjunkturberichte. Deutsche Wirtschaft im Sommer 2021, no. 80 (2021/Q2).
9 Vogt, G. 2021. Materialknappheiten bremsen Aufschwung. BVR Research Volkswirtschaft Kompakt. July 6. Berlin.
How effective were measures introduced in the COVID-19 crisis in supporting household incomes?
Refereed by: Hans Pitlik, WIFO
We analyze the distributional effects of both, the COVID-19 crisis and the measures introduced to support household incomes, using the microsimulation model developed by the Office of the Fiscal Advisory Council (FISKSIM). In 2020, more than one-third of Austrian households were affected, at least temporarily, by unemployment, short-time work or losses in self-employed income. The fiscal measures to support household incomes clearly cushioned the financial impact of the crisis on households. They proved particularly effective in two ways: First, lower-income households benefited more (vertical effectiveness); second, within individual income brackets, those households that had experienced higher losses due to the COVID-19 shock benefited more strongly from support measures (horizontal effectiveness). This was achieved mostly by the establishment of the hardship fund and one-off payments to unemployed workers.
JEL classification: H53, D30
Keywords: fiscal stabilization measures, income distribution.
The macroeconomic shock triggered by the lockdown measures to contain the COVID-19 pandemic caused a slump in GDP in 2020 (chart 1, dark red line).
As a result, aggregate self-employed income decreased, more than 1,000,000 people were temporarily put on short-time work, and the number of unemployed averaged more than 400,000 over the year, which implied significant losses in employment income. In the aggregate, the compensatory fiscal measures and the automatic stabilizers offset the losses in household income in 2020.
If we adjust for capital income, which is both volatile and very unevenly distributed, the growth of aggregate disposable household income amounted to approximately 3½% in 2020, which roughly corresponds the long-term average (chart 1, blue line). That said, the degree to which households were financially affected varies strongly. Moreover, the packages to support household incomes included not only targeted measures like payments from the hardship fund or one-off payments for people on unemployment benefits. For instance, a cut in income tax and an additional one-off family allowance payment (“child bonus”) also benefited households that did not experience income losses.
In this study, we analyze the effectiveness of the implemented measures with respect to the income distribution by looking at both their vertical and their horizontal effectiveness. We consider measures to be vertically effective if their relative effect in terms of the absolute amount of disposable household income was larger within each quintile than in the wealthier quintiles. Likewise, we consider measures to be horizontally effective if their relative effect within a quintile was larger among households that had experienced higher income losses caused by the COVID-19 shock than among households without income losses. 12
The extent to which interventions to contain the pandemic restricted economic activity varied sharply across sectors. Therefore, we model the COVID-19 shock to the labor market on a sectoral basis, so that household incomes from economic activity in severly hit sectors suffered higher losses. We follow an approach broadly similar to that used by Baumgartner et al. (2020), who analyze the cyclical, fiscal and distributional effects of the measures adopted during the COVID-19 crisis, arriving at consistent results as regards the associated changes in disposable household incomes. However, while Baumgartner et al. (2020) look primarily at the distribution of support among households broken down by the latter’s income levels, we also analyze the distribution among households in relation to actual income losses they experienced. Christl et al. (2021), who also examined the impact of the COVID-19 crisis and countermeasures, find that Austria was mostly successful in avoiding an increase in the risk of poverty 13 ; without government measures, this risk would have risen notably. In our study, we take into account a wider range of measures, including, in particular, measures implemented to compensate for losses in self-employed income.
In the next section, we describe the fiscal measures we included in our analysis. In section 2, we discuss the methods used in the microsimulation model FISKSIM to adapt household data to the COVID-19 shock and to implement the associated government measures. After that, we analyze the distributional effects of the shock and the measures in the aggregate. The extent to which individual measures contributed to the effectiveness of the entire package of measures is shown in section 4, and section 5 concludes.
1 Overview of the analyzed fiscal measures
A significant part of the measures the Austrian government took in 2020 to cushion the impact of the COVID-19 crisis was intended to support households’ disposable incomes and included a range of benefits for workers, unemployed people and families as well as a cut in the lowest rate of personal income tax, which was implemented earlier than originally planned. We included the following measures in the FISKSIM microsimulation model:
- COVID-19 short-time work: Employees working between 10% and 90% of normal hours received minimum pay based on replacement ratios of between 80% and 90% of their ordinary pay. 14
- Hardship fund (administered by the Austrian Economic Chambers): One-person businesses, freelancers and micro businesses that had experienced a decline in sales by at least 50% compared with the same period in 2019 (in up to ten one-month assessment periods in 2020) were eligible to apply for grants of up to EUR 2,600. The 2020 assessment periods were between March 16, 2020, and January 15, 2021 (see Federal Ministry of Finance, 2020a).
- AMA fund for farmers: The AMA fund for farmers was set up in a similar way to the hardship fund, with grants amounting to 80% of the difference between the income from agriculture and forestry in 2020 compared to the same period in 2019 (Federal Ministry of Finance, 2020b).
- Bridge fund and COVID-19 fund for artists: These funds respectively provided for grants of up to EUR 14,000 for self-employed artists covered by the social insurance system and up to EUR 3,500 for artists that are entitled to unemployment benefits or earn very low incomes (Federal Ministry of Arts, Culture, Civil Service and Sport, 2020a and 2020b).
- One-off payments for people on unemployment benefits: The first payment amounted to EUR 450 per person, the second to up to EUR 450, depending on the number of days a person had already been on unemployment benefits (Parliament, 2020).
- Increase of unemployment assistance for the long-term unemployed who have become ineligible for unemployment benefits to the level of regular unemployment benefits.
- Child bonus: one-time payment of EUR 360 per child.
- Increase in the supplement income limit for family allowance and extended entitlement to family allowance and student grants (because of the “neutral” semester).
- Family hardship fund and family crisis fund: payments of up to EUR 3,600 for families affected by short-time work or unemployment after February 2020 or income losses as defined under the hardship fund (Federal Ministry of Labour, Family and Youth, 2020; Arbeiterkammer, 2021).
- Personal income tax cut: reduction of the lowest income tax rate from 25% to 20%.
We only take into account measures that had a direct impact on households’ disposable incomes. 15 We do not include the distributional effects of subsidies for companies beyond short-time work (i.e., in particular, fixed cost grants, compensation for lost sales), nor investment incentives for companies (through grants or tax relief) because these measures are not transfer payments to households, and therefore their effect on individual household incomes cannot be determined.
Short-time work is a special case in this context, given that it is difficult to tell to what extent government funds have benefited employers on the one hand and employees on the other. 16 We compared the new replacement ratios (see above) with those under the short-time work scheme that had been in force before March 2020 to calculate our main results. Also, in section 4 we use an additional scenario to describe the job-saving effect of short-time work.
In chart 2, we show the FISKSIM-simulated costs of the income tax cut as well as the unemployment and family benefit payments and grants from the hardship fund (blue columns) and compare these amounts with actual budgetary costs (red lines). 17 We see that the simulated values match the actual costs (to the extent that related data are available) very well.
2 Methodology 18
Survey-based microdata do not yet include the pandemic shock on the job market and the resulting losses in earned income. The calculations carried out with the microsimulation model developed by the Office of the Fiscal Advisory Council (FISKSIM) currently are based on AT-SILC 2017–2019 data. Ordinarily, the gap between the most recent year of available data and the current year or a projected year can be closed by adjusting the weights applied to persons and households in the microdata to target values of official statistics or from forecast data. This is true if there are only marginal changes in employment, unemployment and earned incomes, which we tend to see in non-crisis times (Bachleitner and Maidorn, 2019, p. 6ff.). In 2020, however, both unemployment and short-time work as well as losses in self-employed incomes increased to such an extent that adjusting weights would not suffice to integrate this increase in the data (Figari et al., 2014, p. 53), all the more so, as economic sectors were affected to varying degrees.
For our analysis, we therefore adjusted the EU-SILC 2017–2019 data to three scenarios:
1. a counterfactual scenario for 2020, which reflects developments according to the outlook prepared by the Austrian Institute of Economic Research (WIFO) in December 2019;
2. a shock scenario for 2020, which simulates the shocks to employed workers triggered by unemployment and short-time work and the income shocks to self-employed persons – for each section of the Austrian Statistical Classification of Economic Activities (ÖNACE); and
3. the factual scenario for 2020, which includes both the COVID-19-related shock and the fiscal measures to cushion its impact that had a direct effect on household incomes.
If we compare the counterfactual scenario with the shock scenario, we see the effect of the COVID-19 crisis including automatic stabilizers playing out, but we do not see the impact of the discretionary fiscal measures introduced to support household incomes. To identify this impact, we look at the difference between the factual scenario and the shock scenario. 19 Our analysis excludes subsidies for companies (in particular, fixed cost grants and compensation for lost sales), except for subsidies for short-time work schemes; these subsidies are implicitly included in the shock scenario, where they cushion the drop in self-employed income in 2020.
We implement the counterfactual (i.e. “no pandemic”) scenario for 2020 on the basis of WIFO’s economic outlook of December 2019 (Glocker, 2020), which projected real economic growth of 1.2% for 2020, by adjusting the weights (Bachleitner and Maidorn, 2019). This implied an increase in the number of actively employed and self-employed by 1.1% and 0.4%, respectively. The number of unemployed persons was projected to rise by 1.7%. We also extrapolate earned incomes on the basis of the WIFO outlook. 20 Details on the implementation of this simulation can be found in annex 1 and 2.
The drop in earned income in the wake of the crisis is attributable to three factors: a sharp increase in the number of people on short-time work, a big rise in unemployment and a steep fall in self-employed income (to varying degrees across sectors). There was also a marked decline in capital income in 2020 21 , but we do not analyze its distributional effect for the following two reasons: First, household surveys tend to very much understate capital income, which is also very unevenly distributed; both these factors make an analysis much more difficult. Second, stock markets recovered quickly after their nosedive at the beginning of the pandemic. This means that an isolated assessment of the decline in capital income in 2020 would overestimate the pandemic-related financial losses for higher-income households.
3 Aggregate effect of COVID-19-related fiscal measures
Comparing the factual scenario with the shock scenario enables us to evaluate the distributional effects and the effectiveness of the pandemic-related fiscal measures. We show the results of the simulation in relation to hypothetical household incomes without the COVID-19 shock and broken down by quintiles of household incomes weighted by household size (household equivalence income). 22
Our simulation shows that more than one-third of households in Austria experienced income losses due to the COVID-19 shock if the effect of the support measures is not factored in 23 (left-hand panel of chart 3). The share of households affected by pandemic-related income losses is similar in Albacete et al. (2021; p. 121), who also analyze differences in the extent to which households were financially affected by the COVID-19 shock, which depends, inter alia, on people’s work status.
Overall, the COVID-19 shock would have reduced the average household income in the bottom quintile by around 2.1% (blue column in the right-hand panel of chart 3) if no fiscal measures had been taken. The relative drop in household income decreases slightly with rising incomes – from 1.9% in the second quintile to 1.6% in the fifth quintile. The calculated reduction in household income takes into account automatic stabilizers (unemployment benefits, short-time work pay under pre-pandemic schemes, lower tax liabilities for lower incomes).
Within quintiles, we see large differences: While many self-employed households and households affected by crisis-related unemployment experienced a sharp drop in incomes, there is a large number of households whose incomes were not directly affected by the pandemic (employees not affected by short-time work or COVID-19-related unemployment, pensioners without earned income). In the second to fifth quintiles, 37% to 43% of households were affected by income losses (left-hand panel of chart 3). In the first quintile, by contrast, due to a lower labor participation rate, only a quarter of households experienced income losses; the losses in this category were notably higher, though (chart 4).
Chart 4 shows the distributional effects of the COVID-19 shock and the related measures on households broken down by severity of shock impact. We split the households of each quintile into different groups, i.e. unaffected households that did not experience income losses due to the COVID-19 shock and households affected by pandemic-related income losses to different degrees; the latter are split along the median of relative income losses into two – equally large – groups: severely affected and less affected households (see also left-hand and middle panel of chart 4). The earned income of severely affected households in the bottom quintile dropped, on average, by around 13%, compared with 7% to 8½% in the other quintiles. In the group of less affected households, the relative losses did not vary much across the income distribution, ranging from 1.9% in the bottom quintile to 1.3% in the top quintile. At the same time, the incomes of unaffected households increased between 0.4% in the bottom quintile and 1.0% in the top quintile.
Thanks to the comprehensive set of fiscal measures (dark red columns in the right-hand panel of chart 3), the combined effect (“net effect”) of the shock and fiscal measures on household incomes is even positive in the lower quintiles; in the two highest quintiles, incomes decreased somewhat (green line in the right-hand panel of chart 3). Severely affected households in the lower two quintiles were compensated, on average, for about two-thirds of their losses, while in the middle and upper quintiles, average compensation amounted to one-half and one-third, respectively. Hence, the average net effect on severely affected households’ incomes was notably more uniform than the effect of the COVID-19 shock, ranging between –2.9% in the second quintile and –5.3% in the top quintile.
The impact of the fiscal measures on disposable income across household groups largely corresponds to that identified by Baumgartner et al. (2020). However, the set of measures covered by our analysis also includes the second one-off payment for people on unemployment benefits in December, the family hardship fund and the family crisis fund. As a result, the share of funds paid out to lower-income households is higher in our analysis, amounting to 26.5% in the bottom tercile, compared with 23.0% in Baumgartner et al. (2020).
4 How did individual instruments contribute to measures’ overall effectiveness?
Overall, the discretionary fiscal measures were highly effective and well-targeted. Lower-income households benefited relatively more; households more severely affected by the COVID-19 shock received significantly more transfers in relation to their incomes compared to less affected households; and the latter received more than households whose incomes did not decline at all due to the COVID-19 shock (chart 4).
In the following, we describe the effects of individual instruments, looking at two different forms of effectiveness, i.e. instruments’ impact
- in relation to household income (chart 5): Here we analyze vertical effectiveness, which requires that the relative effect of a measure is higher in each quintile than in the higher quintiles, and
- in relation to income losses caused by the COVID-19 crisis (chart 6): Here we analyze horizontal effectiveness, which requires that the relative effect of a measure within a quintile is larger among severely affected households than among unaffected households. 24
The main instruments contributing to the measures’ effectiveness in terms of income level were the extra funds paid out to those on unemployment benefits or unemployment assistance because such transfers make up a larger share in household income in the lower quintiles (chart 5). 25 In the bottom quintile, the effect of these measures on changes in income averaged 1.2 percentage points, compared with 0.3 percentage points in the middle quintile. In addition, the one-off child bonus had a larger percentage impact on lower-income households, accounting for, on average, 0.8 percentage points in the bottom and 0.4 to 0.1 percentage points in the second to fifth quintiles. Likewise, the impact of payments from the family hardship fund was significantly stronger in the lower quintiles because these payments were subject to income thresholds. While lower-income households, on average, also benefited more from the hardship fund, its effect was substantial also in the higher-income quintiles because of the higher share of self-employed. The COVID-19 short-time work scheme provides for higher replacement rates for lower-income earners compared with pre-pandemic schemes, thereby increasing household incomes in the bottom quintile by an average 0.2%; we do not see this positive effect, on average, in the other quintiles. At the same time, the percentage impact of the income tax cut was smaller for the bottom income quintile than for medium- to high-income households, who benefited more from the reduction of the lowest income tax rate.
The measures’ effectiveness in relation to income losses caused by the COVID-19 shock is expressed by the difference in the effects on severely affected households compared to unaffected households (chart 6). For severely affected households, the hardship fund turned out to be the most important measure, next to short-time work and its job-saving effect (see below). In the bottom quintile, severely affected households received more transfers than unaffected households: the difference amounted to 6.2% of the former’s household income; 3.4 percentage points were attributable to payments from the hardship fund. The one-off payment for people on unemployment benefits were also targeted at households hit particularly hard by the crisis, but the payment also benefited households affected by unemployment not caused by the crisis. Likewise, households affected by the crisis benefited to a larger extent from the child bonus because households with children were more likely to experience income losses than households without children (above all, due to pensioner households without children, which hardly lost any income at all). Given that the purpose of the family hardship fund was to compensate people for actual income losses (similar to the hardship fund), its effect was also relatively strong for the lowest two quintiles.
That said, we see the high effectiveness of the set of fiscal measures only if we compare its relative effect among the severely affected, less affected and unaffected household groups; we do not see it, if we compare the net effect of the COVID-19 shock and the measures among these groups. In particular, we find that the negative effect of the COVID-19 shock was larger than the positive effect of the measures among severely affected households in the quintile averages (chart 4).
We paid particular attention to the COVID-19 short-time work scheme, which was much more employer friendly compared to similar pre-pandemic schemes so as to provide stronger incentives for companies to preserve jobs (in particular, employers were exempt from paying social security contributions, and the scheme also provided for more flexibility in reducing working hours). In some cases, the minimum pay an employee is entitled to under the COVID-19 short-time work scheme may be lower than under previous schemes (especially if both the regular pay and the number of hours worked are relatively high), which implies that the effect of the new replacement rates was even slightly negative for some (above all in the fourth quintile, see charts 5 and 6). Still, the job-saving effect of the COVID-19 short-time work scheme was very high for all household income groups. An additional shock scenario illustrates this effect: it assumes that if the scheme had not been adjusted as a response to the COVID-19 shock, use of short-time work would have been lower by half, and unemployment would have been correspondingly higher. The shaded blue columns illustrate that in this case, the estimated effect of the fiscal measures would have been ½ to 1 percentage point higher (chart 5) 26 and that average household incomes would have been ½ to 1 percentage point lower. This effect is higher still if we look at differences in income between households severely affected by the crisis and unaffected households, amounting to 2½ to 3 percentage points (chart 6; these effects are not shown in charts 3 and 4). Table 1 offers an overview of the vertical and horizontal effectiveness of the measures under analysis.
|Income tax cut||–||~|
|One-off unemployment payment||++||+|
|Higher unemployment assistance||++||~|
|Child bonus/family allowance||++||+|
|Family hardship fund||++||++|
|Replacement rate of short-time work||+||+|
|Use of short-time work scheme||~||++|
|Source: Office of the Fiscal Advisory Council, OeNB.|
Note: Relative to household income, measures benefit
lower-income households and/or households more
severely affected (in financial terms) by the COVID-19
shock much more (++), more (+), more or less
equally (~), less (–) than higher-income households
and/or households that have not been affected
financially by the COVID-19 shock.Meaning of “benefit
much more (++)” in terms of vertical equity: in
percentage terms, the 1st quintile benefits at least
twice as much as the overall average, and the 2nd
quintile by at least 50% more than the overall average.
Meaning of “benefit much more (++)” in terms
of horizontal equity: at least in the first four quintiles,
measures benefit households severely affected by
the COVID-19 shock at least twice as
much as the quintile average.
The fiscal measures implemented to cushion the impact of the COVID-19 crisis in Austria prevented a steep drop in aggregate household incomes in 2020. The measures proved effective in two ways: both lower-income households and households that had experienced particularly large income losses benefited more on average and relative to their incomes.
We show that especially the measures aimed to compensate for actual COVID-19-related losses were horizontally effective; these measures included the hardship fund and the family hardship fund. This also applies indirectly to the use of short-time work arrangements, which helped avoid higher income losses caused by unemployment. Vertical effectiveness was best achieved through measures aimed to support especially lower-income households (e.g. family hardship fund) or funds paid only to households affected by unemployment, which are more often found in the lower quintiles.
At the same time, other measures aimed to increase overall consumer demand instead of providing support specifically to those affected by the crisis; these measures included the reduction of the lowest income tax rate, which was put into force earlier than originally planned, and the child bonus. Interestingly, the child bonus also achieved relatively good vertical and horizontal effectiveness (see also table 1) because it accounted for a larger part of household income in the lower quintiles and because families with children were affected by the crisis more severely due to their higher share of earned icome from labor market participation compared to e.g. pensioner households. The reduction of the lowest income tax rate was the only measure we found to be neither vertically nor horizontally effective.
Albacete, N., P. Fessler, F. Kalleitner and P. Lindner. 2021. How has COVID-19 affected the financial situation of households in Austria? In: Monetary Policy and the Economy Q4/20–Q1/21. OeNB. 111–130.
AMS (Public Employment Service Austria). 2020. Bundesrichtlinie Kurzarbeitsbeihilfe (KUA-COVID-19). BGS/AMF/0702/9931/2020.
AMS (Public Employment Service Austria). 2021. Kurzarbeit sichert nachhaltig Arbeitsplätze und Know-how. Special labor market analysis. January 2021.
Arbeiterkammer. 2021. AK Gerechtigkeitscheck der Regierungsarbeit im ersten Corona Jahr. Vienna.
Bachleitner, A. and S. Maidorn. 2019. Das Mikrosimulationsmodell des Büros des Fiskalrates (FISKSIM). Interaktionseffekte ausgewählter Transferleistungen in Österreich. Vienna.
Baumgartner, J., M. Fink, C. Moreau, S. Rocha-Akis, S. Lappöhn, K. Plank, A. Schnabl and K. Weyerstrass. 2020. Wirkung der wirtschaftspolitischen Maßnahmen zur Abfederung der COVID-19-Krise. Mikro- und makroökonomische Analysen zur konjunkturellen, fiskalischen und verteilungspolitischen Wirkung. WIFO.
Christl, M., S. De Poli, D. Kucsera and H. Lorenz. 2021. COVID-19 and (gender) inequality in income: the impact of discretionary policy measures in Austria. JRC Working Papers on Taxation and Structural Reforms 05/21.
Federal Ministry of Arts, Culture, Civil Service and Sport. 2020a. Richtlinie für die Gewährung von Überbrückungsfinanzierungen im Rahmen des Bundesgesetzes über die Errichtung eines Fonds für eine Überbrückungsfinanzierung für selbständige Künstlerinnen und Künstler.
Federal Ministry of Arts, Culture, Civil Service and Sport. 2020b. Richtlinien für die Gewährung von nicht rückzahlbaren Beihilfen aus dem COVID-19-Fonds gemäß § 25b iVm § 25c Abs. 3a Künstler-Sozialversicherungsfondsgesetz.
Federal Ministry of Finance. 2020a. Richtlinie zur Regelung der Auszahlungsphase 2 im Rahmen des Härtefallfonds für Ein-Personen-Unternehmen, Freie Dienstnehmer und Kleinstunternehmen, BMF - II/10.
Federal Ministry of Finance. 2020b. Richtlinie gemäß § 1 Abs. 4 Härtefallfondsgesetz für Einkommensausfälle bei land- und forstwirtschaftlichen Betrieben sowie Privatzimmervermietungen, BMF - II/9.
Federal Ministry of Labour, Family and Youth. 2020. Richtlinien für die Corona(Covid-19)-Hilfe aus dem Familienhärteausgleich.
Figari, F., A. Paulus and H. Sutherland. 2014. Microsimulation and Policy Analysis, ISER Working Paper Series.
Glocker, C. 2020. Schwäche der Weltwirtschaft dämpft Konjunktur in Österreich. Prognose für 2020 und 2021. WIFO-Monatsberichte 1/2020.
Parliament. 2020. Parlamentskorrespondenz Nr. 1238 vom 20.11.2020: Nationalrat: Arbeitslose erhalten im Dezember weitere Einmalzahlung von 450 Euro.
Annex 1: The simulation of the COVID-19 shock in detail
According to reports by the Public Employment Service Austria (AMS), around 1.2 million people in Austria received support through COVID-19 short-time work arrangements in 2020. To calculate the average reduction in working hours across ÖNACE 2008 sections of economic activities, FISKSIM uses a special AMS (2021) analysis as the basis for calibrating data from the microcensus labor force survey of the second quarter of 2020. 27 In the entire economy, working hours declined by an average of 43% in 2020; the reduction was significantly higher in accommodation and food services (62%), compared with 33% and 43% in manufacturing and wholesale and retail trade. We assume the distribution of lost working hours within a section of economic activity to be normally distributed, with the mean corresponding to sector averages and the standard deviation amounting to half of the gap between averages and the permitted highest or lowest reduction of working hours (i.e. 10% and 90%). 28
The given target value of the number of people on short-time work in each sector is obtained by randomly sampling payroll employees in the SILC data on the basis of their information about their employer’s NACE sector 29 . Apart from that, no other correlation between the probability of short-time work and, for instance, an employee’s pay or position in the company is assumed, as no such correlation can be derived from microcensus data. The average duration of short-time work is calculated on the basis of a breakdown of AMS payments by economic activity, the number of persons on short-time work, and short-time work subsidy per person calculated on the basis of SILC data and taking into account the reduction of working hours. This yields an average duration between around two months (e.g. in manufacturing) and around four months (e.g. in accommodation and food services).
Under the counterfactual scenario (without the COVID-19 pandemic), we calculated, on the basis of the WIFO economic outlook of December 2019, the number of people on unemployment benefits and unemployment assistance to have averaged around 148,000 and around 155,000, respectively, in 2020. According to AMS data, in 2020, an average of around 202,000 persons received unemployment benefits and around 190,000 persons received unemployment assistance; compared with the counterfactual scenario, these numbers are around 54,000 and 35,000, respectively, higher. The number of additional persons on unemployment benefits for each sector was generated on the basis of the monthly unemployment statistics by sector from the stock of counterfactual employment taking into account information on the NACE sector of the relevant companies; the probability of unemployment was modeled contingent on gross incomes. The number of additional persons on unemployment assistance was generated from the counterfactual stock of persons on unemployment benefits. 30
A shorter duration of unemployment is assumed for some of the additional people on unemployment benefits. Their number and the duration of unemployment was derived from the monthly stock of unemployed by economic sectors, in particular from the drop in unemployment seen in the months May to August compared to the high recorded in April 2020. We thus obtain an average duration of approximately three months with only minor sector-specific fluctuations. For all other benefits recipients, the duration of unemployment was assumed – on the basis of SILC data – to be four months. 31
The COVID-19 shock scenario without fiscal measures uses the tax and transfer regime (including short-time work scheme) that had been in place until March 2020. Under the phase II COVID-19 short-time work scheme, which entered into force in June 2020, support was higher the lower beneficiaries’ hourly pay and number of hours worked were. This, in turn, may imply less support in certain cases under the new scheme. At the same time, the new scheme was more attractive for employers: The public purse covers employer and employee social security contributions and the minimum pay rule means that employers are required to pay only the replacement rate and not the full hourly wage for actual hours worked. We can therefore assume that the COVID-19 short-time work scheme contributed more to saving jobs than previous schemes. Hence, the COVID-19 shock scenario without fiscal measures is calculated in the following two variants: in the first variant, all persons on COVID-19 short-time work are assumed to be in the pre-pandemic short-time work scheme; in the second variant, half of this group is unemployed. The latter implies an additional 120,000 jobless on average over the year.
The decline in self-employed income was derived from quarterly national accounts data broken down by economic activity. The subsidies (which, like the data for all individual sectors, are only part of not yet published annual national accounts data) were calculated on the basis of data on the compensation for lost sales and short-time work by sector. The change in self-employed income under the 2020 shock scenario compared with 2019 is assumed to be a normal distribution with a mean 32 corresponding to the adjusted change in a sector’s net operating surplus 33 .
Annex 2: The simulation of the fiscal measures in detail
Some measures are implemented in FISKSIM through the tax and transfer systems implemented in the model, e.g. the income tax rate cut, the child bonus, one-off payments to people on unemployment benefits and the increase of unemployment assistance. In order to implement payments from the hardship fund in FISKSIM, the model limits the group of self-employed to one-person businesses, freelancers and micro businesses, relying on structural business statistics. The maximum number of businesses eligible for support within a sector is derived under the assumption that these businesses employ fewer than ten people. For the implementation in FISKSIM we use an approximation of the average income of businesses in this group. On this basis, it is assumed that applications are submitted by those self-employed in the AT-SILC data whose incomes are at similar levels 34 and who, taking into account income thresholds, experienced the highest income losses. 35 In combination with data from the monthly advance VAT returns and PRODCOM statistics on monthly sales, these data yield the number of self-employed eligible for support as well as the number of applications. 36 This is a crude approximation of the number of applications and the sum of hardship fund payouts, which, despite numerous assumptions, is sufficiently empirically founded to reflect the impact of fiscal measures on self-employed income in the analysis thanks to the AT-SILC data on self-employed income.
For payouts from the bridge fund and the COVID-19 fund for artists, FISKSIM also assumes that those self-employed that experienced the highest losses in income apply for support. For the bridge fund, only payouts to persons whose self-employed activity is their main economic activity are taken into account, whereas for the COVID-19 fund, only payouts to persons on very low income or unemployment benefits or assistance are included.
10 Office of the Fiscal Advisory Council, email@example.com.
11 Oesterreichische Nationalbank, Economic Analysis Division, firstname.lastname@example.org.
12 The degrees to which households were financially affected by the COVID-19 shock are defined in section 3. We look at financial effects in terms of income losses of households, not in terms of their ability to compensate potential losses through dissaving.
13 Defined as the risk of households of a decrease in income beyond the poverty line of 60% of median eqivalized household disposable income.
14 For reasons of simplicity, we use the COVID-19 short-time work scheme in force from June 2020 in the shock scenario with fiscal measures for all persons in a short-time work scheme. Under this scheme, employees received minimum pay regardless of actual hours worked (AMS, 2020). Modeling two different short-time work schemes would require additional assumptions on the allocation of workers on short-time work to the different schemes.
15 Therefore, measures such as rent deferrals are not considered.
16 In this context, it is interesting to note that in some countries, short-time work subsidies are classified as social transfers to households in the national accounts; in other countries, they are considered subsidies for employers. In line with established practice, Statistics Austria applies the latter variant for Austria.
17 Chart 2 does not show the total costs of short-time work because the aggregate effects of this instrument’s overhaul are difficult to quantify (see also section 4).
18 For a detailed description of the simulation of the COVID-19 shock and the fiscal measures in FISKSIM, see annexes 1 and 2.
19 The difference between the factual scenario and the shock scenario represents the static effects of the measures adopted to support household incomes; the multiplier effects (e.g. the slower increase in unemployment) of these measures are not taken into account.
20 Self-employed incomes are extrapolated on the basis of their historical growth differential vis-à-vis nominal GDP growth.
21 As suggested by aggregate data on household income (chart 1) and capital income tax revenues.
22 The weighting of persons follows the modified OECD scale: Main earners are assigned a factor of 1.0, other household members aged 15 and over 0.7, and all other household members 0.5.
23 As suggested by data on household income (chart 1) and capital income tax revenues.
24 The sum of all columns (without the shaded areas representing “use of short-time work”) in charts 5 and 6 corresponds to the difference between the dark red columns in the left-hand and the right-hand panel of chart 4 (severely affected and unaffected households, respectively).
25 See also Christl et al. (2021), who arrive at very similar results regarding the effect of one-off payments for people on unemployment benefits or families in the lower part of the income distribution.
26 These shaded blue columns show the additional effect of this assumption on the total amount paid out under the measures; in other words, the effects of higher unemployment on the total amount of payments under other measures (in particular, higher one-off payments for people on unemployment benefits and a smaller effect of the income tax rate cut) are directly set off.
27 The average reduction in working hours in each ÖNACE 2008 economic sector is based on information on regular working hours and hours worked, provided that short-time work was the reason for the reduction in working hours.
28 In phases I and II of COVID-19 short-time work until September 30, 2020.
29 Generating the short-time work status by random sampling provides a good approximation in each sector. Due to the large number of persons on short-time work in 2020, weights are adjusted to ensure an exact alignment with the target values.
30 Hence, the target value of additional people on unemployment benefits to be generated increases by the number of additional people on unemployment assistance.
31 According to SILC data, people are on unemployment benefits for an average of 3.4 months. However, this may be an underestimation because months during which a person was both employed and unemployed are counted as employment months.
32 Empirically deriving the standard deviation of, e.g. the distribution of self-employed incomes in previous years yields implausible results, therefore a standard deviation of 10% was assumed.
33 Most SILC data lack information about the NACE sectors of the self-employed. For this reason, sectors were assigned on the basis of occupational activities or functions, where possible; for instance, assemblers as well as construction workers and builders were assigned to ÖNACE 2008 F (“construction”), professionals or comparable workers and engineers in information and communications technology were assigned to ÖNACE 2008 J (“information and communication”). In all other cases, economic sectors were assigned according to the distribution resulting from information about both categories, e.g. for managers.
34 This means that the income is within two standard deviations of self-employed income of the sector in the SILC data.
35 The income losses serve as a proxy for sales losses. The criteria for hardship fund payouts follow a similar approach: by looking at the return on sales in the reference period, a fixed relationship between sales and income is used to calculate income losses in the assessment period on the basis of lost sales.
36 Self-employed in economic sectors for which no structural business statistics data are available are assumed to employ fewer than ten persons. These sections of economic activities are ÖNACE 2008 MN “professional, scientific, technical and other business activities,” PQ “education, human health and social work activities” and RS “arts, entertainment and recreation, other service activities.”
Corporate equity finance in Austria – impediments and possible improvements
Peter Breyer, Eleonora Endlich, Dieter Huber, Doris Oswald, Christoph Prenner, Lukas Reiss, Martin Schneider, Walter Waschiczek 37
This study examines the state of play of equity financing in Austria and highlights challenges Austrian companies face in raising equity capital. The equity ratios of Austrian companies had been improving steadily before the onset of the COVID-19 pandemic, which has been weighing considerably on corporate equity levels. The decrease of equity levels would, however, be about twice as high in the absence of the support measures taken to alleviate the economic effects of the pandemic. The bulk of Austrian companies’ equity is sourced from the rest of the world, while the domestic financial sector plays only a minor role in providing equity funding. Impediments to raising capital externally include business owners’ reluctance to share control with external investors, information deficits and data gaps as well as differences in the tax treatment of debt and equity (“debt bias”). Equity supply is limited because investors lack information on the economic situation of capital-seeking companies and because investments in unlisted companies are less liquid. Together with representatives of national and international institutions and market participants, we identified ways to strengthen the equity base of Austrian companies. Cases in point are providing both tax incentives and intermediation support for equity finance and establishing public-private partnerships.
JEL classification: E61, G1, G2, G32
Keywords: corporate finance, equity, institutional investors
The economic setback triggered by the COVID-19 pandemic has affected different economic sectors to different extents. In some sectors, the related containment measures have caused massive sales losses, which has had a direct impact on corporate liquidity and equity levels. As Austrian companies were facing frictions between capital supply and demand even before the current economic crisis, numerous economic policy actors have been calling for measures to strengthen the equity base of companies. This would improve the balance of supply and demand (figure 1) and generate a range of favorable macroeconomic effects.
In this study, we give an overview of the equity structure of Austrian companies, or nonfinancial corporations to be conceptually precise. We highlight challenges in raising equity capital and present ways to increase equity finance. Our goal is to provide more comprehensive data and a better understanding of the underlying mechanisms, explain the issues in more detail and share best practices from other countries.
The study is structured as follows: In section 1, we present data on the equity ratios of Austrian companies before and during the COVID-19 pandemic. In section 2, we look at equity ownership structures to answer the question: who is investing in Austrian companies? Section 3 discusses the concept of the funding escalator and frictions between capital supply and demand. In section 4, we outline possible avenues for strengthening corporate equity in Austria and present international best practices. Section 5 summarizes.
1 Understanding the facts: equity ratios of Austrian companies
1.1 Pre-crisis equity ratios were improving but bottom quartile ratios remained weak in an international peer comparison
Before the onset of the COVID-19 crisis, the equity ratios of Austrian companies had been improving steadily, rising from an average ratio of 31.5% in 2005 to 40.4% in 2018, based on BACH data. 38 Among the nine countries for which BACH data are available from 2005, Austria moved up from rank 9 to rank 4 in this period (chart 1). 39
Breaking down corporate equity structures by business sectors enables us to identify vulnerable areas in Austria (chart 2). Using weighted averages for 2018, we see that the corporate equity ratios measured for Austria were broadly aligned with the ratios measured for other countries in most business sectors (other than the hospitality sector). However, Austrian companies performing in the bottom quartile tended to be negative outliers. In other words, Austrian companies in the bottom quartile face heightened insolvency risk from debt overhang. Overall, only about 10% of all insolvencies in Austria result from debt overhang problems, whereas 90% of all insolvencies arise from liquidity issues. The propensity for liquidity problems is driven above all by small companies, which account for 89% of all companies covered by the BACH database. Of all size classes, the best equity capital ratios are in all countries attributable to medium-sized companies (with an annual sales volume of between EUR 10 million and EUR 50 million) and large companies (with an annual sales volume of more than EUR 50 million). This holds true in particular for companies in the bottom quartile. Among the companies in the bottom quartile, Austrian medium-sized companies are closer to the lower end while large Austrian companies tend to be aligned with the average of the other countries under review.
Apart from the BACH data, which are aggregated balance sheet data, we also draw on corporate data from the Sabina database, which provide for a more granular view of the corporate equity structure in Austria. 40 Based on the Sabina data, we see that 17.4% of all Austrian companies had a negative equity balance in 2018. The share of companies with a negative equity balance was particularly high among companies in the hospitality industry (32.1%) and companies providing arts, entertainment, recreation and other services (28.4%).
|Equity ratio by quartiles||
Share of companies
with an equity ratio of
|Average||Bottom quartile||Median||Third quartile||< –30%||< 0||Cash and bank < 0|
|Energy supply (D)||36.1||2.7||18.8||50.5||6.8||20.9||0.2||1,527||33,016|
waste management (E)
|Transport and storage (H)||32.7||6.3||29.2||58.4||10.6||19.6||0.2||4,672||10,631|
Accomodation and food
service activities (I)
|Real estate activities (L)||38.8||2.3||24.6||73.7||5.8||19.4||13.7||21,261||7,674|
Scientific and technical
activities (M, excl. head
Education (P), health
and social actitivies (Q)
other services (S)
|Source: Sabina database, OeNB calculations.|
1.2 OeNB insolvency model reveals substantial impact of pandemic support measures on corporate equity levels
In this section, we present the results of simulations run with the OeNB’s insolvency model. 41 Specifically, we calculated two COVID-19 scenarios, one with and one without support measures, 42 and cross-checked the resulting estimates with a counterfactual scenario without COVID-19 in order to isolate the pandemic impact.
The results show that the pandemic-related crisis had a major impact on corporate equity in Austria. In the absence of support measures and when we factor out the effects of COVID-19, the equity level of Austrian companies would have been EUR 25 billion lower in 2020. The support measures diminish the decline in equity to EUR 17 billion, thus improving equity availability by EUR 8 billion in 2020 (chart 3). Equity losses until 2022 add up to EUR 47 billion (without support measures) or EUR 34 billion (with support measures).
However, note the caveat that these results must not be interpreted as equity finance forecasts, as the insolvency model simulations are conditional on the validity of numerous restrictive assumptions, and as they contain only the losses resulting from the projected decline in sales. Moreover, the simulations do not reflect the (substantial amount of) capital transfers from the household sector and from nonresidents observed in 2020, which means that the decline is overstated. The estimated pandemic-related decline in equity is also likely to constitute an upper bound as our insolvency model does not factor in any corporate investments. 43 Gross fixed capital formation by companies contracted by 3.9% in 2020 in view of lost sales. In other words, lower investment levels cushioned losses in sales to some extent, causing the impact of the COVID-19 pandemic on capital ratios to be smaller in actual fact than implied by the model.
According to the OeNB’s financial accounts data, corporate equity levels contracted by EUR 5.5 billion in 2020. While this figure provides a benchmark, it cannot be used to cross-check the simulation results because of underlying conceptual differences. The insolvency model results are based on simulated monthly balance sheet data. The financial/national accounts framework, by contrast, uses a point-in-time approach to calculate equity levels. Moreover, the two frameworks differ with regard to the coverage of companies. Last but not least, the insolvency model maps the simulated capital losses against a counterfactual scenario without the pandemic, whereas the financial accounts data reflect annual changes.
2 Corporate equity ownership in Austria
One starting point for identifying possible strategies to strengthen corporate equity in Austria is to establish the underlying investor structure. In other words, we need to know how much of the companies’ equity is currently being held by which economic sectors. To this effect, we provide a breakdown of the equity raised by Austrian companies from the individual financing sectors, using year-end 2020 data. The overview is based on the financial accounts data that the OeNB compiles. The financial accounts capture the flow of funds between the individual sectors of the economy, including the flow of funds between different units of the same sector, and the resulting stocks using unconsolidated data. For the purpose of this paper, we exclude the equity stakes of Austrian companies in other Austrian companies, presuming that a large share of such financing is intragroup financing. Both the financial accounts and the national accounts are based on the definition of nonfinancial corporations. Specifically, nonfinancial corporations include stock corporations, limited liability companies and cooperatives as well as partnerships, such as limited partnerships or sole proprietorships with more than 50 employees and/or sales or more than EUR 10 million (OeNB, 2018). While being published in a timely manner, financial accounts data are available only for the corporate sector as a whole, without any breakdowns by firm characteristics like size, business sector or the like.
According to financial accounts data, the amount of equity held by Austrian nonfinancial corporations totaled EUR 353 billion at the end of 2020 (table 2). 44 Stocks accounted for about 30% of this amount (quoted shares: 20%, unquoted shares: 10%). The by far bigger part, namely 70%, was attributable to other equity. Other equity refers to equity held in companies that have not been set up as stock corporations. 45
2.1 Equity ownership structures in Austria at the end of 2020
The bulk of Austrian corporate equity tends to be sourced from the rest of the world. At the end of 2020, nonresident investors accounted for 44% of the (consolidated) equity of Austrian companies. The share of nonresident investors exceeded 40% for all three types of equity instruments discussed here. According to the OeNB’s securities statistics, three-quarters of all quoted shares acquired by nonresident investors qualified as portfolio investment. 14% of corporate equity was held by the government sector, with the average masking large differences among individual financing instruments. The government share was as high as 43% for unquoted shares but below 6% for other equity. Households 46 held close to 24% of Austrian corporate equity (mostly in the form of other equity) at the end of 2020, but only close to 14% of all quoted shares issued by Austrian companies. Private foundations held close to 12% of corporate equity, typically in the form of other equity. Taken together, domestic households and private foundations accounted for somewhat more than 35% of the consolidated equity of Austrian nonfinancial corporations. This figure masks considerable differences when it comes to individual financing instruments: The combined share, for instance, ranged from about 44% for other equity to 19% for quoted shares. Private foundations apart, which are classified in the financial sector, the amount of equity sourced from the financial sector is limited. Banks (or monetary financial institutions (MFIs), to be conceptually precise) provided only 1.7% of all corporate equity (but 52% of consolidated corporate debt) at the end of 2020. The share of institutional investors (insurance companies, mutual funds and pension funds) in total corporate equity also added up to 1.7%. (Even quoted shares accounted for just 4.6% of their portfolio.) Last but not least, other financial corporations (including holding companies and special purpose entities) supplied 3.7% of total corporate equity in Austria, mostly by investing in unquoted shares and other unquoted equity.
|Assets in EUR million (end-2020)|
|Debt and equity||185,553||12,118||19,119||40,974||74,640||99,414||266,321||698,139|
|Share of individual sectors in corporate equity in %|
corporate equity (%)
|Source: OeNB (financial accounts).|
Note: Based on consolidated figures = capital of nonfinancial corporations minus (asset-side)
debt instruments held by the nonfinancial corporations sector. MFIs (monetary financial institutions)
= the OeNB, credit institutions and money market funds; SPEs = special purpose entities.