Call for applications: Klaus Liebscher ­Economic Research Scholarship

Please e-mail applications to scholarship@oenb.at 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

Nontechnical summaries

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

Table 1: National accounts data for Austria (Q2 data published on September 2, 2021)  

	GDP	Private consumption	Government consumption	Gross fixed capital formation	Exports	Imports	Domestic demand (without inventories)	Net exports	Changes in inventories	Statistical discre­pancy
	Change on previous period in %						Contribution to GDP growth in percentage points
Q1 20	–2.2	–3.4	+0.6	–0.3	–5.0	+0.2	–1.7	–3.0	2.0	0.5
Q2 20	–10.9	–12.4	+1.3	–8.4	–18.6	–16.8	–8.1	–1.4	–0.6	–0.9
Q3 20	+11.0	+14.8	+1.1	+7.7	+17.5	+11.8	9.5	3.0	–2.0	0.4
Q4 20	–2.5	–6.0	+1.7	–1.2	+1.2	+5.4	–3.0	–2.1	2.2	0.4
Q1 21	–0.2	–1.6	+1.2	+4.7	–2.9	+2.3	0.6	–2.9	1.9	0.2
Q2 21	+3.6	+3.2	+2.9	+1.8	+7.1	–2.1	2.6	5.0	–2.4	–1.7
2017	+2.5	+1.9	+0.9	+4.0	+5.7	+5.8	2.1	0.2	0.1	0.1
2018	+2.5	+1.1	+1.2	+4.0	+4.9	+4.6	1.7	0.3	0.4	0.1
2019	+1.4	+0.8	+1.4	+3.9	+2.9	+2.5	1.6	0.3	–0.7	0.2
2020	–6.3	–8.1	+2.4	–5.3	–11.5	–9.0	–5.0	–1.8	0.1	0.4
Source: Statistics Austria.

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.

Table 2: National accounts data for Austria (production-side data published on September 2, 2021)  

	Q2 21	Q1 21	Q4 20	Q3 20	Q2 20	Q1 20	2020	2019	2018	2017
	Change on previous period in %
GDP	+3.6	–0.2	–2.5	+11.0	–10.9	–2.2	–6.3	+1.4	+2.5	+2.5
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
Trade, transport/storage, hospitality (NACE G–I)	+20.4	–7.4	–16.3	+29.2	–21.7	–6.9	–15.4	+1.1	+2.0	+1.5
Information and communication (NACE J)	–0.4	+2.9	+1.2	+0.5	–3.4	–1.4	–1.6	+3.8	+9.8	+2.1
Financial and insurance services (NACE K)	+0.8	–2.2	+1.9	+2.3	0.0	+2.6	+4.1	+3.1	+2.9	+5.3
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 (NACE M–N)	+0.6	+0.0	+5.9	+13.5	–18.4	–0.1	–7.9	+2.1	+3.6	+3.7
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
Public administration (NACE O–Q)	+0.7	+0.8	+0.4	+0.7	–0.6	–0.5	–0.4	+0.7	+0.9	+1.3
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
Source: OeNB.

2 GDP slightly surpassed its pre-crisis levels for the first time in late July

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

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

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.

5 August 2021: surge in overnight stays by foreign visitors

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

7 Inflation significantly on the rise since early 2021

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.

Analyses

How effective were measures introduced in the COVID-19 crisis in supporting household incomes?

Susanne Maidorn ¹⁰ , Lukas Reiss ¹¹

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.

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

• 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%.

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

For our analysis, we therefore adjusted the EU-SILC 2017–2019 data to three scenarios:

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

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?

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 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.

Table 1: How did individual measures affect horizontal and vertical effectiveness?  

	Vertical ­effectiveness	Horizontal ­effectiveness
Income tax cut	–	~
One-off unemployment payment	++	+
Higher unemployment assistance	++	~
Child bonus/family allowance	++	+
Hardship fund	+	++
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.

5 Conclusions

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.

References

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

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

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.

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

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

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%).

Table 1: Equity ratio by sectors (Sabina data for 2018)  

	Equity ratio by quartiles				Share of companies with an equity ratio of		Share of firms with	Number of ­companies	Average assets (EUR thousand)
	Average	Bottom quartile	Median	Third quartile	< –30%	< 0	Cash and bank < 0
Total	39.9	8.7	37.7	71.1	9.9	17.4	2.5	129,239	5,506
Agriculture (A)	55.5	6.1	29.5	63.3	7.6	16.2	0.1	956	2,549
Mining (B)	50.3	16.4	42.1	70.0	10.1	14.4	35.0	303	20,774
Manufacturing (C)	45.9	15.1	39.2	66.5	8.8	14.0	0.1	10,981	14,402
Energy supply (D)	36.1	2.7	18.8	50.5	6.8	20.9	0.2	1,527	33,016
Water supply, waste management (E)	32.1	16.7	40.5	67.6	6.1	11.6	28.0	621	7,585
Construction (F)	31.4	10.8	36.1	64.9	6.8	14.2	0.1	15,648	2,426
Trade (G)	42.7	11.1	38.4	69.5	12.0	17.8	0.1	27,337	4,067
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)	26.3	–14.9	19.2	51.5	20.4	32.1	0.2	8,782	1,984
Information and communication (J)	44.6	14.2	49.3	77.3	12.9	17.6	0.1	7,877	2,815
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 office activities)	49.5	25.9	58.3	83.9	6.9	10.4	0.1	18,427	1,537
Support service activities (N)	27.5	10.7	36.3	67.0	10.3	16.3	0.2	5,505	5,059
Education (P), health and social ­actitivies (Q)	30.9	9.4	37.4	70.6	12.1	18.2	0.1	2,287	1,805
Arts, entertainment, recreation (R), other services (S)	28.8	–8.2	29.1	65.3	19.4	28.4	0.2	3,055	2,410
Source: Sabina database, OeNB calculations.

1.2 OeNB insolvency model reveals substantial impact of pandemic support measures on corporate equity levels

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

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

Table 2: Corporate equity and debt ownership in Austria  

	MFIs	Institutional investors	Other ­financial ­corporations incl. holdings and SPEs	Private ­foundations	Government sector	Households and ­nonprofit ­institutions serving households	Rest of the world	All sectors
	Assets in EUR million (end-2020)
Quoted shares	466	3,271	735	4,105	20,160	9,691	32,809	71,237
Unquoted shares	1,016	453	1,850	1,166	14,937	1,476	14,209	35,108
Other equity	4,475	2,145	10,648	35,409	14,262	72,864	106,893	246,695
Total equity	5,957	5,869	13,232	40,680	49,359	84,031	153,911	353,039
Total debt	179,597	6,249	5,887	294	25,281	15,383	112,410	345,100
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 %
Quoted shares	0.7	4.6	1.0	5.8	28.3	13.6	46.1	100.0
Unquoted shares	2.9	1.3	5.3	3.3	42.5	4.2	40.5	100.0
Other equity	1.8	0.9	4.3	14.4	5.8	29.5	43.3	100.0
Total equity	1.7	1.7	3.7	11.5	14.0	23.8	43.6	100.0
Total financial assets of individual sectors (EUR million)	1,178,334	347,777	134,193	55,465	301,092	779,071	847,298	3,643,228
of which: corporate equity (%)	0.5	1.7	9.9	73.3	16.4	10.8	18.2	9.7
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.