Call for applications: Klaus Liebscher Economic Research Scholarship

Please e-mail applications to scholarship@oenb.at by the end of October 2022. Applicants will be notified of the jury’s decision by end-November 2022.

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

Exchange rate index update for Austria shows lower effective appreciation than previously measured

Ursula Glauninger, Thomas Url, Klaus Vondra

How competitive are Austrian exports and services? The first step toward answering this question is to compare the value of the Austrian currency against a basket of other currencies in a way that reflects the relative importance of trade with other countries. This is what the so-called nominal effective exchange rate for Austria, calculated by the Oesterreichische Nationalbank (OeNB) and the Austrian Institute of Economic Research (WIFO), does. When this rate increases, Austrian exports become more expensive in other countries. When this rate goes down, Austrian exports become less expensive. By adding the price and cost dimension, i.e. by looking at both the costs of producing such goods and services and the prices foreign manufacturers or consumers have to pay for them, we arrive at the real effective exchange rate. Austria’s real effective exchange rate is, thus, an indicator of Austria’s international price or cost competitiveness.

With this article, we publish the latest nominal and real exchange rates for Austria, as calculated in index form (i.e. by comparing the changes against a fixed standard based on either prices or unit labor costs), for four segments of the economy: (1) manufactured goods, (2) food and beverages, (3) raw materials and energy products, and (4) services. For the services segment, we provide additional in-depth information by calculating, for the first time, a separate exchange rate for tourism services. Our calculations relate to up to 56 trading partners, which account for more than 95% of all exports from and imports to Austria. The key factor in updating the effective exchange rate is the reweighting of the individual currencies, to reflect ongoing changes in the relative importance of the individual trading partners. In this article, we update the calculations published in 2017 by reflecting more recent data on trade flows in the weighting matrix. The index recalculation confirms that Austria has lost competitiveness over time, but that the loss has been less pronounced than suggested by the previous calculations. The competitiveness indicators for 2020 and 2021 must be interpreted with some caution, because economic measures taken to cushion the impact of COVID-19 have introduced a bias into the data.

The newly developed real effective exchange rate for the tourism industry shows that Austria’s tourism exports have become more expensive than its services exports in general. This would imply that Austria has lost competitiveness compared with other vacation destinations. However, cross-checks with the number of tourist overnight stays and the amounts spent by foreign tourists in Austria in recent years indicate that Austria’s tourism industry has continued to thrive. One explanation may be that Austria’s tourism industry has been catering increasingly to more demanding visitors who stand ready to pay more for higher quality.

Private consumption and savings during the COVID-19 pandemic in Austria

Martin Schneider, Richard Sellner

During the COVID-19 pandemic, Austrian households have saved more money than ever before. In this study, we first try to find out how high these additional (“excess”) savings are. We then look at where they come from, how they were used and why they were piled up. Finally, we estimate how much demand for goods and services has built up and discuss what this so-called “pent-up demand” means for the Austrian economy.

We find that from the first quarter of 2020 to the second quarter of 2021, total household savings in Austria were EUR 10.8 billion higher than if the pandemic had not happened.

Savings went up mainly because people bought fewer services. That people earned a lot less from their investments was not enough to bring total savings down. We see that in 2020, households’ excess savings mainly went into cash holdings and bank deposits. In the first half of 2021, however, the opposite happened: Excess cash holdings and bank deposits went down and thus helped reduce the savings ratio.

Looking at a range of reasons for saving we know from the literature, we try to find out which of them are responsible for the strong increase in savings we have seen. It turns out that the traditional reasons cannot explain this increase, so the main reason might be that people could not buy many things (mostly services) while shops and businesses were closed during the lockdowns. We estimate that these so-called “forced savings” of Austrian households come to between EUR 17 billion and EUR 23 billion.

We expect that savings out of people’s current income will quickly reach the levels seen before the crisis, but that Austrians will not spend a lot of their excess savings on meeting pent-up demand. We calculate that if Austrians spend 25% or EUR 2.7 billion of their excess savings on pent-up demand, Austrian GDP will increase by EUR 2.4 billion (or 0.4%) until 2023.

These figures are of course uncertain because we do not know which course the pandemic will take and which parts of private consumption it will affect most.

A new instrument to measure wealth inequality: distributional wealth accounts

Arthur B. Kennickell, Peter Lindner, Martin Schürz

This study outlines the data restrictions we face when analyzing the distribution of wealth in Austria, and it identifies ways to improve the relevant data basis. National accounts (NA) data on corporations, the general government and households do not provide a suitable basis for analyzing the concentration of wealth. More useful data come from the Eurosystem’s Household Finance and Consumption Survey (HFCS). The Oesterreichische Nationalbank (OeNB) has carried out this sample survey among Austrian households since 2010. Participation in the HFCS is voluntary. However, one of the key problems of the HFCS is that, given the voluntary character of the survey, particularly wealthy households tend not to participate or not to (fully) disclose their financial circumstances. This sets a limit to any serious analysis of wealth concentration on the basis of HFCS data. It also means that total wealth as recorded in the HFCS remains considerably below total wealth as estimated in the national accounts.

The European System of Central Banks intends to close this gap by introducing distributional wealth accounts, which would bring together the information on wealth distribution that is available from HFCS microdata with NA macrodata.

Additional information could be obtained from the rich lists published regularly by Forbes and the Austrian weekly trend magazine and from assumptions on changes in household wealth distribution and household debt.

This study presents various scenarios resulting from simulations of wealth concentration. The results of these simulations show that the net wealth of the richest, i.e. top, 1% of Austrian households accounts for a share in total household net wealth that ranges from at least 23% to more than 50%. All available information suggests that in fact this share comes to around 50%. Precise assessments of potential distortions and estimation uncertainties remain difficult, however. This data gap could only be closed by introducing a statutory asset register.

Payment behavior in Austria during the COVID-19 pandemic

Dominik Höpperger, Codruta Rusu

This study discusses the latest survey on the use of payment instruments in Austria. The 2020/21 survey was the fifth survey on this topic that was conducted for the Oesterreichische Nationalbank (OeNB). It addressed Austrian households, which means women and men aged 15 or older. Its results are representative of the payment behavior of people living in Austria no matter how old they are, whether they are women or men, and in which Austrian province they live.

The first part of our study presents the results of the survey. We see that cash remains the most popular means of payment at the point of sale in Austria. About 66% of these payments are made in cash. Cash payments went down compared with 2019, however, and the pandemic supported this trend. For a number of reasons, people used cash less often when making everyday payments during the pandemic. Electronic payments have been becoming more popular in general. Moreover, the restrictions in force to fight the pandemic affected activities which tend to involve cash payments: travel, leisure activities and cultural events, for instance. All in all, the pandemic seems to have sped up the trend toward paying with cards. It remains to be seen whether, and how, the pandemic will influence the way people pay at the point of sale or online in the long term. Much will depend on when the pandemic-related restrictions will be removed on a large scale and when economic and social life will return to normal. Another important factor is the range of options for digital payments enterprises will offer their customers.

The second part of our study analyzes the connection between the drop in cash payments and the contagion risk people answering the survey said they felt when using cash. Our results show: The higher people considered the risk to catch the coronavirus via banknotes and coins, the fewer cash payments they made. Often, they thought the risk was a lot higher than it actually is. In fact, many scientific studies have shown that the risk of contracting the coronavirus from using cash is very low.

Nontechnical summaries in German

Neugewichtung der effektiven Wechselkurse für Österreich ergibt geringfügigere Aufwertung als bisher gemessen

Ursula Glauninger, Thomas Url, Klaus Vondra

Ein nominal effektiver Wechselkurs ist ein handelsgewichteter Durchschnitt der bilateralen Wechselkurse eines Landes und seiner wichtigsten Handelspartner. Ein steigender nominal effektiver Wechselkurs signalisiert aus makroökonomischer Sicht eine Aufwertung gegenüber den Handelspartnern, ein sinkender eine Abwertung. Durch die Integration der relativen Preis- oder Kostenbewegungen in den nominellen Wechselkurs erhält man einen real effektiven Wechselkurs. Dieser ist ein Indikator für die internationale Preis- oder Kostenwettbewerbsfähigkeit eines Landes, je nachdem ob Preis- oder Lohnkostenindizes verglichen werden. Im vorliegenden Beitrag berechnen die Oesterreichische Nationalbank (OeNB) und das Österreichische Institut für Wirtschaftsforschung (WIFO) nominelle und reale Wechselkurse für Österreich insgesamt sowie für vier Branchen: Handelswaren, Nahrungsmittel und Getränke, Rohstoffe und Energieprodukte und Dienstleistungen. Als Spezialfall der Dienstleistungen wird zudem erstmals ein Wechselkurs für den Tourismus berechnet. In den Berechnungen werden bis zu 56 Handelspartner und damit mehr als 95 % des österreichischen Handels berücksichtigt.

Die entscheidende Komponente in der Berechnung der Wechselkurse ist die Gewichtsmatrix, in der das Gewicht der einzelnen Handelspartner festgelegt wird. Im vorliegenden Artikel wurde diese Gewichtsmatrix mit nun zur Verfügung stehenden Daten neu berechnet und somit die Ergebnisse der letzten OeNB/WIFO-Berechnungen aus dem Jahr 2017 aktualisiert. Der nun berechnete neue Indikator für die Wettbewerbsfähigkeit zeigt eine mittelfristige Verschlechterung der Wettbewerbsposition Österreichs, wobei die Aufwertung im Vergleich zum vorherigen Gewichtungsschema weniger ausgeprägt ist. Die COVID-19-Krise verzerrt in den Jahren 2020 und 2021 mehrere zugrundeliegende Indikatoren und schränkt damit eine umfassende Interpretation der Wettbewerbsindikatoren am aktuellen Rand ein.

Im vorliegenden Beitrag widmet sich ein Spezialkapitel der Entwicklung im Tourismusbereich. Der neu entwickelte reale effektive Wechselkurs für die Tourismusbranche zeigt eine stärkere Aufwertung als für den gesamten Dienstleistungssektor. Dies bedeutet eigentlich eine Verschlechterung der österreichischen Wettbewerbsfähigkeit im Vergleich zu anderen Urlaubsdestinationen. Allerdings verzeichneten die Anzahl der Nächtigungen sowie die Ausgaben ausländischer Touristen in Österreich in den vergangenen Jahren klare Aufwärtstrends. Eine Erklärung hierfür könnte die Verlagerung zu höherwertigen Angeboten im Tourismus sein.

Konsum- und Sparverhalten der privaten Haushalte in Österreich während der COVID-19-Pandemie

Martin Schneider, Richard Sellner

Die österreichischen Privathaushalte haben während der COVID-19-Pandemie mehr gespart als je zuvor. In der vorliegenden Studie untersuchen wir, wie hoch diese zusätzlichen Ersparnisse sind, woher sie kommen und welche Überlegungen ihnen zugrunde liegen. Darüber hinaus interessiert uns, wofür diese zusätzlichen Ersparnisse verwendet werden. Außerdem schätzen wir ab, wie viel Nachfrage nach Gütern und Dienstleistungen sich aufgestaut hat, und untersuchen, was dieser Konsumnachholbedarf für die österreichische Wirtschaft bedeutet.

Vom ersten Quartal 2020 bis zum zweiten Quartal 2021 waren die Ersparnisse der österreichischen Haushalte insgesamt um 10,8 Mrd EUR höher, als im selben Zeitraum ohne Ausbruch der Pandemie erwartet worden wäre.

Die Ersparnisse sind vor allem deshalb gestiegen, weil die Menschen weniger Dienstleistungen in Anspruch genommen haben. Obwohl die Investitionen der privaten Haushalte deutlich weniger Erträge abwarfen als sonst, gingen die Sparguthaben insgesamt dadurch nicht zurück. Der während der Pandemie aufgebaute Ersparnisüberschuss floss 2020 vor allem in Bargeld- und Bankguthaben. Im ersten Halbjahr 2021 kam es zu einer Umkehr dieser Entwicklung: Die überschüssigen Bargeld- und Bankguthaben verringerten sich, wodurch auch die Sparquote zurückging.

In der Literatur wurde vielfach zu den Gründen, warum Menschen sparen, geforscht. Unsere Untersuchung zeigt, dass keines der üblichen Motive den starken Anstieg der Ersparnisse während der Pandemie erklären kann. Vielmehr dürfte ausschlaggebend gewesen sein, dass die Menschen vieles (insbesondere Dienstleistungen) nicht kaufen konnten, weil Geschäfte, Betriebe und Lokale im Lockdown geschlossen waren. Die dadurch entstandenen unfreiwilligen Ersparnisse der österreichischen Haushalte betragen unseren Schätzungen zufolge zwischen 17 Mrd EUR und 23 Mrd EUR.

Wir gehen davon aus, dass die Beträge, die die Menschen von ihrem laufenden Einkommen zur Seite legen, rasch wieder dasselbe Niveau erreichen werden wie vor der Krise. Wir rechnen jedoch nicht damit, dass die Österreicherinnen und Österreicher einen großen Teil der zusätzlichen Ersparnisse verwenden werden, um versäumten Konsum nachzuholen. Wenn 25 % (oder 2,7 Mrd EUR) des aufgebauten Ersparnisüberschusses für diesen Zweck verwendet wird, würde das österreichische Bruttoinlandsprodukt (BIP) nach unseren Berechnungen bis 2023 um 2,4 Mrd EUR (oder 0,4 %) wachsen.

Diese Angaben sind jedoch unsicher, da schwer abzuschätzen ist, wie sich die Pandemie weiterentwickeln und welche Bereiche des privaten Konsums sie am stärksten betreffen wird.

Ein neues Instrument zur Messung der Vermögensverteilung: Distributional Wealth Accounts

Arthur B. Kennickell, Peter Lindner, Martin Schürz

In dieser Studie wird beschrieben, welchen Datenrestriktionen die Analyse der Vermögensverteilung in Österreich unterliegt und wie eine Verbesserung der Datenbasis erreicht werden kann. Die Daten zu Unternehmen, Staat und privaten Haushalten aus der Volkswirtschaftlichen Gesamtrechnung (VGR) ermöglichen zur Vermögenskonzentration keine Analysen. Eine bessere Datenquelle stellt der Household Finance and Consumption Survey (HFCS) des Eurosystems dar, eine seit dem Jahr 2010 von der Oesterreichischen Nationalbank (OeNB) in Österreich durchgeführte freiwillige Stichprobenerhebung zu Finanzen und Konsum der privaten Haushalte. Ein Kernproblem des HFCS besteht aber darin, dass – im Rahmen der Freiwilligkeit – besonders vermögende Haushalte ihre Vermögensverhältnisse nicht, oder nicht ganz, offenlegen. Das schränkt seriöse Analysen der Vermögenskonzentration ein. Dementsprechend liegt das im HFCS erhobene Gesamtvermögen weit unter dem in der VGR geschätzten Gesamtvermögen.

Mit Hilfe von Distributional Wealth Accounts möchte das Europäische System der Zentralbanken diese Lücke schließen. Dabei sollen die in den HFCS-Mikrodaten vorhandenen Informationen zur Vermögensverteilung auf die VGR-Makrodaten übertragen werden.

Die regelmäßig von Forbes und trend veröffentlichten Reichenlisten könnten zusammen mit Annahmen über die Verläufe der Vermögensverteilung und Verschuldung zusätzliche Informationen liefern.

In dieser Studie werden unterschiedliche Szenarien aus Simulationen der Vermögenskonzentration dargestellt. Die Ergebnisse dieser Simulationen zeigen, dass das Nettovermögen des reichsten Prozents der Haushalte (Top-1-Prozent) einen Anteil am gesamten Nettovermögen aller Haushalte von zumindest 23 % bis mehr als 50 % hat. Sämtliche Informationen deuten darauf hin, dass dieser Anteil tatsächlich bei etwa 50 % liegt, Eine präzise Einschätzung potenzieller Verzerrungen und Unsicherheiten der Schätzungen ist aber weiterhin schwierig. Diese Datenlücke ließe sich nur mit der Einführung eines gesetzlich verpflichtenden Vermögensregisters schließen.

Das Zahlungsverhalten in Österreich während der COVID-19-Pandemie

Dominik Höpperger, Codruta Rusu

Diese Studie befasst sich mit der jüngsten Umfrage zur Verwendung von Zahlungsmitteln in Österreich. Diese Umfrage ließ die Oesterreichische Nationalbank 2020/21 bereits zum fünften Mal durchführen. Befragt wurden private Haushalte, und zwar Frauen und Männer ab dem 15. Lebensjahr. Die Ergebnisse sind also in Bezug auf Alter, Geschlecht und Bundesland aussagekräftig für das Zahlungsverhalten der in Österreich lebenden Menschen.

Der erste Teil der Studie befasst sich mit den Ergebnissen der Umfrage. Für rund 66 % aller Zahlungen an der Kassa wird Bargeld verwendet. Bargeld ist und bleibt das beliebteste Zahlungsmittel im stationären Handel in Österreich. Der Rückgang gegenüber 2019 wurde durch die Pandemie verstärkt. Die Menschen haben während der Pandemie bei alltäglichen Zahlungen aus unterschiedlichen Gründen seltener Bargeld verwendet. Einerseits besteht ein allgemeiner Trend zu elektronischen Zahlungen, andererseits wirkten sich die Beschränkungen, die zur Bekämpfung der Pandemie eingeführt wurden, auf Tätigkeiten aus, bei denen sonst viel bar bezahlt wird. Dazu gehören etwa Reisen, Freizeitaktivitäten und kulturelle Veranstaltungen. Insgesamt scheint die Pandemie den Trend zur Zahlung mit Karten beschleunigt zu haben. Ob und wie die Pandemie die Art und Weise, wie die Bevölkerung an der Kassa oder im Internet bezahlt, langfristig verändern wird, bleibt abzuwarten. Dabei wird es darauf ankommen, wann die Maßnahmen zur Pandemiebekämpfung umfassend gelockert werden und sich unser wirtschaftlicher und gesellschaftlicher Alltag wieder normalisiert, aber auch darauf, welche Möglichkeiten für digitales Bezahlen die Unternehmen ihren Kundinnen und Kunden bieten.

Im zweiten Teil der Studie untersuchen wir den Zusammenhang zwischen dem Rückgang der Barzahlungen und dem von den Befragten subjektiv wahrgenommenen Ansteckungsrisiko durch Bargeld. Die Ergebnisse zeigen: Je höher die Befragten das Risiko einstuften, sich über Banknoten und Münzen mit dem Corona-Virus anzustecken, desto seltener bezahlten sie in bar. Das wahrgenommene Risiko wurde dabei oft stark überbewertet. Tatsächlich wird das Ansteckungsrisiko, das von Bargeld ausgeht, in zahlreichen wissenschaftlichen Untersuchungen als äußerst gering eingestuft.

Exchange rate index update for Austria shows lower effective appreciation than previously measured

Ursula Glauninger, Thomas Url, Klaus Vondra ² Refereed by: Benjamin Bitschi (WIFO), Julia Grübler (WTO)

This article reports on the most recent update of Austria’s effective exchange rate indices, which serve to aggregate data on bilateral exchange rates and relative prices or costs into indicators of Austria’s short- to medium-term international competitive position. As before, the weighting scheme builds on bilateral trade data for Austria’s 56 most important trading partners and a three-year averaging period, which we were able to move forward to the period 2013–2015. Having recalculated existing observations from January 2013 onward, we find confirmation for the medium-term worsening of Austria’s competitive position, but in a less pronounced form than suggested by the previous weighting scheme. On the tail end of the curve, the COVID-19 crisis in general and short-time work subsidies in particular have distorted several indicators in 2020 and 2021. With regard to the geographical focus of Austria’s international trade relations, we observe a shift away from the large EU economies towards the USA and China, plus a weaker shift from Northeastern Europe towards Eastern Europe and Turkey. Given the economic relevance of tourism for Austria, we newly created a real effective exchange rate for the tourism industry. In this segment of the economy, we see a more pronounced appreciation than in the service sector as a whole from 2015 onward, which would normally imply a decline in tourism services output. That Austria’s tourism industry clearly continued to thrive indicates that the appreciation coincided with an upward shift of prices and supply toward higher quality segments.

JEL classification: C43, F14, F47

Keywords: international competitiveness, COVID-19, tourism services

Recent examples for the practical use of effective exchange rate indices in analyzing the response of small open economies to exchange rate fluctuations are Fauceglia et al. (2018) and Dao et al. (2021). The real effective exchange rate can also be used to evaluate the transmission of foreign monetary and financial shocks to the tradable goods and services sectors of the economy. In sum, accurate measures of the effective exchange rate are essential input for market participants as well as policymakers.

To avoid a plethora of incompatible effective exchange rate indices across the euro area, member countries committed themselves in 1999 to apply a harmonized methodology (Schmitz et al., 2012) and to revise their weighting schemes for trading partners at regular intervals. This ensures comparability and incorporates changing trading patterns. The Austrian indices were last revised by the OeNB and WIFO in 2017 (see Köhler-Töglhofer et al., 2017). Upon release of the 2018 set of OECD-TiVA input-output tables on bilateral foreign trade flows, we were able to move forward the three-year averaging period for adjusting the exchange rate weights from 2010–2012 to 2013–2015.

As outlined below, the new weights produce a less pronounced appreciation throughout the review period from 2013 to 2021, particularly for the nominal effective exchange rate. In terms of individual shifts, the trade weight of Germany was scaled down most, while the United States showed the most vigorous gain.

Besides, we broadened the range of real effective exchange rates by developing a novel indicator for the price competitiveness of the Austrian tourism industry, using relative prices for tourism-related services in the consumer price index. After all, the COVID-19 pandemic has highlighted the high dependency of Austria’s economic output on a thriving tourism sector.

The tourism-specific real effective exchange rate is illustrative of the strengths and weaknesses of the real effective exchange rate as a measure of competitiveness. First, a trade-weighted scheme implicitly assumes that countries trade homogenous goods with a constant elasticity of substitution (Armington, 1969). If the degree of product differentiation among countries is high, e.g. skiing in the Alps versus visiting a tropical destination, the elasticity of substitution between imports from different regions varies and the fluctuations of different foreign currencies will have different effects on tourism demand. Second, the homogenous-goods assumption ignores different price and income elasticities of demand for individual goods (Klau and Fung, 2006). Effective exchange rate changes will affect the relative demand for, or the relative prices of, any pair of goods differently. If the countries covered by the weighting scheme have similar economic structures, the homogenous-goods assumption will not result in serious misjudgment; but if the scheme mixes countries with highly different export product structures, conclusions about the economic consequences of effective exchange rate appreciation become more uncertain.

In what follows, section 1 reviews the main characteristics of Austria’s price/cost competitiveness indicators, which continue to apply. Section 2 addresses the recalculation of the country weights based on the trade relations prevailing during the period 2013–2015. Section 3 provides a snapshot of Austria’s competitiveness position among other economies based on updated weights. Section 4 presents and analyzes the new real effective exchange rate for tourism services.

1 Main characteristics of competitiveness indicators for Austria remain unchanged

• The aggregate index is a trade-weighted average of four subindices calculated separately for manufactured goods, food and beverages, raw materials/energy products, and services. Introducing subindices alleviates possible violations of the homogeneity assumption underlying the single weighting structure and therefore allows for differences in the degree of substitutability (Turner and Van’t dack, 1993). Moreover, this allows us to use a higher number of trading partners (56 instead of 43) covering 96% of total export flows.
• The index is based on geometric weighting, i.e. it represents the weighted geometric average of a basket of bilateral exchange rates, which yields the price or cost competitiveness indicator when adjusted for the respective relative price or cost indices.
• The individual country weights in the subindex for manufactured goods continue to be calculated on the basis of single (bilateral) import and double (multilateral) export weights. Double export weights are the method of choice to catch third-market effects, as they reflect both home and external market competition with individual trading partners (depicted in competition matrices; see table A2 in the annex). The drawback of double export weights is that they are more difficult to calculate, ³ less intuitive, and require data based on OECD-TiVA input-output tables with a larger publication lag.
• The index base period was left unchanged at the first-quarter average (arithmetic mean) of 1999 (i.e. 1999 Q1 = 100), which is the base period established by the harmonized Eurosystem framework.
• The new weights based on the 2013–2015 period apply to all observations beginning with January 2013. Earlier observations have been chain-linked to the new exchange rate indices. ⁴
• We use a range of deflators to calculate the Austrian competitiveness indicators: the HICP/CPI and its tourism-related components (COICOP division 11), producer prices (PPI), and unit labor costs (ULC) for the whole economy. ⁵ In practice, we use both the HICP/CPI and the PPI to calculate the subindex for the manufacturing sector, and both the HICP/CPI and ULC to calculate the subindices for the service sector and the index for the total economy. ⁶ Additionally, we use the components of the HICP/CPI related to tourism spending to deflate the subindex for the service sector. The subindices for food and beverages and for raw materials/energy are based solely on the HICP/CPI.

The regular revisions of the harmonized competitiveness indicators generally provide room for adjustment in the sample of trading partners, reflecting changes in export patterns. Since the current sample of 56 countries covers 96% of Austrian exports, we left the number of countries unchanged. We continue to add the export shares of countries not included in the index to the weight of the USA, based on the assumption that these trade flows are invoiced in US dollars (Gopinath et al., 2020; see table A1 in the annex).

2 Country weights – comparatively stable ranking of Austria’s trading partners

The changes in Austria’s regional trade structure are noteworthy particularly given the further opening of the Austrian economy in recent decades and continued efforts to integrate members of the European Single Market more seamlessly. Comparing the data for the current reference period 2013–2015 with the base period 1998–2000, we see a substantial decline in the weight of Austria’s EU trading partners (by 7.3 percentage points to 65.3%) and other euro area countries (by 9.5 percentage points to 53.8%).

Ultimately, Austria would thus not appear to have gained measurable positive trade effects from the elimination of exchange rate uncertainty within the euro area (EA-19). The empirical evidence on the effects of exchange rate uncertainty on foreign trade is mixed. Clark et al. (2004) find a negative relation indicating that higher uncertainty lowers export flows, but their result is not robust against reasonable changes in the specification. Bahmani-Oskooee and Hegerty (2007) find inconclusive evidence for the relation between exchange rate volatility and export flows. Ambiguity can arise from the coincidence of deeper integration and the remaining exchange rate uncertainty with respect to non-euro members of the EU. For example, the weight of countries outside of the euro area but within the EU-27 ¹⁰ was shown to have increased by 2.2 percentage points to 11.5%. In this case, positive effects from trade integration dominate the higher degree of exchange rate uncertainty with respect to trading partners outside the currency union. Similarly, the weight of CESEE countries grew by 4.6 percentage points to 14.7%. Thus, the potentially negative effect from increased exchange rate uncertainty has been more than compensated by stronger economic integration with Eastern Europe, favored by geographical proximity and the higher economic dynamism of this region. Furthermore, some Eastern European countries have managed to hold a stable exchange rate against the euro.

Southeast and East Asian countries also benefited from highly dynamic economic growth and the more intensified international division of labor. The trade weight of this group of countries moved up by 5.3 percentage points.

The regional relocation of foreign trade was mainly driven by two large economies: Germany and China. While Germany’s country weight declined by 5.8 percentage points to 31.1% over the last 15 years, China gained 6 percentage points to 7.7% and now ranks second among the 56 countries, having even surpassed the USA (7.1%).

The long-run regional shift proceeded also in the short run between 2010–2012 and 2013–2015. Figure 1 shows a world map where all countries included in the weighting scheme are colored corresponding to the size of this short-run change in their weight. Dark green indicates countries with a visibly higher trade weight following the latest update of the index (USA, China, and Switzerland, with gains ranging from 0.4 to 1 percentage points), while dark blue indicates a substantial decline (Germany, France, Italy, with losses between 0.5 and 1.1 percentage points). Countries not included in the currency basket for the effective exchange rate are colored in white.

Furthermore, trade relations with Brazil and countries in the northeast of Europe weakened, whereas they remained stable with neighboring countries like Czechia and Hungary (colored gray). Overall, the trading pattern shifted toward CESEE, the UK, the Netherlands, and Turkey. For detailed values for all weights, see table A1 in the appendix.

The calculation of the weights for the manufactured goods subindex is based on double export weights and therefore reflects direct bilateral trade flows as well as the indirect effects of competition from third countries on the destination markets of Austrian exports. For instance, Austrian exports to Germany face competition from German firms on the German market but also from firms located in other countries also exporting to Germany. The size of this effect can be seen by comparing single export weights with double export weights in chart 1. The axis in chart 1 has been cut at 10% to facilitate the comparison for countries with smaller weights. For exact numbers, including the full figures for Germany, see table A3 in the appendix.

For most of the countries, the difference between single export weights and double export weights is small. Exceptions include Germany, with a single export weight of 31.0% and a double export weight of 23.6% (the single highest measures of all countries included in the index). In other words, German firms are less of a competition for Austrian firms on international export destinations than on the German market itself. This may be so because German exporters target other regions or export different goods, e.g. a higher share of final consumer goods. Two other countries with distinctively higher single export weights are Switzerland and Hungary. In contrast, there are several countries with a relatively higher double export weight. In particular, China’s double export weight is almost three times the size of its single export weight. This makes Chinese exporters stronger competitors for Austrian firms internationally than on China’s home market for manufactured products. To a lesser extent, this also holds for firms from the Netherlands, Italy, Belgium, Japan, South Korea, the USA, Spain, and India.

Table A3 in the appendix also presents values for previous reference periods, thus facilitating long-term comparisons. Over time, French and US exporters have become increasingly less relevant as competitors for Austrian exporters. The same holds, to a lesser extent, for producers located in Japan, the UK, Germany and Italy. In contrast, firms from China leapt forward, to the second double-weight rank. Furthermore, Dutch firms, which used to have a neutral position with respect to third-market competition, have turned into competitors.

The country weights for Austrian services exports are more stable and show only minor changes in bilateral trade flows. For example, 72.4% of services trade occurs between Austria and other EU member states and 58.6% of Austria’s services trade is concentrated within the euro area. The most important destination for Austria’s services exports is Germany with a country weight of 36.3%, followed by the USA (7.4%), Switzerland (6.1%), Italy (5.2%), the UK (4%) and the Netherlands (3.5%). The weights for the services subindex are mainly determined by trade flows in travel including international passenger transport (34%), as well as other business-related service exports (22%), transport services excluding passenger transport (20%) and telecommunication and information services (9%).

Imports and exports of raw materials and energy are less concentrated on trading partners located in the EU. Total imports to Austria from EU member countries amount to 57.6%, with 28.4% coming from Germany. The second biggest source of raw materials and energy imports is the USA (18.8%), followed by Russia (12.2%). In contrast, the subindex on food and beverages is dominated by trading partners from the EU, which account for 82.4% of imports and 73.9% of exports. Again, Germany tops the list, with 38.7% of imports and 33.7% of exports. Italy comes in a strong second, supplying 11.1% of Austria’s food and beverages imports and taking 13.5% of Austria’s exports.

3 Price competitiveness after the European government debt crisis

The ECB announcement of unlimited support for the euro in summer 2012 continued to support the euro and induced an appreciation of the nominal effective exchange rate throughout 2013 until doubts about the political stability in Greece and the common support for the bailout plan designed by the EU Commission, the ECB, and the IMF emerged (chart 2). Political uncertainty about the common currency project was accompanied by an effective nominal depreciation of 5.1% between March 2014 and April 2015. The agreement about the third Greek bailout in August 2015 supported another rally of the euro, peaking in February 2016, which was followed by a cycle of ups and downs, leaving the nominal effective exchange rate in August 2021 almost 4% above its level in early 2013. The emergence of the COVID-19 pandemic in Europe in March 2020 coincided with a month-on-month jump of the nominal effective exchange rate by 1.6%; this started another appreciation cycle. Based on the weights for the new base period 2013–2015 (left-hand panel of chart 2), the nominal appreciation appears to have been less pronounced since early 2013, however.

3.1 Recent appreciation not yet corrected by lower inflation in Austria

When we change the perspective, using the producer price index (PPI) for Austrian manufacturers to deflate the export-weighted real effective exchange rate, we find almost no change in price competitiveness (+0.3%) since early 2020 (chart 3, left-hand panel). In a long-term perspective since 1999, a PPI-based comparison reveals a decline of the real effective exchange rate by 5.8%, i.e. a distinct gain in price competitiveness compared to the HICP/CPI-based index. This deviation may be due to the smaller sample (26 countries for the PPI-based index, 56 countries for the HICP/CPI-based index). Or, it may reflect the comparatively moderate increases in Austrian producer prices, based on higher productivity growth and comparatively low wage increases.

European monetary union restricts adjustments of the real effective exchange rate between member countries of the euro area to changes in relative prices, i.e. deviations in relative inflation rates. Austria’s long-term position against other euro area countries has, indeed, remained almost stable (chart 3, right-hand panel). In the 22 years since 1999, we observe a small appreciation of the real effective exchange rate based on HICP/CPI with respect to the EA-19 of 2.7%. Vis-à-vis non-euro area members of the EU, Austria visibly gained in price competitiveness. The USA shows marked variations but was almost back to its starting level in 2021. Japan is an outlier, featuring low inflation rates but at the same time a considerable appreciation of its currency during the European government debt crisis.

3.2 Loss of cost competitiveness prolonged

3.3 COVID-19 crisis characterized by euro area-wide convergence of total unit labor costs

At the end of the euro area sample, we again see signs of a crisis, but this time in the context of the global COVID-19 pandemic. Widespread lockdowns restricting social life and economic activity have been accompanied by government subsidies to firms and monetary transfers to households. The development of unit labor costs has been affected above all by short-time work schemes. Government support created a divergence between value added and the wage bill, which is visible in chart 5 as sharp spikes during 2020 and 2021. Extreme output reductions have been met by the deliberately smoothed wage bill and the structural effect resulting from higher unemployment of low paid unskilled workers (OECD, 2021), for whom employers were less inclined to take up short-time work arrangements. The COVID-19 crisis created a jump in unit labor costs throughout the euro area in the first half of 2020, which was swiftly corrected in the fall but ultimately gave way to a renewed sharp increase in total labor costs amid adverse developments in spring 2021.

The COVID-19-related lockdowns and short-time work schemes were associated with further ULC convergence throughout Europe until mid-2021, as short-term work schemes were being phased out at different speeds and supply-side bottlenecks related to intermediate products were putting increasing strain on the economy.

4 Price competitiveness in the service sector, and in the accommodation sector in particular

In analyzing the international competitiveness position of Austria’s service sector, we need to distinguish between those services provided only or mainly domestically and those services which are traded. Within the latter, there are three main categories of services: tourism, transport and business services. Current account data show that – before the COVID-19 crisis – almost ⅓ of Austrian service exports were related to tourism, ¼ to transport and ½ to business services. ¹¹ Since business services are closely linked to manufacturing activity, they are also closely interrelated to goods exports. In contrast, tourism has much lower linkages to the manufacturing industry, but it is nevertheless characterized by competition between regions/countries. However, to our knowledge until now no comprehensive measure of competitiveness, such as a real effective exchange rate, has been developed with respect to tourism trade flows.

4.1 Domestic service providers lost price competitiveness in the years before the COVID-19 crisis

In the first years after euro area accession, the Austrian service sector managed to strongly improve its competitiveness position (chart 6), both in terms of costs and prices, in line with a depreciation trend. This development reversed until mid-2000, with a stronger backlash from prices than costs. Thereafter, both indicators converged to similar levels and stagnated (with slight ups and downs) in a synchronized manner for about ten years. In the years before the outbreak of the COVID-19 crisis, both exchange rates had started to trend upward, indicating a loss in Austrian tradable services competitiveness (appreciation) vis-à-vis trading partners.

The upward trend in the two real effective exchange rates intensified in the last two years of the sample. However, the development during the COVID-19 crisis must be taken with caution, as all competitiveness indicators are biased during that phase. ¹³ Viewed over the full sample period, the initial improvement in service competitiveness melted away over the past 20 years. Both real effective exchange rates – deflated by unit labor costs and deflated by HICP – are currently very close to their values in the first quarter of 1999.

4.2 Tourism as a key pillar of the Austrian economy ¹⁴

There is no straightforward way to statistically capture the importance of tourism for the national economy, as within the framework of the System of National Accounts this sector can only be approximated by the sum of the NACE service sectors I (accommodation and food services) and R (arts, entertainment and recreation). Without further information, it is not possible to distinguish activities consumed by residents from services bought by tourists. Still, the sum of these two sectors may serve as an approximation for the importance of tourism in Austria. Together, these two sectors accounted for 6.6% of total value added in 2019 (2020 data not yet available).

Table 1: The importance of tourism for the Austrian economy  

	2019
National accounts data – value added	EUR million	Share in value added in %
Accommodation and food service activities (NACE I)	18,869	5.3
Arts, entertainment and recreation (NACE R)	4,485	1.3
Sectors I and R	23,354	6.6
Tourism satellite accounts (TSA) – GDP	EUR million	Share of GDP in %
Direct value added excl. business trips	22,135	5.6
Direct value added incl. business trips	23,545	5.9
Direct and indirect value added	29,171	7.3
Source: Statistics Austria, Eurostat.

An alternative and conceptually more precise way to assess the importance of this sector are tourism satellite accounts (TSA), which have been available for Austria since 1999. ¹⁵ Counting only direct effects and excluding business trips, tourism in Austria contributed 5.6% to total GDP in 2019 (see table 1). In a broader sense – including indirect effects and business trips – the share was 7.3%. Since 2000 this proportion has remained almost unchanged. If – on top of that – the whole leisure industry is included as well, then the share doubles to almost 15% (Laimer et al., 2013). ¹⁶

Capturing the importance of the tourism industry is even more complicated for cross-country comparisons, as TSAs are not available or comparable (due to different concepts, data and/or publishing periods) for many countries. But in general, for most countries the TSA results are broadly similar to the share of the sectors I and R in total value added. Within Europe, the share of tourism in value added is highest among Mediterranean countries, followed by Austria with a markedly higher share than for example in Germany, Switzerland, or Denmark.

4.3 Real effective exchange rates for the tourism industry

In a second step, we deflate the nominal effective exchange rate with the country-specific consumer price deflators for the accommodation sector (COICOP 11 classification). The resulting real effective exchange rate index (green line in chart 7) ¹⁹ shows a much stronger depreciation than the nominal effective exchange rate between 1999 and 2002, indicating an improvement by almost 10% in this early stage of monetary union. In the following countermovement – up until mid-2003 – the accommodation sector lost around half of its prior gains. In the subsequent period until 2015, the real effective exchange rate evolved at a rather stable rate with some ups and downs. Thereafter, the overall stable corridor gave way to a steady upward trend. Between April 2015 and July 2021, the relative competitiveness position of Austria’s accommodation sector deteriorated by 11% mainly due to higher inflation rates in this sector compared to the main trading partners. Due to the antecedent depreciation, this later appreciation resulted in an overall loss of competitiveness of 4% compared to early 1999 vis-à-vis Austria’s 43 most important trading partners for accommodation exports.

Next, we cross-check these results with data on overnight stays and export revenues generated by tourism in Austria (chart 8). These data mark the structural break brought on by the ongoing COVID-19 pandemic, but they are not as current as the exchange rate data. In late 2021, it is still unclear whether the drop in overnight stays and revenues will ultimately be found to have been a temporary phenomenon or what the long-run consequences will be. The steady upward trend in overnight stays, ²⁰ which are a measure of quantity but disregard the price aspect, got steeper from early 2015 onward until the onset of the COVID-19 crisis. The tourist travel expenditures taken from the balance of payment statistics, ²¹ which cover both quantity and price effects, show a similar pattern over the recent years. They indicate a weak upward trend between 1999 and 2007, followed by a weak downward trend until 2014. Since 2015, there has been a distinct upward trend. Higher (nominal) expenditures in a longer perspective are a result of increasing prices, but this increase in Austria is very much related to the improvements in the quality of the product “vacation.” Between 2015 and 2020, we recorded a 14% increase of upper price class hotels (with four and five stars). ²² As pointed out by Smeral (2015), demand for holidays is directly related to household income; the income elasticity for holidays is bigger than one, indicating that the quality improvement has triggered additional demand, which is characterized by the willingness to spend more.

The appreciation of the real effective exchange rate since 2015, which (1) indicates a loss in price competitiveness and (2) a distinct upward trend in the number of overnight stays and expenditures of foreign tourists since 2015, appear to be conflicting developments in a demand-side interpretation under ceteris paribus conditions (e.g. no change in quality). Higher relative costs should normally lead to less demand. However, when viewed from the supply side, the Austrian tourism sector succeeded in raising prices relative to its competitors, as demand for its product – holiday stays in Austria – increased. As already stated before, this development is also in line with the observed trend toward higher price segments, i.e. four and five star hotels. Furthermore, the share of holiday packages in the high-expenditure segment – conference, sports (skiing) and culture tourism – has increased in Austria. These developments hint towards a low price elasticity of demand of foreign tourists: Their decision to visit Austria is driven by quality and unique experience and less by relative prices. ²³ Hence, nonprice competitiveness aspects play a major role. ²⁴

5 Summary

Using data on bilateral trade flows with Austria’s 56 most important trading partners over the period 2013–2015 to adjust the weighting scheme, the OeNB and with WIFO (Austrian Institute of Economic Research) again joined efforts to recalculate the Austrian competitiveness indicator and its four subindices, from January 2013 onward. Our four subindices cover manufactured goods, food and beverages, raw materials and energy products, and services. Individual country weights in the subindex for manufactured goods continue to be calculated on the basis of single (bilateral) import and double (multilateral) export weights. The remaining subindices use only single (bilateral) import and export weights. All in all, we use four different deflators to calculate the harmonized competitiveness indicators, each having its own pros and cons in terms of timely availability across countries, international comparability, and the degree of focus on tradable goods. The four deflators are the HICP/CPI, the tourism-related components of the HICP/CPI, producer prices, and unit labor costs of the economy as a whole.

Austria’s biggest neighboring economy, Germany, remains the key trading partner with a share of 32.5% in the total weighting scheme, but its share has been declining over time. The USA (7.8%) emerged as the second-largest trading partner as Italy (6.5%) moved down by one rank. With a weight of 5%, China continues to hold the fourth position. From a regional perspective, trade patterns shifted away from large EU member countries toward the USA and China. Trading relations weakened somewhat with respect to North Eastern Europe and Brazil, while the UK, some Eastern European countries, and Turkey reinforced their trading relations with Austria.

The new competitiveness indicator shows that Austria’s competitive position has been weakening in the medium term, starting with the government debt crisis in the euro area. The real effective exchange rate has been appreciating, with sharper spikes since the COVID-19 pandemic hit Europe. At the same time, the appreciation has been somewhat less pronounced than suggested by the previous weighting scheme. The loss of competitiveness is highest when using unit labor costs as the deflator. This measure, however, is biased at the moment due to the extensive use of short-term work schemes in Austria, which smoothed the development of per capita wages against the sharp drop in per capita GDP during lockdowns.

For the service sector, the real effective exchange rates (both for the price and the cost side) indicate a gradual appreciation since 2015. The same holds for the accommodation industry, but at a higher pace. From a demand perspective, this indicates a loss in Austria’s competitiveness position. Ceteris paribus and under the assumption of a homogenous good, this loss in competitiveness should go along with a reduction in demand. Despite the recent appreciation, the Austrian tourism industry, measured by foreign overnight stays and by tourist expenditures in the current account statistics, has clearly improved its “output” in recent years. In other words, the appreciation should not be interpreted as a pure demand-dampening effect; rather, it shows the ability of the tourism industry to set higher prices.

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ECB. 2000. The nominal and real effective exchange rates of the euro. Monthly Bulletin. (April). 39–48.

ECB. 2003. Developments in the euro area’s international cost and price competitiveness. Monthly Bulletin, (August). 67–74.

ECB. 2010. International trade developments and revision of the effective exchange rates of the euro. Monthly Bulletin, (January). 55–58.

ECB. 2012. The effective exchange rates of the euro – revised trade weights in the light of global economic integration. Monthly Bulletin, (March). 17–21.

ECB. 2015. Revised trade weights for the effective exchange rates of the euro reflect the increasing importance of emerging market economies. Economic Bulletin 6. 45–48. https://www.ecb.europa.eu/pub/pdf/ecbu/eb201506.en.pdf?b58dfa168a33d6dc69a2d397d0982e45

ECB. 2020. The ECB’s enhanced effective exchange rate measures. Economic Bulletin (6). 50–54. https://www.ecb.europa.eu/pub/pdf/ecbu/eb202006.en.pdf

Appendix

Table A1: Exchange rate index update for Austria – new weighting scheme  

Competing countries	Austrian exports						Austrian imports
	Manufactured goods	Raw materials, energy products	Food	Goods	Services	Total	Manufactured goods	Raw materials, energy products	Food
	Country weights in %, calculated for the period from 2013 to 2015
Belgium	2.53	0.46	0.99	2.30	1.62	2.11	1.72	0.39	1.74
Bulgaria	0.34	0.34	0.43	0.35	0.48	0.38	0.35	0.09	0.30
Croatia	0.47	1.03	1.20	0.55	0.79	0.62	0.45	0.31	0.41
Cyprus	0.02	0.04	0.10	0.03	0.33	0.11	0.03	0.00	0.07
Czechia	2.81	4.84	2.55	2.90	2.37	2.75	3.68	7.40	2.98
Denmark	0.66	0.17	0.66	0.64	0.63	0.63	0.43	0.14	0.73
Estonia	0.08	0.05	0.07	0.08	0.06	0.07	0.03	0.04	0.01
Finland	0.55	0.12	0.28	0.50	0.61	0.53	0.40	0.18	0.07
France	5.18	2.02	2.17	4.79	2.28	4.08	3.13	0.88	3.45
Germany	23.56	26.86	33.67	24.47	40.41	29.01	39.34	28.45	38.67
Greece	0.27	0.13	0.65	0.29	0.26	0.28	0.10	0.09	0.73
Hungary	1.57	7.73	4.05	2.09	2.58	2.23	2.51	3.35	5.17
Ireland	0.75	0.02	0.08	0.66	0.61	0.65	0.63	0.09	0.40
Italy	6.66	17.89	13.47	7.77	4.89	6.95	6.32	3.33	11.12
Latvia	0.08	0.02	0.10	0.08	0.09	0.08	0.02	0.04	0.04
Lithuania	0.16	0.12	0.14	0.16	0.08	0.14	0.06	0.03	0.19
Luxembourg	0.13	0.04	0.05	0.12	0.75	0.30	0.23	0.02	0.12
Malta	0.02	0.03	0.10	0.02	0.22	0.08	0.01	0.00	0.00
Netherlands	3.14	0.92	2.54	2.97	4.09	3.29	2.61	1.89	4.93
Poland	3.19	1.16	1.99	3.00	1.51	2.57	1.96	1.92	3.81
Portugal	0.41	0.12	0.15	0.37	0.15	0.31	0.45	0.08	0.14
Romania	1.27	1.20	1.01	1.25	1.49	1.32	0.97	0.68	0.79
Slovakia	1.52	3.96	1.82	1.68	1.59	1.65	1.81	4.94	1.90
Slovenia	0.78	9.08	3.45	1.43	1.11	1.34	1.14	2.40	0.88
Spain	2.44	0.76	1.22	2.26	0.79	1.84	1.80	0.28	3.49
Sweden	1.28	0.23	0.98	1.20	1.47	1.28	1.11	0.63	0.21
Australia	0.43	0.13	0.75	0.43	0.28	0.39	0.04	0.12	0.12
Bosnia and Herzegovina	0.15	0.18	0.48	0.18	0.22	0.19	0.35	0.41	0.09
Brazil	0.76	0.11	0.42	0.70	0.18	0.56	0.11	0.61	1.24
Canada	0.80	0.03	0.14	0.71	0.40	0.62	0.33	0.28	0.09
Chile	0.11	0.00	0.17	0.11	0.11	0.11	0.04	0.25	0.34
China	8.16	2.45	0.48	7.29	0.97	5.49	7.16	0.33	0.61
Hong Kong	0.81	0.20	0.19	0.73	0.19	0.57	0.08	0.00	0.00
Iceland	0.02	0.00	0.01	0.02	0.03	0.02	0.01	0.00	0.01
India	1.08	0.47	0.09	0.98	0.23	0.76	0.58	0.11	0.32
Iran	0.16	0.03	0.08	0.15	0.08	0.13	0.01	0.02	0.08
Israel	0.29	0.27	0.32	0.29	0.21	0.27	0.14	0.03	0.11
Japan	2.10	1.46	0.80	1.97	0.47	1.54	1.73	0.05	0.06
Malaysia	0.52	0.02	0.03	0.46	0.09	0.35	0.31	0.02	0.03
Mexico	0.77	0.02	0.02	0.68	0.16	0.53	0.21	0.66	0.17
New Zealand	0.08	0.00	0.06	0.08	0.07	0.07	0.02	0.02	0.18
Norway	0.36	0.05	0.22	0.34	0.42	0.36	0.16	0.43	0.32
Russian Federation	2.23	0.49	1.76	2.10	2.21	2.13	0.32	12.20	0.09
Saudi Arabia	0.41	0.10	0.36	0.39	0.28	0.36	0.03	1.77	0.00
Serbia	0.32	0.39	0.47	0.33	0.35	0.34	0.24	0.25	0.73
Singapore	0.66	0.00	0.06	0.58	0.16	0.46	0.11	0.00	0.01
South Africa	0.44	0.03	0.25	0.40	0.13	0.32	0.08	1.62	0.39
South Korea	1.66	0.73	0.61	1.53	0.22	1.16	0.75	0.02	0.03
Switzerland	3.61	7.72	4.09	3.87	7.40	4.88	4.80	0.79	3.90
Taiwan	0.67	0.14	0.10	0.60	0.12	0.47	0.55	0.01	0.01
Thailand	0.54	0.04	0.03	0.47	0.10	0.37	0.46	0.08	0.33
Turkey	1.46	1.17	0.70	1.39	1.10	1.31	1.06	0.38	1.87
Ukraine	0.35	0.31	0.50	0.36	0.44	0.38	0.17	2.08	0.24
United Arab Emirates	0.40	0.11	0.40	0.39	0.62	0.45	0.16	0.01	0.01
United Kingdom	3.29	1.59	1.94	3.10	3.80	3.30	1.95	1.00	1.10
USA	7.49	2.42	10.51	7.42	7.72	7.51	6.76	18.82	5.17
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00
Source: OeNB/WIFO.

Table A1 continued: Exchange rate index update for Austria – new weighting scheme  

Competing countries	Austrian imports			Exports and imports
	Goods	Services	Total	Manufactured goods	Raw materials, energy products	Food	Goods	Services	Total
	Country weights in %, calculated for the period from 2013 to 2015
Belgium	1.52	1.77	1.58	2.14	0.41	1.37	1.91	1.69	1.85
Bulgaria	0.31	1.07	0.49	0.35	0.16	0.36	0.33	0.75	0.44
Croatia	0.43	2.33	0.88	0.46	0.50	0.80	0.49	1.48	0.75
Cyprus	0.03	0.44	0.13	0.03	0.01	0.09	0.03	0.38	0.12
Czechia	4.17	2.96	3.88	3.22	6.70	2.77	3.54	2.63	3.30
Denmark	0.41	0.39	0.41	0.55	0.15	0.70	0.52	0.52	0.52
Estonia	0.03	0.17	0.06	0.05	0.04	0.04	0.05	0.11	0.07
Finland	0.34	1.12	0.53	0.48	0.16	0.18	0.42	0.83	0.53
France	2.83	2.42	2.73	4.21	1.19	2.83	3.80	2.34	3.42
Germany	37.70	31.14	36.14	31.07	28.01	36.23	31.14	36.29	32.50
Greece	0.15	0.99	0.35	0.19	0.10	0.69	0.22	0.59	0.31
Hungary	2.82	3.46	2.98	2.02	4.55	4.62	2.46	2.97	2.59
Ireland	0.54	1.38	0.74	0.69	0.07	0.24	0.60	0.95	0.69
Italy	6.24	5.67	6.10	6.50	7.29	12.27	7.00	5.24	6.54
Latvia	0.02	0.14	0.05	0.05	0.04	0.07	0.05	0.11	0.07
Lithuania	0.07	0.41	0.15	0.11	0.06	0.16	0.11	0.23	0.14
Luxembourg	0.19	1.08	0.40	0.18	0.02	0.09	0.16	0.90	0.35
Malta	0.01	0.23	0.06	0.02	0.01	0.05	0.02	0.22	0.07
Netherlands	2.68	2.68	2.68	2.89	1.63	3.76	2.82	3.46	2.99
Poland	2.09	2.55	2.20	2.61	1.71	2.92	2.54	1.97	2.39
Portugal	0.38	0.45	0.39	0.43	0.09	0.14	0.37	0.29	0.35
Romania	0.91	2.22	1.22	1.13	0.82	0.90	1.08	1.81	1.27
Slovakia	2.28	3.02	2.45	1.66	4.67	1.86	1.98	2.23	2.05
Slovenia	1.30	1.85	1.43	0.95	4.22	2.14	1.37	1.44	1.39
Spain	1.70	1.89	1.75	2.13	0.41	2.38	1.98	1.28	1.80
Sweden	0.97	1.96	1.21	1.20	0.53	0.58	1.09	1.69	1.25
Australia	0.06	0.24	0.10	0.24	0.12	0.43	0.24	0.26	0.25
Bosnia and Herzegovina	0.34	0.28	0.33	0.25	0.35	0.28	0.26	0.24	0.26
Brazil	0.26	0.27	0.27	0.45	0.47	0.84	0.48	0.22	0.41
Canada	0.31	0.50	0.35	0.58	0.21	0.11	0.51	0.44	0.49
Chile	0.10	0.09	0.09	0.08	0.18	0.26	0.10	0.10	0.10
China	5.68	0.87	4.54	7.68	0.91	0.55	6.48	0.92	5.02
Hong Kong	0.06	0.26	0.11	0.46	0.06	0.09	0.39	0.22	0.35
Iceland	0.01	0.04	0.02	0.02	0.00	0.01	0.01	0.03	0.02
India	0.49	0.34	0.46	0.84	0.21	0.21	0.73	0.28	0.61
Iran	0.01	0.05	0.02	0.09	0.02	0.08	0.08	0.07	0.08
Israel	0.12	0.19	0.14	0.22	0.09	0.21	0.21	0.20	0.20
Japan	1.36	0.27	1.10	1.92	0.44	0.42	1.66	0.38	1.33
Malaysia	0.24	0.29	0.25	0.42	0.02	0.03	0.35	0.18	0.30
Mexico	0.28	0.12	0.24	0.51	0.49	0.10	0.47	0.14	0.39
New Zealand	0.04	0.07	0.04	0.06	0.01	0.12	0.06	0.07	0.06
Norway	0.21	0.28	0.23	0.27	0.33	0.27	0.27	0.36	0.29
Russian Federation	2.04	1.70	1.96	1.32	9.01	0.91	2.07	1.98	2.05
Saudi Arabia	0.28	0.13	0.24	0.23	1.32	0.18	0.33	0.21	0.30
Serbia	0.27	0.45	0.32	0.28	0.28	0.60	0.30	0.39	0.33
Singapore	0.08	0.17	0.10	0.39	0.00	0.03	0.33	0.17	0.29
South Africa	0.32	0.44	0.35	0.27	1.18	0.32	0.36	0.27	0.34
South Korea	0.59	0.14	0.48	1.23	0.21	0.31	1.06	0.19	0.83
Switzerland	4.15	4.54	4.24	4.18	2.68	3.99	4.01	6.12	4.57
Taiwan	0.43	0.15	0.36	0.61	0.05	0.05	0.52	0.13	0.42
Thailand	0.40	0.34	0.38	0.50	0.07	0.18	0.44	0.21	0.38
Turkey	1.02	1.21	1.06	1.27	0.60	1.30	1.20	1.15	1.19
Ukraine	0.45	0.74	0.52	0.26	1.60	0.37	0.41	0.57	0.45
United Arab Emirates	0.12	0.61	0.24	0.29	0.03	0.20	0.25	0.62	0.35
United Kingdom	1.75	4.34	2.37	2.65	1.16	1.51	2.42	4.04	2.84
USA	8.40	7.08	8.09	7.14	14.35	7.78	7.92	7.44	7.79
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00
Source: OeNB/WIFO.

Table A2: Competition matrix for manufactured goods exports from Austria  

	Destinations
Competing countries	Belgium	Bulgaria	Croatia	Cyprus	Czechia	Denmark	Estonia	Finland	France	Germany	Greece	Hungary
	Market shares in %: calculated for the period from 2013 to 2015
Belgium	10.27	2.21	1.32	1.97	1.96	2.53	1.89	1.53	6.45	3.42	2.05	2.41
Bulgaria	0.28	35.17	0.39	0.51	0.18	0.10	0.14	0.03	0.11	0.16	1.41	0.31
Croatia	0.05	0.15	36.71	0.10	0.08	0.04	0.04	0.02	0.03	0.08	0.05	0.18
Cyprus	0.00	0.03	0.01	11.19	0.01	0.02	0.04	0.00	0.00	0.00	0.34	0.00
Czechia	1.26	2.40	2.06	1.54	32.56	1.45	1.56	0.64	0.99	2.77	0.42	4.33
Denmark	0.41	0.26	0.41	0.30	0.33	40.49	1.09	1.35	0.30	0.67	0.25	0.57
Estonia	0.05	0.03	0.01	0.37	0.03	0.19	22.30	1.33	0.02	0.03	0.01	0.04
Finland	0.46	0.19	0.14	0.99	0.18	0.69	6.91	61.90	0.18	0.38	0.13	0.27
France	8.96	2.49	1.66	2.25	2.52	2.16	1.57	1.37	43.86	4.37	2.62	3.98
Germany	14.09	11.26	12.00	9.11	22.58	13.96	10.46	7.30	13.85	55.61	6.87	24.13
Greece	0.06	3.07	0.24	11.02	0.10	0.08	0.04	0.03	0.07	0.07	56.63	0.09
Hungary	0.54	2.94	3.69	0.33	2.23	0.64	1.22	0.24	0.55	1.53	0.45	19.53
Ireland	5.07	0.28	0.19	0.39	0.33	0.51	0.12	0.19	0.59	0.44	0.48	0.36
Italy	4.64	7.02	10.83	6.27	3.25	2.28	2.64	1.26	5.71	3.06	5.67	4.70
Latvia	0.03	0.08	0.02	0.72	0.08	0.29	5.62	0.14	0.02	0.03	0.02	0.05
Lithuania	0.08	0.16	0.04	0.10	0.14	0.48	3.95	0.22	0.06	0.09	0.03	0.15
Luxembourg	0.46	0.09	0.02	0.13	0.08	0.10	0.06	0.04	0.20	0.17	0.03	0.06
Malta	0.00	0.02	0.08	0.03	0.01	0.01	0.00	0.00	0.02	0.02	0.02	0.01
Netherlands	10.57	2.53	1.85	3.60	4.40	4.21	4.88	2.39	3.61	4.04	2.25	3.13
Poland	1.19	2.21	1.77	3.71	6.11	2.32	6.28	0.99	1.19	2.54	0.88	4.94
Portugal	0.35	0.17	0.09	0.40	0.21	0.28	0.18	0.09	0.76	0.39	0.18	0.26
Romania	0.32	3.91	0.56	0.30	0.85	0.21	0.44	0.13	0.50	0.68	0.61	2.61
Slovakia	0.38	1.45	1.58	1.11	5.19	0.64	0.46	0.16	0.51	0.99	0.31	3.76
Slovenia	0.11	0.67	7.21	0.20	0.40	0.27	0.22	0.05	0.18	0.32	0.11	0.72
Spain	1.79	1.25	1.08	2.59	1.12	0.97	0.93	0.60	4.55	1.44	2.27	1.56
Sweden	1.96	0.38	0.45	0.39	0.64	7.26	5.51	5.82	0.62	0.71	0.35	0.68
Australia	0.06	0.02	0.01	0.02	0.01	0.05	0.02	0.02	0.04	0.03	0.01	0.02
Bosnia and Herzegovina	0.01	0.06	1.83	0.05	0.05	0.00	0.00	0.00	0.01	0.04	0.02	0.10
Brazil	0.30	0.01	0.05	0.03	0.02	0.31	0.21	0.09	0.10	0.12	0.03	0.18
Canada	0.67	0.06	0.07	0.36	0.07	0.10	0.10	0.19	0.23	0.10	0.06	0.10
Chile	0.20	0.01	0.00	0.00	0.00	0.02	0.00	0.00	0.09	0.01	0.23	0.00
China	5.23	3.68	5.64	10.43	4.83	5.49	6.31	3.79	3.38	3.88	5.34	6.03
Hong Kong	0.98	0.21	0.20	0.34	0.55	0.51	0.91	0.65	0.62	0.54	0.21	1.63
Iceland	0.01	0.00	0.00	0.00	0.00	0.02	0.00	0.00	0.01	0.02	0.00	0.06
India	1.63	0.61	0.57	0.68	0.26	0.65	0.39	0.23	0.46	0.40	0.37	0.38
Iran	0.01	0.05	0.00	0.00	0.00	0.01	0.00	0.00	0.00	0.01	0.00	0.00
Israel	0.94	0.29	0.10	9.62	0.09	0.06	0.09	0.08	0.18	0.09	0.43	0.16
Japan	1.66	0.24	0.17	3.63	0.71	0.33	0.98	0.33	0.67	0.95	0.26	1.34
Malaysia	0.27	0.12	0.05	0.10	0.20	0.12	0.13	0.10	0.18	0.27	0.32	0.23
Mexico	0.22	0.00	0.00	0.02	0.09	0.11	0.02	0.05	0.15	0.19	0.01	0.38
New Zealand	0.01	0.00	0.00	0.00	0.00	0.03	0.00	0.00	0.01	0.01	0.00	0.00
Norway	0.18	0.04	0.15	0.26	0.08	1.29	0.67	0.47	0.10	0.18	0.12	0.06
Russian Federation	1.53	1.78	0.31	1.82	0.53	0.43	4.08	1.47	0.08	0.30	0.35	0.51
Saudi Arabia	0.56	0.02	0.01	0.09	0.00	0.00	0.47	0.01	0.03	0.01	0.19	0.00
Serbia	0.04	0.62	1.39	0.56	0.19	0.04	0.02	0.01	0.04	0.09	0.15	0.26
Singapore	1.57	0.06	0.04	0.10	0.32	0.13	0.09	0.12	0.42	0.29	0.04	0.36
South Africa	0.66	0.05	0.01	0.05	0.06	0.02	0.09	0.01	0.08	0.21	0.04	0.07
South Korea	0.69	0.37	0.75	0.55	1.16	1.50	0.64	0.27	0.35	0.41	1.61	1.67
Switzerland	1.87	1.17	1.07	0.80	0.94	0.88	0.68	0.57	1.79	2.18	1.35	1.10
Taiwan	0.37	0.38	0.20	0.30	0.26	0.32	0.72	0.47	0.18	0.33	0.20	0.55
Thailand	0.49	0.07	0.07	0.17	0.46	0.36	0.31	0.20	0.16	0.21	0.15	0.50
Turkey	0.74	5.46	1.09	2.52	0.44	0.84	0.96	0.21	0.71	0.71	1.51	0.72
Ukraine	0.04	1.57	0.16	0.23	0.17	0.10	0.42	0.04	0.02	0.07	0.16	1.07
United Arab Emirates	1.36	0.06	0.06	0.28	0.02	0.01	0.07	0.08	0.06	0.04	0.06	0.03
United Kingdom	4.62	1.74	0.97	6.10	1.83	2.65	1.72	1.53	2.72	2.22	1.69	1.94
USA	9.71	0.82	0.63	1.25	1.09	1.47	1.33	1.18	2.16	2.09	0.67	1.72
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00
Austria’s single export weights	1.41	0.51	0.93	0.10	3.41	0.56	0.09	0.41	5.09	29.78	0.29	2.91
Source: OeNB/WIFO.

Table A2 continued: Competition matrix for manufactured goods exports from Austria  

	Destinations
Competing countries	Ireland	Italy	Latvia	Lithuania	Luxembourg	Malta	Netherlands	Poland	Portugal	Romania	Slovakia	Slovenia
	Market shares in %: calculated for the period from 2013 to 2015
Belgium	1.88	2.08	2.01	3.11	17.07	0.94	6.13	2.30	2.10	1.38	1.39	1.95
Bulgaria	0.02	0.22	0.17	0.17	0.01	0.29	0.08	0.15	0.04	1.46	0.20	0.30
Croatia	0.01	0.11	0.06	0.05	0.29	0.07	0.04	0.05	0.04	0.10	0.20	2.61
Cyprus	0.07	0.00	0.04	0.13	0.00	0.64	0.04	0.00	0.00	0.01	0.04	0.01
Czechia	0.55	0.57	1.57	1.93	0.62	0.36	0.88	2.82	0.56	1.82	12.75	1.94
Denmark	0.61	0.14	1.47	1.30	0.17	0.37	0.50	0.57	0.16	0.25	0.29	0.23
Estonia	0.02	0.01	4.51	1.90	0.02	0.02	0.03	0.07	0.01	0.02	0.03	0.02
Finland	0.25	0.13	1.74	1.04	0.10	0.22	0.65	0.48	0.13	0.16	0.14	0.24
France	2.11	3.08	1.50	1.70	6.57	4.26	2.88	2.71	4.47	3.81	3.37	2.82
Germany	6.24	6.26	9.67	10.02	19.70	5.50	13.02	16.34	8.69	11.34	15.66	14.08
Greece	0.04	0.14	0.07	0.07	0.02	0.59	0.06	0.09	0.11	0.58	0.04	0.09
Hungary	0.19	0.41	0.87	0.71	0.39	0.10	0.53	1.20	0.32	3.48	5.10	2.28
Ireland	46.63	0.27	0.10	0.14	0.36	0.19	0.90	0.36	0.43	0.42	0.09	0.15
Italy	1.23	69.11	2.90	3.30	2.25	10.98	1.94	4.05	4.14	6.92	3.42	9.98
Latvia	0.01	0.01	27.27	5.04	0.05	0.04	0.02	0.14	0.01	0.02	0.07	0.03
Lithuania	0.08	0.03	7.56	35.61	0.02	0.04	0.08	0.32	0.09	0.07	0.09	0.06
Luxembourg	0.05	0.05	0.08	0.05	26.34	0.10	0.13	0.10	0.04	0.05	0.07	0.11
Malta	0.01	0.01	0.00	0.01	0.00	11.08	0.01	0.01	0.01	0.03	0.01	0.00
Netherlands	2.95	1.77	2.34	2.42	4.07	1.82	30.93	3.27	2.72	1.75	1.83	2.21
Poland	0.44	0.83	8.57	8.40	0.91	0.55	1.24	46.03	0.71	2.33	4.78	1.74
Portugal	0.22	0.16	0.14	0.16	0.21	0.22	0.32	0.16	47.00	0.33	0.18	0.11
Romania	0.11	0.70	0.16	0.19	0.08	0.12	0.27	0.52	0.16	48.58	1.24	0.84
Slovakia	0.13	0.39	1.16	0.63	0.35	0.17	0.39	2.01	0.24	1.53	33.29	1.77
Slovenia	0.04	0.25	0.26	0.29	0.10	0.05	0.09	0.30	0.08	0.38	0.54	32.29
Spain	1.19	1.59	0.77	0.90	1.23	1.30	1.07	1.42	17.25	1.68	0.93	1.29
Sweden	0.44	0.35	2.21	2.38	0.47	0.25	1.11	1.08	0.41	0.28	0.31	0.36
Australia	0.04	0.02	0.03	0.01	0.02	0.01	0.09	0.01	0.01	0.03	0.00	0.03
Bosnia and Herzegovina	0.00	0.07	0.00	0.00	0.15	0.01	0.02	0.01	0.00	0.06	0.08	1.10
Brazil	0.33	0.14	0.03	0.06	0.08	0.03	0.96	0.04	0.29	0.04	0.03	0.05
Canada	0.36	0.12	0.35	0.20	0.47	5.21	0.35	0.09	0.15	0.05	0.03	0.07
Chile	0.00	0.12	0.00	0.00	0.00	0.00	0.19	0.00	0.00	0.00	0.00	0.00
China	2.80	2.96	7.62	5.62	9.01	24.48	12.20	4.64	3.01	2.87	3.60	6.40
Hong Kong	0.28	0.34	0.65	0.34	0.57	0.20	1.23	0.35	0.29	0.29	0.31	0.21
Iceland	0.00	0.01	0.00	0.00	0.00	0.00	0.26	0.00	0.01	0.00	0.01	0.00
India	0.56	0.45	0.52	0.24	0.04	1.09	0.67	0.32	0.64	0.30	0.16	0.68
Iran	0.00	0.04	0.00	0.00	0.00	0.00	0.01	0.00	0.03	0.00	0.00	0.00
Israel	0.12	0.10	0.28	0.15	0.09	2.01	0.39	0.07	0.12	0.11	0.03	0.38
Japan	1.26	0.36	0.30	0.22	1.16	1.91	2.44	0.54	0.39	0.29	0.34	0.29
Malaysia	0.15	0.07	0.28	0.14	0.03	0.45	1.00	0.13	0.06	0.07	0.12	0.35
Mexico	0.22	0.08	0.01	0.10	0.07	0.00	0.21	0.06	0.04	0.06	0.02	0.01
New Zealand	0.02	0.02	0.01	0.01	0.00	0.01	0.04	0.00	0.00	0.00	0.00	0.01
Norway	0.33	0.05	0.26	0.36	0.13	0.58	0.59	0.30	0.10	0.14	0.03	0.03
Russian Federation	0.18	0.34	4.71	3.57	0.02	0.37	1.71	0.51	0.14	0.25	0.48	0.24
Saudi Arabia	0.01	0.09	0.01	0.01	0.00	0.89	0.09	0.12	0.04	0.01	0.00	0.02
Serbia	0.01	0.25	0.03	0.08	0.03	0.03	0.02	0.09	0.03	0.34	0.25	1.17
Singapore	0.50	0.05	0.07	0.07	0.08	0.83	1.24	0.07	0.05	0.06	0.04	0.09
South Africa	0.06	0.06	0.01	0.06	0.09	0.13	0.17	0.04	0.04	0.03	0.00	0.06
South Korea	0.42	0.37	0.62	0.77	1.44	12.74	0.54	1.17	0.47	0.50	5.08	5.18
Switzerland	1.09	1.41	0.86	0.63	0.90	0.41	0.98	0.74	0.98	0.75	0.68	1.45
Taiwan	0.21	0.19	0.81	0.45	0.07	0.30	0.86	0.26	0.21	0.13	0.41	0.45
Thailand	0.43	0.11	0.16	0.10	0.02	0.14	0.77	0.14	0.14	0.10	0.19	0.13
Turkey	0.43	0.64	0.97	1.22	0.19	1.82	0.50	0.70	0.60	2.51	0.57	2.36
Ukraine	0.00	0.17	0.71	0.57	0.02	0.02	0.07	0.47	0.07	0.32	0.33	0.04
United Arab Emirates	0.01	0.06	0.08	0.05	0.15	0.19	0.22	0.03	0.03	0.02	0.00	0.01
United Kingdom	17.88	1.30	1.66	1.51	1.32	5.12	3.33	1.73	1.57	1.33	0.84	1.03
USA	6.78	1.32	1.75	1.80	2.40	0.72	5.56	0.80	0.53	0.56	0.31	0.59
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00
Austria’s single export weights	0.42	5.16	0.12	0.14	0.15	0.03	1.61	3.20	0.25	1.54	1.93	1.43
Source: OeNB/WIFO.

Table A2 continued: Competition matrix for manufactured goods exports from Austria  

	Destinations
Competing countries	Spain	Sweden	Australia	Bosnia and Herzegovina	Brazil	Canada	Chile	China	Hong Kong	Iceland	India	Iran
	Market shares in %: calculated for the period from 2013 to 2015
Belgium	1.81	2.57	0.62	0.77	0.39	0.33	0.68	0.07	0.43	1.82	0.88	0.21
Bulgaria	0.06	0.06	0.01	0.57	0.00	0.01	0.01	0.01	0.01	0.03	0.00	0.05
Croatia	0.03	0.03	0.00	7.91	0.00	0.00	0.00	0.00	0.00	0.03	0.00	0.01
Cyprus	0.00	0.00	0.00	0.02	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Czechia	0.73	1.06	0.18	1.56	0.04	0.04	0.10	0.02	0.05	1.12	0.05	0.02
Denmark	0.23	3.19	0.17	0.17	0.08	0.08	0.28	0.02	0.04	8.13	0.02	0.05
Estonia	0.02	1.01	0.00	0.02	0.00	0.00	0.00	0.00	0.00	0.33	0.00	0.00
Finland	0.16	2.04	0.16	0.05	0.06	0.07	0.35	0.02	0.03	0.86	0.04	0.03
France	5.38	2.29	0.83	1.24	0.58	0.44	1.12	0.17	0.74	1.70	0.28	0.41
Germany	7.37	10.44	2.92	11.45	1.39	1.64	4.00	0.84	0.96	9.43	0.93	1.43
Greece	0.08	0.05	0.03	0.32	0.00	0.01	0.02	0.00	0.01	0.05	0.00	0.01
Hungary	0.54	0.46	0.10	3.14	0.04	0.04	0.08	0.02	0.04	0.28	0.02	0.02
Ireland	0.71	0.39	0.26	0.19	0.03	0.13	0.13	0.01	0.08	0.65	0.05	0.05
Italy	3.76	1.83	1.19	8.79	0.60	0.49	1.49	0.12	0.96	2.93	0.31	1.00
Latvia	0.01	0.18	0.00	0.04	0.00	0.00	0.00	0.00	0.00	0.34	0.00	0.00
Lithuania	0.04	0.38	0.01	0.03	0.00	0.01	0.00	0.00	0.01	0.54	0.00	0.00
Luxembourg	0.05	0.09	0.01	0.01	0.01	0.02	0.01	0.00	0.01	0.06	0.00	0.00
Malta	0.01	0.01	0.00	0.00	0.00	0.01	0.00	0.00	0.04	0.09	0.00	0.00
Netherlands	2.09	2.98	0.64	1.27	0.28	0.23	0.57	0.06	0.17	8.75	0.16	0.26
Poland	0.85	2.08	0.16	1.84	0.05	0.13	0.11	0.02	0.05	1.35	0.03	0.03
Portugal	1.89	0.19	0.03	0.03	0.04	0.04	0.15	0.01	0.02	0.38	0.01	0.01
Romania	0.25	0.25	0.01	0.99	0.03	0.02	0.03	0.00	0.01	0.06	0.02	0.08
Slovakia	0.35	0.51	0.09	1.17	0.01	0.03	0.05	0.02	0.01	0.30	0.00	0.01
Slovenia	0.07	0.13	0.03	7.78	0.01	0.01	0.03	0.00	0.01	0.06	0.01	0.02
Spain	61.06	0.87	0.54	0.63	0.33	0.17	2.01	0.03	0.12	0.68	0.10	0.22
Sweden	0.37	54.55	0.44	0.30	0.13	0.15	0.48	0.05	0.08	6.03	0.10	0.19
Australia	0.02	0.03	55.91	0.00	0.02	0.05	0.17	0.04	0.17	0.05	0.04	0.00
Bosnia and Herzegovina	0.01	0.02	0.00	32.96	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Brazil	0.10	0.08	0.09	0.01	85.04	0.09	3.31	0.03	0.06	0.01	0.05	0.01
Canada	0.07	0.12	0.34	0.02	0.18	51.01	0.66	0.05	0.15	0.54	0.11	0.01
Chile	0.05	0.01	0.16	0.00	0.22	0.09	41.73	0.09	0.00	0.28	0.01	0.00
China	3.83	3.03	11.09	1.88	3.49	4.32	18.33	90.61	51.40	3.54	4.55	13.11
Hong Kong	0.36	0.41	1.32	0.26	0.19	0.42	0.77	2.46	12.68	0.20	1.04	0.08
Iceland	0.04	0.00	0.00	0.00	0.00	0.00	0.01	0.00	0.00	30.03	0.00	0.00
India	0.53	0.31	0.59	0.20	0.29	0.27	0.90	0.08	1.92	0.52	83.96	1.32
Iran	0.02	0.00	0.00	0.01	0.00	0.00	0.00	0.04	0.00	0.00	0.17	67.08
Israel	0.18	0.06	0.16	0.02	0.10	0.08	0.20	0.03	0.84	0.15	0.19	0.00
Japan	0.42	0.52	3.44	0.07	0.50	1.18	2.01	1.10	3.95	1.06	0.66	0.16
Malaysia	0.07	0.13	1.29	0.00	0.09	0.11	0.23	0.20	1.46	0.03	0.34	0.13
Mexico	0.11	0.04	0.18	0.00	0.46	1.30	2.41	0.03	0.08	0.00	0.03	0.00
New Zealand	0.00	0.00	0.87	0.00	0.01	0.02	0.06	0.00	0.02	0.02	0.01	0.00
Norway	0.11	1.20	0.07	0.02	0.04	0.04	0.08	0.02	0.02	6.19	0.01	0.00
Russian Federation	0.14	0.19	0.02	0.28	0.20	0.05	0.03	0.04	0.11	0.50	0.33	0.37
Saudi Arabia	0.14	0.04	0.11	0.00	0.03	0.00	0.08	0.07	0.04	0.04	0.25	0.07
Serbia	0.02	0.02	0.00	8.67	0.00	0.00	0.00	0.00	0.00	0.01	0.00	0.01
Singapore	0.06	0.04	1.90	0.02	0.19	0.14	0.09	0.42	5.37	0.03	0.83	0.09
South Africa	0.09	0.06	0.21	0.01	0.05	0.03	0.08	0.02	0.15	0.06	0.05	0.01
South Korea	0.37	0.36	1.80	0.20	0.87	0.73	2.80	1.33	3.89	0.68	0.96	2.89
Switzerland	1.06	0.62	0.68	0.74	0.23	0.50	0.37	0.09	1.20	0.46	0.18	0.28
Taiwan	0.17	0.26	0.81	0.02	0.16	0.35	0.43	0.76	5.49	0.17	0.27	0.47
Thailand	0.12	0.19	2.59	0.02	0.19	0.12	0.81	0.16	1.64	0.04	0.41	0.15
Turkey	0.76	0.47	0.11	3.47	0.07	0.08	0.27	0.01	0.05	0.75	0.03	2.13
Ukraine	0.03	0.03	0.01	0.07	0.01	0.01	0.00	0.00	0.00	0.01	0.03	0.10
United Arab Emirates	0.05	0.01	0.07	0.03	0.01	0.02	0.07	0.02	0.48	0.03	0.64	7.27
United Kingdom	1.94	2.72	1.60	0.44	0.37	0.76	0.91	0.16	1.11	6.46	0.49	0.08
USA	1.21	1.40	6.17	0.27	2.87	34.12	11.47	0.65	3.84	2.15	1.30	0.06
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00
Austria’s single export weights	1.76	1.17	0.62	0.26	0.62	0.87	0.14	2.75	0.46	0.02	0.55	0.19
Source: OeNB/WIFO.

Table A2 continued: Competition matrix for manufactured goods exports from Austria  

	Destinations
Competing countries	Israel	Japan	Malaysia	Mexico	New Zealand	Norway	Russian Federation	Saudi Arabia	Serbia	Singapore	South Africa	South Korea
	Market shares in %: calculated for the period from 2013 to 2015
Belgium	2.95	0.16	0.20	0.21	0.43	1.39	0.61	0.83	1.01	0.41	1.07	0.11
Bulgaria	0.06	0.00	0.01	0.00	0.01	0.03	0.07	0.02	1.44	0.01	0.02	0.00
Croatia	0.04	0.00	0.00	0.00	0.00	0.05	0.04	0.02	1.88	0.00	0.01	0.00
Cyprus	0.08	0.00	0.00	0.00	0.00	0.02	0.00	0.01	0.01	0.04	0.00	0.00
Czechia	0.74	0.03	0.06	0.10	0.13	0.56	0.63	0.21	1.73	0.08	0.33	0.04
Denmark	0.13	0.03	0.04	0.06	0.20	3.91	0.12	0.15	0.44	0.15	0.21	0.05
Estonia	0.01	0.00	0.00	0.02	0.00	0.38	0.25	0.01	0.02	0.00	0.01	0.00
Finland	0.11	0.03	0.06	0.05	0.11	1.26	0.61	0.16	0.15	0.09	0.23	0.07
France	1.48	0.29	0.85	0.53	1.11	1.14	0.95	1.41	1.48	1.78	1.41	0.41
Germany	4.70	0.88	1.91	1.93	2.32	7.01	4.53	4.47	9.32	2.48	7.23	1.44
Greece	0.16	0.00	0.00	0.01	0.02	0.02	0.03	0.04	0.81	0.02	0.03	0.00
Hungary	0.37	0.03	0.04	0.10	0.08	0.18	0.31	0.07	3.84	0.08	0.23	0.03
Ireland	0.77	0.13	0.08	0.22	0.17	0.20	0.06	0.37	0.28	0.23	0.19	0.04
Italy	2.83	0.26	0.42	0.65	0.86	1.28	1.44	2.08	7.96	0.73	1.42	0.36
Latvia	0.01	0.00	0.00	0.00	0.01	0.20	0.10	0.00	0.02	0.00	0.01	0.00
Lithuania	0.02	0.00	0.00	0.00	0.01	0.48	0.56	0.01	0.06	0.00	0.01	0.00
Luxembourg	0.02	0.00	0.00	0.01	0.01	0.05	0.02	0.02	0.03	0.01	0.04	0.00
Malta	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.03	0.09	0.00	0.00
Netherlands	1.60	0.13	0.30	0.18	0.52	2.51	0.76	1.00	1.54	0.74	1.18	0.33
Poland	0.40	0.02	0.06	0.09	0.12	2.50	0.99	0.15	3.17	0.27	0.38	0.04
Portugal	0.12	0.00	0.01	0.04	0.03	0.10	0.03	0.06	0.04	0.02	0.10	0.01
Romania	0.16	0.00	0.01	0.03	0.01	0.54	0.21	0.07	2.11	0.02	0.11	0.03
Slovakia	0.24	0.01	0.01	0.03	0.06	0.18	0.34	0.07	1.58	0.01	0.07	0.01
Slovenia	0.05	0.00	0.00	0.01	0.01	0.06	0.15	0.03	3.74	0.01	0.03	0.00
Spain	1.57	0.08	0.19	0.68	0.51	0.66	0.32	1.23	0.65	0.22	0.94	0.08
Sweden	0.35	0.07	0.12	0.11	0.26	10.10	0.35	0.54	0.57	0.27	0.61	0.09
Australia	0.09	0.08	0.62	0.03	8.90	0.07	0.01	0.39	0.00	0.47	0.25	0.15
Bosnia and Herzegovina	0.00	0.00	0.00	0.00	0.00	0.01	0.01	0.00	0.84	0.00	0.00	0.00
Brazil	0.13	0.06	0.04	0.59	0.05	0.05	0.02	0.12	0.02	0.44	0.63	0.06
Canada	0.33	0.07	0.17	0.58	0.46	0.26	0.09	0.34	0.04	0.26	0.24	0.07
Chile	0.00	0.01	0.03	0.10	0.05	0.01	0.00	0.01	0.00	0.00	0.06	0.14
China	8.87	5.68	14.23	5.18	8.09	2.05	5.82	9.05	1.80	14.85	10.82	7.17
Hong Kong	2.64	0.72	1.22	0.58	1.02	0.23	0.28	0.39	0.23	2.46	0.63	0.60
Iceland	0.00	0.00	0.00	0.00	0.00	0.04	0.00	0.00	0.00	0.00	0.00	0.00
India	2.46	0.11	0.98	0.42	0.48	0.24	0.21	1.51	0.25	1.50	2.20	0.20
Iran	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.04	0.01	0.00	0.01	0.01
Israel	40.33	0.03	0.48	0.07	0.11	0.04	0.07	0.00	0.06	0.23	0.22	0.04
Japan	1.17	84.98	4.17	1.68	3.41	0.81	1.09	3.18	0.08	4.66	2.09	3.43
Malaysia	0.00	0.42	51.98	0.24	0.82	0.19	0.06	0.35	0.01	6.77	0.34	0.29
Mexico	0.11	0.05	0.05	52.14	0.12	0.01	0.03	0.19	0.00	0.14	0.11	0.05
New Zealand	0.02	0.04	0.04	0.02	57.10	0.01	0.00	0.01	0.00	0.06	0.04	0.02
Norway	0.02	0.02	0.12	0.01	0.04	51.89	0.03	0.04	0.04	0.35	0.06	0.16
Russian Federation	0.97	0.08	0.08	0.15	0.02	0.18	73.61	0.13	1.35	0.06	0.04	0.08
Saudi Arabia	0.00	0.03	0.41	0.00	0.22	0.00	0.00	50.82	0.00	1.06	0.32	0.08
Serbia	0.01	0.00	0.00	0.00	0.00	0.01	0.09	0.00	45.77	0.00	0.00	0.00
Singapore	0.56	0.62	9.17	0.18	1.87	0.27	0.06	0.38	0.02	37.23	0.31	1.08
South Africa	0.41	0.15	0.10	0.02	0.13	0.02	0.02	0.12	0.00	0.12	56.64	0.05
South Korea	1.43	0.95	2.03	1.74	1.46	2.02	1.10	3.94	0.28	4.08	1.11	79.03
Switzerland	1.24	0.28	0.28	0.27	0.36	0.67	0.35	1.10	0.97	1.20	0.45	0.22
Taiwan	0.64	0.77	2.23	0.32	0.76	0.18	0.16	0.73	0.04	5.26	0.49	0.95
Thailand	0.67	0.69	2.85	0.36	2.00	0.16	0.11	1.25	0.01	2.10	1.28	0.23
Turkey	2.80	0.01	0.04	0.04	0.11	0.37	0.54	1.19	2.87	0.07	0.30	0.02
Ukraine	0.25	0.00	0.02	0.02	0.00	0.02	1.05	0.16	0.21	0.04	0.01	0.01
United Arab Emirates	0.00	0.02	0.13	0.01	0.10	0.09	0.06	1.91	0.05	0.38	0.13	0.03
United Kingdom	1.94	0.28	0.73	0.26	1.61	3.45	0.76	2.61	0.80	1.85	1.95	0.35
USA	13.94	1.68	3.40	29.93	3.72	1.86	0.86	7.02	0.36	6.57	3.40	2.34
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00
Austria’s single export weights	0.22	1.00	0.41	0.57	0.11	0.41	2.42	0.59	0.40	0.34	0.42	0.67
Source: OeNB/WIFO.

Table A2 continued: Competition matrix for manufactured goods exports from Austria  

	Destinations
Competing countries	Switzerland	Taiwan	Thailand	Turkey	Ukraine	United Arab Emirates	United Kingdom	USA	Rest of the world	Double export weight
	Market shares in %: calculated for the period from 2013 to 2015
Belgium	1.67	0.16	0.34	1.11	1.04	1.57	3.76	0.38	1.35	2.53
Bulgaria	0.06	0.01	0.01	0.28	0.22	0.02	0.06	0.01	0.12	0.34
Croatia	0.05	0.00	0.00	0.02	0.05	0.01	0.02	0.00	0.09	0.47
Cyprus	0.00	0.00	0.00	0.00	0.02	0.01	0.01	0.00	0.04	0.02
Czechia	0.86	0.04	0.05	0.45	1.66	0.30	1.02	0.06	0.65	2.81
Denmark	0.26	0.04	0.06	0.12	0.34	0.13	0.48	0.08	0.39	0.66
Estonia	0.01	0.00	0.00	0.01	0.17	0.01	0.03	0.00	0.03	0.08
Finland	0.17	0.04	0.06	0.17	0.42	0.10	0.27	0.07	0.31	0.55
France	4.40	0.36	0.65	1.66	1.27	1.78	3.71	0.53	3.41	5.18
Germany	17.10	1.66	1.46	5.43	7.27	5.67	11.14	2.07	7.72	23.56
Greece	0.04	0.00	0.01	0.16	0.07	0.11	0.10	0.01	0.14	0.27
Hungary	0.30	0.03	0.02	0.43	2.53	0.19	0.47	0.06	0.45	1.57
Ireland	2.36	0.03	0.06	0.16	0.08	0.16	1.24	0.46	0.24	0.75
Italy	6.49	0.26	0.50	2.28	2.43	2.49	2.76	0.59	3.31	6.66
Latvia	0.01	0.00	0.00	0.01	0.12	0.02	0.03	0.00	0.04	0.08
Lithuania	0.03	0.00	0.00	0.01	0.46	0.01	0.08	0.01	0.15	0.16
Luxembourg	0.06	0.00	0.01	0.03	0.02	0.02	0.06	0.01	0.04	0.13
Malta	0.01	0.00	0.00	0.00	0.00	0.00	0.02	0.00	0.03	0.02
Netherlands	1.71	0.69	0.30	1.14	1.27	1.02	3.73	0.27	1.47	3.14
Poland	0.55	0.03	0.05	0.66	5.40	0.32	1.38	0.07	0.66	3.19
Portugal	0.16	0.01	0.01	0.09	0.03	0.06	0.40	0.03	0.45	0.41
Romania	0.12	0.01	0.01	0.41	0.64	0.07	0.31	0.02	0.30	1.27
Slovakia	0.46	0.01	0.01	0.24	0.65	0.07	0.49	0.03	0.36	1.52
Slovenia	0.15	0.00	0.01	0.06	0.38	0.03	0.07	0.01	0.19	0.78
Spain	1.31	0.11	0.14	1.24	0.49	0.72	1.96	0.15	2.03	2.44
Sweden	0.40	0.10	0.20	0.32	0.40	0.29	0.76	0.16	0.46	1.28
Australia	0.09	0.21	0.40	0.02	0.01	0.34	0.21	0.09	0.38	0.43
Bosnia and Herzegovina	0.02	0.00	0.00	0.01	0.01	0.00	0.00	0.00	0.05	0.15
Brazil	0.44	0.05	0.13	0.10	0.08	0.15	0.13	0.29	2.19	0.76
Canada	0.14	0.11	0.12	0.07	0.10	0.32	0.38	3.20	0.46	0.80
Chile	0.05	0.24	0.03	0.05	0.00	0.01	0.01	0.07	0.40	0.11
China	1.12	8.56	10.37	4.07	8.54	14.44	6.92	6.68	24.88	8.16
Hong Kong	1.25	1.86	1.93	0.22	0.27	2.32	0.88	0.76	1.99	0.81
Iceland	0.02	0.00	0.00	0.00	0.00	0.00	0.02	0.00	0.00	0.02
India	0.34	0.20	0.85	0.73	0.50	8.38	1.03	0.59	3.06	1.08
Iran	0.00	0.04	0.03	0.28	0.02	0.41	0.01	0.00	0.53	0.16
Israel	0.48	0.15	0.15	0.49	0.41	0.01	0.48	0.31	0.43	0.29
Japan	0.71	7.36	9.48	0.47	0.55	3.49	1.15	2.14	5.16	2.10
Malaysia	0.08	0.83	3.01	0.13	0.08	1.25	0.27	0.30	1.12	0.52
Mexico	0.16	0.05	0.10	0.03	0.02	0.12	0.13	4.45	1.11	0.77
New Zealand	0.01	0.02	0.03	0.00	0.00	0.03	0.03	0.02	0.07	0.08
Norway	0.08	0.01	0.01	0.05	0.04	0.10	0.31	0.04	0.14	0.36
Russian Federation	0.38	0.30	0.13	1.16	9.91	0.35	0.11	0.09	1.93	2.23
Saudi Arabia	0.01	0.23	0.20	0.42	0.06	2.30	0.10	0.02	1.05	0.41
Serbia	0.03	0.00	0.00	0.04	0.13	0.00	0.02	0.01	0.09	0.32
Singapore	0.45	2.93	4.14	0.10	0.04	1.90	0.34	0.38	3.29	0.66
South Africa	0.49	0.08	0.12	0.05	0.02	0.35	0.31	0.10	1.38	0.44
South Korea	0.31	2.40	2.15	1.36	0.60	2.43	0.76	1.11	5.98	1.66
Switzerland	46.00	0.36	0.40	0.42	0.55	1.27	1.52	0.48	1.24	3.61
Taiwan	0.16	65.64	1.78	0.34	0.21	0.61	0.50	0.57	1.68	0.67
Thailand	0.32	0.62	57.23	0.20	0.11	1.15	0.34	0.34	2.56	0.54
Turkey	0.23	0.01	0.05	70.05	2.21	1.26	1.09	0.09	2.41	1.46
Ukraine	0.01	0.01	0.01	0.48	46.05	0.08	0.03	0.01	0.52	0.35
United Arab Emirates	0.49	0.06	0.19	0.21	0.08	32.10	0.29	0.04	2.17	0.40
United Kingdom	3.04	0.28	0.69	1.02	0.83	3.49	44.07	0.94	1.80	3.29
USA	4.35	3.82	2.32	0.92	1.10	6.12	4.18	71.81	7.48	7.49
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00
Austria’s single export weights	5.11	0.32	0.24	1.03	0.40	0.59	3.26	6.58	4.05	100.00
Source: OeNB/WIFO.

Table A3: Comparison of the weights for Austrian manufactured goods across different calculation periods  

	1998 to 2000				2001 to 2003				2004 to 2006
Competing countries	Austrian exports (single weights)	Austrian exports (double weights)	Austrian imports	Total	Austrian exports (single weights)	Austrian exports (double weights)	Austrian imports	Total	Austrian exports (single weights)	Austrian exports (double weights)	Austrian imports	Total
	In %
Belgium	1.82	2.77	2.21	2.48	1.72	2.88	1.89	2.38	1.73	2.96	1.71	2.35
Bulgaria	0.34	0.19	0.11	0.15	0.38	0.20	0.17	0.18	0.52	0.28	0.28	0.28
Croatia	0.98	0.51	0.34	0.42	1.26	0.62	0.50	0.56	1.35	0.66	0.65	0.65
Cyprus	0.05	0.02	0.00	0.01	0.09	0.02	0.00	0.01	0.04	0.01	0.01	0.01
Czechia	2.78	2.14	2.13	2.14	3.12	2.39	2.72	2.56	3.22	2.63	3.11	2.86
Denmark	0.86	0.80	0.64	0.72	0.77	0.76	0.59	0.68	0.74	0.69	0.55	0.63
Estonia	0.05	0.04	0.03	0.03	0.08	0.06	0.03	0.04	0.18	0.09	0.03	0.06
Finland	0.62	0.91	1.12	1.02	0.59	0.86	1.11	0.99	0.58	0.81	1.06	0.93
France	4.75	6.61	5.22	5.89	4.69	6.52	4.23	5.36	4.12	5.87	4.17	5.04
Germany	36.82	29.95	43.28	36.86	33.43	27.23	42.28	34.85	31.93	25.25	43.07	33.89
Greece	0.45	0.34	0.15	0.24	0.59	0.41	0.13	0.27	0.52	0.38	0.12	0.25
Hungary	4.93	2.50	3.02	2.77	4.46	2.22	3.24	2.74	3.62	1.93	2.38	2.15
Ireland	0.32	0.82	0.75	0.78	0.31	0.90	1.27	1.08	0.48	0.80	0.86	0.83
Italy	6.85	8.74	7.80	8.25	6.93	8.83	7.22	8.02	7.15	8.60	7.07	7.85
Latvia	0.06	0.03	0.02	0.03	0.10	0.05	0.03	0.04	0.13	0.07	0.02	0.05
Lithuania	0.08	0.06	0.04	0.05	0.11	0.09	0.04	0.06	0.15	0.12	0.04	0.08
Luxembourg	0.20	0.18	0.17	0.18	0.19	0.18	0.17	0.17	0.23	0.19	0.23	0.21
Malta	0.02	0.02	0.01	0.02	0.02	0.02	0.01	0.02	0.11	0.02	0.01	0.02
Netherlands	2.45	2.40	2.95	2.68	2.26	2.46	2.78	2.62	1.83	2.52	2.74	2.62
Poland	1.69	1.61	0.76	1.17	1.80	1.82	0.96	1.39	2.24	2.21	1.12	1.68
Portugal	0.49	0.58	0.56	0.57	0.50	0.57	0.61	0.59	0.45	0.48	0.49	0.48
Romania	0.68	0.50	0.42	0.46	1.24	0.69	0.74	0.72	1.79	0.96	0.94	0.95
Slovakia	1.11	0.78	1.07	0.93	1.45	0.90	1.46	1.18	1.67	1.00	1.46	1.22
Slovenia	1.68	0.93	1.00	0.97	1.74	0.98	1.19	1.09	1.79	0.89	1.19	1.04
Spain	3.06	3.15	1.41	2.25	2.87	3.15	1.53	2.33	2.99	3.15	1.57	2.38
Sweden	1.22	1.58	1.49	1.53	1.12	1.44	1.42	1.43	1.10	1.42	1.46	1.44
Australia	0.50	0.41	0.03	0.22	0.54	0.44	0.05	0.24	0.67	0.52	0.07	0.30
Bosnia and Herzegovina	–	–	–	–	0.21	0.10	0.04	0.07	0.24	0.12	0.12	0.12
Brazil	0.42	0.55	0.13	0.33	0.31	0.46	0.10	0.28	0.30	0.58	0.18	0.39
Canada	0.76	0.68	0.55	0.61	0.85	0.78	0.47	0.62	1.00	0.91	0.43	0.68
Chile	0.05	0.07	0.01	0.04	0.05	0.07	0.01	0.04	0.08	0.11	0.01	0.06
China	0.74	1.71	1.66	1.68	1.41	2.99	2.26	2.62	1.42	4.27	3.65	3.97
Hong Kong	0.57	0.88	0.34	0.60	0.70	0.88	0.34	0.61	0.52	0.83	0.21	0.53
Iceland	0.03	0.03	0.02	0.02	0.02	0.02	0.01	0.02	0.04	0.03	0.00	0.02
India	0.17	0.38	0.24	0.30	0.22	0.48	0.27	0.37	0.37	0.67	0.34	0.51
Iran	0.32	0.30	0.03	0.16	0.37	0.30	0.02	0.16	0.37	0.27	0.02	0.14
Israel	0.23	0.29	0.15	0.22	0.17	0.26	0.12	0.19	0.13	0.23	0.09	0.16
Japan	1.03	3.14	2.97	3.05	1.02	2.88	2.66	2.77	1.07	2.87	2.52	2.70
Malaysia	0.13	0.35	0.31	0.33	0.13	0.37	0.62	0.50	0.25	0.43	0.33	0.38
Mexico	0.23	0.41	0.14	0.27	0.21	0.44	0.19	0.31	0.24	0.49	0.16	0.33
New Zealand	0.07	0.05	0.01	0.03	0.08	0.06	0.01	0.04	0.09	0.07	0.02	0.05
Norway	0.47	0.44	0.15	0.29	0.40	0.40	0.12	0.26	0.42	0.41	0.18	0.30
Russian Federation	0.92	1.03	0.29	0.64	1.45	1.35	0.28	0.81	2.08	1.95	0.27	1.13
Saudi Arabia	0.27	0.17	0.01	0.09	0.25	0.18	0.01	0.10	0.36	0.26	0.01	0.14
Serbia	–	–	–	–	–	–	–	–	0.17	0.16	0.05	0.11
Singapore	0.28	0.54	0.20	0.37	0.29	0.61	0.27	0.44	0.27	0.75	0.17	0.47
South Africa	0.38	0.41	0.07	0.23	0.47	0.50	0.07	0.28	0.56	0.59	0.10	0.35
South Korea	0.34	0.96	0.51	0.73	0.41	1.12	0.73	0.92	0.49	1.44	1.02	1.24
Switzerland	6.24	3.68	3.39	3.53	6.04	3.34	3.61	3.47	5.26	2.72	3.69	3.19
Taiwan	0.37	0.90	0.94	0.92	0.31	0.84	0.82	0.83	0.33	0.78	0.70	0.74
Thailand	0.20	0.31	0.26	0.28	0.15	0.35	0.28	0.32	0.15	0.39	0.37	0.38
Turkey	0.78	0.94	0.54	0.73	0.73	1.01	0.78	0.89	0.86	1.23	0.88	1.06
Ukraine	0.29	0.32	0.12	0.22	0.41	0.43	0.17	0.30	0.55	0.54	0.20	0.37
United Arab Emirates	0.22	0.10	0.01	0.05	0.32	0.23	0.01	0.12	0.34	0.24	0.03	0.14
United Kingdom	4.71	5.47	3.37	4.38	4.95	5.16	2.67	3.90	4.43	4.51	2.28	3.43
USA	4.93	7.32	6.86	7.08	5.71	7.67	6.72	7.19	6.28	7.63	5.60	6.65
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00
Source: OeNB/WIFO.

Table A3 continued: Comparison of the weights for Austrian manufactured goods across different calculation periods  

	2007 to 2009				2010 to 2012				2013 to 2015
Competing countries	Austrian exports (single weights)	Austrian exports (double weights)	Austrian imports	Total	Austrian exports (single weights)	Austrian exports (double weights)	Austrian imports	Total	Austrian exports (single weights)	Austrian exports (double weights)	Austrian imports	Total
	In %
Belgium	1.67	3.04	1.79	2.43	1.59	2.63	1.81	2.24	1.47	2.53	1.72	2.14
Bulgaria	0.68	0.38	0.29	0.34	0.56	0.41	0.35	0.38	0.54	0.34	0.35	0.35
Croatia	1.34	0.63	0.61	0.62	0.97	0.60	0.57	0.59	0.97	0.47	0.45	0.46
Cyprus	0.06	0.01	0.02	0.02	0.10	0.02	0.04	0.03	0.10	0.02	0.03	0.03
Czechia	3.63	2.86	3.31	3.08	3.69	2.91	3.58	3.23	3.56	2.81	3.68	3.22
Denmark	0.73	0.70	0.48	0.59	0.59	0.59	0.45	0.52	0.58	0.66	0.43	0.55
Estonia	0.11	0.07	0.03	0.05	0.09	0.06	0.03	0.05	0.09	0.08	0.03	0.05
Finland	0.57	0.79	0.60	0.70	0.46	0.61	0.49	0.55	0.42	0.55	0.40	0.48
France	4.07	5.59	3.59	4.63	4.87	5.96	3.32	4.69	5.30	5.18	3.13	4.21
Germany	31.65	23.97	42.72	33.00	32.06	23.95	41.11	32.19	31.04	23.56	39.34	31.07
Greece	0.58	0.41	0.10	0.26	0.38	0.32	0.10	0.22	0.30	0.27	0.10	0.19
Hungary	3.25	1.85	2.21	2.02	2.74	1.66	2.44	2.03	3.03	1.57	2.51	2.02
Ireland	0.26	0.69	0.54	0.62	0.27	0.69	0.55	0.63	0.44	0.75	0.63	0.69
Italy	6.80	8.23	7.08	7.67	5.99	7.20	6.70	6.96	5.38	6.66	6.32	6.50
Latvia	0.15	0.07	0.02	0.05	0.11	0.07	0.02	0.04	0.13	0.08	0.02	0.05
Lithuania	0.14	0.13	0.05	0.09	0.14	0.11	0.05	0.08	0.14	0.16	0.06	0.11
Luxembourg	0.13	0.16	0.17	0.17	0.15	0.15	0.24	0.19	0.15	0.13	0.23	0.18
Malta	0.03	0.02	0.02	0.02	0.02	0.02	0.01	0.01	0.03	0.02	0.01	0.02
Netherlands	1.78	2.64	2.72	2.68	1.60	2.73	2.71	2.72	1.68	3.14	2.61	2.89
Poland	2.86	2.61	1.35	2.00	3.04	2.95	1.63	2.32	3.34	3.19	1.96	2.61
Portugal	0.41	0.46	0.37	0.42	0.31	0.42	0.45	0.44	0.26	0.41	0.45	0.43
Romania	2.04	1.15	0.72	0.95	1.64	1.05	0.93	0.99	1.60	1.27	0.97	1.13
Slovakia	1.87	1.13	1.64	1.38	1.91	1.20	1.93	1.55	2.01	1.52	1.81	1.66
Slovenia	1.90	0.84	1.10	0.96	1.57	0.44	1.11	0.76	1.49	0.78	1.14	0.95
Spain	2.73	2.99	1.63	2.33	1.83	2.44	1.73	2.10	1.84	2.44	1.80	2.13
Sweden	1.21	1.44	1.44	1.44	1.26	1.42	1.31	1.37	1.22	1.28	1.11	1.20
Australia	0.70	0.51	0.06	0.29	0.66	0.49	0.04	0.28	0.65	0.43	0.04	0.24
Bosnia and Herzegovina	0.30	0.14	0.19	0.17	0.28	0.11	0.27	0.18	0.27	0.15	0.35	0.25
Brazil	0.64	0.88	0.18	0.55	0.93	1.04	0.16	0.61	0.64	0.76	0.11	0.45
Canada	0.85	0.78	0.45	0.62	0.81	0.78	0.34	0.57	0.91	0.80	0.33	0.58
Chile	0.10	0.13	0.01	0.07	0.14	0.15	0.05	0.10	0.14	0.11	0.04	0.08
China	1.96	6.16	4.99	5.60	2.84	7.80	6.47	7.16	2.87	8.16	7.16	7.68
Hong Kong	0.41	0.81	0.15	0.50	0.55	0.76	0.11	0.45	0.48	0.81	0.08	0.46
Iceland	0.03	0.03	0.01	0.02	0.02	0.02	0.01	0.02	0.02	0.02	0.01	0.02
India	0.59	0.96	0.43	0.71	0.69	1.14	0.53	0.84	0.57	1.08	0.58	0.84
Iran	0.34	0.25	0.01	0.13	0.28	0.23	0.02	0.13	0.19	0.16	0.01	0.09
Israel	0.18	0.26	0.09	0.18	0.22	0.28	0.17	0.23	0.23	0.29	0.14	0.22
Japan	0.82	2.57	2.05	2.32	1.08	2.49	1.95	2.23	1.04	2.10	1.73	1.92
Malaysia	0.28	0.42	0.25	0.34	0.30	0.48	0.30	0.39	0.43	0.52	0.31	0.42
Mexico	0.35	0.56	0.19	0.38	0.44	0.65	0.23	0.45	0.60	0.77	0.21	0.51
New Zealand	0.08	0.07	0.03	0.05	0.07	0.06	0.03	0.05	0.12	0.08	0.02	0.06
Norway	0.60	0.50	0.19	0.35	0.45	0.42	0.19	0.31	0.42	0.36	0.16	0.27
Russian Federation	2.65	2.22	0.31	1.30	2.76	2.46	0.38	1.46	2.52	2.23	0.32	1.32
Saudi Arabia	0.47	0.22	0.02	0.12	0.48	0.32	0.03	0.18	0.62	0.41	0.03	0.23
Serbia	0.53	0.32	0.22	0.27	0.47	0.31	0.21	0.26	0.42	0.32	0.24	0.28
Singapore	0.32	0.72	0.13	0.43	0.34	0.69	0.14	0.43	0.36	0.66	0.11	0.39
South Africa	0.53	0.57	0.10	0.34	0.49	0.54	0.08	0.32	0.44	0.44	0.08	0.27
South Korea	0.54	1.68	0.65	1.19	0.77	1.79	0.63	1.24	0.70	1.66	0.75	1.23
Switzerland	5.01	2.55	4.25	3.37	5.22	3.07	4.47	3.74	5.33	3.61	4.80	4.18
Taiwan	0.23	0.70	0.63	0.66	0.31	0.67	0.57	0.62	0.34	0.67	0.55	0.61
Thailand	0.18	0.48	0.41	0.45	0.25	0.53	0.39	0.46	0.25	0.54	0.46	0.50
Turkey	0.83	1.35	0.86	1.11	1.03	1.40	0.90	1.16	1.07	1.46	1.06	1.27
Ukraine	0.72	0.62	0.21	0.42	0.63	0.54	0.20	0.38	0.42	0.35	0.17	0.26
United Arab Emirates	0.52	0.31	0.02	0.17	0.51	0.30	0.08	0.19	0.62	0.40	0.16	0.29
United Kingdom	3.57	3.57	2.16	2.89	3.27	3.16	1.83	2.52	3.39	3.29	1.95	2.65
USA	5.04	6.82	6.11	6.48	5.78	6.68	5.56	6.14	6.85	7.49	6.76	7.14
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00
Source: OeNB/WIFO.

Private consumption and savings during the COVID-19 pandemic in Austria

Martin Schneider, Richard Sellner ²⁵

Refereed by: Tobias Schmidt and Alexander Erler, Deutsche Bundesbank

The economic disruptions caused by the COVID-19 pandemic have driven up household savings in Austria to unprecedented levels. We quantify the excess household savings accumulated so far during the pandemic (Q1 20 to Q2 21) at EUR 10.8 billion relative to a counterfactual scenario without the pandemic. In this paper, we perform three decompositions of Austrian households’ excess savings. The decomposition by source reveals that a drop in the consumption of services fueled savings despite a strong fall in property income. The decomposition by allocation shows that in 2020 excess savings were mainly used to accumulate currency and deposits. This development reversed in the first half of 2021. The decomposition by saving motives employs econometric models for the savings ratio. It shows that traditional determinants behind saving motives cannot explain the observed sharp increase in savings. We therefore conclude that in the observation period, savings were driven by forced savings. These forced savings come to between EUR 17 billion and EUR 23 billion, depending on the model specification used. Based on our results and a literature survey, we expect that households’ marginal propensity to save out of current income will quickly return to pre-crisis levels but that the scope for satisfying pent-up demand out of accumulated excess savings will remain limited.

JEL: classification: E21, E32, E37

Keywords: COVID-19, excess savings, pent-up demand

This article is structured as follows: In section 1, we take a detailed look at the development of household income and household consumption in Austria during the pandemic. In section 2, we perform the above-mentioned decompositions. In section 3, we discuss the implications for future consumption and saving patterns in Austria and their potential effect on GDP.

1 Household income and consumption during the pandemic

Throughout the second quarter of 2020, several sectors of the Austrian economy remained closed and the fear of infection led to voluntary mobility restrictions that put a further drag on economic activity. Real output shrank by 11.5% compared to the first quarter of 2020, marking the strongest economic contraction in Austrian post-war history. Disposable household income dropped by EUR 5.5 billion and private consumption by EUR 6.1 billion, contributing to a further rise in the savings ratio to 13%. Due to massive government support measures, most prominently the short-time work scheme, households’ purchasing power was backed while opportunities to consume – especially to consume contact-intensive services – were still heavily restricted.

As the lockdown measures were gradually lifted, the economy experienced a sharp recovery in the third quarter of 2020. Strengthend by both the economic upturn and continued government support, Austrian households’ disposable income grew by EUR 7.2 billion while consumption fell short of a full recovery, partly due to traveling restrictions and travel warnings that were in place during the summer of 2020. As a result, the quarter-on-quarter growth in net savings accelerated further to EUR 1.5 billion.

A second wave of COVID-19 infections after the summer led to the next lockdown in Austria by early November 2020. Still backed by huge government support, disposable household income continued to expand while consumption declined in the fourth quarter as nonessential businesses were closed down again. These developments drove households’ net savings further up, causing the (net) savings ratio to peak at nearly 20% in Q4 20.

Apart from a temporary lifting of measures before Christmas 2020, the lockdown in Austria went on until early February 2021, resulting in a further drop in real GDP by 0.5% in the first quarter of 2021. Household income fell by EUR 5.8 billion, which caused the savings ratio to drop to 11.1%. In the second quarter of 2021, the Austrian economy recovered strongly. Disposable household income and household consumption recovered only partially, however. The savings ratio increased somewhat, to 11.4%.

Table 1: Economic development during the COVID-19 pandemic in Austria  

	Q1 20	Q2 20	Q3 20	Q4 20	Q1 21	Q2 21	2019	2020
GDP, real1	–2.5	–11.5	11.0	–2.0	–0.5	4.0	1.5	–6.8
HICP inflation (change to previous year in %)	2.0	1.1	1.4	1.1	1.5	2.6	1.5	1.4
Unemployment rate (Eurostat), %	4.6	7.0	6.5	6.5	7.0	6.7	4.8	6.1
	Difference to previous period in EUR billion						EUR billion
Net disposable household income	–0.9	–5.5	7.2	1.4	–5.8	1.6	222.3	220.8
Private consumption	–1.5	–6.1	5.7	–2.2	–0.1	1.3	204.8	190.1
Net savings	0.5	0.7	1.5	3.7	–5.8	0.3	19.0	32.1
	% of household disposable income
Net savings ratio	10.6	13.0	13.9	19.8	11.1	11.4	8.5	14.3
Source: Statistics Austria, Eurostat, OeNB calculations.
1 Change to previous period in %. Based on seasonally and working day-unadjusted data, GDP growth in 2020 was 6.7%.
Note: All data are seasonally and working day adjusted and, except for GDP, HICP and the unemployment rate, given in current prices.

Table 2: Final consumption expenditure by purpose  

	2020	Growth in 2020			Share in 2019
	EUR billion	EUR billion	%	Contribution in percentage points	%
Restaurants, hotels, package holidays	20.8	–9.7	–31.8	–4.7	14.9
Transport	20.4	–4.5	–17.9	–2.2	12.2
Recreational and cultural services	5.7	–2.6	–31.2	–1.3	4.0
Clothing and footwear	9.1	–2.4	–20.7	–1.2	5.6
Other close-contact services	5.3	–0.5	–8.5	–0.2	2.8
Health and education	9.5	–0.4	–3.7	–0.2	4.8
Other goods	12.4	–0.2	–1.8	–0.1	6.2
Other services	16.0	–0.03	–0.2	–0.02	7.8
Food, beverages, housing and related goods	89.1	3.5	4.1	1.7	41.8
Total	188.2	–16.6	–8.1	–8.1	100.0
Source: Eurostat, OeNB calculations.
Note: Consumption at current prices. The following COICOP 2- and 3-digit headers have been aggregated: transport (CP07), restaurants, hotels, package holidays (CP11, CP096), recreational and cultural services (CP094), food, beverages, housing and related goods (CP01, CP02, CP04, CP05), health and education (CP06, CP10), other close-contact services (CP081, CP121), other services (CP083, CP124-126, CP122, CP127), clothing and footwear (CP03), other goods (CP082, CP091-093, CP095, CP123).

The pandemic-related containment measures and voluntary social distancing considerably affected the pattern of private consumption. The decline in private consumption in Austria was largest for close-contact services involving crowds of people, i.e. those sectors that were intentionally targeted by the containment measures. In current prices, final consumption expenditure shrank by 8.1% in 2020 (table 2). Expenditure on restaurant visits, hotels and package holidays accounted for more than half of this decline (–4.7 percentage points). The second largest contributor was the consumption of transport goods and services (–2.2 percentage points). Another –1.3 percentage points were accounted for by expenditure on recreational and/or cultural activities as well as clothing and footwear. Even though recreational and cultural activities were among the industries most affected by the containment measures, their share in total consumption is small by comparison (4%). By contrast, the brick-and-mortar distribution channel of the clothing and footwear industry was only affected during the strictest lockdown stages, and the online and click-and-collect channel was available during most of 2020. The strong decline in expenditure on clothing and footwear may be due to social distancing, the decrease in mobility and the increasing numbers of people working from home. Fewer opportunities to wear new items of apparel may have dampened demand. ²⁶ At the other end of the spectrum, the consumption of necessities like food, water, electricity and housing but also home furnishing products such as furniture and household appliances grew by 4.1%. Given their large share in total consumption of 41.8%, these products and services contributed 1.7 percentage points to the percentage change in total consumption.

2 Excess savings during the pandemic

2.1 Excess savings in Austria came to EUR 10.8 billion during the pandemic

2.2 Decomposition by source: drop in consumption of services fueled savings despite strong fall in property income

The contraction in services consumption during the first lockdown in Austria in Q2 20 was compensated by a decline in labor and property income. Due to lagged transfer payments, transfer income drove up households’ total income only marginally in Q2 20, whereas short-time work payments helped stabilize income. Hence, the savings ratio increased only marginally. In the following quarters, transfer income was an important driver of savings, which peaked in the fourth quarter of 2020. In the first half of 2021, substantial income losses and a strong decline in durable goods consumption brought savings almost back to the counterfactual level.

2.3 Decomposition by allocation: accumulation of currency and deposits in 2020 reversed in first half of 2021

Net savings (B.8n) = A net disposable income – consumption + + changes in pension entitlements (D8)(1) ²⁹

Savings (B.8n) + net wealth transfers (D9.r – D9.p) + net financing (F.LIAB) = = net capital formation (P.5g – P.51c) + financial investment (F.ASSETS)(2)

Savings (B.8n) = net capital formation (P.5g – P.51c) + + financial investment (F.ASSETS) – net wealth transfers (D9.r – D9.p) – – net financing (F.LIAB)(3)

Our calculation of excess financial investment differs from that of excess savings. Since we did not produce forecasts for financial investment in the December 2019 Broad Macroeconomic Projection Exercise (BMPE) and since seasonal adjustment yields questionable results for some series, we calculate excess financial investment in the following way: For each category of financial investment, we first calculate the deviation of quarterly levels during the period from Q1 20 to Q2 21 from the respective average quarterly level for the years from 2017 to 2019 and then rescale it, so that excess financial investment plus excess real investment match total excess funds available.

The bottom left-hand panel of chart 1 shows the results of this decomposition. On average over the six quarters under observation, all components of excess savings show similar contributions to households’ total savings. Over time, a distinct profile emerges. During the first lockdown in Austria (Q1 20), households’ excess savings were mostly used to invest in unlisted equity, i.e. to support households’ own businesses classified in the household sector. In the second half of 2020, the bulk of excess savings were used for the accumulation of currency and deposits, whereas in the first half of 2021, the decline in the savings ratio was driven by the reduction of this position. Other financial investment played a dominant role in the buildup of savings in Q1 20 and in their reduction in Q3 20, while shares only played a minor role.

2.4 Decomposition by motive: increase of savings ratio driven by forced savings

First, we consider consumption smoothing, which is a direct consequence of the permanent income hypothesis (Friedman, 1957) and the life cycle hypothesis (Modigliani and Brumberg, 1956). The permanent income hypothesis states that current consumption depends on expected long-term permanent income and not on current income, as suggested by Keynesian theory. According to the life cycle hypothesis (Modigliani, 1956), households want to maintain a stable consumption path over their entire lifetime. Both theories imply that savings increase in periods with higher income and decrease in periods with lower income. Our proxy for consumption smoothing is the percentage deviation of real disposable household income from the quarterly sectoral accounts from its trend. ³¹ In periods when disposable income is above (below) its trend, savings should increase (decrease); thus, we expect the respective coefficient to have a positive sign.

Our second determinant is the composition of income. Empirical literature ³² shows that the marginal propensity to consume (MPC) differs considerably across income components. The MPC is defined as the amount of additional consumption generated by one additional unit of income and/or wealth. De Bondt et al. (2019) reviewed the empirical literature and estimated MPCs for the euro area and its four largest economies (Germany, France, Italy, Spain). They distinguish between labor income, transfer income, property income, financial wealth and nonfinancial (housing) wealth. The bulk of estimates for the MPC out of labor and transfer income ranges from 0.8 to 1, whereas the MPC out of property income is much lower in most cases (0.07 to 0.3). The estimates for the MPC out of wealth are substantially lower, between 0 and 0.01. ³³ According to our own estimates for Austria (see annex 3, table A1), the MPC for nonproperty income is always higher (0.75 to 1.05) than that for property income (0.54 to 0.71). ³⁴ Our proxy variable for income composition is the share of net property income in total (net) disposable household income. Net labor income, net transfer income and net property income are calculated from quarterly sectoral accounts data. ³⁵ On aggregate, a higher share of income from property should, ceteris paribus, lower consumption and thus increase savings. Hence, we expect the coefficient of income composition to have a positive sign.

Third, we consider the precautionary savings motive. The theory of precautionary savings states that uncertainty about future income developments triggers the accumulation of wealth. Although this hypothesis has been tested in many studies, empirical results are not conclusive about the prevalence of precautionary savings and the best proxy variables for empirical work (Lugilde et al., 2017). Kennickell and Lusardi (2005) found that the role of precautionary savings is low, at an aggregate level, for the USA. Desired precautionary wealth accounts for only 8% of households’ total net worth and is mainly accumulated by older households and business owners. We use two proxies, namely “adequate for savings” from the European Commission’s business and consumer survey (“the current economic situation is adequate for savings”) and unemployment expectations (“How do you expect the number of people unemployed in this country to change over the next 12 months?”, quoted in Dossche and Zlatanos, 2020) from the same source. Assuming that consumers find it more adequate to save and expect higher future unemployment in periods of high individual or general economic uncertainty, an increase in these proxy variables should be associated with higher savings; thus, we expect both coefficients to have a positive sign.

Fourth, we look at the role of Ricardian savings. According to Ricardian theory, additional government transfers or tax reliefs to households do not increase consumption because forward-looking households end up saving all their additional income from these transfers or reliefs since they expect future tax increases to pay the deficits incurred at present. It is a well-known stylized fact that Ricardian equivalence in its purest from rarely holds in practice. Recently, Armantier et al. (2020) and Baker et al. (2020) investigated the impact of US government support to households during the pandemic. They found that between 25% and 40% of these funds were spent, indicating Ricardian equivalence only to some degree. We use the year-on-year absolute difference of the government debt-to-GDP ratio from the quarterly national accounts as a proxy for the Ricardian savings motive. An increasing government debt-to-GDP ratio may signal a future need for budget consolidation, i.e. tax increases, which according to Ricardian theory should lower consumption and thus increase savings in the present period. Thus, we expect the coefficient attached to this proxy variable to have a positive sign.

Fifth, the savings target addresses the role of wealth in saving decisions. The buffer stock savings theory postulates that households have a certain stock of savings that they target. If their target wealth falls below this target, they increase their savings, and vice versa. We use three different variables for financial wealth, namely the total wealth-to income-ratio, the net financial wealth-to-income ratio and the real (housing) wealth-to-income ratio (see footnote 10 for details on the calculation of the wealth variables). These variables capture different aspects of wealth, with the intuition that, on aggregate, increasing wealth should enable more households to reach their savings target and thus decrease their savings related to this motive. Therefore, we expect negative coefficients on these three proxy variables.

Finally, we include two additional variables: the real interest rate and the debt-to-income ratio. The real interest rate is an important determinant of saving decisions. In theory, higher interest rates make saving more attractive and should therefore drive up savings. In reality, additional other factors (“income effect”) are at play which render the relationship between interest rates and savings less clear. ³⁶ The empirical literature finds mixed evidence (see e.g. Elmendor, 1996, or Beznoska and Ochmann, 2013). We use the interest rate on consumer loans, deflated by annual HIPC inflation. The basic idea behind including the household debt-to-income ratio is that, in the national accounts, debt repayments are counted as savings. Higher debt would thus lead to higher savings, and including this variable should control for this accounting effect.

We estimate a full (general) model (M0) that includes all of the above-mentioned motives with their described proxy variables and serves as our benchmark model in the decomposition by saving motives. Based on this general model, we estimate a set of submodels ³⁷ which focus on specific saving motives and also serve as robustness checks (M1 to M8). We limit the estimation sample to the period prior to the pandemic (Q1 01 to Q4 19). We include the lagged endogenous variable in all models to account for autocorrelated residuals. Table 2 shows the estimation results for our benchmark model (M0) and the eight different submodel specifications (M1 to M8). All coefficients have the expected sign in all models. There is only one insignificant variable, namely the total wealth-to-income ratio, in the general model. All other variables are significant, most of them at the 1% level.

Consumption smoothing and income composition are included in all submodels. Compared with the benchmark model, the coefficient of consumption smoothing decreases in the submodels, whereas the coefficient of the income composition proxy variable increases. In specifications with proxies for precautionary savings (M0, M2, M3, M5), the coefficient of the share of property income is lower (0.20 to 0.26) than in specifications without this determinant (0.30 to 0.41). Precautionary savings hence reduce the explanatory power of the property income share. This result supports the findings of Kennickell and Lusardi (2005), who found that precautionary savings are mainly accumulated by older households and business owners. Since our measure of property income includes the net operating surplus of businesses that are part of the household sector, our proxy variables for precautionary savings and income composition partially overlap.

Ricardian effects are significant in the benchmark model (M0) and two submodels (M4, M5). The coefficients of our proxy variables for the savings target show the expected negative sign and are significant in submodels M6 to M8. In the general model, the coefficient is considerably lower and not significant. The effects of the debt-to-income ratio and the lending rate are also somewhat lower in our fully specified benchmark model (M0) than in the two submodels in focus (M1, M3).

Table 3: Estimation results of the savings ratio equations  

	M01	M1	M2	M3	M4	M5	M6	M7	M8
Coefficients
Constant	–3.05	–3.03	0.36	–5.38	–2.13	0.58	4.11	2.66	4.35
Lagged dependent variable
T–1	0.02				0.38 ***
T–2		0.26 ***	0.27 ***			0.26 ***	0.36 ***	0.37*	0.36 ***
Consumption smoothing
Trend deviation of disposable income	0.70 ***	0.36 ***	0.40 ***	0.43 ***	0.35 ***	0.39 ***	0.36 ***	0.37 ***	0.35 ***
Income composition
Share of property income in total income	0.20 ***	0.41 ***	0.24 ***	0.22 ***	0.39 ***	0.26 ***	0.31 ***	0.33 ***	0.30 ***
Precautionary savings
Consumer survey (“save now”)	0.05 ***		0.03 ***	0.07 ***		0.03 ***
Unemployment expectations	0.01 **		0.01 **	0.02 ***
Ricardian savings
Government debt-to-GDP ratio	0.06 ***				0.06 **	0.06 ***
Savings target
Total wealth-to-income ratio	–0.19						–0.80 **
Net financial wealth-to-income ratio								–1.68 **
Housing wealth-to-income ratio									–1.33 *
Other variables
Debt-to-income ratio	0.07 ***			0.09 ***
Lending rate	0.19 **	0.40 ***
R2	0.95	0.86	0.85	0.84	0.82	0.86	0.82	0.83	0.82
Durbin-Watson statistic	1.93	2.09	2.07	2.06	2.14	1.92	1.89	1.92	1.86
Source: Authors’ calculations.
1 M0 = benchmark model; M1 to M8 = submodels.

Chart 2 shows the individual models’ in-sample and out-of-sample predictions of the savings ratio. The left-hand panel shows the fitted values of the savings ratio in the benchmark model (M0) and the eight submodels (M1 to M10) together with the actual values over the estimation horizon. All specifications capture the historical development of the savings ratio well. The middle panel of chart 2 shows the conditional forecasts of the savings ratio with the realized values of the conditioning variables for the period from Q1 19 to Q2 21. For Q1 20 to Q2 21, these forecasts are out of sample. None of the specifications come close to predicting the observed path of the savings ratio; instead, they predict a sharp decline for Q2 20, when, in fact, the savings ratio was already increasing. The subsequent movements of the savings ratio (increase until Q4 20, fall afterward) are predicted correctly (regarding the direction) but the predicted level of the savings ratio remains too low.

The right-hand panel of chart 2 shows a decomposition into saving motives of the excess savings Austrian households accumulated during the pandemic (EUR 10.8 billion) for each of our nine models (see also table A2). Although the importance of the individual motives differs between specifications, the main result is robust: The fall in disposable household income and the even stronger decline in property income combined with the role of consumption smoothing suggest that the savings ratio decreased instead of increased during the pandemic. The effect of precautionary savings on the savings ratio is positive but small. This might be an effect of the short-time work scheme, which reduced unemployment expectations considerably. Consequently, forced savings (defined as the residual) substantially contributed to the increase in the savings ratio. ³⁸ Our estimates of the different specifications for forced savings range from EUR 17 billion to EUR 23 billion (accumulated over the period from Q1 20 to Q2 21). Specifications including the precautionary savings motive (our benchmark M0 and submodels M2, M3, M5) exhibit lower forced savings. The quarterly results of this decomposition for our benchmark model M0 can be found in the bottom right-hand panel of chart 1.

3 Implications for future consumption and saving patterns

There is a consensus among most forecasters that the MPS will quickly decline as soon as all lockdown measures are lifted (see e.g. Deutsche Bundesbank, 2021; Banque de France, 2021; European Commission, 2021; or OECD, 2021). In the first half of 2021, savings in Austria almost reverted to pre-pandemic levels. However, this was not due to the release of pent-up demand but to a decline in income, notably property income. Household consumption in Austria declined in the first quarter of 2021 due to the second lockdown and recovered in the second quarter of 2021 as containment measures were lifted. All components of private consumption were still below pre-crisis levels in Q2 21 except for durable goods, which were 24% above the level recorded in Q4 19.

There is a lot more disagreement in the literature regarding the amount or share of excess savings that will be spent to meet pent-up demand, however. Early evidence from Germany (Bernard, Tzamourani and Weber, 2020) suggests that consumers were significantly more cautious in their spending intentions after the relaxation of the first pandemic-induced lockdown in 2020. To shed light on this question, we focus on two different perspectives. First, we consider information from surveys asking households how they plan to use their excess savings. The available surveys exhibit a huge variation, depending on their timing and design and on country characteristics. They point toward some pent-up demand, which is of apparently limited magnitude, however. ³⁹ This is mainly because the lion’s share of excess savings is expected to have accrued in high-income households, which saw a strong increase in their savings during the pandemic while lower-income households did not increase their savings by much or even drew on existing savings. On average, one-quarter to one-third of excess savings is expected to be consumed. There is little information on the transition dynamics, i.e. the length of the period until this pent-up demand is satisfied, and on the respective spending profile over time.

Second, we draw on theoretical and empirical studies on households’ MPC out of savings. The MPC out of wealth is considerably lower than the MPC out of income (Albacete and Lindner, 2017; Jappelli and Pistaferri, 2014). The main problem with this approach, therefore, is whether households perceive their excess savings as additional income or as wealth. During the pandemic, most additional savings were accumulated by high-income households, which may have a higher tendency to perceive these excess savings as additional wealth. Some low-income households, by contrast, were unable to accumulate excess savings during the pandemic; they even incurred higher debts (Raja, 2021). Hence, it is reasonable to assume that their spending on pent-up demand after the pandemic will be limited and that a large share of excess savings will not be spent.

If we assume that 25% of excess savings (EUR 2.7 billion) will be spent on pent-up demand, this will result in a cumulated increase in Austrian GDP of EUR 2.4 billion (or 0.4%) until 2023 according to a simulation performed with the OeNB’s macroeconomic model (Austrian Quarterly Model – AQM). This assessment is, of course, subject to considerable uncertainty about the course of the pandemic and the consumption components ⁴⁰ that will be most affected.

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Annex 1

Definition of the counterfactual scenario

Annex 2

Income decomposition

We follow de Bondt et al. (2019) and define the following three income categories ⁴¹ : Labor income is the sum of compensation of employees (D1R) and gross mixed income (B3G); transfer income is social benefits other than social transfers in kind (D62) plus other current transfers (D7); and property income is the sum of gross operating surplus (B2G, which consists mostly of imputed rents for owner-occupied housing) and net property income (D4, i.e. net interest earnings plus net other property income). However, our approach differs in the way how direct taxes (D5P) and social security contributions (D61) are distributed across these three income categories. While de Bondt et al. (2019) subtract social security contributions from labor income and distribute direct taxes proportionally among the income components, we use administrative tax data to distribute direct taxes among income components. This approach results in a much better estimate for income components after taxes, since the approach used by de Bondt et al. (2019) underestimates net property income. The main reasons for this underestimation are that de Bondt et al. (2019) consider financial intermediation services indirectly measured (FISIM), i.e. imputed interest payments, and imputed rents for owner-occupied housing, which are not taxed, and the fact that dividend payments in the national accounts are too high when compared with other statistics.

Annex 3

Table A1: Estimation results of the private consumption models1  

	C14	C2	C3	C4	C5	C6	C7
Elasticities2
Labor + transfer income	0.92 ***	0.72 ***	0.65 ***	0.67 ***
Labor income					0.54 ***	0.29 ***	0.27 ***
Transfer income					0.42 ***	0.41 ***	0.34 ***
Property income	0.10 ***	0.12 ***	0.13 ***	0.13 ***	0.13 ***	0.15 ***	0.16 ***
Total wealth		0.14 ***				0.18 ***
Net financial wealth			–0.06 *				–0.09 **
Housing wealth			0.25 ***	0.18 ***			0.32 ***
Marginal propensity to consume3
Labor + transfer income	1.05	0.83	0.75	0.77
Labor income					0.97	0.51	0.49
Transfer income					1.31	1.27	1.08
Property income	0.54	0.65	0.71	0.70	0.66	0.78	0.83
Total wealth		0.01				0.01
Net financial wealth			–0.01				0.00
Housing wealth			0.01	0.01			0.02
Source: Authors’ calculations.
1 All variables are real and expressed in logs. We report only equations with significant estimation results.
2 = percentage change of private consumption per 1% change of respective variable.
3 = change of private consumption in euro per EUR 1 change in respective variable.
4 C1 to C7 denote the seven different private consumption models estimated in this exercise.

Table A2: Decomposition of excess savings into saving motives  

	M01	M1	M2	M3	M4	M5	M6	M7	M8
	Contributions to accumulated excess savings in EUR million
Constant	0.6	0.6	–0.1	1.0	0.4	–0.1	–0.8	–0.5	–0.8
Inertia		–0.4	–0.4		–0.7	–0.4	–0.5	–0.6	–0.5
Consumption smoothing	–7.5	–3.9	–4.3	–4.6	–3.8	–4.2	–3.8	–3.9	–3.7
Income structure	–4.2	–8.7	–5.2	–4.6	–8.2	–5.5	–6.5	–6.9	–6.4
Precautionary savings	2.0		1.4	2.6		0.8
Ricardian savings	2.0				1.8	1.9
Savings target	–0.1						–0.3	–0.2	–0.4
Other variables	0.2	0.6		–0.1
Residual (= forced savings)	17.9	22.7	19.4	16.5	21.3	18.4	22.8	22.9	22.7
Excess savings	10.8	10.8	10.8	10.8	10.8	10.8	10.8	10.8	10.8
Source: Authors’ calculations.
1 M0 = benchmark model; M1 to M8 = submodels.