Monetary Policy and the Economy Q3/20

Call for applications: Klaus Liebscher ­
Economic Research Scholarship

Please e-mail applications to scholarship@oenb.at by the end of October 2021.
Applicants will be notified of the jury’s decision by end-November.

The Oesterreichische Nationalbank (OeNB) invites applications for the “Klaus ­Liebscher Economic Research Scholarship.” This scholarship program gives outstanding researchers the opportunity to contribute their expertise to the research activities of the OeNB’s Economic Analysis and Research Department. This contribution will take the form of remunerated consultancy services.

The scholarship program targets Austrian and international experts with a proven research record in economics and finance, and postdoctoral research experience. Applicants need to be in active employment and should be interested in broadening their research experience and expanding their personal research ­networks. Given the OeNB’s strategic research focus on Central, Eastern and Southeastern Europe, the analysis of economic developments in this region will be a key field of research in this context.

The OeNB offers a stimulating and professional research environment in close proximity to the policymaking process. The selected scholarship recipients will be expected to collaborate with the OeNB’s research staff on a prespecified topic and are invited to participate actively in the department’s internal seminars and other research activities. Their research output may be published in one of the department’s publication outlets or as an OeNB Working Paper. As a rule, the consultancy services under the scholarship will be provided over a period of two to three months. As far as possible, an adequate accommodation for the stay in Vienna will be provided. 1

Applicants must provide the following documents and information:

  • a letter of motivation, including an indication of the time period envisaged for the consultancy
  • a detailed consultancy proposal
  • a description of current research topics and activities
  • an academic curriculum vitae
  • an up-to-date list of publications (or an extract therefrom)
  • the names of two references that the OeNB may contact to obtain further information about the applicant
  • evidence of basic income during the term of the scholarship (employment contract with the applicant’s home institution)
  • written confirmation by the home institution that the provision of consultancy services by the applicant is not in violation of the applicant’s employment contract with the home institution.

1 We assume that the coronavirus crisis will abate in the course of 2021. We are also exploring alternative formats to continue research cooperation under the KLERS program for as long as we cannot resume visits due to the ­pandemic situation.

Nontechnical summaries
in English and German

Nontechnical summaries in English

Financial literacy in Austria – Focus on millennials

Pirmin Fessler, Marilies Jelovsek, Maria Antoinette Silgoner

This article summarizes the main findings from the second wave of the Austrian Survey of Financial Literacy (ASFL) which was conducted in spring 2019. As compared to the previous survey round in 2014, the financial knowledge of Austrian residents seems to have increased significantly. While men outperform women in terms of financial knowledge, they score slightly worse in terms of financial behavior and attitudes. Austrian residents are rather prudent, risk averse and forward looking and have a good overview of their finances. In general, financial literacy is rather equally distributed across age groups. However, 15- to 38-year-olds (hereinafter called millennials) differ from other age cohorts in several respects: They have relatively low levels of financial literacy, are less financially organized, and they show more risky and less forward looking behavior. At the same time, they are more open to digital means of payments and financial innovations in general. Even though the observed differences are not very large and may vanish as millennials mature and gain experience with business and finance, we deem it important to monitor the financial literacy development for this group, given the rising complexity of financial decisions many among this group will face and the tremendous ­financial resources they will ultimately inherit.

A spatial analysis of access to ATMs in Austria

Helmut Stix

This study analyzes the access to cash via automated teller machines (ATMs) for consumers in Austria, providing information on how far people have to walk or travel, and how much time it takes, to reach the nearest ATM in both urban and rural areas. The fine-grained analysis is based on a 100x100 m geographical grid of Austria. For each grid cell, we calculated the corresponding travel distance and travel time to the next ATM. The results show that, in Austria, the average distance people must travel to access an ATM is 1.2 km. The median distance comes to 630 m, which means that 50% of the population travel less far to withdraw cash from an ATM. In terms of travel time, we find that, in Austria, it takes 2.9 minutes on average to reach the nearest ATM, with the median time being 2.1 minutes. A total of 85% of the ­population reside within a 2 km travel distance and an approximate five-minute travel time of an ATM. When looking at a travel distance of 5 km, this percentage rises to 97%. We also found that the larger a municipality is, the smaller the distance and the shorter the travel time is to get to the next ATM. In Vienna, residents live, on average, less than 400 m from the nearest ATM; in small municipalities with less than 2,000 inhabitants the average travel distance is 2.1 km. Yet, even in these municipalities, the share of the ­population that has access to an ATM within 5 km of where they live comes to more than 90%. Overall, our results indicate that the domestic ATM network seems to be relatively dense, on average. This is supported by the finding that even in rural areas most people can reach the next ATM within less than 5 km. Based on the geographical grid of Austria used in this study, we can also identify regions with longer distances to the nearest ATM. When breaking down the results by municipalities, we find that the share of municipalities where a large part of the population faces longer distances is relatively small. For example, in 108 of Austria’s 2,096 municipalities, more than 40% of inhabitants face a distance of more than 5 km to get to the next ATM. These municipalities have an average population of some 840 people.

A new long-run consumer price index for Austria (1800–2018)

Gerald Hubmann, Clemens Jobst, Michaela Maier

Indices measuring the development of consumer prices in Vienna or Austria date back to the year 1800. This article presents the first systematically documented consumer price index for Austria spanning the period from 1800 to today without time gaps. We calculated the new index from existing shorter index series, discussing the selection of the available time series at length. To be able to merge existing shorter time series and to adequately integrate war periods and currency reforms, we had to make a number of adjustments to deal with methodological issues, which at the same time enabled us to fix unresolved problems affecting the existing indices. In contrast to the existing time series, our new long-run index thus yields significantly higher inflation rates during the Napoleonic Wars and a more pronounced ­decline in the level of prices after their end as well as a steeper price increase in 1948 and 1949. Finally, this article examines the suitability of consumer price indices for the conversion of historical prices. The article includes a table with annual index figures; monthly series are available online.

Nontechnical summaries in German

Finanzbildung in Österreich – Millennials im Fokus

Pirmin Fessler, Marilies Jelovsek, Maria Antoinette Silgoner

Dieser Artikel fasst die ersten Ergebnisse der im Frühjahr 2019 zum zweiten Mal durchgeführten Erhebung zur Finanzbildung in Österreich. Gegenüber der ersten Welle (2014) ist eine signifikante Verbesserung beim Finanzwissen ­festzustellen. Allgemein schneiden Männer bei Finanzwissensfragen besser ab als Frauen, erzielen punkto Finanzverhalten und Einstellungen zum Thema Geld und Finanzen aber etwas schlechtere Ergebnisse. Insgesamt agiert die österreichische Bevölkerung in Finanzfragen eher vorsichtig, risikoavers und vorausschauend und hat die Finanzen generell gut im Blick. Nach Altersgruppen sind die Finanzbildungsergebnisse recht gleichmäßig verteilt, doch sticht die Gruppe der 15- bis 38-Jährigen („Millennials“) mehrfach hervor: Ihr Finanzbildungsniveau ist vergleichsweise niedrig; sie sind nicht so gut organisiert, wenn es um ihre Finanzen geht; und sie agieren risikofreudiger und weniger vorausschauend als die anderen Generationen. Zugleich stehen sie digitalen Zahlungsmitteln und Finanzinnovationen insgesamt offener gegenüber. Selbst wenn die dargestellten Unterschiede insgesamt nicht sehr groß sind und mit zunehmendem Alter bzw. mehr Finanz- und Geschäftserfahrung immer geringer werden dürften, erscheint es angebracht zu verfolgen, wie sich das Finanzbildungsniveau dieser Gruppe weiterentwickelt. Immerhin werden viele Millennials früher oder später komplexere finanzielle Entscheidungen als jetzt zu treffen haben und eines Tages durch Erbschaften über ein in Summe sehr großes Vermögen verfügen.

Die Erreichbarkeit von Geldautomaten in Österreich

Helmut Stix

Im vorliegenden Beitrag wird die Erreichbarkeit von Bankomaten in Österreich in hoher geografischer Auflösung untersucht. Damit liegen Ergebnisse darüber vor, innerhalb welcher Wegstrecken bzw. welcher Wegzeiten Bankomaten im städtischen und ländlichen Raum Österreichs erreichbar sind. Konkret wurde Österreich in 100 x 100 m Rasterzellen eingeteilt und von jeder dieser Rasterzellen die Wegstrecke und die Wegzeit zum nächstgelegenen Bankomaten berechnet. Die Ergebnisse zeigen, dass der nächste Bankomat in Österreich im Durchschnitt 1,2 km entfernt ist. Die mittlere Entfernung (der Median) liegt bei 630 m; das heißt, dass für 50 % der heimischen Bevölkerung die nächste Geldquelle weniger weit entfernt liegt. Zeitlich ausgedrückt sind in Österreich durchschnittlich 2,9 Minuten einzuplanen, um zum nächsten Bankomaten zu gelangen; der Median liegt bei 2,1 Minuten. Für 85 % (97 %) der Bevölkerung liegt die nächstgelegene Geldquelle innerhalb von 2 km (5 km) ihres Wohnsitzes; etwa 85 %, brauchen weniger als
5 Minuten um diese zu erreichen. Je größer die Gemeinde, desto kürzer ist der Weg für die Bargeldbehebung bzw. desto weniger Zeit muss dafür eingeplant werden. In Wien ist der nächste Bankomat im Durchschnitt binnen 400 m erreichbar. In kleinen Gemeinden mit weniger als 2.000 Einwohnerinnen und Einwohnern liegt die durchschnittliche Entfernung bei 2,1 km. Auch in kleineren Gemeinden mit weniger als 2,000 Einwohner haben mehr als 90% der ­Einwohner einen Bankomaten innerhalb von 5 km. Die Ergebnisse zeigen, dass die österreichische Bevölkerung im Durchschnitt gut mit Bankomaten versorgt ist. Auch im ländlichen Raum steht für den überwiegenden Teil der Bevölkerung innerhalb von 5 km ein Bankomat zur Verfügung. Mittels des Rasternetzes ist es möglich, Regionen und ­Gemeinden zu identifizieren, in denen ein höherer Anteil der Bevölkerung weitere Wegstrecken hat. Dies ist hauptsächlich in kleineren Gemeinden mit weniger als 2.000 Einwohnerinnen und Einwohnern der Fall – solche Gemeinden haben im Durchschnitt 840 Einwohner und sind in allen österreichischen Bundesländern außer Wien zu finden.

Ein neuer langer Verbraucherpreisindex für Österreich (1800–2018)

Gerald Hubmann, Clemens Jobst, Michaela Maier

Indizes zur Entwicklung der Verbraucherpreise in Wien bzw. Österreich gehen bis zum Jahr 1800 zurück. Der ­vorliegende Beitrag präsentiert den ersten systematisch dokumentierten, durchgehenden Verbraucherpreisindex für Österreich von 1800 bis heute. Dieser neue Index wird auf Basis bereits bestehender Indexreihen, die für kürzere Zeitabschnitte verfügbar sind, erstellt. Die Auswahl der zugrunde liegenden Reihen wird ausführlich besprochen. Für ihre Zusammenführung zum neuen, langen Verbraucherpreisindex über Kriege und Währungsreformen hinweg sind ­Anpassungen vorzunehmen, die einerseits methodische Gründe haben und durch die andererseits Probleme der ­bestehenden Indizes behoben werden. Im Unterschied zu den bisher verwendeten Indexreihen ergeben sich damit eine signifikant höhere Inflation während der Napoleonischen Kriege und ein stärkerer Rückgang des Preisniveaus nach deren Ende sowie ein stärkerer Preisanstieg in den Jahren 1948 und 1949. Abschließend widmet sich der Beitrag der Frage, wieweit Verbraucherpreisindizes zur Umrechnung historischer Preise verwendet werden können. Der Artikel enthält eine Tabelle mit den jährlichen Indexwerten. Monatliche Reihen sind online verfügbar.


Rising infection rates threaten to derail economic recovery

Gerhard Fenz, Friedrich Fritzer, Ernst Glatzer , Martin Schneider, Helmut Stix, Klaus Vondra 2

Economic activity in Austria has been sharply curbed by the ongoing COVID-19 pandemic. During the first-wave lockdown, the OeNB’s weekly GDP indicator registered a decline of economic output by one quarter. After the exit from lockdown, the GDP gap narrowed very rapidly, amounting to –3½% compared to previous year levels in the first half of October. Among the hardest-hit sections of the economy, tourism benefited from markedly stronger domestic ­demand during the summer, which limited the year-on-year decline in overnight stays to 15% in July and August. Meanwhile, the travel alerts newly issued by a number of countries for Austria since mid-September have been taking their toll, though. For October, real-time data on card payments already point to a 40% decrease in overnight stays. In contrast, export performance has been improving, mirroring the slight upward trend in the production sector. By September, the decline in goods exports had dropped to a small percentage according to the OeNB’s export indicator. Looking ahead, the ongoing rapid rise in infection rates constitutes downside risks to growth, however. While the GDP forecasts for 2020 (about –7%) are fairly solid given strong third-quarter performance, the recovery projected for 2021 may turn out to be below the range currently expected (+4½ to +5%). The recovery in the labor market has ­already been slowing down. Registered unemployment exceeded the year-earlier mark by 71,000 unemployed individuals by mid-­October and thus a mere 30% of the peak measured in April, but unemployment has been shrinking at a decreasing pace. The early warning system for impending layoffs implemented by Public Employment Service Austria points to more layoffs coming in the weeks ahead. Inflation has been highly volatile in 2020 so far, reflecting energy price fluctuations as well as one-off effects (fashion clearance sales started later usual) and price measurement problems. In September, HICP inflation came to 1.3%. In line with the OeNB’s inflation forecast of September 2020, HICP inflation is expected to run to 1.4% in 2020 and to climb to 1.7% in 2021.

1 The OeNB’s weekly GDP indicator

Third-quarter economic output down 4½% from 2019

Economic activity in Austria has been sharply curbed by the ongoing COVID-19 pandemic. Coming up with timely estimates of how badly the economy has been hit and how soon it will recover has created new challenges for economic research. The economic indicators that have been used in the past tend to be lagging indicators and tend to be limited to providing monthly and quarterly snapshots. This is why the OeNB developed a new indicator that measures economic activity in Austria in real time using daily and weekly data. The new indicator has been published at weekly intervals since mid-May 2020 (https://www.oenb.at/Publikationen/corona.html).

According to the OeNB’s weekly GDP indicator, the domestic economy contracted by up to one-quarter during lockdown in late March/early April. This setback was driven by all major demand components other than public consumption; even private consumption, which tends to have a stabilizing impact on economic activity, weighed in. When the lockdown measures were lifted, the economy started a brisk recovery. The easing of health policy measures, rising consumer spending supported by pent-up demand and public relief measures caused economic conditions to improve significantly in the run-up to the summer months. Even so, the annual change in output still amounted to a 7% decline at the end of June. All in all, second-quarter output was 14.5% lower than a year earlier – as indeed projected most accurately by the OeNB’s weekly economic indicator for early July.

The summer did not bring any significant improvement in economic conditions, with economic measures moving steadfastly sideways. The GDP gap hovered around close to –5% for weeks, improving only slightly over time. The gradual recovery in individual sections of the economy, such as tourism or construction, was offset by dwindling pent-up demand for consumer spending in particular and ongoing challenges faced by a range of service providers. At the end of September, the GDP gap measured –4%. Preliminary estimates based on the weekly GDP indicator imply that third-quarter GDP was down 4½% in 2020 compared with 2019. In other words, we see an improvement by 10 percentage points compared with second-quarter growth, yet a downturn echoing the maximum contraction during the 2008/2009 economic and financial crisis (second quarter of 2009: –5¼%). While the GDP growth pattern clearly followed a V-shape in the initial months of the COVID-19 pandemic, the right-hand recovery curve has been flattening in recent months.

Chart 1 “Weekly GDP indicator for Austria” shows how the weekly GDP indicator newly developed by the OeNB changed during the period from week 4 to week 41 2020. The indicator shows the decline of real GDP below the level measured for the same period of 2019 in percent and displays the contributions of import-adjusted demand components to GDP growth. A detailed description of the indicator's development over time is provided in the main text. Source: OeNB.

Growth risks until end-2020 and for 2021 increasingly on the downside

Late in the third quarter and early in the fourth quarter of 2020, economic growth was moderate and characterized by divergent developments. Rising infection rates, travel alerts issued by numerous countries for Austria or for individual regions in Austria, and the re-tightening of coronavirus containment measures negatively ­impacted the service industry, while manufacturing and exports remained largely unaffected for the time being. The tourist industry is faced with a second drop-off of overnight stays, as implied by real-time data on card payments (see section 2). The export-oriented manufacturing industry, meanwhile, experiences a continuation of the positive trend seen in recent weeks. Judging from estimates of export volumes (excluding tourism) derived from truck mileage data, week 41 was the first week during the pandemic period to finally see a slight increase on the corresponding week of 2019 (see section 3).

Overall, the weekly GDP indicator result for week 40 was 3.5% below the level of economic output measured for the same week of 2019, and 3.7% below the result for week 41 in 2019. Given the high infection rates and the containment measures required in Austria and worldwide, and given increasing concerns about job losses, the risks to growth will remain on the downside during the weeks ahead.

As the recovery was stronger than expected until early summer, there was no actual need to revise the OeNB’s June 2020 economic outlook for 2020 (–7.2%). The downside risks to the projections for 2021 (+4.9%) have increased, though, as of late. Thus, the OeNB’s projections are very close to the forecasts published by WIFO in the first half of October (2020: –6.8%, 2021: +4.4%), IHS (2020: –6.7%, 2021: +4.7%) and IMF (2020: –6.7%, 2021: +4.6%).

Tourism is among the economic sectors that have been hit hardest by the COVID-19 pandemic. In Austria, the tourist industry contributes as much as 7.3% to economic value added, which is more than in many other countries. With the restrictions on travel, overnight stays dropped by close to 100% during the April lockdown. This was followed by a gradual recovery in the following months and a summer respite for the tourist industry. In July and August, the tourist industry made up all but 15% of the year-on-year gap in overnight stays, as Austrian destinations attracted 20% more domestic guests than in 2019 and the same numbers of German tourists as in 2019.

So far, overnight statistics have only become available up to August. However, real-time data on card payments made by tourists visiting Austria allow us to produce estimates for September and October as well as a first review for the summer season of 2020 (May to October). September is expected to have seen the smallest decline in overnight stays (–10%) compared with pre-pandemic levels. The travel alerts for Austria issued by a number of countries since mid-September have, however, started to take their toll already in the first two weeks of October. The value of card payments made by foreign tourists dropped by as much as 60% – which is twice as much as the decline registered in September. The amounts spent by ­domestic tourists exceeded the 2019 levels just by a small margin. If we extrapolate this trend for the second half of October, we arrive at a decline in overnight stays by close to 40%. Regarding the projections for domestic tourists, the newly harmonized fall school break (for the first time, all schools in Austria closed from October 26 to October 30) constitute some upward risks. In a risk scenario, which assumes higher levels of vulnerability even for provinces not directly affected by the travel alerts, the dropoff is projected to be as high as 60%.

Chart 2 “Weekly travel-related card payments” consists of two panels. The left panel depicts the transaction value of card payments made by domestic and foreign tourists in Austria, showing year-on-year changes from week 4 to week 41. In week 12, card payments declined by nearly 100% in both categories. However, payments made by domestic tourists started to rebound in week 22 and started to exceed the corresponding spending levels of 2019 in week 24. During summer, card payments made by foreign tourists stabilized at about 25% below the 2019 levels. Starting with week 38, card payments made by domestic tourists were declining and barely above the 2019 levels in week 41. Foreign tourists' card payments dropped off sharply in week 40 and continue to trail the levels recorded in 2019 by 60%. In the right panel, the card payments made by foreign tourists (as shown in the left panel) are broken down by country of origin, depicted as bars with their respective growth contributions. With most incoming tourists stemming from Germany, German tourists also account for the largest contributions to growth. Thus, they accounted for the lion’s share of the lockdown-related decline and spent even more in the 2020 summer season than in the summer of 2019. Conversely, the most recent decline in card payments in the last two weeks shown in the chart is also attributable to significantly negative growth contributions from German tourists. Source: Payment card providers, OeNB calculations.
Table 1: Summer season overnight stays in Austria
Total Domestic tourists Foreign tourists German tourists
Annual change in %
May – August 2020 –33.1 –7.4 –43.5 –24.4
July – August 2020 –14.0 19.4 –25.9 –0.4
September 2020 forecast –10.5 12.9 –20.7 5.1
October 2020 forecast
Baseline scenario –37.2 6.4 –58.7 –53.2
Risk scenario –56.1 6.4 –86.9 –89.7
2020 summer season forecast (May to October)
Baseline senario –30.2 –2.7 –41.8 –21.6
Risk scenario –32.3 –2.7 –44.7 –25.4
Source: Statistics Austria, payment card providers, OeNB.
Note: Data for May to August provided by Statistics Austria; September and October forecasts derived from data collected by payment card providers.

Based on these assumptions, the baseline scenario yields a drop in overnight stays of 30.2% for the summer season of 2020 (May to October) against the summer season of 2019. The current travel alerts for October account for 2.3 percentage points of the decline. The risk scenario yields a decrease by 32.3%. At any rate, the setback in October does not bode well for the upcoming winter season.

3 Exports reflect slight upward trend in manufacturing

According to Statistics Austria, nominal goods exports shrank by 10.4% in the first seven months of 2020, with April and May witnessing declines by about one-quarter and exports to regions beyond Europe taking visibly larger hits (Asia: –13%, the Americas: –16.7%). Exports to other European countries shrank by 9.4%. Within Europe, exports to countries hardest hit by the pandemic (Spain: –28.6%, United Kingdom: –19.5%) topped the list of export contractions, while exports to Germany, Austria’s number-one trading partner, saw a below-average dent in exports (–7.9%). In terms of product groups, the loss leaders (–17%) were machinery, transport equipment and manufactured goods, which together account for more than 60% of goods exports. The only product groups to show export gains were food (+2.7%) and chemical products (+6.5%, including pharmaceutical products (+23.8%)).

As implied by the OeNB’s truck mileage-based export indicator, manufacturing trade continued to recover in late summer/early fall, with the year-on-year decline narrowing to 4.7% in August and 1.7% in September. The September result is, however, upward-biased because the number of working days was one day higher in 2020 than in 2019. When adjusted for this bias, the projections yield a decline by 3.9% for September. In other words, exports have continued to rebound, but at a lessening pace.

Chart 3 “Summer season overnight stays in Austria” consists of two panels, which are both line charts and both cover the period from January 2017 to September 2020. The left panel shows the trend in nominal goods exports expressed in EUR as well as the OeNB’s forecast for August and September 2020 and changes in truck mileage on Austrian highways in million kilometers. The right panel visualizes the trend in nominal exports expressed in EUR and the OeNB’s forecast for August and September 2020 expressed as percentage change compared with a year earlier; it also depicts the leading indicators for the export sector explained in the main text where a more detailed description is provided. Source: Eurostat, Statistics Austria, Bank Austria, OeNB.

Looking ahead, most of the available economic indicators suggest that the ­recovery process will continue. In the Bank Austria’s Purchasing Managers Index, the subindex measuring expectations of export orders registered 52.7 in September. This is clearly above the expansion threshold of 50 and more closely aligned with the overall new order index than in previous months. This contrasts with a significantly more pessimistic view of export order books evident from the European Commission’s latest survey. While the September reading did improve slightly, to –46.4, ­it remains well below the long-term average (–25). The positive view is bolstered by weekly truck mileage data as collected by Austria’s highway operator, ASFINAG. In early October (weeks 40 and 41), ASFINAG truck mileage figures for Austria’s highways slightly exceeded 2019 levels for the first time since the COVID-19 pandemic reached Austria. Regarding truck traffic in the sections near Austria’s borders, which is a particularly good gauge for export growth, the figures reverted to positive territory for the first time in week 41. Thus, while large parts of the service industry continue to suffer rather heavily from the COVID-19 fallout, the slight recovery of Austria’s export-oriented industry is evidently ongoing.

Chart 4 “Weekly truck mileage growth during the COVID-19 pandemic” displays the year-on-year changes in truck mileage on Austrian highways for the period from week 3 to week 41 2020 compared with the same weeks a year earlier. The changes are shown in the form of a bar chart and in percent. At the peak of the COVID-19 pandemic in late March and early April, truck mileage was down a quarter compared with the same months of 2019. Most recently, week 40 and 41 marked a slight increase. Source: ASFINAG (Austria’s highway network operator), OeNB.

4 Mixed impact on different sectors of the economy

While the lockdown had affected business activity across the board, the current crisis is characterized by the heterogeneity of vulnerability across the economy.

Travel agencies and tour operators recorded the heaviest lockdown toll by far as they saw 89% of the sales generated in the second quarter of 2019 evaporate in the second quarter of 2020. Hotels and other tourist accommodation and the restaurant/catering industry also suffered a drastic blow, seeing their sales contract by 73% and 54%, respectively. Likewise, the lockdown severely affected sports and entertainment activities as well as other personal service activities. In the transport sector, air transport was the loss leader (–84%).

Meanwhile, conditions have been improving in many sections of the economy. Chart 5 shows business sentiment as measured in May and September by European Commission surveys, looking three months back and looking three months ahead. In this comparison, we see the current-conditions component of business sentiment to have improved significantly in manufacturing, construction, retail and services. Consumer sentiment was the only outlier with a more negative assessment in September than back in May. At the same time, the future-expectations component remains below long-term averages outside the construction industry.

Chart 5 “Mixed impact on different economic sectors” shows two business cycle clocks consisting of four quadrants depicting recession, upturn, boom and downturn. The two panels visualize business sentiment concerning current conditions on the horizontal scale and future expectations on the vertical scale. The left panel, depicting the business cycle clock for all sectors, indicates that business sentiment concerning current conditions from May to September has improved, except for households. The right panel, showing the business cycle clock for selected services, indicates improving business sentiments as well, except for restaurants, hotels and travel agencies, which continue to hover at the bottom of the recession quadrant. Source: Eurostat.

5 Labor market: decline in unemployment leveling off

Following steep increases until mid-April, unemployment levels dropped somewhat in the following months. On October 19, 2020, the number of individuals registered as either unemployed or receiving training stood at 348,000. This corresponds to an increase by 71,000 people compared with the same month of 2019 (see left panel and middle panel). Thus, the unemployment figures have continued to go down, but the improvement has been weakening since July. In parallel, the number of registered vacancies has been going up since mid-April, but the increase has been stagnating in recent weeks.

With regard to the outlook for unemployment in the months ahead, two ­factors are playing a role: First, seasonal unemployment is bound to increase. We know from past experience that the unemployment figures for late January typically ­exceed the unemployment figures for late August by 80,000. Second, there are signs that the ebb and flow of unemployment with the business cycle may be about to stagnate. Apart from media reports about layoffs in manufacturing – a sector which has so far added very little to unemployment given support through the coronavirus short-time work scheme – incoming data from the early warning system for impending layoffs installed by Austria’s Public Employment Service imply that more people may be losing their jobs in the weeks ahead. In September 2020, the number of employees who had been given early warning of layoffs was close to 16,500 higher than in September 2019, having risen substantially from the corresponding figures for July (+6,000) and August (+8,200). The stagnation of registered vacancies referred to above would also point in this direction.

6 Inflation projected to reach 1.4% in 2020 and 1.7% in 2021 despite recession 3

Having stood at 2.2% at the start of 2020, HICP inflation in Austria dropped to 0.6% in May. Thereafter, July marked the high point for inflation (1.8%), and by September inflation had declined to 1.3%. To some extent, the temporary peak in July reflects one-off effects (with fashion clearance sales starting later than usual) as well as price measurement problems concerning the service industry. Moreover, the inflation spurt until July was also driven by the moderate rise in oil prices since June (starting from very low levels). Core inflation (excluding energy and food) surged to 2.7% from May to July before dropping back to 2.0% by September amid the normalization of fashion sales. The large gap between core inflation and headline inflation can be explained by the fact that headline inflation was diminished by the energy inflation component, while core inflation was not.

In line with the OeNB’s inflation forecast of September 2020, HICP inflation is expected to run to 1.4% in 2020 and to climb to 1.7% in 2021 (chart 7). Monthly inflation rates will be going down visibly until the end of 2020 before starting to rebound in January 2021. The energy component of inflation is expected to retain its dampening impact until early 2021. Moreover, the COVID-19 pandemic and the ensuing fall in aggregate demand are expected to have a moderating impact on the components of core inflation (industrial goods excluding energy and services). The energy price effects of this year’s slump in crude oil prices will peter out in the second quarter of 2021. As the diminishing impact of the COVID-19 pandemic on inflation weakens gradually in 2021, HICP inflation is projected to rise to 1.7% in 2021. With the oil price effect dropping out, core inflation excluding energy and food prices is set to mirror headline inflation thereafter and drop from 2.0% in 2020 to 1.6% in 2021.

Chart 7 “The OeNB’s inflation forecast of September 2020” shows year-on-year changes in HICP inflation and core inflation as well as the inflation contributions stemming from the main components of HICP in percentage points. The main HICP components are food, industrial goods excluding energy, energy and services. These indicators relate to the period from January 2019 to December 2021, with actual data for the period up to September 2020 and projections from October 2020 onward. HICP inflation is expected to decline to under 1% until the end of 2020 and climb to 1.9% until December 2021. This trend is largely related to the HICP components services and industrial goods as well as energy. Source: OeNB, Statistics Austria.

To provide financial support to the hospitality industry, the VAT rate for food and accommodation services was temporarily cut to 5% in July 2020. This rate will probably apply until December 2021. In line with government intentions, the lower VAT rate is unlikely to be passed on to consumers, as the hospitality industry faces higher costs and lower incomes resulting from capacity constraints imposed with a view to containing the COVID-19 pandemic (hygiene rules, social distancing) and as numerous business are struggling with liquidity problems.

The current inflation projections well exceed the forecast published in June 2020 (+0.6 percentage points for 2020, +0.9 percentage points for 2021). Underlying reasons include the sharp increase in HICP inflation in recent months (above all in July 2020), which had not been anticipated, and the upward revision of commodity price assumptions for both crude oil and nonenergy commodities (table 2). In addition, price measurement problems in the area of food and accommodation services are likely to prevail for the time being, which means that the inflation rate for services is going to respond more slowly than expected in the latest projections. Last but not least, services prices tend to be downward rigid, which makes a rapid adjustment to changing demand patterns unlikely.

2 Oesterreichische Nationalbank, Economic Analysis Division, gerhard.fenz@oenb.at, friedrich.fritzer@oenb.at, ernst.glatzer@oenb.at, martin.schneider@oenb.at, klaus.vondra@oenb.at; Economic Studies Division, helmut.stix@oenb.at.

3 These figures were obtained by mechanically updating the September 2020 inflation forecast, i.e. by incorporating the HICP data published for September.

Table 2: Assumptions underlying the OeNB’s September 2020 inflation forecast
September 2020
Revisions to June 2020
2019 2020 2021 2022 2020 2021 2022
Energy and exchange rates %
Oil price (EUR/barrel Brent) 57.2 37.6 40.2 41.6 13.4 17.0 10.6
USD/EUR exchange rate 1.1 1.1 1.2 1.2 4.8 9.2 9.2
Nonenergy commodity prices Index 2005=100 %
Total 129.1 131.0 138.6 142.4 4.1 6.4 6.0
of which world market prices for food 138.8 143.9 154.1 158.8 0.1 3.7 4.9
of which world market prices for metal commodities 116.8 116.4 128.8 131.9 12.1 22.0 20.7
EU food production prices 110.7 109.1 104.4 104.8 –6.2 –11.8 –11.8
Interest rates % Percentage points
Three-month interest rates –0.4 –0.4 –0.5 –0.5 –0.1 –0.1 –0.1
10-year government bond yields 0.1 –0.2 –0.2 –0.1 –0.1 –0.2 –0.2
Source: Eurosystem.

Financial literacy in Austria – focus on millennials

Pirmin Fessler, Marilies Jelovsek, Maria Silgoner 4
Refereed by: Brent Kigner, Fachhochschule Kufstein Tirol, University of Applied Sciences, emeritus;
Eveline Wuttke, Goethe University Frankfurt, Department of Business Education

This article summarizes the main findings from the second wave of the Austrian Survey of Financial Literacy (ASFL), the Austrian contribution to the OECD/INFE survey on adult financial literacy, which was conducted in spring 2019. As compared to the previous survey round in 2014, the financial knowledge of Austrian residents seems to have increased significantly. While men outperform women in terms of financial knowledge, they score slightly worse in terms of financial behavior and attitudes. Austrian residents are rather prudent, risk averse and forward looking and have a good overview of their finances.

In general, financial literacy is rather equally distributed across age groups. However, ­15- to 38-year-olds (hereinafter called millennials) differ from other age cohorts in several respects: They have relatively low levels of financial literacy, are less financially organized, and they show more risky and less forward looking behavior. At the same time, they are more open to digital means of payments and financial innovations in general. Even though the observed differences are not very large and may vanish as millennials mature and gain experience with business and finance, we deem it important to monitor the financial literacy development for this group, given the rising complexity of financial decisions many among this group will face and the tremendous financial resources they will ultimately inherit.

JEL classification: A20, D12

Keywords: financial literacy, financial education, financial stability, survey data

In the aftermath of the global financial crisis, the issue of financial education has come to the fore, and financial literacy has gained international recognition as a critical life skill for individuals (Hilgert et al., 2003). Innovations and advanced technologies have increased the number of financial products and services offered, creating a complex and fast-paced financial landscape. In light of the complexity of the financial market, financial education efforts have been stepped up, and relevant strategies and programs have been developed in recent years (Alsemgeest, 2015).

At the same time, scientific interest in the topic has increased almost exponentially, as suggested by chart 1, which shows the number of citations of the term “financial literacy” in scientific journals (SSCI index).

The OeNB works in close contact with the education community in Austria to improve financial literacy. The OeNB’s main goal in this area is to help consumers make sound financial decisions. Topics such as monetary policy, inflation and price stability are regular features of interactive programs (workshops, presentations and teacher seminars) that are created in line with the OeNB’s mission statement (“We support financial literacy by offering a broad range of information and education services”).

An important precondition for any financial education program is sound information about the state of financial literacy. A mere decade ago, research on what people know about economics and finance was scarce, primarily due to a lack of a universally accepted approach of how to measure financial literacy. Annamaria Lusardi and her coauthors (e.g. Lusardi and Mitchell, 2008) were pioneers in designing a small set of financial knowledge questions that later became known as the Big Three 5 and were adopted in surveys in dozens of countries around the world. The set of questions was extended subsequently in the following years, but overall the coverage of the survey – both in terms of financial literacy questions and in terms of demographic and control variables – remained limited. Numerous other financial literacy surveys were adopted at the national level. Most of these surveys share the common weakness that they lack theoretical foundations and cover only limited dimensions (see e.g. Aprea and Wuttke, 2016).

Chart 1, a column chart, shows a citation index for the term “financial literacy” in scientific journals between 2000 and 2019. The number of citations is in the single digits for the first four years and rises moderately until 2008. After that, it increases sharply by about 40% per year on average. The last observation is 4,606 citations in 2019. Source: Social Sciences Citation Index, January 2020.

About a decade ago, the OECD’s International Network on Financial Education (INFE) started an ambitious project to design an extensive blueprint survey on adult financial literacy, the so-called Toolkit for measuring financial literacy and financial inclusion (OECD, 2015), with the aim of rolling it out in a decentralized way to its member countries and other countries participating in the INFE. After a pilot study in 2010/2011, the first regular survey wave in 2014/2015 covered about 35 countries on different continents and at different levels of development, including Austria. The design of the OECD/INFE survey follows the OECD’s ­approach of defining financial literacy as “a combination of financial awareness, knowledge, skills, attitude and behavior necessary to make sound financial decisions and ultimately achieve individual financial wellbeing” (Atkinson and Messy, 2012). The questionnaire therefore covers not only financial knowledge but also several aspects of financial behavior and attitudes. One of the strengths of the OECD’s approach – in addition to providing data – is that it constructs a set of financial literacy scores from the individual survey questions to allow international rankings. The descriptive results were published in OECD (2016) and OECD (2017), while analysis and research papers based on the Austrian contribution (the ASFL 2014) were summarized in Silgoner et al. (2015) and Cupak et al. (2018).

In spring 2019, the OECD repeated the exercise. Countries were expected to deliver national data by spring 2020. The OeNB again participated in the exercise. This article describes the first results from the second wave of the Austrian Survey of Financial Literacy (ASFL 2019). We investigate the current state of financial knowledge, behavior and attitudes in Austria, highlighting also – wherever possible – changes as compared to the previous survey round. We explore differences among sample subgroups by gender, education and age, with a special focus on the subgroup of millennials.

Throughout this article, we split the sample into age bands, for convenience giving each of them customary labels. Such sample splits and labels are by nature arbitrary, as there is no universally accepted definition of generations. We refer to millennials as the demographic cohort born after 1980, also known as Generation Y. Most of the time, we divide this group further into young millennials (age 15–28) and old millennials (age 29–38). Generation X (age 39–58) are those born in the 1960s and 1970s. The rest of the sample either belong to the baby boomers (age 59–74) or the silent generation (age 75+), i.e. those born before the end of World War II.

This article is structured as follows: In section 1, we describe the dataset and the OECD’s financial literacy scores. Section 2 presents the key results from the ASFL 2019. Section 3 focuses on the results for millennials and investigates special characteristics of this group, and section 4 concludes.

1 Dataset and calculation of scores

This article is based on the ASFL 2019, the Austrian contribution to the second wave of the regular OECD/INFE financial literacy survey. It was conducted by the OeNB among about 1,500 Austrian residents in spring 2019.

The survey setting – some technical details

The sample used in the ASFL was based on stratified multistage clustered random sampling. NUTS 3 regions, municipality size as well as districts in Vienna were used for regional stratification. Replacement of unit nonresponse by drawing new addresses was allowed. Ultimately, the gross sample consisted of 3,356 households (3,201 after neutral dropouts). Respondents within households were drawn randomly. The final net sample comprised 1,418 computer-assisted personal interviews (CAPIs) conducted in April and May 2019. The nonresponse rate was about 55.7%. We used survey weights to produce descriptive population statistics throughout the article. The weights consist of a combination of (sample) design weights and poststratification weights based on external population statistics on age and gender at the province level.

The survey questionnaire is based on the OECD toolkit (OECD, 2018), but as in 2014, the OeNB included several additional survey questions that are of special interest for the Austrian case. The complete set of financial knowledge questions is reported in box 2; the remainder of the questionnaire, including financial behavior and attitude questions, is available from the authors upon request.

According to OECD methodology (OECD, 2018), the survey data are used to calculate a set of financial literacy scores:

  • The financial knowledge score is given by the total number of financial knowledge questions answered correctly (as opposed to a wrong answer, “don’t know” or “refused to answer”) out of the seven questions marked with an asterisk (*) in box 2. The score ranges from 0 to 7.
  • The financial attitude score is based on a set of three statements (“I find it more satisfying to spend money than to save it for the long term,” “Money is there to be spent,” “I tend to live for today and let tomorrow take care of itself”). Respondents are asked how much they agree with a statement on a scale from 1 to 5, where 1 indicates “completely agree” and 5 “completely disagree.” The financial attitude score is the arithmetic average agreement with the three statements and ranges from 1 to 5.
  • The calculation of the financial behavior score is far more complex. It is based on a total set of ten questions that cover several aspects: active participation in financial decisions, savings behavior, product comparison and information sources before taking financial decisions, money management and financial planning. The financial behavior score ranges from 0 to 9. For details of the calculation, see annex A in OECD (2018).
  • The total financial literacy score simply adds up these three scores, so it can take a maximum value of 21. This corresponds to the OECD/INFE approach that all three aspects of financial literacy in the end contribute to financial wellbeing.

Financial knowledge questions in the survey

The ASFL 2019 covers 10 questions on financial knowledge. Questions used to calculate the OECD’s financial knowledge score are denoted with an asterisk. The correct answers are indicated in brackets after each question. In addition to the various answer choices, participants could refuse to respond or state that they don’t know the answer. Overall, the mix of questions – multiple choice questions, true/false questions and questions requiring respondents to do some math – is in line with common recommendations to design surveys in a way that they work equally well for respondents, regardless of their socioeconomic background, gender or culture.

Time value of money 6 (*): Five brothers receive a gift of EUR 1,000 in total and are asked to share the money equally. Imagine that the brothers have to wait for one year to get their share of the EUR 1,000 and inflation stays at around 2%. In one year’s time, will they be able to buy (a) more with their share of the money than they could today, (b) the same amount or (c) less than they could buy today? (c)

Interest paid on a loan (*): You lend EUR 25 to a friend one evening and he gives you EUR 25 back the next day. How much interest has he paid on this loan? (0)

Interest plus principal (*): Imagine that someone puts EUR 100 into a no fee savings account with a guaranteed interest rate of 2% per year. They don’t make any further payments into this account and they don’t withdraw any money. How much would be in the account at the end of the first year, once the interest payment is made? (EUR 102)

Compound interest (*): And how much would be in the account at the end of five years? Would it be (a) more than EUR 110, (b) exactly EUR 110, (c) less than EUR 110 or (d) impossible to tell from the information given? (a)

Risk and return (*): Is the following statement (a) true or (b) false? An investment with a high return is likely to be high risk. (a)

Definition of inflation (*): Is the following statement (a) true or (b) false? High inflation means that the cost of living is increasing rapidly. (a)

Diversification (*): Is the following statement (a) true or (b) false? It is usually possible to reduce the risk of investing in the stock market by buying a wide range of stocks and shares. (a)

Real interest: Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per year: After 1 year, how much would you be able to buy with the money in this account (disregarding any bank fees)? Would it be (a) more than today, (b) the same amount or (c) less than today? (c)

Overdrawing an account: Is the following statement (a) true or (b) false? It usually does not matter whether I overdraw my checking account or take out a loan because in both cases the interest rates do not differ significantly. (b)

Exchange rate: Suppose you have taken out a loan in Swiss francs. Then the euro ­depreciates against the Swiss franc. How does this change the amount of euro you need to make your loan installments? Does it (a) increase, (b) stay exactly the same or (c) decrease? (a)

Simplifications tend to come with some drawbacks. For example, asking people about interest rates or inflation may not really give an accurate picture of their ­real-life ability to take sound economic and financial decisions. Furthermore, the distinction between financial behavior and attitude is sometimes not clear cut. Also, calculating the total financial literacy score as the sum of the other three scores leads to some sort of double-counting: If we expect knowledge to ­impact on behavior, we would expect people with a high financial knowledge score to also show high financial behavior scores (see e.g. Fessler et al., 2019). This way, people with sound financial knowledge are credited twice for this advantage.

Chart 2 is a column chart presenting the OECD financial knowledge score for Austria based on 2019 data. The x-axis shows the number of financial knowledge questions answered correctly in a range from 0 to 7. The eight corresponding bars indicate the percentage share of respondents who achieved each score. Score of 0: 0.8% of respondents; score of 1: 2.2%; score of 2: 4.6%; score of 3: 7.1%; score of 4: 11.7%; score of 5: 17.5%; score of 6: 27.8%; score of 7: 28.3%. Source: Austrian Survey of Financial Literacy 2019, Oesterreichische Nationalbank.

These caveats need to be kept in mind, especially when interpreting cross-country differences in the OECD’s financial literacy scores. They share a common feature of most internationally comparable data sets: The methodology is always a compromise to account for the different traditions, conditions and circumstances in place in a wide range of countries. The attempt to make data suit all purposes may lead to data not fully matching national needs in the end. The World Bank, for example, follows a different, outcome-driven approach to designing national financial literacy programs (e.g. Holzmann et al., 2013). In line with its behavior-oriented ­definition of financial capability, the World Bank identifies country-specific key vulnerabilities and challenges of the financial system to develop a well-­targeted set of measures. The Austrian approach is somewhere in between in that it relies on the OECD’s method­ology but extends the scope of the ­survey in directions that are of special importance for Austria.

2 Key results from the ASFL 2019

2.1 Knowledge gaps are largest for the youngest, the oldest and women

Chart 2 shows the distribution of the ­financial knowledge score for Austria. The bars indicate the share of respondents who get a specific financial knowledge score, defined as the number of financial knowledge questions answered correctly from the list of items denoted with an asterisk in box 2, i.e. those questions that the OECD uses to calculate the ­financial knowledge score. 7

More than one-half of respondents perform rather well – they answered all (or almost all) knowledge questions correctly. 28% of respondents get the highest ­possible financial knowledge score of 7. On the other hand, a non-­negligible share of respondents (15%) show a rather poor performance, answering less than four questions correctly. This is a source of concern, especially since none of the ­questions require ­expert knowledge, but all of them are essential when dealing with standard ­financial products. We therefore see scope for improvement.

Chart 3 is a stacked column chart that compares respondents’ self-assessment of financial knowledge with the knowledge scores they achieved. The knowledge scores, which range from 0 to 7, are represented by eight corresponding columns. Each column is subdivided into five sub-bars representing the five options given to respondents (ranging from “very low” to “very high”). About 40% of respondents across all score groups believe their financial knowledge is about average compared with that of other adults in Austria. The share of those who believe their financial knowledge is quite or very high among those who answered six or seven knowledge questions correctly is of a similar magnitude. The tendency to overestimate one’s own financial knowledge is pronounced among those who answered less than three knowledge questions correctly: About 30% of them believe that their financial knowledge is quite or very high. Source: Austrian Survey of Financial Literacy 2019, Oesterreichische Nationalbank.

At the same time, people are not fully aware of their knowledge gaps. We asked people to rate their own knowledge about financial matters as compared with other adults in Austria on a scale from 1 to 5 (1 stands for “very high,” 5 for “very low”). Respondents were asked to answer this question ­before starting the knowledge quiz so we would get an idea of their self-­assessment, unaffected by their actual performance. Chart 3 shows that people have a tendency towards overconfidence in their own financial knowledge. What is of special concern is the high share of those who ­answered less than three questions correctly (bars 0 to 2 in chart 3) and nevertheless believe that their ­financial knowledge is “quite” or “very” high compared with that of other adults in Austria (light and dark green areas, respectively). Overconfidence can breed risky financial behavior.

Chart 4 shows in more detail how respondents perform on the knowledge questions. The questions without an ­asterisk are Austria-specific and were added to the existing OECD toolkit. Note that the OECD financial knowledge score, which we also use in this study, focuses entirely on the number of questions ­answered correctly and thereby largely ignores the difference between “don’t know” – indicating awareness of one’s own knowledge – and a wrong answer.

Chart 4 consists of ten stacked bars indicating the answers to the seven OECD and three additional knowledge questions asked in the survey. Each bar is subdivided into three sub-bars showing the respective percentage share of respondents who gave a correct answer, an incorrect answer or declared that they don’t know the answer. The best results were observed for the questions on risk and return, interest paid on a loan and definition of inflation (about 90% of respondents chose the correct answer), followed by interest plus principal and real interest (almost 80% each), followed by time value of money, exchange rate, and overdrawing an account (about 70% each). The poorest results were achieved for the questions on compound interest (49% correct; 36% incorrect; 15% “don’t know”) and diversification (61% correct; 15% incorrect; 23% “don’t know”). Source: Austrian Survey of Financial Literacy 2019, Oesterreichische Nationalbank.

Most respondents manage to give the correct answer to rather simple questions, like the one on the link between risk and return. However, while most (89%) know that high inflation means that the cost of living is increasing rapidly, one-quarter of respondents do not understand that you can buy less with the same amount of money after one year of inflation (time value of money). About 30% do not understand the implications of exchange rate movements for foreign currency debt, and the same fraction is not aware that overdrawing an account is far more costly than taking out a loan. Only about 60% of respondents grasp the key principle of risk diversification, and the concept of compound interest is understood by less than one-half of respondents.

Table 1: Means of financial literacy scores across personal socioeconomic characteristics
sample size
Knowledge Behavior Attitude Literacy (sum)
Young millennials (15–28) 158 5.0 5.5 2.7 13.2
Old millennials (29–38) 198 5.3 5.9 3.1 14.3
Generation X (39–58) 515 5.4 6.1 3.2 14.8
Baby boomers (59–74) 354 5.4 5.8 3.2 14.4
Silent generation (75+) 193 5.2 5.5 3.1 13.9
Male 668 5.6 5.8 3.1 14.5
Female 750 5.1 5.9 3.2 14.1
Primary 744 5.0 5.5 3.0 13.6
Secondary 512 5.5 6.2 3.2 14.9
Tertiary 162 6.1 6.3 3.1 15.6
Self-employed, business owner 101 6.1 6.4 3.1 15.6
White collar worker 737 5.5 6.0 3.2 14.6
Public servant 115 5.5 6.1 3.3 14.9
Farmer 31 5.0 5.8 3.3 14.1
Blue collar worker 391 4.9 5.5 3.0 13.4
Homemaker 8 5.1 4.0 2.9 12.0
Overall mean 5.3 5.8 3.0 14.2
Possible maximum 7 9 5 21
Source: ASFL 2019, OeNB.

Table 1 provides a breakdown of the average financial knowledge score by age, gender, education or job status. The first column shows unweighted numbers of observations. 8 Splitting the sample by gender, we observe that men significantly outperform women in terms of financial knowledge – a common finding in the international literature. The average ­financial knowledge score of men is 5.6, while that of women is only 5.1. Greimel-­Fuhrmann and Silgoner (2017) used ASFL 2014 data to investigate the potential reasons behind the comparatively weak performance of women. They identified a mix of determinants, including differences in personal endow­ments (such as education or income), the level of interest and involvement in financial matters as well as gender differences in the answer behavior in survey settings. 9 Especially the level of personal involvement in financial decisions seems to be crucial: Greimel-Fuhrmann and Silgoner (2017) do not find a gender gap when focusing on respondents who are widowed or divorced or live in single-person households and presumably are alone responsible for their financial decisions.

Chart 5 combines a binned scatter plot and a curve chart to highlight the relationship between age (x-axis, 20 to 80+ years) and level of knowledge (y-axis, financial knowledge score ranging from 0 to 7) for women and men separately. Each dot (men) and triangle (women) represents 5% of the sample in terms of age and shows the average financial knowledge score for that age bin. For both men and women, financial knowledge peaks at middle age (35 to 65 years) and is lower for younger and older age bins. The fitted curve for men and women, respectively, confirms an inverse U-shaped link between age and financial knowledge. The curve for women is below that for men and more bent, indicating stronger age effects. Source: Austrian Survey of Financial Literacy 2019, Oesterreichische Nationalbank.
Chart 6, a double column chart, compares the OECD financial knowledge score results of the 2014 and 2019 survey waves. The x-axis shows the number of correctly answered financial knowledge questions underlying the OECD’s knowledge score (range from 0 to 7), with corresponding columns indicating the share of respondents who achieved each score. The number of people who were able to answer six or seven questions correctly increased between the two waves. In 2019, 28% of respondents answered all seven questions correctly, compared with only 19% in 2014. Overall, the mass of the distribution shifted to the right between 2014 and 2019. Source: Austrian Survey of Financial Literacy 2014 and 2019, Oesterreichische Nationalbank.

Table 1 also shows that knowledge is lowest in the youngest age cohort ­(referred to as young millennials in the following). Chart 5 gives more insight into the link between age and the knowledge score: In this binned scatter plot, the blue dots (men) and red triangles (women) each represent 5% of the ­respective sample and show the average knowledge score for each age bin. The chart confirms, for both women and men, the typical inverse U-shaped relationship ­described in the literature, indicating that people in the middle of their professional careers score highest in terms of financial knowledge. Young people, who have not yet ­acquired that much experience with financial products and business life in general, perform comparatively poorly. The same is true for the oldest age cohorts, who are used to very standard and safe financial products, such as savings books, and who never invested in financial knowledge. The red line in chart 5 also shows that these age effects are more pronounced for women than men. This ­evidence calls for ­financial education initiatives that focus on young people as well as targeted training programs for women.

Finally, table 1 shows – not surprisingly – that financial knowledge increases with the level of education. Self-employed people tend to score highest in terms of financial knowledge.

2.2 Financial literacy has improved from 2014

Chart 6 compares the financial knowledge score results from the two survey rounds, the ASFL 2014 (orange) and the ASFL 2019 (blue). 10 Again, the bars show the share of respondents exhibiting a specific financial knowledge score, defined as the number of financial knowledge questions answered correctly.

Chart 7, a double column chart, shows a breakdown of the OECD financial knowledge score by gender for the two survey waves. From 2014 to 2019, the score for men increased by 10% to 5.6, up from 5.1, while the score of women rose by 12% to 5.1, up from 4.5, indicating that the gender gap has declined. Source: Austrian Survey of Financial Literacy 2014 and 2019, Oesterreichische Nationalbank.

A comparison of the orange and blue bars in chart 6 shows that financial knowledge has increased significantly over the last five years. In 2019, 28% of respondents were able to answer all seven questions correctly, as compared to only 19% in 2014. About three-quarters of respondents gave the right answer to at least five questions, which the OECD considers a minimum target, as compared to only 65% in 2014. On average, people today give the correct answer to about half a question more than in the ASFL 2014. This improvement is not due to composition effects, such as a higher share of men or university graduates within the samples. Today, respondents score significantly better than in 2014 for all questions ­except the one about risk diversification. See chart A1 in annex 1 for regression results, which confirm that the differences between 2014 and 2019 are statistically significant at the 5% level for ­almost all individual questions and ­remain so even if we control for a set of socioeconomic covariates.

A comparison of the two survey waves shows that both men and women accumulated knowledge (chart 7), but women improved more than men (12% and 10%, respectively), which helps ­reduce the gender gap somewhat. Improvements were also observed for all levels of education and all age groups.

From the survey alone, it is impossible to derive definitive explanations for the improvement in financial knowledge over time. One hypothesis (that would need to be confirmed with further ­research, however) could be that the extensive media coverage of challenges related to the financial and economic crisis that started in 2008 has sparked people’s ­interest in core economic concepts, so they have become more knowledgeable about them. After all, Norvilitis et al. (2006) found that financial knowledge, unlike financial behavior and ­attitudes, is very susceptible to changing external conditions. All types of news media focused on the major economic challenges associated with the crisis, such as deep recession, high unemployment, private and public debt and persistently low ­inflation. Economic and monetary policy reactions as well as their limitations (over­indebtedness, zero lower bound of monetary policy, effectiveness of unconventional monetary policy measures) were also widely discussed. The ­intensive financial education initiatives launched in Austria by the OeNB and other key stakeholders of financial education may have contributed to this improvement. But these effects are hard to isolate, given the long-term orientation of most ­education initiatives.

Chart 8 is a column chart presenting the distribution of the OECD financial behavior score. The x-axis shows the financial behavior score ranging from 0 to 9, with corresponding columns indicating the share of respondents who achieved each score. 0.3% of respondents have a behavior score of 0; 1.1% a score of 1; 2.4% a score of 2; 3.9% a score of 3; 9.6% a score of 4; 19.2% a score of 5; 28.2% a score of 6; 21.3% a score of 7; 11.8% a score of 8; and 2.2% a score of 9. Source: Austrian Survey of Financial Literacy 2019, Oesterreichische Nationalbank.
Chart 9, a column chart, shows the distribution of the OECD financial attitude score within a range of 1 to 5. The scores are plotted on the x-axis, with corresponding columns indicating the share of respondents who achieved a given score (as rounded). It turns out that 3.6% of respondents have an attitude score of 1; 18.5% a score of 2; 43.7% a score of 3; 31.1% a score of 4; and 3.0% a score of 5. Source: Austrian Survey of Financial Literacy 2019, Oesterreichische Nationalbank.

2.3 Population rather prudent, forward oriented and risk averse

The distribution of the financial behavior score, as measured by OECD methodology, 11 is tilted to the right, indicating that Austrian residents self-report rather positive financial behavior (chart 8). The distribution of the financial attitude score is shown in chart 9.

Again, we find interesting differences across sociodemographic subgroups (table 1). While men outperformed women in terms of financial knowledge, women scored better in terms of behavior and attitudes (although the difference is significant only for attitudes). One of the questions investigated in Greimel-Fuhrmann and Silgoner (2017) was how this difference affects financial wellbeing. The authors use the period of time that people would get by after losing their main source of income as a proxy for financial wellbeing and find no gender gap there. Apparently, there are different ways of achieving the same level of financial wellbeing: Women may partly compensate a lack of ­knowledge with extra prudent and ­forward-looking behavior, while men – equipped with a higher level of financial knowledge – can potentially afford more risky behavior. But causality may also run in the other direction: The more willing individuals are to take risk, the higher might be their incentive to invest in knowledge so they can assess risks properly.

Chart 10 summarizes the information provided in table 1 on the three scores for the different age cohorts. Generally, financial literacy seems to be rather equally distributed across generations. However, all three scores peak for Generation X, i.e. people in the middle of their professional career (age 39–58). The lowest scores in all three dimensions of financial literacy are achieved by the youngest age group (young millennials), followed by the silent generation. Overall, this mirrors the slight inverse U-shape we see for financial knowledge in chart 5 for the other two financial literacy scores. Given the increasing complexity of ­financial decisions and the enormous ­financial resources millennials will eventually inherit, monitoring the development of their financial literacy seems reasonable.