COVID-19 and public support for the Euro

Roth, Felix; Jonung, Lars; Most, Aisada Article — Published Version COVID-19 and public support for the Euro Empirica Provided in Cooperation with: Springer Nature Suggested Citation: Roth, Felix; Jonung, Lars; Most, Aisada (2023) : COVID-19 and public support for the Euro, Empirica, ISSN 1573-6911, Springer US, New York, NY, Vol. 51, Iss. 1, pp. 61-86, https://doi.org/10.1007/s10663-023-09596-7 This Version is available at: https://hdl.handle.net/10419/318030 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Empirica (2024) 51:61–86 https://doi.org/10.1007/s10663-023-09596-7 1 3 ORIGINAL PAPER COVID‑19 andpublic support fortheEuro FelixRoth1 · LarsJonung2· AisadaMost1 Accepted: 20 October 2023 / Published online: 16 December 2023 © The Author(s) 2023 Abstract The COVID-19 pandemic had disastrous effects on health and economic activity worldwide, including in the Euro Area. The application of mandatory lockdowns contributed to a sharp fall in production and a rise in unemployment, inducing an expansionary fiscal and monetary response. Using a uniquely large macro database, this paper examines the effects of the pandemic and the ensuing economic policies on public support for the common currency, the euro, as measured by the Eurobarometer survey. It finds that public support for the euro increased in a majority of the 19 Euro Area member states and reached historically high levels in the midst of the pandemic. This finding suggests that the expansionary fiscal policies initiated at the EU level significantly contributed to this outcome, while the monetary measures taken by the European Central Bank did not have a similar effect. Keywords COVID-19· Support for the euro· Unemployment· Monetary policies· Fiscal policies· ECB· EMU JEL Classification C23· E24· E42· E52· E62· I18 Responsible Editor: Harald Oberhofer. * Felix Roth [email protected] Lars Jonung [email protected] Aisada Most [email protected] 1 Department ofEconomics, Faculty ofBusiness, Economics andSocial Sciences, University ofHamburg, Von-Melle-Park 5, Postfach # 17, 20146Hamburg, Germany 2 Department ofEconomics, Lund University, Tycho Brahes väg 1, 22363Lund, Sweden 62 Empirica (2024) 51:61–86 1 3 1 Introduction The coronavirus pandemic that erupted in early 2020 triggered an unprecedented health crisis across the globe, including within the member countries of the Euro Area (EA). In response to the pandemic and in the hope of arresting its spread, governments introduced far-reaching lockdowns in many countries. These policy measures had a strong negative effect on growth, employment and trade,1 inducing some observers to talk about the “Great Lockdown Recession”.2 The lockdowns had a particularly negative impact on specific sectors of the economy, such as hospitality (Gursoy and Chi 2020). Many industries reacted to the pandemic by implementing short-term work schemes and laying off employees. In sum, the lockdown policies led to a rise in unemployment, a sharp drop in economic activity and a rapid rise in public debt (for an extended analysis of these phenomena, see, for example, Bauer and Weber 2021; Baek etal. 2021; IMF 2020b; Ping Ang and Dong 2022). COVID-19 became an urgent policy challenge for the EA member states. They were pressed to dampen the spread of the pandemic as well as to reduce the economic damage created by lockdowns. In response to the downturn in economic activity, loss of income in many households, and rising unemployment, national and European Union (EU) policymakers turned to large-scale fiscal and monetary policy initiatives. How did these economic policy measures influence public support for the euro? This question is a pertinent one to ask, as broad public support for the euro is crucial for the long-term sustainability of the common currency. As long as it prevails, it acts as a shield against attempts to dismantle the euro and grants political legitimacy to the European Central Bank (ECB) “to do whatever it takes” to preserve the EA in times of crisis (Roth and Jonung 2020a). In addition, given that the economic and unemployment crisis in the EA following the financial and sovereign debt crisis from 2008 to 2013 had a negative impact on public support for the euro (Roth etal. 2016, 2019; Roth and Jonung 2020a, b; Roth 2022), we are interested to examine if the rise in unemployment during the COVID-19 pandemic had a similarly negative effect on public support for the euro. This paper analyses the evolution and determinants of public support for the euro at the macro-economic level using a Fixed Effect Dynamic Feasible General Least Squares (FE-DFGLS) to derive statistical inferences on the causal impact of our macro-economic variables, adding dummies for fiscal and monetary policy measures. We use a EA19 database running from 3–4/1999 (EB51) to 6–7/2022 (EB97), thus covering the intense phase of the COVID-19 pandemic in 2020–2021 as well. We develop a daily data frequency approach for the matching procedure. By applying the above mentioned tailor-fit estimation approach, data and research design during the pandemic, we observe a striking feature: public support for the euro 1 A rise in unemployment, business closures, income losses, disruptions in trade and the travel industry are among the pandemic consequences; see for example Barua (2021). 2 See IMF (2020a) where the COVID-19 recession is compared to the Great Depression of the 1930s. 63 1 3 Empirica (2024) 51:61–86 increased, especially during the winter of 2020–2021, reaching a historical peak at that time despite of an increase in unemployment at the same time. The results of our paper suggest that the fiscal policy initiatives taken by EU policy makers led to the significant and immediate increase in public support for the euro during the COVID-19 pandemic in the EA19, although unemployment rose at the same time. For the monetary initiative by the ECB, we find only a limited significant positive influence (in the winter of 2020/2021) on public support for the euro. Much suggests that the increased political legitimacy bestowed on the EU institutions during the pandemic can be attributed to the fiscal policy initiatives. Our study is an explorative one. We had two hypotheses to start with. First, a rise in unemployment would decrease support for the euro judging from our previous work (Roth etal. 2016, 2019; Roth and Jonung 2020a). Second, a rise in the volume of fiscal transfers/expenditures to countries hit by unemployment would increase support for the common currency just as, as an empirical rule, increased public expenditures strengthen the popularity of the government that carries out an expansionary fiscal policy. A priori, there is no theory that can answer which of these two effects would dominate. There is a huge literature on voting functions and popularity functions that suggests that expansionary economic policies do have a significant impact on voter behavior and voter sentiment (for a literature review see Lewis-Beck and Stegmaier 2013). The aim of this paper is two-fold: first, to analyze how public support for the euro evolved during the COVID-19 pandemic, and second, to investigate the extent to which the fiscal and expansionary monetary responses were driving the increase in public support for the euro. As far as we have seen, the drivers of public support behind the euro during the pandemic have not been studied before. In this sense, our study is unique. Our paper covers the workings of euro area by focusing on the role of monetary and fiscal policies during an economic crisis induced by the corona pandemic. The paper belongs to the field of political economy—using an approach pioneered by economists. The article is structured in the following manner. The following section summarizes previous studies on public support for the euro and highlights the importance of public support for the euro. The third section elaborates on the various policy initiatives adopted across the EU in response to the COVID-19 pandemic. The fourth section reviews the fiscal and monetary responses by national and EU policymakers during the pandemic. The fifth section presents the model specification used by the authors. The sixth section offers econometric results. The seventh section discusses the fiscal and monetary policy interventions during the COVID-19 crisis and their effects on public support for the euro. The last section offers conclusions. 2 Public support fortheEuro: areview oftheliterature 2.1 The role ofpublic support fortheEuro Public support for the common currency is a crucial prerequisite for the existence and sustainability of a common currency. History contains many cases where the 64 Empirica (2024) 51:61–86 1 3 lack of a public support is translated via the political system into a break-up of the common currency area (Bordo and Jonung 2003). During the euro crisis in the early 2010s, some politicians in some EA member states proposed a return to domestic currency units. Public support for the euro among the voters proved, however, strong and sustainable, dampening the requests for a return to a national currency. In France and Italy, for example, populists have muffled their demands for an end of the euro (Roth and Jonung 2020a). Given these events, the determinants behind public support for the common currency is thus a central and current research issue. We can identify at least five distinct strands of research on the role of public support for the euro. First, economists argue that the European Monetary Union (EMU) benefits from public support for its common currency. If the euro maintains strong public support, policymakers are able to address the challenges arising from political, economic, and financial disturbances and crises by making necessary adjustments (Bordo and Jonung 2003). Second, high levels of public support for the euro, defined by the economics of the optimum currency area (OCA) as a shared sense of a ‘commonality of destiny’, is crucial for a smooth functioning of a monetary union. Baldwin and Wyplosz (2022) assert that the primary reason for the survival of the euro is this political OCA criterion. Third, public support is identified as a key stabilizer for the process of European integration in the political science literature (Banducci etal. 2003; Verdun 2022). This literature argues that public support for the euro is necessary so that citizens are willing to transfer power from national to European institutions (Kaltenthaler and Anderson 2001). Fourth, a relatively new strand of research links public support for the euro to the fact that Greece and other crisis-prone Mediterranean economies have not exited the EA despite the fact that a majority of citizens oppose austerity measures (Walter etal. 2018; Jurado etal. 2020; Xezonakis and Hartmann 2020). Fifth, economists conclude that public support for the euro is crucial in times of economic and political distress (Roth etal. 2016, 2019; Roth and Jonung 2020a; Roth 2022). As evidenced by the Italian case, large public support for the euro in the EA19 served as a shield against “populist governments” efforts to dismantle EA cooperation. It also granted political legitimacy to the ECB’s independence against growing criticism of its actions in times of diminished institutional trust. 2.2 The empirical evidence Research on public support for the euro and EMU neatly follows a timeline. It encompasses studies of public support in the years before the introduction of the common currency (Gärtner 1997; Kaltenthaler and Anderson 2001; Banducci etal. 2003), during the pre-crisis period from 1999 to 2008 (Banducci etal. 2009; Deroose etal. 2007), during the crisis from 2008 to 2013 (Hobolt and Leblond 2014; Hobolt and Wratil 2015; Roth etal. 2016) and during the economic recovery from 2013 to 2018 (Roth etal. 2019; Roth and Jonung 2020a). A central finding of these studies is that, with a few exceptions, the euro has enjoyed strong support in all EA19 countries since its introduction, including during the crisis from 2008 to 2013 (Roth et al. 2016, 2019). The research on the 65 1 3 Empirica (2024) 51:61–86 macroeconomic determinants of public support for the euro is not conclusive. While Hobolt and Leblond (2014) find no significant relationship between unemployment and net support for the euro, Roth etal. (2016, 2019) and Roth and Jonung (2020a) establish a significant and negative relationship during the economic crisis and recovery period from 2008 to 2018. A similarly controversial conclusion applies to the impact of inflation. Banducci etal. (2009) and Hobolt and Leblond (2014) conclude that there is no significant relationship between inflation and public support for the euro, while Roth etal. (2016, 2019) and Roth and Jonung (2020a) find a strong negative coefficient in the pre-crisis period and during the crisis period for an EA-19 country sample. The above studies form the background for our present study, which deals with the impact on the support for the euro of the economic downturn and of the fiscal and monetary measures taken during the COVID-19 pandemic, starting in early 2020 and lasting through 2022. 3 Covid‑19 andpublic support fortheEuro 3.1 The COVID‑19 pandemic In response to the pandemic, EA19 member countries introduced compulsory restrictions on the mobility of the public, commonly referred to as lockdowns.3 These measures included non-pharmaceutical interventions such as school closures, workplace closures, and stay-at-home requirements. The commonly stated goal of these mandatory measures was to flatten the epidemiological curve (Baldwin and Wyplosz 2022), thereby reducing the spread of the pandemic and holding down the rise in mortality rates. These measures were the primary tools, as vaccines only reached a minority of EA19 member countries in 2020 and 2021 (Moore etal. 2021; Burki 2021). The actual effect of the lockdowns on mortality is a subject of lively debate. Some argue that lockdowns, that is, increased stringency, decreased the growth of COVID-19 cases and mortality rates (Hale etal. 2020; Violato etal. 2021). Others hold a more skeptical view, such as Herby etal. (2023), concluding that lockdowns had a negligible effect on excess mortality.4 Figure1 displays the 14-day moving average of the stringency index, the common measure of the extent of lockdowns and the mortality rate per million people in the EA19 next to the mean unemployment rate matched according to the respective bi-annual standard Eurobarometer (EBs 92–97) fieldwork periods considered in the analysis. 3 The World Health Organization declared COVID-19 to be a pandemic on 11 March 2020 (Ducharme 2020) and ended the global emergency status for COVID-19 on 5 May 2023 (Rigby and Satija 2023). 4 Herby etal. (2023), based on a meta-analysis of the effects of lockdowns on mortality, offer the most comprehensive review of the evidence on lockdowns. They argue that the costs of lockdowns to society far outweigh any benefits. See Table18 in Herby etal. (2023). 66 Empirica (2024) 51:61–86 1 3 During the first two waves of the COVID-19 pandemic,5 from March until May 2020 and again from November 2020 until the end of April 2021, the lockdown measures remained at a high level, mostly above 70. Only during the summer of 2020 and after the second wave, at the end of March 2021, was partial control achieved, with a decline in the stringency measures in response to the decreasing infections and death rates. Overall, in 2020 and 2021, the stringency index remained at a high level, mostly above 50. Then in early 2022, the stringency index started to decline and converge toward pre-crisis levels in June 2022. The reason for this decline was the continuous increase in COVID-19 vaccinations in many member countries. The rising frequency of vaccinations, however, led to a decreasing hospitalization rate despite a strong increase in confirmed cases due to the emergence of the Omicron variant6 (Ritchie etal. 2020; Ulloa etal. 2021). Fig. 1 14-day Moving Average Stringency Index, Mortality Rate per Million People and Mean Unemployment Rate, EA19, 2019–2022. The stringency index is aggregated from 0 to 100 (100 = strictest) and is calculated based on nine response indicators (school closures, workplace closures, cancellations of public events, limits on size of gatherings, public transport closures, stay-at-home requirements, restrictions on internal movement, travel bans, and record presence of public information campaigns). PEPP = Pandemic Emergency Purchase Programme, SURE = Support to mitigate Unemployment Risks in an Emergency, RRF = Recovery and Resilience Facility (RRF), EB = Eurobarometer, Disb. = Disbursement. Values of the left-hand y-scale stringency index are in percent. Values on the right-hand y-scale (showing mean unemployment rates) are in percent. Values on the right-hand y-scale (showing mortality rates) are displayed per million people. X-scale displays 14-day moving averages. Source: Data for the stringency index and confirmed deaths are taken from the Oxford COVID-19 Response Tracker (Hale etal. 2021; Oxford COVID-19 Government Response Tracker 2020), and data for the unemployment rate and population are from Eurostat. *This includes the SURE disbursements on 2 February 2021 5 There is no scientifically agreed definition of a “wave” or a “driving force” (Cacciapaglia etal. 2021). We derive the first two waves based on the daily-confirmed cases as seen in Fig. A1 in Appendix A in the online supplementary information. 6 The Omicron variant caused a massive increase in the number of confirmed cases since late 2021 as seen in Fig. A1 (arrow) in Appendix A in the supplementary information, although it was not associated with higher mortality, as shown in Fig.1. 67 1 3 Empirica (2024) 51:61–86 The high stringency measures taken in the spring of 2020 and in the winter of 2020–2021 led to a significant decline in economic activity, strongly reflected in the increase in the mean unemployment rate, as shown in Fig.1 in the winter of 2020–2021 before the standard EB94 fieldwork (2–3/2021). Several studies show that a high stringency index significantly increased the unemployment rate (Bauer and Weber 2021; Baek etal. 2021; Ping Ang and Dong 2022). In parallel with the decreasing stringency index from April 2021 onwards, the mean unemployment rate also fell below the pre-pandemic levels in the summer of 2022. EU policymakers addressed the economic downturn and the rise in unemployment via rapid large-scale fiscal policy and monetary initiatives. As seen in Fig.1, the temporary Support to mitigate Unemployment Risks in an Emergency (SURE) was activated by the European Commission (EC) on 22 September 2020. The centerpiece Recovery and Resilience Facility (RRF) of the European recovery plan, NextGenerationEU (NGEU), was approved on 10–12 February 2021 by the European Parliament and the Council of the EU. The ECB activated the Pandemic Emergency Purchase Programme (PEPP) on 24 March 2020. With the decision of the Governing Council, PEPP net purchases were discontinued at the end of March 2022. These three initiatives are described in more detail in Sect.4. To sum up: we identify the start of the COVID-19 crisis with a rapid increase in the stringency index at the end of February 2020 and its end with the convergence toward pre-crisis levels in June 2022. 3.2 COVID‑19 andpublic support fortheEuro Let us start by examining the evolution of net public support for the euro and the rate of unemployment in the EA and its 19 individual member countries since the introduction of the euro in 3–4/1999 (EB51) until 6–7/2022 (EB97). A striking feature in Fig.2a is the increase in public support for the euro of 6.7-percentage points from 57.9 percent before the pandemic in 11/2019 (EB92) compared to 64.6 percent of net support in 2–3/2021 (EB94) (see TableA1 in Appendix A in the online supplementary information). However, whereas the increase from 11/2019 (EB92) to 7–8/2020 (EB93) was only 1-percentage point, the increase from 7–8/2020 (EB93) to 2–3/2021 (EB94) was 5.7-percentage points (see Table A2 in Appendix A in the online supplementary information). This pronounced increase in the winter of 2020/2021 in 2–3/2021 (EB94) established the highest level of support in the history of the euro at that time, although unemployment had risen in the meantime. As the pandemic progressed, net support dropped after 2–3/2021 (EB94) but remained higher than before the pandemic. The unemployment rate follows the same pattern as net support, peaking in 2–3/2021 (EB94). With the start of the war in Ukraine we see a “rally-around-the-flag” effect with a renewed increase in public support to 64.7 percent in 6–7/2022 (EB97), representing a new historical high level of support for the euro. This positive correlation during 2020/2021 between support and unemployment runs counter to the negative correlation during the financial and economic crisis of 2008–2013 (Roth etal. 2016, 2019; Roth and Jonung 2020a). The sharp increase in 68 Empirica (2024) 51:61–86 1 3 (a) (b) Fig. 2 a, b Unemployment and Net Public Support for the Euro in the EA19 and in the 19 individual EA19 Countries, 1999–2022. Note: As the figure depicts net support, all values above 0 indicate that a majority of the respondents support the euro. Net support measures are constructed as the number of ‘For’ responses minus ‘Against’ responses, according to the equation: Net support = (For − Against)/ (For + Against + Don’t know). The vertical dashed lines represent four milestones in the history of the single currency: the physical introduction of the euro in January 2002, the start of the global financial crisis in September 2008, the start of the recovery at the end of 2013, the start of the COVID-19 crisis at the beginning of 2020 and its end in June 2022. Source: Standard Eurobarometer data 51–97 75 1 3 Empirica (2024) 51:61–86 Table 1 Summary statistics and overview of variable construction, EA19, 1999–2022 N = number of observations. SD = standard deviation. Min. = minimum. Max. = maximum. ld = loan disbursements. pag = pre-allocated grants. np = net purchase. m. = mean. avg = average. EB = Standard Eurobarometer. EC = European Commission. EU = European Union. ECB = European Central Bank. ES = Eurostat. OC19 = Oxford COVID-19 Government Response Tracker. ES = Eurostat. Ann. = Annual. F = For. A = Against. DK = Don’t Know. d. = Dummy. Un = Unemployment. *daily fieldwork information utilized if needed. **interpolated to monthly data Sources: Standard Eurobarometer data 51–97. Eurostat. European Commission (2022a). European Union (2021). European Central Bank (2022b). Oxford COVID-19 Government Response Tracker (2020) Variable N Mean SD Min Max Data range Data frequency Variable construction Source Net support for the euro 731 51.2 19.2 − 9.0 90.0 3–4/99–17/7/22 Bi-Ann.* (F − A)/(F + A + DK) EB Unemployment rate 731 8.5 4.3 1.9 28.0 10/96–5/22 Monthly Seasonaly adjusted ES Inflation 731 1.0 1.4 − 3.7 11.8 10/96–5/22 Monthly Change of HCIP ES GDP per capita growth 731 0.7 2.2 − 12.9 14.8 10/96–5/22 Quarterly** Chain-linked volumes ES Covid dummy × Un. Rate 731 0.9 2.7 0 17.8 7–8/20–6-7/22 Bi-Ann Covid d. × Un. rate EB, ES Covid dummy 731 0.1 0.3 0 1 7–8/20–6-7/22 Bi-Ann = 1 if EB > 92 & < 98 EB SURE dummy 731 0.0 0.2 0 1 27/10/20–29/3/22 Daily = 1 if l.d. > EA-19 avg EC 2022a, ES SURE dummy (EB94) 731 0.0 0.1 0 1 27/10/20–2/2/21 Daily = 1 if l.d. > EA-19 avg EC 2022a, ES RRF dummy 731 0.1 0.2 0 1 10–12/2/21–16/6/22 Daily = 1 if pag > EA-19 avg EU 2021, ES RRF dummy (EB94) 731 0.0 0.1 0 1 10–12/2/21 Daily = 1 if pag > EA-19 avg EU 2021, ES PEPP dummy 731 0.0 0.2 0 1 3/20–3/22 Monthly = 1 if np > EA-19 avg ECB 2022b, ES PEPP dummy (EB94) 731 0.0 0.1 0 1 7/20–1/21 Monthly = 1 if np > EA-19 avg ECB 2022b, ES Stringency index dummy 731 0.1 0.3 0 1 22/1/20–16/6/22 Daily = 1 if mn > EA-19 avg OC19 2020, ES Mortality dummy 731 0.1 0.2 0 1 22/1/20–16/6/22 Daily = 1 if mn > EA-19 avg OC19 2020, ES Cases dummy 731 0.1 0.2 0 1 22/1/20–16/6/22 Daily = 1 if mn > EA-19 avg OC19 2020, ES 76 Empirica (2024) 51:61–86 1 3 With t = 46 and n = 12 (n = 19) and thus with a ratio of t/n = 3.8 (t/n = 2.4), Eq.1 is estimated via a panel time-series estimation. The analysis focuses on the period from 1999 to 2022. We apply a matching procedure between macroeconomic variables, fiscal and monetary initiatives, COVID-19-related variables and standard Eurobarometer (EB) survey data. For the macroeconomic variables and our monetary initiative (PEPP) dummy, we applied a monthly data frequency approach. Following the previous literature (Roth etal. 2016, 2019), we assume that citizens consider macroeconomic developments and the net purchases under PEPP between two EB surveys. This means that the citizens, in responding to the EB94 (2–3/2021) survey, considered macroeconomic developments and net purchases under PEPP between July 2020 (the first month of the previous EB93 (7–8/2020) survey) up to January 2021 (the month before the standard EB94 (2–3/2021) survey). For the fiscal initiative dummies (SURE and RRF pre-allocated grants) and COVID-19-related variables, we applied a daily data frequency approach for the matching procedure.19,20 In examining the pre-allocated RRF grants, we utilize the pre-allocated grants for the bi-annual EB periods from the RRF activation from 10 to 12 February 2021 onwards.21 This matching procedure is applied to all bi-annual EB fieldwork up to the latest 6–7/2022 (EB97) survey.22 An overview of the matching strategy can be found in Fig. A5 in Appendix A in the online supplementary information. Figures3, 4 and 5 illustrate the base for the construction of our dummy variables. Countries that have received SURE loan disbursements, RRF pre-allocated grants and PEPP net purchases per GDP that are above the EA19 average have been marked with 1 (for an overview, see Tables A6–8 in Appendix A in the online supplementary information). COVID-19 control dummies are similarly calculated, with the values for the mean confirmed cases per million people, mean deaths per million people and mean stringency index that lie above the EA19 average indicated by a 1.23 19 The EB surveys were conducted during the following periods: 9 July-26 August 2020 (EB93), 12 February-18 March 2021 (EB94), 14 June-15 July 2021 (EB95), 18 January-14 February 2022 (EB96) and 17 June-17 July 2022 (EB97). 20 Thus, the SURE loan disbursement on 2 February 2021 would have been reflected in the previous EB94 (2–3/21) survey data, since the EB94 fieldwork started on 12 February 2021. 21 This means that the pre-allocated RRF grants have been considered by citizens when answering EB surveys, starting from 2–3/2021 (EB94). 22 The latest EB97 (6–7/22) survey was conducted between 17 June and 17 July 2022. Thus, the fiscal initiatives dummy and COVID-19 control variables include the data up to 16 June 2022, one day before the EB97 survey started. 23 The underlying figures and tables on the dummy construction are available from the authors upon request. 77 1 3 Empirica (2024) 51:61–86 6 Estimation approach andeconometric results We utilize a Fixed Effect Dynamic Feasible General Least Squares (FE-DFGLS)24 approach, which is represented by Eq.(2)25: with 𝛼i being the country fixed effect and Δ indicating that the variables are in first differences. By applying DFGLS, unemployment, inflation and growth become exogenous and the coefficients 𝛽1 , χ1 , 𝛿1 and φ1 follow a t-distribution. This property permits us to derive statistical inferences on the causal impact of the unemployment, inflation and growth variables. The asterisk (*) indicates that the variables have been transformed and that the error term uit fulfils the requirements of the classical linear regression (i.e. no autocorrelation). Table2 shows our econometric results within our EA12 and EA19 country samples. Utilizing an FE-DFGLS estimation approach for the EA12 and EA19 over the 23-year period 3–4/1999 to 6–7/2022 with 549 and 720 observations and including the introduced fiscal and monetary stimulus dummies yields the following results. First, we present the results for our macro-economic variables. Looking at the EA12 and EA19, our long-term variables unemployment and growth of GDP per capita have the usual signs. Whereas an increase in the unemployment rate is associated with a significant decline in net public support for the euro (ranging from − 1.0 to − 1.6), no significant relationship between GDP per capita growth and public support for the euro could be detected. The size of the coefficient can be interpreted as follows: a 1-percentage point increase in unemployment is associated with a 1.0–1.6 percentage point decline in net support for the euro. Overall, these results support previous empirical evidence in Roth etal. (2016, 2019) and Roth and Jonung (2020a). Contrary to these results, inflation has become insignificant. This is due to the inflation dynamics in the Euro Area starting in the winter of 2021/2022 (from EB96 onwards), which has not led to a significant decline, but an actual increase (2) Support∗ it =𝛼 i +𝛽 1 Unemployment∗ it +χ 1 Inflation∗ it +𝛿 1 Growth∗ it +φ 1 Z∗ it + p=+1 ∑ P=−1 𝛽2pΔUnemployment∗ it−p+ p=+1 ∑ P=−1 χ2pΔInflation∗ it−p+ p=+1 ∑ P=−1 𝛿2pΔGrowth∗ it− p + p=+1 ∑ P=−1 φ2pΔZ∗ it−p+𝜃FMit +𝛾Cov19it +uit, 24 All series are integrated of order 1, i.e. they are I(1) (non-stationary); non-stationary of the variables inflation and GDP per capita growth is due to non-stationarity (non-constancy) of the variance of these series, and they are cointegrated. The Pesaran’s CADF panel unit root tests and Kao’s residual cointegration test are displayed in Tables A4–A5 in Appendix A in the online supplementary information. 25 Appendix B in the online supplementary information explains the detailed steps from Eqs. (1) to (2) and discusses the properties and benefits of the utilized FE-DFGLS estimation approach. 78 Empirica (2024) 51:61–86 1 3 Table 2 Unemployment, inflation, GDP per capita growth, fiscal and monetary dummies and support: FE-DFGLS estimations, EA12 and EA19, 1999–2022 Values which are depicted in bold represents a significance level of at least **P < 0.05, FS = full sample. El. = Elimination. Standard errors are in parentheses. **P < 0.05, ***P < 0.01. aOnly includes fiscal and monetary stimulus dummies in EB94 (2–3/2021) dataset Regression (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Dependent variable Euro Euro Euro Euro Euro Euro Euro Euro Euro Euro Euro Euro Country sample EA12 EA19 EA12 EA19 EA12 EA19 EA12 EA19 EA12 EA19 EA12 EA19 Period FS FS FS FS FS FS FSaFSaFSaFSaFSaFSa Unemployment − 1.0** − 1.5*** − 1.0** − 1.5*** − 1.1** − 1.5*** − 1.1** − 1.5*** − 1.1** − 1.5*** − 1.1** − 1.6*** (0.48) (0.42) (0.47) (0.41) (0.49) (0.42) (0.49) (0.42) (0.49) (0.42) (0.49) (0.42) Inflation 1.1 1.0 0.4 0.4 0.9 0.8 1.2 0.9 1.1 0.8 1.1 0.9 (1.18) (0.79) (1.19) (0.79) (1.20) (0.79) (1.19) (0.78) (1.19) (0.78) (1.19) (0.78) GDP per capita growth − 0.1 − 0.1 − 0.5 − 0.4 − 0.4 − 0.3 − 0.2 − 0.2 − 0.2 − 0.3 − 0.2 − 0.2 (0.68) (0.56) (0.68) (0.55) (0.68) (0.56) (0.68) (0.56) (0.68) (0.55) (0.68) (0.56) SURE dummy 7.8*** 5.2** 9.5*** 6.7*** (2.87) (2.04) (3.55) (2.49) RRF pre-allocated Grants dummy 16.3*** 11.0*** 10.2*** 6.6*** (4.56) (2.97) (3.78) (2.34) PEPP dummy 1.7 2.0 10.2*** 7.9** (2.88) (2.17) (3.78) (3.08) COVID control dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Durbin–Watson statistic 2.33 2.29 2.32 2.26 2.33 2.29 2.33 2.29 2.32 2.29 2.32 2.28 Adjusted R-squared 0.81 0.83 0.82 0.83 0.81 0.83 0.81 0.83 0.81 0.83 0.81 0.83 Country fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Control for endogeneity Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes El. of first-order autocorr Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 549 720 549 720 549 720 549 720 549 720 549 720 Number of countries 12 19 12 19 12 19 12 19 12 19 12 19 79 1 3 Empirica (2024) 51:61–86 in public support for the euro—a “rally-around-the-flag” effect—in 6–7/2022 (EB97).26 More importantly for this paper, we detect that the dummies for the SURE and RRF pre-allocated grants in the EA12 (Regressions 1 and 3) and the EA19 (Regressions 2 and 4) display highly significant and positive coefficients ranging from 5.2 to 7.8 for SURE and from 11.0 to 16.3 for RRF. The size of the coefficient can be interpreted as follows: Member states of the EA12 that benefited from the SURE program27 during the COVID-19 pandemic showed on average a 7.8 percentage point increase in net euro support (Regression 1).28 In addition, EA12 member states that were net beneficiaries of the RRF grants pre-allocation29 experienced on average, an increase in net public support for the euro by 16.3 percentage points (Regression 3). With a coefficient of 5.2 for SURE (Regression 2) and a coefficient of 11 for RRF pre-allocated grants (Regression 4), these values are nevertheless lower when looking at the complete set of EA19 economies. Our PEPP coefficients in Regressions 5 and 6 are neither significant for the EA12 nor for EA19 when analyzing the full net purchases under PEPP between March 2020 to March 2022.30 Regressions 7–12 show our results when focusing on the peak of the COVID-19 crisis in the winter of 2020/2021, particularly in 2–3/2021 (EB94). Regressions 7 and 8 show that the coefficients for SURE increase to 9.5 for EA12 and 6.7 for EA19 member countries when solely analyzing the SURE loan disbursements in 2–3/2021 (EB94) during our full sample period 3–4/1999 to 6–7/2022.31 These results indicate that our results for SURE for the overall period are driven by the peak of the COVID-19 crisis in 2–3/2021 (EB94). We find contrasting results for the RRF pre-allocated grants in Regressions 9 and 10, where coefficients are lower (10.2 for EA12 and 6.6 for EA19) when considering only pre-allocated grants in winter of 2020/2021 (EB94), compared to our sample of pre-allocated grants for the whole COVID-19 period. 26 See here TableA3 in Appendix A in the online supplementary information, which shows that the negative coefficient for inflation loses significance from 1–2/2022 (EB96) onwards. 27 As can be seen from Fig.3 and TableA6 in Appendix A in the online supplementary information, those countries are Belgium, Greece, Ireland, Italy, Portugal, and Spain. 28 Figure A3 in Appendix A in the online supplementary information shows the variation of the effect of fiscal and monetary policies in the individual EA12 and EA19 countries throughout the COVID-19 pandemic (from 11/2019 to 6–7/2022). Figure A3 suggests that the overall average impact of 7.8 percentage points of the SURE Program in the EA12 as reported in regression 1 in Table2 is driven by the increases of net public support for the euro in Portugal by 9 percentage points, in Belgium and Spain by 10 percentage points, respectively and in Italy and Greece by 15 percentage points, respectively. 29 As can be inferred from Fig.4 and TableA7 in Appendix A in the online supplementary information, those countries are Greece, Portugal, Italy, and Spain. 30 Our results are robust if we exclude the COVID-19 pandemic-related dummies (stringency index, cases, and mortality) in our analysis. 31 Figure A4 in Appendix A in the online supplementary information shows the variation of the effect of fiscal and monetary policies in the individual EA12 and EA19 countries at the peak of the COVID-19 crisis in the winter 2020/21 (from 7–8/2020 to 2–3/2021). The figure suggests that the overall average impact of 9.5 percentage points of the SURE Program in the EA12 as reported in regression 7 in Table2 is driven by the large increases of net public support for the euro in Italy and Portugal by 19 and 18 percentage points, respectively. 80 Empirica (2024) 51:61–86 1 3 More interestingly, the PEPP coefficients are highly significant when solely analyzing the net purchases under PEPP in 2–3/2021 (EB94) within our full sample period in Regressions 11 and 12. Those EA12 and EA19 member countries that benefited from the net purchases under PEPP during the winter of 2020/2021 saw an increase of 10.2 and 7.9 percentage points respectively in net public support for the euro.32 In summary, we detect a significant positive influence of the fiscal initiatives on public support for the euro throughout the whole COVID-19 period until 6–7/2022 (EB97), while an overall significantly positive influence of monetary initiatives can be found when solely analyzing net purchases under PEPP in the period 2–3/2021 (EB94). In the case of SURE, the positive effect of the fiscal initiatives is driven by its strong positive impact in the winter 2020/2021 and in 2–3/2021 (EB94) period. This evidence suggests that the increase during the COVID-19 pandemic of net public support for the euro is significantly connected to the immediate responses of SURE and RRF pre-allocated grants, as well as to the immediate effect of PEPP in winter 2020/2021. This happens despite the simultaneous rise in unemployment— we actually detect positive and significant coefficients of 1.7 and 1.6 for our EA12 and EA19 samples, respectively when analyzing an interaction effect between a COVID-19 dummy and the unemployment rate in Table3.33 7 Discussion Our results allow us to draw several conclusions about how public support for the euro was affected by the fiscal and monetary initiatives introduced at the beginning of the COVID-19 pandemic. First, public support for the euro increased amidst a rise in unemployment during the COVID-19 pandemic. This runs counter to previous empirical findings of the negative relationship between unemployment and public support for the euro during the economic crisis recovery period from 2008 to 2018 (Roth etal. 2016, 2019; Roth and Jonung 2020a). This raises the question: Why has public support for the euro increased even though unemployment has also risen? Coming back to our two theoretical hypotheses as discussed initially, we suggest that the fiscal policy initiatives taken by EU policy makers led to the significant and immediate increase in public support for the euro during the COVID-19 pandemic in the EA19, although unemployment rose at the same time. This might be related, amongst other factors, to the fact that the fiscal policy measures SURE and NGEU, with its centerpiece RRF, were communicated clearly by EU authorities to the public 32 These results remain robust if we exclude the COVID-19 pandemic dummies (stringency index, cases and mortality) in our analysis. 33 This econometric finding supports the visual evidence of a positive relationship between the unemployment rate and net public support for the euro in times of COVID-19 crisis, as highlighted in Fig.2a, b. 81 1 3 Empirica (2024) 51:61–86 Table 3 Interaction effect between Covid dummy and unemployment: FE-DFGLS estimations, EA12 and EA19, 1999–2022 FS = full sample. El. = Elimination. EA = euro area; Standard errors are in parentheses. **P < 0.05, ***P < 0.01 (1) (2) Euro Euro EA12 EA19 FS FS Unemployment − 1.0** − 1.4*** (0.47) (0.40) Inflation 0.2 0.7 (1.31) (0.83) GDP per capita growth − 0.4 − 0.2 (0.71) (0.58) Covid dummy * unemployment 1.7** 1.6*** (0.70) (0.61) Covid dummy Yes Yes Covid control dummies Yes Yes Durbin–Watson statistics 2.31 2.25 Adjusted R-squared 0.81 0.83 Country fixed effects Yes Yes Control for endogneity Yes Yes El. of first-order autocorr Yes Yes Observations 549 720 Number of Countries 12 19 in member states.34 In addition, recent research has shown that on average support for European financial solidarity during the pandemic is substantial (Beetsma etal. 2022; Bauhr and Charron 2022). For the monetary initiative, PEPP, we could only find a limited significant positive influence (in the winter of 2020/2021) on public support for the euro. Most likely, the public did not notice in the media the expansionary monetary measures taken by the ECB as much as the expansionary fiscal measures. The magnitude of these measures is unprecedented in the history of the EU. In the aftermath of the financial crisis in 2008/2009, economic stimuli took mostly the form of intergovernmental initiatives. Additionally, the early crisis management of the EU has been described as slow and indecisive and thus hardly visible (Begg 2012; Puetter 2012; Menz and Smith 2013). In contrast, the fiscal policies SURE and RRF mark an important step towards a common fiscal integration. Moreover, 34 The effectiveness of NGEU is even queried from EB94 (2–3/2021) onwards. As shown in TableA9 in Appendix A in the online supplementary information, a majority of all EA19 countries, with the exceptions being Finland and Latvia, is of the opinion that the NGEU has been effective from 2–3/2021 (EB94) to 6–7/2022 (EB97). 82 Empirica (2024) 51:61–86 1 3 the SURE initiative introduced a step toward a new EU risk-sharing model (Andersson and Jonung 2023). Overall, the early pandemic management of the EU deserves to be described as swift, comprehensive, and decisive. Much suggests that without these fast and decisive fiscal initiatives, the economic consequences of the increased unemployment associated with the lockdowns would have become even more pronounced. The increased political legitimacy bestowed on the EU institutions during the pandemic can be attributed to the new policy initiatives SURE and RRF, which might have laid the foundation for a new stabilization framework at the supranational level. This implies a move towards fiscal federalism but one that is not universally supported by all of the member states.35 Unfortunately, academia and the business/industrial sector in particular, pay little attention to the EB surveys on public support for the euro. Data from these surveys are not fed into the standard macroeconomic forecasts produced by commercial banks, financial institutions, and other institutions supplying and marketing forecasts. One reason is that EB data are of low frequency and only produced biannually. Most forecasters work with high-frequency data like monthly, weekly, and even daily. In addition, data on public support for the euro requires expertise treatment to be consistent over time. Before we turn to our conclusions, we consider the limits to our study. One limitation of our research is that we examine the impact of the pandemic on public support for the euro using only macroeconomic determinants and outcomes. Analyzing the microeconomic determinants using micro data would broaden the results and reveal a more comprehensive picture. We intend to investigate the micro-economic determinants in an additional paper in the future. 8 Conclusions Using a uniquely large panel dataset and applying a tailor-fit FE-DFGLS estimation approach and a daily-data-frequency research design at the macro-economic level for an EA country sample over the period 1999–2022, our analysis arrives at four major conclusions. First, during the COVID-19 pandemic, net public support for the euro among a majority of the 19 EA economies increased and reached new historically high levels at the peak of the COVID-19 crisis in the winter of 2020/2021. Second, we find a significant positive relationship between the highly expansionary fiscal initiatives taken by the European Commission and the rise in support for the euro in the EA. Third, the expansionary monetary measures by the European Central Bank are only marginally associated with a positive effect on public support for the euro. We speculate that this modest effect is attributable to the fact that the public was more informed about the expansionary fiscal measures than about the monetary policy of the ECB. Fourth, the increase in unemployment and the downturn in economic activity triggered by the COVID-19 pandemic and the subsequent lockdowns did not 35 For a detailed review, see Andersson and Jonung (2023). 83 1 3 Empirica (2024) 51:61–86 negatively affect public support for the euro. This finding contrasts with the pattern observed in the aftermath of the financial and sovereign debt crisis in 2008/2009, when the rise in unemployment was associated with a significant fall in public support for the euro. Returning to our two theoretical hypotheses as discussed initially, the findings of our paper suggest that the fiscal policy initiatives implemented by EU policy makers led to the significant and immediate increase in public support for the euro during the COVID-19 pandemic in the EA19, although unemployment rose at the same time. In contrast, the impact of the monetary initiatives was limited, with only a slight significant positive influence observed (in the winter of 2020/2021). Much suggests that the increased political legitimacy bestowed on the EU institutions during the pandemic can be attributed to the new fiscal policy initiatives. Overall, our research results open up two promising avenues for future research, which we have not covered in this paper due to space limitations. The first avenue for future research is a comprehensive analysis of the micro-economic determinants of public support for the euro, focusing on the COVID-19 crisis. The second avenue for future research is an in-depth analysis of the impact of inflation on public support for the euro in times of accelerating inflation and a “rally-around-the-flag” effect as witnessed from late 2021 and early 2022 onward. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s1066302309596-7. Acknowledgements The authors would like to thank Thomas Straubhaar, Michael Bauer, Ole Wilms and two anonymous reviewers for excellent comments. Author contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by FR and AM. The first draft of the manuscript was written by FR and AM and LJ commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding Open Access funding enabled and organized by Projekt DEAL. Data availability The data that support the findings of this study are available on request from the corresponding author Felix Roth. The underlying datasets were derived from the following public domain resources: Standard Eurobarometer (51–97) survey: https:// www. gesis. org/ en/ eurob arome terdataservi ce/ surveyseries/ stand ardspeci aleb and https:// europa. eu/ eurob arome ter/ screen/ home. Inflation, Unemployment, GDP per capita and population: Eurostat. SURE loan disbursements: https:// econo myfinan ce. ec. europa. eu/ eufinan cialassis tance/ sure_ en. RRF grants pre-allocation: https:// eurlex. europa. eu/ legalconte nt/ EN/ TXT/? uri= CELEX% 3A320 21R02 41. PEPP net purchases: https:// www. ecb. europa. eu/ mopo/ imple ment/ pepp/ html/ index. en. html. COVID-19 variables (stringency index, confirmed cases and confirmed deaths: https:// github. com/ OxCGRT/ covidpolicytrack er/ tree/ master/ data/ times eries. Material and/or code availability All supplementary material and codes are supplied in standard file formats. Declarations Conflict of interest The authors declare that they have no conflict of interest. 84 Empirica (2024) 51:61–86 1 3 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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