Which crisis support fiscal measures worked during the Covid-19 shock in Europe?

Pappa, Evi; Ramos, Andrey; Vella, Eugenia Article Which crisis support fiscal measures worked during the Covid-19 shock in Europe? SERIEs - Journal of the Spanish Economic Association Provided in Cooperation with: Spanish Economic Association Suggested Citation: Pappa, Evi; Ramos, Andrey; Vella, Eugenia (2024) : Which crisis support fiscal measures worked during the Covid-19 shock in Europe?, SERIEs - Journal of the Spanish Economic Association, ISSN 1869-4195, Springer, Heidelberg, Vol. 15, Iss. 4, pp. 327-348, https://doi.org/10.1007/s13209-023-00288-w This Version is available at: https://hdl.handle.net/10419/326959 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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Evi Pappa1·Andrey Ramos1·Eugenia Vella2 Received: 13 March 2023 / Accepted: 18 July 2023 / Published online: 5 August 2023 © The Author(s) 2023 Abstract We build a comprehensive database that categorizes COVID-19 fiscal measures announcements in 12 European Union countries into 7 distinct spending categories. Through our empirical analysis, we investigate the impact of these support packages on the economy. Overall, the fiscal measures played a crucial role in promoting output recovery without significant inflationary pressures. However, we observed substantial variations across different spending categories. Assistance provided to small and medium enterprises and specific sectors proved to be highly effective in stimulating the output while maintaining inflation. Direct pandemic spending and measures aimed at sustaining employment levels generated substantial output and employment multipliers and enhanced business sentiment without leading to inflationary costs. Conversely, universal help had inflationary effects and transfers to households primarily aroused consumer and business sentiment without producing significant economic impacts. Keywords COVID-19 crisis ·Fiscal measures ·Multipliers ·Sentiment ·Transfers · Assistance to SMEs ·Inflation JEL Classification C23 ·E62 The replication material for the study is available at https://doi.org/10.5281/zenodo.8154763. We would like to thank the editor, Virginia Sanchez Marcos and two anonymous referees for their useful guidance. We are grateful to José Manuel Claros and Kostantinos Mavrigiannakis for excellent research assistance. BEvi Pappa [email protected] Andrey Ramos [email protected] Eugenia Vella [email protected] 1Universidad Carlos III de Madrid, Getafe, Spain 2Athens University of Economics and Business and Fundació MOVE, Athens, Greece 123 328 SERIEs (2024) 15:327–348 1 Introduction In response to the record-breaking COVID-19 recession, governments worldwide implemented extraordinary fiscal stimuli, effectively mitigating the most severe consequences of the crisis. According to Fig. 1, the pandemic-related cumulative fiscal spending between October 2020 and July 2021 varied from 45% of GDP in Italy to around 8% in Romania and the average spending in the 27 EU countries was a bit below 20%. These figures are surely exceptional and, moreover, mask a lot of heterogeneity in spending categories across countries. While all countries seem to have used to some extent fiscal measures to support small and medium enterprises, some activated measures to support employment and others opted for non-targeted help to both consumers and firms or used direct transfers to households to assist the economic recovery. In this paper, we construct a unique database on fiscal spending categories announced during the COVID-19 crisis in 12 European Union (EU) countries.1The database is constructed using three primary sources: (a) the Fiscal Monitor database of country fiscal measures in response to the COVID-19 pandemic by the International Monetary Fund (IMF), (b) the European Commission (EC) report on European COVID-19 measures, and (c) the report on COVID-19 policy measures compiled by Bruegel, an economics-focused think tank based in Brussels. We also use national sources for some countries to overcome data availability problems when, for example, theIMFdatabaseprovidesonlythemeasurebutnottheassociatedexpenditureamount. The database includes the EU countries for which we found the relevant information. These measures, which encompass a range of functions, are classified into seven distinct categories to provide a comprehensive understanding of their intended purposes: (1) Assistance to small and medium enterprises (SMEs) and specific sectors, (2) Measures targeted to transform the economy, (3) Pandemic spending (e.g., on healthcare), (4) Transfers to households, (5) Unemployment benefits and measures to sustain employment, (6) Universal help, and (7) Other COVID-19 government spending. Toillustratetheusefulnessofourdataset,weconductanempiricalanalysis utilizing a Generalized Method of Moments (GMM) approach to investigate the effectiveness of the different COVID-19 fiscal measures. In particular, we estimate their impact on several key economic indicators, including quarterly Gross Domestic Product (GDP) growth, changes in consumer confidence and businesssentiment,inflationandemployment. To account for the evolving nature of the pandemic, our regressions incorporate control variables such as an index reflecting the stringency of lockdown measures and the number of COVID-19 fatalities per million inhabitants. Our empirical findings indicate that the fiscal measures implemented during the COVID-19 crisis were effective in stimulating economic recovery without leading to notable inflationary effects. Theoutputmultiplierisestimatedtobesmallerthanoneforthetotalfiscalpackages. However, there issubstantialheterogeneity regardingthedifferentfiscalmeasures. The multipliers associated with assistance to SMEs and specific sectors are in line with the aggregate spending multipliers. By contrast, we obtain output multipliers larger than 1It is important to point out that our data include fiscal measures that were either announced or actually implemented. 123 SERIEs (2024) 15:327–348 329 Fig. 1 Pandemic-related cumulative fiscal spending (% GDP) in 27 EU countries, from October 2020 to July 2021. Source: IMF Fiscal Monitor database of country fiscal measures in response to the COVID-19 pandemic. The GDP share of each measure in every quarter is calculated using data for 2020Q3 GDP to avoid changes caused by GDP variations. The horizontal line depicts the average across the 27 EU countries one for (i) direct pandemic spending and (ii) unemployment benefits and measures to sustain employment levels. Employment rates and business sentiment also react substantially and significantly to these measures. For transfers to households, the estimated output multipliers are not statistically significant but consumer and business confidence multipliers are. Therefore, even if transfers did little to regain economic losses, they were important in backing up sentiment.2Finally, non-targeted assistance (universal help) to both firms and households had positive effects on inflation and boosted business sentiment mostly. Hence, we conclude that the fiscal packages during the COVID-19 crisis successfully helped the economic recovery in Europe. Different fiscal measures had differential effects with unemployment benefits and measures to sustain employment as the best measure to maintain employment and business sentiment and assistance to SMEs as the most effective in stimulating output. Non-targeted help was the measure that put upward pressure on the inflation dynamics. Recent studies that quantify the macroeconomic effects of fiscal actions in response to the COVID-19 pandemic using fiscal announcements or aggregate fiscal data also suggest that the measures helped the economies recover (e.g., Gourinchas et al. 2021; Chudik et al. 2021; Deb et al. 2021). In this body of research, Gourinchas et al. (2021) conclude that fiscal policy prevented a large increase in firm failures by halving the failure rate, but it was inefficiently targeted. Using detailed regional variation in economic conditions in US data, Auerbach et al. (2022) recently document that the effects of government spending were stronger during the peak of the pandemic recession, but only in cities that were not subject to strong stay-at-home orders. 2Using the ECB Consumer Expectations Survey, Georgarakos and Kenny (2022) find that improving perceptions about the adequacy of fiscal interventions stimulated spending, equally strongly for consumers who received government support and for those who did not. 123 330 SERIEs (2024) 15:327–348 Guerrieri et al. (2022), using a theoretical framework, suggest that fiscal policy can display a smaller multiplier in the case of the COVID-19 shock but suggest that the insurance benefit of fiscal transfers can be enhanced. Faria-e Castro (2021) finds, in a nonlinear Dynamic Stochastic General Equilibrium (DSGE) model, that the COVID19 pandemic shock changes the ranking of policy multipliers in the United States. Unemployment benefits are the most effective tool to stabilize income for borrowers, while liquidity assistance programs are the most effective if the policy objective is to stabilizeemploymentintheaffectedsector.InaHeterogeneousAgentsNewKeynesian (HANK) framework, Bayer et al. (2020) quantify for the US economy the impact of a rise in fiscal transfers in the presence of the COVID-related lockdown. For the short run, they find large differences in the transfer multiplier: it is 0.25 for unconditional transfers and 1.5 for conditional (on recipients being unemployed) transfers. Overall, they conclude that the transfers reduce the output loss due to the pandemic by up to 5 percentage points. The theory in Auerbach et al. (2021) predicts that pandemic fiscal stimulus has weaker economic effects on impact, as households are unable or reluctant to spend on services that potentially pose health risks. But as restrictions are removed and consumers become less reserved, there is a surge in spending and therefore in inflation. Jordà and Nechio (2023) exploit the differences in pandemic support to identify the effect of these programs on inflation and the pass-through to wages using a sample of both European and non-European countries. Their estimates suggest that a 5 percentage points increase in real disposable income relative to trend (their indirect measure of changes in pandemic fiscal support) translates into roughly 3 percentage points additional inflation after 4 quarters. Using data for 10 large economies, Hale et al. (2023) find that fiscal support measures to consumers, but not firms, had inflationary effects that manifested 5 weeks following the announcement and peaked at 12 weeks. The impact was stronger in an environment of boosting consumer sentiment. Focusing on inflation through February 2022, de Soyres et al. (2022) show that countries with large fiscal stimulus, or with high exposure to foreign stimulus through international trade, experienced stronger inflation outbursts. Their back-of-the-envelope calculations suggest that US fiscal stimulus during the pandemic contributed to a surge in inflation of about 2.5 percentage points in the USA and 0.5 percentage points in the United Kingdom. Relative to the existing literature, we focus on EU countries and provide evidence on the effectiveness of different fiscal measures for different economic indicators. We look at the effects of the measures for output growth, employment and business sentiment but also in terms of consumer confidence, an important factor for demand recovery, and inflation. The rest of the paper is organized as follows: Section2lays out the data on COVID19 fiscal measures. Section3discusses the empirical methodology. Section4presents the main findings. Finally, Sect. 5concludes. 123 SERIEs (2024) 15:327–348 331 2 COVID-19 fiscal measures in European countries In this section, we provide a detailed description of the methodology used to construct our database of public spending categories in 12 EU countries during the COVID19 pandemic. Furthermore, we conduct a comparative analysis among countries to identify the specific measures adopted and examine the evolution of fiscal measures over time. 2.1 Construction of the database We have constructed a novel database that covers the period of 2020-Q2, 2020-Q3, 2020-Q4, 2021-Q1 and 2021-Q2. The database incorporates data from three main sources: (a) the IMF Fiscal Monitor database, which provides information on country fiscal measures implemented in response to the COVID-19 pandemic, (b) the EC report on COVID-19 measures, and (c) the COVID-19 policy measures report by Bruegel.3We also used national sources for some countries to overcome data availability problems when, for example, the IMF database provided only the measure but not the associated expenditure amount (see the data methodology in Annex A of our companion policy paper Pappa and Vella 2022). This approach allowed us to compile a comprehensive and reliable dataset that captures the discretionary measures announced and implemented by governments to complement existing automatic stabilizers in selected economies in response to the COVID-19 pandemic (as of September 27th, 2021). Let us briefly discuss the IMF Fiscal Monitor database, since we follow its classification of expenditure types. The database summarizes key fiscal measures announced or taken by governments in response to the COVID-19 pandemic. The database categorizes different types of fiscal support since January 2020, focusing on government discretionary measures. The IMF data are organized on the basis of the following categories: 1. Above the line: •Additional spending or foregone revenues (tax cuts) in health and non-health sectors. •Accelerated spending/deferred revenue (mostly tax deferrals). 2. Below the line support: •Equity injections, loans, asset purchases or debt assumptions. •Contingent liabilities in form of guarantees and quasi-fiscal operations (financial schemes used during the pandemic). The novelty of our work lies in collecting and classifying the above mentioned individual measures taken in EU countries into the following categories, for which we also provide a concrete example taken from the case of Belgium: 3See https://www.bruegel.org/publications/datasets/covid-national-dataset/. 123 332 SERIEs (2024) 15:327–348 1. Assistance to small and medium enterprises (SMEs) and specific sectorsv fiscal measures targeted to the firms or self-employed that suffered losses due to the pandemic; an example here is the Federal loan to Brussels Airlines and various (subordinated) loans provided by regional governments for companies and selfemployed affected by COVID-19. 2. Fiscal measures targeted to transform the economy: fiscal measures to promote investment activities, particularly in the areas of environmental sustainability and digitization;an examplehereis the Flemish fiscal stimulus amountingto e1.66billion for one-off investments in various priority areas, e.g. 5G, hydrogen, water management, infrastructure etc. 3. Spending caused by the pandemic: fiscal measures to face the direct effects of the pandemic (e.g., on healthcare); an example here is spending on medical equipment and tests. 4. Transfers to households: fiscal measures designed to help households; an example here is the one-off payment of e100 for households to pay their electricity bills and e75 to pay their gas bills in 2020-Q2. 5. Unemployment benefits and measures to sustain employment levels: measures that covered the cost of short-time work schemes and maintain jobs; an example here is government payments of a part of employees’ salaries when are temporarily laid off due to the circumstances. 6. Universal help: non-targeted fiscal measures, mostly tax cuts, to support businesses, employees, and households; an example here in 2020-Q2 is the deferred payment of tax and social security contributions for affected firms, self-employed, and households, without application of interest charges and penalties, estimated at about e10 bn, and exemption of advanced VAT payment in December. 7. Other: all COVID-related fiscal measures that do not belong to the previous categories. An example here is foregone revenue. The sample of EU countries included in our analysis comprises Belgium, Bulgaria,theCzechRepublic,Denmark, Finland,France,Germany,Italy, the Netherlands, Poland, Romania, Spain, and Sweden. However, it is important to note that we have limited data availability for Portugal and Poland. For Portugal, we only have data for June 2021, while for Poland, we encountered data issues in the previous quarters, and therefore, we could only use the data for the last quarter. As a result, these two countries are excluded from the econometric analysis discussed in Sect. 3of our study. For a complete understanding of the data construction process, we refer readers to Annex A of Pappa and Vella (2022).4 2.2 Cross-country comparison Next, we compare the fiscal measures adopted by different countries. Figure2shows the percentage distribution of various expenditure types within the total public expenditure related to COVID-19 for a subset of EU countries with available data. Notably, the variations in expenditure allocation across different countries and categories reflect 4The full database is available online in the following address: https://sites.google.com/site/ evipappapersonalhomepage/home/research. 123 SERIEs (2024) 15:327–348 333 the diverse approaches adopted by different countries in response to the COVID-19 pandemic. All countries but Bulgaria allocated more than half of their exceptional fiscal measures to “Assistance to SMEs and specific sectors”. Italy and Germany stand out, with over 80% of their measures directed towards this category. Regarding “Fiscal measures targeted to transform the economy”, Spain emerges as the clear leader with 17%, followed by France and Poland, both at around 11%. Other countries in our sample generally have values close to or below 4%, or even zero. In terms of “Spending caused by the pandemic”, Romania and Bulgaria take the lead with approximately 17% and 15%, respectively. Most other countries hover around or below 10%, except for Finland, which stands at 12%. All countries in the sample have engaged part of the extra spending to finance “Unemployment benefits and measures to sustain employment rates”. Bulgaria shows the highest figure (26%), followed by Portugal (21%) and Poland (19%). Contrary to general perceptions, the numbers in Fig. 2indicate that “Transfers to households” were not widely used during the pandemic in the countries included in our sample. Bulgaria and Finland had the highest shares, with approximately 18% and 6% of total expenses allocated to this category, respectively. The remaining eight countries that implemented transfers as a fiscal measure dedicated less than 2% of their total expenditures to this category. Regarding “Universal help”, Denmark stands out with a remarkably high value close to 40% in non-targeted fiscal measures. In contrast, other economies have values well below 15%, and countries such as Italy, Poland, Spain, Sweden, and the Netherlands did not implement any non-targeted measures. In Fig. 3, we examine the quantitative evolution of COVID-19 expenditure types as percentage of GDP over the period from 2020Q2 to 2021Q2 for countries with available information by presenting cumulative data. The analysis reveals shifts in fiscal measures across time, particularly towards “Assistance to SMEs”. Here are the key observations. Romania primarily increased its “Assistance to SMEs” from 3% of GDP initially to roughly 5% of GDP in the last quarter. Belgium and Italy increased over time “Assistance to SMEs”, which reached a cumulative of 14% of GDP in Belgium and 40% of GDP in Italy, as well as “Unemployment benefits”, which reached roughly 2% of GDP in both countries. Bulgaria adjusted upwards “Pandemic spending” in the last quarter of 2020, from less than 0.5% of GDP initially to 1.5% of GDP, and “Transfers to households”, from 0.3% of GDP initially to 1.4% of GDP in 2020Q4 and increased again these transfers in 2021Q2. The fiscal measures in the Czech Republic increased continuously during the whole period with most expenditures destined to the assistance of small and medium enterprises and with other types of assistance increasing markedly in 2021Q2. Denmark adjusted upwards in 2021Q1 its “Assistance to SMEs”, roughly tripling its share in GDP, and “Universal help”, roughly doubling its share in GDP. France followed a similar pattern for assistance to SMEs, which reached cumulatively 20% of GDP in the last quarter, and increased measures to “Transform the economy” in 2020Q4. Spain and the Netherlands adjusted persistently upwards “Assistance to SMEs”, “Unemployment benefits” and “Pandemic spending”. Specifically, in the Netherlands “Assistance to SMEs” climbed from 9% of GDP to more than 15% of GDP cumulatively in the second quarter of 2021, whereas in Spain it reached cumula123 334 SERIEs (2024) 15:327–348 Fig. 2 Cross-country comparison of COVID-19 expenditure types (% of total spending, cumulative data in July 2021). Authors’ own calculations based on the constructed database (see Sect. 2) tively 13% of GDP. Finally, Germany and Sweden did not adopt additional measures after the initial period. 3 Econometric methodology In this section, we outline the econometric methodology and complementary data sources that we use in the empirical analysis. Our goal is to test whether the fiscal choice in response to the pandemic makes a difference for the economic and sentiment recovery and whether it matters for inflationary pressures in the economy. 3.1 Model specification Given that our data include fiscal measures that were either announced or actually implemented, we adopt the following regressions specification. We estimate the multiplier effect of a change in spending as a percentage of GDP on the dependent variable of interest up to hperiods ahead, where h∈{1,2,3}, as follows: h  j=1 yi,t+j=αi,h+ h  j=1 p  l=1 γl,hyi,t+j−l+β1,h h  j=1 SPENDi,t+j−1 +β2,hXi,t+i,t,(1) where iand tdenote countries and periods, respectively; yi,t+jis the variable affected by the fiscal measures (GDP growth, confidence change, CPI change, employment rate, and the Economic Sentiment Indicator (ESI) change); SPENDi,trepresents the change either in total COVID-19 spending (% GDP) or in a specific COVID-19 spending component. We also incorporate a set of controls, represented by Xi,t,to account for endogenous movements in the dependent variable. These controls include 123 SERIEs (2024) 15:327–348 341 4.1.4 Employment Table 1also reveals a positive impact of total spending on employment rates, with the highest multiplier estimated to be around 1.152% at h=2.12 We further find that specific categories of fiscal measures also exhibit significant effects on employment. In particular, the multipliers for unemployment benefits and measures to sustain employment are significant at horizon h=2, highlighting the effectiveness of these measures in sustaining and even boosting employment rates. Additionally, significant effects are observed for pandemic spending at h=2. 4.1.5 Business sentiment Regarding the Economic Sentiment Indicator (ESI) change, our estimates in Table 1 indicate statistically significant and positive effects of total spending, as well as of specificcategoriessuchas pandemic spending,transferstohouseholds,unemployment benefits and measures to sustain employment levels, and universal help. Pandemic spending and unemployment benefits and measures to sustain employment generate the highest multipliers for business sentiment. For assistance to SMEs and measures to transform the economy, the estimated multipliers are not statistically significant.13 In summary, our analysis reveals that assistance to SMEs and pandemic-related spending played a crucial role in stimulating the economy, leading to positive effects on GDP growth and employment rates, and boosting confidence and sentiment. Importantly, these measures did not generate inflationary pressures. Similarly, unemployment benefits and measures aimed at sustaining employment levels showed strong positive effects on GDP growth and employment, without contributing to inflation. On the other hand, transfers to households had limited impact, primarily influencing confidence and sentiment but not significantly affecting other economic indicators. Surprisingly, non-targeted measures to both firms and households and only affected the employment recovery with several lags. 4.2 Diagnostic tests and robustness In terms of diagnostic tests, we performed the Arellano–Bond tests to assess the presence of firstand second-order autocorrelation in the first-differenced errors. The results, presented in Table 2, indicate that the null hypothesis of no first-order autocorrelation is rejected in most cases. This is a common finding when the idiosyncratic errors are independent and identically distributed. However, we find evidence of no second-order autocorrelation in the majority of cases, at a significance level of 5%, for the regressions involving GDP growth, consumer confidence change, CPI change, and employment. This suggests that the model is not misspecified for these dependent variables. However, for the ESI index regressions, the null hypothesis of no secondorder autocorrelation is rejected for the horizons h=2 and h=3, indicating that caution should be taken when interpreting these estimates. 12 In this regressions, we control for 2020-Q2 fixed effects and stringency of the lockdown. 13 In this set of regressions, we control for 2020-Q2 fixed effects and stringency of the lockdown. 123 342 SERIEs (2024) 15:327–348 Table 2 Arellano–Bond test for auto-correlation, multiplier spending regressions Spending category GDP growth Confidence change CPI change Employment rate ESI change Order h=1h=2h=3h=1h=2h=3h=1h=2h=3h=1h=2h=3h=1h=2h=3 Total spending m=1 0.030 0.002 0.013 0.038 0.037 0.045 0.009 0.018 0.022 0.629 0.003 0.191 0.001 0.002 0.001 m=2 0.088 0.253 0.941 0.072 0.648 0.405 0.048 0.116 0.125 0.589 0.024 0.293 0.202 0.004 0.019 Assistance SMEs m=1 0.039 0.001 0.010 0.021 0.014 0.035 0.014 0.016 0.024 0.097 0.046 0.006 0.045 0.003 0.014 m=2 0.160 0.030 0.836 0.128 0.122 0.391 0.047 0.070 0.120 0.375 0.389 0.042 0.188 0.005 0.207 Transform m=1 0.048 0.002 0.007 0.030 0.008 0.021 0.003 0.013 0.023 0.129 0.031 0.007 0.045 0.002 0.017 m=2 0.289 0.044 0.779 0.128 0.171 0.247 0.083 0.381 0.275 0.316 0.982 0.081 0.134 0.015 0.201 Pandemic spending m=1 0.035 0.001 0.023 0.027 0.018 0.011 0.004 0.013 0.015 0.127 0.367 0.005 0.034 0.003 0.048 m=2 0.293 0.039 0.940 0.119 0.444 0.327 0.061 0.198 0.115 0.226 0.542 0.076 0.071 0.019 0.222 Transfers m=1 0.037 0.001 0.006 0.022 0.021 0.065 0.006 0.018 0.022 0.072 0.030 0.009 0.031 0.003 0.026 m=2 0.367 0.056 0.586 0.124 0.246 0.307 0.034 0.204 0.308 0.218 0.786 0.089 0.174 0.021 0.364 Unemployment m=1 0.021 0.001 0.003 0.037 0.013 0.036 0.005 0.015 0.013 0.227 0.010 0.090 0.069 0.003 0.353 m=2 0.161 0.060 0.930 0.077 0.161 0.318 0.040 0.230 0.143 0.433 0.609 0.108 0.383 0.019 0.190 Universal help m=1 0.040 0.001 0.005 0.026 0.021 0.042 0.009 0.017 0.016 0.108 0.043 0.018 0.034 0.003 0.016 m=2 0.353 0.037 0.573 0.101 0.245 0.370 0.039 0.384 0.113 0.260 0.986 0.112 0.118 0.021 0.373 The table reports the pvalues of the Arellano–Bond test for first (m=1)-order and second (m=2)-order auto-correlation in the first-differenced errors of each panel regression in Table 1 123 SERIEs (2024) 15:327–348 343 To ensure the robustness of our findings, we estimated different variants of Eq. 1 incorporating various combinations of additional control variables. These controls aimed to capture specific economic conditions and the evolving nature of the pandemic across countries. Examples of these additional controls included variables such as the current account to GDP ratio, market openness, tourism flows, electricity prices, and industrial production, among others. Despite the inclusion of these additional controls, our main regression results reported in Table 1remained unchanged. Taking into account the reduced number of observations, the models we reported above were the more parsimonious ones that satisfy the diagnostic checks.14 As a complementary exercise, we also estimated the cumulative effect of a change in the spending category based on the following equation: h  j=1 yi,t+j=αi,h+ h  j=1 p  l=1 γl,hyi,t+j−l+β1,hSPENDi,t+β2,hXi,t+i,t,(2) for h∈{0,1,2,3}. Results are reported in the“Appendix” and provide similar insights to the ones discussed earlier. 5 Conclusion During the COVID-19 pandemic, fiscal measures implemented by EU countries successfully contributed to the recovery of output growth and employment without substantially contributing to inflationary pressures in the economy, except for universal help spending. Assistance to SMEs emerged as the primary measure adopted by most EU countries in our sample, with increased support during the crisis. According to our estimates and findings from other studies (see Gourinchas et al. 2021), this kind of measures, although effective in stimulating output and maintaining inflation, were not sufficiently targeted and generated output multipliers below one. If policymakers and academics are to take a lesson from the COVID-19 crisis for the different fiscal measures one can use in such circumstances, the results of our exercise suggest that the best fiscal crisis support measure is clearly unemployment benefits and measures to maintain employment levels. According to our estimates, such measures induce sizeable output multipliers and stimulate employment without creating inflationary pressures. Conversely, transfers to households did not assist the economic recovery and only generated stimulative demand effects by recouping confidence and economic sentiment. Finally, our analysis can be useful for studying in the future the effects of other large fiscal policy packages in the EU such as the Next Generation EU package (NextGen EU), a union-wide equivalent to the CARES and ARP Acts in the USA. This is potentially important to the extent that the Next Gen EU is focused on some of the spending categories analyzed in our paper (transforming the economy is a very important component of NextGen EU for Spain, Portugal or Greece, for example). The program is also unequally distributed across countries, with certain countries receiving a larger 14 Detailed results are available from the authors upon request. 123 344 SERIEs (2024) 15:327–348 share of it (relative to population or GDP) and basing their post-pandemic fiscal plans on that program. Funding The research has received funding under a contract with DG Internal Policies of the Union Directorate A (project: “Phase out of the crisis support measures: How successful are Member States in moving from broad support measures towards more targeted support?”). The opinions expressed do not represent the contracting authority’s official position. 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A: Appendix A.1 Alternative specification See Table 3. 123 SERIEs (2024) 15:327–348 345 Table 3 Effects of COVID-19 fiscal measures on output, sentiment and inflation controlling for the effects of total COVID-19 government spending: alternative specification Spending category GDP growth Confidence change CPI change Employment rate ESI change h=1h=2h=3h=1h=2h=3h=1h=2h=3h=1h=2h=3h=1h=2h=3 Total spending 0.372*** 0.441*** 0.527*** 0.142* 0.151 0.318*** −0.012 −0.022** −0.031*** 0.035 0.077*** 0.021 1.780*** 1.798*** 2.132*** (0.000) (0.000) (0.000) (0.058) (0.106) (0.001) (0.121) (0.047) (0.000) (0.721) (0.003) (0.457) (0.000) (0.000) (0.000) Assistance SMEs 0.230** 0.346*** 0.556*** 0.055 0.118 0.346*** −0.038*** −0.023 −0.038*** 0.004 0.087*** 0.026 −0.247** 1.096*** 1.257*** (0.021) (0.000) (0.000) (0.608) (0.194) (0.004) (0.000) (0.115) (0.000) (0.876) (0.006) (0.441) (0.015) (0.000) (0.000) Transform 0.851* −0.046 0.331 0.358 0.564 1.699 0.071 −0.085 0.573*** −0.287 0.105 −0.603 −0.951 1.368 5.477 (0.062) (0.979) (0.928) (0.642) (0.747) (0.191) (0.304) (0.520) (0.000) (0.242) (0.859) (0.407) (0.342) (0.750) (0.387) Pandemic spending 0.730 8.465*** 15.063*** 2.109 8.317*** 14.418*** 0.239 −0.719*** −1.046** −1.673** 2.870*** 0.872 9.051** 30.833*** 45.868*** (0.505) (0.000) (0.000) (0.179) (0.002) (0.000) (0.417) (0.002) (0.023) (0.025) (0.000) (0.511) (0.015) (0.000) (0.000) Transfers −2.183 3.083 12.824 1.292 5.200*** 19.571** 0.096 −0.018 0.227 −1.875 1.935** 1.311 2.979 21.401*** 40.625** (0.279) (0.142) (0.119) (0.666) (0.004) (0.015) (0.833) (0.931) (0.828) (0.338) (0.019) (0.590) (0.372) (0.009) (0.024) Unemployment1.658* 3.041*** 6.136*** 0.303 2.968** 5.237*** −0.015 −0.120** −0.458*** 0.739 0.892*** 0.895* 6.260*** 13.540*** 15.731*** (0.096) (0.003) (0.000) (0.782) (0.038) (0.000) (0.878) (0.012) (0.007) (0.489) (0.006) (0.062) (0.006) (0.000) (0.000) Universal help −0.237 0.699*** 2.351** 0.007 0.653** 1.380** 0.127*** −0.025 −0.010 −0.157 0.067 −0.216 0.167 3.832*** 7.870*** (0.237) (0.003) (0.028) (0.983) (0.034) (0.012) (0.000) (0.537) (0.827) (0.593) (0.442) (0.262) (0.819) (0.000) (0.000) 123 346 SERIEs (2024) 15:327–348 Table 3 continued Spending category GDP growth Confidence change CPI change Employment rate ESI change h=1h=2h=3h=1h=2h=3h=1h=2h=3h=1h=2h=3h=1h=2h=3 Observations 60 60 60 56 56 56 60 60 60 60 60 60 60 60 60 Number of countries 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 Lags 222222 222222222 2020-Q2 fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No No Stringency No No No No No No No No No Yes Yes Yes Yes Yes Yes Fatalities No No No Yes Yes Yes No No No No No No No No No Interest rates No No No No No No Yes Yes Yes No No No No No No Oil price No No No No No No Yes Yes Yes No No No No No No The table contains the estimated cumulative impacts associated to each spending category for GDP growth, confidence change, and CPI change based on equation (2) of the paper. Pvalues of the significance tests are in parentheses. *, **,*** denote significance at the 10, 5, and 1% levels, respectively. Observations begin in 2020Q2 and end in 2021Q2. There are 56 observations in the regressions for confidence due to lack of data for Romania after 2020Q3. All the regressions include total spending as percentage of the GDP as a control 123 SERIEs (2024) 15:327–348 347 B: Arellano–Bond test for auto-correlation See Table 4. Table 4 Arellano–Bond test for auto-correlation in regressions of Table 3 Spending category GDP growth Confidence change CPI change Employment rate ESI change Order h=1h=2h=3h=1h=2h=3h=1h=2h=3h=1h=2h=3h=1h=2h=3 Total spending m=1 0.030 0.019 0.026 0.038 0.085 0.064 0.009 0.021 0.026 0.629 0.003 0.031 0.002 0.001 0.010 m=2 0.088 0.154 0.556 0.072 0.082 0.238 0.048 0.160 0.183 0.589 0.030 0.227 0.202 0.228 0.034 Assistance SMEs m=1 0.039 0.010 0.019 0.021 0.039 0.057 0.014 0.020 0.021 0.097 0.058 0.005 0.045 0.015 0.003 m=2 0.160 0.575 0.462 0.128 0.321 0.293 0.047 0.172 0.270 0.375 0.804 0.116 0.188 0.052 0.006 Transform m=1 0.048 0.001 0.006 0.030 0.023 0.051 0.003 0.012 0.010 0.129 0.029 0.009 0.045 0.002 0.010 m=2 0.289 0.042 0.469 0.128 0.133 0.415 0.083 0.190 0.055 0.316 0.980 0.086 0.134 0.015 0.126 Pandemic spending m=1 0.035 0.008 0.014 0.027 0.059 0.045 0.004 0.047 0.018 0.127 0.037 0.059 0.034 0.012 0.020 m=2 0.293 0.293 0.103 0.119 0.113 0.185 0.061 0.142 0.135 0.226 0.982 0.111 0.071 0.756 0.129 Transfers m=1 0.037 0.001 0.007 0.022 0.029 0.072 0.006 0.010 0.017 0.072 0.034 0.008 0.031 0.005 0.014 m=2 0.367 0.059 0.688 0.124 0.455 0.296 0.034 0.204 0.126 0.218 0.961 0.125 0.174 0.027 0.146 Unemploymentm=1 0.021 0.004 0.004 0.037 0.037 0.057 0.005 0.021 0.010 0.227 0.025 0.012 0.069 0.022 0.015 m=2 0.161 0.813 0.264 0.077 0.225 0.091 0.040 0.144 0.154 0.433 0.594 0.058 0.383 0.217 0.539 Universal help m=1 0.040 0.002 0.008 0.026 0.029 0.063 0.009 0.014 0.018 0.108 0.040 0.006 0.034 0.003 0.003 m=2 0.353 0.095 0.984 0.101 0.369 0.253 0.039 0.171 0.104 0.261 0.793 0.092 0.118 0.019 0.126 The table reports the pvalues of the Arellano–Bond test for first (m=1)-order and second (m=2)-order auto-correlation in the first-differenced errors of each panel regression in Table 3 123 348 SERIEs (2024) 15:327–348 References Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58(2):277–297 Auerbach A, Gorodnichenko Y, McCrory PB, Murphy D (2022) Fiscal multipliers in the COVID19 recession. J Int Money Financ 126:102669 Auerbach AJ, Gorodnichenko Y, Murphy D (2021) Inequality, fiscal policy and COVID19 restrictions in a demand-determined economy. Eur Econ Rev 137:103810 Bayer C, Born B, Luetticke R, Müller GJ, Series MW (2020) The coronavirus stimulus package: how large is the transfer multiplier? Econ Policy 500:600 Chudik A, Mohaddes K, Raissi M (2021) COVID-19 fiscal support and its effectiveness. Econ Lett 109939 de Soyres F, Santacreu AM, Young H (2022) Fiscal policy and excess inflation during Covid-19: a crosscountry view. Technical report, FEDS Notes. Board of Governors of the Federal Reserve System, Washington Deb P, Furceri D, Ostry JD, Tawk N, Yang N (2021) The effects of fiscal measures during COVID-19. In: IMF Working papers 2021 (262) Faria-e Castro M (2021) Fiscal policy during a pandemic. J Econ Dyn Control 125:104088 Georgarakos D, Kenny G (2022) Household spending and fiscal support during the COVID-19 pandemic: insights from a new consumer survey. J Monet Econ 129:S1–S14 Gourinchas P-O, Kalemli-Özcan e, Penciakova V, Sander N (2021) Fiscal policy in the age of COVID: does it ‘Get in all of the Cracks?’. Technical report, National Bureau of Economic Research Guerrieri V, Lorenzoni G, Straub L, Werning I (2022) Macroeconomic implications of Covid-19: Can negative supply shocks cause demand shortages? Am Econ Rev 112(5):1437–74 Hale G, Leer JC, Nechio F (2023) Inflationary effects of fiscal support to households and firms. Technical report, National Bureau of Economic Research Jordà Ò, Nechio F (2023) Inflation and wage growth since the pandemic. Eur Econ Rev 156:104474 König M, Winkler A (2021) The impact of government responses to the COVID-19 pandemic on GDP growth: Does strategy matter? PLOS ONE 16(11):e0259362 Pappa E, Vella E (2022) Phase out of the crisis support measures: How successful are member states in moving from broad support measures towards more targeted support?. Briefing for the EU Parliament’s in-house think tank. https://www.europarl.europa.eu/thinktank/en/document/IPOL_ STU(2022)689448 Ritchie H, Mathieu E, Rodés-Guirao L, Appel C, Giattino C, Ortiz-Ospina E, Hasell J, Macdonald B, Beltekian D, Roser M (2020) Coronavirus pandemic (COVID-19). Our world in data. https:// ourworldindata.org/coronavirus Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 123