## 1 Introduction

## 2 Literature review and motivation

## 3 Methodology and data

### 3.1 Methodology

^{1}which is based on a specification of panel-data decomposition of the variable of interest that allows for heterogeneous transitional dynamics. In particular, panel data, X

_{it}, can be represented by:

_{it}is a systematic component, α

_{it}is a transitory component, and both g

_{it}and α

_{it}may consist of common and idiosyncratic elements. To isolate idiosyncratic from common elements, (1) can be modified as follows:

_{it}now consists of two components, a shared component μ

_{t}, and an idiosyncratic component b

_{it}, which includes a transitory element that absorbs α

_{it}. In country-panel data, the interpretation is that μ

_{t}could be common among economies, while idiosyncratic dynamics are described by the b

_{it’}s. Convergence of all countries to a common long run path implies \(\underset{k\to \infty }{\mathrm{lim}}{b}_{it+k}=b\) for i = 1, 2,…N. Transitional dynamics may differ, so convergence is investigated via the evolution of the b

_{it}‘s.

_{it}, can, more conveniently, be used, describing transition paths with respect to panel average:

_{it}’s measure countries’ relative departure from μ

_{t}and thus reflect possible divergence. To formulate a null hypothesis of convergence, the following semiparametric model for the transition coefficients is proposed by Phillips and Sul (2007, 2009):

^{it}is iid (0,1) over i and weakly dependent over t, and α represents the convergence rate. Based on (4), the hypothesis of convergence can be presented as \({H}_{0}: {b}_{i}=b\) for all i with \(\alpha \ge 0,\) versus \({H}_{\alpha }: {b}_{i}\ne b\) for all i with \(\alpha <0.\) To test for convergence, Phillips and Sul suggest the estimation of (5):

(i) Sort the N countries in descending order according to the last-period value of the time series. (ii) Combine all possible core groups/clubs C_{k}, by taking the first k highest countries for 2≤k≤N and use the logt_{k}test within each subgroup of size k to test for convergence. Then, set the core club C* of size k* as the club for which the maximum computed logt_{k}occurs, provided that it complies with the convergence hypothesis. (iii) After the core club C* is detected, run the logt regression adding one country at a time to the core club C*. If the logt test strongly satisfies the convergence hypothesis, then add the country to group C*. Countries included initially in the core group C* and those added constitute the first convergence club. (iv) Repeat steps (i)–(iii) in order to determine whether there are other subgroups that constitute convergence clubs. If there are no further convergence clubs, the remaining countries diverge.

### 3.2 Data

^{2}This set of data measures, on a daily basis, the percentage change in people’s visits to specific places during the pandemic compared to a pre-Covid-19 baseline, which corresponds to the median value of the 5‑week period from January 3 to February 16, 2020. We focus on mobility to essential places (grocery markets, farmer’s markets, specialty food shops, and pharmacies) given that, compared to other places, is least likely to have been directly connected to lockdowns (in most countries lockdowns did not apply to essential places), while especially in periods of extreme crisis, it can be taken to reflect people’s beliefs and confidence about the future. We use the trend component of this series by applying a Hodrick-Prescott filter to the raw data since daily observations are likely to contain a large amount of short-run variability that may cause problems of interpretation regarding people’s sentiments/expectations.

^{3}The sample consists of 26 EU economies,

^{4}plus the UK, for two periods (of equal length), i.e., 17/2/2020-30/11/2020 and 17/12/2020-30//9/2021, which correspond to the pandemic’s pre- and post-vaccine phases. Focusing on these two periods allows us to investigate how the initiation of the vaccination process has affected the outcomes.

^{5}

^{6}which records the overall strictness of government-imposed ‘lockdown-type’ measures during the pandemic, and takes values between 0 and 100. Although in most countries, ‘lockdowns’ were not applied directly to essential places, they are expected to have affected people’s sentiments/ expectations indirectly, through anticipations of lower incomes due to the slowdown of economic activity and/or infection-induced mortality risks by reflecting the pandemic’s contagiousness. To isolate effects resulting from mortality-related risks, the interaction of the restrictions variable with the announced number of new Covid-19 fatalities is included in the set of regressors, with the respective fatality data obtained from ECDC (European Center for Disease Prevention & Control).

^{7}Fiscal measures to mitigate the pandemic’s adverse economic consequences are proxied by the Hale et al. (2021) OxCGRT economic-support index,

^{8}which records how the intensity of fiscal support by governments has varied across countries and includes income support as well as debt relief. This index also takes values in the range of 0–100, and a higher value indicates greater support. To proxy monetary easing by the central banks, we use data on a set of short-term interest rates (1-, 6- and 12-month money market rates), obtained from Eurostat (Economy & Finance Database)

^{9}and measured as deviations from pre-Covid-19 levels (values in 2019).

^{10}To allow for cumulative effects on sentiments/expectations of the pandemic’s various aspects, right-hand-side variables in the MNL regressions are measured as monthly averages. Moreover, as the Phillips-Sul convergence testing procedure discards the first 5% of observations, the sample period in MNL regressions is March-November 2020 and January–September 2021.

^{11}as well as diversity in the extent of restrictions and fiscal support, with fiscal support showing greater diversity than restrictions (Panel B). The table also indicates variations in interest rates, although overall the data show a decrease in the cost of borrowing relative to pre-covid levels.

^{12}

Variable | Obs | Mean | SD | Min | Max |
---|---|---|---|---|---|

Panel A: Variables in convergence testing | |||||

Changes in human mobility to essential places (relative to a pre-Covid-19 baseline, %) | |||||

1st period | 7776 | − 5.92 | 7.78 | − 30.12 | 18.84 |

2nd period | 7776 | 4.535 | 11.585 | − 32.045 | 41.274 |

Panel B: Explanatory variables in MNL regressions | |||||

Restrictions | 486 | 55.887 | 15.967 | 18.101 | 95.436 |

Announced new Covid-19 fatalities (per 10,000 population) | 486 | 0.391 | 0.613 | 0 | 4.302 |

Fiscal support | 486 | 68.015 | 23.326 | 0 | 100.00 |

Interest rates, 1-month (in deviations from 2019 levels) | 468 | − 0.246 | 0. 484 | − 1.86 | 1.35 |

Interest rates, 6-month (in deviations from 2019 levels) | 450 | − 0.258 | 0.505 | − 1.86 | 1.61 |

Interest rates, 12-month (in deviations from 2019 levels) | 432 | − 0.298 | 0.481 | − 1.80 | 2.04 |

## 4 Results

Countries | β coefficient | \({t}_{\widehat{\beta }}\) | |
---|---|---|---|

Full sample | 26 EU countries plus the UK | − 2.696 | − 4.910 |

First club | Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, Germany, Greece, Hungary, Ireland, Latvia, Lithuania, The Netherlands, Sweden | − 1.447 | − 1.470 |

Second club | France, Spain | − 0.479 | − 1.565 |

Third club | Belgium, Italy, Malta, Slovakia | 0.473 | 4.385 |

Fourth club | Austria, Luxembourg, UK | 0.192 | 1.088 |

Fifth group–non-converging | Poland, Portugal, Romania, Slovenia | − 2.626 | − 19.153 |

Club | β coefficient | \({t}_{\widehat{\beta }}\) |
---|---|---|

Club 1 + 2 | − 1.663 | − 10.382 |

Club 2 + 3 | − 1.049 | − 1.693 |

Club 3 + 4 | − 0.606 | − 3.080 |

Club 4 + non-conv | − 1.995 | − 10.979 |

Countries | β coefficient | \({t}_{\widehat{\beta }}\) | |
---|---|---|---|

Full sample | 26 EU countries plus the UK | − 0.741 | − 201.608 |

First club | Greece, Lithuania, Portugal | 0.107 | 1.105 |

Second club | Croatia, Poland | 0.011 | 0.130 |

Third club | Belgium, Bulgaria, Czech Republic, Estonia, France, Germany, Hungary, Ireland, Latvia, Malta, Romania, Slovenia, Slovakia | 0.083 | 1.186 |

Fourth club | Austria, Finland, Italy, Luxembourg, The Netherlands, Spain, Sweden | 0.196 | 18.516 |

Fifth club | Denmark, UK | 1.593 | 9.616 |

Club | β coefficient | \({t}_{\widehat{\beta }}\) |
---|---|---|

Club 1 + 2 | 0.005 | 0.059 |

Club 2 + 3 | − 0.329 | − 18.204 |

Club 3 + 4 | − 0.168 | − 33.342 |

Club 4 + non-conv | − 0.011 | − 2.281 |

Countries | β coefficient | \({t}_{\widehat{\beta }}\) | |
---|---|---|---|

First club | Croatia, Greece, Lithuania, Poland, Portugal | 0.005 | 0.059 |

Second club | Belgium, Bulgaria, Czech Republic, Estonia, France, Germany, Hungary, Latvia, Malta, Romania, Slovenia, Luxembourg, The Netherlands | 0.083 | 1.186 |

Third club | Austria, Finland, Italy, Ireland, Slovakia, Spain, Sweden | 0.196 | 18.516 |

Fourth club | Denmark, UK | 1.593 | 9.616 |

Dependent variable | Regressors | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) |
---|---|---|---|---|---|---|---|---|---|

CLUB 2 | (res) | 1.042*** (2.93) | 1.042*** (2.74) | 1.041*** (2.67) | 1.038*** (2.51) | 1.027*** (2.08) | 1.025*** (1.77) | 1.024*** (1.66) | 1.016 (1.12) |

(res*mrisk) | 1.008*** (2.05) | 1.008*** (2.12) | 1.008*** (2.18) | 1.011*** (2.51) | |||||

(fiscal) | 1.017 (1.47) | 1.020 (1.59) | 1.020 (1.56) | 1.019 (1.54) | 1.015 (1.27) | 1.018 (1.41) | 1.019 (1.40) | 1.017 (1.31) | |

(interest rates) | 1.416 (0.73) | 1.727 (1.09) | 2.576*** (1.75) | 1.645 (0.98) | 1.941 (1.30) | 3.460*** (2.20) | |||

CLUB 3 | (res) | 1.027*** (2.29) | 1.026*** (2.04) | 1.025*** (1.96) | 1.022*** (1.75) | 1.016 (1.24) | 1.014 (0.94) | 1.013 (0.86) | 1.005 (0.31) |

(res*mrisk) | 1.007 (1.56) | 1.007 (1.58) | 1.007*** (1.67) | 1.010*** (2.09) | |||||

(fiscal) | 1.009 (1.00) | 1.011 (1.20) | 1.011 (1.21) | 1.010 (1.14) | 1.008 (0.82) | 1.010 (1.06) | 1.010 (1.07) | 1.008 (0.91) | |

(interest rates) | 1.316 (0.63) | 1.713 (1.30) | 2.594*** (2.11) | 1.453 (0.80) | 1.855 (1.32) | 3.366*** (2.52) | |||

CLUB 4 | (res) | 0.992 (-0.56) | 0.984 (-0.98) | 0.983 (-0.99) | 0.982 (-1.08) | 0.985 (-0.84) | 0.997 (-1.14) | 0.976 (-1.17) | 0.973 (-1.37) |

(res*mrisk) | 1.004 (1.06) | 1.004 (0.96) | 1.005 | 1.006 (1.30) | |||||

(fiscal) | 1.102*** (2.95) | 1.108*** (2.87) | 1.109*** (2.85) | 1.107*** (2.75) | 1.102*** (2.95) | 1.108*** (2.88) | (1.02) 1.110*** (2.86) | 1.106*** (2.77) | |

(interest rates) | 0.485 (-1.16) | 0.716 (-0.64) | 0.857 (-0.31) | 0.497 (-1.15) | 0.721 (-0.65) | 0.899 (-0.22) | |||

CLUB 5 | (res) | 1.027*** (2.16) | 1.032*** (2.43) | 1.035*** (2.53) | 1.030*** (2.23) | 1.025*** (1.75) | 1.036*** (2.48) | 1.039*** (2.61) | 1.034*** (2.22) |

(res*mrisk) | 1.002 (0.36) | 0.998 (-0.37) | 0.997 (-0.44) | 0.998 (-0.27) | |||||

(fiscal) | 0.992 (-0.96) | 0.991 (-1.08) | 0.990 (-1.19) | 0.991 (-1.11) | 0.992 (-0.99) | 0.992 (-0.98) | 0.991 (-1.10) | 0.991 (-1.08) | |

(interest rates) | 0.163*** (-3.10) | 0.178*** (-3.43) | 0.198*** (-3.41) | 0.155*** (-3.31) | 0.170*** (-3.69) | 0.193*** (-0.27) | |||

Observations | 243 | 234 | 225 | 216 | 243 | 234 | 225 | 216 | |

Wald X*** | 28.74 | 72.26 | 57.63 | 58.01 | 33.06 | 82.27 | 71.56 | 72.55 | |

Pseudo-R*** | 0.092 | 0.136 | 0.142 | 0.140 | 0.100 | 0.145 | 0.152 | 0.153 |

Dependent variable | Regressors | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) |
---|---|---|---|---|---|---|---|---|---|

CLUB 1 | (res) | 1.008 (0.63) | 1.023 (1.52) | 1.022 (1.47) | 1.022 (1.43) | 1.009 (0.59) | 1.032 (1.53) | 1.030 (1.50) | 1.029 (1.39) |

(res*mrisk) | 0.999 (-0.19) | 0.994 (− 0.70) | 0.994 (− 0.68) | 0.996 (− 0.53) | |||||

(fiscal) | 1.028*** (3.47) | 1.057 ^{***}(6.06) | 1.057*** (6.03) | 1.067*** (5.74) | 1.028*** (3.47) | 1.057*** (6.06) | 1.056*** (6.03) | 1.066*** (5.71) | |

(interest rates) | 0.889 (− 0.31) | 0.867 (− 0.37) | 0.985 (− 0.04) | 0.864 (− 0.37) | 0.845 (− 0.42) | 0.959 (− 0.10) | |||

CLUB 3 | (res) | 1.014 (1.32) | 1.017 (1.41) | 1.021*** (1.80) | 1.033*** (2.19) | 1.032*** (2.34) | 1.038*** (2.46) | 1.040*** (2.61) | 1.046*** (2.56) |

(res*mrisk) | 0.985*** (− 1.96) | 0.985*** (− 2.13) | 0.987*** (− 1.92) | 0.990 (− 1.25) | |||||

(fiscal) | 1.059*** (5.43) | 1.088*** (5.91) | 1.082*** (5.78) | 1.137*** (8.37) | 1.058*** (5.46) | 1.089*** (6.20) | 1.082*** (5.95) | 1.136*** (8.51) | |

(interest rates) | 23.092*** (5.15) | 11.521*** (5.19) | 28.241*** (5.67) | 32.331*** (5.36) | 13.408*** (5.24) | 29.341*** (5.71) | |||

CLUB 4 | (res) | 1.008 (0.48) | 1.007 (0.40) | 1.008 (0.44) | 1.009 (0.47) | 1.032 (1.44) | 1.030 (1.21) | 1.031 (1.25) | 1.029 (1.16) |

(res*mrisk) | 0.978 (− 1.05) | 0.982 (− 1.00) | 0.981 (− 0.99) | 0.983 (− 0.93) | |||||

(fiscal) | 1.028*** (1.66) | 1.038*** (2.12) | 1.036*** (2.01) | 1.043*** (2.03) | 1.028*** (1.66) | 1.038*** (2.10) | 1.035*** (1.99) | 1.042*** (1.98) | |

(interest rates) | 2.037*** (1.86) | 1.573 (1.25) | 2.144*** (1.99) | 1.931 (1.54) | 1.440 (0.92) | 2.007*** (1.67) | |||

Observations | 243 | 234 | 225 | 216 | 243 | 234 | 225 | 216 | |

Wald X*** | 37.78 | 81.74 | 86.95 | 96.37 | 46.69 | 91.76 | 92.24 | 114.35 | |

Pseudo-R*** | 0.094 | 0.183 | 0.173 | 0.242 | 0.106 | 0.193 | 0.182 | 0.247 |