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This paper assesses the nature and correlation of shocks in Visegrad countries and investigates the role of labour mobility in the process of adjustment to the effects of asymmetric shocks. Structural vector autoregression (SVAR) models are employed to assess the nature and correlation of shocks while dynamic cointegrated panel autoregressive distributed lag (ARDL) models are used to determine the role of labour mobility in the adjustment process. The data- set for the SVAR models is quarterly time series and covers the period 2000–2020. The dataset for the cointegrated panel ARDL models is annual and covers the period 2000–2019. The results show more asymmetries in external supply, domestic supply, demand and monetary shocks before the financial crisis. The findings also show that more symmetries occurred in Visegrad countries after the financial crisis in relation to external and domestic supply shocks. Asymmetries persisted with regard to demand and monetary shocks after the financial crisis. With labour mobility as an adjustment mechanism to asymmetric shocks, the paper finds that the capacity of labour mobility is very low. The percentage of net migration in the total population is less than 1% in the four countries compared to 15% in the United States. The size of the adjustment coefficients shows that it takes 3–5 years for countries to adjust to asymmet - ric shocks through labour mobility. Keywords: Demand shocks, Supply shocks, Symmetry, Asymmetry, Labour mobility JEL codes: F15, F41, J01, J08, J61 1 Introduction the scope of normal economic transactions. They cause This paper uses the approach of Blanchard and Quah recessions and business cycles in countries. Shocks can (1989) and Bayoumi and Eichengreen (1993, 2017) to be symmetric or asymmetric. They are asymmetric when assess the nature, size and correlation of shocks in Viseg- they affect countries in a region differently. Symmetric rad countries for the period 2000–2020. The paper also shocks occur when the effect of an unexpected event is investigates the role of labour mobility as an adjustment uniform. This paper focuses on four types of shocks: mechanism to asymmetric shocks. Economic shocks are external supply shocks, domestic supply shocks, domes- defined simply as unexpected events that have either a tic demand shocks, and monetary shocks. The study is positive or a negative impact on macroeconomic varia- based on the optimum currency area theory developed bles in the economy. These shocks are frequently beyond by Mundell (1961) and elaborated by McKinnon (1963) and Kenen (1969), in which mobility of labour and other factors of production are considered as the main condi- tions for smooth adjustment to asymmetric shocks. This *Correspondence: dennis.nchor@mendelu.cz Mendelova univerzita v Brne, Brno, Czech Republic theory was further elaborated by De Grauwe (2005), © The Author(s) 2020. 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. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. 16 Page 2 of 19 D. Nchor Krugman (1993) and Frankel and Rose (1996) with a labour mobility across European countries was lower focus on labour and factor mobility, openness and intra- than that within countries. Similarly, De Grauwe and regional trade, and symmetric macroeconomic shocks Vanhaverbeke (1991) investigated regional and national across countries. The theory postulates that balancing labour mobility across several Western European coun- the costs and benefits of regional economic integra - tries and found that the yearly flow of migrants among tion depends largely on the synchronisation of shocks the investigated countries was less when compared to that occur in the region. In other words, if shocks are interregional migration. The information above shows distributed symmetrically across countries of a region, that labour mobility in Visegrad countries is low, imply- the shocks are correlated, and therefore a single policy ing the low capacity of labour mobility as an adjustment response is sufficient to counteract the negative effects mechanism. However, as concluded by Puhani (1999), if across the countries. the lack of economic incentives to migrate is the cause Visegrad is a sub-regional bloc in the European Union of the low labour mobility, the conclusion about labour (EU) consisting of four countries: the Czech Republic mobility having low capacity to counteract the effects of (Czechia), Hungary, Poland, and Slovakia. The countries asymmetric shocks is wrong. are heterogeneous with different economic structures. A large body of literature on symmetric and asym- For instance, the structure of production and the level metric shocks in Europe focuses on Western European of income per capita vary across these countries even countries. The case of Visegrad as a regional economic though there is a degree of convergence. The presence of bloc is rarely studied. This study therefore seeks to fill a certain degree of heterogeneity makes the region vul- this gap. The aim of the paper is twofold. First, the paper nerable to shocks. The four countries joined the EU in applies SVAR models to determine the nature and cor- 2004, and of the four, only Slovakia joined the Economic relation of shocks across the Czech Republic, Hungary, and Monetary Union. Therefore, the Czech Republic, Poland and Slovakia. Second, the paper uses cointegrated Hungary and Poland still maintain national currencies panel ARDL models to assess the role of labour mobil- while Slovakia has replaced the national monetary policy ity in counteracting the impact of asymmetric shocks with the common and independent policy managed by across the region. The paper makes unique contributions the European Central Bank. The implication of this is that to theory and development in that rather than using the the exchange rate, which is used frequently as an adjust- conventional two-variable modelling approach consist- ment mechanism in the face of asymmetric shocks in ing of only supply and demand shocks, the paper uses a countries, cannot be used in Slovakia but can be used in four-variable SVAR model to capture the correlation of the other three countries. In the event of shocks, several external shocks including the global financial crisis and options are available for countries to adjust their econo- the correlation of domestic monetary shocks among the mies to recovery. This study focuses on labour mobility countries, which has been given minimal attention in across the region since the countries are highly integrated previous research. The other contribution of the paper in terms of trade openness and interregional trade. is the use of cointegrated panel ARDL models to assess This paper focuses on the role of labour mobility as an the impact of migration and technological progress on adjustment mechanism in the Visegrad region, which employment during periods of asymmetric shocks. has been emphasised as one of the methods that coun- The structure of the paper is as follows. The first chap - teracts the effects of asymmetric shocks. As it is difficult ter introduces the concept of macroeconomic shocks, to measure labour mobility, the paper uses migration labour mobility and its related terms. It also provides a as a proxy, which is also used in the studies conducted brief overview of the motivation for and contribution of by Arpaia et al. (2016), Dao et al. (2014) and Beyer and the paper. The second chapter reviews the literature. The Smets (2015). Globalisation has accelerated the move- third chapter outlines the methodology used in the study. ment of labour and capital as well as goods and ser- The fourth chapter presents and discusses the results. vices across countries. Moreover, it has become easier The fifth chapter presents the conclusions. to travel and work in other countries. Consequently, international labour mobility has increased significantly 2 Literature review and has become a topic of growing policy importance. Visegrad was created in 1991 to promote cooperation Piracha and Vickerman (2002) found that labour mobil- initially among three states: Czechoslovakia, Hungary, ity in Europe as a percentage of the total population and Poland. In 1993, Slovakia separated from the Czech was lower than that in the United States (US). Martin Republic, thus making it a group of four countries, the and Taylor (1996) found that there was lower migration Czech Republic, Hungary, Poland and Slovakia. The ini - among European countries than expected. Obstfeld and tial goal of the group was to promote these states as can- Peri (1998) obtained similar results and concluded that didates for membership of the EU and the North Atlantic Labour mobility as an adjustment mechanism to asymmetric shocks in Europe: evidence from the… Page 3 of 19 16 Treaty Organization (NATO). They were accepted into to macroeconomic shocks has increased significantly in NATO in 1999 and into the EU in 2004. Beyond these Europe. goals, the group has continued to foster regional and The motivation for this study is that although there is a economic cooperation. Slovakia was the first Visegrad large body of literature on the nature of shocks in Europe, country to join the Eurozone in 2009. The Eurozone is there are no studies that focus on the Visegrad region. a monetary union of countries in the EU that adopt the For instance, using SVAR, Frenkel and Nickel (2002) euro as their currency. By joining the Eurozone, Slovakia studied the relationship between shocks in the euro area became more tied to the EU core than the other three and in Central and Eastern European countries (CEECs). Visegrad countries. The four countries follow their own Their results showed that there were some differences in individual interests with regard to the Eurozone and the the shocks and adjustment processes between the euro common currency. The Czech Republic, Hungary and area and the CEECs. Fidrmuc and Korhonen (2003) con- Poland have not joined the Eurozone. tinued the analysis of Bayoumi and Eichengreen (1993) All the Visegrad countries have a high export depend- but included CEECs. Their results showed that Hungary ency ratio, which, according to data from Eurostat (2020), and Estonia had the highest share of correlated supply has increased over the years. Slovakia has the highest shocks with the euro area. The authors attributed this share of exports in gross domestic product (GDP) with observation to the impact of foreign direct investment 90%, followed by Hungary with 80%, the Czech Repub- inflow and high trade relations. They also found a low lic with 75% and Poland with 50%. Real GDP per capita degree of correlation of shocks for other CEECs. Fidrmuc in all the countries is rising. The highest real GDP per and Korhonen’s (2003) study of business cycle correla- capita is in the Czech Republic. Inflation is declining in tions between the euro area and the transition countries all the countries and converging. Poland has the lowest showed that Poland, Slovenia and Hungary had a very inflation rate. Similarly, unemployment is declining and high degree of correlation of shocks with the euro area. converging in all the countries. The lowest level of unem - Horvath (2000) analysed the correlation of demand ployment is in the Czech Republic. The Czech Repub - and supply shocks between the Visegrad countries and lic, Hungary and Slovakia have positive net migration the Baltic countries. His results showed that Hungary while Poland has negative net migration, implying that had the highest correlation of supply shocks and the low- there are more immigrants than emigrants in the Czech est correlation of aggregate demand shocks. Weimann Republic, Hungary and Slovakia and the opposite in (2002) reported that the Czech Republic, Bulgaria and Poland. The employment rate of foreign workers is high Hungary had the strongest correlation of demand shocks in all the Visegrad countries, with the Czech Republic with the euro area. Given the structural differences in the having the highest rate of employment of both EU and economies of European countries, Frenkel and Nickel non-EU workers. The percentage of immigrants is less (2002) concluded that there was a significant difference than 1%. The highest percentage is in the Czech Republic, in shocks and the speed of adjustment between CEECs followed by Hungary and Poland. Slovakia has the lowest and the euro area. immigrants to population ratio. Its emigration to popula- Arfa (2009) found that several new member countries tion ratio increased after the financial crisis. Poland and of the EU had high correlation of demand shocks with Slovakia overtook the Czech Republic as the countries the euro area while the observed supply shocks were with the highest emigration to population ratio after the asymmetric. Socol and Soviani (2010) and Socol and crisis. The percentage of net migration in the Visegrad Măntescu (2011) associated weak correlation of demand region is too low, however, at less than 1%. shocks with differences in national fiscal policies among Movement of workers from one EU member coun- the countries. According to Janus and Beck (2014), the try to another has become an important and necessary Visegrad region is characterised by low correlation of alternative adjustment mechanism for the EU coun- shocks or disturbances. In their study, correlation of sup- tries (see European Commission 2011). Labour mobility ply shocks in the Visegrad countries was lower than in and migration are complex phenomena; however, they any other analysed sample, with Poland and the Czech are used interchangeably following the approach of the Republic characterised by high correlation of demand European Commission (European Commission 2011). shocks. Their results also showed a sharp increase in A study conducted by Arpaia et al. (2016) showed that the correlation of supply shocks at the start of the global labour mobility increases significantly when a country financial crisis. becomes a member of the EU. It also showed that mem- Baxter and Koutraparitsas (2004) concluded that bership of the euro area is not necessarily associated with trade plays an influential role in the synchronisation a rise in the level of mobility flows. The European Com - of shocks. Beck (2014) found that structural similari- mission (2011) reported that labour mobility in response ties and differences in GDP per capita play a significant 16 Page 4 of 19 D. Nchor role in the synchronisation of economic shocks. Sachs 3 Methodology and Schleer (2013) showed that structural reforms and This study focuses on two research questions. The first labour market institutions play a crucial role in the aims to investigate the nature and correlation of demand coherence of shocks. Lehwald (2012) concluded that and supply shocks in the Visegrad countries (the Czech the synchronisation of shocks in the Eurozone is attrib- Republic, Hungary, Poland and Slovakia) while the sec- uted to global factors rather than regional ones. In rela- ond aims to assess the role of labour mobility in the pro- tion to this, Lee and Azali (2010) found that the main cess of adjustment to asymmetric shocks in the Visegrad driver of synchronised shocks was international trade. region. The first research question is answered using In support of this conclusion, Silvestre et al. (2009) SVAR models following the approach of Blanchard and emphasised the role of diminishing trade in the correla- Quah (1989) and Bayoumi and Eichengreen (1993). This tion of shocks. approach focuses on the aggregate demand and aggre- Although many studies explain the nature and syn- gate supply (AD − AS) framework of demand and sup- chronisation of shocks, the question of how important ply shocks. The second question is answered using panel labour mobility (migration) is in the process of adjust- cointegrated ARDL models following the approach of ment of the Visegrad countries to shocks has not been Pesaran et al. (1999). This approach is chosen because of addressed. Since the formation of a monetary union the efficacy of pooled mean group (PMG) models to esti - reduces the potential of countries to respond to asym- mate both short-term and long-term effects of phenom - metric shocks through macroeconomic policy, the role ena. It allows for a variation in short-term coefficients of labour mobility has received significant attention. according to specific conditions in each country but This study focuses on labour mobility and its ability to restricts long-term coefficients to be homogenous across act as an adjustment mechanism to counteract asym- the Visegrad countries. metric shocks in the Visegrad countries following the The main variables for the first research question approach of Dao et al. (2014) and Beyer and Smets include the World Uncertainty Index (WUI), real domes- (2015). tic GDP, exchange rate, and price level or inflation. The According to Mayda (2006), labour mobility faces some WUI is used to measure the impact of external sup- limitations due to negative individual perceptions and ply shocks. The index is the unbalanced GDP weighted attitudes towards migration, thus reducing its influence average for 142 countries. It takes into consideration all as a mechanism of adjustment to asymmetric shocks. global events that affect economic activities in countries. A study conducted by the Organization for Economic The use of this variable instead of the conventional global Cooperation and Development (OECD) (2012) con- GDP follows the approach of Ahir et al. (2019). Real cluded that labour mobility across EU member countries domestic GDP is used to measure the effect of domes - was low despite a rise in migration across countries. Sim- tic supply shocks. It is measured in millions of euros. ilarly, Barslund and Busse (2014) reported that in 2013, The exchange rate is measured using the real effective only 4% of working-age EU citizens lived in a different EU exchange rate, and the price level or inflation is meas - country. ured using the Consumer Price Index (CPI). These vari - Decressin and Fatás (1995) assessed regional labour ables are chosen following the approach of Blanchard and mobility in the EU, comparing their results to those of the Quah (1989) and Bayoumi and Eichengreen (1993). US. Their results show a low response of labour mobil - Data for the variables is obtained from Eurostat and ity to macroeconomic activity rates in the EU. They con - the OECD. The WUI and real domestic GDP are trans - cluded that in Europe, it is only after the third year that formed into natural logarithmic forms. The inflation rate the influence of labour mobility is observed in response and the exchange rate are in percentage, and therefore no to changes in activity rates. Dao et al. (2014) found logarithms are applied. A shock to real domestic GDP is that labour mobility has increased in Europe though chosen as supply shocks since it has been shown that this it is still lower than in the US. Beyer and Smets (2015) is the main driver of output fluctuations in countries (see investigated labour mobility in the US and Europe and e.g. Rand and Tarp 2001). Shocks to the real exchange concluded that labour mobility’s responsiveness to asym- rate are aligned with aggregate demand shocks. Shocks to metric shocks is higher in the US than in Europe. Simi- inflation are restricted to nominal shocks that only have larly, L’Angevin (2007) found a low rate of labour mobility temporary effects due to short-term price stickiness. The in the euro area countries than in the US. He concluded paper assumes that in the long term, money is neutral. that unemployment takes a longer time to return to an The study performs time series verifications, includ - equilibrium after a shock in the euro area. Beyer and ing a stationarity test, lag order selection and a coin- Smets (2015) also found that labour mobility in the EU tegration test. The stationarity test is performed using region was low. the Augmented Dickey−Fuller test (Dickey and Fuller Labour mobility as an adjustment mechanism to asymmetric shocks in Europe: evidence from the… Page 5 of 19 16 1979). Lag order selection is performed using Akaike Ay = C y + ··· + C y + Bµ 1 t−1 t k t−k (5) Information Criterion (Akaike 1974), Hannan-Quinn Information Criterion (Hannan-Quinn 1979) and Given that A is invertible, the SVAR is written as Schwarz Information Criterion (Schwarz 1978). The −1 −1 −1 y = A C y + ··· + A C y + A Bµ (6) t 1 t−1 k t−k t cointegration test is performed using the Johansen test of cointegration (Johansen 1988). Optimum lags were which implies the following set of relationships: decided using a lag frequency test. The results of the −1 unit root test show that all the variables are integrated A C = A (7) i i of order one. The cointegration test shows no case of For i = 1,2,3 . . . cointegration among the variables. Therefore, the first difference stationary specification for all the SVAR ′ ′ −1 −1 A BB A = (8) models is chosen, which was also applied by authors such as Bayoumi and Eichengreen (1993) and Campos Cholesky identification is used to derive the B matrix, and Macchiarelli (2016). The VAR model is specified in and the study relies on some assumptions based on eco- Eq. (1): nomic theory. First, external supply is assumed to be y = A y + ··· + A y + e strictly exogenous. This is plausible since all the Viseg - t 1 t−1 k t−k t (1) rad economies are relatively small and open economies, making no significant contribution to global output. E e e = t (2) Second, domestic supply is affected only by shocks to external supply and shocks from itself. Third, the real where y is a vector of n endogenous variables, ′ effective exchange rate is assumed to be affected by EX y = y ,y ,d ,m t t t t shocks to external supply, shocks to domestic supply, EX y represents the first-order difference of the WUI, and domestic demand shocks. The domestic price level y represents the first-order difference of real domes - is assumed to be strictly endogenous, implying that tic GDP, d represents the first-order difference of real prices are affected by shocks to external supply, shocks effective exchange rate, and m represents the first- to domestic supply, demand shocks, and monetary order difference of CPI inflation. A are coefficient matri - shocks. ces, e are error terms, and Σ is the covariance matrix of The study normalises the variance–covariance matrix the errors. The modification of VAR to allow for contem - of structural shocks to identity following the identifi - poraneous relationships among the model variables gives cation of Blanchard and Quah (1989) and Clarida and an SVAR model as expressed in (3). Gali (1994), which is the extended form of the for- mer. The method uses a C-Model, as used by Amisano Ay = C y + ··· + C y + e 1 t−1 t k t−k (3) and Giannini (1997). The theoretical foundations are A new notation (C ) is formulated because when matrix explained using an AS − AD framework. The assump - A is not an identity matrix, the C will generally dif- tions behind structural shocks are expressed in Eq. (9): fer from the A in the reduced-form VAR. The A matrix EX EX a a a a �y 11i 12i 13i 14i t characterises the contemporaneous relationships among � DS a a a a ε �y i 21i 22i 23i 24i t t the variables in the VAR. Error terms are decomposed = L DD �d a a a a 31i 32i 33i 34i ε into mutually orthogonal shocks. The solution is to write i=0 �m a a a a 41i 42i 43i 44i t ε the errors as a linear combination of structural shocks: (9) e = Bµ EX t t (4) where y , y , d and m denote the first differ - t t t t ence form of WUI, real domestic GDP, the real effec - where B is a 4 × 4 matrix of structural coefficients and is a EX tive exchange rate, and the price level respectively. ε , vector of structural shocks such that. DS DD M EX DS DD M ε , ε and ε represent external shocks, real domestic t t t ε = ε , ε , ε , ε ,consisting of external GDP t t t t EX DS supply shocks, domestic demand shocks, and monetary shock ( ε ), domestic supply shock ( ε ), domestic t t DD M shocks respectively. The matrix of ‘a’ coefficients is a 4 × 4 demand shock ( ε ), and monetary shock ( ε ) resp e c- t t matrix defining the impulse responses of the variables to tively. It is assumed that they are serially uncorrelated structural shocks. Detailed information about the VAR and orthonormal, with a variance–covariance matrix and SVAR models is provided in the work of Amisano normalised to the identity matrix. The condition of and Giannini (1997) and Lütkepohl (2005). Demand E µ µ = I is imposed. Equations 3 and 4 are com- shocks and nominal shocks have no effect on output in bined to obtain the SVAR model: the long term; thus: 16 Page 6 of 19 D. Nchor ∞ ∞ unemployment and economic growth among the Viseg- a = a = 0 13i 14i (10) rad countries. i=0 i=0 The variables for the second research question are employment, migration (labour mobility), technology, Furthermore, exchange rate is not affected by nomi - and real value added. Employment is measured in per- nal shocks in the long term (see Lütkepohl, 2005; Amis- centages and represents the percentage of employment ano and Giannini, 1997); thus: of EU labour except the reporting country. Migration or labour mobility is measured using the net migra- a = 0 34i (11) tion value, which is the difference between immi - i=0 gration and emigration. It is in thousands of people. Technology is measured using total factor productiv- Therefore, the matrix of structural coefficients (see Lüt - ity, as used by Ngai and Pissarides (2007). Real value kepohl, 2005; Amisano and Giannini, 1997) is expressed added is measured in millions of euros and is derived as: c c c c d d d d .0 0 0 11i 12i 13i 14i 11i 12i 13i 14i c c c c d d d d . . 00 21i 22i 23i 24i 21i 22i 23i 24i (12) = c c c c d d d d .. .0 31i 32i 33i 34i 31i 32i 33i 34i i=0 c c c c d d d d . ... 41i 42i 43i 44i 41i 42i 43i 44i Pairwise correlations of shocks are examined across as the ratio of gross value added to the GDP deflator. the four Visegrad countries. When the correlation coef - The paper acknowledges that there are other deter - ficients of shocks between two countries are positive and minants of employment, but it seeks to estimate the statistically significant, it implies symmetry, whereas neg - unique impact of migration and technological progress. The effect of other employment determinants is incor ative correlation coefficients or statistically insignificant - coefficients indicate asymmetry. Statistical significance in porated into real value added. Data for all the variables the results is depicted by an asterisk (*). If there is sym - is in quarterly frequencies and ranges from 2000q1 to 2020q1 (where q represents quarter). All the variables metry of shocks between or among countries, it means are transformed into logarithmic forms except employ that the countries require a synchronous policy response - to that macroeconomic shock. The test for the statisti - ment since it is in percentage. cal significance of the coefficients is carried out using There has been increased interest in dynamic panel models in recent years due to cross-country analyses. In Kendall and Stuart’s (1973) correlation statistic at the 5% level. This statistic is chosen since it offers more power this regard, the study considers Mean Group (MG) and than other parametric tests such as the Pearson product– PMG models. The PMG approach proposed by Pesaran moment correlation coefficient (Yue et al. 2002). et al. (1999) accounts for panel heterogeneity and endo The second research question is answered using a geneity of variables and allows for estimation with I(0) cointegrated panel ARDL model following the approach and I(1) variables. It estimates both short run and long of Pesaran et al. (1999). It seeks to assess the effective run coefficients but uniquely restricts short run coef - - ness of labour mobility as an alternative adjustment ficients to individual countries and imposes common mechanism to shocks in the Visegrad region. This study long run coefficients on all countries. The choice of the uses migration and labour mobility interchangeably, as MG model and PMG model is based on the Hausman used in the report of the European Commission (2011). test (Hausman 1978). The null hypothesis of this test This report highlighted the importance of the move is long run slope homogeneity (PMG). Failure to reject the null hypothesis indicates that PMG is more appro ment of workers from one EU country to another as - an important adjustment mechanism for the European priate. The Hausman test results for this paper show economies. A study conducted by Arpaia et al. (2016) that PMG is more appropriate, and therefore the study showed that countries joining the EU increases labour estimates both the long run and short run coefficients mobility significantly. It also found that membership of for technological progress, migration (labour mobility) the euro area is not associated with a rise in the level and real value added. With PMG models, the focus is of labour mobility. The European Commission (2011) on the long run coefficients and the speed of adjust reported that movements in response to shocks have ment parameters. increased significantly in Europe. Since the start of the The study performs a unit root test (Levin and Lin global financial crisis, attention has turned to labour 1992) to check for the stationary properties of the data mobility as a counter measure for the divergence in and a test of panel cointegration using the Kao test (Kao Labour mobility as an adjustment mechanism to asymmetric shocks in Europe: evidence from the… Page 7 of 19 16 1999). The lags for the variables are determined using the and migration (labour mobility) because of the response Akaike Information Criterion (AIC) (Akaike 1974). Struc- of labour markets to asymmetric shocks. The more effi - tural breaks are taken into consideration, as the period cient the response is, the better the response of coun- covered by the study includes major events such as the tries to asymmetric shocks (see Dao et al. 2014; Beyer global financial crisis. Ignoring structural breaks can lead and Smets 2015). Technology is also considered as one to inaccurate inferences. In this regard, the paper divides of the variables because of the recent wave of techno- the dataset into two samples: 2000–2007 and 2008–2019. logical evolution in workplaces, where there is growing The first period is the period before the crisis and the sec - automation of production processes. It thus serves as a ond is the period after the crisis. The Chow test (Chow shock and therefore plays a crucial role in the dynam- 1960) is used to determine the significance of breaks. The ics of the labour markets of the four Visegrad countries null hypothesis of this test is no structural break against (see Goos et al. 2014; Bernardi and Garrido 2008; Oesch the alternative that there is a known structural break in and Rodriquez-Menes 2011). Furthermore, technology is 2008. With this test, the coefficients of the two periods considered as a variable in labour mobility since with a are estimated. The F test is then computed using out-of- regional shock to total factor productivity, labour mobil- sample forecast errors. The stability of the coefficients for ity acts to reduce macroeconomic disturbances, thus the periods is assured if the null hypothesis of no struc- helping in labour market adjustment given a technologi- tural breaks is not rejected. The data is pooled for the cal change. Labour mobility also raises the level of volatil- whole period (2000–2019) if the test fails to reject the ity in the rest of the region to some degree by exporting null hypothesis of no structural break. If the null hypoth- unemployment associated with technological change esis is rejected, ARDL models will be constructed differ - to other areas, where it is absorbed (see Mundell 1961; ently for the periods before the global financial crisis and Hauser 2014; Saks and Wozniak 2011). The magnitude after the crisis. and signs of coefficients are important as they indicate The results of the Chow test show that the null hypoth - the size and direction of the impact. The speed of adjust - esis is rejected, implying that the intercepts and slopes ment parameters and their signs and significance are also changed over the two periods. PMG models are therefore important. A significance level of 0.05 (5%) is used. run for the period before the crisis and after the crisis. As In Eq. (13), the short run relationships are explained mentioned earlier, the PMG model constrains the long by the terms with the summation signs. Long run coef- run coefficients to be identical but allows the intercept, ficients are interpreted as elasticities and they are the short run coefficients and error variances to differ across coefficients of the lagged independent variables ( π , π , 2 3 groups (Baltagi and Griffin 1997). This allows for varia - π ). These long run coefficients are multiplied by nega - tion in the impact of the drivers of employment in the tive one and then divided by ( π ). The study chooses the short run while imposing an identical or similar impact ARDL model by first using the optimal lag structure for from all drivers in the long run across the four Visegrad each country, which is decided with the help of the AIC countries. The economic justification of the PMG model and the Bayesian Information Criterion. Next, the study is the fact that the four countries are in the same regional decides on the maximum number of lags for each vari- economic bloc and share common policies since they are able in the ARDL using the lag frequencies. The preferred also all members of the EU. The basic form of the ARDL specification for the two ARDL models is ARDL (1, 1, 1, model according to Pesaran et al. (1999) is specified in 1) for the model before the crisis and ARDL (1, 3, 1, 1) Eq. (13): for the model after the crisis. With the data being coin- tegrated, the error-correction model is an alternative �lnM = α + θ �lnM it i j it−j with a general form equivalent to the ARDL model. The j=1 presentation of the error correction version of the ARDL model with all the variables in equation one is specified + δ �lnV + σ �lnF + β �lnG j it−j j it−j j it−j as follows: j=0 p−1 + π lnM + π lnV + π lnF + π lnG + µ 1 it−1 2 it 3 it 4 it it �lnM = α + θ �lnM it i j it−j (13) j=1 The paper denotes specific country fixed effects as α . q−1 M represents employment of EU labour measured in it + δ �lnV + σ �lnF + β �lnG j it−j j it−j j it−j percentage, V represents technological innovation, F it it j=0 represents migration or labour mobility, and G repre- it + γ lnM + γ lnV + γ lnF + γ lnG + µ 1 it−1 2 it 3 it 4 it it sents real value added. µ is the error term. Δ denotes the it (14) first-order differencing. The study considers employment 16 Page 8 of 19 D. Nchor i = 1,2,3 . . . n and the subscript t is given by in the process of adjustment to asymmetric shocks in t = 1, 2, 3 . . . T. The number of countries is represented the Visegrad countries. by n. Time is represented by t. The number of lags is rep - resented by j. The speed of adjustment parameter which 4.1 Similarities and differences in economic structures is also called the error correction term (ECT) is repre- of Visegrad countries sented by γ , as in Eq. (14). The signs of its coefficient This section briefly discusses the economic structures of are important in the interpretation of convergence and the four countries with an emphasis on similarities and divergence from the equilibrium. A positive γ indicates differences. This helps to understand the dynamics of a divergence and a negative γ indicates convergence responses to shocks across the region. Figure 1 shows the towards the equilibrium. In the error correction version export dependence of the four Visegrad countries and of the ARDL model, short run coefficients ( θ ,δ ,σ and the trajectory of GDP growth. It is observed that all four j j j β ) are directly estimated. They can differ across coun - countries have experienced an increased contribution tries. The long run coefficients are constrained for the of exports to GDP. The highest share of exports in GDP group of Visegrad countries. The long run coefficients is in Slovakia with a value above 90%. Hungary has the areγ , γ , γ ,γ and γ . Chapter four presents, interprets next highest share of about 80%, the Czech Republic has 1 2 3 3 4 and discusses the results of the study. The results are gen - a share of about 75%, and Poland has the lowest share of erated in STATA version 15. about 50%. The share of exports in GDP partly explains the vulnerability of a country to external demand shocks. High export dependence means a high impact of external 4 Results shocks. This section provides economic interpretations of the Figure 2 shows the level of real GDP per capita, infla - results obtained in the SVAR models and the cointe- tion and unemployment in the four countries. Real GDP grated panel ARDL models. The first section describes per capita is rising in all the countries, with the highest the nature of shocks in the Visegrad countries. The rise occurring in the Czech Republic, followed by Slo- second section explains the impact of labour mobility vakia. Hungary and Poland have a very similar level of real GDP per capita. It is also observed that the level Fig. 1 Exports and GDP Growth ( Source: Author’s own work using data from EUROSTAT ) Fig. 2 Real GDP per Capita, Annual Inflation, and Unemployment ( Source: Author’s own work using data from EUROSTAT ) Labour mobility as an adjustment mechanism to asymmetric shocks in Europe: evidence from the… Page 9 of 19 16 of inflation has declined across all the countries even on the exchange rate and a negative impact on the price though it shows more volatility in its movement. There level. The impact of real domestic supply shocks on is some degree of convergence as all the countries move the exchange rate and the price level was positive. The towards the same level. The lowest inflation rate is in impact of demand shocks on the price level was posi- Poland and the highest is in Hungary. There is a similar tive. After the crisis, a positive shock to the WUI had a result for the level of unemployment, which has declined positive impact on real domestic GDP. The impact on the over the years. It shows some level of convergence across exchange rate and the price level remained the same as the countries. The lowest unemployment rate is in the before the crisis. A positive shock to real domestic supply Czech Republic and the highest is in Slovakia. impacted negatively on the exchange rate and the price Figure 3 shows the trade concentration and trade level. The impact of demand shocks on the price level diversification indices for the four countries. The trade changed to negative. concentration index measures how reliant a country Table 2 shows the SVAR results of Hungary. All the is on a limited group of commodities as its main for- unrestricted entries are statistically significant. The signs eign exchange source. It ranges between 0 and 1. Zero of the coefficients show the nature of response to shocks. implies perfect diversification and 1 indicates concentra - Positive coefficients show a positive response, and vice tion on a single product. The trade concentration index versa. Before the crisis, shock to the WUI had a negative also tells us whether the observed large share of exports impact on real domestic GDP, the exchange rate and the comes from a small number of products or exports are price level. The impact of real domestic supply shocks on distributed widely among many products. This index is the exchange rate and the price level was negative. The important since it signals how vulnerable a country is to impact of demand shocks on the price level was posi- external shocks. How it evolves over time provides vital tive. After the crisis, a shock to the WUI impacted posi- information about the changing productive structure of tively on real domestic GDP and the exchange rate but a country. The trade concentration values observed are negatively on the price level. The impact of real domes - closer to zero than 1, indicating low trade concentration. tic supply shocks on the exchange rate and the price level According to the United Nations Conference on Trade remained the same (negative). The impact of demand and Development (UNCTAD 2020), the trade diversifica - shocks on the price level changed to negative. tion index shows the difference between the trade struc - Table 3 shows the SVAR results of Poland. It shows ture of a country or country group and the world average. that before the global financial crisis, the impact of a It ranges between 0 and 1. A value closer to 1 indicates a shock to the WUI was negative on domestic GDP and bigger difference from the world average. the exchange rate in Poland. The impact on the price level was positive, however. Real domestic supply shocks had 4.2 The nature and correlation of shocks before and after negative impact on the exchange rate and positive impact the financial crisis on the price level. The impact of demand shocks on the Table 1 shows the SVAR results of the Czech Republic. price level was negative. After the crisis, the impact of a Positive coefficients indicate a positive response to shocks shock to the WUI remained negative on domestic GDP and negative coefficients imply a negative response to but positive on the exchange rate and negative on the shocks. Before the global financial crisis, a shock to the price level. A shock to real domestic supply impacted WUI had a negative impact on real domestic GDP in the negatively on the exchange rate and the price level. The Czech Republic. Furthermore, it had a positive impact Fig. 3 Trade Concentration and Trade Diversification Indices ( Source: Author’s own work using UNCTAD data) 16 Page 10 of 19 D. Nchor Table 1 SVAR results of Czech Republic before and after Table 2 SVAR results of Hungary before and after crisis crisis Before crisis After crisis Before crisis After crisis Coefficient Std. error Coefficient Std. error Coefficient Std. error Coefficient Std. error /a_1_1 1 (Constrained) 1 (Constrained) /a_1_1 1 (Constrained) 1 (Constrained) /a_2_1 − 0.612*** 0.045 0.1** 0.041 /a_2_1 − 0.36*** 0.023 0.1*** 0.025 /a_3_1 − 0.528*** 0.011 0.041** 0.016 /a_3_1 0.12*** 0.011 0.18*** 0.008 /a_4_1 − 0.147** 0.0662 − 0.309 0.621 /a_4_1 − 0.423*** 0.106 − 0.42*** 0.0518 /a_1_2 0 (Constrained) 0 (Constrained) /a_1_2 0 (Constrained) 0 (Constrained) /a_2_2 1 (Constrained) 1 (Constrained) /a_2_2 1 (Constrained) 1 (Constrained) /a_3_2 − 0.111** 0.05 − 0.083** 0.041 /a_3_2 0.62*** 0.1 − 0.118** 0.049 /a_4_2 − 0.396*** 0.115 − 2.276*** 0.238 /a_4_2 1.122*** 0.095 − 4.578*** 0.329 /a_1_3 0 (Constrained) 0 (Constrained) /a_1_3 0 (Constrained) 0 (Constrained) /a_2_3 0 (Constrained) 0 (Constrained) /a_2_3 0 (Constrained) 0 (Constrained) /a_3_3 1 (Constrained) 1 (Constrained) /a_3_3 1 (Constrained) 1 (Constrained) /a_4_3 0.39*** 0.113 − 3.248*** 0.6464 /a_4_3 4.018*** 0.211 − 5.515*** 0.946 /a_1_4 0 (Constrained) 0 (Constrained) /a_1_4 0 (Constrained) 0 (Constrained) /a_2_4 0 (Constrained) 0 (Constrained) /a_2_4 0 (Constrained) 0 (Constrained) /a_3_4 0 (Constrained) 0 (Constrained) /a_3_4 0 (Constrained) 0 (Constrained) /a_4_4 1 (Constrained) 1 (Constrained) /a_4_4 1 (Constrained) 1 (Constrained) /b_1_1 0.298*** 0.041 0.292*** 0.035 /b_1_1 0.231*** 0.037 0.29*** 0.031 /b_2_1 0 (Constrained) 0 (Constrained) /b_2_1 0 (Constrained) 0 (Constrained) /b_3_1 0 (Constrained) 0 (Constrained) /b_3_1 0 (Constrained) 0 (Constrained) /b_4_1 0 (Constrained) 0 (Constrained) /b_4_1 0 (Constrained) 0 (Constrained) /b_1_2 0 (Constrained) 0 (Constrained) /b_1_2 0 (Constrained) 0 (Constrained) /b_2_2 0.068*** 0.009 0.087*** 0.011 /b_2_2 0.024*** 0.004 0.048*** 0.005 /b_3_2 0 (Constrained) 0 (Constrained) /b_3_2 0 (Constrained) 0 (Constrained) /b_4_2 0 (Constrained) 0 (Constrained) /b_4_2 0 (Constrained) 0 (Constrained) /b_1_3 0 (Constrained) 0 (Constrained) /b_1_3 0 (Constrained) 0 (Constrained) /b_2_3 0 (Constrained) 0 (Constrained) /b_2_3 0 (Constrained) 0 (Constrained) /b_3_3 0.017*** 0.002 0.027*** 0.003 /b_3_3 0.011*** 0.002 0.016*** 0.002 /b_4_3 0 (Constrained) 0 (Constrained) /b_4_3 0 (Constrained) 0 (Constrained) /b_1_4 0 (Constrained) 0 (Constrained) /b_1_4 0 (Constrained) 0 (Constrained) /b_2_4 0 (Constrained) 0 (Constrained) /b_2_4 0 (Constrained) 0 (Constrained) /b_3_4 0 (Constrained) 0 (Constrained) /b_3_4 0 (Constrained) 0 (Constrained) /b_4_4 1 (Constrained) 1 (Constrained) /b_4_4 1 (Constrained) 1 (Constrained) LR test of identifying restrictions: chi2(1) = 93.66, Prob > chi2 = 0.000, Number of obs. = 26, Log likelihood = 72.19042. LR test of identifying LR test of identifying restrictions: chi2(1) = 43.18, Prob > chi2 = 0.000, restrictions: chi2(1) = 18.48, Prob > chi2 = 0.000, Number of obs. = 34, Log Number of obs. = 20, Log likelihood = 91.10228. LR test of identifying likelihood = 67.38943 restrictions: chi2(1) = 14.16, Prob > chi2 = 0.000, Number of obs. = 45, Log ** *** likelihood = 136.9166 denotes statistical significance of coefficients at 5% level, and denotes statistical significance of coefficients at 1% level /a_1_1 represents own shock for global GDP, /a_2_1 represents a shock from global GDP to domestic supply, /a_3_1 represents a shock from global GDP to the exchange rate, /a_4_1 represents a shock from global GDP to price level. /a_1_2 represents a domestic supply shock to global GDP, /a_2_2 represents impact of demand shocks on the price level remained own domestic supply shock, /a_3_2 represents domestic supply shock to exchange rate, /a_4_2 represents domestic supply shock to price level. /a_1_3 negative. represents demand shock to global GDP, /a_2_3 represents demand shock to Table 4 shows the SVAR results of Slovakia. All the domestic supply, /a_3_3 represents own demand shock, /a_4_3 represents demand shock to price level, /a_1_4 represents monetary shock to global entries are statistically significant. Before the financial GDP, /a_2_4 represents monetary shock to domestic supply, /a_3_4 represents crisis, the impact of a shock to the WUI was positive on monetary shock to exchange rate, /a_4_4 represents monetary shock to real domestic GDP and the price level but negative on the price level. /b_1_1, /b_2_2 and /b_3_3 are own shocks. ** denotes statistical significance of coefficients at 5% level, and *** denotes statistical significance of coefficients at 1% level Labour mobility as an adjustment mechanism to asymmetric shocks in Europe: evidence from the… Page 11 of 19 16 Table 3 SVAR results of Poland before and after crisis Table 4 SVAR results of Slovakia before and after crisis Before crisis After crisis Before crisis After crisis Coefficient Std. error Coefficient Std. error Coefficient Std. error Coefficient Std. error /a_1_1 1 (Constrained) 1 (Constrained) /a_1_1 1 (Constrained) 1 (Constrained) /a_2_1 − 0.14*** 0.044 − 0.09** 0.045 /a_2_1 0.458*** 0.027 − 0.035** 0.015 /a_3_1 − 0.071*** 0.021 0.24*** 0.02 /a_3_1 − 0.36*** 0.095 − 0.021*** 0.0002 /a_4_1 0.233*** 0.068 − 0.31*** 0.063 /a_4_1 0.110** 0.0401 − 0.23*** 0.066 /a_1_2 0 (Constrained) 0 (Constrained) /a_1_2 0 (Constrained) 0 (Constrained) /a_2_2 1 (Constrained) 1 (Constrained) /a_2_2 1 (Constrained) 1 (Constrained) /a_3_2 − 0.238** 0.091 − 0.163** 0.076 /a_3_2 − 0.329*** 0.068 − 0.08*** 0.002 /a_4_2 0.124*** 0.0338 − 0.450** 0.221 /a_4_2 − 1.663*** 0.535 0.785*** 0.0535 /a_1_3 0 (Constrained) 0 (Constrained) /a_1_3 0 (Constrained) 0 (Constrained) /a_2_3 0 (Constrained) 0 (Constrained) /a_2_3 0 (Constrained) 0 (Constrained) /a_3_3 1 (Constrained) 1 (Constrained) /a_3_3 1 (Constrained) 1 (Constrained) /a_4_3 − 1.444** 0.648 − 0.891** 0.337 /a_4_3 8.976*** 0.144 1.350*** 0.452 /a_1_4 0 (Constrained) 0 (Constrained) /a_1_4 0 (Constrained) 0 (Constrained) /a_2_4 0 (Constrained) 0 (Constrained) /a_2_4 0 (Constrained) 0 (Constrained) /a_3_4 0 (Constrained) 0 (Constrained) /a_3_4 0 (Constrained) 0 (Constrained) /a_4_4 1 (Constrained) 1 (Constrained) /a_4_4 1 (Constrained) 1 (Constrained) /b_1_1 0.289*** 0.04 0.283*** 0.034809 /b_1_1 0.281*** 0.038 0.286*** 0.036 /b_2_1 0 (Constrained) 0 (Constrained) /b_2_1 0 (Constrained) 0 (Constrained) /b_3_1 0 (Constrained) 0 (Constrained) /b_3_1 0 (Constrained) 0 (Constrained) /b_4_1 0 (Constrained) 0 (Constrained) /b_4_1 0 (Constrained) 0 (Constrained) /b_1_2 0 (Constrained) 0 (Constrained) /b_1_2 0 (Constrained) 0 (Constrained) /b_2_2 0.065*** 0.009 0.074*** 0.009097 /b_2_2 0.039*** 0.005 0.033*** 0.004269 /b_3_2 0 (Constrained) 0 (Constrained) /b_3_2 0 (Constrained) 0 (Constrained) /b_4_2 0 (Constrained) 0 (Constrained) /b_4_2 0 (Constrained) 0 (Constrained) /b_1_3 0 (Constrained) 0 (Constrained) /b_1_3 0 (Constrained) 0 (Constrained) /b_2_3 0 (Constrained) 0 (Constrained) /b_2_3 0 (Constrained) 0 (Constrained) /b_3_3 0.03*** 0.004 0.032*** 0.003988 /b_3_3 0.013*** 0.001893 0.0003*** 5.04e-05 /b_4_3 0 (Constrained) 0 (Constrained) /b_4_3 0 (Constrained) 0 (Constrained) /b_1_4 0 (Constrained) 0 (Constrained) /b_1_4 0 (Constrained) 0 (Constrained) /b_2_4 0 (Constrained) 0 (Constrained) /b_2_4 0 (Constrained) 0 (Constrained) /b_3_4 0 (Constrained) 0 (Constrained) /b_3_4 0 (Constrained) 0 (Constrained) /b_4_4 1 (Constrained) 1 (Constrained) /b_4_4 1 (Constrained) 1 (Constrained) LR test of identifying restrictions: chi2(1) = 40.21, Prob > chi2 = 0.000, Number LR test of identifying restrictions: chi2(1) = 20.74, Prob > chi2 = 0.000, of obs. = 26, Log likelihood = 58.42637. LR test of identifying restrictions: Number of obs. = 21, Log likelihood = 154.1757. LR test of identifying chi2(1) = 48.35, Prob > chi2 = 0.000, Number of obs. = 33, Log likelihood = 68.486 restrictions: chi2(1) = 38.88, Prob > chi2 = 0.000, Number of obs. = 31, Log ** *** likelihood = 224.4755 denotes statistical significance of coefficients at 5% level, and denotes ** *** statistical significance of coefficients at 1% level denotes statistical significance of coefficients at 5% level, and denotes statistical significance of coefficients at 1% level exchange rate. The impact of real domestic supply shocks on the exchange rate and the price level was negative. The 4.3 Correlation of shocks before and after the global impact of demand shocks on the price level was positive. financial crisis After the financial crisis, a shock to the WUI impacted Table 5 shows the correlation of shocks in the Visegrad negatively on domestic GDP, the exchange rate and the countries before the global financial crisis. This section is price level. A shock to real domestic supply impacted relevant as it shows whether the Visegrad countries will negatively on the exchange rate and positively on the have similar responses to macroeconomic shocks or vice price level. The impact of domestic demand shocks on versa. Shocks are correlated if they have the same effect the price level remained positive. on macroeconomic aggregates in different countries. For example, if external supply shocks affect real domestic 16 Page 12 of 19 D. Nchor Table 5 Correlation of shocks before the global financial Table 6 Correlation of shocks after the global financial crisis crisis Czechia Hungary Poland Slovakia Czech Hungary Poland Slovakia External supply shocks External supply shocks Czechia 1 Czech 1 Hungary 0.0768 1 Hungary 0.2591 1 a a Poland 0.2766 0.7911 1 Poland 0.6949 0.3326 1 a a a a Slovakia − 0.1114 0.6636 0.3646 1 Slovakia 0.5624 0.3766 0.5209 1 Domestic supply shocks Domestic supply shocks Czechia 1 Czech 1 Hungary − 0.3121 1 Hungary 0.3730 1 a a Poland − 0.1541 0.1726 1 Poland 0.4580 0.5061 1 a a Slovakia − 0.1089 0.1294 0.2242 1 Slovakia 0.3333 0.3918 0.4158 1 Demand shocks Demand shocks Czechia 1 Czech 1 Hungary − 0.398 1 Hungary 0.2349 1 a a Poland − 0.1627 0.0664 1 Poland 0.4972 0.4514 1 Slovakia − 0.3088 0.1454 0.239 1 Slovakia − 0.1362 0.0324 − 0.3521 1 Monetary shocks Monetary shocks Czechia 1 Czech 1 Hungary − 0.262 1 Hungary − 0.0622 1 Poland 0.2782 0.1452 1 Poland 0.1071 0.1884 1 Slovakia − 0.116 0.4812 − 0.0394 1 Slovakia 0.0742 − 0.0374 − 0.0191 1 a a Denotes symmetric shocks at the 5% level Denotes symmetric shocks at the 5% level GDP in the same direction in two different countries, of the data coverage (2000−2007) and before the crisis. It there is a correlation of external supply shocks between is therefore understandable that there was less coordina- the two countries and the policy response can be syn- tion of policies, resulting in the high proportion of asym- chronised. Positive and statistically significant correlation metric shocks in the region. coefficients indicate symmetry while negative correlation Table 6 shows the correlation of shocks after the coefficients indicate asymmetry. Statistical significance global financial crisis. It is observed that more symme - in the results is depicted by an asterisk (*). Positive and tries occurred with regard to external supply shocks and significant correlation coefficients imply that countries domestic supply shocks. There were more asymmetric require a synchronous policy response to counteract shocks with respect to demand shocks and monetary similar macroeconomic shocks, and vice versa. The only shocks. Slovakia had the highest correlation of exter- significant correlation before the crisis relates to external nal supply shocks, followed by the Czech Republic and supply shocks and monetary shocks. Hungary correlates Poland. Hungary had the highest correlation of domes- with Poland and Slovakia with regard to external supply tic supply shocks and Poland had the highest correlation shocks and with Slovakia only with regard to monetary of demand shocks. A variety of structural differences shocks, making Hungary the country with highest cor- account for these asymmetric demand and monetary relation of shocks before the crisis. These correlations of shocks, including the influence of the political systems, shocks among the countries are referred to as symmet- fiscal policies, differences in legal systems, public pur - ric shocks. In such cases, the countries involved require chasing, and the political cycle. Monetary policy had a similar or synchronous policy to counteract the effects asymmetric shocks largely because of the differences in of the shocks. Other correlation coefficients are either financial structure among the Visegrad countries. These negative or not statistically significant, and those indicate differences include the influence of banks, levels of con - asymmetry. Therefore, the countries require different sumer debts, and the nature of borrowing (whether policy responses to mitigate the effects of the shocks. All at fixed or variable interest rates). The asymmetry in four countries joined the EU in 2004, which is the middle demand and monetary shocks had persisted for some time even before the financial crisis. Labour mobility as an adjustment mechanism to asymmetric shocks in Europe: evidence from the… Page 13 of 19 16 4.4 The role of labour mobility as an adjustment but all four countries had a high employment rate of both mechanism to asymmetric shocks EU and non-EU nationals. Figure 4 shows the case of net migration in the four coun- Figure 5 shows the case of employment of foreign EU tries. It also shows the total employment of EU workers workers by gender. The essence of this chart is to show and non-EU workers. The significance of this figure is the that there is less discrimination in the labour market connection it has with labour mobility. The figure shows with regard to employment. The rate of employment is that the Czech Republic, Hungary and Slovakia had posi- high in all the countries for both men and women. This tive net migration while Poland had negative net migra- is therefore an encouragement for workers who intend to tion. In the case of the Czech Republic, Hungary and seek jobs in other countries in the event of any negative Slovakia, there were more immigrants than emigrants. demand and supply shocks. The opposite occurred in Poland. The Czech Republic Figure 6 presents information about the total num- had the highest rate of employment of foreign workers ber of immigrants, emigrants, and net migration as a Fig. 4 Net Migration and Employment Rate of EU Labour Except Reporting Country ( Source: Author’s own work using data from EUROSTAT ) Fig. 5 Employment of EU Labour Except Reporting Country by Gender ( Source: Author’s own work using data from EUROSTAT ) Fig. 6 Migration as a Percentage of the Total Population in Visegrad Countries ( Source: Author’s own work using data from EUROSTAT ) 16 Page 14 of 19 D. Nchor percentage of the total population. For all four Visegrad According to the Migration Policy Institute (2020), the countries, the percentage of immigrants is less than 1%. percentage of legal immigrants in the US is about 15% The highest percentage is in the Czech Republic, fol - and has been rising since the 1960s. This is a big con - lowed by Hungary and Poland. Slovakia has the lowest trast to the case of the Visegrad countries, where the immigrants to population ratio. The graph for emigrants percentage of immigrants is less than 1%. The Institute is more dynamic with cases of emigration to popula- also noted that the percentage of immigrant labour in the tion ratio increasing after the financial crisis, indicating civil service has been growing since the 1980s and is cur- increased labour mobility. Poland overtook the Czech rently around 20% of the labour force in the US (Migra- Republic as the country with the highest emigration to tion Policy Institute 2020). The same cannot be said for population ratio after the crisis. Similarly, Slovakia had the Visegrad countries, where policy restrictions and lan- an increased emigration to population ratio exceed- guage barriers make it virtually impossible for immigrant ing that of the Czech Republic. The third part of Fig. 6 workers to participate in the civil service. shows net migration to population ratio. Before the cri- Given the information on labour mobility in Figs. 4, 5 sis, the Czech Republic had a fast and positive increasing and 6, the study further investigates the impact of labour net migration to population ratio. However, it declined mobility on employment using a cointegrated panel sharply after the crisis to negative. It is now positive and ARDL model. A significant and positive coefficient for increasing. Net migration to population ratio has been migration (labour mobility) indicates that labour mobil- positive and more stable in Hungary and Slovakia. The ity aids in adjustment to asymmetric shocks and a nega- graph shows that Poland had a negative net migration to tive or insignificant coefficient indicates otherwise. The population ratio for the most part, implying that more results are for the period before the financial crisis and Poles travel outside for work than the other way round. after the crisis. Table 7 shows the ARDL model for the The Czech Republic, Hungary and Slovakia had a positive period before the financial crisis for all four countries. net migration to population ratio, indicating that more The coefficient for migration (labour mobility) is sig - EU migrants travel to these three countries to work. The nificant and positive, indicating that labour mobility percentage of net migration is too low, however, at less contributes to the adjustment to asymmetric shocks. than 1%, which highlights the low movement of labour The adjustment parameters are negative and statisti - across the Visegrad region. cally significant, indicating long-term convergence to the Table 7 ARDL model before the financial crisis Variable ECT Czechia Hungary Poland Slovakia ECT − 0.313** − 0.189*** − 0.200*** − 0.168*** (0.154) (0.059) (0.016) (0.061) Employment 0.293 0.558*** 0.671*** 0.575*** t−1 (0.188) (0.199) (0.230) (0.135) lnTechnology 0.176 − 4.097 0.645 0.445 t−1 (2.406) (3.035) (1.507) (1.329) lnMigration 0.009 0.013 0.076** 0.012* t−1 (0.015) (0.062) (0.034) (0.006) lnRealvalueadded 9.498*** − 3.317 0.107 0.743 t−1 (3.627) (3.966) (2.368) (2.785) lnTechnology 8.138*** (0.570) lnMigration 0.105*** (0.038) lnRealvalueadded 6.857* (3.688) Constant 22.622 − 14.731*** − 18.302 − 11.378** (16.138) (5.470) (11.998) (4.860) Each variable has a maximum lag set to five. The study determined the optimal lag lengths using the AIC. Standard errors are represented in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1, represents first-order difference. t-1, t-2..t-j represent lags. L is used to represent the long run. ECT means error correction term Labour mobility as an adjustment mechanism to asymmetric shocks in Europe: evidence from the… Page 15 of 19 16 Table 8 ARDL Model After Financial Crisis Variable ECT Czechia Hungary Poland Slovakia ECT − 0.158*** − 0.193*** − 0.259*** − 0.215** (0.041) (0.0103) (0.224) (0.0214) Employment 0.075 0.605** -0.814*** 0.584 t−1 (0.195) (0.289) (0.195) (0.432) lnTechnology − 7.649** − 22.270*** 4.877 − 4.626 t−1 (3.888) (5.601) (5.796) (6.252) lnTechnology 6.208 21.774*** − 9.703** 6.405 t−2 (4.167) (5.833) (4.002) (5.794) lnTechnology − 2.146 − 6.630*** 3.440*** − 2.039 t−3 (1.746) (2.532) (1.222) (2.377) lnMigration − 0.000 − 0.072 0.052 0.005 t−1 (0.013) (0.064) (0.041) (0.017) lnRealvalueadded 9.957*** 0.339 − 2.998* 5.256 t−1 (2.725) (5.811) (1.609) (8.553) lnTechnlogy 4.603*** (0.2877) lnMigration 0.186*** (0.020) lnRealvalueadded 6.195*** (0.486) Constant − 0.330 − 0.697 − 16.632 − 1.865 (1.229) (1.715) (11.630) (2.727) Each variable has a maximum lag set to five. The study determined the optimal lag lengths using the AIC. Standard errors are represented in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1, represents first order difference. t-1, t-2..t-j represent lags. L is used to represent the long run. ECT means error correction term equilibrium. Furthermore, 31.0% of deviations in employ- the speed of adjustment shows that it takes 4−5 years ment is corrected per period in the Czech Republic, for countries to adjust to asymmetric shocks through 18.9% in Hungary, 20.0% in Poland, and 16.8% in Slova- labour mobility. The coefficient of migration (labour kia. The convergence is slow, however, given the sizes of mobility) is positive and statistically significant, indi - the speed of adjustment parameters. The size of adjust - cating the positive contribution of labour mobility to ment coefficients shows that it takes about 3−5 years for adjustment to asymmetric shocks. the economies to adjust to an asymmetric shock through Both ARDL models are checked for violation of labour mobility. The fastest speed of adjustment occurs assumptions, such as autocorrelation, normality and in the Czech Republic. The coefficient for migration or heteroscedasticity. No assumptions were violated. The labour mobility is significant and positive, indicating that paper carries out bounds testing to check for long run labour mobility contributes to adjustment to asymmetric relationships in the cointegrated panel ARDL mod- shocks. els. The observed F statistics from the Wald tests are Table 8 shows the ARDL results for the period after greater than the upper bound of the bounds test in all the financial crisis. The speed of adjustment parameters the models, indicating the presence of long run rela- for all the countries is negative and statistically signifi - tionships among the variables. cant, indicating convergence. All the countries converge in the long run but the size of the speed of adjustment 5 Discussion parameters shows slow convergence. Moreover, 15.8% This section discusses the results of this paper and com - of deviations in employment is corrected per period in pares them with findings of other authors. It covers the the Czech Republic, 19.3% in Hungary, 25.9% in Poland, results generated from the SVAR models for all the coun- and 21.5% in Slovakia. The fastest convergence to the tries before and after the global financial crisis, as well equilibrium in Poland is attributed to the higher rate of as the results of the ARDL models. One of the key find - emigration than the other three countries. The size of ings of this study is that there were both symmetric and 16 Page 16 of 19 D. Nchor asymmetric shocks before and after the global financial and that the highest correlation occurred with regard to crisis. Before the global financial crisis, in the Czech external supply shocks dominated by Hungary. Exter- Republic, external supply shocks impacted negatively on nal supply shocks and domestic supply shocks became real domestic GDP and the price level but positively on asymmetric after the global financial crisis. Demand the exchange rate. Real domestic supply shocks impacted shocks and monetary shocks remained asymmetric. positively on the exchange rate and the price level. The Slovakia had the highest correlation of external supply impact of demand shocks on the price level was positive. shocks, Hungary the highest correlation of domestic After the crisis, a positive shock to the WUI impacted supply shocks, and Poland the highest correlation of positively on real domestic GDP, the exchange rate and demand shocks. The results also show that there were the price level. A positive real domestic supply shock more changes in the correlation of supply shocks than impacted negatively on the exchange rate and the price in demand shocks. For the period before the crisis and level. The impact of demand shocks on the price level after the crisis, there were positive changes in the cor- changed to negative. relation of supply shocks and negative changes in the Regarding Hungary, there were also differences in correlation of demand shocks. For the Visegrad group, responses to shocks before and after the financial crisis. the correlation of demand shocks was lower than that Before the crisis, the impact of external supply shocks of supply shocks. The weak correlation of demand on real domestic GDP, the exchange rate and the price shocks is explained by the differences in the economic, level was negative. The impact of real domestic supply trade and financial structures of the countries. The pos - shocks on the exchange rate and the price level was nega- itive synchronisation of supply shocks is explained by tive. The impact of demand shocks on the price level was the process of structural convergence within the Viseg- positive. After the crisis, external supply shocks impacted rad group through trade integration. The few cases of positively on domestic GDP and the exchange rate but asymmetric shocks are explained by industrial speciali- negatively on the price level. The impact of real domes - sation across the Visegrad countries, thus making the tic supply shocks on the exchange rate and the price level effect of industry-specific disturbances more likely to remained the same (negative). The impact of demand be concentrated in single countries, as suggested by De shocks on the price level changed to negative. Grauwe and Vanhaverbeke (1991). In Poland, before the global financial crisis, the impact The findings align with those of Horvath (2000), who of external supply shocks on real domestic GDP and the analysed the correlation between demand and supply exchange rate was negative. The impact on the price level shocks for the Baltic countries and the Visegrad group. In was positive. Real domestic supply shocks had a negative this case, Hungary was characterised by the highest cor- impact on the exchange rate and a positive impact on the relation of aggregate supply shocks and the lowest cor- price level. The impact of demand shocks on the price relation of aggregate demand shocks. Weimann (2002) level was negative. After the crisis, the impact of exter- found that Bulgaria, the Czech Republic and Hungary nal supply shocks remained negative on real domestic registered the strongest correlation of demand shocks. GDP but positive on the exchange rate and negative on Konopczak and Marczewski (2011) concluded that the the price level. A shock to real domestic supply impacted response of the economy of Poland to the crisis of 2008– negatively on the exchange rate and the price level. The 2009 was different from that of other CEECs owing to impact of demand shocks on the price level remained the structural characteristics. The findings of this paper negative. correspond to those of Blanchard and Quah (1989), who In Slovakia, before the financial crisis, the impact of found that for the EU countries, there are more asym- external supply shocks was positive on domestic GDP metric shocks than for US regions. and the price level but negative on the exchange rate. The Frenkel and Nickel (2002) concluded that there are still impact of domestic supply shocks was negative on the differences in shocks and adjustment processes between exchange rate and the price level. The impact of demand the euro area and many CEECs. Arfa (2009) found that shocks was positive on the price level. After the finan - several new member countries of the EU had high cor- cial crisis, external supply shocks impacted negatively relation of demand shocks with the euro area while sup- on domestic GDP, the exchange rate and the price level. ply shocks were asymmetric. Socol and Soviani (2010) A shock to real domestic supply impacted negatively on and Socol and Măntescu (2011) attributed the weak cor- the exchange rate and positively on the price level. The relation of demand shocks to differences in national fis - impact of domestic demand shocks on the price level cal policies. The existence of asymmetric shocks in the remained positive. Visegrad region is accounted for by a variety of factors. The outcome of the analysis of shocks shows that The higher percentage of the asymmetries observed most shocks were asymmetric before the financial crisis is attributed to political and governmental factors, for Labour mobility as an adjustment mechanism to asymmetric shocks in Europe: evidence from the… Page 17 of 19 16 example, the differences in the political cycle, fiscal poli - external supply and domestic supply shocks after the cies and legal systems. global financial crisis in the four Visegrad countries. Regarding the question of whether labour mobility Demand and monetary shocks remained asymmetric in helped in the adjustment to asymmetric shocks in the the region even after the financial crisis. Visegrad region, the results show that the share of labour Moreover, the results show that the highest correla- mobility in the four countries was low. This was meas - tion of shocks occurred with regard to external supply ured using the percentage of net migration to total popu- shocks dominated by Hungary. External supply shocks lation in each country. The value was less than 1%. The and domestic supply shocks became asymmetric after results from the ARDL models before and after the finan - the global financial crisis. Demand shocks and mon - cial crisis show that migration has a positive impact on etary shocks remained asymmetric after the crisis. employment in the Visegrad region. However, the adjust- Slovakia had the highest correlation of external supply ment from migration or labour mobility was slow due shocks, Hungary the highest correlation of domestic to the small size of the speed of adjustment obtained for supply shocks, and Poland the highest correlation of each of the countries. The size of the speed of adjustment demand shocks. In addition, the results show that there suggests that a shock is absorbed in 3−5 years, which is were more changes in the correlation of supply shocks similar to the results of Decressin and Fatás (1995), who than the correlation of demand shocks. For the period concluded that a significant proportion of the shock is before the crisis and after the crisis, there were positive absorbed through labour migration after four years. changes in the correlation of supply shocks and nega- Furthermore, the results of this paper are similar to tive changes in the correlation of demand shocks. those of Pelagidis (1996), who showed that migration The results of this paper also show that labour mobil - within the EU as a percentage of total population was ity helps in the process of adjustment to asymmetric less than 1% on average. The results also correspond shocks in the region, as the coefficient for migration with those of Martin and Taylor (1996), Obstfeld and in the models is positive, indicating a positive impact. Peri (1998) and Piracha and Vickerman (2002), who con- However, the size of the adjustment coefficients indi - cluded that labour mobility as a share of total popula- cates that the countries converged after asymmetric tion was lower in European countries than in the US. De shocks but in a very slow process. Even though there Grauwe and Vanhaverbeke (1991) studied labour mobil- was high employment of migrant labour in the region ity across several Western European countries and con- ranging between 60 and 70%, the total number of cluded that the annual flow of migrants was less at the migrants in the region was low compared to the total national level than interregional migration. The findings population (less than 1%). The number of migrants in above suggest that labour mobility in Visegrad countries the US was higher than in the Visegrad region. is too low to act as an efficient adjustment mechanism to Furthermore, there was high participation of migrant asymmetric shocks. However, as concluded by Puhani workers in the civil service in the US but not in the (1999), if the cause of the low labour mobility stems from Visegrad countries. These and other factors contribute the fact that there are not enough economic incentives significantly to the slow process of adjustment to asym - to migrate, the above conclusion might change if the metric shocks in terms of labour mobility. Plausibly, low obstacles or challenges and incentives are given policy labour mobility points partly to the fact that the costs consideration. of large-scale labour movement in Visegrad countries are greater than the benefits in the areas of migra - tion. Other obstacles include the non-transferability of 6 Conclusion pension rights, the restrictions on the right to social This paper investigates the nature and correlation of security, and nationality restrictions with respect to shocks among the Czech Republic, Hungary, Poland and recruitment in the civil service. There are other issues Slovakia. It also explains the role of labour mobility in the such as non-recognition of qualifications from member adjustment to asymmetric shocks among the countries states and asymmetry of information with regard to jobs that form a regional economic and political bloc called in other member countries. The consequence is that the Visegrad. The paper applies a SVAR model to explain role of labour mobility as an adjustment mechanism to the nature and correlation of shocks. Cointegrated panel asymmetric shocks might improve through policies but ARDL models are employed to assess the role of labour the change will be marginal and gradual in the Visegrad mobility in the adjustment process. The results show countries. Given the obvious picture of an ageing popu- that all shocks were asymmetric in the period before the lation across the Visegrad region, migration is a force global financial crisis, with a few symmetries occurring that if given the necessary attention, will play a signifi - in external supply shocks and domestic supply shocks. cant role in the economies of these four countries. The results also show that there were more symmetric 16 Page 18 of 19 D. Nchor Acknowledgements Clarida, R., Gali, J.: Sources of real exchange rate fluctuations: how important Not applicable. are nominal shocks? Accessed 4 Apr 2020 <https ://www.nber.org/paper s/w4658 .pdf (1994). Authors’ contributions Dao, M. D., Furceri, D., Loungani, P.: Regional labour market adjustments in the I wrote the entire paper. I also read it through before approving it for submis- United States and Europe, IMF Working Paper 2014/26. https ://www. sion. 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