Baltic Journal of Economics Taylors.Francis Group Routledge ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rbec20 The ins and outs of Central European unemployment Vladislav Flek, Martin Hála & Martina Mysíková To cite this article: Vladislav Flek, Martin Hála & Martina Mysíková (2022) The ins and outs of Central European unemployment, Baltic Journal of Economics, 22:1,49-67, DOI: 10.1080/1406099X.2022.2083306 To link to this article: https://doi.org/10.1080/1406099X.2022.2083306 © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis G r o u p m Published online: 08Jun 2022. Submit your article to this journal 07 Mil Article views: 4 5 7 5l View related articles Gf View Crossmark data 07 Full Terms & Conditions of access a n d use can be f o u n d at https://www.tandfonline.com/action/journallnfo rmation?journalCode=rbec20 BALTIC JOURNAL OF ECONOMICS 2022, VOL. 22, NO. 1, 49-67 https://doi.org/! 0.1080/1406099X.2022.2083306 !J Routledge Taylor & Francis Croup 9 OPEN ACCESS |*> Check for updates The ins and outs of Central European unemployment Vladislav Flek © , Martin Hála © and Martina Mysíková Department of Economics and Law, ŠKODA AUTO University, Mladá Boleslav, Czech Republic A B S T R A C T We examine the role of unemployment inflows and outflows in contributing to unemployment cyclically in Czechia and Poland, using data from the European Union Statistics on Income and Living Conditions, and a three-state model of unemployment variance decomposition. We find that the labour market fluidity is higher in Poland than in Czechia, with Polish workers moving in and out of unemployment more frequently than their Czech counterparts. For both countries, the upward unemployment dynamics was during 2008-2011 driven by counter-cyclical increases in the job-separation rate, rather than by pro-cyclical declines in the job-finding rate. The inflow-outflow split was nonetheless more balanced in Czechia. The two economies further diverged across 2015-2018: Czech unemployment declined prevailingly due to diminishing job separations, while in Poland it was mostly due to improving job-finding prospects. This signals a deeper insider-outsider fragmentation of the Czech labour market, even during the period of economic expansion. ARTICLE H I S T O R Y Received 6 September 2021 Accepted 25 May 2022 K E Y W O R D S Central Europe; labour market; unemployment variance decomposition; worker flows 1. Introduction This study focusses on the less highlighted aspect of labour market dynamics in Central Europe - on the frequency of worker flows in and out of unemployment, and how these flows account for variation in unemployment. Though there has been a fair amount of evidence collected particularly for Western/Southern Europe and the US (see, among others, Elsby et al., 2009,2011,2013,2015; Fujita & Ramey, 2009; Petrongolo & Pissarides, 2008; Shimer, 2012), we know relatively less about the inflow-outflow split to unemployment variation in Central European economies (Baranowska-Rataj & Magda, 2013; Flek et al., 2018; Strawinsky, 2009 are among the few exceptions known to us). Our study seeks to bridge this gap, assuming that identification of the labour-marketdriven sources of unemployment variation is of policy interest. In the words of Elsby et al. (2011, p. 339),'... policy that focused on encouraging outflows from unemployment may not be as relevant in an economy in which rises in unemployment were driven by changes in the rate of outflows from employment'. CONTACT Vladislav Flek @ vladislav.flek@savs.cz Q Department of Economics and Law, ŠKODA AUTO University, Na Karmeli 1457, 293 01 Mladá Boleslav, Czech Republic © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 50 @ V. F L E K E T A L . We assess the contributions of variations in unemployment inflow and outflow rates to variations in Czech and Polish unemployment during 2008-2011 and 2015-2018, using data on economic activity from the European Union Statistics on Income and Living Conditions (EU-SILC), and a three-state model of unemployment variance decomposition borrowed from Elsby et al. (2011). The chosen time periods are characterised by contrasting unemployment trends, and are instructive in assessing the key drivers of unemployment cyclically and the nature of labour market adjustments to macroeconomic fluctuations. Specifically, we address the following research questions: Is rising unemployment associated decisively with (counter-cyclical) increases in the job-separation rate, with (pro-cyclical) declines in the job-finding rate, or with a relatively balanced contribution of the two? Do unemployment reductions occur mainly due to diminishing job-loss risks of insiders, or due to improving job-finding prospects of unemployed workers? How relevant are the contributions of flow rates involving economic inactivity? Is there any link between the 'ins' and 'outs' of unemployment and labour market regulatory frameworks? What can the results tell us about economic policies adopted to counteract rises in unemployment? The 'retrospective' nature of our study is given by the fact that more recent developments do not yet display comparable degrees and lengths of cyclical swings in unemployment dynamics. This includes the pandemic crisis which has been accompanied by massive fiscal support aimed at preventing abrupt rises in unemployment, which would otherwise result from lockdowns and other measures that hindered normal economic activity. We believe that the novelty of our study lies in its longer-run view of labour market adjustment mechanisms, and how they respond to cyclical disturbances in two Central European economies, which remain almost completely unstudied in prior analyses. In addition, we apply in an innovative way the matched longitudinal monthly data of the EU-SILC database, as opposed to more frequently used quarterly data from the European Labour Force Survey. In the next section, we discuss theoretical, methodological, institutional and empirical issues linked with unemployment variance decomposition. In particular, we address the economic importance and interpretation of unemployment variance decomposition over various business cycle stages, highlight various methodological approaches, explain reasons for our model choice, and refer to results in pre-existing literature. Then we explain why we utilise monthly data for the purposes of unemployment variance decomposition, describe our data processing procedures, and specify the model. Next, we report results which point to non-negligible differences in the sources of unemployment variation between the less protective/more fluid Polish labour market and the more protective/less fluid Czech labour market. Based on these results, the concluding section offers an implicit assessment of policies shaping the dynamics of both labour markets during the 'post-transition' stage of their development. 2. Literature overview Mortensen and Pissarides (1999) popularised the idea that the strictness of employment protection legislation (EPL) matters in affecting the patterns of unemployment variation.1 Strict EPL imposes explicit and implicit constraints on hiring/layoff decisions, and reduces worker turnover. Countries with more stringent EPL are thus likely to experience relatively BALTIC JOURNAL OF ECONOMICS © 51 low worker flows in and out of unemployment. This is frequently the case in the 'sclerotic' labour markets of continental Western and Southern Europe, which record much lower unemployment inflow and outflow rates than Anglo-Saxon and Nordic countries (see, for instance, Elsby et al., 2013; Garda, 2016 for empirical evidence). But this is obviously not to say that firms operating in less fluid and/or more protective national labour markets may not need to adjust the size of their workforce to product demand fluctuations. Lay-offs remain an option, particularly in recessions. However, as lay-offs are formally complicated and costly, a pro-cyclical variation (reduction) in unemployment outflows may decisively account for increasing unemployment. This leads to labour market segmentation (fragmentation) into insiders enjoying long job-tenures and outsiders suffering from prolonged unemployment spells. Petrongolo and Pissarides (2008) apply a two-state model of steady-state unemployment variance decomposition in continuous time along the lines of Fujita and Ramey (2008),2 and list France as a typical example, where the unemployment rate dynamics has been driven decisively (and in recessionary periods almost exclusively) by a pro-cyclical variation in the job-finding rate of unemployed workers since 1991. In contrast, studies which deal with the more liberal British labour market point to relatively high, or even decisive, importance of unemployment inflows in accounting to unemployment variation (Elsby et al., 2011; Petrongolo & Pissarides, 2008; Smith, 2011 represent prominent examples). The results obtained by Elsby et al. (2011) are perhaps most instructive: They apply a three-state model of logarithmic steady-state variance decomposition in discrete time, and point to an approximately 70:30 inflow-outflow split. Specifically, they find that, since 1975, the decisive contribution to unemployment variation in the UK has been a counter-cyclical variation (rise) in the job-separation rate during recessions. The drivers of unemployment cyclically have been examined by many other studies, and the results are evidently sensitive to data and model selections. Leaving aside the frequently controversial evidence collected for the highly fluid labour market in the US (see, for instance, Darby et al., 1986; Elsby et al., 2009; Fujita & Ramey, 2009; Shimer, 2012), Elsby et al. (2013) offer a contribution which includes the behaviour of European labour markets. Based on a two-state model in continuous time developed by Shimer (2012), they apply a new recursive formula for log changes in the non-steady-state unemployment rate, which enables them to decompose unemployment variation when unemployment deviates from its steady state. They conclude that variation in the job-finding rates of unemployed workers contributes decisively to unemployment variation in Anglo-Saxon and Nordic countries, while the labour markets of continental Western and Southern Europe display a balanced inflow-outflow split. Space for further research is even more open in the case of formerly centrally planned economies. The two economies of our research interest differ in size and structure, but are similar in their communist past, history of economic transition, GDP per capita, and relatively recent European Union entry. In formal institutional and policy terms, both countries maintain relatively ungenerous unemployment benefit replacement ratios, devote relatively low amounts of their GDP to expenditures on active labour market policies, record comparable average effective ages of labour market exit, and experience weakening union density (Fadejeva, 2019; Martin, 2014; OECD, 2020, 2022). 52 @ V. F L E K E T A L . However, the strictness of Czech employment protection legislation is consistently among the highest in the OECD and European Union countries, while in Poland it is less stringent, being roughly comparable to those in Nordic countries. The extent of the tax wedge is also considerably higher in Czechia than in Poland, with the countries actually occupying different positions relative to the OECD average. The countries differ also in the share of temporary jobs in total employment, which is far higher in Poland (Fadejeva, 2019; Lewandowski & Magda, 2018; OECD, 2020).3 Business cycle fluctuations were more moderate in Poland than in Czechia during the global financial and economic crisis, and were not fully synchronised. While Poland actually avoided any absolute declines in real GDP, Czechia was hit more strongly, particularly in 2009. Yet, both economies faced a negative output gap opening in 2008. Negative figures prevailed until 2011, as did an increasing unemployment trend. The 2015-2018 period was in turn commonly characterised by the output gap remaining mostly in positive values, along with diminishing unemployment (CNB, 2012,2019; Galuscak et al., 2021; IMF, 2011, 2022). With a degree of simplification, the 2008-2011 and 2015-2018 periods serve to represent two different business cycle stages with contrasting unemployment trends and output gap evolutions. Which type of worker flows was decisive in contributing to unemployment fluctuations in Czechia and Poland, given the above highlighted institutional and cyclical characteristics? Were the contributions of 'ins' and 'outs' to variation in unemployment comparable across the countries studied, thus possibly displaying a common 'liberal' or 'protective' pattern, or did they differ remarkably? To our best knowledge, the existing evidence is rather scarce and would benefit from updated insights. Strawinsky (2009) utilises two main approaches towards the no-log steady-state variance decomposition in continuous time. Applying a two-state model along the lines of Shimer (2007), he argues that the main driver of unemployment variation in Poland is variation in the job-finding rate of unemployed workers. However, when following the threestate approach of Petrongolo and Pissarides (2008), he finds that unemployment volatility is driven predominantly by variation in unemployment inflow rates. Baranowska-Rataj and Magda (2013) follow the model of Elsby et al. (2013), and concentrate on Polish youth and prime-age groups using a gender breakdown. They find that the contributions of variations in the job-finding rates to variation in unemployment range between approximately 40 and 60 per cent (meaning that the contributions vary by age and gender), while the contributions of variations in the job-loss rates amount to some 50-60 per cent. Flek et al. (2018) focus on various Czech and Polish age groups. The study applies a three-state model in continuous time developed by Smith (2011) and concludes that variations in the job-finding rates contribute decisively to variation in unemployment (except for Polish youth, where the contribution of variation in the job-separation rate dominates). Given the relative lack of knowledge of the drivers of unemployment dynamics in Central European economies, we believe that the transition channels which involve economic inactivity should not be omitted, so we adopt a three-state model to demonstrate how unemployment evolves not only in response to variations in direct worker flows between unemployment and employment, but also to show the corresponding contributions of unemployment inflows and outflows via economic inactivity. We decompose variations in steady-state unemployment rates, and not in actual unemployment rates. BALTIC JOURNAL OF ECONOMICS © 53 Variance decomposition of actual unemployment rates is analytically more feasible for two-state models (Elsby et al., 2013) than for a three-state model, which is explored in our analysis. 3. Data and model specification 3.7. Data selection and organisation Most European studies devoted to worker flows between employment (E), unemployment (U) and inactivity (/) are based on quarterly data stemming from Labour Force Surveys (LFS).4 Quarterly flow rates are more prone to time-aggregation bias than monthly rates, because they omit a higher number of multiple transitions (e.g. from unemployment to employment and back to employment) within the unit of observation. Controls for time-aggregation bias represent a research challenge in their own right, and these controls are typically missing when quarterly rates are used. Quarterly rates thus underestimate the degree of labour market fluidity, compared to monthly rates. On the other hand, if an individual experiences a transition once in a quarter, the quarterly data would simply register the change, while the monthly data would record one change and two unchanged statuses, i.e. a monthly average 1/3 flow rate. That is why, almost by definition, monthly rates differ from quarterly rates, and monthly rates are more accurate for our research purposes. EU-SILC datasets (as released in 2020 and 2021) are the only source available to us for calculation of monthly rates. EU-SILC is an annual survey harmonised by Eurostat across European countries. Its longitudinal version is designed as a four-year rotational panel, in which approximately one quarter of respondents are replaced each year. The data involve retrospective, selfreported monthly information on each respondent's labour market status (employment, unemployment, inactivity). The longitudinal EU-SILC allows us to utilise four-year pure panels, and create matched chains of monthly flow rates over these periods, which refer to a constant number of the working-age population. This is the key condition for estimating the steady-state unemployment rate (and its variance decomposition) consistently, as stated explicitly below Formulas (1)- (3) in the following subsection. In contrast, the quarterly flow rates published by Eurostat stem from respondents' responses made just for two consecutive points of time, and do not satisfy the above noted condition for longer time periods. Unlike the cross-sectional European Union Labour Force Survey (EU-LFS), the longitudinal version of EU-LFS is not yet routinely available for research purposes. This represents another key reason for adopting the longitudinal EU-SILC. We utilise two longitudinal EUSILC datasets covering January 2008-January 2011 and March 2015-September 2018, as these two time periods are the most illustrative of the contrasting unemployment trends (see Figure 1). They include 37(43) monthly observations of the labour market status of each respondent, and are, in all cases, shorter than 48 months, which is the maximum length of observations one could extract from the panels.5 Because of methodological differences, the unemployment rates calculated from longitudinal EU-SILC are typically higher than those reported in Labour Force Surveys.6 However, Figure A l in the Appendix documents that their quarterly correlations are high, ranging between 0.75 and 0.95. As noted above, we limit our dataset to respondents 54 @ V. F L E K E T A L . Figure 1. Monthly unemployment rates in Czechia (CZ) and Poland (PL). Source: Longitudinal EU-SILC 2012 and 2019 (2020-03 and 2021-09 versions); authors' computations. Note: In per cent of workforce. who remained in each selected sample over its full time horizon, so that the monthly labour market states of each respondent are continually matched, and their chains are of the maximum possible length.7 For the respective time periods, the unweighted matched Czech samples contain 3,284 (2,600) individuals, while the Polish ones consist of 4,491 (3,944) individuals.8 We apply longitudinal weights designed by Eurostat to account for non-response and attrition biases. As a result, the weighted matched samples are organised as pure panels, in which the total amount of working-age populations (E+ U+ I) remains constant over the chosen time periods. The respondents' ages range between 16 and 64 in the first year of observation, and between 19 and 67 in its fourth year. Gross worker flows involve the numbers of individuals changing their labour market status in month f + 1, compared to month f. (Er -> Ur + 1 ) represents flows from employment to unemployment, and so forth for (Er -> /r + 1 ); (Ut ->• Ef + 1 ); (Ut ->• /t + 1 ); (/r ->• Et + 1 ); [lt ->• Ut+-\). Individuals remaining in their initial status are defined as ( E r ^ E r + 1 ) ; {u < ~> u ^ (/f - / f + l ) -Then < = 0^+7) + iUt -> UW ) + iUt -> M — — ^f + 1 ^ expresses an individual's probability of transitioning from unemployment in month f to employment in month t + 1, and so forth for X^1 ; Afu ; Af; Af; A'r u . After multiplying by 100, the figures can be interpreted as flow transition rates, that is, as percentages of a given labour market stock (E; U; I) in month f, which are subject to a specific gross flow in month t + 1 . The monthly series of transition rates are seasonally adjusted by the XI3 filter. We also calculate the average monthly transition rates. For instance, A U f = U t f f + 1 • 100 stands for the average monthly jobUt -> E f + 1 + Ut -> Ut+i + Ut -> finding rate of unemployed workers. 3.2. The model Based on the arguments discussed earlier, we utilise monthly matched chains of flow transition rates in discrete time, presume the proximity of actual and steady-state BALTIC JOURNAL OF ECONOMICS © 55 unemployment rates, and distinguish two periods with opposite unemployment trends. In line with Elsby et al. (2011), we define the following stock-flow identity in expressing a change in the total number of unemployed persons: AL7t + 1 =Ut+,-Ut = (XEU Et + A'f u /f) - (Xf + \f)Ut (1) We can analogously express a change in total employment: AEf+i - E f + 1 -Et = {XfUt + Af/f ) - (XEU + Af)Et . (2) Finally, the following identity holds for a change in the total number of inactive individuals: A/t + 1 = / t + 1 - lt = {X?Ut + AfEt ) - (X't u + Af)/t (3) Formulas (1)-(3) involve three labour market stocks (states) and six flow transition rates, as defined above. Under a constant working-age population P (P = Ut + Et + lt), setting A U r + 1 = A E f + 1 = 0 implies A/t + 1 = 0. Then all three labour market stocks remain constant between months f and f + 1, and the unemployment rate also remains constant. The corresponding value of the unemployment rate u* (in per cent) is labelled as the steadystate unemployment rate. It can be expressed in terms of flow transition rates as follows: u '=^ ( 4 ) where sf = A? + Af - j * . and ft = X f + A f ' - / h ^ ? A f + A f A f + A f The unemployment inflow rate st in formula (4) consists of two terms: Interpretation of the first term (XEU ) is evident, as it stands for the rate of direct inflows to unemployment from employment. When we refer to this rate later in the text, we label it the job-separ/ A / u \ ation rate. The second component I Af —^ I represents the inflow rate to unemployV Af + At / ment from employment via inactivity. The unemployment outflow rate ft analogously splits into two components, with A ^ representing the direct job-finding rate of unem/ A/ £ \ ployed workers, and the second one I A f ' - ^ — ^ I defining the rate of outflow from V At + At / unemployment to employment via inactivity. Unemployment inflow and outflow rates do not necessarily remain constant over time, and the steady-state unemployment rate may vary accordingly. In terms of logarithmic decomposition: A i n u * « «t [A In st - A In ft] (5) where A i n u * = I n U f - I n u * ^ , at = 1 - u * _ v A l n s r = InSf-lnSf-^ etc. We can approximate the log changes in unemployment inflow and outflow rates in formula (5) as follows: A In st « cos tA In Afu + (1 - a5 t)A In XE t lu , where G?t=-*-, and A f u = Af f 5r Af + Af UE XlE A In ft ^ ojf tA In \f + (1 - Jt)A In Xu t SE , where o/t = and AJ^ = AJ7 ' f ft Af + Af (6) (7) 56 @ V. F L E K E T A L . Combining equations (5-7), we obtain the final approximation: A i n u * & ar[a>;A In \ E U + (1 - w*)A In A £ , u - • st(Et + Ut) - (st + ft)Ut = 0 =>• s, = ^ +f f ) £r + Ut Et + Ut St + ft f St + ft 10. Some results, which are actually close to zero, may bear a negative sign. This applies particularly to J5BU and/or J5UIE. Such seemingly counter-intuitive results are rather exceptional (see, for instance, Elsby et al., 2011), but may nonetheless occur due to a weak negative correlation BALTIC JOURNAL OF ECONOMICS © 63 between the series of logarithmic differences of the steady state unemployment rate (A In u*) and the series of terms at(1 - wpA In A f u and/or at[o/t — 1)A In A"/ E . 11. Relatively better job-finding prospects, compared to Czechia, apply particularly to the least qualified and older Polish unemployed workers (Flek & Mysíková, 2015). 12. In Section 2 we note that formal employment protection legislation (EPL) is more relaxed in Poland, compared to Czechia. At the same time, results in Table 1 suggest that the labour market fluidity is higher in Poland than in Czechia. That is why we suggest (for these two countries) that higher labour market fluidity appears to be consistent with more relaxed formal EPL and vice versa. In contrast, Eamets and Masso (2005) refer to Baltic countries and argue that the relatively high effective fluidity of their labour markets does not stem from relaxed formal EPL, but rather from problems with enforcement of formally strict employment protections. 13. According to Strawinski (2009), the inflow-outflow split in Poland is 60:40 (see Table 2). This result differs somewhat from ours, not least because it refers to the different time period and involves more business cycle stages. Alternative results for the Czech aggregate unemployment variance decomposition are not known to us. 14. In sum, J5BU and /3UIE contribute to unemployment variation by 3-17 per cent, depending on the country and time period analysed (see Table 2). Galuščák et al. (2021) apply a net-flow approach towards investigating unemployment cyclically in Czechia and P oland. They also conclude that the role of inactivity-related (net) flows in explaining the cyclical properties of unemployment rates is rather small. In contrast, Elsby et al. (2015) report for the US that the participation margin accounts for around one-third of unemployment fluctuations. Acknowledgements The datasets of the European Union Statistics on Income and Living Conditions (EU-SILC LT UDB 2012 - version of 2020-03; EU-SILC LT UDB 2019 - version of 2021 -09) were made available to the authors on the basis of the Research project proposal No. 242/2016-EU-SILC, granted to ŠKODA AUTO University by the European Commission, Eurostat. The authors thank Aleš Bulíř and Kamila Fialová for discussion on earlier versions of this study, and two anonymous referees for their helpful suggestions and comments on the submitted version. The responsibility for all results and conclusions drawn from the data lies entirely with the authors. Disclosure statement No potential conflict of interest was reported by the author(s). Notes on contributors Vladislav Flek is Associate P rofessor at SKODA AUTO University. He obtained P h.D. in Economics in 1992 from the Czechoslovak Academy of Sciences. From 1997 to 2020 cooperated with Charles University in Prague as Associate Professor in Labour Economics. Between 1992 and 2007 he worked in the Czech National Bank as an economic research coordinator and an advisor to the Bank Board. Afterwards he moved to the Ministry of Finance as Head of the Organizational Committee for Euro Adoption, a post he held until 2011. Research Fellow at the Catholic University of Leuven LICOS (1992), Stanford University - GSB (1995-1996), and University of Oxford - Pembroke College (1996). Martin Hála is Research Fellow at SKODA AUTO University. He received his P h.D. in Statistics at the Charles University in Prague, Faculty of Mathematics and Physics. He teaches at the University of New York in Prague, the Czech Technical University, and the Metropolitan University, P rague. Martina Mysíková is Research Fellow at SKODA AUTO University. She obtained P hD. in Economics from the Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague. 64 @ V. FLEK ET AL. Since 2008, she has been affiliated with the Institute of Sociology of the Czech Academy of Sciences. She worked in the Czech Statistical Office (2004-2008) and participated in the methodology and coordination of the international household survey Statistics on Income and Living Conditions (EU-SILC). ORCID Vladislav Flek © http://orcid.org/0000-0002-3132-3983 Martin Hála http://orcid.org/0000-0003-4535-0330 Martina Mysíková http://orcid.org/0000-0002-6340-4753 References Abbritti, M., & Weber, M. (2018). Reassessing the role of labor market institutions for the business cycle. International Journal of Central Banking, 74(1), 1-34. Baranowska-Rataj, A., & Magda, I. (2013). Decomposition of trends in youth unemployment - the role of job accessions and separations in countries with different employment protection regimes. Working Paper No. 26, Institute of Statistics and Demography, Warsaw School of Economics, Warsaw. CNB (2012). Inflation Report 1/2012. Czech National Bank. CNB (2019). Inflation Report 1/2019. Czech National Bank. Darby, M., Haltiwanger, J. C , & Plant, M. W. (1986). The ins and outs of unemployment: The ins win. NBER Working Paper No. 1997. National Bureau of Economic Research, Cambridge, MA. Eamets, R., & Masso, J. (2005). The paradox of the Baltic states: Labour market flexibility but protected workers? European Journal of Industrial Relations, 7 7(1), 71-90. https://doi.org/10.1177/ 0959680105050403 Elsby, M. W. L., Hobijn, B., & Sahin, A. (2013). Unemployment dynamics in the OECD. Review of Economics and Statistics, 95(2), 530-548. https://doi.Org/10.1162/REST_a_00277 Elsby, M. W. L., Hobijn, B., & Sahin, A. (2015). On the importance of the participation margin for labor market fluctuations. Journal of Monetary Economics, 72(May), 64-82. https://doi.Org/10.1016/j. jmoneco.2015.01.004 Elsby, M. W. L., Michaels, R., & Solon, G. (2009). The ins and outs of cyclical unemployment. American Economic Journal: Macroeconomics, 7(1), 84-110. https://doi.Org/10.1257/mac.1.1.84 Elsby, M. W. L., Smith, J. C , & Wadsworth, J. (2011). The role of worker flows in the dynamics and distribution of UK unemployment. Oxford Review of Economic Policy, 27(2), 338-363. https://doi.org/ 10.1093/oxrep/grr014 Fadejeva, L. (2019). Labour market reforms in the European Union: An overview. On-line report (Macroeconomics), Latvijas Banka, Riga. Accessed December 18, 2021. https://www. macroeconomics.lv/labour-market-reforms-european-union-overview Feldmann, H. (2005). Labour market institutions and labour market performance in transition countries. Post-Communist Economies, 17{\), 47-82. https://doi.org/10.1080/14631370500052720 Feldmann, H. (2009). The unemployment effects of labor regulation around the world. Journal of Comparative Economics, 37(1), 76-90. https://doi.org/10.1016/jjce.2008.10.001 Fialová, K., & Schneider, O. (2009). Labor market institutions and their effect on labor market performance in the new EU member countries. Eastern European Economics, 47{3), 57-83. https://doi.org/ 10.2753/EEE0012-8775470303 Flek, V., Hála, M., & Mysíková, M. (2018). Unemployment and age-based labour market segmentation. Politická ekonomie, 66(6), 709-731. https://doi.Org/10.18267/j.polek.1227 Flek, V., & Mysíková, M. (2015). Unemployment dynamics in Central Europe: A labour flow approach. Prague Economic Papers, 24{\), 73-87. https://doi.Org/10.18267/j.pep.501 Fujita, S., & Ramey, G. (2008). The cyclically of separation and job finding rates. Working Paper No. 07- 19. Federal Reserve Bank of Philadelphia. BALTIC JOURNAL OF ECONOMICS © 65 Fujita, S., & Ramey, G. (2009). The cyclically of separation and job finding rates. International Economic Review, 50(2), 415-430. https://doi.Org/10.1111/j.1468-2354.2009.00535.x Galuscäk, K., Sole, J., & Strzelecki, P. (2021). Labour market flows over the business cycle: The role of the participation margin. Eastern European Economics, 59(5), 449-471. https://doi.org/10.1080/ 00128775.2021.1958688 Garda, P. (2016). The ins and outs of employment in 25 OECD countries. OECD Economics Department Working Papers No. 1350, Organisation for Economic Co-operation and Development, Paris. Haltiwanger, J. S., Scarpetta, S., & Schweiger, H. (2014). Cross country differences in job reallocation: The role of industry, firm size and regulations. Labour Economics, 26(C), 11-25. https://doi.org/10. 1016/j.labeco.2013.10.001 IMF (2011). Republic of Poland: Selected Issues. IMF Country Report No. 7 7/767, International Monetary Fund, Washington, DC. IMF (2022). Republic of Poland: Selected Issues. IMF Country Report No. 22/59, International Monetary Fund, Washington, DC. Lehmann, H., & Muravyev, A. (2012). Labour market institutions and labour market performance: What can we learn from transition countries? Economics of Transition, 20(2), 235-269. https:// doi.org/10.1111/j.1468-0351.2012.00435.x Lewandowski, P., & Magda, I. (2018). The labor market in Poland, 2000-2016. IZA World of Labor, Issue 426, Institute for the Study of Labor (IZA), Bonn. Martin, J. M. (2014). Activation and active labour market policies in OECD countries: Stylized facts and evidence on their effectiveness. IZA Policy Paper No. 84, Institute for the Study of Labor (IZA), Bonn. Mortensen, D. T., & Pissarides, C. A. (1999). Job reallocation, employment fluctuations and unemployment. In J. B. Taylor & M. D. Woodford (Eds.), Handbook of macroeconomics (Vol. 1, pp. 1171-1228). Elsevier. OECD (2020). Employment outlook 2020. Organisation for economic co-operation and development. OECD Publishing. OECD (2022). Pensions at a glance. Organisation for Economic Co-operation and Development. Accessed February 23, 2022. https://stats.oecd.org/lndex.aspx7Queryld =69218 Petrongolo, B., & Pissarides, C. (2008). The ins and outs of European unemployment. American Economic Review, 98(2), 256-262. https://doi.Org/10.1257/aer.98.2.256 Shimer, R. (2007). Reassessing the ins and outs of unemployment. NBER Working Paper No. 13421, National Bureau of Economic Research, Cambridge, MA. Shimer, R. (2012). Reassessing the ins and outs of unemployment. Review of Economic Dynamics, 15 (2), 127-148. https://doi.Org/10.1016/j.red.2012.02.001 Smith, J. C. (2011). The ins and outs of UK unemployment. Economic Journal, 727(552), 402-444. https://doi.Org/10.1111/J.1468-0297.2011.02428.x Strawinsky, P. (2009). Ins and outs of Polish unemployment. Central European Journal of Economic Modelling and Econometrics, 7(3), 243-259. Appendix Table Al. Average monthly flow transition rates between employment and inactivity Czechia Poland 1/2008-1/2011 3/2015-9/2018 1/2008-1/2011 3/2015-9/2018 From employment to inactivity (Af^) From inactivity to employment (A, f ) 0.35 (9.1) 0.28 (21.2) 0.53 (20.5) 0.66 (26.3) 0.48 (13.2) 0.52 (15.4) 0.89 (19.1) 0.88 (17.3) Note: In per cent of the respective labour market stock, as defined in Subsection 3.1. Coefficients of variation are reported in parentheses. Source: Longitudinal EU-SILC 2012 and 2019 (2020-03 and 2021-09 versions); authors' computations. 66 @ V. F L E K E T A L . CZ 2008Q1-2011Q1 (0.75) 12 i 10 8 6 4 I 1 1 1 1 1 1 1 1 1 1 1 08Q1 08Q3 09Q1 09Q3 10Q1 10Q3 11Q1 PL 2008Q1-2011Q1 (0.88) 12 6 4 I , , , , , , , , , , , 1 08Q1 08Q3 09Q1 09Q3 10Q1 10Q3 11Q1 EU-SILC EU-LFS Figure Al. Quarterly unemployment rates in Czechia (CZ) and Poland (PL). Source: Longitudinal EUSILC 2012 and 2019 (2020-03 and 2021-09 versions); EU-LFS (Eurostat database, variable lfsq_urgan); authors' computations. Note: In per cent of the workforce; EU-SILC unemployment rates are averaged from monthly data. Correlation coefficients between both unemployment rates are reported in parentheses. BALTIC JOURNAL OF ECONOMICS @ 67 CZ 1/2008-1/2011 (logarithmic scale) PL 1/2008-1/2011 (logarithmic scale) 1/2008 7/2008 1/2009 7/2009 1/2010 7/2010 1/2011 1/2008 7/2008 1/2009 7/2009 1/2010 7/2010 1/2011 CZ 3/2015-9/2018 (logarithmic scale) .Af (left scale) 1.6% 0.8% wvi«=>4* (right scale 0.2% 0.2% 3/2015 9/2015 3/2016 9/2016 3/2017 9/2017 3/2018 9/2018 PL 3/2015-9/2018 (logarithmic scale) If (left scale) 3/2015 9/2015 3/2016 9/2016 3/2017 9/2017 3/2018 9/2018 Figure A2. Monthly flow transition rates from employment to inactivity (Af), and from inactivity to employment (Af) in Czechia (CZ) and Poland (PL). Source: Longitudinal EU-SILC 2012 and 2019 (2020-03 and 2021-09 versions); authors' computations.