Open Access

Transitions in labour market status in EU labour markets

IZA Journal of European Labor Studies20143:17

https://doi.org/10.1186/2193-9012-3-17

Received: 25 November 2013

Accepted: 27 June 2014

Published: 9 September 2014

Abstract

We discuss the ease with which individuals can move between employment, unemployment and inactivity over time in the EU. Transitions from unemployment and inactivity back into employment are relatively weak in Central Eastern, Mediterranean and Continental European labour markets compared to Nordic European countries. On the basis of a mobility index used in the paper (Shorrocks, Econometrica, 46:1013-1024, 1978), labour markets in Spain, Luxemburg, the Netherlands, Denmark and Sweden are the most mobile, with these results mainly reflecting mobility of people below the age of 29, highly educated and female workers. Looking at some “macro” explanatory factors, the results suggest a mixed picture.

Jel codes

J21, J60, J82, E24

Keywords

Transition probabilities Labour market mobility LFS micro data EU countries

1 Introduction

This paper utilises the available microeconomic data behind the Eurostat’s Labour Force Survey (LFS) to present alternative measures of labour market mobility across EU countries over time, and in particular the ease of transition between the labour market statuses of unemployment, employment and out of the labour market (inactivity) over the period 1998–20081. As well as identifying stylized facts, the aim of this paper is to shed some light on the functioning of the EU labour markets.

Until the onset of the crisis, the EU experienced a reduction in unemployment rate, essentially driven by a fall in long term unemployment and unemployment duration (Table 1)2. A quick look at the standardized unemployment (employment) rates by country confirms that most EU countries were successful in reducing (improving) unemployment (employment) before the crisis. However, across the EU, unemployment (employment) rates behaved very differently, with some countries displaying steadily declining (increasing) unemployment (employment) rates over time, while others exhibiting more marked unemployment (employment) fluctuations; i.e. with unemployment (employment) increasing (decreasing) after the 2001–02 global recession and – in many central eastern European EU countries – raising (waning) following the 1998 Russia crisis, before declining again (improving) in the light of EU membership (see also Epstein and Macchiarelli 2010; Macchiarelli 2013a, [b]).
Table 1

Unemployment and employment rates in the EU (1998–2008)

EU (changing composition)

Unemployment rate (%)

Long-term unemployment (12 months or >) as a % of the total unemployment

Employment rate (%)

Average unemployment duration in months

1998

10.3

48.0

61.2

18.3

1999

9.5

46.1

62.2

17.7

2000

8.5

45.4

63.2

17.4

2001

7.4

44.0

63.9

16.0

2002

7.7

40.1

64.2

15.6

2003

8.1

41.3

64.4

16.1

2004

8.3

41.0

64.6

15.7

2005

9.1

45.5

64.0

15.7

2006

8.3

45.3

64.8

15.7

2007

7.2

42.7

65.4

14.8

2008

7.1

37.0

65.9

12.4

Sources: Eurostat and OECD statistics (last column).

Alongside the macroeconomic picture of a decrease in unemployment rate and duration, the use of micro data can help assess if such developments at the EU level reflected an increase in the number of people transitioning from unemployment to employment, or, on the contrary, an increase in the transitions from unemployment to inactivity. Similarly, microeconomic data can help highlight whether the increase in the employment rate resulted from an increase in employment persistence (more people remaining in employment), an increase in transitions from unemployment to employment, or an increase in transitions from inactivity to employment. Finally, the use of microeconomic data also allows for the construction of measures of the degree of labour market flexibility, and how this varied across countries and over time. The analysis of transitions into and out of unemployment thus offers significant advantages over an analysis of macroeconomic developments, allowing us to observe the directions of flows and levels of status mobility behind any particular change in the aggregate employment, unemployment or inactivity rate. Moreover, the proposed methodology allows quantitatively assessing the role played by labour market flows, by readily analysing how mobility measures evolved over time and across worker groups (gender, age and education).

The contribution of the paper can be gauged under two perspectives. First, we provide results for a large set of countries, by providing a systematic, unconditional approach to estimate labour market transitions in most EU countries. Secondly, we exploit cross country differences in the size and the speed with which labour market changes took place over time.

In our analysis, a number of stylized facts are documented. Drawing on the labour market classifications of Boeri (2002) and Sapir (2006), we find that the probability of remaining in the same labour market status between two consecutive periods is high for all country groupings. Nonetheless, transitions from unemployment and inactivity back into the labour market are relatively weak in Central Eastern, Mediterranean and Continental European countries compared to the Nordic European countries. Secondly, comparisons of transition probabilities over time suggest that – until the onset of the financial crisis – the probability of remaining in unemployment over two consecutive periods decreased in Nordic and Continental countries, while it increased in the average Central Eastern and Mediterranean countries. At the same time, however, successful labour market entries (from outside the labour market) increased in Central Eastern European countries and the Nordics.

Finally, on the basis of an index for labour markets turnover used in the paper (Shorrocks 1978), labour markets in Spain, Luxemburg, the Netherlands, Denmark and Sweden are the most mobile on average, with these results mainly reflecting higher mobility of people below the age of 29, highly educated and female workers. We also find that mobility of all worker groups has generally increased over time in Continental and Mediterranean countries, as well as in Denmark and Sweden.

In the last section, we look at the link between macroeconomic developments and changes in mobility indexes. The results suggest that countries who experienced an increase in mobility are also those which increased their percentage of time limited (e.g., temporary) contracts and part time work, and viceversa. However, looking at unemployment rates and some structure indicators the results provide a mixed picture, suggesting that the sense of mobility and its implications strongly vary across countries.

The remainder of the paper is organized as follows. Section 2 presents the methodology and our main results. Section 3 looks at some explanatory factors behind the observed labour market mobility in each country. Section 4 concludes.

2 Labour market transitions

2.1 Transitions in labour status in the EU

A number of papers have focused on establishing the persistence of both unemployment incidence and duration using longitudinal data with a relatively short time horizon (Boeri and Garibaldi 2009; Petrangolo and Pissarides 2008; Brandolini et al.2006 for Europe; Kilponen and Vanhala 2009; Elsby et al. 2013 for OECD countries)3. These papers document an increase in status mobility during the last two decades, with differences in the extent of mobility across countries being attributed to institutional factors. Boeri and Garibaldi (2009) ask, for instance, why the decrease in unemployment does not show up as increased satisfaction in the labour market, a result they attribute to the increased risk of job loss that higher mobility implies. Elsby et al. (2013) instead question the validity of the assumption of a steady state decomposition for unemployment which forms the basis of a number of theoretical models. Petrangolo and Pissarides (2008) identify the relative role of inflow and outflow rate from unemployment in explaining labour market dynamics and conclude that the relative contribution of each depends on labour market institutions. In the same vein, Kilponen and Vanhala (2009) argues that European countries generally have low unemployment inflow and outflows rates which contribute to high rates and unemployment persistence. Brandolini et al. (2006) emphasise the need to acknowledge the group of non-participants (or potentially unemployed) when looking at labour market dynamics; accordingly the distinction provided for by the ILO definition of unemployment is only “artificial” and indeed non-participants and unemployed do not differ substantially in their job search activity.

We use gross data flows from the Eurostat’s Labour Force Survey (LFS) microdata for 23 countries. The UK, Germany (DE), Malta (MT) and Ireland (IE) are excluded from the analysis owing to a lack of data4. The remaining countries are grouped as follows:
  • Nordics, including The Netherlands (NL), Finland (FI), Denmark (DK) and Sweden (SE);

  • Continental, including Belgium (BE), France (FR), Luxemburg (LU) and Austria (AT);

  • Mediterranean, including Greece (GR), Spain (ES), Italy (IT), Cyprus (CY) and Portugal (PT);

  • Central Eastern, including Czech Republic (CZ), Estonia (EE), Latvia (LV), Lithuania (LT), Hungary (HU), Poland (PL), Romania (RO), Slovakia (SK) and Slovenia (SI).

The grouping above clusters countries according to social policy models, drawing on the definition of Boeri (2002) and Sapir (2006)5.

We use a relatively comprehensive sample which focuses on the period between 1998 and 2008. Stopping the sample in 2008 is motivated by the idea that EU labour markets sensitively lagged the slack in the real activity, showing a worsening of unemployment figures mainly starting from 2009 (see European Commission 2010). Hence, with the purpose of identifying stylized labour market facts, the crisis and ensuing labour adjustments are for now excluded.

Eurostat Labour Force Survey Statistics are available in yearly frequencies and are constructed from a rotating panel reporting information based on anonymous interviews. The LFS microdata dataset provides the longest time series of comparable and consistently defined individual level data that is available for the EU, and our sample consists of individuals between the ages of 16 and 64.

Year-on-year transitions are obtained based on the subjective assessment of the respondent’s current and past working situation6. In this way, the labour market status in the initial (t-1) and the final period (t) is the subjective assessment of the respondent’s current and past working status, reported at the time of the survey (t).

Using data from subjective classifications prompt several methodological questions. First, whether subjective classifications capture actual levels of labour market turnovers, or they capture, in fact, the behaviour of individuals potentially moving across labour market statuses (see Brandolini et al.2006)3. Secondly, retrospective data can go wrong as people can forget, make mistakes or simply do not respond, naturally giving rise to spurious changes in statuses. Third, period-censoring (or, collecting answers referring to the survey year and the year before) does not allow capturing flows between survey dates7.

The anonymous nature of the LFS data does not allow tracking individuals over time. This breaks down any form of serial correlation between classification errors in our sample. In other words, reporting errors at a given survey date are independent of errors in previous LFS waves. Furthermore, we rule out the possibility that non-responses are captured as spurious changes in status, by necessarily excluding the number of individuals for which labour market classifications are not reported for the survey year and, retrospectively, for the year before. Finally, by construction of transition probabilities (i.e. the labour market status in the initial and the final period is the subjective assessment of the respondent’s current and past working situation, reported at the time of the survey), any subjective bias between the “official” labour market status (i.e. as defined by the ILO) and its “reported” counterpart naturally simplifies out under the, likely, assumption that each individual’s subjective bias is constant over time.

From the LFS, we construct raw probabilities of moving or remaining in any labour market status, together with an index of mobility (Shorrocks 1978). Particularly, we consider nine possible transition probabilities across the statuses of employment, unemployment and out of the labour market (inactivity). The (ex post) probability of remaining in any particular labour market status is defined on the basis of the number of individuals being in that particular status i in both year t and t-1, as a percentage of individuals in the same status i in year t-1. Conversely, the probability of moving from one labour market status to another is defined as the ratio of the probability of remaining in any labour market status i, as defined previously, over the probability of an individual in status k in period (t-1) turning to status i in period t.

For each country (j) the probability of moving across n labour market statuses between year t-1 and year t is thus a (n x n) matrix (P i,k jt ) in which each individual element p i,k jt = Pr{S t = i | S t-1 = k} records the transition probability, with i,k = employment (e), unemployment (u), out of the labour market or inactivity (na).

The measure of mobility used is the Shorrocks’ (1978) mobility index, defined as:
M jt = n trace P i , k jt / n - 1
(1)

By definition, the mobility index is bounded between [0,1], where, a value of zero implies no probability of leaving any labour market status, and a value of one implies full mobility.

At this stage, it should be noted that flows from and into the labour market are very different among them. In fact, people moving from inactivity to unemployment are different from people moving from inactivity to employment, as the former re-enter the labour market but do not find a job immediately. In this vein, distinguishing between flows into and out of inactivity can be retained in the probability of successfully re-entering the labour market (Marston 1976; Theeuwes et al.1990). The latter is defined as:
S L jt = p nan , e jt / p nan , e jt + p nan , u jt ,
(2)

which is the percentage of people successfully entering the labour market (p nan,e ) as a percentage of the number of people entering the labour market as a whole.

Analogously, people leaving unemployment to get back into employment are different from those who, once separated from their job, stop searching for a new one (i.e. they move from unemployment into inactivity). Thus, unsuccessful labour market outcomes are computed as:
F L jt = p u , nan jt / p u , nan jt + p u , e jt ,
(3)

which is the percentage of people withdrawing from the labour market, as a percentage of people generally leaving unemployment (moving either back into employment or inactivity). It should be noted, however, that unsuccessful labour market outcomes may not represent labour market withdrawals per sé, as flows into inactivity also capture shifts into retirement or education. For this reason, when computing (un)successful labour market outcomes we control for the statuses of retirement and education. A discussion is warranted in the next section.

2.2 Results

Table 2 provides a snapshot of average transition probabilities, over time and across countries, between different labour market statuses during the period 1998–2008 for all country groupings. The table shows that the average probability of being employed in year t-1 and year t, i.e. the probability of remaining employed for two consecutive periods, is 94% on average in Central Eastern and Mediterranean countries and around 93% in Continental countries. The same probability is around 92% in Nordic countries. The probability of remaining unemployed is around 61% in Central Eastern European countries, about 45% in the Nordic countries, and 55% in Continental countries8. The probability of remaining inactive is between 86-92% in the Central Eastern, Continental and Mediterranean countries, but below 80% in the Nordic countries. Clearly, the probability of moving from employment to inactivity or the probability of moving from unemployment to inactivity is strongly associated with retirement flows and/or flows into the status of education. Controlling for education and retirement flows – setting up a 5-dimensional transition matrix including the statuses of e = employment, u = unemployment, nan = inactivity (this time, excluding education and retirement), plus ie = education and re = retirement – shows that the likelihood of remaining inactive (excluding retirement and education) for two consecutive periods falls to about 74% in the Nordic countries. The same probability is about 77% in Central Eastern countries, 82% in Continental Europe and about 87% in Mediterranean countries9.
Table 2

Transition probabilities (full period, 1998 – 2008)

  

Labour market status year t

   

Central Eastern

Nordics

Continental

Mediterranean

Labour market status

Year t-1

1998-2008

E

U

NA

E

U

NA

E

U

NA

E

U

NA

E

93.980

3.134

3.486

91.210

2.717

6.229

93.082

3.496

3.601

94.969

2.688

2.450

U

28.325

61.117

14.506

38.242

45.929

18.384

32.215

55.515

18.434

28.215

67.464

5.161

NA

7.250

3.876

86.198

16.175

5.120

79.102

8.831

3.548

88.012

4.574

3.562

92.192

1998-2003

E

U

NA

E

U

NA

E

U

NA

E

U

NA

E

92.505

4.373

4.291

91.252

2.883

5.981

92.775

3.921

3.100

94.910

2.386

2.786

U

28.151

57.547

15.788

33.852

49.789

19.009

30.334

60.619

9.512

31.676

63.235

5.750

NA

8.851

4.949

87.711

16.892

5.032

78.893

9.058

3.608

88.898

5.623

3.270

91.282

2004-2008

E

U

NA

E

U

NA

E

U

NA

E

U

NA

E

94.261

2.723

2.985

91.267

2.595

6.227

93.217

2.936

3.844

94.987

2.771

2.290

U

28.371

61.654

13.742

39.739

43.673

18.343

33.140

44.936

21.960

26.487

68.914

4.925

NA

6.545

3.455

86.181

16.028

4.991

79.104

9.430

3.651

86.877

3.805

3.657

92.601

Note: E = employed; U = unemployed; NA = inactive so that EE = remains in employment between one year and the next; UU = remains in unemployment, NANA = remains in inactivity. Observations are weighted according to the labour force share (15–64) in each country over the aggregate. Elements showing a probability of remaining in the same labour market state (employment, unemployment and inactivity) are in bold.

Sources: LFS microdata, authors’ computations.

From Table 2, in the Mediterranean and Central Eastern European countries the probability of moving from unemployment to employment is just below 29%, whereas it is above 32% in Continental countries and over 38% in the Nordic countries. In the Central Eastern, Mediterranean and Continental countries this probability is much lower than the probability of remaining in unemployment, compared to Nordic countries. In the case of Nordic EU countries, the picture is consistent with relatively fast hiring and firing dynamics, compared to other EU social models.

Comparisons of labour transition probabilities over time shows that in the Central Eastern and Mediterranean countries the number of people remaining in unemployment has increased over the last decade, whereas it decreased in Nordic and Continental countries (Figure 1)10. For Continental countries, of those individuals unemployed in period t-1, the percentage remaining unemployed in period t decreased from 64% to 45%. For Nordic countries this number decreased from 42% to 39% and for Sweden from 50% to 44%. For Central Eastern countries the same number increased instead from 53% to 62%, possibly as the result of economic growth after 1998 not being very employment intensive, as evidenced by the number of people remaining in employment during the period 1998–2003, compared to the period 2004–200811. The same number increased in the Mediterranean countries, from 63% to 69%.
Figure 1

Changes in transition probabilities over time (2004–2008 minus 1998–2003). Sources: LFS microdata, authors’ computations.

By contrast, the probability of remaining inactive slightly fell over time in the Central Eastern and Continental EU countries, while it remained broadly stable in Mediterranean and Nordic countries. Finally, the probability of remaining in employment increased strongly in the Central Eastern countries as well as – but to a smaller degree – in Continental European countries. In Nordic and Mediterranean countries, the number of people remaining in employment remained broadly stable over the last decade.

Turning to transitions between different labour market statuses, unemployment-to-employment flows have increased by about 6 percentage points over the last decade in Nordic European and Continental countries (see Figure 1), while they remained constant in Central Eastern countries and even declined in Mediterranean countries12. Flows in the opposite direction (i.e. employment to unemployment) have decreased overall in Central Eastern countries, but also in Continental Europe, and, to a lesser extent, in Nordic and Mediterranean countries.

The figures also shows that changes from unemployment to inactivity have overall fallen in the Central Eastern, Mediterranean and Nordic countries, whereas they strongly increased in Continental European countries13. In the latter case, a change in definition for France also explains such high rates of transition out of the labour market14. The figure also suggests that transitions from inactivity into employment have decreased by about 2–3 percentage points in Central Eastern and Mediterranean countries, while they remained broadly constant in Nordic and Continental countries.

Looking at the percentage of people entering successfully the labour market (successful labour market entries, SL), we find that this percentage has increased in Central Eastern countries (from 59% to 60%), the Nordics (from 66% to 72%), while it has decreased in the Continental and Mediterranean countries (from 66% to 61% and from 59% to 48%, respectively) over the period 1998–2008, controlling for education and retirement flows (i.e. in fact, the notation p nan,. jt in (2) refers to the number of people moving from inactivity (excluding retirement and education) into another state, and analogously for the formula in (3); see Table 3). Alternatively, the percentage of unsuccessful labour market outcomes (UL) has decreased in Central Eastern countries (from 33% to 31%) and Nordic countries (from 20% to 16%). UL have increased only in Continental European countries (from 17% to 35%), net of transitions out of the labour market driven by education and retirement decisions, while they remained broadly stable in Mediterranean countries15.
Table 3

Successful and unsuccessful labour market outcomes

 

Central Eastern

Nordics

Continental

Mediterranean

 

Successful labour market outcome

1998-2003

59.489

66.142

66.291

59.439

2004-08

59.997

71.673

61.696

48.285

 

Unsuccessful labour market outcome

1998-2003

33.255

19.710

16.627

12.334

2004-08

31.003

15.878

35.231

12.121

Note: Results are based on a 5-dimensional transition probability matrix where statuses are defined as E = employed; U = unemployed; NAN = inactive (excluding education and retirement); RE = in retirement; IE = in education. Compared to the results where a 3-dimensional transition matrix is used (with E = employed; U = unemployed; NA = inactive), the results here holds in the light of NA = NAN + IE + RE. In other words, in computing successful and unsuccessful labour market outcomes we control for education and retirement flows when defining the status of inactivity. Following Theeuwes et al. (1990) a successful labour market entry is computed as the percentage of people successfully entering the labour market (p nan,e ) as a percentage of the total number of people entering the labour market, i.e. SL jt = p nan,e jt /( p nan,e jt + p nan,u jt ).

Analogously, an unsuccessful labour market outcome is the percentage of people withdrawing from the labour market (but not moving to either retirement or education), as a percentage of people leaving unemployment, i.e. FL jt = p u,nan jt /( p u,nan jt + p u,e jt ).

Sources: LFS microdata, authors’ computations.

Turning to changes in labour market inflows and outflows by worker group (Figure 2), the reduction in people leaving the labour market in Central Eastern European countries over the last decade was mainly driven by females, the highly educated and the 55 to 64 age group. At the same time, these countries experienced on average a reduction in people leaving inactivity and going back to the labour market, mainly driven by people between the ages of 15 and 24, males and low educated people16. In Nordic countries the fall in the unemployment to inactivity and, viceversa inactivity to employment flows, is mostly driven by people between the ages of 15 and 24. For continental countries, the number of people transitioning from unemployment to inactivity has overall increased (in 2004–2008 against the period 1998–2003) on average, mainly triggered by females and low educated workers and the 55–64 year olds17. The probability of moving from inactivity to employment in Continental countries increased overall, driven by females and the 25–29 year olds. Finally, for Mediterranean countries, the fall in the probability of transitioning from unemployment to inactivity is found to be mainly driven by males, low educated workers and the 15–24 year olds, whereas the decrease in flows in the opposite direction is mainly driven by males, high educated workers and the 30–54 year olds.
Figure 2

Changes in the probability of moving from unemployment to inactivity (lhs) and in the probability of moving from inactivity to employment (rhs). (2004–2008 minus 1998–2003). Note: The chart on the lhs presents the percentage change in unemployment to inactivity flows by different workers groups. Bars refer to a weighted country grouping average (Central Eastern, Nordics, Continental, Mediterranean), where observations are weighted according to the proportion in each country over the aggregate. The chart on the rhs presents inactivity to employment reshuffles under the same reasoning. Sources: LFS microdata, authors’ computations.

2.2.1 Labour mobility

Decomposing the results by worker group shows that the chance of unemployed youths finding a job is in all countries much higher than for older groups. Analogously, the probability to remain in unemployment is found to increase with age and is highest for individuals with lower educational attainment (Table 4).
Table 4

Transition probabilities by worker group

Labour market status year t

Labour market status year t-1

  

Central Eastern

Nordics

Continental

Mediterranean

Males

 

E

U

NA

E

U

NA

E

U

NA

E

U

NA

1998-2003

E

93.245

4.705

3.130

92.353

2.938

4.878

94.018

3.641

2.425

95.623

2.144

2.317

 

U

30.950

58.559

12.394

34.708

51.257

16.568

32.590

61.282

6.850

34.789

61.845

4.029

 

NA

9.734

5.352

86.706

16.526

4.841

79.731

11.119

3.777

87.441

8.989

4.286

87.018

2004-2008

E

95.328

2.792

1.995

92.891

2.548

4.618

94.094

2.795

3.059

95.896

2.507

1.643

 

U

30.147

61.747

11.399

40.153

46.234

15.623

34.496

47.746

17.764

29.169

68.048

2.952

 

NA

7.028

3.608

89.968

15.327

4.825

80.050

10.097

3.540

86.318

4.949

4.162

90.935

Females

 

E

U

NA

E

U

NA

E

U

NA

E

U

NA

1998-2003

E

91.604

4.007

5.701

89.993

2.862

7.255

91.220

4.296

3.956

93.787

2.791

3.524

 

U

25.193

56.422

19.740

33.206

48.477

21.387

28.484

59.799

12.138

28.395

64.841

7.535

 

NA

8.287

4.690

88.411

17.279

5.211

78.220

6.940

3.443

89.798

3.999

2.786

93.342

2004-2008

E

92.935

2.661

4.212

89.422

2.673

8.077

92.196

3.103

4.758

93.651

3.165

3.246

 

U

26.614

61.687

16.495

39.468

41.124

20.989

31.897

42.174

25.956

24.145

69.553

6.965

 

NA

6.247

3.362

84.196

16.609

5.125

78.349

8.985

3.742

87.244

3.245

3.422

93.429

Low education

 

E

U

NA

E

U

NA

E

U

NA

E

U

NA

1998-2003

E

88.732

5.616

7.268

86.591

3.797

10.118

90.184

5.440

4.230

93.662

2.828

3.618

 

U

21.069

61.114

20.158

27.572

55.099

20.989

24.580

65.824

10.283

29.013

65.372

6.167

 

NA

6.430

1.908

93.626

10.149

3.228

87.161

4.374

2.933

93.339

3.985

2.122

94.186

2004-2008

E

89.918

4.722

5.206

86.744

4.001

9.406

90.216

4.154

5.636

93.804

3.389

2.865

 

U

19.299

68.509

17.773

31.646

49.062

21.088

25.456

49.311

25.555

22.241

73.148

4.920

 

NA

2.977

1.329

91.580

8.387

4.115

87.456

4.657

2.616

92.691

1.906

2.312

95.836

Medium education

 

E

U

NA

E

U

NA

E

U

NA

E

U

NA

1998-2003

E

92.508

4.774

3.877

90.849

3.238

5.979

93.144

3.838

2.834

95.839

2.175

2.036

 

U

30.486

56.390

14.573

36.121

47.854

18.179

34.295

57.085

9.085

33.197

61.578

6.023

 

NA

10.284

7.607

83.601

21.271

7.519

72.734

11.820

4.472

86.162

8.120

4.638

87.529

2004-2008

E

94.218

2.940

2.877

91.180

2.793

6.139

93.232

2.997

3.796

95.565

2.578

1.912

 

U

31.040

59.890

12.426

40.892

42.302

18.749

36.922

42.650

20.287

29.975

65.349

5.058

 

NA

7.814

4.751

83.955

20.497

6.239

73.654

11.206

4.413

84.342

5.495

4.461

90.164

High education

 

E

U

NA

E

U

NA

E

U

NA

E

U

NA

1998-2003

E

96.393

1.895

2.492

95.176

1.733

3.178

95.147

2.371

2.235

97.359

1.258

1.477

 

U

40.909

47.924

14.882

41.378

46.207

16.006

43.082

49.453

8.209

44.938

54.006

3.181

 

NA

21.510

9.129

70.648

29.188

7.025

65.601

20.603

4.986

75.311

21.654

13.906

64.930

2004-2008

E

96.533

1.192

2.337

94.352

1.605

4.138

95.551

1.929

2.521

96.780

1.666

1.604

 

U

41.427

51.792

10.706

47.453

40.204

14.474

43.830

39.251

17.190

36.449

58.907

5.212

 

NA

21.255

7.847

70.319

30.768

6.600

63.201

22.201

5.536

72.251

15.199

12.842

72.289

15-24 year olds

 

E

U

NA

E

U

NA

E

U

NA

E

U

NA

1998-2003

E

86.184

8.691

6.630

67.533

4.531

28.716

84.492

10.268

5.337

90.498

5.738

3.912

 

U

32.528

54.780

15.116

39.723

34.376

29.650

42.075

49.440

9.132

29.849

63.545

6.929

 

NA

10.818

5.799

86.677

20.864

3.693

76.934

11.905

3.699

86.247

6.135

4.804

89.189

2004-2008

E

88.751

6.141

5.073

66.369

5.141

28.857

87.848

7.218

5.352

90.343

6.784

3.041

 

U

33.789

55.491

13.077

43.578

30.444

27.296

43.014

43.881

13.465

30.130

65.238

4.940

 

NA

6.480

4.096

88.579

16.159

4.900

79.125

13.436

4.436

82.053

4.983

4.566

90.485

25-29 year olds

 

E

U

NA

E

U

NA

E

U

NA

E

U

NA

1998-2003

E

91.749

5.803

3.713

88.882

3.541

7.623

91.195

5.864

2.283

94.765

3.984

1.382

 

U

33.423

55.659

13.002

44.203

39.350

18.187

40.310

52.178

8.416

32.251

64.274

4.169

 

NA

18.507

10.566

72.434

32.669

8.984

60.255

24.474

10.159

66.622

12.257

8.505

79.667

2004-2008

E

93.546

3.536

2.968

88.356

3.174

8.723

91.572

4.721

3.687

93.682

4.600

1.770

 

U

34.758

57.259

11.937

49.428

34.088

19.271

45.885

41.096

13.290

31.511

64.551

4.248

 

NA

17.416

8.889

65.291

33.957

8.452

58.421

27.994

10.532

61.306

12.118

10.441

77.671

30-54 year olds

 

E

U

NA

E

U

NA

E

U

NA

E

U

NA

1998-2003

E

94.452

3.880

2.671

95.307

2.615

2.091

95.131

3.110

1.601

96.645

1.915

1.493

 

U

26.174

59.655

15.657

36.844

50.093

16.101

30.190

63.030

7.269

33.519

62.001

5.464

 

NA

9.097

6.570

85.321

19.224

10.580

73.314

8.285

5.296

87.150

7.483

3.051

90.052

2004-2008

E

95.995

2.407

1.566

95.158

2.239

2.702

95.614

2.499

1.876

96.345

2.387

1.303

 

U

27.008

64.579

13.157

43.243

45.134

13.396

34.820

49.809

15.556

24.936

70.709

4.873

 

NA

7.927

4.432

78.998

22.769

8.106

69.964

9.556

5.619

84.807

4.058

4.273

91.920

55-64 year olds

 

E

U

NA

E

U

NA

E

U

NA

E

U

NA

1998-2003

E

85.043

2.096

15.287

89.532

2.975

7.851

81.747

3.041

15.588

86.903

1.365

12.038

 

U

16.774

50.835

36.484

17.948

65.101

21.694

5.518

73.821

21.538

28.008

64.215

9.896

 

NA

3.498

0.957

95.914

1.808

2.797

96.838

0.734

1.275

98.536

1.063

0.814

98.243

2004-2008

E

87.518

1.532

11.259

89.995

2.730

7.442

84.457

1.409

14.206

90.017

1.571

8.586

 

U

15.695

63.970

29.805

25.324

55.813

23.375

7.670

33.260

57.741

13.753

77.843

9.315

 

NA

3.364

0.626

94.626

2.697

2.174

96.027

1.131

0.482

98.397

0.559

0.998

98.518

Note: E = employed; U = unemployed; NA = inactive so that EE = remains in employment between one year and the next; UU = remains in unemployment, NANA = remains in inactivity. Observations are weighted according to the labour force share (15–64) in each country over the aggregate. Elements showing a probability of remaining in the same labour market state (employment, unemployment and inactivity) are in bold.

Sources: LFS microdata, authors’ computations.

Table 5 also provides a summary measure (the Shorrocks’ index explained earlier) of labour market mobility18. Importantly, the index summarizes the extent of the transitions between different economic activity statuses (employment, unemployment and inactivity).
Table 5

Mobility index

  

Central Eastern

Nordics

Continental

Mediterranean

Total

1998-2003

0.311

0.400

0.289

0.253

 

2004-2008

0.290

0.430

0.375

0.217

 

Total

0.310

0.419

0.317

0.227

Males

1998-2003

0.307

0.383

0.286

0.278

 

2004-2008

0.265

0.404

0.359

0.226

 

Total

0.275

0.396

0.313

0.240

Females

1998-2003

0.318

0.417

0.296

0.240

 

2004-2008

0.306

0.456

0.392

0.217

 

Total

0.332

0.441

0.323

0.222

Low-education

1998-2003

0.283

0.356

0.253

0.234

 

2004-2008

0.250

0.384

0.339

0.186

 

Total

0.275

0.371

0.277

0.199

Medium-education

1998-2003

0.338

0.443

0.318

0.275

 

2004-2008

0.310

0.464

0.399

0.245

 

Total

0.331

0.457

0.347

0.253

High-education

1998-2003

0.425

0.465

0.400

0.419

 

2004-2008

0.407

0.511

0.465

0.360

 

Total

0.411

0.499

0.426

0.372

16-24 years olds

1998-2003

0.362

0.606

0.399

0.284

 

2004-2008

0.336

0.620

0.431

0.270

 

Total

0.341

0.617

0.413

0.273

25-29 years olds

1998-2003

0.401

0.558

0.450

0.306

 

2004-2008

0.420

0.596

0.530

0.320

 

Total

0.425

0.582

0.478

0.315

30-54 years olds

1998-2003

0.303

0.406

0.273

0.255

 

2004-2008

0.302

0.449

0.349

0.207

 

Total

0.337

0.434

0.299

0.218

55-64 years olds

1998-2003

0.341

0.243

0.229

0.253

 

2004-2008

0.269

0.291

0.419

0.168

 

Total

0.303

0.271

0.259

0.186

Notes: Measures are based on the Shorrocks’ mobility index (mobility is higher the closer the index is to 1). Observations are weighted according to the labour force share (15–64) in each country over the aggregate.

Highest mobility indexes for each sub-category across the periods 1998–2003 and 2004–2008 are in bold.

Sources: LFS microdata, authors’ computations.

The mobility index reflects an increase in labour market churning over time in Nordic and Continental countries. On the contrary, the Shorrocks summary index for the periods 1998–2004 and 2004–2008 reveals a decrease in labour market mobility over time both in Mediterranean and the Central Eastern European countries. In the latter case, following the changes in the labour market structure for some Central Eastern European countries, a high mobility during the period 1998–2003 suggest higher returns to job changes and a less stringent labour market segmentation in the allocation of job offers after the reforms, as reported e.g., in Boeri and Flinn (1999). Conversely, the observed decline of mobility after 2004 – to values “converging” to what observed for the average Mediterranean and Continental Countries (and the euro area, see Macchiarelli and Ward-Warmedinger 2013) – suggests a stabilization of labour markets in the region, but also a less efficient matching of individuals with jobs, as evidenced by the increase in the probability to remain in unemployment19. For Mediterranean countries, a lower mobility over time analogously reflects an increase in the likelihood to remain unemployed over time. In Nordic and Continental countries, mobility increased over the whole period 1998–2008, essentially as the result of a fall in the probability of remaining in unemployment.

The mobility index also confirms that, in Continental countries, mobility is particularly high for people between the ages of 25 and 29 and highly educated people, and has overall increased over time. Also, in the latter countries mobility has generally increased for females. In Continental European countries, women and young people exhibit higher mobility over time through a decreasing probability to remain in both unemployment and inactivity. Analogously, highly educated workers are more mobile through a decreased probability to remain in unemployment over time.

From Table 5, in Nordic countries people between the ages of 16–24 are the most mobile on average and their mobility has increased over time. Such behaviour is always driven by a lower probability of remaining in employment, unemployment and inactivity compared to Continental and Mediterranean countries (see Table 4). In Nordic countries, highly educated individuals generally display both a higher probability of remaining in employment and a lower probability of remaining in unemployment and inactivity over time, while female workers display a lower probability of remaining in both employment and unemployment over time (Table 4).

In Central Eastern European countries mobility is higher for females, highly educated people and workers between the ages of 25 and 29, though this pattern has overall decreased over time. In these countries, the higher mobility of women is driven by a lower probability over time of remaining in employment and unemployment. Highly educated individuals in the CEE EU countries are more mobile through a lower probability over time of remaining in inactivity and employment.

Finally, for Mediterranean countries, mobility is higher for males, highly educated workers and the 25–29 year olds. While mobility of the former two groups has generally decreased over time, the mobility of the 25–29 year olds has increased, essentially reflecting a lower probability to remain both in employment and inactivity over time.

2.2.2 Pooling the results

As well as over time, it is interesting to consider how labour market mobility and transitions varied across EU countries and workers groups. While some empirical patterns are observed in all countries (e.g. the probability of remaining unemployed is several times higher than the probability of an employed individual turning unemployed), cross-country differences in the degree of mobility among different labour market statuses do exist. Particularly, by pooling results, we find that the probability of remaining in employment and, to a lesser extent, inactivity over two periods (t-1 and t) is very similar across countries (Figure 3). The results also emphasises the very small variation across countries in the low probability of moving from employment into either unemployment or inactivity. Significant differences across countries are found in the probability of remaining unemployed over two consecutive periods, and in the transitions out of unemployment. Looking at cross-country differences, the probability of remaining unemployed is on average over 70% in, Belgium, Greece and Slovenia, or slightly below in Italy, Bulgaria, Latvia and Slovakia. This probability is almost twice that of the probability in Denmark, Sweden, Spain, The Netherlands and Cyprus and more than two-thirds that of the probability in France, Austria, Portugal, Estonia and Romania. This probability is around 60% in Finland, Czech Republic, Lithuania, Hungary and Poland and about only 24% in Luxembourg.
Figure 3

Transition probabilities across countries. Notes: The chart refers to pooled transition probabilities results for 23 EU countries. Spain (ES), Italy (IT), France (FR), The Netherlands (NL), Belgium (BE), Austria (AT), Cyprus (CY), Finland (FI), Greece (GR), Luxemburg (LU), Portugal (PT), Slovenia (SI); Czech Republic (CZ), Estonia (EE), Latvia (LV), Lithuania (LT), Hungary (HU), Poland (PL), Romania (RO) and Slovakia (SK); Denmark (DK) and Sweden (SE). Sources: LFS microdata, authors’ computations.

Furthermore, while the probability of remaining in unemployment has increased over time in Italy, Portugal, Cyprus, Czech Republic, Hungary, Poland, Romania and Slovakia, it has fallen in Belgium, Greece, France, Austria, Slovenia, the Baltic countries (Estonia, Latvia and Lithuania), Denmark and Sweden (Additional file 1: Table S1).

Further, on the basis of the Shorrocks’ mobility index, labour markets in some countries are characterised by more mobility than others (see Table 6). As expected, labour markets in Denmark and Sweden are more mobile on average, together with that of Spain, the Netherlands and Luxemburg. This is evidenced by a higher Shorrocks’ mobility index, which is twice as high in these countries relative to Bulgaria, the Slovak Republic, Poland, Latvia, Hungary, Italy, Belgium, Greece and Slovenia. A group of countries reporting intermediate mobility is represented instead by the Czech Republic, Estonia, Lithuania, Romania, Austria, Finland, Cyprus and Portugal. Table 6 also shows that on average highly educated individuals and people between the ages of 25–29 are the most mobile across labour market statuses. Moreover, while for Denmark, Sweden, the Continental and Mediterranean counties mobility of all worker groups has increased over the last decade (particularly for females) there is no clear pattern for the disaggregated Central Eastern European countries. The highest mobility groups overall are the 16 to 24 age group in Denmark and Sweden, the 25 to 29 year olds in Romania, people with high educational attainment in the Slovak Republic, the 25 to 29 age group in Spain and the 16–24 age group in Finland (Table 6).
Table 6

Mobility index across country and worker group

  

Central Eastern

Nordics

Continental

Mediterranean

  

BG

CZ

EE

LV

LT

HU

PL

RO

SK

SI

NL

FI

DK

SE

BE

FR

LU

AT

GR

ES

IT

PT

CY

Total

1998-2003

--

0.332

0.345

0.149

0.318

0.279

0.307

0.366

0.252

0.147

--

0.348

0.447

0.403

0.189

0.304

0.406

0.301

0.207

--

0.241

0.361

0.401

 

2004-2008

0.224

0.291

0.364

0.300

0.311

0.262

0.249

0.388

0.221

0.222

0.426

0.359

0.453

0.458

0.204

0.412

0.444

0.320

0.199

0.447

0.199

0.324

0.364

 

Total

0.224

0.303

0.349

0.250

0.301

0.266

0.260

0.384

0.226

0.204

0.426

0.352

0.449

0.440

0.199

0.337

0.436

0.317

0.201

0.447

0.211

0.330

0.371

Males

1998-2003

--

0.324

0.337

0.143

0.311

0.261

0.307

0.368

0.243

0.147

--

0.312

0.429

0.398

0.198

0.303

0.393

0.272

0.238

--

0.269

0.361

0.404

 

2004-2008

0.238

0.283

0.341

0.282

0.307

0.245

0.249

0.299

0.212

0.213

0.088

0.321

0.436

0.434

0.211

0.392

0.446

0.308

0.232

0.457

0.205

0.323

0.362

 

Total

0.238

0.295

0.332

0.235

0.296

0.249

0.260

0.306

0.217

0.196

0.088

0.315

0.433

0.422

0.207

0.333

0.433

0.303

0.233

0.457

0.224

0.330

0.371

Females

1998-2003

--

0.344

0.360

0.158

0.319

0.306

0.309

0.367

0.264

0.150

--

0.384

0.464

0.407

0.186

0.307

0.414

0.353

0.193

--

0.225

0.367

0.403

 

2004-2008

0.213

0.302

0.387

0.322

0.313

0.281

0.251

0.433

0.232

0.234

0.558

0.397

0.468

0.482

0.201

0.433

0.439

0.340

0.186

0.450

0.200

0.328

0.367

 

Total

0.213

0.315

0.367

0.270

0.303

0.285

0.263

0.423

0.237

0.214

0.558

0.389

0.465

0.459

0.196

0.342

0.435

0.342

0.188

0.450

0.207

0.334

0.374

Low-education

1998-2003

--

0.245

0.321

0.140

0.307

0.242

0.268

0.393

0.176

0.120

--

0.278

0.440

0.351

0.161

0.263

0.386

0.296

0.181

--

0.222

0.347

0.392

 

2004-2008

0.192

0.217

0.334

0.274

0.283

0.224

0.198

0.391

0.130

0.206

0.342

0.295

0.441

0.401

0.172

0.373

0.421

0.302

0.174

0.398

0.165

0.305

0.320

 

Total

0.192

0.225

0.321

0.222

0.277

0.228

0.213

0.388

0.138

0.184

0.342

0.284

0.438

0.382

0.168

0.292

0.413

0.301

0.176

0.398

0.181

0.312

0.335

Medium-education

1998-2003

--

0.377

0.366

0.167

0.332

0.321

0.338

0.367

0.301

0.167

--

0.419

0.462

0.445

0.217

0.338

0.438

0.301

0.228

--

0.263

0.386

0.405

 

2004-2008

0.271

0.332

0.383

0.324

0.332

0.294

0.265

0.393

0.263

0.238

0.453

0.409

0.472

0.492

0.231

0.437

0.440

0.335

0.202

0.457

0.234

0.335

0.364

 

Total

0.271

0.345

0.368

0.275

0.319

0.300

0.279

0.390

0.269

0.221

0.453

0.414

0.468

0.476

0.227

0.370

0.439

0.330

0.209

0.457

0.243

0.342

0.373

High-education

1998-2003

--

0.454

0.408

0.196

0.416

0.380

0.460

0.402

0.481

0.259

--

0.441

0.516

0.449

0.331

0.415

0.395

0.385

0.300

--

0.417

0.546

0.495

 

2004-2008

0.302

0.421

0.430

0.397

0.415

0.399

0.405

0.399

0.441

0.386

0.549

0.440

0.537

0.537

0.326

0.502

0.536

0.372

0.313

0.520

0.342

0.499

0.501

 

Total

0.302

0.429

0.416

0.343

0.411

0.395

0.411

0.397

0.445

0.362

0.549

0.440

0.531

0.514

0.328

0.451

0.511

0.373

0.310

0.520

0.358

0.505

0.499

16-24 years olds

1998-2003

--

0.434

0.411

0.193

0.366

0.351

0.344

0.397

0.332

0.221

--

0.601

0.700

0.551

0.304

0.414

0.462

0.414

0.261

--

0.256

0.456

0.461

 

2004-2008

0.231

0.377

0.437

0.383

0.401

0.307

0.327

0.326

0.284

0.454

--

0.584

0.701

0.593

0.341

0.443

0.463

0.455

0.268

0.563

0.241

0.417

0.437

 

Total

0.231

0.396

0.418

0.301

0.381

0.317

0.330

0.336

0.292

0.383

--

0.593

0.700

0.582

0.329

0.422

0.462

0.450

0.264

0.563

0.246

0.426

0.443

25-29 years olds

1998-2003

--

0.420

0.442

0.201

0.422

0.364

0.423

0.400

0.383

0.298

--

0.533

0.615

0.537

0.358

0.472

0.476

0.411

0.297

--

0.276

0.475

0.514

 

2004-2008

0.313

0.384

0.446

0.388

0.464

0.362

0.388

0.528

0.347

0.448

--

0.547

0.639

0.598

0.391

0.572

0.497

0.409

0.309

0.590

0.292

0.472

0.526

 

Total

0.313

0.395

0.438

0.325

0.436

0.362

0.395

0.488

0.353

0.411

--

0.535

0.631

0.579

0.381

0.505

0.495

0.409

0.304

0.590

0.286

0.468

0.521

30-54 years olds

1998-2003

--

0.304

0.360

0.157

0.312

0.259

0.299

0.379

0.207

0.118

--

0.380

0.421

0.413

0.164

0.290

0.378

0.287

0.169

--

0.256

0.332

0.369

 

2004-2008

0.246

0.275

0.355

0.294

0.314

0.255

0.220

0.515

0.203

0.162

0.276

0.397

0.446

0.481

0.194

0.383

0.432

0.297

0.179

0.416

0.192

0.306

0.339

 

Total

0.246

0.284

0.350

0.249

0.305

0.256

0.236

0.486

0.204

0.151

0.276

0.386

0.437

0.460

0.184

0.319

0.420

0.295

0.171

0.416

0.209

0.308

0.345

55-64 years olds

1998-2003

--

0.364

0.333

0.129

0.202

0.269

0.374

0.378

0.289

0.175

--

0.206

0.305

0.226

0.134

0.242

0.215

0.265

0.167

--

0.277

0.203

0.335

 

2004-2008

0.204

0.276

0.352

0.230

0.259

0.271

0.215

0.376

0.226

0.232

0.472

0.230

0.313

0.313

0.125

0.497

0.300

0.248

0.169

0.320

0.159

0.213

0.238

 

Total

0.204

0.292

0.336

0.204

0.245

0.270

0.230

0.377

0.232

0.222

0.472

0.215

0.309

0.281

0.127

0.284

0.290

0.251

0.169

0.320

0.184

0.211

0.254

Notes: Measures are based on the Shorrocks’ mobility index. Highest mobility indexes for each sub-category across the periods 1998–2003 and 2004–2008 are in bold. The table refers to 23 EU countries: Spain (ES), Italy (IT), France (FR), The Netherlands (NL), Belgium (BE), Austria (AT), Cyprus (CY), Finland (FI), Greece (GR), Luxemburg (LU), Portugal (PT), Slovenia (SI); Czech Republic (CZ), Estonia (EE), Latvia (LV), Lithuania (LT), Hungary (HU), Poland (PL), Romania (RO) and Slovakia (SK); Denmark (DK) and Sweden (SE).

Sources: LFS microdata, authors’ computations.

3 What’s behind mobility? A quick look

While the analysis carried out in earlier was aimed at providing a description of the degree of labour market turnover in the EU, in this section we complement this information by looking at macroeconomic trends in employment (both part-time and temporary), unemployment and the evolution of structure indicators (EPL, product market regulation, etc.). Our objective is to understand whether part of the observed changes in mobility can be broadly restraint to some “macro” explanatory factors.

Not surprisingly, the increase in mobility observed in some countries can be linked to the use of time-limited contracts and part-time work, and viceversa. Figure 4 (top and medium panels) shows that, broadly speaking, those countries where mobility increased over time are also those where the percentage of time limited contracts and part time work increased. However, the correspondence is not one-to-one. Further, Latvia represents a major exception, as the observed increase in mobility is not found to be associated with an increase in the share of temporary or part-time jobs.
Figure 4

Mobility index vs. employment and unemployment. Notes: Where available, the chart refers to pooled transition probabilities results for 23 EU countries. Spain (ES), Italy (IT), France (FR), The Netherlands (NL), Belgium (BE), Austria (AT), Cyprus (CY), Finland (FI), Greece (GR), Luxemburg (LU), Portugal (PT), Slovenia (SI); Czech Republic (CZ), Estonia (EE), Latvia (LV), Lithuania (LT), Hungary (HU), Poland (PL), Romania (RO) and Slovakia (SK); Denmark (DK) and Sweden (SE). Changes for the variables on the x-axis are the difference between 2004–08 and 1998–2003 averages. The results are not presented for the all 23 EU countries, depending on data coverage and availability. The figure fits a linear regression line. Estimated values of the regression are reported in the top right angle of each figure. Sources: Eurostat and LFS microdata, authors’ computations.

In addition, there is no clear correspondence between unemployment rate and mobility. In most countries increases in mobility are associated with a reduction of unemployment over time (Figure 4, bottom panel). Overall, however, in some countries mobility decreased and so too did unemployment rates (notably, Slovakia, Italy, Poland and the Czech Republic), suggesting that while a certain level of turnover is necessary for healthy labour markets (see also Boeri and Garibaldi 2009), it may not be sufficient (also depending on the direction in which changes in labour market statuses are observed; see Section 2).

Focusing on structure indicators (Figure 5), changes in mobility over time seem to be negatively related with changes in the strictness of Employment Protection Legislation (EPL)20, i.e. less regulation favours labour market turnovers and viceversa, especially in Sweden, Czech Republic and Poland. A similar pattern does not exist for Italy and Portugal, among the euro area countries, or Slovakia. Further, changes in the mobility index are, in most cases, correlated with changes in the expenditure on ‘active’ labour market policies, such as direct job creation, and, to a lesser extent, employment incentives21. A reduction in direct job-creation expenditures is associated with decreasing mobility over time in Italy and Portugal – among the euro area countries – and Slovakia. On the contrary, in France and Sweden a reduction in direct-job creation expenditure is positively associated with increased mobility.
Figure 5

Mobility index vs. structure indicators. Notes: Where available, the chart refers to pooled transition probabilities results for 23 EU countries. Spain (ES), Italy (IT), France (FR), The Netherlands (NL), Belgium (BE), Austria (AT), Cyprus (CY), Finland (FI), Greece (GR), Luxemburg (LU), Portugal (PT), Slovenia (SI); Czech Republic (CZ), Estonia (EE), Latvia (LV), Lithuania (LT), Hungary (HU), Poland (PL), Romania (RO) and Slovakia (SK); Denmark (DK) and Sweden (SE). Changes for the variables on the x-axis are the difference between 2004–08 and 1998–2003 averages. The expenditure on direct-job creation and out-of work income maintenance and support are intended as a percentage of GDP. The results are not presented for the all 23 EU countries, depending on data coverage and availability. The figure fits a linear regression line. Estimated values of the regression are reported in the top right angle of each figure. Sources: OECD and LFS microdata, authors’ computations.

The expenditure on out-of-work maintenance and support (including unemployment benefits, expenditure on early retirement22, etc.…) is found to be negatively related with mobility over time. This is particularly clear for countries such as Italy, Portugal and Sweden, where increases (decreases) in the expenditure on out-of-work benefits are coupled with lower (higher) mobility over time. Poland and Slovakia provide the opposite picture.

Finally, a decrease in product market regulation is related with increased mobility over time in almost all countries – with the exceptions of Italy and Portugal – among euro area countries – and mainly Poland, Czech Republic and Slovakia – among the CEE EU countries23.

4 Conclusions

This paper presented information on labour market mobility in 23 EU countries for the period 1998 to 2008 using Eurostat Labour Force Survey (LFS) data. The analysis presented evidence by country and worker group.

Transitions from unemployment and inactivity back into employment are found to be less frequent in the Central Eastern, Mediterranean and Continental European countries than in the Nordic countries. Moreover, in Continental Europe and the Nordics, the number of people remaining in unemployment decreased over the period 1998–2008 whereas this number increased in the average Central Eastern and Mediterranean countries. At the same time, however, successful labour market entries (from outside the labour market) increased in Central Eastern European countries and the Nordics.

Summary mobility measures for the periods 1998 – 2004 and 2004 – 2008 show a decrease in labour market mobility over time in the Central Eastern European and Mediterranean countries and an increase in Continental and Nordic countries. This decline of labour market mobility in the Central Eastern European and Mediterranean countries may stem from a less efficient matching of individuals with jobs than in other countries, as evidenced by an increase in the probability to remain in unemployment. In contrast, in Continental and Mediterranean countries, mobility increased over this period, essentially as the result of a fall in the probability of remaining in unemployment. All in all, the highest degree of labour market mobility among the countries covered in this paper is consistently observed in Spain, Luxemburg, The Netherlands, Denmark and Sweden, with these results mainly reflecting higher mobility of people below the age of 29, highly educated and female workers. We also find that mobility of all worker groups has generally increased over time in Continental Europe and the Nordics.

Looking at some explanatory factors, the results suggest that countries who experienced an increase in mobility are also those which increased their percentage of time limited (e.g., temporary) contracts and part time work, and viceversa. However, looking at unemployment rates and some structure indicators the results provide a mixed picture, suggesting that the sense of mobility strongly varies across countries24.

Endnotes

1The anonymized version of this data (which is used in this analysis and is the only version for many countries currently available to the ECB) suffers from some limitations in its use for economic analysis since individuals cannot be tracked over time and there are significant changes in the information collected, variable definitions and coding which limit the time series dimension of the data.

2A decrease in the average unemployment duration from 18 months (1998) to 11 months (2008) can be overall observed in Europe (Table 1).

3See, inter alia, Fujita and Ramey (2006; 2009); Shimer (2012) for the US.

4Due to missing data, some countries are also excluded when computing aggregated results. Based on the LFS, data are not available for Spain prior to 2006, for France for the 2003–2005 period, for Luxemburg and Slovenia prior to 1999 and 2000 respectively. For the Netherlands data availability reduces to 2008 for transitions from unemployment, and to 2006–2008 for transitions from employment and inactivity. For Latvia, Lithuania and Slovakia data are missing prior to 2001, for Romania and Hungary prior to 1999. For Sweden data are missing in 2005.

5The latter definition differs from the one used in Macchiarelli and Ward-Warmedinger (2013) in that it does not classify countries according to euro area membership or not.

6The LFS questionnaire asks about (i) the individual’s socio-economic situation one year before the survey date and (ii) their current professional status during the reference week (i.e. in period t). Our measure is therefore an ‘annual’ transition measure and presents a lower bound for labour market mobility. No information is available about labour market mobility within a particular year. In addition, a similar analysis using objective classifications for each labour market state (i.e. ILO definitions) is not feasible, owing to a lack of data. For further details see http://epp.eurostat.ec.europa.eu/portal/page/portal/employment_unemployment_lfs/documents/.

7The latter limitation – common to such kind of studies (Boeri and Flinn 1999; Boeri and Garibaldi 2009) – allows only observing labour market flows between the survey date (t) and the year before (t-1), without transitions in and out of a particular status (be it employment, unemployment or out of the labour market) in the interval (t; t-1) can be observed. This, clearly, represents a major concern in our analysis, given the interval considered across two subsequent periods is relatively long, i.e. one year. This limitation is likely to underestimate the degree of labour market turnover, especially for those individuals who often make transitions in and out of the labour market (e.g., part-time workers). A feasible alternative would be that of drawing on matched records across different LFS waves using national LFS data. However, the results might be anyway imprecise owing to the merging procedure and possible attrition and nonresponse issues, or errors in the classification of the labour market statuses across countries. For a discussion see Boeri and Flinn (1999); Caliendo and Uhlendorff (2008).

8Those results are broadly consistent with Macchiarelli and Ward-Warmedinger (2013), where it is shown that the probability of remaining in unemployment is about 40% in both Denmark and Sweden.

9Those results are available upon request from the authors. An analysis of shifts into retirement or education is not provided here. For a discussion on retirement decisions see, inter alia, Aranki and Macchiarelli (2013).

10The probability of remaining in unemployment has increased in Czech Republic, Hungary, Poland, Romania and Slovakia over the last decade, but has fallen in the Baltic countries (Estonia, Latvia and Lithuania). In Latvia and Lithuania the fall in the probability of remaining in unemployment was accompanied by a higher probability of transiting from unemployment to inactivity over time, while for Estonia this probability remained roughly similar across time.

11Changes in the institutional arrangements and labour market composition (also in the light of labour market migration to Western Europe stemming from the EU accession in 2004) have contributed to this trend.

12Country-specific results point to the fact that flows from employment to unemployment or inactivity do not vary much across countries, whereas movements from unemployment to employment or inactivity as well as transitions from inactivity to employment show more pronounced cross- country variation.

13A change in definition for France explains the high rates of transition into inactivity for the euro area aggregates. These results do not change when controlling for education and retirement transitions.

14Results for Continental countries must be taken cautiously, as the effect of this recodification cannot be exactly quantified. As reported by the French National Institute of Statistics (INSEE) such an adjustment was adopted to make the unemployment definition conformable to the ILO criteria after 2003. For further details please see http://www.insee.fr/fr/methodes/sources/pdf/estimations_chomageBIT_enquete_emploi.pdf.

15Possibly, also in the light of the aforementioned change in definition for unemployment in France.

16While we recognize the role of out-migration in Central Eastern European countries to be extremely relevant – especially after EU accession – the LFS data do not specifically target migrants, being aimed instead at the resident population. Matching migration from origin to destination countries (outflows and inflows) after the 2004 and 2007 EU enlargements is thus very difficult in practice as “some migrants will be missing from the sampling frame […] which is design to ensure a representative coverage of the overall population, rather than specifically migrants […]”. For a further discussion see Eurostat (2011).

17From Figure 2, the results of labour market outflows increasing in Continental European countries are shown to be mainly driven by France, where the aforementioned change in the definition for unemployment is likely to over-estimate labour market quits (see also Macchiarelli and Ward-Warmedinger 2013) See footnote 14.

18As summarized before, the Shorrocks’ index is a proxy index for mobility. For example, with respect to the results in Tables 2 and 3, the decrease in state persistence over time (i.e. the reduction of the elements on the main diagonal from 1998–2003 to 2004–2008) implies an increase in the mobility index across the two sub-periods.

19Particularly, the fall in mobility in Central Eastern European countries from 2004 should be read in light of the political demand for social security after the transition period (early 90s). At that time several program of unemployment benefits, social security, income support and severance pay were put in place, with the (often mistaken) aim to enhance flexibility of workers and reduce long-term unemployment. Such active labour market spending seemed not to have crucially enhanced stagnation on unemployment pools before 2004 but, on the contrary, they seemed to create inefficiencies by means of displacement effects in the second period (2004–2008).

20EPL is likely to proxy institutional factors such as the degree of unionization, minimum wage policies, etc.

21With employment incentives we mean benefits paid to beneficiaries with low earning from part-time or intermittent jobs. See OECD.stat database.

22This type of expenditure refers to a scheme which allows (older) workers – already on unemployment benefits – to move to a similar benefit scheme where the work availability requirement is no longer necessary.

23For the former, the patters is, however, in line with the idea that a higher regulation is expected to reduce employment by slowing down the pace at which displaced workers find new jobs (see also Burgess et al. 2000), resulting into a lower level of labour turnover.

24As discussed in Section 2, also depending on the direction in which transitions across labour market statuses are observed – be it from unemployment to employment, from unemployment to inactivity and so on. The effectiveness of labour market measures and their interactions are likely to affect the degree of labour market turnover as well.

Authors’ information

CM is a Fellow in European Political Economy at the London School of Economics’ European Institute. MW is a Principal Economist in the EU Countries Division of the European Central Bank, a Research Fellow at the Institute for the Study of Labor (IZA) and a CEPR Research Affiliate.

Declarations

Acknowledgements

The views expressed are those of the authors only and should not be reported as representing the views of the European Central Bank (ECB). The authors are grateful to Julian Morgan, Giulio Nicoletti, José Marín Arcas and other participants at an internal seminar organized by the Directorate Economic Developments of the ECB. The paper also benefited from comments provided by participants at an internal seminar organized by the Centre for European Economic Research (ZEW). Finally, the authors are thankful to Vassilis Monastiriotis for further input and discussion.

Responsible editor: Alan Barrett

Authors’ Affiliations

(1)
European Central Bank
(2)
London School of Economics and Political Science

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Copyright

© Ward-Warmedinger and Macchiarelli; licensee Springer. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.