A Markov system analysis application on labour market dynamics: The case of Greece

Transcription

1 + A Markov system analysis application on labour market dynamics: The case of Greece Maria Symeonaki Glykeria Stamatopoulou This project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No Panteion University of Social and Political Sciences

2 + The structure of the presentation 2 n Motivation Objectives n Data n Methodology n Results Conclusions n Future work 2

3 + Motivation Objectives 3 n To examine: n the flow data and transition rates between labour market states on a micro-level, i.e. the labour market outcomes of the individuals n Using: n quantitative analysis and Markov system theory for Greece * *Source: Hellenic Statistical Authority 3

4 + Data 4 EU LFS n The EU LFS is a large household sample survey providing results on labour participation of people aged 15 and over as well as on persons outside the labour force n All definitions apply to persons aged 15 years and over living in private households n Persons carrying out obligatory military or community service are not included in the target group of the survey, as is also the case for persons in institutions/collective households

5 + Data 5 EU LFS n Information about the labour market transitions in Greece from 2006 to n To study and compare the distribution of transition probabilities from employment, unemployment and inactiveness and vice versa n The current labour status (MAINSTAT) and the situation one year before the survey (WSTAT1Y) are the variables that will be used in the present analysis

6 Country Year: 2013 Country Year: Belgium 8.4 Luxembourg 5.9 Bulgaria 13.0 Hungary 10.2 Czech Republic 7.0 Malta 6.4 Denmark 7.0 Netherlands 7.3 Germany 5.2 Austria 4.9 Estonia 8.6 Poland 10.3 Ireland 13.1 Portugal 16.4 Greece 27.5 Romania 7.1 Spain 26.1 Slovenia 10.1 France 10.3 Slovakia 14.2 Croatia 17.3 Finland 8.2 Italy 12.2 Sweden 8.0 Cyprus 15.9 United Kingdom 7.6 Latvia 11.9 Iceland 5.4 Lithuania 11.8 Norway 3.5 Source: Eurostat, Labour Force Survey (lfsa_urgan), + Unemployment rates (total population) by country (%), 2013

7 Country Year: 2013 Country Year: Belgium 23.7 Luxembourg 16.9 Bulgaria 28.4 Hungary 26.6 Czech Republic 18.9 Malta 13.0 Denmark 13.0 Netherlands 13.2 Germany 7.8 Austria 9.2 Estonia 18.7 Poland 27.3 Ireland 26.8 Portugal 38.1 Greece 58.3 Romania 23.7 Spain 55.5 Slovenia 21.6 France 24.8 Slovakia 33.7 Croatia 50.0 Finland 19.9 Italy 40.0 Sweden 23.6 Cyprus 38.9 United Kingdom 20.7 Latvia 23.2 Iceland 10.7 Lithuania 21.9 Norway 9.1 Source: Eurostat, Labour Force Survey (lfsa_urgan), + Youth unemployment rates, by country (%), 2013

8 + Unemployment rates (2013) Total UR Youth UR Belgium Bulgaria Czech Republic Denmark Germany Estonia Ireland Greece Spain France Croatia Italy Cyprus Latvia Lithuania Luxembourg Hungary Malta Netherlands Austria Poland Portugal Romania Slovenia Slovakia Finland Sweden United Kingdom Iceland Norway

9 9

10 10

11 + Basic parameters of a non homogeneous Markov system { P(t) } t=0 { p o (t)} t=0 { p k+1 (t)} t=0 { Q( t) } T( t) { } S = 1,2,...,k t = 0 [ ] N( t) = N1( t), N2 ( t),, N ( t) N( t + 1) = N( t) Q( t) + ΔT( t) p ( t) ΔT( t) = T( t) T( t 1) k o po2( t) p po1( t) p 12( ) p22( t), k+ ( t) 2 1 p11( t) 1 t p13( t) p21( t) p31( t) p32( t) 2 3 p23( t) k = 3 p1, k+ 1( t) p33( t) p, k+ ( t) 3 1 po3( t) Transition diagram of a Markov system with three states

12 + States in LFS Carries out a job or profession, including unpaid work for a family business or holding, including an apprenticeship or paid traineeship, etc 2. Unemployed 3. Pupil, student, further training, unpaid work experience 4. In retirement or early retirement or has given up business 5. Permanently disabled 6. In compulsory military service 7. Fulfilling domestic tasks 8. Other inactive person

13 + States in NHMS and possible 13 transitions 1: Employment (1) : Unemployment (2) : Inactivity (5, 6,7,8)

14 + Basic parameters P(t) = p 11 (t) p 12 (t) p 13 (t) p 21 (t) p 22 (t) p 23 (t) p 31 (t) p 32 (t) p 33 (t) p k+1 (t) = ( I P(t) ) p o (t) = 1 = p k+1,1 (t), p k+2,2 (t), p k+1,3 (t) p o1 (t), p o2 (t), p o3 (t) q ij (t) = p ij (t) + p i,k+1 (t) p oj (t) Q(t) = q 11 (t) q 12 (t) q 13 (t) q 21 (t) q 22 (t) q 23 (t) q 31 (t) q 32 (t) q 33 (t)

15 + Mobility indices M B = 1 k k k i=1 j=1 q ij i j n Bartholomew s index of mobility M(PS) = 1 n 1 (n tr(q)) n Shorrocks index of mobility IM = tr(q) n n Immobility index M T = 1 tr ( Q ) k n Prais Bibby mobility index

16 + Results 2006, Greece 2006 Greece 2007 N = 262,884 N = 257, 228 p o (2006) = p o (2007) = Q(2006) = Q(2007) =

17 + Results 2008, Greece 2008 Greece 2009 N = 255,701 N = 258, 428 p o (2008) = p o (2009) = Q(2008) = Q(2009) =

18 + Results 2010, Greece 2010 Greece 2011 N = 262,172 N = 236, 551 p o (2010) = p o (2011) = Q(2010) = Q(2011) =

19 + Results 2012, Greece 2012 Greece 2013 N = 212,307 N = 215,244 p o (2012) = p o (2013) = Q(2012) = Q(2013) =

20 + Mobility and Immobility Indices 20 Greece M(PS) IM M(B) M(T)

21 + Mobility indices M(PS) M(B) M(T)

22 + Immobility index IM

23 + Conclusions 23 Transition probabilities Input probabilities Eè E Uè U Eè U è E è U è IA

24 + Future work 24 n First, we will study the asymptotic behaviour of the system n The same methodology will be applied for all European countries (starting with the Southern European countries) n We will also study gender and age differences