With financialsupportfrom the EuropeanUnion –EaSIProgress Program

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PANEL 2

Labor Market and Wealth

Presenters: Chiara Puccioni, Elena Fabrizi, Michele Bavaro Discussants: Giovanni Gallo, Paolo Acciari, Federico Belotti

With financial support from the European Union – EaSI Progress Program

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• General features & novelties

• Data

• Evidence from Italian labor market

• Econometric models & estimates results

• Future implementations

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Novelties

• Allowing retired and students to work. After being assigned to work status, retired workers labour

market transitions and monthly wages are estimated separately

• Multinomial model to estimate transitions

• New evidences of interesting labor market

phenomena from the estimates, not highlighted in

previous report

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The Labor market dataset

DATASET

AD-SILC is an unbalanced panel with retrospective (forward-looking) information on individuals’ working conditions before (after) the year of survey of SILC, based on:

 Panel INPS - longitudinal data of individuals’ working history since their entry in the LM: occupational status, income evolution, contribution accumulation, etc.

 Panel SILC - longitudinal data of individual socio-economic characteristics (up to 4 years): education, marital status, number of children, etc.

SAMPLE DESCRIPTION

• Sample size : 551,682 observations and 211,555 individuals aged 15-80

• Time span: unbalanced panel data from 2004 to 2017, individual appear 2.6 times on average

• Demographic and socio-economic individual or household characteristics such as country of birth, marital status, age, nr of children, disability status etc. from SILC and labour market-related information from INPS

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The Italian Labor Market over 2004-2017

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21.87 21.05 20.73 20.55 20.27 20.3 20.2 19.83 19.29 18.94 18.88 18.78 18.45 18.15 29.86 30.01 29.38 28.78 28.46 27.99 27.78

26.41 25.85 25.62 24.82 23.87 24.12 23.27 34.36 34.47 35.13 35.77 35.99 36.38 37.14 38.41 39.09 39.12 39.67 40.49 40.6 40.98 13.91 14.48 14.76 14.9 15.28 15.33 14.89 15.35 15.77 16.32 16.63 16.86 16.82 17.6

0 10 20 30 40 50 60 70 80 90 100

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Employment distribution by age class

15-29 30-44 45-64 65-74

Elaboration on AD-SILC , 2004-2017

Overtime workforce is continuously ageing and therefore changing.

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6 55.77 54.14 51.84 50.32 49.32 48.23 48.33 46.82 44.41 42.17 41.28 39.26 40.14 40.22

35.48 37.05 38.46 39.3 40.01 40.4 40.16 40.22 42.48 44.13 42.07 42.67 43.25 43.65 8.75 8.81 9.69 10.38 10.68 11.36 11.51 12.96 13.1 13.7 16.65 18.07 16.62 16.13

0 20 40 60 80 100

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Employment distribution by education

lower sec. degree and below upper sec. degree tertiary degree

The Italian Labor Market over 2004-2017

Decrease of the share of workers with a lower secondary degree at most, consequently favoring the component with the highest educational qualification

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The immigrant workforce share

gradually rose

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1.08 1.13 1.23 1.44 1.76 2.12 2.01 2.26 2.51 2.53 2.7 2.66 3.21 3.51 3.73 3.63 3.78 3.93 4.24 4.34 4.31 4.81 4.94 5.02 5.49 5.5 6.82 7.24 95.19 95.25 94.99 94.62 94 93.54 93.68 92.93 92.55 92.45 91.8 91.84 89.97 89.25

0 10 20 30 40 50 60 70 80 90 100

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Immigrant workers on the Italian labor market

EU born Extra-EU Italian

55.93 53.03 49.88 50.09 47.1 44.7 48.27 46.26 41.62 39.9 40.75 39.92 51.38 52.87 36.12 38.94 41.44 40.95 43.39 44.5 41.85 41.03 45.64 47.21 44.6 44.61 37.75 37.53 7.94 8.03 8.68 8.96 9.52 10.8 9.88 12.71 12.74 12.89 14.65 15.46 10.88 9.6

0 20 40 60 80 100

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Immigrant workers on the Italian labor market, by education

lower sec. degree and below upper sec. degree tertiary degree Elaboration on AD-SILC , 2004-2017

The Italian Labor Market over 2004-2017

Immigrant

influx tend to be more educated over time

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4.2 4.1 4.3 4.1 3.9 3.4 3.5 3.5 3.7 3.1 3.3 3.3 2.7 2.1

8.5 9.0 9.6 9.9 10.0 10.1 10.8 10.7 10.6 10.4 10.7 10.2 10.4 13.3

1.5 1.8 1.9 2.1 1.9 1.6 1.6 1.5 1.3 0.8 0.8 0.8 1.0 1.4

51.5 50.7 49.3 49.1 49.2 49.3 49.1 49.1 49.9 50.9 50.5 50.7 52.3 51.1

13.0 13.2 13.9 13.9 14.0 14.6 14.5 14.9 14.7 14.7 14.7 15.4 14.9 14.0

2.5 2.6 2.9 3.1 3.2 3.4 3.3 3.4 3.6 4.2 4.3 4.3 4.2 4.1

18.8 18.7 18.2 17.8 17.9 17.7 17.3 17.0 16.4 15.8 15.6 15.3 14.5 14.0

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Distribution by employment status of the workforce

Atypical Fixed-term private Fixed-term public Open-ended private Open-ended public Professionals Self-employed

The Italian Labor Market over 2004-2017

over time:

Growth in fixed-term positions; reduction of self-employed and slight surge in Professionals

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9 Elaboration on AD-SILC , 2004-2017

The Italian Labor Market over 2004-2017

Among males higher self-employment occurred, compensated by a lower amount of public positions.

Workforce with tertiary education is more frequent having an open-ended contract in public or Professional fields, in comparison to those with lower education.

3 3 2 3 5

111 100 140 101 62

50 51 54 55

39

21 11 7 14

28 4

5 0 2 13

10 19 22 15 7

0 20 40 60 80 100

female male lower sec upper sec tertiary

Distribution by employment status of the workforce by Gender and education, year 2015

Atypical Fixed-term private Fixed-term public Open-ended private Open-ended public Professionals Self-employed

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Evidence from labor earnings dynamics

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Comparing annual and monthly

evidence on

earnings, it is very clear that earning inequality strictly depends on

duration aspects

0 5000 10000 15000 20000 25000 30000

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

median annual earnings, full time workers

Perm Fixed Self-empl. Atypical

0 500 1000 1500 2000 2500

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

median montly earnings, full time workers

Perm Fixed Self-empl. Atypical

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Evidence from intensity of work

Differences in the number of work days therefore differences in earnings

0 50 100 150 200 250 300 350 400

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Number of days worked per year, full time workers

Perm Fixed Self-empl. Atypical part time

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Evidence from intensity of work

Strong differences are observed between the gender work days

280 285 290 295 300 305 310 315 320

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

number of days worked per year, by gender. Full time workers

Female Male

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Evidence from intensity of work

Work days are dramatically different concerning the age and the educational aspect .

The gap between the youngest and the rest of the working population increases over time.

260 270 280 290 300 310 320 330 340

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

number of days worked per year, by education. Full time workers

lower sec upper sec tertiary

250 260 270 280 290 300 310 320 330 340

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

number of days worked per year, by age. Full time workers

45-54 35-44 15-34

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Evidence from intensity of work

Concerning work intensity immigrant workforce is penalized with respect to native workers.

250 260 270 280 290 300 310 320

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Number of days worked per year, by Area of birth. Full time workers

EU born Extra-EU Italian

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Labor market transitions

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2005 Perm Fixed Self-empl. Atypical out of work

Perm 92.5 2.5 0.7 0.4 4.0

Fixed 21.7 66.7 1.3 1.5 8.7

Self-empl. 1.2 0.7 92.3 0.9 5.0

Atypical 7.1 6.0 5.0 72.2 9.8

2006

Transition matrix: working conditions after 1 year

Perm 92.6 2.1 0.4 0.2 4.8

Fixed 22.2 62.7 0.5 1.5 13.1

Self-empl. 2.2 0.6 91.6 0.5 5.1

Atypical 14.3 9.3 3.5 53.3 19.6

2016

Over time, an increasing number of workers downgrade to an «out of work»

state

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Transition matrix: working conditions after 2 years

Perm 88.0 3.7 1.4 0.5 6.3

Fixed 32.0 52.1 2.3 1.6 12.0

Self-empl. 2.5 1.4 86.6 1.5 8.1

Atypical 13.9 8.3 5.8 55.8 16.2

2007

Perm 86.9 4.0 0.7 0.1 8.3

Fixed 28.6 54.7 1.1 0.7 14.9

Self-empl. 3.4 1.8 85.7 0.7 8.4

Atypical 15.6 14.8 4.1 41.5 24.0

2017

Labor market transitions

After two years, the number of workers observed in an «out of work» state particularly concerns the precarious individuals.

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Transition matrix: working conditions after 2 years, by education

Labor market transitions

Workforce with a tertiary degree is more protected from the risk of falling in a jobless condition.

Perm 83.0 4.8 0.6 0.0 11.5

Fixed 21.7 59.0 0.3 0.3 18.7

Self-empl. 2.2 1.6 84.3 0.4 11.5

Atypical 9.8 16.7 2.9 30.4 40.2

Perm 86.8 4.2 0.8 0.2 8.1

Fixed 29.3 55.4 1.2 0.3 13.8

Self-empl. 3.3 1.9 85.7 0.5 8.5

Atypical 15.3 15.3 2.6 42.4 24.5

Perm 91.1 2.8 0.8 0.1 5.3

Fixed 41.0 43.9 2.3 2.6 10.3

Self-empl. 5.2 1.9 87.2 1.2 4.5

Atypical 19.2 13.2 6.6 46.7 14.3

2017

At most lower-secondary

Upper-secondary

Ter ary 2017

2017

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2016 2070…

AD-SILC 2016

Demographic Module

Labor Market Module

Pension Module

Wealth Module

Tax-Benefit Module

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General features of Labor Market Module

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Are you in ‘active age’

(15-80)?

Are you employed?

(probabilistic + alignment)

What type of worker are you?

(probabilistic, 6 possible

categories) • Private permanent employee

• Public permanent employee

• Temporary employee

• Atypical (Co.co.co)

• Professionals

• Self-employed

(Artisan/Merchant/Farmer)

NO _• On inability pensions

• On unemployment benefits

• Other unemployed

YES

• Part-time

• Full-time

Real monthly wages

Months worked

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The Labor market estimates

Probability of being employed

• Sample: all individual aged between 15 and 80, including students and retired observed over the entire 2004-2017 span

• Model: for the time being, logit is used clustering s.e. at individual level but RE yields very similar results

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Among those working, the probability of being a certain typology of worker

• Sample: all individual working in t excluding those retired (dedicated &

simplified regression)

• Model: Multinomial logit for preliminary estimates

• Further logit regressions distinguish between between part-time/full- time employees and private/public fixed-term employees

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Probability of being employed by sex, log odds

AD-SILC , 2004-2017, all individuals aged 15-80 21 Socio-demographic

Labor market related

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22 AD-SILC , 2004-2017, all individuals working in t aged 15-80

Multinomial logit (ref. category: open-ended private), male, log odds

- Among demographic characteristics only being foreign increase likelihood of work as private open-ended employee - Cumulate past work experience in a category increases chances of persistence in that type

- Movements between types occur for fixed-term towards stability and among professional, self-employed and atypical

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Multinomial logit (ref. category: open-ended private), female, log odds

23 - Motherhood is more associated with open-ended private contract than any other category, except for self-employed - Very similar patterns with respect to men for both socio-demographic and labor-related characteristics

- Exception: women have ‘upgrading’ transitions if working as professionals (do not move towards atypical as men do) or working as atypical (move also towards open-endend public contracts, while men did not)

AD-SILC , 2004-2017, all individuals working in t aged 15-80

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once employment typology is assigned:

Real gross monthly wages**:

• Sample: all individual working in t excluding those retired (dedicated regression) and assigned to a specific employment category (fixed-term and open-ended employee are considered together, by sector)

• Model: GLS RE model with a permanent error component (i.e. soft skills, motivation), that represent constant wage deviation for each individual + a transitory component (white noise error with no memory and i.i.d. )

Number of months worked

• Sample: fixed-term employees or atypical only, other categories work all year by default

• Model: GLS RE model as for real wages

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Yearly individual labour income gross of personal income taxation is the product of two components:

monthly gross wages months worked

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changes

AD-SILC , 2004-2017, all individuals working in t aged 15-70 – excluding retired 25 Socio-

demographic

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Number of months worked

26 AD-SILC , 2004-2017, all individuals 15-80 aged working as fixed-term employee or atypical

Socio-

demographic

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Future Implementations

Existing regression models:

• Use RE models whenever relevant, adding individual mean of time- variant covariates to relax RE assumption of independence with individual effects if needed

• Investigate if any of the economic relationships analysed with panel data model is dynamic in nature, adding lagged dependent variable

• Investigate if error component show serial correlation, taking into account the unbalanced and sometimes irregularly spaced nature of observations

New regression models:

• Model unemployment among those out of work

• Distinguish between a first and a second employment relationships

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