Economic transitions amongst

the over-50s

Lorenzo Cappellari

Richard Dorsett

Getinet Haile

Format

• Policy background and research questions

• Analytical approach

• Summary of the data used in the analysis

• A description of the over-50s, 1993-2003

• Results of modelling transitions

• Results of modelling time until change

• Summary

Policy background and research questions

• Pensions Green Paper states commitment to raising employment among older people

• Research questions:– Who are the over-50s?

– How should over-50s employment be characterised?

– How much movement between states is there?

– How quickly does this movement happen?

– What factors are associated with movement?

Analytical approach

• Focus on those aged 50 – SPA

• Use longitudinal data to examine changes

• Two approaches:– Markov models examine transitions between

two points in time (state dependence)– Duration models examine the time taken until a

change from initial state (duration dependence)

Data – longitudinal LFS

• 5 quarter LLFS observes households 5 times (quarterly) over a year

• Data are weighted – nationally representative

• Data cover 1993-2003

• Resulting dataset has 26,000 individuals

Descriptive statistics

Size of the 50-SPA population

0123456

mill

ion

s

men

women

The changing size of the older working age population

Summary of characteristics

• Mostly white (97, 97%: men, women)• Mostly partnered (82, 77)• Mostly without dependent kids (86, 90)• Mostly own/mortgage accom (78, 79)• Long-term health probs common (45, 41)• About a quarter on benefit (23, 24)• Men better qualified: no quals (26, 39); NVQ3+

(51, 27); apprenticeship (32, 5)

Economic status when first observed by gender (col %) male female Employee 51 56 Self-employed 16 6 Unpaid family worker 0 1 ILO unemployed 5 3 Inactive 1 - inactive but would like work - looking after family/ home 0 1 - long term sick or disabled 4 3 - believes no job available 1 1 - not looked 1 1 Inactive 2 - inactive and would not like work - looking after family, home 1 10 - long-term sick or disabled 11 9 - not need or want job 1 3 - retired 8 4 - other reason 0 1 Base 14,857 11,174

Grouped economic status (col %) male female Employed 67 62 Unemployed 5 3 Inactive 1 7 6 Inactive 2 22 29 Base 14,857 11,174

020

4060

80

jul93 jan96 jul98 jan01 jul03 jul93 jan96 jul98 jan01 jul03

perc

ent

perc

ent

male female

Employed Unemployed

Inactive 1 Inactive 2

date

Changes over time in status

Transitions over a year between broad groups (row %) Emp Unemp Inac 1 Inac 2 base Men: Employed 91 2 2 5 10,154 Unemployed 25 48 15 12 579 Inactive 1 6 7 46 41 934 Inactive 2 3 1 13 83 3,190 Women: Employed 90 1 1 8 7,109 Unemployed 33 32 16 19 271 Inactive 1 6 4 40 50 628 Inactive 2 4 1 10 85 3,166

Examining transitions between states with Markov models

Markov models• transition probabilities: estimating probability of being

in a particular state conditional on the state occupied at an earlier time.

• control for differences in characteristics (but do not consider duration issues)

• allow assessment of state dependence. Policy relevance:– causal effects of states – via eg signalling, human capital

changes, loss of motivation – may be more important than personal characteristics

– so all individuals may become trapped in, for example, unemployment once they experience the state.

State dependence status in current quarter: status in previous quarter: Emp Unemp Inac 1 Inac 2

men (n=39,696)

Unemployed -0.403** 0.111** 0.123** 0.169**

Inactive type 1 -0.596** 0.084* 0.167** 0.344**

Inactive type 2 -0.723** -0.014** 0.053 0.684**

women (n=27,735) Unemployed -0.396** 0.056** 0.114** 0.225*

Inactive type 1 -0.565** 0.045 0.126** 0.394**

Inactive type 2 -0.734** -0.007 0.025 0.716**

status in current quarter:

status in previous quarter FT emp PT emp Unemp Inac 1 Inac 2

men (n=39,696)

Part-time employed -0.384** 0.126** 0.072** 0.053 0.133**

Unemployed -0.523** 0.108* 0.098** 0.105* 0.212**

Inactive type 1 -0.628** 0.026 0.076* 0.144** 0.382**

Inactive type 2 -0.665** -0.058* -0.011 0.04 0.695**

women (n=27,735) Part-time employed -0.341** -0.005 0.011 0.035** 0.3**

Unemployed -0.389** -0.133 0.015* 0.038** 0.469**

Inactive type 1 -0.393** -0.236* 0.01 0.039* 0.581**

Inactive type 2 -0.394** -0.336** -0.015 -0.011 0.756**

The role of FT and PT employment

Examining timing of transitions with duration analysis

Duration analysis

• Models the ‘hazard’: the probability of a transition at some point for those who had not experienced a transition before that point

• Allows assessment of duration dependence. Policy relevance: the longer individuals are in an adverse labour market state the harder it may become to help them.

Exits from employment .6

.7.8

.91

0 12 24 36 48 60 72 84 96 108120132144156168180192204216228Spell length - quarters

men women

Pro

babi

lity

of

rem

ain

ing

em

plo

yed

• Age: older men & women more likely to exit employment• Qualification: no significant marginal effect• Accommodation: men and women with a mortgage less

likely to exit employment• Having a partner: women: less likely to exit. Non-working

partner more likely to exit (for both men and women) • Occupation: no significant marginal effect• Industry: men in all industries but real estate & ‘other’ more

likely to exit• Hours of work: men with <30 hrs/wk more likely to exit;

women <17 hrs more likely to exit• Region: Men in all but York & H., Wales, Scotland and N.

Ireland less likely to exit; Women in York & H., EM, rest of SE, WM, & Wales less likely to exit.

Employment exits: marginal effects

Exits from type 2 inactivity

.2.4

.6.8

1

0 12 24 36 48 60 72 84 96 108120132144156168180192204216228Spell length - quarters

men women

Pro

babi

lity

of

rem

ain

ing

Inac

t2

• Age: older men & women less likely to exit

• Disability: temp and perm more likely to exit

• Mortgage & rented: more likely to exit

• Dependent children: more likely to exit

• Partner: non-working partners less likely to exit

• Region: NI less likely to exit

Marginal effects of covariates

Summary

• Evidence of state dependence and duration dependence. Former shows potential risk for all individuals of being trapped in inactivity. Duration dependence shows importance of escaping this trap relatively early. Policy faces trade-off between intervening soon and risking deadweight or intervening later and risking individuals becoming hard-to-help. However, inactivity spells are mostly long (average about 7 years for men and 10-14 for women).

• For those in employment, need to help individuals remain in work for long enough to overcome early shake-out (most exits are concentrated in first 3 years).

• Part-time work as bridge to full-time work. Scope for policy to support bridging role of part-time work.