Job Loss, Retirement and the Mental Health of Older Americans Bidisha Mandal Brian Roe The Ohio State University

Job Loss, Retirement and the Mental Health of

Older Americans

Bidisha MandalBrian Roe

The Ohio State University

Outline

Motivation Literature Data Model Results Conclusion Future Research

Motivation

Increasing percentage of older individuals in the population.

General decline in job security in U.S. labor market.

Physical limitations, cognitive changes, bereavement are commonly associated with aging.

Does work displacement cause additional distress? Are there any long-term effects? Job loss – skills may not be transferable, loss of

income Retirement – lifestyle changes.

Policy implication – increased private medical expenditure, increased public spending for government medical programs.

Relevance

Mental health affects social behavior, morale, as well as work productivity.

Deteriorating mental health can manifest in weakened physical health and increase likelihood of suicide.

Declines in the mental health may negatively influence the well-being of other household members.

Older Americans may be less inclined to seek help for psychological problems (as compared to physical decrements).

Job loss affects the quality of life

Literature

Retirement Kim and Moen (2002): 458 New York employees; 1994,

1996, 1998 waves of Cornell Retirement and Well-being study.Results: short-term boost in morale, and long-term increase in distress levels for men.

Drentea (2002): 2 different cross-sectional national surveys Mixed results: lower sense of control, but lower anxiety levels among retirees.

Midanik et al. (2005): 595 members of a health maintenance organization; short-term effect.Result: lower stress levels among retirees.

No clear trend; No long-run national panel have been studied yet.

Related Literature on Retirement Kerkhofs et al. (1999): health and retirement are

endogenously related. Dwyer and Mitchell (1999), Disney et al. (2006): health

problems influence retirement plans more strongly than economic variables.

Involuntary Job Loss (business shut-down or lay-off) Gallo et al. (2000): 1992 and 1994 waves of Health and

Retirement Study. Different methodology to handle endogeneity

Reverse causality and unobserved heterogeneity OLS vs. HT/IV and 2SLS

Alternative coding

Framework

Work displacement

RetirementInvoluntary

job loss

Easily adjusts tonew lifestyle

Unable to adjust to new lifestyle

Retirementplans affected

ReemploymentReentry

Long spell or forced to retire

CESD Score Mental health measure

Developed by Radloff (1977) – short, self-reporting scale (20 items) for general population.

HRS only includes 8 items – 6 negative and 2 positive binary indicators Negative items – felt depressed, everything an effort,

sleep was restless, felt lonely, felt sad, could not get going

Positive items – was happy, enjoyed life

CESD = sum (negative items) – sum (positive items)Thus, higher score (0 to 8) means worse mental health.

Both versions commonly used in other studies to measure distress and psychological well-being.

Summary – CESD Score

Reliability Cronbach’s alpha coefficient for 20 items = 0.85 Cronbach’s alpha coefficient for 8 items = 0.71

Mean change in CESD score among those who suffered involuntary job loss is 0.19

Mean change in CESD score among retirees is 0.17

Maximum increase in CESD score is reported between the first two waves (1992 to 1994), when job loss rates were high.

CESD scores improve during latter waves for all.

Coding Involuntary Job Loss and Retirement Unbalanced panel data

6 waves – 1992, 1994, 1996, 1998, 2000 and 2002 N=7,780 (all those employed in 1992, 51-61 years old)

Coding Survey does not ask R if suffered involuntary job-loss,

but reason for unemployment. Involuntary job loss

If R reports business closure or layoff, and started looking for job immediately.

Retirement (voluntary) If R accepts early retirement incentives, and does not

look for job immediately. These individuals also call themselves – ‘self-retired’.

Plus, those who report retirement as labor market status.

Data limitations

Data

Survey year Lost job Retired Employed

1994 97 792 6153

1996 87 1451 5116

1998 67 1907 4373

2000 32 2365 3605

2002 47 2883 2932

Labor market status

Employed

Invol. exit

Vol. exit Self-retired

Retired

(coded) (survey instrument)

ΔCESD 0.44 0.95 0.61 0.63 0.65

‘Lost job’ Collapsed as ‘Retirees’Distribution of HRS respondents in different labor market situations

Mean change in CESD score between 1992 and 1994

Summary Statistics (selected variables)

Variables (Change) 1992-1994

1994-1996

1996-1998

1998-2000

2000-2002

Job loss (%) 3.32 6.25 4.54 3.17 4.13

Retirement (%) 11.25 25.23 31.80 43.31 53.75

Separated/divorced (%)

1.65 1.08 0.98 0.68 0.56

Married/re-married (%)

1.07 1.19 0.84 0.91 0.81

Widowed (%) 1.05 1.08 1.31 1.42 1.52

Worse physical health (%)

14.4 15.9 16.9 17.6 20.8

Δ ADLA 0.03 0.07 0.02 0.03 0.02

Δ Wealth ($10,000) 3.31 3.01 5.71 4.66 - 1.03

Response rate 91.04 86.58 83.06 78.77 76.62

Unobserved Heterogeneity

Compare fixed effects, random effects and Hausman-Taylor IV random effects model using Hausman specification test

FE:

where, are time-varying independent variables

RE:

where, are time-invariant independent variablesand, denotes individual-specific effects

HT-IV:

where, the subscripts distinguish between exogenous and endogenous variables

First difference model:

... )( iitiitiit XXYY

itiiitit ZXY

itiiiititit ZZXXY 22112211

itX

iZ

i

ititit XY

Comparing Model Properties FE

Subtracts off group means Along with time-invariant regressors, latent effects are left out

FD Similar, but subtracts off last period’s observations Again, gets rid of both time-invariant factors and latent effects Unbiased, consistent estimates from both FE and FD

RE Can use time-invariant variables, as long as independent of

latent effects Efficiency gain

HT-IV RE Allows time-invariant variables under lesser constraints Correct specification produces consistent, unbiased and

efficient estimates Limitation – single-equation model; model misspecification

FE, RE and HT-IV RE Models

Variables FE RE HT-IV RE

Job loss 0.191* (0.042)

0.204* (0.040)

0.189* (0.038)

Retired 0.051** (0.023)

0.036 (0.021)

0.051** (0.021)

Time 0.424* (0.019)

0.413* (0.019)

0.425 (0.018)

Time (squared)

- 0.051* (0.003)

- 0.052* (0.003)

- 0.052* (0.002)

Separated 0.037 (0.127)

0.168** (0.069)

0.034 (0.116)

Widowed 0.405* (0.133)

0.409* (0.073)

0.402* (0.121)

Married - 0.276** (0.128)

- 0.210* (0.066)

- 0.282** (0.117)

Physical health

0.190* (0.016)

0.248* (0.009)

0.199* (0.014)

Estimates (SE) from different models for selected variables

Dependent variable: CESD score

* p < 0.01; ** p < 0.05

Model Choice Latent effects – motivation, productivity

Time-varying endogenous variables – involuntary and voluntary exits, marriage/remarriage, separation/divorce, ADLA index, physical health condition

Time-invariant endogenous variables – age, education, white/blue-collar job

Choice of model (FE vs. RE) depends on cost of efficiency gain

Only one time-invariant variable significant - gender First difference model is adequate in controlling for

latent effects and is able to capture the change in mental health due to a shock

Specification test RE HT-IV RE

χ2 (df) 374.01 (21) 8.14 (7)

p-value 0.00 0.32

Compare with Previous Study Gallo et al. (2000) use data from 1992 and 1994

HRS.

Sample selection is sufficient to take care of latent effects – exclude retirees, self-employed individuals, disabled, and those who left their jobs for reasons other than plant closure and lay-off.

Involuntary job loss – plant closure and lay-off

Method – OLS regression.

Replicate their coding and methodology, and obtain estimate of involuntary exit similar to theirs.

Problem – unobserved heterogeneity still existsSpecification

testRE HT-IV RE

χ2 (df) 102.32 (18) 16.62 (5)

p-value 0.00 0.01

Reverse Causality

Suspect endogenous variables Involuntary exit Voluntary exit Separation/Divorce Marriage/Re-marriage

Instruments (excluded exogenous variables) Unemployment rate Age at the beginning of each survey Parents’ level of education R’s level of education If R’s parents are/were married to each other or to step-

parents Number of divorces and widowhoods reported in 1992

Validity Three basic tests to

Check if endogeneity actually exists Ho: suspect endogenous variables are exogenous

Compare 2 regressions – one where suspect regressors are treated as endogenous, and the other where they are exogenous. Test statistic is distributed χ2 with df = number of endogenous regressors

Check for weak instruments LR test - Ho: equation is underidentified

To check if the instruments are poor proxies for the endogenous variables. Test statistic is distributed χ2 with df = total number of exogenous regressors - endogenous regressors + 1

Check the validity of the instruments J statistic - Ho: instruments are uncorrelated with error

Test statistic is distributed χ2 with df = number of instruments - 1

Results from Labor Market Exit

Variables (Change) All (N = 7780)

Estimate SE

Involuntary exit E 0.244* 0.042

Retirement E - 0.261* 0.034

Sep./divorce E - 0.121* 0.038

Married E 0.037 0.035

Widowed 0.897* 0.092

Death of child 0.220* 0.059

ΔADLA 0.426* 0.027

Worse physical health

0.331* 0.032

Endogeneity χ2 (4) 87.98*

LR statistic χ2 (6) 24.79*

J statistics χ2 (5) 4.44

2SLS regression: Dependent variable – ΔCESD score

E - endogenous

* p < 0.01

Results from Re-entry after Job Loss

Variables (Change) Only those who lost job (N = 418)

Estimate SE

Reemployment E - 0.426* 0.137

Sep./divorce 0.862** 0.440

Married 0.341 0.504

Widowed 1.999* 0.425

ΔADLA 0.723* 0.133


0.451* 0.126


LR statistic χ2 (3) 112.42*



E – endogenous; * p < 0.01; ** p < 0.05

Results from Re-entry after Retirement

Variables (Change) Only retirees (N = 3578)

Estimate SE


Sep./divorce E 0.170 0.041

Married E - 0.065 0.042

Widowed 0.973* 0.125

ΔADLA 0.334* 0.037


0.175* 0.040


LR statistic χ2 (4) 16.67**



E – endogenous; * p < 0.01; ** p < 0.05

Involuntary Exit vs. Reemployment

Variables (Change) Only those who lost job (N = 418)

Estimate SE

Involuntary exit E 0.172** 0.075


Sep./divorce 0.579 0.446

Married 0.185 0.545

Widowed 1.073** 0.424

ΔADLA 0.517* 0.118


0.208 0.135

Ho: Effect of exit = - Effect of re-entry

F-value (p-value) 0.36 (0.55)


E – endogenous; * p < 0.01; ** p < 0.05

Summary of Steps

First Difference Model Accounts for Unobserved Heterogeneity To capture the effect of change in labor market

status on change in mental health

Reverse Causality Mental health may decide labor market status Two Stage Least Squares (2SLS) Endogenous Regressors

Labor market status Marital status (except widowhood)

Effect of Reemployment

Conclusion

Endogenous regressors are labor market exit and re-entry, separation or divorce, and marriage or remarriage

Involuntary job loss negatively impacts mental health

Similar in magnitude and direction to effect of death of child

Highest negative effect due to death of spouse

Retirement has a positive effect on mental health (short-term)

Re-entering labor market has a positive effect on the mental health for all

Re-entry recaptures the previous mental health status of those who lost job involuntarily

Documents

Job Loss, Retirement and the Mental Health of Older Americans Bidisha Mandal Brian Roe The Ohio State University