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Centre for Research on theEconomics of Ageing

Retirement Adequacy in Singapore - Insights from the Singapore Life Panel®

Conference held at Singapore Management University11 November 2016

Copyright © Singapore Management University 2016

The contents of this document are the property of SINGAPORE MANAGEMENT UNIVERSITY and no part of the document may be reproduced or transmitted in any form or by any means, electronic or mechanical, for

any purpose, without the expressed written permission of SINGAPORE MANAGEMENT UNIVERSITY

1For Internal Use Only

PLEASE DO NOT CIRCULATE

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Table of Contents (bookmarked)

• Programme

• Background And Methodology Of The Singapore Life Panel®

• Labour Market Outcomes

• Economic Position And Preparation For Retirement

• Household Spending And Its Components

• Financial Knowledge, Wealth And Portfolio Complexity

• Health Shocks And Household Expenditures Of The Elderly

• Housing Wealth and Monetisation

• Behavioural Risk Factors And Chronic Conditions

• Subjective Well-being

For Internal Use OnlyPLEASE DO NOT CIRCULATE

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Programme


08:30 – 09:00 REGISTRATION

09:00 – 09:30 WELCOME ADDRESSProfessor Arnoud De MeyerPresident, Singapore Management University

OPENING SPEECH BY GUEST-OF-HONOURDr Amy KhorSenior Minister of State, Ministry of the Environment & Water Resources and Ministry of Health

09:30 – 09:50 BACKGROUND AND METHODOLOGY OF THE SINGAPORE LIFE PANEL®Bryce Hool, Michael Hurd, Susann Rohwedder, Rhema Vaithianathan

09:50 – 10:20 LABOUR MARKET OUTCOMES Michael Hurd, Seonghoon Kim, Susann Rohwedder

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Programme (cont.)


10:20 – 11:00 ECONOMIC POSITION AND PREPARATION FOR RETIREMENTMichael Hurd, Seonghoon Kim, Susann Rohwedder

11:00 – 11:15 TEA BREAK

11:15 – 11:30 Q&A SESSION 1

11:30 – 12:10 HOUSEHOLD SPENDING AND ITS COMPONENTSMichael Hurd, Susann Rohwedder

12:10 – 12:40 FINANCIAL KNOWLEDGE, WEALTH AND PORTFOLIO COMPLEXITY Benedict Koh, Olivia S. Mitchell, Susann Rohwedder

12:40 – 13:30 LUNCH

13:30 – 13:40 Q&A SESSION 2

13:40 – 14:20 HEALTH SHOCKS AND HOUSEHOLD EXPENDITURES OF THE ELDERLYTerence Cheng, Jing Li, Rhema Vaithianathan

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Programme (cont.)


14:20 – 15:00 HOUSING WEALTH, MONETISATION AND CONSUMPTIONLiang Jiang, Sock Yong Phang, Jun Yu

15:00 – 15:15 TEA BREAK

15:15 – 15:30 Q&A SESSION 3

15:30 – 15:50 BEHAVIOURAL RISK FACTORS AND CHRONIC CONDITIONSYuanyuan Deng, Rhema Vaithianathan

15:50 – 16:20 SUBJECTIVE WELL-BEINGSusann Rohwedder

16:20 – 16:30 Q&A SESSION 4

16:30 – 16:35 CLOSING REMARKSBryce Hool

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Introduction to the Singapore Life Panel

Bryce Hool, Michael Hurd, Susann Rohwedder,

Rhema Vaithianathan



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Background to the Singapore Life Panel

• Large-scale research programme

• Funded by a Ministry of Education Tier 3 grant

• Research team: SMU + International collaborators


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Sampling Frame

• Recruitment 8th May 2015 through to July 2015

• Singaporeans (including Permanent Residents) aged between 50 and 70 and their spouses were eligible

• The sampling frame is a probability sample acquired from the Singapore Department of Statistics

• Population representative


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Initial Recruitment• In May 2015, 25,000 households were sent letters inviting

them to participate

• Letters were followed up with phone calls and personal visits

• Around 10% of sample were not eligible

• By August 2015, more than 11,000 households had been recruited to the panel

• This represented more than 14,000 eligible respondentsFor Internal Use Only


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Baseline Survey

• The respondents who were recruited to the panel completed a baseline survey

• This survey included questions on demographics (age, gender, education, martial status)

• Health and housing characteristics were also asked


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Monthly Survey

• From August 2015, panel members were invited to complete a monthly online survey

• Of the active panel of just over 11,000, 58% have completed at least 3/4 of the waves and 72% have completed at least 1/2. More than 8,000 responses are being received each month

• The survey opens at the start of the month (typically on the first Friday) and stays open for 3 weekends

• Respondents who have no Internet access / familiarity with computers meet SMU students at centrally located libraries for help with inputting

• Ongoing personal phone calls and re-engagement campaign maintain panel


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Monthly Survey

• One-off, quarterly and monthly repeated questions

• Monthly questions include income, spending, health, well-being and work

• Other questions include housing, pensions

• We ask respondents about Silver SupportGST Voucher SchemeWorkfareS and C rebates


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Number of Respondents Completing Each Monthly Survey

Remarkably stable - with minimal attrition

January was the asset wave where we undertook additional engagement

01,0002,0003,0004,0005,0006,0007,0008,0009,000

10,000

Nu

mb

er

of

Re

spo

nd

en

ts

Series1


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Panel Attrition

• Not everyone answers every wave

• People move in and out

• Every month we have people re-engaging with the survey due to intensive engagement efforts


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Time Taken to Answer Each Wave (Median, 75th and 25th percentile)

0

10

20

30

40

50

60

70

1 (A

ug 1

5)

2 (Sep

t 15

)

3 (O

ct 15

)

4 (N

ov 1

5)

5 (D

ec 15

)

6 (Jan

16

)

7 (Feb

16

)

8 (M

ar 16

)

9 (A

pril 1

6)

10

(May 1

6)

11

(Jun

e 16

)

12

(Jul 1

6)

13

(Au

g 16

)

14

(Sept 1

6)

Tim

e (

Min

s.)


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SLP – Baseline Data compared with Official Statistics


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Validation of SLP Data Collection

• SLP baseline data compared with official statistics published by Department of Statistics, to confirm sample is representative of population

• Comparison with 2010 Singapore Census of Population

• Comparison with 2014 Singapore Labour Force Survey

• Comparison with 2012–13 Singapore Household Expenditure Survey (HES)

Note: the SLP sample statistics shown in the following tables and charts are derived from the unweighted sample data


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Marital Status and Age Group: SLP vs Census (2010)

Single Married

SLP Census SLP Census

50 - 54 16% 20% 84% 80%

55 - 59 18% 21% 82% 79%

60 - 64 21% 22% 79% 78%65 - 69 25% 27% 75% 73%

Census data from Census 2010, Table 13 Resident Population Aged 15 Years and Over by Age Group, Marital Status, Sex and Residential Status.

Note: SLP asks “married and marriage-like”. “Marriage-like” is included in single.


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Ethnicity: SLP vs Census (2010)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Chinese Malay Indian Others

SLP Census


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Education: SLP vs Census (2010)

29

41

30 27

42

31

-

5

10

15

20

25

30

35

40

45

No formal schooling orPrimary

Secondary Post-secondary orTertiary

Pro

po

rtio

n (

%)

Census

SLP

*Note that the data have been matched on cohorts and not on age.


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Labour Force Participation Rate: SLP vs Labour Force Survey (2014)

Male Female

Age Groups Male (LF) Male (SLP) Female(LF)Female

(SLP)

50-54: 94% 90% 70% 70%

55-59: 88% 87% 61% 62%

60-64: 77% 73% 46% 48%

65-69: 54% 52% 30% 29%NotesLABOUR FORCE IN SINGAPORE, 2014 . LF participation defined as persons aged fifteen years and over who are either employed (i.e. working) or unemployed (i.e. actively looking for a job and available for work) during the reference period.

SLP defined LF participation as people who answered the question “what is your current status” with working for pay, unemployed and looking for work, temporarily laid off, on sick leave, self-employed.


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Monthly Gross Earnings (ex CPF) of Employed Residents Aged 50-70: SLP vs HES (2012-13)

0.0%2.0%4.0%6.0%

8.0%10.0%12.0%14.0%16.0%18.0%20.0%

Under500

500 -999

1000 -1499

1500 -1999

2000 -2499

2500 -2999

3000 -3999

4000 -4999

5000 -5999

6000 -6999

7000 -7999

8000 -8999

9000 -9999

10000 -10999

11000 -11999

12000 -

Monthly Income ($)

FROM SLP (Unweighted) FROM SINGSTAT

Labour Force Survey data from 2014, Table 29 “Employed Residents Aged Fifteen and Over by Gross Monthly Income from Work (Excluding Employer CPF)”. Employed persons is defined as work for one hour or more either for pay, profit or family gains; or (ii) have a job or business to return to but are temporarily absent because of illness, injury, breakdown of machinery at workplace, labour management dispute or other reasons.)

SLP: Employed defined as answering the question “What is your current employment situation?” with Working for pay, Temporarily laid off, On sick or other leave, or Self-employed. Income is response to question “How much was your total income from work in the month of [last month], before taxes and other deductions?


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Monthly Household Expenditure of Employed Residents Aged 50-70: SLP Monthly Waves 1-4 vs HES (2012-13)

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

< 1000 1000 -1999

2000 -2999

3000 -3999

4000 -4999

5000 -5999

6000 -7999

8000 -9999

10000 -11999

12000 -14999

15000 < =

SLP (Unweighted) Singstat

Report on the Household Expenditure Survey from 2012/13, Table 6 “Households by Monthly Household Income and Working Status/ Occupation of Main Income Earner (excluding imputed rental of owner-occupied accommodation)”. Employed persons is defined as work for one hour or more either for pay, profit or family gains; or (ii) have a job or business to return to but are temporarily absent because of illness, injury, breakdown of machinery at workplace, labour management dispute or other reasons.)

SLP: Employed defined as answering the question “What is your current employment situation?” with Working for pay or Self-employed.


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Labour Force Participation: SLP Monthly Waves 2-4 vs Labour Force Survey (2014)

Male Female

Age Groups Male (LF) Male (SLP) Female(LF) Female (SLP)

50-54: 94% 90% 70% 70%

55-59: 88% 87% 61% 62%

60-64: 77% 73% 46% 48%

65-69: 54% 52% 30% 29%NotesLABOUR FORCE IN SINGAPORE, 2014 . LF participation defined as persons aged fifteen years and over who are either employed (i.e. working) or unemployed (i.e. actively looking for a job and available for work) during the reference period.

SLP defined LF participation as people who answered the question “What is your current status?” with working for pay, unemployed and looking for work, temporarily laid off, on sick leave, self-employed.


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The Research Programme


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Are we prepared for Ageing?

How well prepared (financially) are Singaporeans for their retirement?

Who is not well prepared?


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Financial Preparedness

Risks

Health Shocks

Unemployment

Death of a Spouse

Housing prices

Longevity Risk

Financial Situation

Financial Assets

Work

Spending

Family Support

House


© 2016 Singapore Management University

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Financial Preparedness

Risks Financial Situation

Perception of Preparedness

Working Expectations

Self-assessed preparedness

Subjective Well-being



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Establishing Financial Preparedness of a 50-year-old

Wealth

Second health Shock Stops work and lives on savings

Enters Nursing Home

55 65 72 80

Runs Out of Wealth



30

The exact trajectory depends on how healthy the person is, whether married, his education etc.

Wealth

55 65 72 80

Runs Out of Wealth

Health Shock

healthy

Retires



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How do the SLP data help?

• Establish the various shocks that people are subject to

• Understand how people’s finances adjust to these shocks

We can then simulate the trajectories of respondents’ wealth, income and consumption over their lifetime

How many people will run out of wealth?



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Conclusion

We cannot reverse the ageing demographic

But we can be the best prepared for ageing

SLP and CREA research aim to inform our preparedness



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End of Presentation


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Labour Market Outcomes

Michael Hurd, Seonghoon Kim, Susann Rohwedder

34For Internal Use OnlyPLEASE DO NOT CIRCULATE

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Outline

• Selected Features of Labour Market Outcomes

• By Age, Gender, Education, Wealth, Health

• Labour Force Status and Transitions

• Retirement and Reverse Retirement

• Employment Expectation of 50-59 Year-Old Individuals at Ages 65/70



36

Lower Labour Force Participation among Older Individuals

36

0.9480.901

0.764

0.497

0.749

0.665

0.512

0.315

0.2

.4.6

.81

50-54 55-59 60-64 65-69 50-54 55-59 60-64 65-69

Male Female

Parti

cipa

tion

Rat

es

Labour Force Participation by Age and Gender


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Lower Labour Force Participation among Low-educated Females (ages 50-59)

37

0.919 0.920 0.926

0.574

0.715

0.773

0.2

.4.6

.81

Primary Secondary Post-secondary Primary Secondary Post-secondary

Male Female

Parti

cipa

tion

Rat

es

Labour Force Participation by Education and Gender


38

Higher Self-employment Rates at Older Ages

38

0.2

.4.6

.8

50 55 60 65 70Age

Self-employment Working for PayLabour Force Participation

Labour Force Status by Age


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Higher Unemployment Rates if in Poor Health (ages 50-59)

39

0.054 0.054

0.044

0.062

0.126

0.0

5.1

.15

Une

mpl

oym

ent R

ates

Excellent Very good Good Fair Poor

Unemployment Rates by Self-reported Health

Note: 5.6% of respondents report poor healthFor Internal Use Only


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1-year Labour Force Transition Probabilities (ages 50-59)

45% of 50-59 Year-Old Unemployed Workers are Unemployed 12 months later

After 12 months (t+12)

Unemployed (t+12)

Out of LabourForce (t+12)

Employed(t+12)

Unemployed (t) 45.2% 19.4% 35.4%

Out of Labour Force (t) 3.6% 80.5% 15.9%

Employed (t) 3.3% 4.5% 92.1%



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Men are more likely to be unemployed andWomen are more likely to be out of the labour force 12 months later (ages 50-59)

41

31.1

54.5

14.3

40.7

33.5

25.8

020

4060

Employed Unemployed Out of LF Employed Unemployed Out of LF

Male Female

1 ye

ar T

rans

ition

Pro

babi

lity

Labour Force Status 1 Year After Being Unemployed


42

Large Differences in Earnings by Education (ages 50-59, excl. zero, excl. employer CPF contribution, monthly, full-time working)

42

19062479

3000

4168

7000

11345

12001606

2600

4285

5900

8292

0

5,00

010

,000


Male Female

Median Average

Indi

vidu

al la

bor i

ncom

e la

st m

onth

Individual Monthly Earnings by Education and Gender


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Higher Proportion Employed among Less-educated Males (ages 65-69)

43

0.578

0.418 0.407

0.254

0.338

0.299

0.2

.4.6


Male Female

Prop

ortio

n Em

ploy

ed

Proportion Employed by Gender and Education


44

About 10% of individuals out of the LabourForce are back to work 12 months later

44

0.113 0.113

0.100

0.079

0.070

0.091

0.0

5.1


60-64 65-69

1-ye

ar R

etur

n-to

-Wor

k R

ates

Proportion Returning to Work by Age and Education


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Expectation of Future Work

• SLP asks probabilities of working full-time past ages 62, 65, and 70

• “Now, please think about work in general and not just your present job. On a scale from 0 percent to 100 percent where "0" means that you think there is absolutely no chance, and "100" means that you think the event is absolutely sure to happen, what do you think are the chances that you will be working full-time after you reach age 65?”

• Useful for predicting labour supply of younger workers when they reach 62/65/70

• We present the expectation of working full-time past 65 and 70 among the 50-59 year olds



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Lower Expectation of Working Full-time at Older Ages

46

0.425

0.247

0.308

0.172

0.1

.2.3

.4

FT E

mpl

oym

ent E

xpec

tatio

n

Male Female

Expectation of Working Full-time past 65/70 by Gender

65 70


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Higher Expectation of Working past 70 among the Less-educated (ages 50-59)

47

0.365

0.249

0.379

0.220

0.343

0.177

0.1

.2.3

.4

FT E

mpl

oym

ent E

xpec

tatio

n

Primary Secondary Post-secondary

Expectation of Working Full-time past 65/70 by Education

65 70


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Less Wealthy Individuals Expect to Work Longer (ages 50-59, married)

48

0.420

0.277

0.389

0.236

0.368

0.203

0.298

0.139

0.1

.2.3

.4

FT E

mpl

oym

ent E

xpec

tatio

n

Bottom 25% 26-50% 51-75% Top 25%

Expectation of Working Full-time past 65/70 by Wealth

65 70


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Expectations Predict Growing Presence of Older Workers in the Labour Market

49

0.362

0.264

0.207

0.156

0.1

.2.3

.465 70

Expected FT Employment (50-59) Actual FT Employment

Expe

ctat

ion

of w

orki

ng fu

lltim

e

FT Employment Expection vs Actual FT Employment


50

A Greater Increase in Full-time Employment at Older Ages expected among Females

50

0.425

0.348

0.307

0.178

0.247 0.239

0.172

0.067

0.1

.2.3

.4

Male Female Male Female

65 70

Expected FT Employment (50-59) Actual FT Employment

Prob

abilit

y of

wor

king

fullt

ime

FT Employment Expection vs Actual FT Employment


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Employment Expectations Consistent with LabourForce Participation Trend

51Source: Statistics Singapore

0.0

10.0

20.0

30.0

40.0

50.0

60.0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Lab

ou

r Fo

rce

Par

tici

pat

ion

Rat

es

Labour Force Participation Rates by Age and Gender, 1996-2015

Males 65 - 69 Years

Males 70 - 74 Years

Females 65 - 69 Years

Females 70 - 74 Years


52

Concluding Remarks

• SLP → Better Understanding of Labour Market Activity and Retirement Readiness of Workers over 50 in Singapore

• Heterogeneous Labour Market Outcomes

• by Age, Gender, Education, Health, Wealth

• The less-educated expect to work longer

• Rising labour force participation of older workers will continue in the near future (esp. for females)



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Future Work

• Testing hypotheses to explain the observed patterns in the data

• Impact evaluations of labour market policies

• Counterfactual simulations of new or adjusted policies

SLP a useful basis for evidence-based policy modelling



54

End of Presentation


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Economic Position and Preparation for Retirement

Income, Wealth and Spending

Michael Hurd, Seonghoon Kim and Susann Rohwedder


55

56

Demographics

• Rapidly increasing education levels



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Education and age: Higher percent with post-secondary at younger ages

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

50-54 55-59 60-64 65-69

Primary

Secondary

Post-secondary



58

Males have more education than females

58

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

primary secondary post-secondary

male

female

Pattern same whether married or single


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59

About 90% of males married

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

50-54 55-59 60-64 65-69

Single male

married male



60

Older females less likely to be married

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

50-54 55-59 60-64 65-69

Single female

married female



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Distribution of marital status among singles: increasing widowed with age

0.0

10.0

20.0

30.0

40.0

50.0

60.0

50-54 55-59 60-64 65-69

Never married

Divorced

Widowed



62

Income and spending, married persons. Mean and median. 5-year moving ave

62

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69

mean inc mean spend med inc med spend


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Income and spending, single persons. Mean and median. 5-year moving ave

63

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69

mean inc mean spend med inc med spend


64

Percent composition of income by age, marrieds, 5-year moving average

64

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69Earnings Self-employment Financial CPF


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65

Percent composition of income by age, singles, 5-year moving average

65

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68Earnings Self-employment Financial CPF


66

Married persons, mean income, education

66

0

20,000

40,000

60,000

80,000

100,000

120,000


male

female


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Single persons, mean income, education

67

0

20,000

40,000

60,000

80,000

100,000

120,000


male

female

Singles’ income about half of marrieds’ income


68

WealthHow measured• Aim for total accounting

• Fielded in January and February, 2016

• Some 33 questions, respondent and spouse

• Housing, CPF, financial, business, real estate, vehicles, private pension, life insurance, other

• Used bracketing to reduce item nonresponse

• “Reconciliation” screen



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Total wealth, marital status

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

Mean Median

Married

Single



70

Median wealth couples, education

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000


male

female



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71

Median wealth singles, education

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000


male

female



72

Comparison of wealth, single males and females, medians

72

- 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000

50-54

male

female

- 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000

55-59

male

female


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73

Comparison of wealth, single males and females, medians

73

-

200,000

400,000

600,000

800,000

1,000,000

1,200,000

60-64

male

female

-

200,000

400,000

600,000

800,000

1,000,000

1,200,000

65-69

male

female


74

Total wealth, couples, 5-year moving average

74

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69Mean Median


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75

Housing wealth couples, 5-year moving average

75

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69Mean Median


76

CPF wealth, couples, 5-year moving average

76

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69Mean Median


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77

Financial wealth, couples, 5-year moving average

77

0

50,000

100,000

150,000

200,000

250,000

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69Mean Median


78

Percent composition of wealth, medians, married persons

78

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69Housing CPF Financial


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79

Total wealth, singles, 5-year moving average

79

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69Mean Median


80

Percent composition of wealth, medians, singles

80

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69Housing CPF Financial


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81

Distribution of wealth

• Highly skewed distribution

• Will show examples of extremes



82

Distribution of CPF+financial wealth: marital status, age and education

PercentileSingle, 50-59,

primarymarried 60-69

post-secondary2.5 0 29,0005.0 0 64,556

10.0 400 104,54625.0 10,350 219,54650.0 42,000 503,50075.0 82,000 1,019,00090.0 170,000 1,767,58095.0 215,282 2,342,40097.5 323,050 2,780,000



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83

Distribution of CPF+financial wealth: marital status, age and education

PercentileSingle, 50-59,

primarymarried 60-69

post-secondary2.5 0 29,0005.0 0 64,556

10.0 400 104,54625.0 10,350 219,54650.0 42,000 503,50075.0 82,000 1,019,00090.0 170,000 1,767,58095.0 215,282 2,342,40097.5 323,050 2,780,000



84

Importance of wealth components at the individual or household level

• 10% of singles have almost all wealth in housing (95% or more of total wealth)

• 10% of couples have almost all wealth in housing (91% or more of total wealth)

• 10% of singles and 10% of couples have almost no CPF wealth; 5% have none

• 25% of singles and couples have almost no financial wealth; 10% have none



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85

Economic preparation for retirement• Self-rated

• Compare spending to income• Can wealth support the gap?



86

Self-rated preparation for retirement: percent good, very good or excellent

86

0

10

20

30

40

50

60

70

Primary Secondary Post-secondary Total

50-59

60-69


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87

Bringing income, spending and assets together


87

88

Comparing mean after-tax income to mean spending. Married females

50-59income spending Saving

Primary 22,147 22,398 -251Secondary 51,652 42,872 8,780Post-secondary 107,430 77,604 29,826

60-69Primary 17,192 17,349 -157Secondary 33,570 31,504 2,066

Post-secondary 78,998 61,067 17,931



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89

Comparing mean annual saving to mean total wealth, married females

50-59Annual saving Mean wealth

Primary -251 571,482Secondary 8,780 970,453Post-secondary 29,826 1,990,026

60-69Primary -157 518,137

Secondary 2,066 1,073,643

Post-secondary 17,931 2,467,477

89

Married males about the sameFor Internal Use Only


90

Comparing mean after-tax income to mean spending. Single females

50-59income spending saving

Primary 7,759 13,371 -5,612Secondary 19,598 23,537 -3,939Post-secondary 58,125 47,392 10,733

60-69Primary 5,884 12,233 -6,349Secondary 20,216 20,941 -725

Post-secondary 45,288 38,288 7,000



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91

Comparing mean annual saving to mean total wealth, single females

50-59

Annual saving Mean wealthPrimary -5,612 263,180Secondary -3,939 536,990

Post-secondary 10,733 1,223,63160-69

Primary -6,349 405,820Secondary -725 786,505Post-secondary 7,000 1,357,548

91

Single males about the sameFor Internal Use Only


92

How do economic transfers influence finances?• Money transfers to others already included in

spending

• Money transfers from others not included in income

• Show annual frequencies and amounts



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93

Percent giving or receiving financial transfers; targets and sources

0.0

5.0

10.0

15.0

20.0

25.0

Give

Receive



94

0

5

10

15

20

25

30

35

40

45


Give

Receive

Percent giving or receiving, marrieds 50-59, education

94

Similar pattern for singles and ages 60-69


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95

Amounts given or received, singles 50-59 by education; population averages

95

0

500

1000

1500

2000

2500


Give

Receive


96

Amounts given or received, singles 60-69 by education; population averages

96

0

500

1000

1500

2000

2500


Give

Receive


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97

Amounts given or received, marrieds 50-59

97

0

500

1000

1500

2000

2500

3000

3500

4000


Give

Receive


98

Amounts given or received, marrieds 60-69

98

0

500

1000

1500

2000

2500

3000

3500

4000


Give

Receive


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99

Conclusions about money transfers• May be important in individual cases

• Topic of future investigations

• At population level of limited importance

• Mean money transfers received less than $400

• Mean money transfers given $1700 or less• Several targets

• Recipients likely relatively well-to-do (correlation of economic status across generations)



100

Means of income and wealth distort situation of typical person• Take households in middle of income distribution:

those in the 40-60 percentiles

• Find for those households

• Mean • Income (after-tax)

• Spending

• Wealth: total, housing, CPF, financial, other

• How well can wealth support dissaving under varying assumptions about what wealth is available?



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101

Mean after-tax income and spending: 40-60 percentile of income distribution

101

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Single 50-59 Single 60-69 Married 50-59 Married 60-69

Net income

Spending


102

Mean wealth: households in 40-60 percentile of income distribution

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

Single 50-59 Single 60-69 Married 50-59 Married 60-69

wealth

housing

CPF

Financial



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103

Saving and wealth, households in 40-60 percentile of income distribution

Saving total wealth CPF+financial

Single 50-59 -9,329 513,771 214,505

Single 60-69 -11,605 572,233 155,988

Married 50-59 7,426 845,709 324,843

Married 60-69 -7,901 824,114 304,113Single 60-69: 13 years of spending for CPF+financial

103For Internal Use OnlyPLEASE DO NOT CIRCULATE

104

Conclusions about economic position and economic preparation

• Large variation in income, wealth and spending by education

• Especially post-secondary

• Modest variation by marital status and sex

• Average total wealth quite substantial• But wealth distribution highly skewed

• Critical aspect of wealth: much is in housing• Can some be monetized to support non-housing

consumption?



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105

Conclusions (cont.)

• To understand economic preparation need dynamic model of behaviour

• What will income, spending and wealth be at age 80 of today’s 65 year-old?

• Model under development: importantly all components are in the SLP data

• But even current data show some groups are spending more than income and have low levels of wealth



106

End of Presentation


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107

Household Spending and its Components

Michael Hurd and Susann Rohwedder


107

108

Study Household Spending to Understand:

Life cycle patterns • Compare spending to income at different ages• Find patterns of saving and dissaving and whether spending

can be sustained

Budget shares • What fraction of total spending do households spend on

certain categories?• How does this vary by household characteristics?

Response to shocks • How does spending change in case of

• Unemployment• Health change: good to bad or bad to good



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109

Family structure and resource sharing

• Unit of observation: Single person or couple, embedded in a household and extended family

• We measure• What our respondent and spouse spend, not what they

consume

• Transfers given are another type of spending• About 20% report spending on behalf of others in a typical month.

• Transfers received are another type of income• About 32% report receiving income from family or friends in a

typical month.



110

Spending Queried Every Month

• 45 categories comprising total spending

• Examples• Food and beverage for consumption at home

• Dining and/or drinking out

• Clothing, footwear, jewelry, watches, accessories

• Property tax

• Prescription medications

• Big ticket durables• Refrigerator, TV, Washing Machine, …

• Cash gifts to others



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111

Total Spending by Age: Singles

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69

mean inc

mean spend

med inc

med spend



112

Total Spending by Age: Couples

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69

mean inc

mean spend

med inc

med spend



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113

Highest Education Group has spending at least 3 times as high as lowest group

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

Mean Median Mean Median

Singles Couples

Monthly Spending by Education

no formal/prim

Secondary

Post-Secondary



114

Highest Wealth Quartile Spends a Lot More than the other Wealth Quartiles

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

Mean Median Mean Median

Singles Couples

Monthly Spending by Wealth Quartile

Lowest

2

3

Highest



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115

Monthly spending higher in households with larger number of persons

0

1,000

2,000

3,000

4,000

5,000

6,000

mean median mean median

Singles Couples

Monthly spending by #other household members

0

1

2

3

4+



116

Spending per person in household:lower when older

0

200

400

600

800

1000

1200

1400

50-54 55-59 60-64 65-69



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117

Aggregate spending into 14 categories

cash gifts housing

clothing insurance

contributions leisure

durables house operations

education transportation

food utilities

health other



118

Budget shares (total 100%)= spending on category/total spending

Food 17.9 leisure 10.1

Clothing 2.2 education 5.0

Housing 9.9 insurance 4.1

HH operations 4.2 contributions 1.9

utilities 9.7 cash gifts 4.0

transportation 16.2 durables 3.3

health care 8.2 other 3.1



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119

Budget Shares Vary By Age

-

0.050

0.100

0.150

0.200

0.250

50-54 55-59 60-64 65-69

Budget Share: Food

0.120

0.130

0.140

0.150

0.160

0.170

0.180

50-54 55-59 60-64 65-69

Budget Share: Transportation

Higher among Older Age Groups:- Food - Utilities- Health Care

Lower among Older Age Groups:- Transportation- Housing- Education- Life insurance- Cash gifts - Clothing- Leisure



120

Why?Lifecycle effect?Health effect?

Both?

Budget Shares Vary By Age



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121

Household Spending by Health: When in fair or poor health, households spend …

… larger fraction of total spending on:

- Health care

- Household operations

… smaller fraction of total spending on:

- Transportation

- Leisure

- Clothing

- Contributions

- Cash gifts



122

Fraction spent on Health Care isHigher if in Bad Health, at all ages

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

50-54 55-59 60-64 65-69

bad

good

Fraction of spending for health, singles

Also for married persons.



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123

Fraction of spending for leisure, singles

0

0.02

0.04

0.06

0.08

0.1

0.12

50-54 55-59 60-64 65-69

bad

good

Fraction spent on Leisure is Lower if in Bad Health, irrespective of age

Also for married persons.



124

Does a Health Shock Cause Changes in Spending?

Or is it selection?

People in bad health may differ in other characteristics leading to differences in spending.

To find out, follow the same people over time:

What happens to spending when health changes?



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125

Use Self-rated Health to Define Two Health States: Bad and Good

• Recoded from 5-point scale asked in survey: Poor, fair, good, very good, excellent

• Prior research found substantial differences between groups using this classification.

• Consistent with patterns in SLP

• How does spending change when health changes• Good to bad; bad to good



126

When Health Worsens: spending onhealth increases, declines on non-health

-20

-15

-10

-5

0

5

10

15

health nonhealth total spendingg-b

b-g

Deviation in change in spending from normal change, percent

good-bad

bad-good

Reverse is true when health improves.



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127

Health change: “good to bad” or “bad to good.” Changes in Budget Shares

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

g-b

b-g

Deviation in change in budget shares from normal change, total spending

Complementary to health:spend more in good healthSubstitutes to health:

spend more in bad healthgood-bad

bad-good



128

The Effect of Income Shocks on Spending:

Analyse Change in Spending following Job Loss

- Total Spending

- Categories of Spending

- Longitudinal


128

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129

Regression Framework for Analysis of Effect of Job Loss on Household Spending

• Compare spending when employed with spending while unemployed for the same households (fixed effects regressions)

• Left-hand variable: Log spending

• Regressed on indicators for employment state • employed, retired, disabled, unemployed by duration, not

working

• Separately for single persons and married persons



130

Income falls more than Spending, about 20% lower spending after Job Loss

0

20

40

60

80

100

120

0 1 2 3 4 5 6+

month of unemployment

spending

income

Spending and income following job loss, singles

Similar results for couples.



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131

Which Spending Categories do People Reduce When Unemployed?

All Categories



132

Largest budget items: Some drop by about 20%, then plateau.Others continue to decline.

-60.0

-50.0

-40.0

-30.0

-20.0

-10.0

0.0

2 3 4 5 6+

Food

Housing

Health

Education

Percent drop in spending, by number of months unemployed



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When Unemployed:Spending Declines Across the Board

-40

-35

-30

-25

-20

-15

-10

-5

0Percent change by category after about 3 months of unemployment

Categories with largest budget sharesFor Internal Use OnlyPLEASE DO NOT CIRCULATE


134

What’s next?

Age profile of spending

- Find how spending changes with age, following the same households.

- Likely will find spending declines at older ages. Is this because of life-cycle effects (spend early while alive) or because of health effects?

Important for studying financial preparation for retirement

- Amount of wealth needed for retirement if followingoptimal time path of spending

- Whether to annuitize and time path of annuities



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135

SLP uniquely situated to study these issues

• For some aspects need more waves for statistical power

• Accumulating data rapidly so that we can proceed



136

End of Presentation


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137

Financial Literacy, Wealth and Portfolio Complexity in Singapore:

Findings from the SLP

Benedict Koh, Olivia S. Mitchell, and Susann Rohwedder



138

Global old-age saving, investment, and payout challenges

• People live longer

• Financial resources need to last longer

• Yet many people reach retirement with limited financial wealth

Risk of running out of money in old age, when physically vulnerable and unable to increase income

Why?

Is lack of financial knowledge part of the problem?

Could it be part of the solution?



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139

Past research shows:

Greater financial literacy is associated with:• More financial planning and saving• Better investment behaviour• Greater understanding of retirement drawdowns

Financial knowledge is low among older adults in less sophisticated financial markets such as Russia, as well as developed markets in UK and US

What about Singapore?



140

Focus of Current Research:

Analyse older Singaporeans’ level of financial knowledge and how it compares to other countries

Examine linkages between financial literacy and wealth

Evaluate how financial knowledge is associated with portfolio holdings and asset allocation



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141

Financial Literacy Defined:

“Ability to use knowledge/skills to manage financial resources effectively for a lifetime of financial well-being” (U.S. Department of the Treasury, 2007)

For example, financial literacy: • reduces financial mistakes (e.g. holding high-interest

rate debt; incurring unnecessary fees, …)

• allows achieving higher rates of return

• enables better financial risk management (e.g. portfolio diversification; avoid excessive financial risk exposure)



142142

Financial Literacy: 3 simple questions (Lusardi/Mitchell)

1) Interest Rate:

Let’s say you have $100 in a saving account paying 2% interest/year. How much would you have in the account at the end of 5 years? <$102; =$102; >$102; DK; refuse to answer

2) Inflation: Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per year. After 1 year, with the money in this account, would you be able to buy: > today; = today; < today

3) Safer: True or false? Buying a single company stock usually provides a safer return than a Stock Unit Trust.


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143

SLP ALP179 HRS 04 HRS 10

Interest 82 87 75 77Inflation 73 86 83 86Safer

FinLit Index

47

2.02

43

2.16

62

2.19

73

2.36

How much do people know? (age 50-70)

143

% correct

• SLP vs ALP: Singaporeans score < on interest and inflation Q’s than ALP respondents; but better informed on stock risk

• SLP vs HRS: Singaporeans do better on interest, < on inflation and stock risk

(HRS and ALP weighted)For Internal Use OnlyPLEASE DO NOT CIRCULATE

144

Proportion with perfect scores (age 50-70)



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145

Compared to other countries (% perfect score)

145

0

10

20

30

40

50

60

Singapore


146

Stock risk knowledge lower among oldest group (SLP 50-70)



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147

Larger differences in Financial Literacy by age in US: oldest score lowest (U.S. HRS 50+)

Interest Inflation Stock Risk≤ 60 76% 81% 60%61-70 67% 80% 54%> 70 58% 65% 43%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

147


148

Financial Literacy Lower for Women (SLP 50-70)

148


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149

Financial Literacy Lower among those in Fair/Poor Health (SLP 50-70)


150

Financial Literacy Much Higher for the Best Educated (SLP 50-70)


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151

Multivariate Analysis

Controls:• Age bands (50-54; 55-59; 60-64; 65-70)

• Sex (Male/Female)

• Married

• Education (Primary, Secondary, Post-secondary)

• Health Status (Excellent/Good = ref; Fair/Poor)

• Homeowner

• Financially responsible

Variance explained: 5 – 11%

N = 6,994


152

Interest rate Inflation Safe return FinLit score

Age55-59 0.007 0.022 0.014 0.042

Age60-64 0.018 0.035 ** 0.024 0.075 **

Age65-70 0.036 ** 0.063 *** -0.011 0.091 **

Female -0.002 -0.017 -0.059 *** -0.072 **

Married -0.001 -0.009 -0.007 -0.021

2ndry educ. 0.046 *** 0.092 *** 0.079 *** 0.235 ***

Post-2ndry educ. 0.157 *** 0.278 *** 0.260 *** 0.715 ***

Fair/poor health -0.023 ** 0.002 -0.038 ** -0.055 **

Work for pay -0.004 -0.032 ** -0.005 -0.036

Own home 0.068 *** 0.124 *** 0.080 *** 0.270 ***

R+Other mgs finances 0.028 ** 0.020 -0.003 0.044 *

Other mgs finances 0.021 0.013 -0.009 0.029

Results for Individual FinLit Q’s & Overall FinLit Score (SLP50-70)


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Interest rate Inflation Safe return FinLit score

Age55-59 0.007 0.022 0.014 0.042

Age60-64 0.018 0.035 ** 0.024 0.075 **

Age65-70 0.036 ** 0.063 *** -0.011 0.091 **

Female -0.002 -0.017 -0.059 *** -0.072 **

Married -0.001 -0.009 -0.007 -0.021

2ndry educ. 0.046 *** 0.092 *** 0.079 *** 0.235 ***

Post-2ndry educ. 0.157 *** 0.278 *** 0.260 *** 0.715 ***

Fair/poor health -0.023 ** 0.002 -0.038 ** -0.055 **

Work for pay -0.004 -0.032 ** -0.005 -0.036

Own home 0.068 *** 0.124 *** 0.080 *** 0.270 ***

R+Other mgs finances 0.028 ** 0.020 -0.003 0.044 *

Other mgs finances 0.021 0.013 -0.009 0.029

Results for Individual FinLit Q’s & Overall FinLit Score (SLP50-70)


154

So which Singaporeans are most financially literate?

154

• The older are wiser!

• Women less financially literate

• Better educated more informed

• Those in poor health less financially knowledgeable

• Homeowners much more financially literate


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155

Wealth: Several measures available

155

1. Total Net Wealth: includes pensions, financial wealth, bank accounts, insurance, primary and secondary residences and net of debt

2. Net Wealth without 2ndry residence/debt

3. Total Non-housing Net Wealth

4. Net Financial Wealth (w/o pension assets)


156156

FinLit score 0.567 *** 0.510 *** 1.112 *** 1.012 ***

Age55-59 0.288 ** 0.297 ** 0.526 ** 0.534 **

Age60-64 0.562 *** 0.621 *** 0.64 ** 0.729 **

Age65-70 0.39 ** 0.488 *** -0.406 * -0.248

Female 0.214 ** 0.307 ** 0.217 0.347 **

Married 0.368 ** 0.362 ** 1.256 *** 1.24 ***

2ndry educ. 0.599 *** 0.515 *** 1.44 *** 1.281 ***

Post-2ndry educ. 2.682 *** 2.47 *** 5.464 *** 5.097 ***

Fair/poor health -0.402 *** -0.323 *** -0.702 *** -0.564 ***

Work for pay -0.15 -0.118 0.438 ** 0.487 **

Own home 0.616 *** 0.621 *** 1.265 *** 1.25 ***

R+Other mgs finances 0.337 ** 0.365 *** 0.754 *** 0.79 ***

Other mgs finances -0.059 0.043 -0.005 0.181

FinConfident 0.347 *** 0.749 ***

LongHorizon 0.86 *** 1.699 ***

GenlRiskPrefer 0.585 ** 0.909 **

FinRiskPrefer 0.611 ** 0.753 **

HH financial wealth HH non-housing wealth

Regression of Nonhousing Wealth on Financial Literacy & other Controls


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158

Financial Literacy & other factors associated with wealth

158

Positively:

• Financially literate

• Older

• Married

• Better educated

• Homeowner

• Financially responsible

• Financially confident

• Long investment horizon

• Risk Tolerant

Negatively:

Poor healthFor Internal Use Only


159

Does Financial Literacy Improve Financial Decision Making?

Ideal experiment:

• Two people with same lifetime resources: One has high financial literacy, other low.

• Then compare financial outcomes, such as:

- Realized returns

- Portfolio diversification

- Exposure to financial risk

Current Research:

• Relate portfolio complexity and exposure to financial risk to Financial Literacy and covariatesThus far, we lack measure of lifetime wealth



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160

Measures of Portfolio Complexity

160

1. Non-complex investment holdings• Owner-occupied home

• Checking/Saving/Bank account

• Fixed-term deposits

• Bonds

• Whole life insurance

2. Complex investment holdings• Investment properties

• Shares in publicly listed companies

• Unit trusts/mutual funds

• Gold/gold funds

• Managed accounts

• Shares in private companies (own business)


161161

FinLit score 0.106 *** 0.054 *** 0.149 ***

Age55-59 -0.001 -0.091 *** -0.077 **

Age60-64 -0.003 -0.174 *** -0.154 ***

Age65-70 0.007 -0.248 *** -0.21 ***

Female 0.015 -0.025 * -0.007

Married -0.078 *** -0.055 ** -0.133 ***

2ndry educ. 0.141 *** 0.073 *** 0.201 ***

Post-2ndry educ. 0.322 *** 0.186 *** 0.471 ***

Fair/poor health -0.010 -0.003 -0.007

Work for pay -0.067 *** 0.117 *** 0.035

Own home -0.136 *** -0.006 -0.100 **

R+Other mgs finances 0.060 ** 0.001 0.058 **

Other mgs finances 0.015 -0.019 0.002

Total net wealth ($100k) 0.020 *** 0.002 *** 0.022 ***

FinConfident 0.033 0.040 ** 0.090 **

LongHorizon 0.098 *** 0.041 ** 0.140 ***

GenlRiskPrefer -0.007 0.000 -0.005

FinRiskPrefer 0.211 *** 0.103 ** 0.319 ***

No of Complex

NonCPF

Total No of

Complex

No of

Complex CPF

Regression of Portfolio Complexity on FinLit & other Controls


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162

How are Financial Literacy & other controls related to Portfolio Complexity?

162

Positively:• Financial literacy

• Better educated

• Wealthier households

• Long investment horizon

• Risk tolerant

Negatively:• Older

• Married

• Home Owners


163

Measures of Portfolio Diversification/Asset Allocation

163

1. Diversified: Hold equity, fixed income, deposits


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164164

FinLit score 0.084 *** 0.029 *** 0.007 *** 0.014 *** 0.001

Age55-59 -0.029 * -0.001 0.014 ** 0.023 ** 0.005

Age60-64 -0.074 *** 0.016 0.027 ** 0.050 *** 0.014 **

Age65-70 -0.122 *** 0.037 ** 0.082 *** 0.062 *** 0.045 ***

Female 0.009 0.012 0.006 * -0.002 0.001

Married -0.003 0.007 -0.009 -0.015 -0.001

2ndry educ. 0.074 *** 0.057 *** 0.028 *** 0.038 *** 0.004

Post-2ndry educ. 0.234 *** 0.079 *** 0.042 *** 0.048 *** 0.010 **

Fair/poor health -0.018 -0.004 -0.001 0.005 0.000

Work for pay 0.050 *** 0.000 -0.005 -0.007 -0.004 **

Own home 0.249 *** 0.021 * 0.002 -0.002 0.002

R+Other mgs finances 0.019 0.005 -0.004 0.001 0.000

Other mgs finances -0.030 0.019 -0.001 0.017 0.000

FinConfident 0.060 ** 0.015 -0.003 -0.008 -0.001

LongHorizon 0.050 *** 0.015 ** 0.007 * 0.008 0.005 **

GenlRiskPrefer 0.023 -0.013 -0.001 -0.011 0.010 *

FinRiskPrefer 0.070 ** 0.042 ** 0.027 ** 0.060 ** -0.002

EquityAsPerTDF (1/0)

Equity amount

/Financial wealth

Equity amount

/Non-housing

wealth

Diversified

asset

EquityAsPerAge (1/0)

Equity amount

/Financial wealth

Equity amount

/Non-housing

wealth

Regression of Portfolio Diversification on Financial Literacy & other Controls


165


165


2. Equity as per Age: Equity share ~ +/- 10% of (100 –age)


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166166

FinLit score 0.084 *** 0.029 *** 0.007 *** 0.014 *** 0.001

Age55-59 -0.029 * -0.001 0.014 ** 0.023 ** 0.005

Age60-64 -0.074 *** 0.016 0.027 ** 0.050 *** 0.014 **

Age65-70 -0.122 *** 0.037 ** 0.082 *** 0.062 *** 0.045 ***

Female 0.009 0.012 0.006 * -0.002 0.001

Married -0.003 0.007 -0.009 -0.015 -0.001

2ndry educ. 0.074 *** 0.057 *** 0.028 *** 0.038 *** 0.004

Post-2ndry educ. 0.234 *** 0.079 *** 0.042 *** 0.048 *** 0.010 **

Fair/poor health -0.018 -0.004 -0.001 0.005 0.000

Work for pay 0.050 *** 0.000 -0.005 -0.007 -0.004 **

Own home 0.249 *** 0.021 * 0.002 -0.002 0.002



FinConfident 0.060 ** 0.015 -0.003 -0.008 -0.001

LongHorizon 0.050 *** 0.015 ** 0.007 * 0.008 0.005 **

GenlRiskPrefer 0.023 -0.013 -0.001 -0.011 0.010 *

FinRiskPrefer 0.070 ** 0.042 ** 0.027 ** 0.060 ** -0.002


Equity amount

/Financial wealth

Equity amount

/Non-housing

wealth

Diversified

asset


Equity amount

/Financial wealth

Equity amount

/Non-housing

wealth

Regression of Portfolio Diversification on FinLit & other Controls


167


167


2. Equity as per Age: Equity share ~ +/- 10% of (100 –age)

3. Equity as per TDF: Equity share ~ +/- 10% of life-cycle or target-date fund


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168

Share Held in Risky Assets in Conservative, Aggressive, and the Vanguard Target Date Glide Path



169169

FinLit score 0.084 *** 0.029 *** 0.007 *** 0.014 *** 0.001

Age55-59 -0.029 * -0.001 0.014 ** 0.023 ** 0.005

Age60-64 -0.074 *** 0.016 0.027 ** 0.050 *** 0.014 **

Age65-70 -0.122 *** 0.037 ** 0.082 *** 0.062 *** 0.045 ***

Female 0.009 0.012 0.006 * -0.002 0.001

Married -0.003 0.007 -0.009 -0.015 -0.001

2ndry educ. 0.074 *** 0.057 *** 0.028 *** 0.038 *** 0.004

Post-2ndry educ. 0.234 *** 0.079 *** 0.042 *** 0.048 *** 0.010 **

Fair/poor health -0.018 -0.004 -0.001 0.005 0.000

Work for pay 0.050 *** 0.000 -0.005 -0.007 -0.004 **

Own home 0.249 *** 0.021 * 0.002 -0.002 0.002



FinConfident 0.060 ** 0.015 -0.003 -0.008 -0.001

LongHorizon 0.050 *** 0.015 ** 0.007 * 0.008 0.005 **

GenlRiskPrefer 0.023 -0.013 -0.001 -0.011 0.010 *

FinRiskPrefer 0.070 ** 0.042 ** 0.027 ** 0.060 ** -0.002


Equity amount

/Financial wealth

Equity amount

/Non-housing

wealth

Diversified

asset


Equity amount

/Financial wealth

Equity amount

/Non-housing

wealth

Regression of Portfolio Diversification on FinLit & other Controls


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170

How are Financial Literacy & other controls related to Portfolio Diversification?

170

Positively:

• Financial Literacy

• Education

• Employment

• Homeownership

• Long Horizon

• Risk tolerance

Negatively:

• Age


Conclusions

171

• Financial Literacy in SingaporeSingaporeans more knowledgeable about interest and

inflation but less about equity investments

Older adult financial literacy in Singapore comparable to older US respondents

Financial literacy higher for older, better-educated, homeowners

FinLit lower for women, those in poor health

• Those more knowledgeable are: Wealthier

Hold more complex investments

Have better diversified portfolio and asset allocationFor Internal Use Only


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• Investigate causality vs correlation between Financial Literacy and observed outcomes;

• Integrate models where Financial Literacy is endogenously determined;

Wish list:

• Link to administrative CPF data • to compare what people think they have, with

what they have; and

• to obtain measure of lifetime earnings

Next steps



173

End of Presentation


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174

Monthly Spending Dynamics of the Elderly Following a Health Shock:

Early Insights from the Singapore Life Panel

Terence Cheng, Jing Li, Rhema Vaithianathan



175

Background

• Singapore has achieved excellent population health outcomes despite spending significantly less than many developed and high-income countries

• Singapore has one of the highest levels of private funding of health care in high income countries

• This means that poor health can cause major impact to household finances and preparation for old age



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176

Objective

• The objective of this research is to understand the impact of a health shock on household spending

• We want to know how long the impact persists and what type of spending increases the most

• To do this, we define a health shock as someone receiving a new diagnosis of a chronic condition



177

Why do we care about health shocks? • Health shocks are a major source of financial

shocks

• They reduce people’s ability to work and earn

• They increase spending on health care

• 18% of 50-70 year olds in the survey, received a health shock over a 14 month period



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178

As people age, they are more exposed to shocks…

0%

5%

10%

15%

20%

25%

30%

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70

Rec

eiv

ed a

new

ch

ron

ic c

on

dit

ion

dia

gno

sis

Age at Start of Survey Male Female

SLP Respondents aged 50 – 70 who answered at least 1 wave.

One-in-four respondents over 65 received a new diagnosis of a chronic condition over a 14 month period



179

Shocks are cumulative… People with 3 conditions are at a higher risk of receiving a shock than those who have no conditions0%

5%

10%

15%

20%

25%

0 1 2 3 4 5 6 7

Pro

po

rtio

n W

ho

Re

ceiv

ed

a

New

Dia

gno

sis

Number of Existing Conditions (in baseline)

SLP respondents aged 50 – 70 who answered at least 1 wave.



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180

Major and Minor Shocks

• We distinguish between major and minor health shocks

• Major Shock: A new diagnosis of Cancer, Heart Disease or Stroke

• Minor Shock: A new diagnosis of Arthritis, Diabetes, Hypertension



181

Major vs. Minor Shocks

0.00

0.05

0.10

0.15

0.20

0.25

0.30

50 52 54 56 58 60 62 64 66 68 70

Re

ceiv

ing

a N

ew D

iagn

osi

s (w

ith

in 1

4 w

ave

s)

Major Minor

8% of respondents aged between 65-70 received a major new chronic diagnosis … this is more than twice the rate of those aged 50 to 55.


181

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182

Total spending increases in the month of the shock.

For major shocks, spending remains higher than pre-shock levels

Monthly Spending

$-

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

$4,000

$4,500

$5,000

Month beforeShock

Month ofShock

Month AfterShock

All MonthsFollowing

ShockTotal Spending (Minor Shock)

Total Spending (Major shocks)


182

183

Major shocks have a much more substantial impact on health spending

Health Spending

$-

$100

$200

$300

$400

$500

$600

$700

$800

$900

$1,000

Month beforeShock

Month of Shock Month AfterShock

All MonthsFollowing Shock

Health Spending (Minor)

Health Spending (major shock)


183

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184

Any Income from Work

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Month beforeShock

Month ofShock

Month AfterShock

All MonthsFollowing

Shock

Rep

ort

ing

Po

siti

ve In

com

e Fr

om

Wo

rk

(pro

po

rtio

n)

Any income from Work (Minor Shock)

Any income from Work (Major Shocks)

Shocks reduce income from work –but it is more substantial for major shocks.


184

185

Insurance

• We compare total spending, health spending and income from work for people who are insured in the baseline and those who are not

• Insured means they have private insurance or are covered by an integrated shield plan

• In the baseline, 70% of the age eligible respondents are insured



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Total Spending

$-

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

Month beforeMajor Shock

Month of MajorShock

Month AfterMajor Shock

All MonthsFollowing Major

ShockTotal Spending (Insured)

Total Spending (Uninsured)

Insured respondents have twice the annual household income of uninsured


186

187

Health Spending

$- $100 $200 $300 $400 $500 $600 $700 $800 $900

$1,000

Month before MajorShock

Month of Major Shock Month After MajorShock

All Months FollowingMajor Shock

Health Spending (Insured) Health Spending (Uninsured)


187

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188

Any Income from Work

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

Month before MajorShock

Month of Major Shock Month After MajorShock

All Months FollowingMajor Shock

Rep

ort

ing

Po

siti

ve In

com

e Fr

om

Wo

rk

(pro

po

rtio

n)

Any income from Work (Insured)

Any income from Work (Uninsured)


188

189

Type and Duration of Health Spending

• We ask respondents to break down their health spending into sub-categories

• These are prescription medication, other medication, outpatient services, hospital services, health insurance and home nursing



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Category of Health Spending

$-

$50

$100

$150

$200

$250

$300

$350

$400

$450

$500

Month beforeMajor Shock

Month of MajorShock

Month AfterMajor Shock

All MonthsFollowing Major

Shock

Prescription Medication Other medications Outpatient services

Hospital services Home Nursing

Significant effect on hospital costs of major health shocks

Note: This does not include health insurance.



191

Duration of Shock

$-

$100

$200

$300

$400

$500

$600

$700

$800

$900

$1,000

-1 0 1 2 3 4 5 6 7

Months After Shock

Major Shock Minor Shock



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Estimated effect of Shocks on Spending in Health Categories

• There are two effects of shocks- they increase the non-zero spending and they increase spending overall

• To calculate the effect of shocks on spending, we estimated a “fixed effects” model



193

Probability of Reporting Non-zero Expenditures in Hospital Service Following Major Shock

193-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

t-2 t-1 t t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+9


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194194

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

t-2 t-1 t t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+9

Probability of Reporting Non-zero Expenditures in Prescription Medicine Following Major Shock


195

Conclusion

• Health shocks increase long term spending and reduce income from work

• Health Insurance does not appear to reduce the impact of these shocks

• Major shocks pose considerable risk to health spending

• As Singaporeans age they are at considerable risk of major health shocks, and these have long term financial implications



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196

End of Presentation


197

Housing Wealth and Monetisation

Liang Jiang, Sock Yong Phang, Jun Yu



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Outline

1. Ageing Population

2. Housing wealth: Evidence from government statistics

3. Housing wealth: Evidence from SLP

4. Housing wealth monetization channels

5. Why do elderly households not extract housing equity?

6. Summary of preliminary findings

7. Ongoing research



199

1. Ageing population

• Rapidly ageing population

• Increased life expectancy

• Decline in fertility rates

• Decreased old-age support ratio



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2. Housing wealth: Evidence from government statistics

• There is concern by elderly that savings may be inadequate for retirement – Cash poor

• Home equity is a major component of household wealth – House rich

• Can housing wealth be extracted during retirement to address retirement adequacy?

• How much housing wealth is there?



201

Housing stock in Singapore: 1970-2015

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1970 1980 1990 2000 2010 2015

Total housing stock

HDB housing stock

Private housing stock

Data source: Department of Statistics, Singapore



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202

Home ownership rate in Singapore: 1970-2015

29

59

8892

8791

20

30

40

50

60

70

80

90

100

1970 1980 1990 2000 2010 2015

Hom

e ow

ners

hip

rate

(%)

Data source: Department of Statistics, Singapore



203

Housing price indices (1990Q4 = 100)

203

Data source: HDB and URAFor Internal Use Only


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Aggregate Housing wealth (S$’M)

Data source: Singapore government websites

216,135

400,951

243,340

436,800

200,000

250,000

300,000

350,000

400,000

450,000

1995 2000 2005 2010 2015

public private



205

Data source: Singapore government websites

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Gross housing wealth /Total assets

205

Housing wealth share of total assets

HDB housing wealth is around 50% of total

residential housing wealth


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206

Distribution of households by house type

DOS (50-69) Department of Statistics 2015 (age 50-69) SLP-Singapore Life Panel (age 50-70)DOS (RP) Department of Statistics 2015 (residents population)

206

5.3%

18.0%

33.1%

26.1%

10.4% 7.2%

3.8%

17.4%

33.1%

29.8%

9.0%6.9%

4.1%

15.6%

34.2%

27.8%

11.7%

6.5%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

1- & 2-Roomflats

3-Room Flats 4-Room flats 5-Room &Executive flats

Condo &Apartments

Landed

DOS (50-69) SLP DOS (RP)


207

3. Housing wealth: Evidence from SLP



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Median value of primary residence S$

Note: for homeowners onlyData source: Singapore Life Panel

208

0

500000

1000000

1500000

2000000

2500000

3000000

1- & 2-Room flats 3-Room Flats 4-Room flats 5-Room &Executive flats

Condo &Apartments

Landed

age 50-64 age 65-70


209

Homeowners with mortgage on primary residence (%)


209

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%


Condo &Apartments

Landed

age 50-64 age 65-70


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210

Median mortgage debt among those having mortgages S$

Note: for homeowners with positive mortgage on primary residence onlyData source: Singapore Life Panel

210

0

100000

200000

300000

400000

500000

600000


Condo &Apartments

Landed

age 50-64 age 65-70


211

Median mortgage to value ratio on primary residence

Note: for homeowners with positive mortgage on primary residence onlyData source: Singapore Life Panel

211

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

1- & 2-Room flats 3-Room Flats 4-Room flats 5-Room & Executiveflats

Condo &Apartments

Landed

age 50-64 age 65-70


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212

Median house equity of primary residence S$


212

0

500000

1000000

1500000

2000000

2500000

3000000


Condo &Apartments

Landed

age 50-64 age 65-70


213

Median housing wealth, financial wealth and CPF S$: age 65-70

Note: for HDB homeowners onlyhousing wealth includes the house values of primary residence and other propertiesfinancial wealth includes savings, fixed deposit, investment (bonds,shares,gold,etc), life insurance

Data source: Singapore Life Panel

213

200000

300000

400000

500000

7500 1500030000

700006960950500

65931

113000

0

100000

200000

300000

400000

500000

1- & 2-Room flats 3-Room Flats 4-Room flats 5-Room & Executive flats

housing wealth financial wealth CPF&Pension


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214

Median housing wealth, financial wealth and CPF S$: age 65-70

Note: for homeowners onlyhousing wealth includes the house values of primary residence and other propertiesfinancial wealth includes savings, fixed deposit, investment (bonds,shares,gold,etc), life insurance

Data source: Singapore Life Panel214

0

500000

1000000

1500000

2000000

2500000

1- & 2-Room flats 3-Room Flats 4-Room flats 5-Room & Executiveflats

Condo &Apartments

Landed

housing wealth financial wealth CPF&Pension


215

Mean home equity to mean net worth ratio in each percentile of net worth:

age 50-70

215

84%

80%

76%

71%

63%

58%

52%

48%

52%

72%

45%

50%

55%

60%

65%

70%

75%

80%

85%

90%

1st-10th 11th-20th 21st-30th 31st-40th 41st-50th 51st-60th 61st-70th 71st-80th 81st-90th 91st-100th

Note: for homeowners onlyhome equity = housing wealth – housing debtnet worth = total wealth – total debt

Data source: Singapore Life Panel For Internal Use OnlyPLEASE DO NOT CIRCULATE

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216

A. Rent out

- rent out whole flat

- rent out room(s)

B. Right-size or down-size

- move to a smaller flat

- Silver Housing Bonus (SHB)

C. Lease Buyback Scheme (LBS)

216

4. Monetization options for HDB households


217

Median annual rental income S$

Note: for homeowners who renting out all or part of their home only0 means no homeowners renting out

Data source: Singapore Life Panel217

5000

8000 8400 8400

30000

38000

0

60007800

9000

17250

28050

0

5000

10000

15000

20000

25000

30000

35000

40000


Condo &Apartments

Landed

age 50-64 age 65-70


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Homeowners who have rental incomes (%)

5.6% 5.6%

6.0%

6.8%

5.0%

5.2%

5.4%

5.6%

5.8%

6.0%

6.2%

6.4%

6.6%

6.8%

7.0%

age 50-54 age 55-59 age 60-64 age 65-70

Data source: Singapore Life Panel



219

Right-size with Silver Housing Bonus (SHB)

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Main Criteria Eligibility

age Aged 55 or aboveIncome Gross monthly household income of $12,000 or less

Existing Property HDB flat or Private property of Annual Value of $13,000 or less;

Property You Are Buying Smaller HDB flat (up to 3-room)

SHB take-up, 92 households from Feb 2013 to Aug 2014

Data source: public media


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HDB Lease Buyback Scheme (LBS)

Main Criteria Eligibility

Age All owners must be at CPF pay-out eligibility age (currently set at age 64) or older

Income Gross monthly household income of $12,000 or lessFlat type HDB 4-room or smaller



221

Source: HDB website221


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Source: HDB website222


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HDB households in SLP, age 64 – 70, LBS eligibility

About 1500 households in Singapore Life Panel are HDB households, age 64-70

223

Note: by eligibility conditions on age, income, flat type and property ownership. Data source: Singapore Life Panel

26%

1%

36%

2%

62%

0%

10%

20%

30%

40%

50%

60%

70%

80%

<=3-Roomeligible

<=3-Roomineligible

4-Roomeligible

4-Roomineligible

totaleligible


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Very low take-up rate

• Lease Buyback Scheme (LBS)

oMar 2009 to Aug 2014 – just under 800 households

oApr to Sep 2015 – 779 new applications

o < 1,600 households over 7 years

224

Data source: public media


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•Consumption habits • Home tenure choice preferences

• Insecurity / risk of rental & other options

• Prefer no mortgage debt

• Precautionary savings for longevity risk & health expenditures

• Cultural / social norms• Low mobility of elderly

• Adult children living with elderly parents

• Home is not recognised as a spendable asset

• Strong bequest motive

• Stigma attached to ‘eating your home’



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• Lack of understanding• Do not understand how to monetize

• Inexperience in using credit

• Age related cognitive decline

• Substitute extraction channels• Implicit extraction by under-maintenance of homes (Davidoff 2006)

• Transfers from children / heirs

226



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6. Summary of preliminary findings for SLP

• Few homeowners have mortgage on their home; those that do pay down their mortgage as they age

• Home equity is a major component of household wealth

• Both the richest and the poorer homeowners have high home equity in their net worth



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7. Ongoing research

• Consumption regression using cross-sectional data on asset variables; Conduct robustness checks with panel data next year and look for valid instruments

• Test hypotheses for resistance to housing wealth monetization

• Examine the relationship between house maintenance expenditures and age

• Link between income transfers and expected housing bequest



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End of Presentation


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Behavioural Risk Factors in the Singapore Elderly

Yuanyuan Deng, Rhema Vaithianathan



231

Background

• There is a concern that Singapore’s rapidly ageing population is at risk of a range of chronic conditions

• Singapore is therefore looking to encourage healthy living amongst its older people

• In this presentation we document some of the findings from the SLP related to the weight of the Singapore elderly population



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Sample for this study

• A one-off behavioural risk factor survey was fielded in July 2016

• We used all respondents aged between 50 and 70 who provided us with self-reported weight and height

• This yields 7,332 respondents

• BMI was calculated as weight/height2



233

BMI by Gender and WHO Cut-offs

0.2

.4.6

.81

15 20 25 30 35 40BMI

De-cumulative of BMI (females)

0.2

.4.6

.81

15 20 25 30 35 40BMI

De-cumulative of BMI (males)

ObeseOverweight ObeseOverweight



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Obesity Rate by EducationOverall obesity rates are low in Singapore amongst this age group…

So we combine obese and overweight categories together for our analysis. 0

.1.2

.3.4

.5.6

.7

Primary Secondary Post-Secondary Primary Secondary Post-Secondary

1 Male 2 Female

Mean 95% c.i

Prop

ortio

n O

bese

Highest Education

Graphs by gender_wave0



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Obese or Overweight by Education

This corresponds to other countries where there is a stronger socio-economic gradient in obesity amongst females than males

0.1

.2.3

.4.5

.6.7


1 Male 2 Female

Mean 95% c.i

Prop

ortio

n O

bese

/Ove

rwei

ght

Highest Education




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Obese or Overweight by Age

0

0.1

0.2

0.3

0.4

0.5

0.6

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69Pro

po

rtio

n O

be

se/O

verw

eig

ht

Age at Baseline

Female Male



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Age National Health Survey (2010)

SLP (2016)

Males Females Males Females

50-59 9.8 13.2 5.3 6.4

60-69 5.7 8.7 6.4 6.3

Comparing National Health Survey Obesity Rates with SLP

Note: National Health Survey data is based on trained interviewers measuring respondents’ weight and height using standardised scales. It is age-standardised as well. SLP is based on self-reported height and weight.

In general, there is a tendency for people to under-report their weight and over-estimate their height leading to self reported BMI being lower than that measured by an interviewer.


237

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Note on Western vs Asian CutOffs

• We have been using the standard WHO cut-points for defining overweight and obesity

• There is an alternative Asian cut-point

Asian Cut-point

Overweight ≥23

Pre-obese 23-30

Obese ≥30



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Over-weight/Obese (Western Cut-Offs)

0.1

.2.3

.4.5

.6.7


1 Male 2 Female

Mean 95% c.i

Prop

ortio

n O

bese

/Ove

rwei

ght (

Usi

ng A

sian

Crit

eria

)

Highest Education


0.1

.2.3

.4.5

.6.7


1 Male 2 Female

Mean 95% c.i

Prop

ortio

n O

bese

/Ove

rwei

ght

Highest Education


Over-weight/Obese (Asian Cut-Offs)


239

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Obesity and Chronic Conditions


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Chronic ConditionsWe asked respondents:

“Has a doctor ever told you that you have any of the following conditions? Please check all that apply.”

1 Hypertension

2 Diabetes

3 Cancer

4 Heart problems

5 Stroke

6 Arthritis

7 Psychiatric problems

8 None of the above



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Rates of Chronic Conditions27.90%

13.60%

8.10%6.70%

3.30%1.50%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

Pro

po

rtio

n R

ep

ort

ing

the

Co

nd

itio

n

(at

bas

elin

e)


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243

Note: These estimates are age, sex and race adjusted

These are estimated marginal effects after adjusting for age, gender and race

16.90%

6.60%

3.90%

1.80%0.80%

-0.80%

-5%

0%

5%

10%

15%

20%

Ad

dit

ion

al R

isk

of

Dis

eas

e if

O

be

se o

r O

verw

eig

ht

Correlation between Obesity/Overweight and Chronic Conditions

Caution ! These are cross-sectional correlations, not causation.


243

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Obesity and Expectationof Chronic Conditions


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Expectation of Developing a Chronic Condition

We asked respondents who do not have a condition:

“On a scale from 0 percent to 100 percent where "0" means that you think there is absolutely no chance, and "100" means that you think the event is absolutely sure to happen, what do you think are the chances that you will develop <disease> in your lifetime?”



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Expectation of Developing a Chronic Condition in Lifetime

0%

5%

10%

15%

20%

25%

30%

35%

40%

Ave

rage

Exp

ect

atio

n



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Expectation of Developing a Chronic Condition by Obesity/Overweight

0

5

10

15

20

25

30

35

40

Hypertension Diabetes Cancer Heart Stroke Arthritis

Ave

rage

Exp

ecta

tio

n o

f C

on

dit

ion

Others Overweight or Obese

People who are obese or overweight do rate their lifetime risk as higher than those who are not…

Is this statistically significant?


247

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The estimated marginal effect of

being overweight or obese on the

expectation of developing a chronic

condition

Hypertension 4.0%**

Heart 2.9%**

Diabetes 2.7%**

Stroke 2.4%**

Arthritis 1.9%**

Cancer 0.4%

Note: These estimates are age, sex and race adjusted ** means that these are significant at less than 5% level.

How to interpret this?

After adjusting for age, gender and race, this estimates the difference in expectations between obese /overweight respondents and the rest.


248

249

We don’t know their personal elevated lifetime risk, but compared to the cross sectional data, they seem to under-estimate their risk

For example those who are over-weight or obese only report a 2.9% higher expectation of lifetime diabetes



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Education and Expectations

• Are respondents with more education more accurate in their expectations?

• We calculate the effect of being obese or overweight on expectations of chronic conditions for respondents with only primary school education and those with post-secondary school education



251

Note: These graphs are age, sex and race adjusted

Expected Risk of Diabetes, Weight and Formal Schooling

Overweight and obese respondents with only primary education actually have similar expectations of getting diabetes as non-overweight.

Those with post-secondary have higher expectations


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Expected Risk of Hypertension, Weight and Formal Schooling



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Expected Risk of Arthritis, Weight and Formal Schooling



253

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Expected Risk of Cancer, Weight and Formal Schooling

Note: These graphs are age, sex and race adjusted For Internal Use Only PLEASE DO NOT CIRCULATE

254

255

Expected Risk of Heart Disease, Weight and Formal Schooling



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Expected Risk of Stroke, Weight and Formal Schooling



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Limitations of this Analysis

• Possible selection effects

• Is it that people who indulge in risky behaviour are more optimistic about their overall health?

• Are they poorly informed?



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Smokers

• We look at whether there is a general pattern of “optimism” amongst respondents who take health risks

• We undertake a similar exercise with respondents who report ever having or currently smoked (for more than 1 year)

• Caution: Smoking questions have not been fully completed – so highly preliminary



259

Effect of Ever or Currently Smoking on Expectations

Effect of Ever Being a Smoker

Expectations of Getting the Disease

Hypertension 1.49%

Diabetes 1.23%

Arthritis 1.21%

Heart Disease 2.76%**

Stroke 3.12%**

Cancer 4.59%**

Smokers have significantly higher expectation of heart disease, stroke and cancer.



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Preliminary Conclusion• Obese and Overweight respondents are more likely to report a

range of chronic conditions

• Obese and Overweight respondents also seem to have heightened expectations about getting these same diseases

• However, those respondents who only have primary level schooling appear not to report higher expectations of the diseases

• There is some evidence that smokers might have a more “accurate” or even an exaggerated sense of their risk



261

Next Steps

• These are early insights – we also have data on where people eat (including at fast food restaurants and hawker centers)

• More work will be required to explore the extent to which these results are causal

• If robust, they suggest that policy-makers should undertake a more intensive education campaign on the dangers of being obese and overweight, similar to smoking



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End of Presentation


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Subjective Well-being

Susann Rohwedder



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Objective: Measure Well-being More Broadly

• Economic resources important determinant of well-being Social policies mostly rely on income to define who is in need/does well

• Other determinants of well-being likely important as well Interest from policy makers to find what these are

(Panel in National Academy of Science, OECD Better Life Initiative, etc.)

• Many countries added SWB measures to national surveys

• Gallup World Poll/World Happiness Report (2015), SG ranks 24

• Sept 2015: Adoption of Social Development Goals at UN, based on concepts of happiness and well-being



265

Evolving literature on Subjective Well-being

Studies of Determinants

• Mostly cross-sectional or time series of cross-sections

• International comparisons, use differences across countries to study determinants

Caveat: different use of response scales across countries

Studies of Evolution of SWB in face of adverse events

- For example, when suffers health shock or job loss: Do people adapt in their SWB as condition persists?



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Subjective Well-Being Measures in SLP

SLP = study of 50-70 year olds

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Evaluative (monthly): How satisfied with …

Experienced (quarterly):How often did you feel …

Life as a whole Worn out

Social contacts and family life Happy

Daily activities and job Difficulty sleeping

Total household income Sad

Economic situation

Health


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1.0 0.64.8 3.6

30.626.3

56.4 61.5

7.1 8.0

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

Life as a whole Social contacts & family lifevery dissatisfied dissatisfied neither sat, nor dissat satisfied very satisfied

Evaluative Subjective Well-beingHow satisfied are you with … (among 50 -69 year olds)

267

Combine and study percent “very satisfied” or “satisfied”


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01020304050607080

Percent satisfied or very satisfied, across domains

Highest for social contacts/family lifeLowest for income/economic situation



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Subjective Well-being Higher at Older Ages

Older … tend to be more satisfied with:

• Life as a whole

• Social contacts, family life

• Job or daily activities

• Economic situation

Older … score higher on experienced well-being:

• Less often worn out

• More often happy

• Less often sad

59.0

60.0

61.0

62.0

63.0

64.0

65.0

66.0

50-54 55-59 60-64 65-69

satisfaction with life as a whole



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Exception: Health satisfaction lower in oldest age group

30.0

35.0

40.0

45.0

50.0

55.0

60.0

65.0

50-54 55-59 60-64 65-69

Health: percent satisfied



271

Strong gradient by wealth quartile:Satisfaction higher among high-wealth group

0

20

40

60

80

100

Lowest 2 3 Highest

Life overall: percent satisfied by wealth quartile



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Satisfaction higher among those with high education

0

20

40

60

80

No formal schoolingor primary

Secondary Post-secondary

Life as a whole: percent satisfied by education



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Satisfaction Much Higher if Healthy

0

20

40

60

80

100

Poor Fair Good Very good Excellent

Life overall: percent satisfied by self-rated health



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Same Patterns in Experienced Measures of Subjective Well-being

Lower educated or in bad health or low wealth …

- feel more often worn out

- feel less often happy

- have more difficulty sleeping

- feel more often sad



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High-frequency surveys:track changes & identify determinants

Effect of shocks on Subjective Well-being:

- Health shock

- Employment shock: job loss

15 waves of monthly data



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A Health Transition Affects Subjective Well-being Immediately

3

3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

4

good to good good to bad bad to good bad to bad

Life Satisfaction, average score by health transition

t t+1

Same pattern for all other dimensions of subjective well-being.



277

Also Employment Transitions Immediately Reflected in Subjective Well-being

3.00

3.10

3.20

3.30

3.40

3.50

3.60

3.70

emp to emp emp to unemp unemp to emp unemp to unemp

Life Satisfaction

t t+1

Same pattern for other dimensions of subjective well-being



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Same for other Domains:Decrease when becoming unemployed,Increase when becoming employed

278

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30Change in SWB by R's employment transition

emp-unemp

unemp-emp


279279

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

1 2 3 4 5 6+months

Life overall

Income

Health

Time Path of Subjective Well-being Following Unemployment

Immediate drop, continued decline


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280280

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

1 2 3 4 5 6

no formal/primary

secondary

post-secondary

Time Path of Subjective Well-being Following Unemployment by Education

Largest and continued decline for those with highest education


281

• Modest immediate increase in satisfaction with life as a whole and with economic situation

• but not always statistically significant

• no suggestion of further improvement

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Time Path of Subjective Well-being Following Re-employment


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282282

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

1 2 3 4 5 6 7 8 9

number of months since onset of bad health

Life overall

Income

Health

Time Path of Subjective Well-being Following Onset of Bad Health


283

Time Path of Subjective Well-being Following Recovery from Bad Health

283

0.0

0.1

0.2

0.3

0.4

0.5

1 2 3 4 5 6 7 8

Life overall

Income

Health

Number of months since returning to good health

Initial increase in subjective well-being, then stable

Satisfaction with


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Link Subjective Well-being to Economic Preparation for Retirement

Now thinking of your financial situation in retirement: Considering all your financial and other assets, including your CPF account, how would you rate your financial preparation for retirement?

1 Excellent2 Very good3 Good4 Fair5 Poor



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Higher Subjective Well-being If ExcellentEconomic Preparation for Retirement

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0

0.2

0.4

0.6

0.8

1

Poor Fair Good Very good Excellent

Life as a whole: percent satisfied by self-rated preparation for retirement


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Future Research:Determinants of Subjective Well-being

- Use longitudinal data (following the same individuals over time) to study causal effects

- Relative importance of determinants

- Scope for using these to gauge effectiveness of public policy?



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End of Presentation


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