February 20, 2013

FAMILY HEALTH AND WEALTH STUDY

INSIGHTS ON WEALTH MEASUREMENTAND CHANGE

To assess the effect of childbearing patterns on family health and wealth outcomesNumber and timing of births

Role of contraception

Family wealth and health outcomes

Household income, employment

Child schooling, nutrition

Maternal health

To assess using a longitudinal design

STUDY AIMS

Addis Ababa UniversityAssefa SemeMeselech Roro

Assiut UniversityOmaima El GibalyGhada Al-Attar

Kwame Nkrumah University of Science and Technology

Easmon OtupiriDenis Yar

Makerere UniversityFred MakumbiVivian Zalwango

Obafemi Awolowo UniversityPeter OgunjuyigbeAbimbola Phillips

University of IbadanMichael OkunlolaImran Morhason-BelloNathanael Afolabi

University of MalawiFrank TauloEddie MalungaWanangwa Chimwaza

FHWS SITE LEADS/DATA COORDINATORS

Andreea CreangaAlain KoffiFunmi OlaOlorunNadia Diamond SmithQingfeng LiAdel TakruriLinnea ZimmermanTimothee FruhaufAnd the rest of the

FHWS team

Saifuddin AhmedMichelle HindinStan BeckerDavid Bishai Julia DriessenWilliam Pan

FHWS ACKNOWLEDGEMENTS (US)

Three rounds of observation

Probability sample of families in peri-urban areaWife of childbearing age (15-49 years)

Husband of childbearing age (20-54 years)

GPS mapping of area (waypoints, households)

Data collection began by Ghana site January 2010Round 2 approximately 2 years later

STUDY DESIGN

Household roster on occupants and their characteristics, GPS Focal woman questionnaire Background characteristics Childbearing history, fertility preferences and contraceptive calendar Child schooling (5 to 24 years) and health history (births in <5 years) Marital relationship quality, decision-making autonomy Self-reported health

Focal man questionnaire Background characteristics Parity, fertility preferences and contraceptive use Marital relationship quality, decision-making autonomy Adult morbidity and self-reported health

Wealth module Housing construction quality, asset ownership, expenditures in <1 year

Physical assessment Height, weight of household members Blood pressure, pulse Anemia (Ghana, Uganda)

MEASUREMENT STRUCTURE AND CONTENT

Country Site Sample size

Egypt Waldeya 548

Ethiopia Sebeta 998

Ghana Asawasa 800

Malawi Lunzu 605

Nigeria Ipetumodu 787

Nigeria Akinyele 502

Uganda Wakiso 505

Total 4745

PERI-URBAN FHWS SITES

SITES VARIED IN ‘PERI-URBAN-NESS’

TRAINING IS EVERYTHING

FHWS Round 2 training for Sebeta site

LEARNING TO TAKE BLOOD PRESSURE, MAPPING AND COMMUNITY SENSITIZATION

July 2011 workshopBlantyre, Malawi

PRECISION AND ENTHUSIASM

KNUST FHWS Team and some of equipment fieldstaff transported during interviews

Analysis workshops in July 2011 and July 2012 Data sharing and authorship agreements Gates Institute role is facilitating comparative analyses on pre-defined

set of topics Panel at International FP Conference 2011, Dakar

Each site has autonomy to share data with analysts within and outside Ghana: Two dissertations Ethiopia: One dissertation

One year spent on data cleaning and linking rounds Follow-up rates and who is missed

Analyses underway Comparative description of 4745 families’ health and wealth Childbearing patterns and child schooling and nutrition Childbearing and family wealth Couple concordance in fertility preferences and contraceptive use Parity and self-rated health (and gender differences)

PROGRESS TO DATE

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Ibadan Ife Kumasi Lunzu Wakiso Sebeta

Parity Composition among Married Women Aged 15‐44 in Six FHWS Sites

6+

4‐5

2‐3

0‐1

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

15-19 20-24 25-29 30-34 35-39 40-44

% u

sing

mod

ern

cont

race

ptiv

e m

etho

d

Age group

Modern Contraceptive Prevalence among Married Women Ages 15-44 by Age Group across Six Africa-based FHWS Sites

Ibadan

Ife

Kumasi

Lunzu

Wakiso

Sebeta

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

0‐1 2‐3 4‐5 6+

% using

 mod

ern contraceptive metho

d

Parity group

Modern Contraceptive Prevalence among Married Women Ages 15‐44 by Parity across Six Africa‐based FHWS Sites

Ibadan

Ife

Kumasi

Lunzu

Wakiso

Sebeta

CAPTURING HOUSEHOLD TRANSITORY WEALTH THROUGH AN INDEX ON

EXPENDITURES AND NON-DURABLES

J. Driessen, P. Ogunjuyigbe, A. Phillips, Q. Li, FHWS Study Team, A. Fatusi, A. Tsui

Common use of wealth quintiles from assets assessed in surveys (EDHS)Wealth measure can be broken down intoPermanent wealth (house, housing quality,

vehicle, ownership of durable goods)Transitory wealth (expenditures on entertainment,

eating out, other consumption reflective of ‘middle class’ lifestyle)

Such data are challenging to collectProxied with asset ownership of durables and non-

durables, expenditures, income, household quality

ANALYSIS OF PERMANENT AND TRANSITORY WEALTH MEASURES

Address overlapping measurement of wealth

Deconstruct household wealth into permanent and transitory components

Create a summative index

Selection of index items

Weights for each item

Dichotomous versus continuous measures

DHS wealth quintiles based on PCA with dichotomous measures

Used principal components analysis

RATIONALE FOR INDEX CONSTRUCTION

Self-rated wealth

9-step ladder of perceived relative economic status

Satisfaction with current income

4-step rating scale

Aspirational wealth

5-step rating scale of relative well-being in one year

OUTCOMES OF INTEREST

PERCENT DISTRIBUTION FOR SATISFACTION WITH CURRENT INCOME

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Fully satisfied Rather satisfied Less than satisfied Not at all satisfied

Regress self-reported economic wellbeing measures on

Permanent wealth index (Fixed Asset Index)

Transitory wealth index (Middle Class Index)

Covariates

Male years of schooling

Number of persons in HH under age 5

Number of persons in HH age 5-14

ANALYTIC APPROACH

Sites Ethiopia Ghana Malawi Nigeria/IfeNigeria/

Ibadan UgandaEigenvalue of component 1 4.89 3.27 4.67 2.87 3.40 4.28% variance explained by component 1 10.4 7.4 14.1 6.2 8.5 10.4Cronbach's alpha 0.774 0.638 0.758 0.575 0.604 0.710Range of predicted score (min max) (-3.92

9.78)(-4.22 6.14)

(-2.67 17.44)

(-5.13 6.12)

(-4.10 11.06)

(-3.20 9.99)

% variance explained by component 1with all fixed asset and middle class index items

9.0 5.9 12.9 6.1 8.2 13.7

PCA RESULTS FOR FIXED ASSETS

EXAMPLE OF FACTOR LOADINGS FOR FIXED ASSET INDEX

Ethiopia Ghana MalawiNigeria/

IfeNigeria/Ibadan Uganda

FurnishingsHas bed 0.12 0.00 0.21 0.21 0.24 0.14Has table 0.08 -0.03 0.21 0.24 0.27 0.21Has chair 0.08 0.01 0.19 0.23 0.26 0.19Has dresser 0.26 0.13 0.17 0.18 0.20 0.23Has refrigerator 0.29 0.24 0.30 0.30 0.25 0.30Has landline telephone 0.28 0.04 0.15 0.04 0.14 0.14Has motorcycle 0.01 0.03 0.06 0.04 0.03 0.06Has bicycle 0.09 0.01 0.05 0.02 0.07 0.13Has car/truck 0.20 0.12 0.25 0.26 0.26 0.29Has horse cart -0.01 0.00 ‐‐ 0.03 ‐‐ 0.15Has generator 0.10 0.03 0.07 0.29 0.27 0.20

PCA RESULTS FOR MIDDLE CLASS INDEX

Sites Ethiopia Ghana MalawiNigeria/

IfeNigeria/

Ibadan UgandaEigenvalue 3.79 3.60 4.83 3.20 3.30 7.67% variance explained by component 1 11.2 10.6 15.1 9.4 9.7 22.6Cronbach's alpha 0.671 0.660 0.755 0.677 0.668 0.522

Range of predicted score (min max)(-2.67

12.64)(-1.57

18.15)(-2.12

11.82)(-4.09 7.77)

(-2.70 14.61)

(-0.93 23.65)

EXAMPLE OF FACTOR LOADINGS FOR MIDDLE CLASS INDEX

Sites Ethiopia Ghana MalawiNigeria/

IfeNigeria/Ibadan Uganda

Consumption/expenditure behaviorsSpent >$2.5 eating out in last 7 days 0.19 0.30 0.14 0.21 0.10 -0.02Spent >$10 in last month on clothes/shoes 0.07 0.29 0.14 0.22 0.16 -0.01Spent >$10 in last month on daily household items 0.19 0.30 0.20 0.30 0.15 -0.01Spent >$5 in last month on medicines 0.07 0.30 0.14 0.23 0.09 0.01Spent >$10 in last month on books, newspapers, school supplies and entertainment 0.18 0.22 0.23 0.22 0.16 0.00Spent >$5 in last month on other products and services 0.12 0.24 0.05 0.22 0.13 -0.01Spent >$20 in last month on child care 0.13 0.26 0.24 0.20 0.10 0.00Spent >$15 in last 7 days on food (less amount spent eating out) 0.23 -0.25 0.20 0.07 0.13 0.00Spent >$10 in last month on utilities 0.28 -0.01 0.25 0.20 0.24 0.01Paid any amount for taxes last year 0.23 0.26 0.20 0.17 0.15 -0.02Household has no debt 0.01 -0.16 0.00 -0.06 -0.04 0.00Household has lent any amount to others 0.05 0.11 0.07 0.11 0.14 -0.01Household currently has savings 0.18 0.08 0.22 0.14 0.18 -0.01

Red frame indicates statistical significance at 5% level.Adjusted for male education, presence of children and youth in household

Red frame indicates statistical significance at 5% level.Adjusted for male education, presence of children and youth in household

Red frame indicates statistical significance at 5% level.Adjusted for male education, presence of children and youth in household

WHAT WE’VE LEARNED ABOUT WEALTH MEASUREMENT

PCA can be applied to other non-asset variables and reduce reliance on reported expenditure data

Constructed Middle Class Index reflective of consumption and short-term well-being

MCI performs similarly to Fixed Asset Index in predicting self-reported wealth outcomes

Middle class index sensitive to Expenditure ‘shocks’ (e.g., unanticipated health expenses) Health expenditure shock likely associated with having

sick children Presence of children Children may drive expenses captured in transitory

wealth score

THE DISTRIBUTION OF THE PERMANENT WEALTH SCORES IN ROUND 1 AND 2 (ETHIOPIA)

0.0

5.1

.15

.2.2

5D

ensi

ty

-5 0 5 10Fixed Assets Scores

Round 1 Round 2

THE DISTRIBUTION OF THE TRANSITORY WEALTH SCORES IN ROUND 1 AND 2 (ETHIOPIA)

0.0

5.1

.15

.2.2

5D

ensi

ty

-5 0 5 10 15MCI Scores

Round 1 Round 2

Factor Adj OR p level

Family income (Round 1/log) 1.011 0.011Length of residence (Husband) 0.997 0.687Length of residence (Wife) 0.988 0.199Borrowed money last year for health expenses 0.609 0.051Own house 0.619 0.010Duration of marriage (Wife report) 0.953 0.001Regret marrying spouse (Husband) 1.470 0.164Regret marrying spouse (Wife) 1.358 0.164Husband has other wives (Wife report) 1.265 0.543Husband has other wives (Husband report) 0.474 0.211

*Model also controls for occupation type of husband and wives (ns)n=950 couples, weighted for loss to follow up

FACTORS ASSOCIATED WITH COUPLE LOSS-TO-FOLLOW UP (ETHIOPIA)

ROC (RECEIVING OPERATING CHARACTERISTICS) CURVE TO ASSESS

PROPENSITY SCORE

0.2

5.5

.75

1S

ensi

tivity

0 .25 .5 .75 11 - Specificity

Area under curve = 0.6743 se(area) = 0.0196

01

23

4D

ensi

ty

0 .2 .4 .6 .8Propensity Score

Captured in 2nd roundMissed in 2nd round

Closeness of curve to diagonal line is favorable to constructed propensity score model

Distribution of propensity scores of those missed and relocated are similar(Ethiopia results only)

Factor Reg Coeff p level

Middle class score (Round 1) 0.777 0.000Fixed asset score (Round 1) 0.088 0.000Husband's years of education (Round 1) 0.020 0.007Borrowed money last year for health expenses -0.126 0.122Number of children < age 71 0.128 0.0612 0.088 0.365

n=693 couples, weighted for loss to follow up

FACTORS AFFECTING ROUND 2 TRANSITORY WEALTH SCORE

Critical importance of training and supervision It’s not worth doing, if it’s not done well

Standardized data-entry formats

Importance of longitudinal study design Under-estimated loss-to-follow up which impacts Round 2 sample

power

Challenges with relocating couples in peri-urban areas Couple follow-up rates are not surprisingly lower than individual

follow-up rates

Household loss due to logical events (marital disruption, migration, death)

Ability to decompose overall wealth into permanent and transitory components

This type of study is rare in the African setting.

WHAT WE’VE LEARNED ABOUT CHANGE THROUGH FHWS

Whether childbearing patterns consistently influence family health and wealth outcomes

WHAT WE HAVEN’T LEARNED AS YET

THANK YOU AND MANY THANKS TO THE PRODIGIOUS EFFORTS OF THE

EXTENDED FAMILY OF RESEARCHERS AND FHWS STUDY PARTICIPANTS AND SUPPORT FROM

THE BILL & MELINDA GATES FOUNDATIONTHROUGH THE GATES INSTITUTE.