Socio-Economic Status Index for a Non-Homogenous Society with - - PowerPoint PPT Presentation

Constructing a Socio-Economic Status Index for a Non-Homogenous Society with Distinct Sets of Variables in Multiple Correspondence Analysis

Sugnet Lubbe & Sheetal Silal

University of Cape Town, South Africa

Niël J le Roux

Stellenbosch University, South Africa

Socio-Economic Status: Wikipedia

• Socio-economic status (SES) is an economic

and sociological combined total measure of a person's work experience and of an individual's or family’s economic and social position relative to others, based on income, education, and occupation.

• A fourth variable, wealth, may also be

examined when determining socio- economic status.

Background

• Maternity ward
• Eight state hospitals

– Population primarily Black South Africans – few Coloured South Africans – Exit interview – Review of hospital record

Aim

– Among other objectives

• Find a Socio-Economic Status Index

– based on three groups of variables

• Asset variables

– Does your household have any of the following in good working

• rder?
• Household physical characteristics

– What is the main material of your house’s walls / roof? – What is your main source of drinking water?

• Demographic variables of household head

– sex / age / education / employment

Asset variables

Technology

• Does your household have any of the

following in good working order?

• Landline
• Cellphone
• Radio
• TV
• Video / DVD
• Electric stove with oven
• Bicycle
• PC
• Internet
• Fridge
• Vehicle

Traditional

• Does your household own

cattle, livestock or chickens?

• How many does your

household own? – cattle – goats – chickens – pigs – other

• 1 Guinea fowl  Poultry

Constructing a SES index: Asset vars

• How do we combine these two ways of living

into a single SES index?

• ... economic and social position

relative to others ...

– technology: more ‘devices’, higher SES – traditional: more livestock, higher SES

• relative to other traditional households

– Single SES, relative to ALL others in South Africa???

MCA of Technology variables

• 1.0
• 0.5

0.0 0.5 0.0 0.5 1.0

landline.0 landline.1 cellphone.0 cellphone.1 radio.0 radio.1 TV.0 TV.1 DVD.0 DVD.1 stove.0 stove.1 bicycle.0 bicycle.1 PC.0 PC.1 internet.0 internet.1 fridge.0 fridge.1 vehicle.0 vehicle.1

higher SES

MCA of all Asset variables (Tech & Trad)

• 6
• 4
• 2

2 4 6

• 10
• 8
• 6
• 4
• 2

landline.0 landline.1 cellphone.0 cellphone.1 radio.0 radio.1 TV.0 TV.1DVD.0 DVD.1 stove.0 stove.1 bicycle.0 bicycle.1 PC.0 PC.1 internet.0 internet.1 fridge.0 fridge.1 vehicle.0 vehicle.1 cattle.0 cattle.2 cattle.6 cattle.7 cattle.8 cattle.9 cattle.10 goats.0 goats.1 goats.2 goats.3 goats.4 goats.5 goats.6 goats.8 goats.26 poultry.0 poultry.1 poultry.2 poultry.3 poultry.4 poultry.5 poultry.6 poultry.8 poultry.10 poultry.14 poultry.15 poultry.20 poultry.34 pigs.0 pigs.2 pigs.3 pigs.4

Cattle

2 6 7 8 9 10

Goats

1 2 3 4 5 6 8 26

Poultry

1 2 3 4 5 6 8 10 14 15 20 34

Pigs

2 3 4

higher SES

• 2
• 1

1 2

• 2
• 1

1 2

Naive combination of components

higher SES MCA 1st component: technology only MCA 1st component: combined components cattle goats poultry pigs

Correlations: all Asset variables

cellphone radio TV DVD stove bicycle PC internet fridge vehicle cattle goats poultry pigs landline 0.04 0.12 0.15 0.19 0.26 0.22 0.33 0.15 0.15 0.33

• 0.04
• 0.02
• 0.03
• 0.04

cellphone 0.17 0.23 0.21 0.12 0.06 0.10 0.02 0.16 0.08 0.03 0.03 0.03 0.03 radio 0.28 0.27 0.15 0.11 0.10 0.06 0.28 0.13 0.03 0.02 0.04 0.04 TV 0.55 0.31 0.14 0.15 0.07 0.45 0.19 0.03

• 0.01
• 0.01

0.03 DVD 0.34 0.19 0.23 0.11 0.36 0.24

• 0.01

0.00

• 0.05
• 0.04

stove 0.20 0.29 0.13 0.39 0.31

• 0.05
• 0.07
• 0.04

0.00 bicycle 0.19 0.08 0.11 0.23

• 0.03
• 0.02

0.06 0.04 PC 0.38 0.14 0.39

• 0.04
• 0.04
• 0.06
• 0.04

Internet 0.06 0.17

• 0.02
• 0.02
• 0.03
• 0.01

fridge 0.02 0.03 0.04 0.00 0.04 vehicle 0.03

• 0.02

0.00 0.03 cattle 0.24 0.11 0.30 goats 0.21 0.11 poultry 0.06

Variable types

• Technology

– 0 / 1

• Traditional

– Count: positive whole numbers – ‘continuous’

• PCA biplot

– Euclidean distance on 0/1 coding  Extended matching coefficient – ‘Generalised biplot’ – Scaled data matrix: all variances = 1

landline cellphone radio TV DVD stove bicycle PC internet fridge vehicle cattle

2

goats

5

poultry

6

pigs

1 5 4 3 1 6 4 3 2 1 8 4 2

PCA biplot of all Asset variables

MCA with indicators only

cattle goats poultry pigs

• wn ‘device’

do not own ‘device’

MCA of livestock owners only

landline.0 landline.1 cellphone.0 cellphone.1 radio.0 radio.1 TV.0 TV.1 DVD.0 DVD.1 stove.0 stove.1 bicycle.0 bicycle.1 PC.0 PC.1 internet.0 fridge.0 fridge.1 vehicle.0 vehicle.1 cattle.0 cattle.2 cattle.6 cattle.7 cattle.8 cattle.9 cattle.10 goats.0 goats.1 goats.2 goats.3 goats.4 goats.5 goats.6 goats.8 goats.26 poultry.0 poultry.1 poultry.2 poultry.3 poultry.4 poultry.5 poultry.6 poultry.8 poultry.10 poultry.14 poultry.15 poultry.20 poultry.34 pigs.0 pigs.2 pigs.3 pigs.4

Cattle

2 6 7 8 9 10

Goats

1 2 3 4 5 6 8 26

Poultry

1 2 3 4 5 6 8 10 14 15 20 34

Pigs

2 3 4

Difficulties in merging components

• Essentially 2 components

– orthogonal to each other

• uncorrelated
• measure different ‘aspects’

– technology vs tradition

• Options

– finite (2) mixtures – principal components – non-parametric principal components (prin. curves) – canonical correlation analysis – ?????

In conclusion

• Statistical plots

– MCA & PCA – INDISPENSIBLE to explore the structure of the data – clearly indicates orthogonality of components

• Statistics

– Show what the data is telling us – Cannot merge contrasting components – Measure different aspects

• Technology vs Tradition