Demographic and poverty dynamics with high AIDS mortality

Ian M. TimæusLondon School of Hygiene & Tropical Medicine

Intellectual justification

The project examines the impact of the AIDS epidemic and measures to mitigate it in sub-Saharan Africa

Most demographic analyses treat socioeconomic status as an exogenous explanation of demographic phenomena

Likewise, microeconomic analysis usually treats demographic change as exogenous or even ignores it entirely.

The challenge to welfare posed by the HIV/AIDS epidemic in Africa demands a more sophisticated understanding of inter-relationships between demographic and poverty dynamics

The response to both demographic and economic shocks can be demographic as well as economic

AIDS and population change

DEMOGRAPHY

HIV AND AIDS

ECONOMY

HIV AIDS

HOUSEHOLD AND FAMILIAL

IMPACTSLIVELIHOODS

AND ACTIVITIES

POVERTY

MORTALITY

Objectives

To synthesize economic and demographic perspectives in order to:

Improve the measurement of poverty dynamics

Understand better the impact of deaths of working-age adults on household welfare, households’ responses, and the determinants of differential vulnerability and resilience

Examine the effects of demographic change, including the AIDS epidemic, on poverty dynamics across the life course in South Africa

Assess social policy interventions designed to mitigate impact and their distributional implications across the life course.

Longitudinal data on AIDS impact

Phase 1 – analysis of two complementary longitudinal population-based studies from KwaZulu-Natal – ACDIS and KIDS (KwaZulu-Natal Income Dynamics Study)

Longitudinal studies provide data on people who later get sick and die They allow analysis of changes in social and economic behaviour that follow shocks such as AIDS and AIDS deathsOne can compare movements into and out of poverty in affected and unaffected households and distinguish transitory from chronic povertyFinally, they can document both early responses to and the longer-term consequences of AIDS sickness and deaths

Phase 2 – development of a micro-simulation model in order to assess different social policy interventions for a population affected by AIDSQualitative study of how households cope with illness and death

Africa Centre DSS (ACDIS)

Surveillance of the entire population of part of the Hlabisa sub-district of KwaZulu-NatalRun by the Africa Centre for Health and Population Studies

part of the University of KwaZulu-Natalprincipal funder: The Wellcome Trust

Data collection started in January 200090,000 household members (88,000 individuals)11,000 householdsTwo rounds of data collection per year

birthsdeaths a verbal autopsy is conducted for all deaths movesdemographic and health datasocioeconomic module (every 2nd/3rd round)

Africa Centre for Health and Population Studies

KwaZulu-Natal Income Dynamics Study

Panel study based on 1354 African and Indian households interviewed in KZN in 1993Uses a World Bank LSMS-style questionnaire with detailed expenditure data2nd wave of interviews in 1998 and 3rd wave in 2004Interviews all branches of households that have split and households established by the next generation as well as the original householdsAlthough the panel has suffered substantial attrition (38%), in aggregate its characteristics remain broadly representative of those of the province according to the 2001 Census

Location of KIDS 2004 households

Deaths by age in 1998 and year, KIDS(* prorated to a full calendar year)

0

25

50

75

100

125

1998* 1999 2000 2001 2002 2003 2004*

Age 0-19 Age 20-44 Age 45+

ADaPT – a multidiscipinary team

The project builds on existing partnerships between:Centre for Population Studies (CPS), LSHTM

Ian Timæus (demography)

Alessandra Garbero (demography, economics)

School of Development Studies (SDS), UKZNJulian May (economics, social policy)

Lucia Knight (demography, sociology – PhD student)

Africa Centre for Health and Population Studies, UKZNVicky Hosegood (demography, social policy)

The core partners are supported by specialist expertise from: University of Southampton

Jane Falkingham (demography, economics, social policy)

University of Cape TownIngrid Woolard (economics)

Work plan and collaborative mechanisms

Three-year project (October 2006 – October 2009)

Funded under a joint initiative of the UK’s Economic and Social Research Council and Department for International Development

North-South collaboration with annual project workshops and periods of intensive face-to-face work

Exchange sabbaticals in Durban and London in 2007 and 2008

Full-time research assistant and linked PhD studentship for a young South African researcher, both based at LSHTM

Final dissemination workshop in South Africa

0

1

2

3

4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15+

Household size (adult equivalents)

Exp

end

itu

re/ p

ove

rty

line

PCE-93

PCE-98

PCE-04

Household size and expenditureKIDS – 1993, 1998 and 2004 waves(Child weights: aged 0-6 = 0.5, aged 7-13 = 0.75; θ = 0.75)

Median household size ≈ 5

Adult death and poverty dynamics

No adult deaths 1998-2004

1+ adult deaths 1998-2004

Number of households, 1998 605 258 Average size in 1998 6.4 9.2 % that died out by 2004 8 6 % that split into 2+ households 34 47 % that fostered out children 25 36 Median expenditure per head, 1998 (as % of a poverty line of R322 per month)

99 63

% change in expenditure by 2004 26 35 Median net wealth per head, 1998 (Rand, 2000 values)

30200 28300

% change in net wealth by 2004 +5 -26

Depending on what an adult who died contributed to their household the impact of premature deaths on per capita expenditure may be negative or positive. A straightforward regression model mixes together these two different regimes

The effect is a data-weighted average of these two regression relationships, which we estimate as negative but, unsurprisingly, insignificant.

To allow for heterogeneous effects of premature deaths, we modify a basic fixed effects regression equation as follows:

where the coefficient 2 allows the impact of a premature adult mortality to change with the household’s level of initial economic well-being.

A heterogeneous effects model of the impact of premature adult deaths

1

1

it

ititit y

yyg

itititititiit yhhyg )]ln([)][ln( 12110498

1992 1994 1996 1998 2000 2002 2004

Year

0

50

100

150

200

250

Perc

ent of P

overt

y L

ine

Impact of HIV/AIDS Death on Predicted Livelihood TrajectoriesFixed Effects Estimates

Poverty Line

50th Percentile Household

80th Percentile Household

20th Percentile Household

Without PAM

Impact of premature adult mortality (PAM) on estimated livelihood trajectories