Demographic and poverty dynamics with high AIDS mortality Ian M. Timæus London School of Hygiene &...
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Transcript of Demographic and poverty dynamics with high AIDS mortality Ian M. Timæus London School of Hygiene &...
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