Demographic and poverty dynamics with high AIDS mortality

Ian M. Tim æus

London 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

HIV AND AIDS MORTALITY HIV AIDS LIVELIHOODS AND ACTIVITIES POVERTY ECONOMY HOUSEHOLD AND FAMILIAL IMPACTS DEMOGRAPHY

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 deaths One can compare movements into and out of poverty in

affected unaffected

and households and distinguish transitory from chronic poverty Finally, 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 AIDS

Qualitative 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-Natal Run by the Africa Centre for Health and Population Studies part of the University of KwaZulu-Natal principal funder: The Wellcome Trust Data collection started in January 2000 90,000 household members (88,000 individuals) 11,000 households Two rounds of data collection per year births deaths  a verbal autopsy is conducted for all deaths moves demographic and health data socioeconomic 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 1993 Uses a World Bank LSMS-style questionnaire with detailed expenditure data 2 nd wave of interviews in 1998 and 3 rd wave in 2004 Interviews all branches of households that have split and households established by the next generation as well as the original households Although 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)

125 100 75 50 25 0 1998* 1999 2000 2001 2002 Age 0-19 Age 20-44 Age 45+ 2003 2004*

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), UKZN

Julian May

(economics, social policy)

Lucia Knight

(demography, sociology – PhD student) Africa Centre for Health and Population Studies, UKZN

Vicky 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 Town

Ingrid 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

Household size and expenditure

KIDS – 1993, 1998 and 2004 waves

(Child weights: aged 0-6 = 0.5, aged 7-13 = 0.75;

θ

= 0.75) 4 3

Median household size ≈ 5

2 1 0 1 2 3 4 5 6 7 8 9 10 11

Household size (adult equivalents)

12 13 14 15+ PCE-93 PCE-98 PCE-04

Adult death and poverty dynamics

Number of households, 1998 Average size in 1998 % that died out by 2004 % that split into 2+ households % that fostered out children Median expenditure per head, 1998 (as % of a poverty line of R322 per month) % change in expenditure by 2004 Median net wealth per head, 1998 (Rand, 2000 values) % change in net wealth by 2004 No adult deaths 1998-2004 605 6.4 8 34 25 99 1+ adult deaths 1998-2004 258 9.2 6 47 36 63 26 30200 +5 35 28300 -26

A heterogeneous effects model of the impact of premature adult deaths

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:

g it



y it



y y it

 1

it

 1

g it

 

i

  98   04   [ln(

y it

 1 )]   1

h it

  2 [

h it

ln(

y it

 1 )]  

it

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

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

Impact of HIV/AIDS Death on Predicted Livelihood Trajectories Fixed Effects Estimates Without PAM 250 200 80 th Percentile Household 150 Poverty Line 100 50 0 1992 50 th Percentile Household 20 th Percentile Household 1994 1996 1998 Year 2000 2002 2004