Poverty and vulnerability: a static vs dynamic assessment of a population subjected to climate change shock in Sub-Saharan Africa.

Transcription

1 Poverty and vulnerability: a static vs dynamic assessment of a population subjected to climate cange sock in Sub-Saaran Africa. Angela Bascieri 1 Abstract Despite te effects of climate cange being evident at a global scale, its negative impact will severely affect tose communities igly dependent on natural resources. Te emerging treat of climate cange to ouseold s welfare calls for more researc to assess te ouseold s probability to fall into poverty. Tis would need a combined analysis of te potential risk of experiencing a climate related extreme (suc as drougts or floods) and te ouseold s ability to cope wit suc events. Tis researc aims to extend a metodology developed by 'micro-economists and explicitly models te unexplained ouseold components (i.e. idiosyncratic ouseold component, error term) using Geograpical Information System tecnique. Using data from te fift round of te Gana Living Standard Survey and detailed maps of rainfall variability and soil types, te researc attempts to model te eterogeneous ouseold sock, provides a profile of te most vulnerable and also igligts te gains obtained from tis approac. [146] XXVI IUSSP International Population Conference Session: 1203 Interrelations between population and climate cange 1 Lecturer in Demograpy and Leverulme Fellow, Centre for Population Studies, London Scool of Hygiene and Tropical Medicine, 50 Bedford Square, London Angela.Bascieri@lstm.ac.uk 1

2 EXTENDED ABSTRACT INTRODUCTION Tere is now convincing scientific evidence tat climate cange is occurring and tat it poses important global risks (IPCC, 2007a). It as been sown tat increases in uman activities suc as burning fossil fuels and canges in land use and land cover ave raised te atmosperic concentrations of greenouse gases (particularly carbon dioxide, metane and nitrous oxide) (IPCC, 1997, 2007b). Since 1900, te global mean temperature as already increased by 0.7ºC (Stern, 2006), wic as a knock-on effect on oter climatic and geograpical penomena. Despite te effects of climate cange being evident at a global scale, its negative impact will be more severely felt in developing countries, particularly by tose communities igly dependent on natural resources and wit limited capacity to cope wit climate variability and extremes. Climate cange increases te vulnerability of poor people by adversely affecting teir ealt and liveliood, tus undermining economic growt opportunities (Hulme, 2001; Davinson et al., 2003; Fields, 2005). Te warming in Africa is expected to exceed te global mean temperature increase. Drier subtropical regions, suc as Gana, are expected to warm more tan moister tropics. Given te drougts experienced by te Saara, Sael and Guinean Coast in te 1970s and 1980s, furter drying and decreases in rainfall are most likely (IPCC, 2007b). A climate cange scenario used by van Boxel (2004) indicates tat tere will be a temperature increase of about 1.5 to 2.5 ºC in Sub-Saaran West Africa. Te contribution of Working Group II to te Fourt Assessment Report of te Intergovernmental Panel on Climate Cange (IPCC) concentrated teir efforts to define vulnerability to climate cange, igligting te need to improve te definition and empirical researc on vulnerability to climate cange in Africa (IPCC, 2007b). Tis was 2

3 considered to be key in order to developed strategies for adaptation and mitigation of its adverse impact on te lives of te poor. CONCEPTUAL FRAMEWORK AND PROPOSED METHOD Altoug related, vulnerability and poverty are different concepts. Moser (1998:3) found tat altoug poor people are usually among te most vulnerable, not all vulnerable people are poor. Vulnerability is a dynamic concept, as it allows for canging processes and circumstances (Lipton & Maxwell, 1992; Moser, 1998). Cauduri, Jlan and Suryaani (2002) pointed out tat vulnerability is te ex ante risk tat ouseolds will be poor, if not so currently, and if tey are currently poor, te risk tat tey will remain poor. Poverty is terefore an ex post measure of wellbeing, and vulnerability an ex-ante measure (Hoddinot & Quisumbing, 2003a, 2003b; Cauduri, Jlan & Suryaani, 2002; Cristiaensen & Subbarao, 2001; Kamanou & Murduc, 2002). In a vulnerability framework, te poor are considered to be active agents, and interventions can build on tose strengts. Measurements of vulnerability usually include bot te sensitivity, wic is te extent of te response, and te resilience, wic is te ability to recover, of economic units to a sock (Ligon & Scecter, 2003; Kamanou & Morduc, 2002; Hulme et al. 2001). Undertaking a vulnerability assessment can be complicated due to bot te multiple definitions of vulnerability and te scarcity of data useable for measuring vulnerability. Te feasibility of applying a particular approac is often dictated by data availability, and most analyses ave ad to rely on cross-sectional data (Hoddinot & Quisumbing, 2003b). A number of approaces for empirically measuring vulnerability to climate cange are possible. One approac is te sustainable liveliood framework, wic provides a socialconceptual approac to examining te ways in wic agricultural researc and tecnologies fit into te liveliood strategies of ouseolds or individuals wit different types of assets and resources (Adato & Meinzen-Dick, 2002). It recognizes tat ouseolds and individuals may pursue multiple strategies, eiter sequentially or 3

4 simultaneously. It suggests te consideration of an asset portfolio of five different types of assets, wic are natural capital, pysical capital, financial capital, uman capital, and social capital. Moser (1998, 2007) explained tat te cannels of resistance are te assets and entitlements tat can be managed in te face of ardsip, noting tat Vulnerability is closely linked to asset ownersip. Te more assets people ave, te less vulnerable tey are, and te greater te erosion of people s assets, te greater teir insecurity (Moser, 1998: 3). An alternative approac is used by micro-economists. It developed from an understanding of te difference between te concepts of vulnerability and poverty. Te metod explicitly models te unexplained ouseold components (i.e. idiosyncratic ouseold component, error term) in order to include an eterogeneous ouseold sock (Elbers et al. 2002; Hoddinot and Quisumbing, 2003a, 2003b; Cauduri, Jlan and Suryaani, 2002; Cristiaensen and Subbarao, 2001; Kamanou and Murduc, 2002). Cauduri, Jalan and Suryaadi (2002) noted tat modelling te eteroskedasticity of te disturbance term as implication for te economic analysis. In oter words eac ouseold as a different probability ( risk ) to incur in a climate related extreme wic does not depend only from te individual and ouseold level caracteristics but also depend on te caracteristics of te wic eac ouseold sare. Tis researc aims to extend a metodology developed by 'micro-economists and explicitly models te unexplained ouseold components (i.e. idiosyncratic ouseold component, error term) using Geograpical Information System tecnique. Using data from te fift round of te Gana Living Standard Survey and detailed maps of rainfall variability and soil types, te researc attempts to model te eterogeneous ouseold sock, provides a profile of te most vulnerable and also igligts te gains obtained from tis approac. 4

5 DATA Tis researc will use ouseold level data on living standards from te fift round of te Gana Living Standards Survey (GLSS 5) 2005 collected from te Gana Statistical service in collaboration wit te World Bank. Te GLSS 5 contains information on te demograpic caracteristics of ouseold members, teir reported ealt status, education, employment, ousing, and income from wages, business activities, and agricultural production, and detailed records of consumption and expenditure data. Furtermore, parallel to te GLSS survey a community questionnaire was also administered to community leaders on services and infrastructures available in te population point. In addition to tese data, tis researc will use data on rainfall variability obtained from te Gana Meteorological service and soil type obtained from LandSat image of te country. METHOD- draft Assessing ouseold vulnerability to poverty from cross-sectional data Adapted from Cauduri, Jlan and Suryaani, 2002: ln c = χ β + e (1) e idiosyncratic sock tat te ouseold will experience in te future te variance of e depend upon ouseold observable caracteristics distributed in some parametric way 2 σ χ θ e, = tis can be estimated wit a 3 step Feasible Generalized Least Square (FGLS) [ c χ ] E ln V v [ ln χ ] c = P r 2 = χ β = σ e, = χ θ ( ln c ln z χ ) ln z χ β = Φ χ θ v is te probability tat an ouseold will be poor= vulnerability 5

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