Isolation and poverty: the relationship between spatially - PowerPoint PPT Presentation

Isolation and poverty: the relationship between spatially differentiated access to goods and services and poverty Kate Bird, Andy McKay & Isaac Shinyekwa Understanding and Addressing Spatial Poverty Traps. Spier Estate, Stellenbosch, 29 March 2007

Overview • What are spatial poverty traps? • How do they drive and maintain poverty and chronic poverty? • Why develop an index of isolation? • The components of our index • Results: the index applied to Uganda

What are spatial poverty traps? • Spatial poverty traps are where ‘geographic capital’ is low & poverty is high – Geographic capital – natural, physical, political, social and human capital of an area • Conceptual framework (typology) – Remote rural areas (frictional distance, locational disadvantage) – Low potential or marginal areas (ecologically disadvantaged) – Less favoured areas (politically disadvantaged) – Weakly integrated areas (poorly linked and economically disadvantaged)

Why are spatial poverty traps important? • Rural poverty is three times higher (incidence) than urban poverty in SSA, E & SE Asia and Latin America (IFAD, 2001) • Approx 1.8 billion people, most of them poor, live in less-favoured or low potential areas • Multi-dimensional poverty & destitution are strongly concentrated - in spatial poverty traps • Poverty persists in spatial poverty traps even where a country has experienced economic growth and aggregate reductions in the poverty headcount

What drives spatial poverty traps: market & state failures • Market failure – Under-investment – Economic activities which extract resources but fail to deliver pro-poor growth • State failure, inadequate provision of – Institutional, political & governance failures – Inadequate provision of infrastructure – Poor security – Limited attention to developing an enabling environment – Poor basic services (esp. education & health) – Limited social protection

What drives spatial poverty traps: agro-ecology, stigma and exclusion • Agro-ecology – High risk (covariate overlaying idiosyncratic) – Drudgery intense livelihoods – Few opportunities for diversification into higher return activities – Migration - seasonal, circular and permanent • Stigma & exclusion – Marginalisation/ minority groups – ethnicity, language, religion, culture, livelihood group (e.g. pastoralists), habits – Discrimination – Blamed for their own poverty – Poorly connected to elites, poorly represented in national discourse

What drives spatial poverty traps: physical isolation & inadequate infrastructure • Physical isolation & inadequate infrastructure – Why is provision poor? • Less favoured area – socially & politically excluded (weak lobby, stigmatised group) • Can be technically difficult to deliver (remote, rugged terrain) • Can be expensive per head (where population densities are low – they aren’t always) – Implications? • Increases ‘frictional distance’ • Isolation, weak access to markets, goods and services • Bangladesh, significant and substantial impact on living standards; • Peru – spatially differentiated household expenditure and income; • Tanzania – people within 100m of a gravel road earn 1/3 more than the rural average; • Ethiopia – more than 8km from markets, people more likely to withdraw from markets • Nepal – frictional distance (journey time from village to market) significantly reduced subjective well-being – Key infrastructure depends on binding constraint (roads, electricity)

What drives spatial poverty traps: communication, media & ICTs • Communication, media & ICTs – National Radio, national TV stations, national newspapers, landline telephone, cell-phone connectivity, internet access – Why is provision poor? • Role of the state as a provider/ enabler/ regulator • Private sector (limited effective demand?) – Implications? • Isolation from mainstream society, new ideas and technical transfer • Impact for shared values, shared narratives, national unity vs marginalisation • Impact on enterprise and markets (information asymmetries, transaction costs, market fragmentation)

What drives spatial poverty traps: crime & insecurity • Greater problem in remote and isolated areas in many developing countries (e.g. Madagascar – Fafchamps & Moser, 2004) • Why? – Social fragmentation & limited livelihood options mean that it is a real option for youth – Few leisure options/ high alcohol consumption – contributes to drunken brawls/ rape – Ineffective policing (police more likely to harass & brutalise local population than solve crime) – Banditry/ armed insurgents/ terrorist groups • Impact? – Constrained movement (esp. women & girls) – Negative impact on well-being and on livelihoods – Low levels of trust – Risk averse behaviour/ Low levels of investment

Poverty in Uganda • Poverty incidence - 56% (1992), 44% (1997/98), 34% (1999/2000), 38% (2002/03) • Post-conflict bounce-back & coffee boom in early 1990s • Then, broadly spread economic growth, UPE, road-building, decentralisation • Now, increasing inequality, growth only benefiting top 20%, poor may be getting poorer • North and West much poorer than Central and Eastern areas (Centre well connected, East – long-run benefits from coffee boom?) • 2 wave panel - Integrated household survey (1992) & Uganda National Household Survey (1999/2000) – nearly 40% of the 1398 panel households experienced transitory poverty (29.6% moved out of poverty, 10.3% moved into poverty) – 18.9% were chronically poor

The components of our index • Extensive fieldwork in Uganda – collecting different dimensions of isolation at the District level – Ministry of Works (density of feeder roads) – UNDP (data on health and safe water) – Uganda Electricity Transmission Company Ltd (availability of electricity at the District level) – Ministry of Information (data on radio and television stations) – Monitor & New Vision (circulation figures) • Uganda National Household Survey data – Average distances to primary and secondary schools – Average distances to the municipality HQ – Average distances to Kampala • Many new Districts. We used the 47 (1998-99) Districts which formed the strata for the Uganda National Household Survey • BUT some indicators could not be constructed for all Districts – data not present for some districts due to conflict

Constructing the index (1) • Want to look at the relationship between isolation and poverty • Many dimensions of isolation important in their own right – relate to different dimensions of the problem • BUT highly desirable to focus on a limited number of measures of isolation (otherwise will have a ‘woods for trees problem’ and won’t see patterns) • We used factor analysis – widely used in the analysis of Demographic and Health Survey data to define asset quintiles (combining a diverse range of assets which a household may or may not own)

Constructing the index (2) • Constructed 2 indices – Average distance in the District to key amenities and locations (e.g. roads, main town of the District) – Availability of key facilities and amenities within the District (schools, health centres etc) • Focus mainly on the first index • Values of these indicators used to classify the 47 Districts into quartiles

Results: the index applied to Uganda (1) • North & West = more isolated • Centre & East = least isolated • We knew this already. How does this add value? • Significant heterogeneity within regions – Mubende & Nakasonsolo (Central – overall, least isolated) top quartile of isolation – Bushenyi (Western) lowest quartile of isolation • Broad similarities for both indices – but some interesting differences – One Northern District in the top quartile (best provided) for facilities – One Central District in the bottom quartile (least well-provided) for facilities

Results: the index applied to Uganda (2) Isolation & Poverty Dynamics • Applied the index to households in the Uganda 2 wave panel to see whether isolated households are more likely to be chronically poor • We found a strong association between isolation & incidence of consumption poverty – poverty incidence increases with isolation quartile – poverty incidence in the most isolated quartile is more than twice that of the least remote quartile • Similar findings for depth of poverty

Results: the index applied to Uganda (3) Isolation & Poverty Dynamics • People in isolated rural areas more likely to be chronically poor – In the most isolated quartile, twice as many households are chronically poor – In the least isolated quartile, hhs are much less likely to have been poor in either of the two periods – Likelihood of escaping poverty decreases systematically with isolation – Likelihood of school age children not being in school increases with isolation (much higher in most isolated quartile) – Use of protected drinking water falls with isolation – Access to electricity falls with isolation – Likelihood of being involved in non-agricultural activity falls with isolation (strong and systematic pattern) – important implication for exit from poverty