What accounts for spatial patterns of aggregate welfare in rural Uganda?

1. What accounts for spatial patterns of aggregate welfare in rural Uganda? An exploration of spatial determinants of poverty and inequality Todd BensonResearch Fellow, IFPRI Uganda Strategy Support Program (USSP) Inception Workshop Kampala 31 October – 1 November 2005

2. Presentation outline • Motivation and components of study. • Salience and conceptual framework. • Sources of data • small-area welfare estimates • limited set relatively high-resolution spatial data • 149 rural counties. • Methods and results. • Global models of the determinants of welfare estimates at rural county level. • Local models. • Implications • For policies & programs to address poverty. • To refine and strengthen the analysis. 2

3. Context & objective • Context lies in commitment of government of Uganda to poverty eradication. • Seek better understanding of openings for policies and programs to reduce poverty and inequality. • Assess the use (often implicit) of a single, global model of the determinants of poverty across Uganda. • Does such a global model provide a useful understanding for taking action? • Or is relationship between welfare measures and their determinants across rural Uganda spatially non-stationary? 3

4. Conceptual framework to select determinants (independent variables) • Risk chain: Risk or risky events, shocks→ Response, coping strategies→ Welfare outcome. • Provides useful framework to identify potential determinants of the welfare measures being considered here. • Drawn from household economic vulnerability literature. 4

5. Risks faced • Data might include: • Climate patterns. • Floods, other natural disasters. • Variability in agricultural production. • Actual or simulated (spatial crop models) • Dynamics of price surfaces in response to market shocks. • Disease patterns – human, crop, livestock. • Here, orphans – percentage of under-18s in population who have lost at least one parent. • Possible proxy for general health status. 5

6. Responses to risk • Elements of coping strategies employed might include: • Household demographic vars. • Educational attainment. • Access to urban centers, markets, social services, safety nets. • Asset ownership. • Assets diversity, weighted by efficacy in dealing with risk. • Social capital proxies. • Diversity of livelihood strategies employed • Both farm & non-farm. • Here, travel time (hrs.) to Kampala from county centerpoint. 6

7. Welfare outcomes • Risk chain outcomes might include: • Poverty measures. • Mean welfare level. • Poverty headcount. • Severity of poverty. • Inequality measures. • Child malnutrition or health status. • Educational attainment. • Level of conflict. 7

8. Welfare outcomes used • Dependent variables are three welfare measures from a poverty mapping analysis for 1999/2000 of aggregate welfare conditions in Uganda. • Poverty headcount (p0 or prevalence of poverty) • Depth of poverty (p1) • Gini coefficient of consumption inequality. 8

9. Uganda poverty map results • Based on analysis of about 1,100 rural households that appeared in both: • 99/00 Uganda National Household Survey, & • 1992 Uganda Integrated Household Survey. • Applied to data from 1991 Population & Housing Census. • Used a single model of household welfare. • Analysis by Hoogeveen, Emwanu, & Okiira-Okwi (2003). • All data used was compiled by Uganda Bureau of Statistics. 9

10. Independent variables 10

11. Same set of independent variables for all three analyses • Not ideal, but reflects the exploratory nature of analysis. • Determinants primarily chosen with reference to p0. • What these are is better understood for the poverty headcount measure. • For p1 and Gini, less clear. • These measures capture some distributional dimensions of welfare. • Consequently, more problematic understanding of their determinants. 11

12. Methods • Three analyses • Two global models • Assume spatial stationarity across rural Uganda in the relationship between determinants and the welfare measures. • Ordinary Least Squares (OLS) regression. • A naïve, ultimately invalid model. • Spatial-lag maximum-likelihood estimation model • To control for spatial autocorrelation in error term of OLS regression. • Local model. • Geographically weighted regression to produce separate models for each of the 149 counties. 12

13. Ordinary least squares regression • Adj. R2s ranged from 0.61 (p0) to 0.46 (Gini). • About half of ind. variables had significant coefs. • For most, expected sign of relationship to p0 confirmed. • However, strong spatial autocorrelation. • Violates OLS assumption that error terms not correlated. • Model estimates likely to be biased and inefficient. 13

14. Spatial regression models • To control for spatial autocorrelation, include independent variable that represents the spatial dependency. • Spatial lag variable – Wy or Wε. • Mean welfare measure or residual for neighboring counties. • Two possible spatial dependence models: • Spatial lag model: y = ρWy + Xβ + ε • Spatial error model: y = Xβ + ε , where ε = λWε + ε • Choice of which to use is done on an empirical basis. • To define ‘neighborhood’ to compute spatial lag for each observation, used 2nd-order Queen’s spatial weight matrix. • All counties that are contiguous with county of interest, plus the counties that are contiguous with those. 14

15. Global spatial-lag model results • R2s - p0: 0.756; p1: 0.742; Gini: 0.638 • Coefficient on spatial lag (ρ) highly significant in all 3 models. • Other significant variables, with sign: • p0: ACC4 (+); ACC4X4 (-) • Increased p0 with time of travel to Kampala, but at a declining rate. • p1: HHSIZE (+); HFACDIST (-); PSCHDIST (+); LRA_IDP (+) • Increased p1 with higher avg. household size, more distant primary schools, and LRA incursions. Lower p1 with more distant health facilities (!). 15

16. Global spatial-lag model results (cont.) • Gini: DEPEND (-); HFACDIST (-); PSCHDIST (+); LRA_IDP (+) • Increased inequality with more distant primary schools, and LRA incursions. Lower inequality with more dependents in population and more distant health facilities (!). • N.B., these models can be interpreted to reflect global processes that apply across rural Uganda in a relatively invariant fashion. • Results should be of interest to national-level program planners. 16

17. Geographically weighted regression • Global OLS regression model: yi = a0 + Σj xij aj + ε where a - regression coefficient, i - index for the location, j - index for the independent variable, and ε - error term. • Reworked as a local regression model to become: yi = a0i + Σj xij aij + ε in which location dependent coefficients are estimated for each of the 149 counties. • For each county, the neighboring counties whose data is used to estimate model are chosen and weighted using a distance decay spatial weighting function. • Here used data from the 112 nearest neighboring counties. • Analytical weight applied to each based on a Gaussian distance decay function (see figure). 17

18. Flood of results produced for each of the 149 models: R2 , residuals, influence statistics for each model. Coefficients, standard errors, t-statistics for each independent variable in each model. Most easily shown as maps. Here, adjusted R2. Overall R2 - p0: 0.807; p1: 0.831; Gini: 0.856. GWR results (1/4) 18

19. Spatial non-stationarity for about 1/3 of variables in each model. Ind. vars. for dependents, orphans, access to local facilities, and for LRA incursions consistently shown to have spatially non-stationary coefficients across the 3 GWR models. Fewer variables with non-stationary coefficients than expected. Results shown as maps of statistical significance and sign of coefficients for each independent variable. Here dependents as a proportion of county population. Spatial non-stationarity seen in p0 and p1, where positive relationship seen in SW Uganda; negative in northern and eastern. GWR results (2/4) 19

20. Here travel time to Kampala. p0 global spatial-lag model had a significant positive coefficient. Here we see that this result is only seen in about ¼ of the local models. p1 and Gini GWR models reflect the insignificant ACC4 coefficient in their global models. GWR results (3/4) 20

21. Here results for proportion of households more than 5 km from primary schools and health facilities. Saw in global spatial-lag models coefficients had opposite signs. Reflected here, too where many areas with positive sign for one variable have negative sign for other. Model specification problem? GWR results (4/4) 21

22. Conclusions – policy related • Some evidence that poverty-reduction programs in Uganda need to be locally targeted and flexible in design. • Few strong and consistent spatial determinants of these welfare measures found. • Spatial non-stationarity in several determinants. • ‘One size fits all’ approaches to design of development programs unlikely to consistently succeed. • Lends support to efforts to decentralize government in Uganda. • Foster local ownership of development efforts to better reflect local needs, local context, and locally-specific key welfare determinants. 22

23. Conclusions - analytical • Analysis not sufficiently robust at this stage for clear policy guidance. • Specification of models needs more work. • Key omitted variables. • In particular, aggregate human capital (e.g., average educational attainment) and structure of local economy (e.g., non-farm employment levels). • Also variables that better capture distribution of welfare in the population. • Assess interaction effects. • Access to health facilities and primary schools, most notably. • Different analytical geographies. • County is not an administrative unit. Try to run analysis at sub-county and district levels, which are administrative levels. • Need experts on rural Uganda’s physical and social landscape to assess the value of these results and where improvements are possible. 23