Identifying Asset Poverty Thresholds New methods with an application to Pakistan and Ethiopia

Understanding how households escape poverty depends on understanding how they accumulate assets over time. Therefore, identifying the degree of linearity in household asset dynamics, and specifically any potential asset poverty thresholds, is of fundamental interest to the design of poverty reduction policies. If household asset holdings converged unconditionally to a single long run equilibrium, then all poor could be expected to escape poverty over time. In contrast, if there are critical asset thresholds that trap households below the poverty line, then households would need specific assistance to escape poverty. Similarly, the presence of asset poverty thresholds would mean that short term asset shocks could lead to long term destitution, thus highlighting the need for social safety nets. In addition to the direct policy relevance, identifying household asset dynamics and potential asset thresholds presents an interesting methodological challenge to researchers. Potential asset poverty thresholds can only be identified in a framework that allows multiple dynamic equilibria. Any unstable equilibrium points would indicate a potential poverty threshold, above which households are expected to accumulate further and below which households are on a trajectory that makes them poorer over time. The key empirical issue addressed in the paper is whether such threshold points exist in Pakistan and Ethiopia and, if so, where they are located. Methodologically, the paper explores what econometric technique is best suited for this type of analysis. The paper contributes to the small current literature on modeling nonlinear household welfare dynamics in three ways. First, it compares previously used techniques for identifying asset poverty traps by applying them to the same dataset, and examines whether, and how, the choice of estimation technique affects the result. Second, it explores whether other estimation techniques may be more suitable to locate poverty thresholds. Third, it adds the first study for a South Asian country and makes a comparison with Ethiopia. Household assets are combined into a single asset index using two techniques: factor analysis and regression. These indices are used to estimate asset dynamics and locate dynamic asset equilibria, first by nonparametric methods including LOWESS, kernel weighted local regression and spline smoothers, and then by global polynomial parametric techniques. To combine the advantages of nonparametric and parametric techniques - a flexible functional form and the ability to control for covariates, respectively - the paper adapts a mixed model representation of a penalized spline to estimate asset dynamics through a semiparametric partially linear model. This paper identifies a single dynamic asset equilibrium with a slightly concave dynamic asset accumulation path in each country. There is no evidence for multiple dynamic equilibria. This result is robust across econometric methods and across different ways of constructing the asset index. The concave accumulation path means that poorer households recover more slowly from asset shocks. Concavity also implies that greater initial equality of assets would lead to higher growth. Moreover, the dynamic asset equilibria are very low. In Pakistan it is below the average asset holdings of the poor households in the sample. In Ethiopia, the equilibrium is barely above the very low mean. This, together with the slow speed of asset accumulation for the poorest households, suggests that convergence towards the long run equilibrium may be slow and insufficient for rural households in Pakistan and Ethiopia to escape poverty.Poverty dynamics, Semiparametric Estimation, Penalized Splines, Pakistan, Ethiopia, Consumer/Household Economics, I32, C14, O12,

“Poor stays poor” - Household asset poverty traps in rural semi-arid India

Although identifying the existence and the nature of household-level poverty traps would have important implications for the design of poverty reduction policies empirical evidence is still scant. A small, but growing empirical literature has begun testing for poverty traps as thresholds in non-linear welfare dynamics. Employing a variety of quantitative methods it has produced a variety of conclusions. This paper uses a uniquely long household panel from three villages in rural India to examine whether the detection of poverty traps may be contingent on the quantitative method used to model household welfare dynamics. It then employs a novel semiparametric panel data estimator that combines the advantages of the existing methods. Since in the context of dynamic poverty traps we are primarily concerned with expected, structural well-being it measures household welfare in assets. Structural immobility in these Indian villages is pervasive. Household asset holdings are stagnant over time. Absent any structural changes, the currently poor are likely to remain poor, suggesting a strong type of poverty trap that is qualitatively different from a dynamic thresholds-type poverty trap. While all types of households face static asset holdings, higher castes, larger landholders and more educated households are significantly less likely to be poor.Household Welfare Dynamics, Semiparametric Estimation, Penalized Splines, India, Panel Data, Asset Poverty, International Development, I32, C14, O12,

A stochastic dominance approach to program evaluation with an application to child nutritional status in arid and semi-arid Kenya

Food Consumption/Nutrition/Food Safety,

Idiosyncratic shocks, risk management and welfare dynamics in rural Ghana

International Development, Risk and Uncertainty,

Targeting maps: An asset-based approach to geographic targeting

Proper targeting of policy interventions requires reasonable estimates of the benefits of the alternative options. To inform such decisions, we develop an integrated approach stemming from the small-area estimation literature that estimates the marginal returns to a range of assets across geographically defined subpopulations. We create a series of maps that can be overlaid with traditional poverty maps to identify strong candidate areas for intervention, though an efficiency/equity tradeoff sometimes exists. We apply our method using recent Ugandan data. Results are consistent with independent empirical findings and suggest asset specific transfer schemes would improve with a spatially targeted strategy

Four Papers on Structural Household Welfare Dynamics

Despite recent progress, abject poverty remains pervasive in many countries around the world. Achieving further sustained reductions in poverty will require more effective poverty eradication policies. The effectiveness of these policies, in turn, depends on how well we understand the structural dynamics of households moving in and out of poverty. The four papers in this dissertation explore several issues in the modeling and measurement of structural household welfare dynamics that to date have received little attention in the academic literature but that are directly relevant for the design of poverty reduction policies. The first paper examines quantitative methods for modeling household welfare dynamics and identifying long-run welfare equilibria and poverty traps. It proposes a new semiparametric panel data estimator that has several advantages over methods used in the extant literature. The empirical application to data from three Indian villages shows deep structural immobility. Structural poverty traps loom large, as rural Indian households who start out asset-poor are likely to remain poor. The second paper proposes and applies a statistical test to examine whether high estimates of economic mobility and transitory poverty in the existing literature are partially driven by stochastic one-off income flows. It finds that these estimates are inversely correlated to the length of the interval between panel observations suggesting that estimates based on short panel spells represent (high) upper bounds of underlying structural economic mobility and (low) lower bounds of chronic poverty. The third paper introduces several new classes of intertemporal poverty measures that can incorporate the variability of household welfare and the distribution of poverty across households over time. Accounting for these intertemporal factors in rural Pakistan leads to greater estimates of poverty than using existing, static poverty indicators. The fourth paper uses a regression-based technique to explore the household characteristics that determine income inequality in rural Pakistan. The level of inequality is determined primarily by land ownership and location. These structural variables are difficult to change by policy, in contrast to the factors that reduced inequality over time, such as access to secondary education and lower dependency ratios

Measuring Poverty Over Time - Accounting for the intertemporal distribution of poverty

Standard poverty measures provide a snapshot of poverty at a single point in time but they cannot capture the distribution of poverty across time. This distribution matters at the individual level where it distinguishes between chronic and transitory poverty and at the aggregate level as it captures economic mobility across the poverty line. This paper proposes three new classes of poverty measures to account for how poverty is distributed across households over time. Each class of measures represents an intertemporal extensions of the standard Foster-Greer-Thorbecke poverty indices. The paper shows that each class of measures is an improvement on the standard practice of applying FGTs directly to intertemporal welfare data. Each class of measures requires choosing a parameter representing society’s preferences towards the intertemporal distribution of poverty. Choosing among them depends on the monitoring and evaluation problem of interest. The applications to panel data from rural Pakistan show that the different methods of accounting for the intertemporal distribution of poverty across households substantially increase estimates of poverty indices suggesting that the standard practice of using snapshot poverty measures underestimates the real level of aggregate poverty

“Poor stays poor” - Household asset poverty traps in rural semi-arid India

Although identifying the existence and the nature of household-level poverty traps would have important implications for the design of poverty reduction policies empirical evidence is still scant. A small, but growing empirical literature has begun testing for poverty traps as thresholds in non-linear welfare dynamics. Employing a variety of quantitative methods it has produced a variety of conclusions. This paper uses a uniquely long household panel from three villages in rural India to examine whether the detection of poverty traps may be contingent on the quantitative method used to model household welfare dynamics. It then employs a novel semiparametric panel data estimator that combines the advantages of the existing methods. Since in the context of dynamic poverty traps we are primarily concerned with expected, structural well-being it measures household welfare in assets. Structural immobility in these Indian villages is pervasive. Household asset holdings are stagnant over time. Absent any structural changes, the currently poor are likely to remain poor, suggesting a strong type of poverty trap that is qualitatively different from a dynamic thresholds-type poverty trap. While all types of households face static asset holdings, higher castes, larger landholders and more educated households are significantly less likely to be poor

Poor stays poor”: Household asset poverty traps in rural semi-arid India, Paper presented at the Joint

Abstract Although identifying the existence and the nature of household-level poverty traps would have important implications for the design of poverty reduction policies empirical evidence is still scant. A small, but growing empirical literature has begun testing for poverty traps as thresholds in non-linear welfare dynamics. Employing a variety of quantitative methods it has produced a variety of conclusions. This paper uses a uniquely long household panel from three villages in rural India to examine whether the detection of poverty traps may be contingent on the quantitative method used to model household welfare dynamics. It then employs a novel semiparametric panel data estimator that combines the advantages of the existing methods. Since in the context of dynamic poverty traps we are primarily concerned with expected, structural well-being it measures household welfare in assets. Structural immobility in these Indian villages is pervasive. Household asset holdings are stagnant over time. Absent any structural changes, the currently poor are likely to remain poor, suggesting a strong type of poverty trap that is qualitatively different from a dynamic thresholds-type poverty trap. While all types of households face static asset holdings, higher castes, larger landholders and more educated households are significantly less likely to be poor