Poverty Dynamics in Europe

The dynamics of poverty in Russia: An analysis in terms of exit and entry Since poverty in Russia is mainly a transitory phenomenon, with many movements in and out, analyzing it necessitates evaluating transition probabilities. Data from the Russian Longitudinal Monitoring Survey and duration models are used to study poverty transitions in Russia between 1994 and 2000. A nonpara- metric method estimates poverty exit, entry and re-entry rates, which depend on how long spells of poverty and nonpoverty last. Logistic discrete-time models are then estimated separately for exits and entries so as to identify the factors associated with poverty transitions. Variables are introduced for taking into account strategic household behaviors; and emphasis is laid on the distinction between promotion and prevention strategies with the aims, respectively, of exiting from, or preventing an entry into, poverty.

While Lithuania is among the countries with the highest at-risk-of-poverty rates in the European Union, povertyresearch has so far been dominated by cross-sectional poverty analysis. This paper is aimed at contributing to better understandingof poverty as a dynamic process in Lithuania by examining poverty risk persistence, transitions and triggers. Theanalysis is based on the longitudinal component of the Survey on Income and Living Conditions. The longitudinal at-risk-ofpovertyrate within the four-year panels is estimated to be at around a third of the Lithuanian population within a period of2005–2012. Poverty risk in Lithuania proved to be both widely spread and persistent. The major role of income events forpoverty risk entries and exits highlights the importance of activation into work, especially as concerns secondary householdearners and better income protection in Lithuania. While no robust evidence of poverty penalty was found for poverty exits,the incidence of poverty re-entries within the initial three-year period after poverty exit substantially exceeds poverty entryrates in the general population.

Poverty Persistence and Transitions in Uganda: A Combined Qualitative and Quantitative Analysis

Exploring the labour productivity of agricultural systems across European regions: A multilevel approach

Longitudinal surveys allow us to understand how markers of (dis)advantage determine present material welfare and economic upward or downward mobility over time. In this paper, we use five waves of panel data to empirically assess the extent and dynamics of poverty in South Africa between 2008 and 2017. Investigating the correlates of poverty entries and exits, we analyse how multidimensional inequalities in terms of household‐ and individual‐level characteristics relate to these dynamics and identify markers of vulnerability. We utilise these markers to classify the South African population into five strata characterised by their present and future risk to poverty.

Research Originality: This study advances the field by employing a comprehensive, multidimensional framework that examines the synergistic interactions between economic, social, educational, and political factors. This study also provides a more nuanced and evidence-based perspective on poverty dynamics, moving beyond the oversimplified, single-dimensional analyses that have dominated prior scholarship. Research Objectives: This study aims to examine the impact of educational participation, crime rates, unemployment, access to Islamic financing, and the democracy index on poverty levels in Indonesia. Research Methods: Panel data from 2015 to 2022 is examined using a fixed effects model to identify the key determinants of poverty. Empirical Results: The findings indicate that higher net enrolment rates and increased access to Islamic financing significantly contribute to poverty reduction y, while rising crime rates and unemployment are associated with increased poverty levels. Interestingly, the democracy index shows no statistically significant effect on poverty during the examined period. Implications: The findings have significant policy relevance, emphasizing the need to enhance access to quality education, promote job creation, and expand Islamic financing as an inclusive economic tool. JEL Classification: I32, E24, G21

Self‐employment and local economic performance: Evidence from US counties

This paper investigates the persistence of poverty over individuals' lifetimes using a hazard rate, or spells approach. Previous research on poverty dynamics using the spells approach has been limited by its failure to take into account multiple episodes of poverty. I estimate hazard models for exiting from and for returning to poverty and use the estimated parameters to calculate distributions of total time spent in poverty over multiple spells, using longitudinal data from the Panel Study of Income Dynamics. These models incorporate observable personal and household characteristics, as well as unobserved heterogeneity. My findings emphasize the importance of considering multiple spells in an analysis of poverty persistence. For black and white individuals falling into poverty in some year, approximately 50 and 30 percent, respectively, will have family income below the poverty line in at least five of the next ten years. A single spells approach predicts comparable figures of only 26 and 13 percent. To check the robustness of these predictions I also utilize two alternative approaches -- direct tabulations from panel data and estimation of a components-of-variance model -- and compare predictions of poverty persistence based on the three methods.

Several studies have shown conceptually that assets form a more robust basis for identifying the poor than do flow variables like expenditures or income. Asset‐based poverty classification can be used to distinguish structural from stochastic poverty and can enable projection of poverty dynamics through time. Nonetheless, little work has empirically compared poverty measurement based on assets and expenditures to indicate the practical implications of the choice of poverty measure. This article uses panel data (between 1994 and 2004) from Ethiopia to generate asset‐ and expenditures‐based poverty classifications. Asset dynamics are then explored to test for the existence of multiple asset index equilibria that could constitute evidence of poverty traps. Results provide evidence of multiple equilibria and show that the asset‐based poverty classifications predict future asset and expenditures poverty status more accurately than expenditures‐based measures. The findings confirm that the asset‐based measure could be used to more carefully target poverty interventions.

This article examines the role of off‐farm employment in poverty reduction over a short period of time by using panel data from 894 rural Ugandan households in 2003 and 2005. Taking advantage of the unique off‐farm labor supply and income data, we analyzed how households respond to negative agricultural shocks, especially through off‐farm labor supply and income to mitigate crop income loss. Our analysis indicates that the flexibility of labor time allocation varies across off‐farm jobs and that only low skilled and low wage jobs tend to be used to mitigate negative shocks, especially among the asset poor.

The geography of education, especially at subnational level, is a huge black box. Little is known about the distribution of educational attainment and inequality across regions in Europe. This paper addresses this gap in the literature by mapping educational attainment and inequality in 102 regions in Western Europe, using data extracted from the European Community Household Panel (ECHP) covering more than 100,000 individuals over the period 1995–2000. The results of this Exploratory Spatial Data Analysis (ESDA) reveal a strong correlation between levels of educational attainment and inequality across regions in Europe. Regions with similar educational conditions tend to cluster, often within national borders. In addition, a North–South and an urban–rural dimension are evident. Northern regions and large European metropoli have not only the most-educated labour force but also the lowest levels of inequality. Educational inequality seems to be, in any case, a fundamentally within-region phenomenon: 90 percent of the educational inequality in Europe occurs among individuals living in the same region.

This article examines quantitative panel analysis techniques appropriate for situations in which the researcher models determinants of change in continuous outcomes. One set of techniques, based on the analysis of pooled time-series data sets, has received little attention in the family literature, although there are a number of situations where these techniques would be appropriate, such as the analysis of multiple-wave panel data. The article explores the fixed effect (change-scare) and random effects models based on pooled time-series data and discusses their advantages and disadvantages for the analysis of survey panel data compared with other available approaches. An empirical example of these methods is presented in which the effect of marital duration and number of children on spousal interaction in intact marriages was examined in a four-wave panel sample. A significant development in the field of family research has been the increased availability of large panel survey data sets containing variables of interest to family researchers (e.g., National Survey of Families and Households Panel, Panel Study of Income Dynamics, National Longitudinal Survey of Youth, Marital Instability Over the Life Course Panel). The potential posed by these panel studies is often unrealized because of the greater complexity of panel analyses and the shortage of clear and accessible guidelines for selecting the appropriate analysis models and statistical software. Several guides to panel methods are available (e.g., Campbell, Mutran, & Parker, 1986; Collins & Horn, 1991; Finkel, 1995; Johnson, 1988; Kessler & Greenberg, 1981; Markus, 1979; Menard, 1991). While these guides can be of value, they have the following drawbacks: Some recent developments are not covered, the focus has been largely on methods for two-wave panels, and situations commonly encountered by family researchers in the analysis of survey data, such as missing waves for some respondents, have been neglected. One type of model commonly found in family research involves the analysis of change over time in a continuous dependent variable. For example, a researcher interested in explaining change in marital interaction over the course of a marriage may model the effects of increased duration of the marriage, changes in the number and ages of the children, spells of employment of both spouses, and changes in family income. This situation involves a continuous dependent variable (degree of spousal interaction) with continuous variables (marital duration, income) and events (addition and subtraction of children, spells of employment and unemployment) as explanatory variables. Panel studies of changes in psychological distress brought about by marital dissolution, changes in frequency of sexual intercourse over the duration of the marriage, and the effect of retirement on marital happiness are research problems with similar analytic needs. With two or more waves of panel data and a continuous dependent variable, the researcher has a choice among five basic panel analysis models. These are (a) regression with lagged dependent variables, (b) structural equation models with reciprocal and lagged effects (e.g., LISREL), (c) repeated measures analysis of variance, (d) growth curve and hierarchical effects models, and (e) fixed (change-score) and random effects regression estimators for pooled time-series data sets. Event history, or hazard, models (Teachman, 1982) and panel models for qualitative variables (Clogg, Eliason, & Grego, 1990) are excluded from this list because they are not designed for use with continuous dependent variables. The first three techniques have been widely used with panel data in the family literature, and are generally accessible to the researcher. Many of the regression techniques have been superseded by the structural equation approaches, which are capable of estimating reciprocal effects and control for biases introduced by measurement errors and autocorrelated errors. …

This article examines poverty dynamics in Ghana using the Ghana Socioeconomic Panel Surveys and a synthetic panel based on the Ghana Living Standards Surveys. It provides insight into the extent of poverty mobility and persistence in Ghana, and the factors that explain poverty transitions. The results show that upward mobility has been a key feature of Ghana's poverty transitions between 2006 and 2019. However, there are still high probabilities of poverty persistence and downward mobility among initially poor and non-poor households, respectively. Furthermore, notable heterogeneities exist in poverty transitions across socioeconomic groups. Poverty is more chronic in rural areas and the northern parts of Ghana compared with urban and southern regions, respectively, and among households headed by persons from opportunity-deprived backgrounds. Other characteristics such as the number of dependants, sex, occupation and level of education of the household head are important correlates of poverty persistence and downward mobility in Ghana. Hence, addressing chronic poverty requires targeted policies that foster more inclusive and sustainable growth in rural areas and northern parts of Ghana, and improved access to opportunities for people from disadvantaged backgrounds, the unemployed, and those in vulnerable employment.

We investigate the factors underlying poverty transitions in Nairobi’s slums focusing on whether differences in characteristics make people more prone to enter poverty and persist in, or whether past experience of poverty matters on future states. Understanding these issues is essential for the design of effective policy programs aimed at enhancing the lives of the poor. The paper uses an endogenous switching model, which accounts for initial conditions, non-random attrition, and unobserved heterogeneity.Estimations are based on a panel dataset from the Nairobi Demographic Surveillance System. Results indicate that true state dependence (TSD) constitutes the major factor driving poverty persistence. There are little heterogeneity effects. Even when household and individual observed characteristics differ notably, the TSD size remains very large. Active anti-poverty programs aimed at breaking the cycle of poverty constitute then the most appropriate policies for taking people out of poverty and preventing them to fall back in.

Poverty Dynamics in Clusters of European Union Countries: Related Events and Main Determinants