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This paper investigates the effect of household size, and in particular of the number of children of different age groups, on poverty, defined as being in a situation of low income. We apply various static and dynamic probit models to control for the endogeneity of the variables of interest and to account for unobserved heterogeneity, state dependence, and serially correlated error components. Using Luxembourg longitudinal data, we show that the number of children of different age groups significantly affects the probability of being poor. However, the magnitude of the effect varies across different specifications. In addition, we find strong evidence of true poverty persistency due to past experience, spurious poverty persistency due to individual heterogeneity, and transitory random shocks.

We consider the link between poverty and subjective well-being, and focus in particular on the role of time. We use panel data on 49,000 individuals living in Germany from 1992 to 2012 to uncover three empirical relationships. First, life satisfaction falls with both the incidence and intensity of contemporaneous poverty. Second, poverty scars: those who have been poor in the past report lower life satisfaction today, even when out of poverty. Last, the order of poverty spells matters: for a given number of years in poverty, satisfaction is lower when the years are linked together. As such, poverty persistence reduces well-being. These effects differ by population subgroups.

In this paper, I examine the concept of ‘vulnerability’ within the context of income mobility of the poor. While the concept of poverty is well developed, the concept of vulnerability is less established in the economic literature. I test for the dynamics of vulnerable households in the United Kingdom using Waves 1–12 of the British Household Panel Survey and find that, of three different types of risks for which I test, household-specific shocks and economy-wide aggregate shocks have the greatest impact on consumption, in comparison to shocks to the income stream. I find vulnerable households up to at least 10 percentile points above the poverty line. Savings and earnings from a second job are not significantly associated with smoothing consumption of all vulnerable households. The results strongly indicate that income transfers and benefits assist the vulnerable in smoothing consumption. Thus, traditional poverty alleviating policies are not likely to assist the vulnerable.

This paper argues that the assumption of a homogeneous workforce, which is implicitly invoked in the decomposition analysis of changes in welfare indicators, hides the role that schooling and its returns may have on the understanding of these changes. Using Peruvian cross-sectional data for a period of 10 years (2004–2013) and counterfactual simulations, this paper finds that the main factor contributing to poverty reduction has been individuals’ changes in labor earnings, and the role of these changes has been less important in reducing income inequality. The main driving force of reduced income inequality has been the fall in returns to education, which at the same time has been one of the important factors to constraining the period’s remarkable progress in poverty reduction and expansion of the middle class.

In this paper I investigate the nature of the differential in poverty by ethnicity in rural China using data from the Chinese Household Income Project in 2002. For that, I compare observed poverty with that in a counterfactual distribution in which ethnic minorities are given a set of relevant village and household characteristics of the Han majority. In particular, I investigate the importance of the location of minorities in explaining their higher poverty levels. The ethnic poverty differential does not change after equalizing the distribution of the population by geographical region (unless we use a higher poverty line). However, it is reduced after equalizing other locational characteristics of minorities (such as them living in less developed and mountainous areas), their larger number of children, their low education, and their fewer skilled non-agriculture workers. Finally, the ethnic per capita (log) income differential is shown to be higher for higher percentiles, with an increasing role of the geographical region as the main driver of these higher differentials.

Regional minimum income (RMI) schemes have been a fundamental tool for fighting poverty in Spain at a regional level. However, the redistributive power of these schemes has not been as effective as was expected in reducing inequality during the last decades. On the other hand, the introduction of the new ‘Minimum Vital Income’ (MVI) by the Spanish Central Government represents a novel measure for fighting poverty, by guaranteeing minimum incomes from a national perspective as a policy response to the asymmetric impact of the COVID-19 crisis upon income distribution. Using EUROMOD,¹ this paper simulates both the distributional effects of the introduction of the MVI and what the effects on inequality and poverty in each Spanish region would be if the national scheme were to substitute the current regional schemes. The results referring to MVI introduction indicate that inequality and poverty would decrease in all dimensions: incidence, intensity, and inequality among the poor (Foster–Greer–Thorbecke poverty measures). Additionally, the negative effects of RMI elimination would be offset by the positive effects of MVI introduction for most regions, leading us to consider that the simulated scenario entails better redistributive results, as well as additional savings for Spanish Public Accounts, in a context of growing debt. Our findings can provide public policy-makers with useful information about the convenience of fighting poverty at a national level as regards distribution and revenue.

This paper examines several measures of poverty and hardship for the United States to illustrate how a single measure of poverty may identify different groups of people as “in need.” Individuals and families may encounter difficulty meeting needs on many dimensions and there are a variety of measures designed to identify those who experience poverty or difficulty making ends meet. In general, there is agreement that all of the approaches capture different pieces of the puzzle while no single indicator can yield a complete picture. To understand this multidimensional aspect of poverty, several measures are examined in this paper: the official U.S. poverty measure, a relative poverty measure, a new supplemental measure that follows recommendations of the U.S. National Academy of Sciences (NAS), an index of material hardship, a measure of household debt, and responses to a question about inability to meet expenses. This study uses the 2008 panel of the Survey of Income and Program Participation (SIPP) and updates a similar analysis that used the 1996 panel of SIPP (Short, 2005). The SIPP is a longitudinal survey that allows us to examine all of these various indicators for the same people over the period from 2009 to 2010. The study uses regression analysis to assess the relationship among several indicators of economic hardship. Results suggest that an understanding of relationships between various indicators can allow only one indicator of poverty alone to be interpreted more appropriately and used more wisely to target the needs of the disadvantaged.

The relationship between income inequality and polarization is an empirical fact: a change in equality might occur together with a change in polarization. At the same time, polarization might emerge while inequality remains constant. The outcome of this process entails relevant information on the evolution of the income distribution. We exploit the LIS database to perform a relative distribution analysis for six European countries. Our aim is describing how disposable income distributions evolved over time. The results indicate that polarization increased in all the considered countries, being the largest in the United Kingdom and the smallest in Italy. Nonetheless, in all the countries downgrading prevails over upgrading: the relevance of the middle class getting poorer is larger than the one of the middle class getting richer.

In recent years, the global economy has undergone major transformations involving the liberalization of markets for traditional goods, services, and capital flows. This has led to the emergence of a world financial market underpinned by digital platforms, innovative and the rapid growth of integrated digital platforms, integration, investment, economic growth, development, and the potential for poverty reduction, especially, in the Global South and, in particular in sub-Saharan Africa. The goal of this chapter is to investigate the increasing accessibility and relationship between digital (e-economy) financial integration and poverty alleviation since the era of structural adjustment programs in sub-Sahara Africa with Ghana as a case study. The emphasis is on the New Digital Economy (NDE) relative to new sources of data from mobile and ubiquitous Internet connectivity. The processes of digitalization and financial sector integration and inclusion become increasingly contestable, decomposable, and reconfigurable, and the capacity to innovate will be a key success factor in policies geared toward poverty alleviation. The multiple linear regression model and its estimation using ordinary least squares (OLS) is doubtless the most widely used tool in econometrics. It helps to estimate the relation between a dependent variable and a set of explanatory variables. An OLS model for macro data set relative to a regression model is applied to provide the empirical estimations of the increasing accessibility and the relationship between digital financial integration, investment, economic growth, development, and poverty alleviation.

This chapter examines self-assessed health (SAH) data of 29 European countries using Eurostat data for the years 2009 and 2018. It first computes the indices recently introduced by Seth and Yalonetzky (2020) and provides confidence intervals for these indices. The ranking of these countries for the year 2018 is then summarized by Hasse diagrams. The chapter then examines first- and second-order stochastic dominance, based again on the recent work of Seth and Yalonetzky. Here also bootstrap confidence intervals were computed. The ranking of the countries in 2018 is then translated again into Hasse diagrams. It appears that Hungary and Latvia are respectively the poorest and least poor countries, both in 2009 and 2018, in terms of their SAH condition. While countries like Ireland, Luxembourg, Romania and Portugal are in the poorer segment of the distribution of countries, Norway, the United Kingdom, Denmark, the Netherlands and Austria are located on the less poor portion. As expected, the Hasse diagrams show also that there are quite a few instances where some countries cannot be ranked.