Research on Economic Inequality: Poverty, Inequality and Shocks: Volume 29

Wellbeing evaluation using ordered categorical response data is hazardous given the scale dependent nature of most measures of wellbeing and inequality. Here, scale independent instruments for measuring levels of wellbeing and inequalities between groups in multidimensional ordered categorical environments are introduced and applied in a study of health and consumption wellbeing and the aging process in twenty‐first century China. Urban/rural location, gender, age and the availability of welfare support were considered circumstances in what is in essence a study of equality of opportunity in the acquisition of health and consumption wellbeing in Chinas’ aging population. Older populations are found to experience diminished and increasingly diverse wellbeing outcomes that are, to some extent, ameliorated by welfare support.

The growth incidence curve of Ravallion and Chen (2003) is based on the quantile function. Its distribution-free estimator behaves erratically with usual sample sizes leading to problems in the tails. The authors propose a series of parametric models in a Bayesian framework. A first solution consists in modeling the underlying income distribution using simple densities for which the quantile function has a closed analytical form. This solution is extended by considering a mixture model for the underlying income distribution. However, in this case, the quantile function is semi-explicit and has to be evaluated numerically. The last solution consists in adjusting directly a functional form for the Lorenz curve and deriving its first-order derivative to find the corresponding quantile function. The authors compare these models by Monte Carlo simulations and using UK data from the Family Expenditure Survey. The authors devote a particular attention to the analysis of subgroups.

The authors propose a framework to estimate the probability of being poor in a dynamic setting based on a large information set that includes individual characteristics and macro-economic variables. The joint inclusion of personal characteristics along with contextual factors allows separation of idiosyncratic shocks from aggregate shocks affecting poverty. The authors combine data from different cross-sectional surveys and fit a dynamic logistic hierarchical model within a Bayesian framework using standard Markov chain Monte Carlo techniques. The authors’ approach is exemplified by estimating household poverty status in Kyrgyz Republic as a function of time, regions, country, regional level variables and household level socio-demographic characteristics.

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.

In the case of ordered categorical data, the concepts of minimum and maximum inequality are not straightforward. In this chapter, the authors consider the Cowell and Flachaire (2017) indices of inequality. The authors show that the minimum and maximum inequality depend on preliminary choices made before using these indices, on status and the sensitivity parameter. Specifically, maximum inequality can be given by the distribution which is the most concentrated in the top or bottom category, or by the uniform distribution.

Post reform India has generated high economic growth, yet progress in income poverty and many other key development outcomes has been modest. This chapter primarily examines how inclusive economic growth has been in India between 2005–2006 and 2015–2016 in reducing multidimensional poverty captured by the global multidimensional poverty index (MPI). The authors employ a constellation of elasticity and semi-elasticity measures to examine vertical, horizontal as well as dimensional inclusiveness of economic growth. Nationally, the authors estimate that a 1% annual economic growth in India during their study period is associated with an annual reduction in MPI of 1.34%. The association of the national growth to state poverty reduction (horizontal inclusiveness) is, however, not uniform. Some states have been successful in reducing poverty faster than the national average despite slower economic growth between 2005–2005 and 2015–2016; whereas, other states have been less successful to do so despite faster economic growth during the same period. The authors’ analyses and findings show how these tools may be used in practical applications to measure inclusive growth and inform policy.

This chapter proposes a definition of pro-middle class growth derived from the approach of Lasso de la Vega, Urrutia, and Diez (2010) to intermediate polarization. The authors show that a sufficient condition for growth to be pro-middle class is for the growth rate of what we define as the “intermediate median income” of the whole population to be higher than that of the weighted average of the growth rates of the rich and smaller than the weighted average growth rate of the poor, the “rich” and the “poor” being respectively those with an income higher and lower than the median income. An empirical illustration based on Israeli data for the period 1995–2018 indicates that in absolute terms growth was not pro-middle class for any income type. In contrast, growth was pro-middle class in relative terms for all market incomes (individual income from salaried work, individual wage per hour worked, household economic income, total household income and total equivalized income). But growth was not pro-middle class for net income and net equivalized income, even in relative terms. These conclusions appear to be related to the combined effect of developments in labor force participation, welfare policy changes and major modifications in income tax rates. The intermediate polarization measures indicate that in general there was no pro-middle class growth except in the case of specific market income types.

The author proposes analyzing the dynamics of income positions using dynamic panel ordered probit models. The author disentangles, simultaneously, the roles of state dependence and heterogeneity (observed and non-observed) in explaining income position persistence, such as poverty persistence and affluence persistence. The author applies the approach to Chile exploiting longitudinal data from the P-CASEN 2006–2009. First, the author finds that income position mobility at the bottom and the top of the income distribution is much higher than expected, showing signs that income mobility in the case of Chile might be connected to economic insecurity. Second, the observable individual characteristics have a much stronger impact than true state dependence to explain individuals’ current income position in the income distribution extremes.

Using real-time data from the University of Luxembourg’s COME-HERE nationally representative panel survey, covering more than 8,000 individuals across France, Germany, Italy, Spain, and Sweden, the author investigates how income distributions and poverty rates have changed from January to September 2020. The author finds that poverty rates increased on average in all countries from January to May and partially recovered in September. The increase in poverty is heterogeneous across countries, with Italy being the most affected and France the least; within countries, COVID-19 contributed to exacerbating poverty differences across regions in Italy and Spain. With a set of poverty measures from the Foster–Greer–Thorbecke family, the author then explores the role of individual characteristics in shaping different poverty profiles across countries. Results suggest that poverty increased disproportionately more for young individuals, women, and respondents who had a job in January 2020 – with different intensities across countries.

In 2020, household incomes were severely hit by the lockdowns imposed across the world in response to the COVID-19 pandemic. Using data from the last available wave of the euro-area harmonized Household Finance and Consumption Survey for 2016, this chapter documents European households’ financial resilience to this shock, based on pre-shock balance sheets, potential exposure to COVID-19, and in the absence of government interventions. The results highlight that there are large and similar shares of the population across European countries that are likely to suffer from the economic fallout of containment measures – albeit through different channels.

This chapter presents a quantitative description of the living conditions in a slum area of an intermediate Argentinean city during the outburst of the Covid-19 crisis using primary data collected four months after the lockdown measures had been introduced. The sample represents 1,500 households which claimed food assistance over this period, and whose deprivations and presence of young members are similar to that of 13% of the city’s population and 23% of the country’s population. Rough estimates suggest a disproportionate drop in employment and a disproportionate increase in unemployment in the area compared to those registered in the aggregate of the main urban agglomerations of the country. Cash transfers implemented during the lockdown, together with in-kind food aid from schools, the municipal government, and the church with non-governmental organizations, entailed a substantial average increase in the coverage of the cost of the basic food basket. However, non-trivial fractions of households were not covered by any of the main cash transfers. Also, and despite efforts, food insecurity could not be avoided. Considering the similarity of the sample to significant fractions of the country’s urban population, the deprivations experienced over 2020 by groups which were already in poverty before the Covid-19 arrival, raise alarms on the future well-being of these populations, especially for infants and children. Novel policies are required, addressing the various critical needs in an interconnected way, integrating the different stakeholders that have proven to be key in assisting these households during such an unprecedented covariate shock.