Relative deprivation and the differential effects of the COVID-19 pandemic on the poor in Mexico

One of the main challenges to studying the effects of the pandemic on poverty has been the lack of adequate data. Very few countries have short-term poverty data and most official surveys are rather rigid and were incapable of adding any special modules to jointly study COVID-19 and poverty. Furthermore, many poverty measures rely on indicators of unsatisfied basic needs (UBN) that, albeit useful to characterise exclusion from essential services, are not sensitive to short-term effects on the resources of the population. This study uses data from the Encovid-19 to jointly study poverty and some of the consequences of the pandemic on the Mexican population. This survey included a short module to measure poverty using the consensual deprivation approach. Once the poor were reliably identified in the survey, the study analysed differences in coping mechanisms, occupation, and perceived COVID-19 consequences between the poor and the not poor. From the methodological point of view, the study shows how the consensual approach has the advantage of producing valid and reliable poverty figures at a low cost. The findings clearly show that the poor were clearly most affected by the COVID-19 pandemic

Housing, Sanitation and Living Conditions Affecting SARS-CoV-2 Prevention Interventions in 54 African Countries

We acknowledge funding from the UK Research and Innovation Global Challenge Research Fund (GCRF) (Grant Ref: ES/ T010487/1), the Conrad N. Hilton Foundation, the Bill and Melinda Gates Foundation, the Elisabeth Blackwell Institute for Health Research and the Beatriu de Pinós fellowship programme.The feasibility of non-pharmacological interventions (NPIs) such as physical distancing or isolation at home to prevent Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) transmission in low-resource countries is unknown. Household survey data from 54 African countries were used to investigate the feasibility of SARS-CoV-2 NPIs in low-resource settings. Across the 54 countries, approximately 718 million people lived in households with ≥6 individuals at home (median percentage of at-risk households 56% (95% confidence interval (CI), 51% to 60%)). Approximately 283 million people lived in households where ≥3 people slept in a single room (median percentage of at-risk households 15% (95% CI, 13% to 19%)). An estimated 890 million Africans lack on-site water (71% (95% CI, 62% to 80%)), while 700 million people lacked in-home soap/washing facilities (56% (95% CI, 42% to 73%)). The median percentage of people without a refrigerator in the home was 79% (95% CI, 67% to 88%), while 45% (95% CI, 39% to 52%) shared toilet facilites with other households. Individuals in low-resource settings have substantial obstacles to implementing NPIs for mitigating SARSCoV-2 transmission. These populations urgently need to be prioritized for COVID-19 vaccination to prevent disease and to contain the global pandemic

Multiple Deprivation, Severity and Latent Sub-Groups:Advantages of Factor Mixture Modelling for Analysing Material Deprivation

Material deprivation is represented in different forms and manifestations. Two individuals with the same deprivation score (i.e. number of deprivations), for instance, are likely to be unable to afford or access entirely or partially different sets of goods and services, while one individual may fail to purchase clothes and consumer durables and another one may lack access to healthcare and be deprived of adequate housing . As such, the number of possible patterns or combinations of multiple deprivation become increasingly complex for a higher number of indicators. Given this difficulty, there is interest in poverty research in understanding multiple deprivation, as this analysis might lead to the identification of meaningful population sub-groups that could be the subjects of specific policies. This article applies a factor mixture model (FMM) to a real dataset and discusses its conceptual and empirical advantages and disadvantages with respect to other methods that have been used in poverty research . The exercise suggests that FMM is based on more sensible assumptions (i.e. deprivation covary within each class), provides valuable information with which to understand multiple deprivation and is useful to understand severity of deprivation and the additive properties of deprivation indicators

Towards an EU measure of child deprivation

This paper proposes a new measure of child material and social deprivation (MSD) in the European Union (EU) which includes age appropriate child-specific information available from the thematic deprivation modules included in the 2009 and 2014 waves of the “EU Statistics on Income and Living Conditions” (EU‑SILC). It summarises the main results of the in-depth analysis of these two datasets, identifies an optimal set of robust children MSD items and recommends a child‑specific MSD indicator for use by EU countries and the European Commission in their regular social monitoring. In doing this, the paper replicates and expands on the methodological framework outlined in Guio, Gordon and Marlier (2012), particularly by including additional advanced reliability tests

Revising the EU material deprivation variables

In March 2017, the European Union (EU) adopted a new indicator of ‘material and social deprivation’. This measure was developed by Guio et al (2012) and covers the entire population of the 28 EU Member States. It includes 13 deprivation items and replaces the 9-item ‘standard’ material deprivation index adopted in 2009, by the then 27 EU countries and the European Commission, to monitor progress in the fight against poverty and social exclusion at national and EU level. Drawing on the methodology developed in the context of the 1999 ‘Poverty and Social Exclusion in the UK Survey’, Guio, Gordon and Marlier (2012) proposed an analytical framework for producing a suitable, valid, reliable and additive deprivation measure for the EU. Their recommendations were based on analyses of the 2009 EU-SILC material deprivation module. This report extends these analyses using the 2014 EU-SILC data and demonstrates that the composition of the new material and social deprivation indicator remains optimal over a five year period of considerable socio-economic chang