Angelo Moretti

Utrecht University | UU · Department of Methodology and Statistics

This article examines the possibilities and pitfalls of using Big Data to address sexual and reproductive health concerns as related to the Sustainable Development Goals (SDGs), paying particular attention to contextual difference in development settings. The global datafication of sexual and reproductive life has taken place at great speed. Howeve...

Large-scale sample surveys are not designed to produce reliable estimates for small areas. Here, small area estimation methods can be applied to estimate population parameters of target variables to detailed geographic scales. Small area estimation for noncontinuous variables is a topic of great interest in the social sciences where such variables...

There is growing interest within National Statistical Institutes in combining available datasets containing information on a large variety of social domains. Statistical matching approaches can be used to integrate data sources through a common set of variables where each dataset contains different units that belong to the same target population. H...

In recent years, the attention to lesbian, gay, bisexual, transgender and intersex (LGBTI) people’ rights from institutions, society and scientific bodies has clearly progressed. Although equal opportunities in employment are promoted within European countries and by the EU legislation, there are still evident discriminations in Europe. Many LGBTI...

Although homopositivity, the attitudinal acceptance of homosexuality, has generally increased across Western societies, there remains considerable homonegativity across certain regions of the world as well as certain demographic and socioeconomic groups. Although previous cross-national research has successfully identified the key factors affecting...

Record linkage brings together information from records in two or more data sources that are believed to belong to the same statistical unit based on a common set of matching variables. Matching variables, however, can appear with errors and variations and the challenge is to link statistical units that are subject to error. We provide an overview...

A variety of data is of geographic interest but is not available at a small area level from large-scale national sample surveys. Small area estimation can be used to estimate parameters of target variables to detailed geographical scales based on relationships between the target variables and relevant auxiliary information. Small area estimation of...

Objectives: Police-recorded crimes are used by police forces to document community differences in crime and design spatially targeted strategies. Nevertheless, crimes known to police are affected by selection biases driven by underreporting. This paper presents a simulation study to analyze if crime statistics aggregated at small spatial scales are...

This paper sheds light on the impact of investor worries about climate change on the pricing of emission (carbon-intensive) and clean (low-emission) stocks. We estimate the carbon risk premium in a cross-section of over 4,800 firms in 21 countries. We do not find evidence of a carbon risk premium when investor worries about climate change are low....

Description Dr. Angelo Moretti, Manchester Metropolitan University, a.moretti@mmu.ac.uk Dr. Adam Whitworth, University of Sheffield, a.whitworth@sheffield.ac.uk Dr. Megan Blake, University of Sheffield, m.blake@sheffield.ac.uk Introduction In the UK many are not food secure. Food security is the ability to consistently afford, access and utilise t...

Spatial microsimulation encompasses a range of alternative methodological approaches for the small area estimation (SAE) of target population parameters from sample survey data down to target small areas in contexts where such data are desired but not otherwise available. Although widely used, an enduring limitation of spatial microsimulation SAE a...

There is growing need for reliable survey-based small area estimates of crime and confidence in police work to design and evaluate place-based policing strategies. Crime and confidence in policing are geographically aggregated and police resources can be targeted to areas with the most problems. High levels of spatial autocorrelation in these varia...

In this article, we aim to stress that the fight of COVID-19 needs clear and timely data collection plans. Without data to support decisions, we can only hope for a fortunate guess. We need synergies between different research communities, policy-makers, Official Statistics, health institutions, and private companies which may provide non-conventio...

Open and crowdsourced data are becoming prominent in social sciences research. Crowdsourcing projects harness information from large numbers of citizens who voluntarily participate in one collaborative project, and allow new insights into people's attitudes and perceptions. However, these data may be affected by a series of biases that limit their...

Police-recorded crimes are used by police forces to map crime patterns and design spatially-targeted strategies. Nevertheless, maps of crimes known to police are affected by selection biases driven by unequal crime reporting rates across social groups. This paper presents a simulation study to analyse the impact of selection biases on crime maps pr...

Public attitudes to welfare are key issues in social policy research and practice given their important roles in shaping demands for different types of welfare policies as well as the political parameters within which those welfare decisions are made by governments. Research into headline trends has shown important hardenings in public attitudes to...

In this article, we aim to stress that the fight of COVID-19 needs clear and timely data collection plans. Without data to support decisions, we can only hope for a fortunate guess. We need synergies between different research communities, policy-makers, Official Statistics, health institutions, and private companies which may provide non-conventio...

Open and crowdsourced data are becoming prominent in social sciences research. Crowdsourcing projects harness information from large crowds of citizens who voluntarily participate into one collaborative project, and allow new insights into people’s attitudes and perceptions. However, these are usually affected by a series of biases that limit their...

Factor analysis models are used in data dimensionality reduction problems where the variability among observed variables can be described through a smaller number of unobserved latent variables. This approach is often used to estimate the multidimensionality of well‐being. We employ factor analysis models and use multivariate empirical best linear...

A range of data is of geographic interest but is not available at a small area level from existing data sources. Small area estimation (SAE) offers techniques to estimate population parameters of target variables to detailed scales based on relationships between those target variables and relevant auxiliary variables. The resulting indirect small a...

Worry about crime is known to be higher in some European regions than others. However, cross-national surveys, which are the main source of information to map worry about crime across Europe, are designed to be representative of large areas (countries), and regions often suffer from small and unrepresentative sample sizes. This research produces re...

Small area estimation (SAE) plays a crucial role in the social sciences due to the growing need for reliable and accurate estimates for small domains. In the study of well-being, for example, policy makers need detailed information about the geographical distribution of a range of social indicators. We investigate data dimensionality reduction usin...

This article deals with the use of sample size dependent composite estimators in spatial microsimulation approaches for small area estimation. This approach has been applied to regression-based small area estimation approaches but never to our knowledge to spatial microsimulation approaches. In this paper, we extend the iterative proportional fitti...

This article deals with mean squared error (MSE) estimation of a multivariate empirical best linear unbiased predictor (MEBLUP) under the unit-level multivariate nested-errors regression model for small area estimation via parametric bootstrap. A simulation study is designed to evaluate the performance of our algorithm and compare it with the univa...

The use of spatially correlated random area effects is increasingly in use in small area estimation field. The spatial Empirical Best Linear Unbiased Predictor (SEBLUP), which borrows strength from correlated random area effects between neighbouring areas, have shown to reduce the estimates' variance and bias, both under simulated and real populati...

Using multivariate statistical models in small area estimation (SAE) may improve the efficiency of the small area estimates over the univariate SAE. In this thesis, we study the multivariate SAE problem of multidimensional well-being indicators. We first investigate the univariate EBLUP for a single latent variable estimated through confirmatory fa...