Quality of government for environmental well-being? Subnational evidence from european regions (with Andrea Vaccaro, and Chiara Gigliarano). 2023. Submitted. [ Abstract ]
This study investigates the relationship between quality of government and environmental wellbeing in 233 European regions at the NUTS-2 level. We find that subnational environmental data is spatially interdependent and construct a set of composite indicators of environmental wellbeing through Bayesian spatial factor analysis. By using these composite indicators in linear regressions, we demonstrate that institutional quality is a key determinant of environmental wellbeing. We also find that the institutions-environment nexus varies across dimensions of environmental wellbeing – institutions matter especially for air and soil quality. Policymakers should be aware that environmental destruction can be tackled by building more effective regional institutions.
Depression in old age has negative individual and societal consequences. With ageing populations, understanding life course factors that raise the risk of clinical depression in old age may reduce healthcare costs and guide resources allocation. In this paper, we estimate the risk of self-reported depression by combining adult life course trajectories and childhood conditions in supervised machine learning algorithms. Our contribution is threefold. Using data from the Survey of Health, Ageing and Retirement in Europe (SHARE), we first implement and compare the performance of six alternative machine learning algorithms. Second, we analyse the performance of the algorithms using different life-course data configurations. While we obtain similar predictive abilities between algorithms, we achieve the highest models' performance when employing high-dimensional and less structured data. Finally, we use the SHAP (SHapley Additive exPlanations) method to extract the most decisive depressive patterns by gender. Age, health, childhood conditions, and low education predict most depression risk later in life. In addition, we identify new predictive patterns in high-frequency emotion-enhancing life events and low utilization of dental care services.
Work in progress
The trickle down effects of parental retirement on adult children well-being: evidence from the UK (with Andrew Clark). 2023. [ Abstract ]
This paper explores the causal effect of parental retirement on adult children's well-being, an area primarily overlooked in current literature. As societies age and retirement rates increase, policymakers concerned with the financial sustainability of pension systems must comprehend these ripple effects. We capitalize on the UK eligibility age for the State Pension and the provisions of the UK 1995 and the UK 2011 Pension Acts, which increased retirement ages to a great extent. Fuzzy Regression Discontinuity Design estimates show maternal retirement increases adult children's life and income satisfaction by 0.22 and 0.19 standard deviations in the short run. Difference-in-differences estimate reveals that paternal retirement negatively impacts life and income satisfaction by 0.11 and 0.08 standard deviations. The well-being response is most significant for adult children in low-income bands, with childcare responsibilities, and living in close geographic proximity to their parents.
Small pictures, big biases: the adverse effects of an Airbnb anti-discrimination policy (with Julio Garbers). 2023. [ Abstract ]
Using scraped data from the Airbnb platform in New York City alongside innovative face classification algorithms, we show that Black minority hosts have a 6-percentage-point lower occupancy rate compared to their white counterparts, with no pricing differences. For Asians and Hispanics, no significant variations were found. Second, we examine the effect of an Airbnb design change, which reduces the prominence of profile pictures on users' personal pages. Difference-in-differences design and event-study methods reveal this intervention does not narrow the occupancy rate gap. Instead, it amplifies the White-Black disparity by 4 percentage points in the short run. Supporting this finding, we observe that Black hosts negatively affected by the design transformation reacted by offering more basic amenities for their listings immediately after the design transformation. Our research indicates that reducing picture prominence exacerbates biases in detecting positive facial cues from profile pictures, disproportionately impacting Black minority hosts.
Pre-PhD Papers
The resilience of the Italian social protection system at the beginning of COVID-19 outbreak: territorial evidence (with Francesco Figari, Carlo Fiorio, Luca Gandullia). 2020 [ Abstract | Link] Published at Journal of Economic Policy.
The article provides a first quantification of the redistributive effects of automatic stabilizers and discretional policies imposed by the Italian government to limit the diffusion of COVID-19 in March 2020 and to compensate for income losses of individuals affected by the shutdown. In particular, we analyse the short term impact on family incomes, using the Italian module of EUROMOD which allow us to simulate the effects on incomes, poverty risks and inequality based on IT-SILC data combined with relevant information needed to identify the workers affected by the shutdown. The article provides timely evidence of the resilience of the Italian welfare state in the different geographical areas of the country facing an asymmetric shock, particularly strong from an economic perspective for some families and less for others even in the presence of compensative policies introduced by the government.
Spatial comprehensive well-being composite indicators: evidence from Italian provinces (with Chiara Gigliarano). 2023. R&R at Social Indicators Research. [ Abstract | Draft ]
This paper proposes spatial comprehensive composite indicators to evaluate the wellbeing levels and ranking of Italian provinces with data from the Equitable and Sustainable Well-Being (BES) dashboard. We use a method based on Bayesian latent factor models, which allow us to include spatial dependence across Italian provinces, quantify uncertainty in the resulting estimates, and estimate data-driven weights for elementary indicators. The results reveal that the inclusion of spatial information changes the resulting composite indicator rankings compared to those produced by traditional composite indicators’ approaches. Estimated social and economic well-being is unequally distributed among southern and northern Italian provinces. In contrast, the environmental dimension appears less spatially clustered, and its composite indicators also reach above average levels in the southern provinces. The time series of well-being composite indicators of Italian macro-areas shows clustering and macro-areas discrimination on larger territorial units.
