Spatial vertical regression for spatial panel data: Evaluating the effect of the Florentine tramway's first line on commercial vitality

G.Grossi, A.Mattei, G.Papadogeorgou

Synthetic control methods are commonly used in panel data settings to evaluate the effect of an intervention. In many of these cases, the treated and control units correspond to spatial units such as regions or neighborhoods. Our approach addresses the challenge of understanding how an intervention applied at specific locations influences the surrounding area. Traditional synthetic control applications may struggle with defining the effective area of impact, the extent of treatment propagation across space, and the variation of effects with distance from the treatment sites. To address these challenges, we introduce Spatial Vertical Regression (SVR) within the Bayesian paradigm. This innovative approach allows us to accurately predict the outcomes in varying proximities to the treatment sites, while meticulously accounting for the spatial structure inherent in the data. Specifically, rooted on the vertical regression framework of the synthetic control method, SVR employs a Gaussian process to ensure that the imputation of missing potential outcomes for areas of different distance around the treatment sites is spatially coherent, reflecting the expectation that nearby areas experience similar outcomes and have similar relationships to control areas. This approach is particularly pertinent to our study on the Florentine tramway's first line construction. We study its influence on the local commercial landscape, focusing on how business prevalence varies at different distances from the tram stops. 

Developing a Synthetic Socio-Economic Index through Autoencoders: Evidence from Florence's Suburban Areas

G.Grossi, E. Rocco

The interest in summarizing complex and multidimensional phenomena often related to one or more specific sectors (social, economic, environmental, political, etc.) to make them easily understandable even to non-experts is far from waning. A widely adopted approach for this purpose is the use of composite indices, statistical measures that aggregate multiple indicators into a single comprehensive measure. In this paper, we present a novel methodology called AutoSynth, designed to condense potentially extensive datasets into a single synthetic index or a hierarchy of such indices. AutoSynth leverages an Autoencoder, a neural network technique, to represent a matrix of features in a lower-dimensional space. Although this approach is not limited to the creation of a particular composite index and can be applied broadly across various sectors, the motivation behind this work arises from a real-world need. Specifically, we aim to assess the vulnerability of the Italian city of Florence at the suburban level across three dimensions: economic, demographic, and social. To demonstrate the methodology's effectiveness, it is also applied to estimate a vulnerability index using a rich, publicly available dataset on U.S. counties and validated through a simulation study. 

The policy is always greener: impact heterogeneity of Covid-19 vaccination lotteries in the US

G.Grossi

Covid-19 vaccination has posed crucial challenges to policymakers and health administrations worldwide. In addition to the pressure posed by the pandemic, government administration has to strive against vaccine hesitancy, which seems to be considerably higher concerning previous vaccination rollouts. To increase the vaccination protection of the population, Ohio announced a monetary incentive as a lottery for those who decided to vaccinate. This first example was followed by 18 other states, with varying results. In this paper, we want to evaluate the effect of such policies within the potential outcome framework, using the penalized synthetic control method. We treat with a panel dataset and estimate causal effects at a disaggregated level in the context of staggered treatment adoption. We focused on policy outcomes at the county, state, and supra-state levels, highlighting differences between counties with different social characteristics and time frames for policy introduction. We also studied the nature of the treatment effect to see whether the impact of these monetary incentives was permanent or only temporary, accelerating the vaccination of citizens who would have been vaccinated in any case. 

Paper

Synthetic control method in presence of Interference: the direct and spillover effect of light rail on neighborhood retail activities

G.Grossi, P.Lattarulo, M.Mariani, A.Mattei, O.Oner

In recent years, Synthetic Control Group (SCG) methods have received great attention from scholars and have been subject to extensions and comparisons with alternative approaches for program evaluation. However, the existing methodological literature mainly relies on the assumption of non-interference. We investigate the use of the SCG method in panel comparative case studies where interference between the treated and the untreated units is plausible. We frame our discussion in the potential outcomes approach. Under a partial interference assumption, we formally define relevant direct and spillover effects. We also consider the "unrealized" spillover effect on the treated unit in the hypothetical scenario that another unit in the treated unit's neighborhood had been assigned to the intervention. Then we investigate the assumptions under which we can identify and estimate the causal effects of interest, and show how they can be estimated using the SCG method. We apply our approach to the analysis of an observational study, where the focus is on assessing direct and spillover causal effects of a new light rail line recently built in Florence (Italy) on the retail density of the street where it was built and of the streets in the treated street's neighborhood. 

Paper Slides

Bayesian Synthetic Control with Spatially Augmented Priors

Giulio Grossi, Leo Vanciu and Falco J. Stoffi Bargagli 

Earmarking Taxation and Compliance: Some Evidence from Car Ownership in Italy

 Lory Barile, Giulio Grossi, Patrizia Lattarulo and Maria Grazia Pazienza

Bayesian Principal Stratification with longitudinal data and truncation by death

G.Grossi,, M.Mariani, A.Mattei, F.Mealli 

Self-enterprise programs are used to promote youth and female employment while stimulating the formation of new businesses and jobs. In this study we are going to analyze the results of the Tuscany region's "Fare Impresa" program, facing with treatment evaluation in presence of censored outcomes. In many causal studies, outcomes are 'censored by death,' in the sense that they are neither observed nor defined for units who die. In order to estimate causal quantities in these contexts, Frangakis and Rubin, 2002 propose the principal stratification approach, which defines partitions of the units in latent groups (the principal strata). In such studies, focus is usually on the stratum of 'always survivors' up to a single fixed time s. Building on a recent strand of the literature, we propose an extended framework for the analysis of longitudinal studies, where units can die at different time points, and the main endpoints are observed and well-defined only up to the death time. We develop a Bayesian longitudinal principal stratification framework, where units are cross-classified according to the longitudinal death status. Under this framework, focus is on causal effects for the principal strata of units that would be alive up to a time point s irrespective of their treatment assignment, where these strata may vary as a function of s. We can get precious insights on the effects of treatment by inspecting the distribution of baseline characteristics within each longitudinal principal stratum, and by investigating the time trend of both principal stratum membership and survivor-average causal effects. 

Fighting Tax Evasion with intergovernemental cooperation: direct and indirect effects

A.Angeli, G.Grossi, P.Lattarulo, M.G.Pazienza

Our study focuses on a cooperation policy between the Revenue Agency and municipalities, devoted to tackling tax evasion. Statements proposed by many international public advisors and scholars focus on the collaboration among institutions as a strategy to contrast evasion. Indeed, institutional collaborative governance is an acknowledged approach in public administration to pursue collective goals in an interdependent multilevel framework. Nonetheless, only little empirical evidence is provided by scholars on the effects of this policy, either on the recovered taxes or on the tax compliance.

 A cooperation policy between Italian Revenue Agency and municipalities to detect evasion of central taxes started in Italy in 2009 and is currently ongoing. We found that the Agency’s successful auditing effort can be an incentive for local administrations to take part in the reporting activity, just as a lack of audits by the tax offices can be a deterrent. Moreover, the growing experience in detecting national taxes that characterize those municipalities participating to the policy for consecutive years results in more qualified reporting activity and growing tax recovery. This represents a case of successful “learning by doing” activity. Our findings suggest that increasing experience in detecting national taxes extends to other municipal revenues and continues in the subsequent years. Policies to tackle tax evasion do not only lead to an immediate tax recovery but induce higher tax compliance that remains in the following years. This implies a most needed benefit for the community, as in fact, it is widely shared among scholars the key role of wide and long-term changes in the individual and institutional attitude toward tax evasion