Marco Forti
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Marco Forti has a total of 32 co-authors. Here are the top co-authors by number of shared publications:
Andrea Salustri
Department of Legal and Economic Studies, University of Rome La Sapienza, Rome, Italy
3 shared publications- Angelo Castaldo
Sapienza University of Rome
3 shared publications Alessia Marrocco
Sapienza University of Rome
2 shared publications- Maria Alessandra Antonelli
Department of Legal and Economic Studies, Faculty of Law Sapienza University of Rome Rome Italy
2 shared publications - Maria Brigida Ferraro
Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy
2 shared publications - Paolo Giordani
Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy
2 shared publications
Latest Publications
- Open Access
- 27 April 2026
- Published by MDPI AG in Sustainability
- Vol. 18 (9) , 4315
Latina’s Proximity Tourism: A Multidimensional Analysis
The COVID-19 pandemic has renewed interest in proximity tourism as a driver for culturally sustainable local development. Against this backdrop, this study examines the Province of Latina, Italy, evaluating the potential of proximity tourism flows originating from the Metropolitan City of Rome to reduce the socioeconomic and infrastructural disparities that affect the provincial territory. Using a multi-stage quantitative framework, the research identifies key determinants of municipal attractiveness and assesses the structural relationships between tourism activities, economic development, and demographic trends. Results reveal a clear spatial polarization: while coastal municipalities successfully leverage natural and cultural assets for development, they increasingly face challenges related to overtourism and gentrification. Conversely, marginalized hilly and mountainous peripheries remain underdeveloped despite their high heritage value. Empirical evidence suggests that while proximity tourism does not directly drive demographic growth, it acts indirectly as a catalyst by stimulating economic development. The study concludes that proximity tourism can foster territorial cohesion and socioeconomic resilience only when coupled within integrated strategies that improve accessibility, valorize peripheral assets, and keep the added value of tourism embedded within host communities. These findings offer actionable insights for policymakers aiming to transition toward more inclusive and culturally sustainable tourism models in the post-pandemic era.
- 27 April 2026
- Published by MDPI AG in Sustainability
- Vol. 18 (9) , 4315
Open AccessLatina’s Proximity Tourism: A Multidimensional Analysis
The COVID-19 pandemic has renewed interest in proximity tourism as a driver for culturally sustainable local development. Against this backdrop, this study examines the Province of Latina, Italy, evaluating the potential of proximity tourism flows originating from the Metropolitan City of Rome to reduce the socioeconomic and infrastructural disparities that affect the provincial territory. Using a multi-stage quantitative framework, the research identifies key determinants of municipal attractiveness and assesses the structural relationships between tourism activities, economic development, and demographic trends. Results reveal a clear spatial polarization: while coastal municipalities successfully leverage natural and cultural assets for development, they increasingly face challenges related to overtourism and gentrification. Conversely, marginalized hilly and mountainous peripheries remain underdeveloped despite their high heritage value. Empirical evidence suggests that while proximity tourism does not directly drive demographic growth, it acts indirectly as a catalyst by stimulating economic development. The study concludes that proximity tourism can foster territorial cohesion and socioeconomic resilience only when coupled within integrated strategies that improve accessibility, valorize peripheral assets, and keep the added value of tourism embedded within host communities. These findings offer actionable insights for policymakers aiming to transition toward more inclusive and culturally sustainable tourism models in the post-pandemic era.
- 9 October 2025
- Published by Taylor & Francis in Journal of Statistical Computation and Simulation
- Vol. 96 (3) , 563-590
Extending finite mixture models with skew-normal distributions and hidden Markov models for time series
- 9 October 2025
- Published by Taylor & Francis in Journal of Statistical Computation and Simulation
- Vol. 96 (3) , 563-590
Extending finite mixture models with skew-normal distributions and hidden Markov models for time series
- Open Access
- 8 February 2025
- Published by Springer Nature in Annals of Operations Research
- p. 1-24
Fuzzy clustering with $\hbox {L}_0$ regularizationThe Fuzzy K-Means algorithm extends the well-known classical K-Means algorithm by replacing the standard allocation matrix with the membership degree one. Consistently with the fuzzy approach to clustering, this allows for obtaining a soft assignment of the units to the clusters. The units are assigned to the clusters with membership degree taking values in the unit interval. In practice, what we get from Fuzzy K-Means is a fuzzy partition where even the units clearly belonging to only one cluster generally present non-zero membership degrees to all the clusters. In order to overcome this drawback, a generalization of Fuzzy K-Means is proposed where an $$\hbox {L}_0$$ regularization term for the membership degree matrix is introduced. This makes it possible to obtain a sparse membership degree matrix, where the units that clearly belong to one cluster have membership degrees strictly equal to one to the cluster involved and zero to the other clusters, without compromising the soft membership degrees of the units with unclear assignments. The adequacy of the proposal is evaluated by means of simulation and real-case studies.- 8 February 2025
- Published by Springer Nature in Annals of Operations Research
- p. 1-24
Open AccessFuzzy clustering with $\hbox {L}_0$ regularizationThe Fuzzy K-Means algorithm extends the well-known classical K-Means algorithm by replacing the standard allocation matrix with the membership degree one. Consistently with the fuzzy approach to clustering, this allows for obtaining a soft assignment of the units to the clusters. The units are assigned to the clusters with membership degree taking values in the unit interval. In practice, what we get from Fuzzy K-Means is a fuzzy partition where even the units clearly belonging to only one cluster generally present non-zero membership degrees to all the clusters. In order to overcome this drawback, a generalization of Fuzzy K-Means is proposed where an $$\hbox {L}_0$$ regularization term for the membership degree matrix is introduced. This makes it possible to obtain a sparse membership degree matrix, where the units that clearly belong to one cluster have membership degrees strictly equal to one to the cluster involved and zero to the other clusters, without compromising the soft membership degrees of the units with unclear assignments. The adequacy of the proposal is evaluated by means of simulation and real-case studies. - 21 January 2025
- Published by Springer Nature
- p. 28-32
L$_0$-Penalized Membership in Sparse Fuzzy Clustering- 21 January 2025
- Published by Springer Nature
- p. 28-32
L$_0$-Penalized Membership in Sparse Fuzzy Clustering- 28 June 2024
- Published by Emerald Publishing in International Journal of Manpower
- Vol. 45 (8) , 1555-1572
Workplace accidents, economic determinants and underreporting: an empirical analysis in Italy
Purpose: This paper proposes an analysis of occupational accidents in Italy at the regional level. For this purpose, our panel is composed of 20 regions over the 2010–2019 time span.Design/methodology/approach: We apply different econometric estimation techniques (pooled OLS model, panel fixed and random effects models and semiparametric fixed model) using INAIL and ISTAT data. Our models investigate workplace accidents at the regional level by accounting for socioeconomic, labour market and productive system variables and controlling for possible underreporting bias.Findings: Overall results reveal the existence of a relevant under-notification phenomenon of accidents at work with respect to moderate accidents, that is higher especially for the southern regions of Italy. However, when considering as outcome variable an alternative set of more severe workplace accidents our model specification remains highly jointly statistically significant. Among our main findings, the analysis shows that worker skills (blue collar) strongly affect the regional pattern of workplace accidents, i.e. an increase of 1% of low paid employees generates about an increase of 1.8 severe workplace accidents per thousand workers. Moreover, we provide evidence that the size of the firm is inversely related to the occupational accident rates. Finally, our results highlight a nonlinear relationship between GDP and occupational accidents for the Italian regional context, confirmed by the high statistical significance of the quadratic term in all the estimated linear models and by the semi-parametric analysis.Originality/value: A first element of originality of our study consists of investigating the macro determinants of occupation accidents at a regional Italian level. Second, the empirical literature (Boone and Van Ours, 2006) highlights the possible bias of underreporting behaviours on nonfatal accidents in contrast to fatal accidents that are always reported. From this perspective, we have identified a few analyses (namely, Boone et al., 2011) considering different accident sets characterised by different severity degrees. Thus, this paper contributes to the literature considering five alternative subsets of accidents stratified by degree of severity (i.e. moderate, severe, moderate plus severe, severe plus fatal and total accident rates) to test for possible underreporting bias affecting our econometric model.
- 28 June 2024
- Published by Emerald Publishing in International Journal of Manpower
- Vol. 45 (8) , 1555-1572
Workplace accidents, economic determinants and underreporting: an empirical analysis in Italy
Purpose: This paper proposes an analysis of occupational accidents in Italy at the regional level. For this purpose, our panel is composed of 20 regions over the 2010–2019 time span.Design/methodology/approach: We apply different econometric estimation techniques (pooled OLS model, panel fixed and random effects models and semiparametric fixed model) using INAIL and ISTAT data. Our models investigate workplace accidents at the regional level by accounting for socioeconomic, labour market and productive system variables and controlling for possible underreporting bias.Findings: Overall results reveal the existence of a relevant under-notification phenomenon of accidents at work with respect to moderate accidents, that is higher especially for the southern regions of Italy. However, when considering as outcome variable an alternative set of more severe workplace accidents our model specification remains highly jointly statistically significant. Among our main findings, the analysis shows that worker skills (blue collar) strongly affect the regional pattern of workplace accidents, i.e. an increase of 1% of low paid employees generates about an increase of 1.8 severe workplace accidents per thousand workers. Moreover, we provide evidence that the size of the firm is inversely related to the occupational accident rates. Finally, our results highlight a nonlinear relationship between GDP and occupational accidents for the Italian regional context, confirmed by the high statistical significance of the quadratic term in all the estimated linear models and by the semi-parametric analysis.Originality/value: A first element of originality of our study consists of investigating the macro determinants of occupation accidents at a regional Italian level. Second, the empirical literature (Boone and Van Ours, 2006) highlights the possible bias of underreporting behaviours on nonfatal accidents in contrast to fatal accidents that are always reported. From this perspective, we have identified a few analyses (namely, Boone et al., 2011) considering different accident sets characterised by different severity degrees. Thus, this paper contributes to the literature considering five alternative subsets of accidents stratified by degree of severity (i.e. moderate, severe, moderate plus severe, severe plus fatal and total accident rates) to test for possible underreporting bias affecting our econometric model.
- Open Access
- 7 May 2024
- Published by Elsevier in Clinical Neurophysiology
- Vol. 163, 152-159
Quantitative EEG analysis of brivaracetam in drug-resistant epilepsy: A pharmaco-EEG study
- 7 May 2024
- Published by Elsevier in Clinical Neurophysiology
- Vol. 163, 152-159
Open AccessQuantitative EEG analysis of brivaracetam in drug-resistant epilepsy: A pharmaco-EEG study
- 25 January 2024
- Published by Taylor & Francis in Applied Economics Letters
- Vol. 32 (11) , 1526-1533
An alternative approach to understanding occupational safety and health (OSH): an empirical insurance cost perspective in Italy
- 25 January 2024
- Published by Taylor & Francis in Applied Economics Letters
- Vol. 32 (11) , 1526-1533
An alternative approach to understanding occupational safety and health (OSH): an empirical insurance cost perspective in Italy
- Open Access
- 17 April 2023
- Published by Taylor & Francis in Applied Economics
- Vol. 56 (24) , 2931-2946
Drivers and frictions of workplace accidents: an empirical investigation of cross-country European heterogeneity
This paper presents an empirical investigation on the determinants of workplace accidents across Europe and focuses on the extent to which production-system characteristics (employment sectoral risk, size of firms, temporary contracts), business cycle and socio-economic factors (GDP, level of investments, unemployment, education) and other territorial controls (crime index) might account for cross-country heterogeneity. We use Eurostat data, and our panel is composed of 27 European countries over the period 2010–2018. Implementing different functional forms and estimation methodologies (pooled OLS, panel fixed and random effects models, system-GMM and semiparametric fixed effects model), we find robust evidence that productive-system structural characteristics, business cycle controls and the other territorial variables are effective in explaining European cross-country heterogeneity. Moreover, we find evidence of a nonlinear relationship between GDP and occupational accidents.
- 17 April 2023
- Published by Taylor & Francis in Applied Economics
- Vol. 56 (24) , 2931-2946
Open AccessDrivers and frictions of workplace accidents: an empirical investigation of cross-country European heterogeneity
This paper presents an empirical investigation on the determinants of workplace accidents across Europe and focuses on the extent to which production-system characteristics (employment sectoral risk, size of firms, temporary contracts), business cycle and socio-economic factors (GDP, level of investments, unemployment, education) and other territorial controls (crime index) might account for cross-country heterogeneity. We use Eurostat data, and our panel is composed of 27 European countries over the period 2010–2018. Implementing different functional forms and estimation methodologies (pooled OLS, panel fixed and random effects models, system-GMM and semiparametric fixed effects model), we find robust evidence that productive-system structural characteristics, business cycle controls and the other territorial variables are effective in explaining European cross-country heterogeneity. Moreover, we find evidence of a nonlinear relationship between GDP and occupational accidents.
- Open Access
- 17 May 2021
- Published by Oxford University Press (OUP) in Interactive CardioVascular and Thoracic Surgery
- Vol. 33 (4) , 510-517
Impact of gender on 10-year outcome after coronary artery bypass grafting
Our goal was to evaluate the impact of gender on the 10-year outcome of patients after isolated coronary artery bypass grafting (CABG) included in the Italian nationwide PRedictIng long-term Outcomes afteR Isolated coronary arTery bypass surgery (PRIORITY) study. The PRIORITY project was designed to evaluate the long-term outcomes of patients who underwent CABG and were included in 2 prospective multicentre cohort studies. The primary end point of this analysis was major adverse cardiac and cerebrovascular events. Baseline differences between the study groups were balanced with propensity score matching and inverse probability of treatment. Time to events was analysed using Cox regression and competing risk analysis. The study population comprised 10 989 patients who underwent isolated CABG (women 19.6%). Propensity score matching produced 1898 well-balanced pairs. The hazard of major adverse cardiac and cerebrovascular event was higher in women compared to men [adjusted hazard ratio (HR) 1.13, 95% confidence interval (CI) 1.03–1.23; P = 0.009]. The incidence of major adverse cardiac and cerebrovascular event in women was significantly higher at 1 year (HR 1.31, 95% CI 1.11–1.55; P < 0.001) and after 1 year (HR 1.11, 95% CI 1.00–1.24; P = 0.05). Mortality at 10 years in the matched groups was comparable (HR 1.04, 95% CI 0.93–1.16; P = 0.531). Women have significantly a higher 10-year risk of myocardial infarction (adjusted HR 1.40, 95% CI 1.17–1.68; P = 0.002) and percutaneous coronary intervention (adjusted HR 1.32, 95% CI 1.10–1.59; P = 0.003). The present study documented an excess of non-fatal cardiac events after CABG among women despite comparable 10-year survival with men. These findings suggest that studies investigating measures of tertiary prevention are needed to decrease the risk of adverse cardiovascular events among women.
- 17 May 2021
- Published by Oxford University Press (OUP) in Interactive CardioVascular and Thoracic Surgery
- Vol. 33 (4) , 510-517
Open AccessImpact of gender on 10-year outcome after coronary artery bypass grafting
Our goal was to evaluate the impact of gender on the 10-year outcome of patients after isolated coronary artery bypass grafting (CABG) included in the Italian nationwide PRedictIng long-term Outcomes afteR Isolated coronary arTery bypass surgery (PRIORITY) study. The PRIORITY project was designed to evaluate the long-term outcomes of patients who underwent CABG and were included in 2 prospective multicentre cohort studies. The primary end point of this analysis was major adverse cardiac and cerebrovascular events. Baseline differences between the study groups were balanced with propensity score matching and inverse probability of treatment. Time to events was analysed using Cox regression and competing risk analysis. The study population comprised 10 989 patients who underwent isolated CABG (women 19.6%). Propensity score matching produced 1898 well-balanced pairs. The hazard of major adverse cardiac and cerebrovascular event was higher in women compared to men [adjusted hazard ratio (HR) 1.13, 95% confidence interval (CI) 1.03–1.23; P = 0.009]. The incidence of major adverse cardiac and cerebrovascular event in women was significantly higher at 1 year (HR 1.31, 95% CI 1.11–1.55; P < 0.001) and after 1 year (HR 1.11, 95% CI 1.00–1.24; P = 0.05). Mortality at 10 years in the matched groups was comparable (HR 1.04, 95% CI 0.93–1.16; P = 0.531). Women have significantly a higher 10-year risk of myocardial infarction (adjusted HR 1.40, 95% CI 1.17–1.68; P = 0.002) and percutaneous coronary intervention (adjusted HR 1.32, 95% CI 1.10–1.59; P = 0.003). The present study documented an excess of non-fatal cardiac events after CABG among women despite comparable 10-year survival with men. These findings suggest that studies investigating measures of tertiary prevention are needed to decrease the risk of adverse cardiovascular events among women.