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Gaetano Perone

University of Pisa, Economics and Management, Post-Doc

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Papers by Gaetano Perone

The relationship between renewable energy production and CO 2 emissions in 27 OECD countries: A panel cointegration and Granger non-causality approach

Journal of Cleaner Production, 2023

Human-caused CO 2 emissions are the primary cause of global warming. In this regard, determining ... more Human-caused CO 2 emissions are the primary cause of global warming. In this regard, determining the most effective approach for lowering CO 2 emissions and the collateral risk of catastrophic natural disasters is crucial. This study examines the long-run relationship between disaggregated renewable energy production and carbon dioxide (CO 2) emissions per capita for a panel of 27 OECD countries from 1965 to 2020. The panelautoregressive distributed lag (ARDL) models of the pooled mean group (PMG), mean group (MG), and dynamic fixed effect (DFE) were used to evaluate the relationship between CO 2 emissions and energy production from biofuel, aggregated geothermal and biomass (GEOB), hydropower, nuclear, solar, and wind. As robustness checks, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and common correlated effects mean group (CCEMG) estimators were used. Then, using a generalized method of moment (GMM) framework for panel vector autoregression (PVAR), the Granger non-causality between CO 2 emissions and renewable energy production was investigated. GEOB, hydropower, nuclear, solar, and wind were found to be negatively and significantly correlated with CO 2 emissions. GEOB, hydropower, and solar were the most effective renewable resources in reducing CO 2 emissions. Granger non-causality approach showed unidirectional causation from hydropower, solar, and wind to CO 2 emissions, bidirectional causation between CO 2 , and biofuel and GEOB, and unidirectional causation from CO 2 emissions to nuclear. The findings were consistent across different model specifications and suggested a faster transition to GEOB, hydropower, and solar energy in OECD countries to reduce CO 2 emissions and enhance environmental sustainability.

The relationship between labor market institutions and innovation in 177 European regions over the period 2000-2015

Structural Change and Economic Dynamics, 2023

The main goal of this paper is to investigate the relationship between labor market institutions ... more The main goal of this paper is to investigate the relationship between labor market institutions (LMIs) and patents in 177 NUTS-1 and NUTS-2 European regions. Fixed effects models, ordinary least squares (OLS), the generalized method of moments estimation of the fixed effects (FE-GMM), multilevel modeling (MLM), and spatial models are employed. Patents are negatively correlated with EPL and union density and positively associated with wage bargaining coverage and centralization. As a result, a uniform wage that is higher than the competitive wage can enable the Schumpeterian creative destruction process, forcing firms to invest in innovation to remain in the market. Spatial analysis emphasizes that regional proximity promotes the flow of knowledge and increases the chance of innovation. Interactions also matter. Increased bargaining power and coordination, in particular, may outweigh the negative consequences of isolated EPL reforms. Thus, policies that strengthen wage-setting institutions are required in Europe to boost innovation.

Assessing the impact of long-term exposure to nine outdoor air pollutants on COVID-19 spatial spread and related mortality in 107 Italian provinces

Scientific Reports

This paper investigates the air quality in 107 Italian provinces in the period 2014–2019 and the ... more This paper investigates the air quality in 107 Italian provinces in the period 2014–2019 and the association between exposure to nine outdoor air pollutants and the COVID-19 spread and related mortality in the same areas. The methods used were negative binomial (NB) regression, ordinary least squares (OLS) model, and spatial autoregressive (SAR) model. The results showed that (i) common air pollutants—nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM2.5 and PM10)—were highly and positively correlated with large firms, energy and gas consumption, public transports, and livestock sector; (ii) long-term exposure to NO2, PM2.5, PM10, benzene, benzo[a]pyrene (BaP), and cadmium (Cd) was positively and significantly correlated with the spread of COVID-19; and (iii) long-term exposure to NO2, O3, PM2.5, PM10, and arsenic (As) was positively and significantly correlated with COVID-19 related mortality. Specifically, particulate matter and Cd showed the most adverse effect on COV...

I fallimenti delle politiche di flessibilità nel mercato del lavoro italiano e internazionale

L'austerità deprime. Ovvero la fallacia dell'ideologia tedesca

L'articolo indaga sugli effetti diretti delle politiche di consolidamento dei bilanci pubblic... more L'articolo indaga sugli effetti diretti delle politiche di consolidamento dei bilanci pubblici (austerity) su tre fondamentali chiave dell'economia: rapporto debito/PIL, tasso di disoccupazione e reddito aggregato. In particolare, l'analisi si concentra su 38 medio/grandi economie mondiali, con un nota specifica sull'Europa.

L'incidenza della criminalità organizzata sul settore ambientale ed agroalimentare italiano: Un'indagine empirica

The Value of the P2 Lodge Connections in the Italian Stock Market

US, 2020

Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries

Econometrics, 2022

The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented shock to t... more The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented shock to the world’s economy, and it has interrupted the lives and livelihood of millions of people. In the last two years, a large body of literature has attempted to forecast the main dimensions of the COVID-19 outbreak using a wide set of models. In this paper, I forecast the short- to mid-term cumulative deaths from COVID-19 in 12 hard-hit big countries around the world as of 20 August 2021. The data used in the analysis were extracted from the Our World in Data COVID-19 dataset. Both non-seasonal and seasonal autoregressive integrated moving averages (ARIMA and SARIMA) were estimated. The analysis showed that: (i) ARIMA/SARIMA forecasts were sufficiently accurate in both the training and test set by always outperforming the simple alternative forecasting techniques chosen as benchmarks (Mean, Naïve, and Seasonal Naïve); (ii) SARIMA models outperformed ARIMA models in 46 out 48 metrics (in forecasting future values), i.e., on 95.8% of all the considered forecast accuracy measures (mean absolute error [MAE], mean absolute percentage error [MAPE], mean absolute scaled error [MASE], and the root mean squared error [RMSE]), suggesting a clear seasonal pattern in the data; and (iii) the forecasted values from SARIMA models fitted very well the observed (real-time) data for the period 21 August 2021–19 September 2021 for almost all the countries analyzed. This article shows that SARIMA can be safely used for both the short- and medium-term predictions of COVID-19 deaths. Thus, this approach can help government authorities to monitor and manage the huge pressure that COVID-19 is exerting on national healthcare systems.

Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy

Coronavirus disease (COVID-19) is a severe ongoing novel pandemic that has emerged in Wuhan, Chin... more Coronavirus disease (COVID-19) is a severe ongoing novel pandemic that has emerged in Wuhan, China, in December 2019. As of October 13, the outbreak has spread rapidly across the world, affecting over 38 million people, and causing over 1 million deaths. In this article, I analysed several time series forecasting methods to predict the spread of COVID-19 second wave in Italy, over the period after October 13, 2020. I used an autoregressive model (ARIMA), an exponential smoothing state space model (ETS), a neural network autoregression model (NNAR), and the following hybrid combinations of them: ARIMA-ETS, ARIMA-NNAR, ETS-NNAR, and ARIMA-ETS-NNAR. About the data, I forecasted the number of patients hospitalized with mild symptoms, and in intensive care units (ICU). The data refer to the period February 21, 2020-October 13, 2020 and are extracted from the website of the Italian Ministry of Health (www.salute.gov.it). The results show that i) the hybrid models, except for ARIMA-ETS, ar...

The effect of labor market institutions and macroeconomic variables on aggregate unemployment in 1990–2019: Evidence from 22 European countries

Industrial and Corporate Change, 2022

This paper investigates the long-run effect of a wide set of labor market institutions (LMIs) and... more This paper investigates the long-run effect of a wide set of labor market institutions (LMIs) and macroeconomic variables on aggregate unemployment for a panel of 22 European countries over the period 1990–2019. First-difference feasible generalized least squares model, Prais-Winsten regression with panel-corrected standard errors, two-step generalized method of moments estimation of the fixed effects, and fixed-effects regression with Driscoll and Kraay standard errors are estimated. The results suggest that employment protection legislation, wage bargaining coordination and centralization, minimum wage, and immigration inflows are significantly and negatively associated with the aggregate unemployment rate. Conversely, union density, product market regulation (PMR), and tax wedge have a positive and significant correlation with unemployment rate. The impact of corporate tax rate and government size is mostly positive. Moreover, the interaction between LMIs does matter and may some...

An ARIMA model to forecast the spread of COVID-2019 epidemic in Italy

Coronavirus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading quickly acr... more Coronavirus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading quickly across the world. Italy, that is widely considered one of the main epicenters of the pandemic, registers the highest COVID-2019 death rates and death toll in the world, to the present day. In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after March 30, 2020, by using the Italian epidemiological data at national and regional level. The data refer to the number of daily confirmed cases officially registered by the Italian Ministry of Health (www.salute.gov.it) for the period February 20 to March 30, 2020. The main advantage of this model is that it is easy to manage and fit. Moreover, it may give a first understanding of the basic trends, by suggesting the hypothetic epidemic's inflection point. Obviously, data need a continuous updating to better explain what is going on.

Assessing the impact of long-term exposure to nine outdoor air pollutants on COVID-19 spatial spread and related mortality in 107 Italian provinces

Scientific Reports, 2022

This paper investigates the air quality in 107 Italian provinces in the period 2014–2019 and the ... more This paper investigates the air quality in 107 Italian provinces in the period 2014–2019 and the association between exposure to nine outdoor air pollutants and the COVID-19 spread and related mortality in the same areas. The methods used were negative binomial (NB) regression, ordinary least squares (OLS) model, and spatial autoregressive (SAR) model. The results showed that (i) common air pollutants—nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM2.5 and PM10)—were highly and positively correlated with large firms, energy and gas consumption, public transports, and livestock sector; (ii) long-term exposure to NO2, PM2.5, PM10, benzene, benzo[a]pyrene (BaP), and cadmium (Cd) was positively and significantly correlated with the spread of COVID-19; and (iii) long-term exposure to NO2, O3, PM2.5, PM10, and arsenic (As) was positively and significantly correlated with COVID-19 related mortality. Specifically, particulate matter and Cd showed the most adverse effect on COVID-19 prevalence; while particulate matter and As showed the largest dangerous impact on excess mortality rate. The results were confirmed even after controlling for eighteen covariates and spatial effects. This outcome seems of interest because benzene, BaP, and heavy metals (As and Cd) have not been considered at all in recent literature. It also suggests the need for a national strategy to drive down air pollutant concentrations to cope better with potential future pandemics.

Comparison of ARIMA, ETS, NNAR, TBATS and Hybrid Models to Forecast the Second Wave of COVID-19 Hospitalizations in Italy

European Journal of Health Economics , 2021

The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, Ch... more The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, in December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 million deaths. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic’s second wave in Italy (the period after October 13, 2020). The autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), the neural network autoregression (NNAR) model, the trigonometric exponential smoothing state space model with Box–Cox transformation, ARMA errors, and trend and seasonal components (TBATS), and all of their feasible hybrid combinations were employed to forecast the number of patients hospitalized with mild symptoms and the number of patients hospitalized in the intensive care units (ICU). The data for the period February 21, 2020–October 13, 2020 were extracted from the website of the Italian Ministry of Health (www.salute.gov.it). The results showed that (i) hybrid models were better at capturing the linear, nonlinear, and seasonal pandemic patterns, significantly outperforming the respective single models for both time series, and (ii) the numbers of COVID-19-related hospitalizations of patients with mild symptoms and in the ICU were projected to increase rapidly from October 2020 to mid-November 2020. According to the estimations, the necessary ordinary and intensive care beds were expected to double in 10 days and to triple in approximately 20 days. These predictions were consistent with the observed trend, demonstrating that hybrid models may facilitate public health authorities’ decision-making, especially in the short-term.

When productivity is limited by the balance of payments. A reflection on the relationship between center and periphery in the European Monetary Union from the perspective of Sylos Labini’s productivity equation

Moneta e Credito , 2020

What does the productivity gap between core and peripheral countries in the Eurozone depend on? T... more What does the productivity gap between core and peripheral countries in the Eurozone depend on? The article proposes a revisiting of Paolo Sylos Labini’s productivity equation aimed at analyzing the phenomenon of balance of payments constrained growth highlighted by Anthony Thirlwall. The analysis tries to verify whether the trade imbalances between the center and the periphery of the Eurozone are relevant to understand the increasing gap in productivity between the two areas. The results seem to confirm the presence of a foreign technological constraint on the periphery. This constraint exhibits a significant correlation with the productivity gap between the center and the periphery, even after the restructuring of production processes undergone in the peripheral countries.

Produttività del lavoro, dinamica salariale e squilibri commerciali nei Paesi dell'Eurozona: un'analisi empirica

Economia & Lavoro, 2018

L'obiettivo del paper è duplice. Da un lato proviamo a determinare se e in quale misura il trente... more L'obiettivo del paper è duplice. Da un lato proviamo a determinare se e in quale misura il trentennale processo di precarizzazione e di progressivo smantellamento dei diritti dei lavoratori nei Paesi dell'Eurozona abbia influito sul funzionamento del mercato del lavoro, e dall'altro cerchiamo di verificare se i cambiamenti di paradigma istituzionale - modificando gli indirizzi di politica economica - abbiano o meno favorito un ri-orientamento virtuoso dei modelli produttivi nazionali. Per pervenire a tale scopo, dividiamo l'elaborato in due sezioni: i) una prima parte di analisi descrittiva degli indici di protezione del lavoro e del rapporto fra salari, produttività e distribuzione del reddito nei Paesi dell'Eurozona, nel periodo 1980-2017; e ii) una seconda parte di analisi empirica sul ruolo svolto dalle istituzioni del lavoro sulla dinamica occupazionale e della produttività rispettivamente nel periodo 1990-2013 e 1999-2013, e sulla funzione svolta dai salari nel riassorbimento degli squilibri commerciali dei Paesi "periferici" dell'area nel periodo 2009-2015. L'indagine mostra come la riduzione trasversale delle tutele a favore del fattore lavoro non abbia generato alcun impatto certo e univoco sui livelli occupazionali; al contrario, essa ha condotto a un incremento della quota dei lavoratori precari e - compatibilmente con l'approccio Kaldoriano-Classico - a una perniciosa spirale di riduzione dei salari, della domanda aggregata e della produttività del lavoro, ponendo de facto seri limiti all'innovazione e alle ristrutturazioni tecnologiche. Infine, verifichiamo come il riequilibrio dei conti con l'estero dei Paesi "periferici" sia stato mediato, più che da una ritrovata competitività sui mercati internazionali, proprio dalle politiche di dumping salariale e di distruzione della domanda interna.

Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy

The European Journal of Health Economics, 2021

The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, Ch... more The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, in December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 million deaths. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic's second wave in Italy (the period after October 13, 2020). The autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), the neural network autoregression (NNAR) model, the trigonometric exponential smoothing state space model with Box-Cox transformation, ARMA errors, and trend and seasonal components (TBATS), and all of their feasible hybrid combinations were employed to forecast the number of patients hospitalized with mild symptoms and the number of patients hospitalized in the intensive care units (ICU). The data for the period February 21, 2020-October 13, 2020 were extracted from the website of the Italian Ministry of Health (www. salute. gov. it). The results showed that (i) hybrid models were better at capturing the linear, nonlinear, and seasonal pandemic patterns, significantly outperforming the respective single models for both time series, and (ii) the numbers of COVID-19-related hospitalizations of patients with mild symptoms and in the ICU were projected to increase rapidly from October 2020 to mid-November 2020. According to the estimations, the necessary ordinary and intensive care beds were expected to double in 10 days and to triple in approximately 20 days. These predictions were consistent with the observed trend, demonstrating that hybrid models may facilitate public health authorities' decision-making, especially in the short-term.

The determinants of COVID-19 case fatality rate (CFR) in the Italian regions and provinces: an analysis of environmental, demographic, and healthcare factors

Science of The Total Environment, 2020

The Italian government has been one of the most responsive to COVID-2019 emergency, through the a... more The Italian government has been one of the most responsive to COVID-2019 emergency, through the adoption of quick and increasingly stringent measures to contain the outbreak. Despite this, Italy has suffered a huge human and social cost, especially in Lombardy. The aim of this paper is dual: i) first, to investigate the reasons of the case fatality rate (CFR) differences across Italian 20 regions and 107 provinces, using a multivariate OLS regression approach; and ii) second, to build a “taxonomy” of provinces with similar mortality risk of COVID-19, by using the Ward’s hierarchical agglomerative clustering method. I considered health system metrics, environmental pollution, climatic conditions, demographic variables, and three ad hoc indexes that represent the health system saturation. The results showed that overall health care efficiency, physician density, and average temperature helped to reduce the CFR. By the contrary, population aged 70 and above, car and firm density, air pollutants concentrations (NO2, O3, PM10, and PM2.5), relative average humidity, COVID-19 prevalence, and all three indexes of health system saturation were positively associated with the CFR. Population density, social vertical integration, and altitude were not statistically significant. In particular, the risk of dying increases with age, as 90 years old and above had a three-fold greater risk than the 80–to–89 years old and four-fold greater risk than 70–to–79 years old. Moreover, the cluster analysis showed that the highest mortality risk was concentrated in the north of the country, while the lowest risk was associated with southern provinces. Finally, since prevalence and health system saturation indexes played the most important role in explaining the CFR variability, a significant part of the latter may have been caused by the massive stress of the Italian health system.

An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy

Health, Econometrics and Data Group (HEDG) Working Paper 07/20. University of York, 2020

Coronavirus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading quickly acr... more Coronavirus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading quickly across the world. Italy, that is widely considered one of the main epicenters of the pandemic, has registered the highest COVID-2019 death rates and death toll in the world, to the present day. In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after April 4, 2020, by using the Italian epidemiological data at national and regional level. The data refer to the number of daily confirmed cases officially registered by the Italian Ministry of Health (www.salute.gov.it) for the period February 20 to April 4, 2020. The main advantage of this model is that it is easy to manage and fit. Moreover, it may give a first understanding of the basic trends, by suggesting the hypothetic epidemic's inflection point and final size.

Covid-19 and the MCO: An Exit Strategy for Malaysia

by Carmelo Ferlito and Gaetano Perone

Brief IDEAS, Apr 18, 2020

Brief IDEAS No 20: "COVID-19 and the MCO: An Exit Strategy for Malaysia", by Dr Carmelo Ferlito (... more Brief IDEAS No 20: "COVID-19 and the MCO: An Exit Strategy for Malaysia", by Dr Carmelo Ferlito (IDEAS Senior Fellow) and Dr Gaetano Perone (Università degli Studi di Bergamo).

Free download here: http://www.ideas.org.my/brief-ideas-no-20-covid-19-and-the-mco-an-exit-strategy-for-malaysia/.

IDEAS welcomes progress in combatting COVID-19 and encourages the government to develop an exit strategy for the eventual end to the Movement Control Order (MCO). In our latest paper written by IDEAS Senior Fellow, Carmelo Ferlito and and Gaetano Perone, Research Fellow at the Department of Management, Economics and Quantitative Methods of the University of Bergamo in Bergamo, Italy, we’ve proposed several steps.

In the short run, we must bring the economy back on-line as quickly as possible, prioritising networked industries. Specific recommendations include:

To include logistics as among the essential services
Provide rapid feedback from MITI for the industry-related firms.

In the medium-run develop protocols for lower restrictions under strict sanitary conditions. Specific recommendations include a gradual relaxation of MCO under strict sanitary conditions (including mass testing), as recently proposed by Dato’ Seri Azmin Ali.

In the medium and long-run the government should accelerate adaptation by firms, to cope with prolonged disruptions. Specific recommendations include fiscal incentives to automation, to reduce dependence on a large labour force.

Media statement here: http://www.ideas.org.my/ideas-welcomes-progress-in-combatting-covid-19-and-proposes-an-exit-strategy-on-mco/.

The impact of agribusiness crimes on food prices: Evidence from Italy

Economia Politica - Journal of Analytical and Institutional Economics, 2019

From the 1990s the Italian agribusiness sector is increasingly threatened by a new and dangerous ... more From the 1990s the Italian agribusiness sector is increasingly threatened by a new and dangerous phenomenon: organized crime in the agribusiness sector. The so-called "Agromafia" imposes its control throughout the whole agricultural supply chain, from production to retail, passing through the processing industry, transports and large-scale distribution. In this paper we examine the relationship between eco-crimes and consumer food and non-alcoholic drinks price index for the 20 Italian regions and 80 Italian provinces in the 1998-2016 period. At regional level, as a proxy for the Agromafia's activities, we build an ad hoc eco-criminal index for every region using data annually elaborated from Legambiente. At province level, as a proxy for Agromafia's activities, we use eight specific variables: extortions, counterfeiting, contraband, forest fires, all types of fires, money laundering, suspicious money transfers, and an ad hoc eco-criminal index. The analysis shows that the Agromafia can consistently affect the whole agribusiness sector, causing an increase in food prices, especially in south of the country. The ten most affected provinces by phenomenon register a food consumer price index about 12% higher than the least ten affected provinces. By the contrast, in the center-north of Italy money laundering seems to reduce food consumer prices through the reinvestment of illicit proceeds in firms with strong cost advantages.