Argentina has the largest Italian population outside of Italy, with around 1.17 million Italians residing in the South American country as of 2023. This community represented almost one fifth of all citizens residing outside the Republic, seven million. Germany hosted the second-largest community, with about 900,000 Italians, while in Brazil lived around 790,000 people with Italian citizenship. In total, three million Italians resided in the Americas, whereas 3.5 million in other European countries. In the nineteenth and twentieth centuries, Argentina was one of the main destinations for Italian emigrants, in particular in the early 1900s. Increasing tendency to emigrate  Between 2006 and 2020, the number of Italians living abroad constantly increased. As of 2020, over five million Italians lived outside their homeland. Data related to the educational level of the emigrated population show that one third of the academics decided to leave the country. In 2017, 32.5 percent of Italians holding a university degree did not reside in Italy. Better jobs and lower taxes  When asked about the reasons why leaving their country, the opportunity to pay lower taxes and have better jobs played an important role. Indeed, about 43 percent of Italians declared to be ready to leave Italy for a place where taxes were lower. In addition, roughly 37 percent could leave Italy for better working chances.

As of 2023, more than 6.1 million Italians lived abroad. In particular, the largest community of migrants was in Argentina, as this country was hosting around 953,000 Italian citizens. Two European states followed in the ranking, Germany and Switzerland, while Brazil had the fourth-largest Italian emigrated population. In total, about 3.32 million emigrants lived in other European countries, while 2.5 million resided in the Americas.

In 2021, some 32 percent of Italians who left Italy lived in Central or South America.

Argentina was the main destination country of Italian migrants. This South American state also hosted the largest Italian population residents abroad.

As of January 2025, about 59 million people lived in Italy. Around 29 million individuals were males and 30 million people were females. The most populated area of the country was the north-west, where 15.9 million people lived. Furthermore, the southern regions counted 13.4 million inhabitants, representing the second most populous area of the country. Regional census The northern region of Lombardy is the most populous region of Italy. One-sixth of all the Italian population is concentrated in this area. Lazio and Campania follow with approximately 5.7 million and 5.6 million individuals, respectively. On the other hand, Aosta Valley, a northern region bordering to France and Switzerland, counted 123,000 inhabitants, representing the smallest region of Italy in terms of residents as well as area. More and more Italians are moving abroad In recent years, the number of Italians reported living abroad increased. In 2022, 5.8 million people lived outside Italy, almost three million more than in 2006. The country hosting the largest Italian population is Argentina, while other large Italian communities reside in Germany, Switzerland, and Brazil.

BackgroundMost studies on immigrant health focus on immigrant groups coming from extra-European and/or low-income countries. Little attention is given to self-rated health (SRH) in the context EU/EEA migration. To know more about health among European immigrants can provide new insights related to social determinants of health in the migration context. Using the case of Italian immigrants in Norway, the aim of this study was to (i) examine the levels of SRH among Italian immigrants in Norway as compared with the Norwegian and the Italian population, (ii) examine the extent to which the Italian immigrant perceived that moving to Norway had a positive or negative impact on their SRH; and (iii) identify the most important factors predicting SRH among Italian immigrants in Norway.MethodsA cross-sectional survey was conducted among adult Italian immigrants in Norway (n = 321). To enhance the sample's representativeness, the original dataset was oversampled to match the proportion of key sociodemographic characteristics of the reference population using the ADASYN method (oversampled n = 531). A one-sample Chi-squared was performed to compare the Italian immigrants' SRH with figures on the Norwegian and Italian populations according to Eurostat statistics. A machine-learning approach was used to identify the most important predictors of SRH among Italian immigrants.ResultsMost of the respondents (69%) rated their SRH as “good” or “very good”. This figure was not significantly different with the Norwegian population, nor to the Italians living in Italy. A slight majority (55%) perceived that their health would have been the same if they continued living in Italy, while 23% perceived a negative impact. The machine-learning model selected 17 variables as relevant in predicting SRH. Among these, Age, Food habits, and Years of permanence in Norway were the variables with the highest level of importance, followed by Trust in people, Educational level, and Health literacy.ConclusionsItalian immigrants in Norway can be considered as part of a “new mobility” of high educated people. SHR is shaped by several interconnected factors. Although this study relates specifically to Italian immigrants, the findings may be extended to other immigrant populations in similar contexts.

Argentina is the country with the second largest Italian population in the world. As of January 2019, around 842.6 thousand Italian citizens who were residents abroad were living in Argentina. This represents almost 16 percent of all Italians living abroad worldwide, according to the Registry of Italian Residents Abroad. Brazil was the second favorite Latin American destination for Italians who lived abroad, with over 447 thousand Italian citizens registered there.

In 2025, the average age of the population in Italy is estimated to be **** years. This figure constantly rose over the last decade. In 2010, the mean age was **** years, steadily growing in the following years. Recent studies indicate that the median age is projected to increase in the future as well. By 2050, it could reach **** years. Few births over the past years Italy has the highest share of the elderly population in Europe. In 2023, ** percent of the Italian inhabitants were aged 65 years and over. One of the main reasons for the population aging is the low number of births recorded in the past years. In fact, Italy counted about *** births every 100,000 inhabitants in 2023, the lowest figure recorded since 2002 at least. Longer lifespan In addition to a low birth rate, Italy is among the countries with the highest life expectancy worldwide. In 2024, life expectancy at birth for Italian women was **** years, whereas Italian men could expect to live up to **** years. A longer life expectancy combined with fewer births explain why the average age of Italian inhabitants has been rising recently.

In 2025, **** million people lived in the Italian north-western regions, the most populated area of the Republic. Moreover, the south of Italy had **** million inhabitants, ranking second in the country. The islands had *** million inhabitants, representing the lowest population among the different macro-areas. Data about the age of the citizens show that ******* Italy has the oldest population, while the youngest Italians live in ******** regions.

Context

This dataset is the result of my study on web-scraping of English Wikipedia in R and my tests on regression and classification modelization in R.

Content

The content is create by reading the appropriate articles in English Wikipedia about Italian cities: I did'nt run NPL analisys but only the table with the data and I ranked every city from 0 to N in every aspect. About the values, 0 means "*the city is not ranked in this aspect*" and N means "*the city is at first place, in descending order of importance, in this aspect* ". If there's no ranking in a particular aspect (for example, the only existence of the airports/harbours with no additional data about the traffic or the size), then 0 means "*no existence*" and N means "*there are N airports/harbours*". The only not-numeric column is the column with the name of the cities in English form, except some exceptions (for example, "*Bra (CN)* " because of simplicity.

Acknowledgements

I acknowledge the Wikimedia Foundation for his work, his mission and to make available the cover image of this dataset, (please read the article "The Ideal city (painting)") . I acknowledge too StackOverflow and Cross-Validated to be the most important focus of technical knowledge in the world, all the people in Kaggle for the suggestions.

Inspiration

As a beginner in data analisys and modelization (Ok, I passed the exam of statistics in Politecnico di Milano (Italy), but there are more than 10 years that I don't work in this topic and my memory is getting old ^_^) I worked more on data clean, dataset building and building the simplest modelization.

You can use this datase to realize which city is good to live or to expand this to add some other data from Wikipedia (not only reading the tables but too to read the text adn extrapolate the data from the meaningless text.)

As of 2024, Romanians were Italy's largest foreign population, with over one million Romanians living in Italy during the period considered. Albania and Morocco followed with 416,000 and 412,000 people, respectively. From a regional perspective, the Northern regions had the largest foreign population. Lombardy had some 1.1 million foreign residents, the largest in the country.

Italy, a country rich in history, culture and traditions, has a diversity of Italian surnames that reflect its heritage and the evolution of its society over the centuries. Italians, proud of their family heritage, often associate their surnames not only with their personal identity, but also with specific regions, ancestral occupations or geographical characteristics. In this article, we will explore a list of the most common surnames in Italy, offering a fascinating insight into how these names have endured and transformed in the context of Italian culture. . From names that evoke stories of ancient nobility to those that emerge from everyday life, Italian surnames are a living testimony of the rich tapestria that makes up the character of this country.

In 2025, 24.7 percent of the total population in Italy is estimated to be 65 years and older. According to data, the share of elderly people in the Italian society has been growing constantly since 2009. Consequently, the share of young population experienced a decrease in the last years. As a result, the average age of Italians has risen. In 2011, it was 43.6 years, whereas in 2024 it was estimated to be 46.8 years. The oldest country in Europe Italy and Portugal are the European countries with the largest percentage of elderly citizens. In 2024, 24 percent of the total population was aged 65 years and older. Bulgaria and Finland followed in the ranking, while Azerbaijan had the lowest share of elder population, less than ten percent. An increasingly longer lifespan might provide an explanation for such a high share of citizens over 65 years in Italy. The Republic ranks among the countries with the highest life expectancy worldwide. In Europe, only people in Switzerland and Spain can expect to live longer. Fewer babies than ever The share of young people is getting slimmer, not only because the elderly are living longer than ever before. In fact, Italians are having fewer children compared to previous years. The birth rate in the country has been constantly decreasing: in 2024, only 6.3 babies were born per 1,000 inhabitants, three children less than in 2010. In the south of Italy, in 2023 the birth rate stood at 6.7 infants per 1,000 inhabitants, whereas in central Italy this figure reached only 5.8, the highest and lowest rates in the country, respectively.

Ladder. A Corpus of Computer-Mediated Communication for the Analysis of the Acquisition of Pragmalinguistic Competences by German-Speaking Learners of Italian.

Project description:

Many recent research projects (Artoni, Benigni, & Nuzzo, 2020; Cortés Velásquez & Nuzzo, 2017; Nuzzo & Cortés Velásquez, 2020) have underlined the usefulness of creating and analyzing corpora for teaching pragmatics, which, unlike other linguistic levels such as syntax, cannot be explained by rules but only by reference to tendential values or more or less appropriate choices in a given context. This is even more true for interactions via digital media, such as email and instant-messaging services, which have little place in manuals or L2 courses and for which learners have few reference models (Brocca, 2021; Trubnikova & Garofolin, 2020).

Data collection:

Data were collected from April 2020 to April 2021 with the help of a discourse completion task (DCT). The data consists of emails and instant messages. The informants are (i) German learners of Italian between A2-C1 level according to the CEFR and most of them are students living in Tyrol (Austria) and (ii) native speakers of Italian most of whom are students from Rome (Italy). The data of the learners were collected by students of the undergraduate seminar “Insegnare la pragmatica” which is part of the compulsory module 2b for student teachers at the Institute of Didactics of the University of Innsbruck. The data of the native speakers were collected in large part from students in foreign languages at the University RomaTre thanks to the collaboration with Prof. Elena Nuzzo.

The DCTs have been conducted with online questionnaires. Along with the texts, metadata were also registered with the help of an online questionnaire giving sociolinguistic information about the informant (age, self-assessed language level, place of residence, native language, etc.). The DCTs aim to elicit linguistic acts of request and refusal in increasing levels of social distance and different media (Taguchi & Roever, 2017, pp. 85, 231; Hinger et al. 2018: 148). The DCTs elicit different speech acts (requests and refusals) with different degrees of formality (study/work or free time), directed at different people (lecturer, friend, boss) and in different media (mail or instant messaging). The scenarios represent authentic circumstances for the students. The following table shows the situations that were studied:

Email

high level of social distance between sender and recipient

Scenario 1: Sender is asking for something that he/she is not entitled to

Scenario 2: Sender is asking for something that he/she is entitled to

WhatsApp messages

a) low level of social distance between sender and recipient

Scenario 1: Request

Scenario 2: Rejecting a request

Scenario 3: Short-notice cancellation of an invitation

b) medium level of social distance between sender and recipient

Scenario 4: Request

Scenario 5: Rejecting a request

Scenario 6: Short-term rejection of an invitation

The WhatsApp messages, which are exemplary of the text type instant messaging, were produced directly with the cell phone. The metadata were subsequently associated with the respective messages in an Excel spreadsheet. All personal data were anonymized.

The prompts were presented in Italian, as follows:

Mail

Mail a) Immagina di star facendo un corso con il Dr. Nicola Brocca. Domani devi fare una presentazione in classe. Non hai avuto tempo per studiare perché dovevi prepararti a un esame di inglese e ti accorgi che il materiale da presentare è più di quello che avevi previsto. Scrivi una mail al professore: la tua speranza è spostare la presentazione.

Engl: Imagine you are taking a course with Dr. Nicola Brocca. Tomorrow you have to give a presentation in class. You had no time to study because you had to prepare for an English exam, and you realize that there is more material to present than you had imagined. You write an email to the professor: your hope is to reschedule the presentation.

Mail b) Hai fatto un corso con il Dr. Brocca. Hai consegnato il tuo portfolio il 01.02.2020 adesso è il 01.03.2020 e non hai ancora ricevuto il voto. Ti serve il voto per registrarti per una borsa di studio. Manda una mail al prof.: il tuo obiettivo è ricevere il voto al più presto

Engl: You have taken a course with Dr. Brocca. You turned in your portfolio on 02/01/2020, it is now 03/01/2020 and you have not received the grade yet. You need the grade to register for a scholarship. Send an email to the professor: your goal is to receive the grade as soon as possible.

WhatsApp messages

1. Sei in Erasmus in Italia. Avete creato una chat con 10 compagni di corso. Hai perso la tua tessera della biblioteca a vuoi chiedere se qualcuno ti può aiutare perché ti serve un libro entro domani...per esempio prestandoti la sua. Cosa scrivi?

Engl: You are taking part in the Erasmus program in Italy. You have created a chat with 10 classmates. You lost your library card and want to ask if someone can help you because you need a book by tomorrow.... E.g. by lending you their card. What do you write?

1. Ricevi questo messaggio da un amico/a che fa un seminario con te: "Ciao, sono a corto di tempo. Ho visto che hai preso 30 all'esame. Potresti darmi una mano e restare con me in biblioteca oggi?" Non vuoi aiutare il tuo amico. Come reagisci?

Engl: You receive this message from a friend who is attending a seminar with you: "Hello, I'm running out of time. I saw that you got a 30 on the exam. Could you help me and stay with me in the library today?" You don't want to help the friend. How do you respond?

1. Cinque giorni fa hai promesso ad un/a amico/a che questa sera sareste andati al cinema assieme. Però hai cambiato idea. Cosa fai? Cosa scrivi?

   Engl: Five days ago, you promised a friend that tonight you would go to the movies together. But you changed your mind. What would you do? What do you write?

2. Sei al lavoro e hai smarrito il documento elettronico per entrare nel parcheggio. Sei nuovo in questo gruppo di lavoro e hai solo il numero del tuo diretto superiore. Gli mandi un messaggio per chiedergli se ti può aiutare.

Engl: You are at work and have lost your electronic badge to enter the parking lot. You are new to this work group and only have the number of your direct supervisor. You send him/her a message and ask if he/she can help you.

1. Ricevi questo messaggio dal/la tuo/a superiore. "Gentile collega, domani c'è una scadenza importante. Per caso sarebbe in grado di restare oggi in ufficio oltre l'orario?" Non vuoi restare in ufficio oltre il normale. Come reagisci?

Engl: You receive this message from your supervisor. "Dear colleague, tomorrow is an important appointment. Would you be able to stay in the office after hours today?" You don't want to stay in the office beyond normal working hours. How do you respond?

1. Cinque giorni fa hai promesso al/la tuo/a superiore che oggi saresti andato a una cena di lavoro. Però devi disdire. Cosa fai?

Engl: Five days ago, you promised your superior that you would go to a business dinner today. However, you have to cancel. What do you do?

The corpus, which was first collected in .xlsx format, was exported to XML format and CSV format in cooperation with Joseph Wang-Kathrein (Brenner Archive Research Center). It was ensured that the emoticons and special characters were also transferred unchanged in the conversion process. These formats allow long-term archiving and significantly facilitate data exchange.

The size of the corpus (as of May 2021, version Ladder 1.0):

The LADDER corpus includes emails and instant-messaging messages amounting to 18,935 tokens and 33,966 tokens respectively. The corpus of WhatsApp messages consists of a total of 1,204 messages from 80 native speakers and 114 learners. The corpus of emails consists of a total of 235 emails from 78 native-speaker informants and 38 learners. The amount of data allows a qualitatively relevant comparison in sub-corpora e.g. language levels.

The size of the corpus is necessarily limited quantitatively, as data collection must be done manually through individual DCT management and metadata checking. The major bottleneck is currently the annotation of socio-pragmatic aspects, a process that is difficult to automate and that needs to be conducted through cross-annotation by multiple annotators.

Some students' works on the corpus have been collected and are accessible via the following link: https://ladder.hypotheses.org/

Bibliography:

Artoni, D., Benigni, V., & Nuzzo, E. (2020), "Pragmatic instruction in L2-Russian: a study on requests and advice" in Instructed Second Language Acquisition, 4(1), 62-95. doi:10.1558/isla.39864

Brocca, N. (2021), "LADDER: La costruzione e analisi di un corpus di scritture digitali per l’insegnamento della pragmatica in L2" in Italiano Lingua Due, 13(1 (2021)).

Cortés Velásquez, D., & Nuzzo, E. (2017), "Disdire un appuntamento: spunti per la didattica dell'italiano L2 a partire da un corpus di parlanti nativi" in Italiano Lingua Due, 1, 17-36.

Hinger, B., Stadler, W., Schmiderer, K., Bauer, M., (Hrg.) (2018). Testen und Bewerten fremdsprachlicher Kompetenzen. Tübingen: Narr Francke Attempto Verlag.

Nuzzo, E., & Cortés Velásquez, D. (2020), "Canceling Last Minute in Italian and Colombian Spanish: A Cross-Cultural Account of Pragmalinguistic Strategies" in Corpus Pragmatics, 4, 1-26. doi:10.1007/s41701-020-00084-y

Taguchi, N., & Roever, C. (2017), Second language pragmatics: Oxford: Oxford University Press.

Trubnikova, V., & Garofolin, B. (2020), Lingua e interazione. Insegnare la pragmatica a scuola. Pisa: ETS.

When asked about "Attitudes towards consumer electronics", most Italian respondents pick "I could not live without my smartphone" as an answer. 52 percent did so in our online survey in 2024.

As of 2020, 131 thousand Italians were living in London. From 2015 to 2017, the number of Italian citizens who lived in the English Capital increased steadily. However, the Italian population living in London fell between 2018 and 2019. Nonetheless, the United Kingdom is the most popular destination country among Italian emigrants. During 2019, about 19 percent of all Italians who moved abroad notably chose the United Kingdom.

Student migration

The United Kingdom is a very common destination among young Italians in particular. In addition, a large percentage of emigrants who moved to the United Kingdom achieved a higher education. Specifically, 31 percent of Italians who live in the United Kingdom hold a university degree. However, the countries with most Italian emigrants holding a university degree are Brazil, Ireland, and the United States.

Historical migration of Italians

Currently, Argentina has the largest Italian population living abroad. Historically, Argentina was one of the most important destinations of Italian emigrants, especially during the nineteenth and twentieth centuries. Currently, Argentina ranks only tenth among the main destination countries of Italians. The most common destinations are, together with the United Kingdom, Germany and France.

In 2023, the average age of Italian women at first marriage was ***years. From a regional perspective, the highest average age was recorded in Emilia-Romagna, where the average age of the bride reached around ** years. Emilia-Romagna was also the region with the oldest grooms in the country. A male inhabitant of the region could get married at the average age of almost ** years. On the contrary, women from the southern regions got married earlier, at ***5 years. Every year less marriages Over the last decade, the marriage rate in Italy has steadily decreased. In 2006, there were about *** marriages celebrated per 1,000 inhabitants, more than one wedding more in comparison with the figures from 2023. Why do not Italians get married? When asked about the cause of not getting married, about ** percent of the Italian respondents who already lived with their partner replied that they do not believe in marriage. Roughly ** percent of them never felt the need to walk down the aisle. On the other hand, around ** percent of the interviewees declared that they would get married in the near future.

The Italian SpeechDat-Car database contains the recordings of 300 Italian speakers (149 females, 151 males) recorded over the GSM telephone network, in a car. This database is partitioned into 14 DVDs. The speech data files are in two formats. Four of the 5 microphones were recorded on the computer in the boot of the car. The speech data are stored as sequences of 16 kHz, 16 bit and uncompressed. The fifth microphone was connected to the cell phone, and was recorded on a remote machine. The data are stored as sequences of 8 kHz 8 bit A-law. Each signal file is accompanied by an ASCII SAM label file which contains the relevant descriptive information. This speech databases was validated by SPEX (the Netherlands) to assess its compliance with the SpeechDat-Car format and content specifications. Each speaker uttered the following items: - 2 voice activation keywords - 1 sequence of 10 isolated digits - 7 connected digits (1 sheet number -4+ digits, 1 spontaneous telephone number ?9/11 digits, 3 read telephone numbers, 1 credit card number -16 digits, 1 PIN code -6 digits) - 3 dates (1 spontaneous date e.g. birthday, 1 prompted date, 1 relative or general date expression) - 2 word spotting phrases using an embedded application word - 4 isolated digits - 7 spelled words (1 spontaneous e.g. own forename or surname, 1 directory city name, 4 real word/name, 1 artificial name for coverage) - 1 money amount - 1 natural number - 7 directory assistance names (1 spontaneous e.g. own forename or surname, 1 city of birth/growing up, 2 most frequent cities, 2 most frequent company/agency, 1 ?forename surname?) - 9 phonetically rich sentences - 2 time phrases (1 spontaneous time of day, 1word style time phrase) - 4 phonetically rich words - 67 application words (13 mobile phone application words, 22 IVR function keywords, 32 car products keywords) - 2 additional language dependent keywords - Prompts for spontaneous sentences The following age distribution has been obtained: 134 speakers are between 16 and 30, 117 speakers are between 31 and 45, 46 speakers are between 46 and 60, and 3 speakers are over 60. A pronunciation lexicon with a phonemic transcription in SAMPA is also included.

IntroductionFood loss and waste are urgent problems to address. Recent estimates highlighted that the highest quantities of waste are generated at the household level and for this reason, the interest in this sector has increased over years.MethodsTo investigate if there is a connection between consumers’ behaviors aiming at reducing food waste and consumers’ choices in adopting healthy eating habits, a survey among a sample (n = 2,869) representative of the Italian population was carried out with the use of validated questionnaires.ResultsResults demonstrated that the higher the adherence to the Italian dietary guidelines indicator (AIDGI) the higher the score measuring household food waste behaviors (HFWB). In particular, the highest AIDGI corresponds to a preponderance of respondents that was more able to plan the shopping and the use of food (38.9%, p < 0.001), to better evaluate the quantities to cook (40.4%, p < 0.001), to avoid impulsive buying (35.2%, p < 0.01), to have a high knowledge of the food stored (38.4%, p < 0.001), to reuse leftovers (35.4%, p < 0.001), to assess food safety (34.7%, p < 0.001), to plan accurately (34.9%, p < 0.01), to know how to prolong the shelf life of a product (34%, p < 0.05), and to cook creatively (32%, p < 0.01). In addition to that, half of the respondents with the lowest AIDGI score did not receive any education regarding food waste (51.1%, p < 0.001). HFWB indicators globally resulted in scores ranging from 40 to 80% revealing the attention of Italians to food waste issues. Regarding eating habits, in half of the sample (50.4%) a consumption pattern with low adherence to nutritional recommendations was found, in particular among men (34.4%), younger age groups (40%), and people living in large families (42.3%).DiscussionThe overall results provided interesting information that could give input for planning nutrition education actions and identifying targets and topics to be addressed.

Several studies have highlighted the role of cross-linguistic influence in determining the over-use of overt subject pronouns in near-native speakers of a null-subject language as Italian. In this work we inquire on the role of factors different from cross-linguistic influence in the choice of anaphoric devices in near-natives, such as age of onset of exposure and dominance. In order to do so, comparing the productions of two groups of natives speakers, we first single out two null-subject languages, Italian and Greek, which do not differ significantly as far as subject anaphoric devices are concerned and thus instantiate a suitable language combination to investigate the role of factors other than cross-linguistic influence in bilingual speakers of these two languages (Study 1). In Study 2, we compare the productions of a group of native speakers and two groups of near-native speakers in Italian: Greek-Italian bilinguals from birth and L2ers of Italian with Greek as an L1. Results reveal that over-use of overt pronouns in near-natives occurs in the absence of cross-linguistic influence and that age of onset of exposure is a relevant factor: while bilinguals from birth do not differ from native speakers, L2ers over-use overt pronouns compared to both native speakers and bilinguals from birth. In order to establish whether dominance is a possible factor determining bilinguals’ choice of subject anaphoric devices, in Study 3, we compare two groups of Greek-Italian bilinguals from birth: bilinguals living in Greece (whose predominant language is Greek) and bilinguals living in Italy (whose predominant language is Italian). Results reveal no effect of dominance in the production of overt subject pronouns. We found, however, an unexpected effect in the predominant language of one group: bilinguals living in Greece produce significantly more null pronouns and less lexical DPs in Greek compared to bilinguals living in Italy. We interpret this effect as stemming from the need to differentiate the two languages that these bilingual speakers have to handle in everyday life. Interestingly, this effect is found in the predominant language rather than in the non-predominant one.

In 2023, the highest regional Gross Domestic Product in Italy was registered in the northern region of Lombardy, roughly 490 billion euros, followed by Lazio, about 239 billion euros, and Veneto, 137 billion euros. The lowest GDP was recorded in Aosta Valley, in the north, and in Molise, in the south of Italy. A deep economic gap Among the top-10 Italian regions with the highest GDP, five are located in the north of the country: Lombardy, Veneto, Emilia Romagna, Piedmont, and Liguria. Campania, the most populous region in the south, ranked only seventh nationally. These results highlight the deep economic disparities between the north and the south of Italy. The GDP of the northwestern regions reached 709 billion euros in 2023, while the south recorded less than half of the northern regions’ figures. Thus, Lombardy, Piedmont, Liguria, and Aosta Valley constitute Italy's economic driving force. In particular, Lombardy is the region with the highest salaries nationwide, 33,635 euros gross per year, 4,300 euros more than in Campania. Actions by policymakers aimed at closing the economic and wage gap are essential for the full development of southern Italian regions. The demographic divide Despite weaker economic indicators compared to the north, southern regions record better demographic figures. Italy’s population is progressively aging and the number of residents has declined recently. The median age of Italians is expected to reach 52.9 years by 2050. However, the south of the country contributes to mitigating the demographic decline. In fact, birth rates are the highest in the southern regions, in Sicily, and in Sardinia, with 6.6 childbirths per 1,000 inhabitants, well above the 6.2 births per 1,000 residents recorded in the northwest. Additionally, the southern population is on average two years younger than the those living in the northern regions.