Italy IT: Birth Rate: Crude: per 1000 People data was reported at 7.800 Ratio in 2016. This records a decrease from the previous number of 8.000 Ratio for 2015. Italy IT: Birth Rate: Crude: per 1000 People data is updated yearly, averaging 10.000 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 19.700 Ratio in 1964 and a record low of 7.800 Ratio in 2016. Italy IT: Birth Rate: Crude: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Population and Urbanization Statistics. Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Unemployment Rate in Italy increased to 6.50 percent in May from 6.10 percent in April of 2025. This dataset provides the latest reported value for - Italy Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Italy IT: Death Rate: Crude: per 1000 People data was reported at 10.100 Ratio in 2016. This records a decrease from the previous number of 10.700 Ratio for 2015. Italy IT: Death Rate: Crude: per 1000 People data is updated yearly, averaging 9.800 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 10.700 Ratio in 2015 and a record low of 9.300 Ratio in 1961. Italy IT: Death Rate: Crude: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Population and Urbanization Statistics. Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Youth Unemployment Rate in Italy increased to 21.60 percent in May from 19.90 percent in April of 2025. This dataset provides the latest reported value for - Italy Youth Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Introduction

The dataset comprises over 10,000 chat conversations, each focusing on specific Retail & E-Commerce related topics. Each conversation provides a detailed interaction between a call center agent and a customer, capturing real-life scenarios and language nuances.

Topic Diversity

The chat dataset covers a wide range of conversations on Retail & E-Commerce topics, ensuring that the dataset is comprehensive and relevant for training and fine-tuning models for various Retail & E-Commerce use cases. It offers diversity in terms of conversation topics, chat types, and outcomes, including both inbound and outbound chats with positive, neutral, and negative outcomes.

Language Variety & Nuances

The conversations in this dataset capture the diverse language styles and expressions prevalent in Italian Retail & E-Commerce interactions. This diversity ensures the dataset accurately represents the language used by Italian speakers in Retail & E-Commerce contexts.

The dataset encompasses a wide array of language elements, including:

This linguistic authenticity ensures that the dataset equips researchers and developers with a comprehensive understanding of the intricate language patterns, cultural references, and communication styles inherent to Italian Retail & E-Commerce interactions.

Conversational Flow and Interaction Types

The dataset includes a broad range of conversations, from simple inquiries to detailed discussions, capturing the dynamic nature of Retail & E-Commerce customer-agent interactions.

Each of these conversations contains various aspects of conversation flow like:

Introduction

The dataset comprises over 10,000 chat conversations, each focusing on specific Delivery & Logistics related topics. Each conversation provides a detailed interaction between a call center agent and a customer, capturing real-life scenarios and language nuances.

Topic Diversity

The chat dataset covers a wide range of conversations on Delivery & Logistics topics, ensuring that the dataset is comprehensive and relevant for training and fine-tuning models for various Delivery & Logistics use cases. It offers diversity in terms of conversation topics, chat types, and outcomes, including both inbound and outbound chats with positive, neutral, and negative outcomes.

Language Variety & Nuances

The conversations in this dataset capture the diverse language styles and expressions prevalent in Italian Delivery & Logistics interactions. This diversity ensures the dataset accurately represents the language used by Italian speakers in Delivery & Logistics contexts.

The dataset encompasses a wide array of language elements, including:

This linguistic authenticity ensures that the dataset equips researchers and developers with a comprehensive understanding of the intricate language patterns, cultural references, and communication styles inherent to Italian Delivery & Logistics interactions.

Conversational Flow and Interaction Types

The dataset includes a broad range of conversations, from simple inquiries to detailed discussions, capturing the dynamic nature of Delivery & Logistics customer-agent interactions.

Each of these conversations contains various aspects of conversation flow like:

What’s Included

Welcome to the Italian Open Ended Classification Prompt-Response Dataset—an extensive collection of 3000 meticulously curated prompt and response pairs. This dataset is a valuable resource for training Language Models (LMs) to classify input text accurately, a crucial aspect in advancing generative AI.

This open-ended classification dataset comprises a diverse set of prompts and responses where the prompt contains input text to be classified and may also contain task instruction, context, constraints, and restrictions while completion contains the best classification category as response. Both these prompts and completions are available in Italian language. As this is an open-ended dataset, there will be no options given to choose the right classification category as a part of the prompt.

These prompt and completion pairs cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more. Each prompt is accompanied by a response, providing valuable information and insights to enhance the language model training process. Both the prompt and response were manually curated by native Italian people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This open-ended classification prompt and completion dataset contains different types of prompts, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. The dataset also contains prompts and responses with different types of rich text, including tables, code, JSON, etc., with proper markdown.

To ensure diversity, this open-ended classification dataset includes prompts with varying complexity levels, ranging from easy to medium and hard. Additionally, prompts are diverse in terms of length from short to medium and long, creating a comprehensive variety. The classification dataset also contains prompts with constraints and persona restrictions, which makes it even more useful for LLM training.

To accommodate diverse learning experiences, our dataset incorporates different types of responses depending on the prompt. These formats include single-word, short phrase, and single sentence type of response. These responses encompass text strings, numerical values, and date and time formats, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers.

This fully labeled Italian Open Ended Classification Prompt Completion Dataset is available in JSON and CSV formats. It includes annotation details such as a unique ID, prompt, prompt type, prompt length, prompt complexity, domain, response, response type, and rich text presence.

Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance.

The Italian version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Ongoing efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to gather custom open-ended classification prompt and completion data tailored to specific needs, providing flexibility and customization options.

The dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy Italian Open Ended Classification Prompt-Completion Dataset to enhance the classification abilities and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.

Context

This dataset has been created to support further publication and analysis activities

Content

Full documentation on sources etc. of the two CSV from this dataset, along with the activities done to produce them from data available on the Statistical Office Website of the Ministero dei Beni Cultural - MIBACT is available within the Kaggle dataset

Please refer to the companion Jupyter Notebook is delivered as a quick overview of the data, as the dataset has been created to support further publications and analysis using different algorithms.

Limitations:

1. See the "Data coverage" section- the CSVs contain the data available (summaries from 1994 until 2018; details by Regione/Provincia, political territorial subdivisions in Italy, from 1998 until 2018)
2. As the data concerning Bologna have been removed from 2000 (transferred to another Ministry, see explanation within the "Data coverage" section), all the further analyses will be done for the timespan 2000-2018

Source: Section "Rilevazioni e data statistici" on the Statistical Office Website of MIBACT - focus: "Biblioteche Pubbliche Statali" (State-owned public libraries in Italy)

Data coverage: the datasets cover 1994-2018 for the summary, as those data were available, and 1998-2018 for the details.

As stated by the source website: "Le unità statistiche di riferimento di questa Rilevazione sono rappresentate dalle 46 Biblioteche Pubbliche Statali, indicate dal D.P.R.5/7/1995, n. 417, modificato dal D.M. del 12/06/2000, che ha disposto il trasferimento della Biblioteca Universitaria di Bologna (BUB) al MURST.".

Or: from 2000, the Bologna library has been transferred to another ministry.

Furthermore, "I dati di questa Rilevazione, disponibili in questa pagina web, riguardano la consistenza del materiale bibliografico, le consultazioni, i prestiti, il personale e le spese di gestione a partire dal 1999."

Or: the data are collected from 1999.

Therefore, while the two CSVs include all the data available: 1. the "BibliotecheStatali_01_published.csv" file contains data from the "Dati storici (quinquennali)" available for 1998-2018 (the 1998 file extends back to 1994); each file contained the data for the current year, plus the four previous years, extending back in time if there was a re-assessment of prior data

following a common practice in business, the data used have been the latest version of each year, i.e. the CSV has been created by using the data from the 2018 "Dati storici (quinquennali)", and then going back in time; the column "Source" clearly states the source table from the website

two columns from the original files have been ignored:

"Spese di gestione", i.e. costs- as it is not within the scope of the publications and analyses

"Personale", i.e. personnel, as instead have been used the details within "Tavola 1. Consistenza del materiale, consultazioni, prestiti e personale (Dati per Provincia)" (represented within the other file)

1. the "BibliotecheStatali_01_Tavola1_published.csv" file contains data from the "Tavola 1. Consistenza del materiale, consultazioni, prestiti e personale (Dati per Provincia)", available for 1998-2018

to ensure consistency, as e.g. the RIETI library data was not available for a number of years, and, as stated above, the BOLOGNA library was removed from 2000, a line containing "empty" has been added to keep both RIETI and BOLOGNA

to ensure consistency, as the "not available" was sometimes in the data as either a left- or right-aligned "-", or a "...", it has been replaced by "empty".

Acknowledgements

Thanks to the publisher of the data

Inspiration

Too many to list

What’s Included

Welcome to the Italian Brainstorming Prompt-Response Dataset, a meticulously curated collection of 2000 prompt and response pairs. This dataset is a valuable resource for enhancing the creative and generative abilities of Language Models (LMs), a critical aspect in advancing generative AI.

This brainstorming dataset comprises a diverse set of prompts and responses where the prompt contains instruction, context, constraints, and restrictions while completion contains the most accurate response list for the given prompt. Both these prompts and completions are available in Italian language.

These prompt and completion pairs cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more. Each prompt is accompanied by a response, providing valuable information and insights to enhance the language model training process. Both the prompt and response were manually curated by native Italian people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This dataset encompasses various prompt types, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. Additionally, you'll find prompts and responses containing rich text elements, such as tables, code, JSON, etc., all in proper markdown format.

To ensure diversity, our brainstorming dataset features prompts of varying complexity levels, ranging from easy to medium and hard. The prompts also vary in length, including short, medium, and long prompts, providing a comprehensive range. Furthermore, the dataset includes prompts with constraints and persona restrictions, making it exceptionally valuable for LLM training.

Our dataset accommodates diverse learning experiences, offering responses across different domains depending on the prompt. For these brainstorming prompts, responses are generally provided in list format. These responses encompass text strings, numerical values, and dates, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers.

This fully labeled Italian Brainstorming Prompt Completion Dataset is available in both JSON and CSV formats. It includes comprehensive annotation details, including a unique ID, prompt, prompt type, prompt length, prompt complexity, domain, response, and the presence of rich text.

Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance.

The Italian version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. We continuously work to expand this dataset, ensuring its ongoing growth and relevance. Additionally, FutureBeeAI offers the flexibility to curate custom brainstorming prompt and completion datasets tailored to specific requirements, providing you with customization options.

This dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy Italian Brainstorming Prompt-Completion Dataset to enhance the creative and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.

What’s Included

Introducing the Italian Newspaper, Books, and Magazine Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the Italian language.

Containing a total of 5000 images, this Italian OCR dataset offers an equal distribution across newspapers, books, and magazines. Within, you'll find a diverse collection of content, including articles, advertisements, cover pages, headlines, call outs, and author sections from a variety of newspapers, books, and magazines. Images in this dataset showcases distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personal identifiable information (PII), and in each image a minimum of 80% space is contain visible Italian text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, further enhancing dataset diversity. The collection features images in portrait and landscape modes.

All these images were captured by native Italian people to ensure the text quality, avoid toxic content and PII text. We used latest iOS and android mobile devices above 5MP camera to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Along with the image data you will also receive detailed structured metadata in CSV format. For each image it includes metadata like device information, source type like newspaper, magazine or book image, and image type like portrait or landscape etc. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of Italian text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native Italian crowd community.

If you require a custom dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific requirements using our crowd community.

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage the power of this image dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the Italian language. Your journey to enhanced language understanding and processing starts here.

Introduction

This Italian Call Center Speech Dataset for the Delivery and Logistics industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Italian-speaking customers. With over 30 hours of real-world, unscripted call center audio, this dataset captures authentic delivery-related conversations essential for training high-performance ASR models.

Curated by FutureBeeAI, this dataset empowers AI teams, logistics tech providers, and NLP researchers to build accurate, production-ready models for customer support automation in delivery and logistics.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Italian speakers. Captured across various delivery and logistics service scenarios, these conversations cover everything from order tracking to missed delivery resolutions offering a rich, real-world training base for AI models.

Topic Diversity

This speech corpus includes both inbound and outbound delivery-related conversations, covering varied outcomes (positive, negative, neutral) to train adaptable voice models.

This comprehensive coverage reflects real-world logistics workflows, helping voice AI systems interpret context and intent with precision.

Transcription

All recordings come with high-quality, human-generated verbatim transcriptions in JSON format.

These transcriptions support fast, reliable model development for Italian voice AI applications in the delivery sector.

Metadata

Detailed metadata is included for each participant and conversation:

This metadata aids in training specialized models, filtering demographics, and running advanced analytics.

Usage and Applications

This dataset

The data in this dataset is a spatial inventory of urban agriculture (UA) carried out in the city of Milan (Italy). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps.

License

Creative Commons CC-BY

Disclaimer

Despite our best efforts to validate the data, some information may be incorrect.

Description of the dataset

Typologies of UA

Residential garden: Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.

Community garden: A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.

Urban farm: Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus on arable crops, horticulture, vineyard, olive groves and orchard.

Institutional garden: Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).

Illegal garden: Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.

Nurseries: A large area subdivided into multiple plots managed for growing ornamental plants and flowers.

Land use typologies

Horticulture: annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).

Vineyard: grape vines grown in order to produce wine or table grape.

Olive groves: olive trees grown in order to produce olive oil or table olives.

Orchards: mixed trees such as orange, stone fruit, pome fruit, olive trees.

Mixed crops: an area grown with a mix of horticulture crops and fruit trees, not divisible.

Nurseries: ornamental plants, trees, flowers.

Credit

Pulighe G., Lupia F. (2019) Multitemporal Geospatial Evaluation of Urban Agriculture and (Non)-Sustainable Food Self-Provisioning in Milan, Italy. Sustainability 2019, 11(7), 1846

https://www.mdpi.com/2071-1050/11/7/1846

Introduction

Welcome to the English-Italian Bilingual Parallel Corpora dataset for the Education domain! This comprehensive dataset contains a vast collection of bilingual text data, carefully translated between English to Italian, to support the development of Education-specific language models and machine translation engines.

Dataset Content

Domain Specific Content

This Parallel Corpus is meticulously curated to capture the linguistic intricacies and domain-specific nuances inherent to the Education industry.

Format and Structure

Usage and Application

1593 handwritten digits from around 80 persons were scanned, stretched in a rectangular box 16x16 in a gray scale of 256 values.

The dataset was created by Tactile Srl, Brescia, Italy (http://www.tattile.it) and donated in 1994 to Semeion Research Center of Sciences of Communication, Rome, Italy (http://www.semeion.it), for machine learning research.

For any questions, e-mail Massimo Buscema (m.buscema '@' semeion.it) or Stefano Terzi (s.terzi '@' semeion.it)

Data Set Information: 1593 handwritten digits from around 80 persons were scanned, stretched in a rectangular box 16x16 in a gray scale of 256 values. Then each pixel of each image was scaled into a boolean (1/0) value using a fixed threshold.

Each person wrote on a paper all the digits from 0 to 9, twice. The commitment was to write the digit the first time in the normal way (trying to write each digit accurately) and the second time in a fast way (with no accuracy).

The best validation protocol for this dataset seems to be a 5x2CV, 50% Tune (Train +Test), and completely blind 50% Validation

Attribute Information: This dataset consists of 1593 records (rows) and 256 attributes (columns). Each record represents a handwritten digit, originally scanned with a resolution of 256 grays scale (28). Each pixel of each original scanned image was first stretched, and after scaled between 0 and 1 (setting to 0 for every pixel whose value was under the value 127 of the grey scale (127 included) and setting to 1 for each pixel whose original value in the grey scale was over 127).

Finally, each binary image was scaled again into a 16x16 square box (the final 256 binary attributes).

Italy IT: Refugee Population: by Country or Territory of Origin data was reported at 47.000 Person in 2017. This records a decrease from the previous number of 51.000 Person for 2016. Italy IT: Refugee Population: by Country or Territory of Origin data is updated yearly, averaging 66.500 Person from Dec 1992 (Median) to 2017, with 26 observations. The data reached an all-time high of 224.000 Person in 2002 and a record low of 1.000 Person in 1992. Italy IT: Refugee Population: by Country or Territory of Origin data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Population and Urbanization Statistics. Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted refugee-like humanitarian status, and people provided temporary protection. Asylum seekers--people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers--are excluded. Palestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict. Country of origin generally refers to the nationality or country of citizenship of a claimant.; ; United Nations High Commissioner for Refugees (UNHCR), Statistics Database, Statistical Yearbook and data files, complemented by statistics on Palestinian refugees under the mandate of the UNRWA as published on its website. Data from UNHCR are available online at: www.unhcr.org/en-us/figures-at-a-glance.html.; Sum;

The benchmark interest rate in Italy was last recorded at 4.50 percent. This dataset provides - Italy Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

IT: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 15.300 % in 2021. This records a decrease from the previous number of 15.600 % for 2020. IT: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 14.050 % from Dec 1977 (Median) to 2021, with 36 observations. The data reached an all-time high of 16.200 % in 1993 and a record low of 9.700 % in 1982. IT: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Italy IT: Refugee Population: by Country or Territory of Asylum data was reported at 167,260.000 Person in 2017. This records an increase from the previous number of 147,370.000 Person for 2016. Italy IT: Refugee Population: by Country or Territory of Asylum data is updated yearly, averaging 48,668.500 Person from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 167,260.000 Person in 2017 and a record low of 5,473.000 Person in 1998. Italy IT: Refugee Population: by Country or Territory of Asylum data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Population and Urbanization Statistics. Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted refugee-like humanitarian status, and people provided temporary protection. Asylum seekers--people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers--are excluded. Palestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict. Country of asylum is the country where an asylum claim was filed and granted.; ; United Nations High Commissioner for Refugees (UNHCR), Statistics Database, Statistical Yearbook and data files, complemented by statistics on Palestinian refugees under the mandate of the UNRWA as published on its website. Data from UNHCR are available online at: www.unhcr.org/en-us/figures-at-a-glance.html.; Sum;

Italy IT: Labour Force Participation Rate: National Estimate: % of Total Population Aged 15+ data was reported at 49.815 % in 2017. This records an increase from the previous number of 49.502 % for 2016. Italy IT: Labour Force Participation Rate: National Estimate: % of Total Population Aged 15+ data is updated yearly, averaging 49.102 % from Dec 1961 (Median) to 2017, with 43 observations. The data reached an all-time high of 51.670 % in 1961 and a record low of 47.294 % in 1995. Italy IT: Labour Force Participation Rate: National Estimate: % of Total Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; The series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.

Italy IT: Labour Force Participation Rate: Modeled ILO Estimate: % of Total Population Aged 15+ data was reported at 48.617 % in 2017. This records a decrease from the previous number of 48.846 % for 2016. Italy IT: Labour Force Participation Rate: Modeled ILO Estimate: % of Total Population Aged 15+ data is updated yearly, averaging 48.399 % from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 50.590 % in 1991 and a record low of 47.334 % in 1995. Italy IT: Labour Force Participation Rate: Modeled ILO Estimate: % of Total Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Labour Force. Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections. National estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.