As part of forthcoming publications, I collect data that might interesting in association with other data.

On 2022-07-18 the Italian business newspaper published an article that reminded me of a book I read few years ago, "Peoplequake" (you can read here a couple of articles that I posted in Italian in 2017 and 2018).

The population of Italy, along with Japan, is old and getting older, and most commentators focus on the health system impacts.

In reality, coupled with a contraction of births well below the "replacement level" (i.e. to keep population steady), this implies the need to rethinksomething more than just the health system.

For the time being, see article in Italian referencing part of the data

As for the data: * it is the same information contained within the article, i.e. at the county ("provincia") level * to ease clustering analysis and comparison with other data that usually are by region or aggregation of regions within Italy, added clustering by Region/Area from ISTAT, the National Statistics Bureau of Italy

More information about other indicators at the county level will be gradually added.

Sources: * for the main table- Il Sole 24 Ore (paper edition, manually re-entered) * for the region and area list ISTAT

The number of internet users in Italy was forecast to continuously increase between 2024 and 2029 by in total *********** users (+***** percent). After the ***** consecutive increasing year, the number of users is estimated to reach ***** million users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here. The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Key information about Italy Labour Force Participation Rate

• Italy Labour Force Participation Rate increased to 66.8 % in Sep 2025, compared with 66.2 % in the previous month.
• Italy Labour Force Participation Rate is updated monthly, available from Jan 2004 to Sep 2025, with an average rate of 64.8 % .
• The data reached the an all-time high of 68.0 % in Oct 2023 and a record low of 59.9 % in Aug 2010.
• Italy Labour Force Participation Rate is reported by reported by CEIC Data.
• In the latest reports, Italy Population reached 58.9 million people in Dec 2024.
• Unemployment Rate of Italy increased to 6.1 % in Sep 2025.
• The country's Monthly Earnings stood at 4,311.3 USD in Sep 2025.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

information about UNHCR's populations of concern for a given year and country of origin. Data is presented as a yearly time series across the page.

This ranking displays the results of the worldwide Made-In-Country Index 2017, a survey conducted to show how positively products "made in..." are perceived in various countries all over the world. During this survey, 81 percent of respondents from Singapore perceived products made in Italy as "slightly positive" or "very positive".

This study contains selected demographic, social, economic, public policy, and political comparative data for Switzerland, Canada, France, and Mexico for the decades of 1900-1960. Each dataset presents comparable data at the province or district level for each decade in the period. Various derived measures, such as percentages, ratios, and indices, constitute the bulk of these datasets. Data for Switzerland contain information for all cantons for each decennial year from 1900 to 1960. Variables describe population characteristics, such as the age of men and women, county and commune of origin, ratio of foreigners to Swiss, percentage of the population from other countries such as Germany, Austria and Lichtenstein, Italy, and France, the percentage of the population that were Protestants, Catholics, and Jews, births, deaths, infant mortality rates, persons per household, population density, the percentage of urban and agricultural population, marital status, marriages, divorces, professions, factory workers, and primary, secondary, and university students. Economic variables provide information on the number of corporations, factory workers, economic status, cultivated land, taxation and tax revenues, canton revenues and expenditures, federal subsidies, bankruptcies, bank account deposits, and taxable assets. Additional variables provide political information, such as national referenda returns, party votes cast in National Council elections, and seats in the cantonal legislature held by political groups such as the Peasants, Socialists, Democrats, Catholics, Radicals, and others. Data for Canada provide information for all provinces for the decades 1900-1960 on population characteristics, such as national origin, the net internal migration per 1,000 of native population, population density per square mile, the percentage of owner-occupied dwellings, the percentage of urban population, the percentage of change in population from preceding censuses, the percentage of illiterate population aged 5 years and older, and the median years of schooling. Economic variables provide information on per capita personal income, total provincial revenue and expenditure per capita, the percentage of the labor force employed in manufacturing and in agriculture, the average number of employees per manufacturing establishment, assessed value of real property per capita, the average number of acres per farm, highway and rural road mileage, transportation and communication, the number of telephones per 100 population, and the number of motor vehicles registered per 1,000 population. Additional variables on elections and votes are supplied as well. Data for France provide information for all departements for all legislative elections since 1936, the two presidential elections of 1965 and 1969, and several referenda held in the period since 1958. Social and economic data are provided for the years 1946, 1954, and 1962, while various policy data are presented for the period 1959-1962. Variables provide information on population characteristics, such as the percentages of population by age group, foreign-born, bachelors aged 20 to 59, divorced men aged 25 and older, elementary school students in private schools, elementary school students per million population from 1966 to 1967, the number of persons in household in 1962, infant mortality rates per million births, and the number of priests per 10,000 population in 1946. Economic variables focus on the Gross National Product (GNP), the revenue per capita per household, personal income per capita, income tax, the percentage of active population in industry, construction and public works, transportation, hotels, public administration, and other jobs, the percentage of skilled and unskilled industrial workers, the number of doctors per 10,000 population, the number of agricultural cooperatives in 1946, the average hectares per farm, the percentage of farms cultivated by the owner, tenants, and sharecroppers, the number of workhorses, cows, and oxen per 100 hectares of farmland in 1946, and the percentages of automobiles per 1,000 population, radios per 100 homes, and cinema seats per 1,000 population. Data are also provided on the percentage of Communists (PCF), Socialists, Radical Socialists, Conservatives, Gaullists, Moderates, Poujadists, Independents, Turnouts, and other political groups and p

Abstract

Sustainable Development Goal (SDG) target 2.1 commits countries to end hunger, ensure access by all people to safe, nutritious and sufficient food all year around. Indicator 2.1.2, “Prevalence of moderate or severe food insecurity based on the Food Insecurity Experience Scale (FIES)”, provides internationally-comparable estimates of the proportion of the population facing difficulties in accessing food. More detailed background information is available at http://www.fao.org/in-action/voices-of-the-hungry/fies/en/ .

The FIES-based indicators are compiled using the FIES survey module, containing 8 questions. Two indicators can be computed:
1. The proportion of the population experiencing moderate or severe food insecurity (SDG indicator 2.1.2), 2. The proportion of the population experiencing severe food insecurity.

These data were collected by FAO through the Gallup World Poll. General information on the methodology can be found here: https://www.gallup.com/178667/gallup-world-poll-work.aspx. National institutions can also collect FIES data by including the FIES survey module in nationally representative surveys.

Microdata can be used to calculate the indicator 2.1.2 at national level. Instructions for computing this indicator are described in the methodological document available in the documentations tab. Disaggregating results at sub-national level is not encouraged because estimates will suffer from substantial sampling and measurement error.

Geographic coverage

National

Analysis unit

Individuals

Universe

Individuals of 15 years or older with access to landline and/or mobile phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

NA Exclusions: NA Design effect: 2.48

Mode of data collection

Computer-Assisted Telephone Interviewing [CATI]

Cleaning operations

Statistical validation assesses the quality of the FIES data collected by testing their consistency with the assumptions of the Rasch model. This analysis involves the interpretation of several statistics that reveal 1) items that do not perform well in a given context, 2) cases with highly erratic response patterns, 3) pairs of items that may be redundant, and 4) the proportion of total variance in the population that is accounted for by the measurement model.

Sampling error estimates

The margin of error is estimated as 4.9. This is calculated around a proportion at the 95% confidence level. The maximum margin of error was calculated assuming a reported percentage of 50% and takes into account the design effect.

The dataset

The Pest Sticky Traps (PST) dataset is a collection of yellow chromotropic sticky trap pictures specifically designed for training/testing deep learning models to automatically count insects and estimate pest populations.

Images were manually annotated by some experts of the Department of Agriculture, Food and Environment of the University of Pisa (Italy) by putting a dot over the centroids of each identified insect. Specifically, we labeled insects as belonging to the category “whitefly” considering two different species, i.e., the sweet potato whitefly (Bemisia tabaci) (Gennadius) and the greenhouse whitefly (Trialeurodes vaporariorum) (Westwood).

The dataset comprises two subsets:- a subset we suggest using for the training/validation phases (contained in the train/ folder)- a subset we suggest using for the test phase (contained in the test/ folder)

Annotations of the two subsets are contained in train/annotations.csv and test/annotations.csv, respectively. They have the following columns:- imageName - filename of the image containing the whiteflies,- X,Y - 2D coordinates of the whitefly in the image space,- class - class index of the insect (always 0 in this dataset).

Citing our work

If you found this dataset useful, please cite the following paper

@inproceedings{CIAMPI2023102384, title = {A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps}, journal = {Ecological Informatics}, volume = {78}, pages = {102384}, year = {2023}, issn = {1574-9541}, doi = {10.1016/j.ecoinf.2023.102384}, url = {https://www.sciencedirect.com/science/article/pii/S1574954123004132}, year = 2023, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, }

and this Zenodo Dataset

@dataset{ciampi_2023_7801239, author = {Luca Ciampi and Valeria Zeni and Luca Incrocci and Angelo Canale and Giovanni Benelli and Fabrizio Falchi and Giuseppe Amato and Stefano Chessa}, title = {Pest Sticky Traps: a dataset for Whitefly Pest Population Density Estimation in Chromotropic Sticky Traps}}, month = apr, year = 2023, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.7801239}, url = {https://doi.org/10.5281/zenodo.6560823} }

Contact Information

If you would like further information about the dataset or if you experience any issues downloading files, please contact us at luca.ciampi@isti.cnr.it

This statistic displays the results of the worldwide Made-In-Country Index 2017, a survey conducted to show how positively products "made in..." are perceived in various countries all over the world. For this statistic, respondents were asked about attributes they associate with products made in Italy. 37 percent of respondents stated they associate "Excellent Design" with products from Italy.

The dataset that we provide is composed of a csv file containing the answers of responders to our questionnaire conducted to explore perceptions and feelings on the COVID-19 pandemic. The survey was conducted from June 27 to July 2 2022 among university students and adult residents of Milan, Italy, and New York City, NY, U.S.A.. The two target demographics for this study were adult residents of the two cities who were employed at the beginning of 2020 and students who attended university during 2020 or joined during the pandemic. The survey was accompanied by a promotional video and an introductory paragraph describing the objective of the study, and it was shared through social media platforms, on specialized social media groups, and on university students’ mailing lists. The total number of questions asked is a maximum of 20, variable depending on answers given by a user since we employed branching based on previous answers. This feature was particularly useful in creating questions that were specific to a subset of the sample population The topics of questions cover the following broad areas: Relationships: Multiple Choice and sorting/ranking questions designed to understand who the respondents spent lockdown with, if they managed to keep in touch with those they could not meet, and to family, friends and intimate relationships during the pandemic Policies: Likert scale questions measuring agreement with measures put in place in both Milan and New York Personal Life: questions about one’s priorities before and during the pandemic Occupation: Multiple Choice questions about one’s occupation during the pandemic and feelings towards work or university Post-pandemic: Likert scale questions about one's perception of contagion threats and feelings of normalcy at the time they responded to the survey Demographics: Multiple choice questions to describe the pool of respondents and control sample bias The types of the questions are of one of the following types: Multiple choice (one or more selections or single selection) Ranking Numeric scale (1-5 or 1-10) The “ranking” question type allowed users to sort a list of items in descending order of importance. In the dataset the column name represents the ranking given to the item, e.g. 1. highest priority.

This graph depicts the main information sources from which individuals have heard about Italy in 2017. According to survey results, Internet was the main source for ** percent of respondents, followed by television which was the source for ** percent of the interviewees, while ** percent of them heard about Italy from their friends.

This statistic displays the main information sources among individuals aged under thirty years old, in Italy in 2016. As of the survey period, Facebook was the main information source for **** percent of the respondents, while **** percent of them watched news on television. Moreover, **** percent of the interviewee declared to use search engines on the internet to keep themselves informed about the matters of their interest.

Since the spread of the coronavirus (COVID-19) in Italy, started in February 2020, many people who contracted the infection died. The number of deaths amounted to 198,683 as of January 8, 2025. On December 3, 2020, 993 patients died, the highest daily toll since the start of the pandemic. The region with the highest number of deaths was Lombardy, which is also the region that registered the highest number of coronavirus cases. Italy's death toll was one of the most tragic in the world. In the last months, however, the country saw the end to this terrible situation: as of November 2023, roughly 85 percent of the total Italian population was fully vaccinated. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

This statistic illustrates the share of population using information websites in Italy in 2013, by operator. In that year, 12.4 percent of the population used Google as online source of information.

As of September 24, 2023, around 50 million people in Italy have completed the regular vaccination cycle against COVID-19, corresponding to roughly 85 percent of the total population. In the age group 80 years and older, the one most vulnerable to the virus, this figure reached almost 96 percent. A vaccine for kids under 12 years of age became available only in December 2021. To build a better protection against the virus over time, the Italian authorities started administering a third vaccine dose during autumn 2021, and a fourth dose in spring 2022. So far, roughly three out of four Italians over 12 years of age have received a booster shot. More statistics and facts about the virus in Italy are available here.For a global overview on the various COVID-19 vaccines' development and distribution, visit Statista's Facts and Figures on the topic.

In late-November 2021, the Omicron variant of SARS-CoV-2 (the virus which causes COVID-19) was designated as a variant of concern by the World Health Organization due to fears about a higher transmissibility from the variant and a possible decrease in the effectiveness of vaccines against it. The Omicron variant has been detected in multiple countries since the discovery, and as of April 1, 2022, almost 965 thousand cases have been sequenced in the United Kingdom.

During spring 2022, Italian authorities started administering a second booster shot of COVID-19 vaccines to the population in order to improve the general protection against the virus. As of November 20, 2023, approximately 6.7 million Italian citizens have received a booster shot, corresponding to roughly 11.4 percent of the population over five years old. This statistic breaks down these figures by age of the vaccinated. In the age group 80+, the one most vulnerable to the virus, over 45.6 percent of the individuals received a fourth shot.

About 85 percent of the total population in Italy has completed the regular vaccination cycle, having received two shots. Moreover, three out of four Italians over 12 years of age have received a third shot. Thanks to this, despite the high number of daily cases, figures for deaths and hospitalizations remain low.

More statistics and facts about the virus in Italy are available here. For a global overview on the various COVID-19 vaccines' development and distribution, visit Statista's Facts and Figures on the topic.

During autumn 2021, Italian authorities started administering booster shots of coronavirus vaccines to the population in order to improve the general protection against the virus, using either Pfizer or Moderna vaccines. As of November 20, 2023, roughly 70.2 percent of the population over 12 years old have received a booster vaccination. This statistic breaks down these figures by age of the vaccinated. In the age group 80+, the one most vulnerable to the virus, around 89.3 percent of the individuals received a third shot. The region achieving the highest booster dose coverage was Lombardy.

About 85 percent of the total population in Italy has completed the regular vaccination cycle, having received two shots. Thanks to this, despite the high number of daily cases, figures for deaths and hospitalizations remain low.

More statistics and facts about the virus in Italy are available here. For a global overview on the various COVID-19 vaccines' development and distribution, visit Statista's Facts and Figures on the topic.

This statistic shows the results of a survey on the opinion about the advance healthcare directive among elderly population in Italy in 2017. According to data, ** percent of respondents declared to support the living will. On the other hand, ** percent of interviewees did not know what the directive was, while ** percent of them was opposed to it.