The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides values for INTEREST RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Reduced Rate Certificate (RRC) report lists the name and addresses of all facilities that are covered by a Climate Change Agreement and the period for which they are covered by an agreement.
We are required to publish the list of facilities as soon as reasonably practicable after the first day of each certification period. The certification periods are as follows: • Certification period 1 (01 April 2013 – 30 June 2015) • Certification period 2 (01 July 2015 – 30 June 2017) • Certification period 3 (01 July 2017 – 30 June 2019) • Certification period 4 (01 July 2019 – 30 June 2021) • Certification period 5 (01 July 2021 – 30 June 2023) (Current) • Certification period 6 (01 July 2023 – 31 March 2025) We are also required to update the list on the last working day of each month if there have been any changes since the date of the last publication. The requirements to publish the list and updates stem from Regulations 9(1) and (2) of the Climate Change Agreements (Administration) Regulations 2012. Facility addresses for the six sectors listed below are not released for reasons of National Security/Site Security: • NFU1 (Pigs) • NFU5 (Eggs & Poultry Meat) • BMPA (Meat) • BPC1 (Poultry Meat) • BPC2 (Poultry) • DATC (Data Centres). The operators of facilities included in this list are entitled to claim a discount on the Climate Change Levy. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved.

This table includes figures on year-on-year developments of expenditure categories of the Harmonised consumer price index (HICP). This table also contains the weighting coefficient. The weighting coefficient shows how much consumers in the Netherlands spend on a product group in relation to their total expenditure.

Furthermore, the table shows the contribution and impact of HICP categories. The contributions of the separate groups add up to the total annual rate of change and show the share of price increases. The impact, on the other hand, answers the question how much higher or lower the annual rate of change would have been, if a specific category would not have been taken into account in calculation. The figures are shown for 141 product groups in 2025. Furthermore, 34 combinations of product groups (special aggregates) are displayed.

HICP figures are published every month. In addition, an annual figure is published at the end of the year. The HICP of a calendar year is calculated as the average of the indices of the twelve months of that year.

Data available from: January 2016.

Status of the figures: The HICP figures in this table are in most cases final immediately upon publication. The figures of the HICP are only marked as provisional in the second publication if it is already known at the time of publication that data are still incomplete, a revision is expected in a later month, or in special circumstances such as the corona crisis.

In most cases, all requested price information is known to Statistics Netherlands when the results are published and no adjustment is made later. However, sometimes certain price information is not available in time and the outcome can be adjusted later. HICP results can then always be revised together with the CPI results, even if they were not published as provisional in the previous month. CPI results are marked as provisional when the index figures are first published, the figures are final the following month.

Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented.

Changes as of 13 February 2025: Starting in the reporting month of January 2025, a price change is published for expenditure category 103000 Post-secondary non-tertiary education. The base period for this index series is December 2024.

Changes as of 23 January 2025: Starting in the reporting month of January 2024, a price change is published for expenditure category 063000 Hospital Services. The base period for this index series is December 2023. Starting from the reporting month of December 2024 a year-on-year change, contribution and impact can be determined. The figures of 2024 for this category have been added to the table.

Changes as of 9 June 2022: The unit of the contribution to annual rate of change and the impact on the annual rate of change has been adjusted to 'percentage point'. Previously, the unit was incorrectly referred to as 'percent' in the table.

When will new figures be published? New figures will usually be published between the first and second Thursday of the month following on the reporting month.

All CPI and HICP publications are announced on the publication calendar.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.

EU Carbon Permits fell to 72.66 EUR on July 31, 2025, down 0.27% from the previous day. Over the past month, EU Carbon Permits's price has risen 2.95%, and is up 2.09% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for EU Carbon Permits.

Corruption remains a serious concern for EU citizens: 68% believe that corruption is still widespread in their country. In focus are national public institutions, where 74% of respondents increasingly believe that corruption is widespread, followed by political parties (58%) and local, regional and national politicians (55%). At the same time, Europeans are pessimistic about actions taken at national level to address corruption as a crime. Only a minority think measures against corruption are applied impartially and without ulterior motives (37%), that there are enough successful prosecutions to deter people from corrupt practices (34%), that their national government’s efforts to combat corruption are effective (31%) or that there is sufficient transparency and supervision of the financing of political parties in their country (31%).

Processed data

Processed data files for the Eurobarometer surveys are published in .xlsx format.

• Volume A "Countries/EU" The file contains frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for each country or territory and for (weighted) EU results.
• Volume AP "Trends" The file compares to previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results.
• Volume AA "Groups of countries" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for groups of countries specified by the managing unit on the part of the EC.
• Volume AAP "Trends of groups of countries" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each groups of countries foreseen in Volume AA and for (weighted) results.
• Volume B "EU/socio-demographics" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for the EU as a whole (weighted) and cross-tabulated by some 20 sociodemographic, socio-political or other variables, depending on the request from the managing unit on the part of the EC or the managing department of the other contracting authorities.
• Volume BP "Trends of EU/socio-demographics" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume B above)and for (weighted) results.
• Volume C "Country/socio-demographics" The file contains (labelled) weighted frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for each country or territory surveyed separately and cross-tabulated by some 20 socio-demographic, socio-political or other variables (including a regional breakdown).

For SPSS files and questionnaires, please contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

Metadata: Title: Cover Management(C-factor) factor of soil erosion by water at European Scale.Description: The C-factor (Cover and Management) is presented at 100m resolution. This C-factor was estimated for a) arable lands based on crop composition and for b) all other land uses (non-arable) based on the vegetation density and land cover type. The management practices (reduced tillage/no till, plant residues and winter cover crops) were taken into account in estimating C-factor in arable lands.Spatial coverage: European Union (28 Countries)Pixel size: 100 mMeasurement Unit: DimensionlessProjection: ETRS89 Lambert Azimuthal Equal AreaTemporal coverage: 2010 C-factor in Europe Land use and management influence the magnitude of soil loss. Among the different soil erosion risk factors, the cover-management factor (C-factor) is the one that policy makers and farmers can most readily influence in order to help reduce soil loss rates. The present study proposes a methodology for estimating the C-factor in the European Union (EU), using pan-European datasets (such as CORINE Land Cover), biophysical attributes derived from remote sensing, and statistical data on agricultural crops and practices. In arable lands, the C-factor was estimated using crop statistics (% of land per crop) and data on management practices such as conservation tillage, plant residues and winter crop cover. The C-factor in non-arable lands was estimated by weighting the range of literature values found according to fractional vegetation cover, which was estimated based on the remote sensing dataset Fcover. The mean C-factor in the EU is estimated to be 0.1043, with an extremely high variability; forests have the lowest mean C-factor (0.00116), and arable lands and sparsely vegetated areas the highest (0.233 and 0.2651 respectively). Conservation management practices (reduced/no tillage, use of cover crops and plant residues) reduce the C-factor by on average 19.1% in arable lands. The methodology is designed to be a tool for policy makers to assess the effect of future land use and crop rotation scenarios on soil erosion by water. The impact of land use changes (deforestation, arable land expansion) and the effect of policies (such as the Common Agricultural Policy and the push to grow more renewable energy crops) can potentially be quantified with the proposed model. The C-factor data per land use and country can be found in the publication while the C-factor maps (at 100m pixel resolution) are available for download here. Data The C-factor dataset is in Raster format. The user can download 2 datasets: Cover management factor (C-factor) in the European Union at 100m resolution A shapefile where the user can map the: Cover-Management factor (C-factor) in arable lands at regional (NUTS2) level in the European Union Influence of Tillage practices on C-factor reduction Influence of plant residues on C-factor reduction Influence of cover crops on C-factor reduction Detailed information about the C-factor per country and land use are also available in the open-access publication.To get access to the data, please compile the Request form; instructions will then follow how to download the datasets. More information about Cover Management in the corresponding section. Note: An update of the C-factor took place for the year 2016 and the results have been published in the paper An indicator to reflect the mitigating effect of Common Agricultural Policy on soil erosion. The elaboration of the EU Farm Structure Survey data 2016 and CORINE Land Cover2012 in the GIS-based LANDUM model allowed to update the knowledge about the most recent changes in land use and arable land management. Using the data on tillage, plant residues and cover crops, we updated the Cover-management (C-factor) in EU for 2016. The increase of land under soil conservation practices and the land cover change have contributed to decreasing the mean C-factor by –0.8%. References A complete description of the methodology and the application in Europe is described in the paper:Panagos, P., Borrelli, P., Meusburger, C., Alewell, C., Lugato, E., Montanarella, L., 2015. Estimating the soil erosion cover-management factor at European scale. Land Use policy journal. 48C, 38-50

Germany DE: Tariff Rate: Applied: Weighted Mean: All Products data was reported at 1.330 % in 2022. This records a decrease from the previous number of 1.390 % for 2021. Germany DE: Tariff Rate: Applied: Weighted Mean: All Products data is updated yearly, averaging 2.020 % from Dec 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 3.370 % in 2001 and a record low of 1.330 % in 2022. Germany DE: Tariff Rate: Applied: Weighted Mean: All Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Trade Tariffs. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead.;World Bank staff estimates using the World Integrated Trade Solution system, based on tariff data from the United Nations Conference on Trade and Development's Trade and Development's Trade Analysis and Information System (TRAINS) database and global imports data from the United Nations Statistics Division's Comtrade database.;;The tariff data for the European Union (EU) apply to EU Member States in alignment with the EU membership for the respective countries/economies and years. In the context of the tariff data, the EU membership for a given country/economy and year is defined for the entire year during which the country/economy was a member of the EU (irrespective of the date of accession to or withdrawal from the EU within a given year). The tariff data for the EU are, thus, applicable to Belgium, France, Germany, Italy, Luxembourg, and the Netherlands (EU Member State(s) since 1958), Denmark and Ireland (EU Member State(s) since 1973), the United Kingdom (EU Member State(s) from 1973 until 2020), Greece (EU Member State(s) since 1981), Spain and Portugal (EU Member State(s) since 1986), Austria, Finland, and Sweden (EU Member State(s) since 1995), Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovakia, and Slovenia (EU Member State(s) since 2004), Romania and Bulgaria (EU Member State(s) since 2007), Croatia (EU Member State(s) since 2013). For more information, please revisit the technical note on bilateral applied tariff (https://wits.worldbank.org/Bilateral-Tariff-Technical-Note.html).

The European Union and its Member States have been working to reduce the use of tobacco and related products through a range of measures, including regulating tobacco and related products, restricting the advertising and sponsorship of tobacco and related products, implementing smoke-free environments and running anti-smoking campaigns. The European Commission regularly carries out public opinion polls to monitor Europeans' attitudes to a range of tobacco-related issues. Less than a quarter (23%) of the respondents smoke boxed cigarettes, cigars, cigarillos or a pipe, a decrease by three percentage points since 2017. 14% of respondents have at least tried e-cigarettes once or twice, while around one in twenty (6%) say the same for heated tobacco products.

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'.
The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.

Dataset for the article: Cortinovis C., Geneletti D., Haase D. (2022). Higher immigration and lower land take rates are driving a new densification wave in European cities. npj Urban Sustainability 2, 19. doi:10.1038/s42949-022-00062-0

The dataset includes three excel workbooks. The main workbook "EU_cities_data.xlsx" calculates residential density in 2006, 2012 and 2018 and its trends in the two periods 2006-2012 and 2012-2018 for 331 European cities and greater cities. The worksheet "CITIES_GREATER_sel" is the basis to reproduce the analyses and figures presented in the paper and related Supplementary Material, using the code R code "EU_cities_plot" available at doi:10.6084/m9.figshare.19773151.

The main workbook is linked to two datasets containing demographic data for the selected cities and greater cities: - EU_cities_population.xlsx: total population at the 1st of January of the three reference years; - EU_cities_demo_balance.xlsx: data on births and deaths in each city during the analysed period, and calculation of net migration figures.

Population data are based on the Eurostat Urban Audit (https://ec.europa.eu/eurostat/web/cities/data/database) and on datasets from national statistical offices. The numerous corrections to the original Urban Audit database and the final source of each value are indicated in the two datasets (see worksheet "sources and legend").

Residential area and land take for residential use are based on the Copernicus Urban Atlas (https://land.copernicus.eu/local/urban-atlas) version 021 for the reference year 2012, version 012 for the reference year 2018, and the consolidated “Revised” version available in 2021 for the reference year 2006.

Cities’ and greater cities’ boundaries were retrieved from the GISCO Eurostat spatial database linked to the Urban Audit, version 2018 ( https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit).

Attitudes towards the energy policy of the EU. Topics: concern about climate change and global warming; impact of national energy production and consumption on climate change and global warming; expected impact of the ongoing climate change on personal energy consumption in ten years’ time: need to change everyday consumption, need to install energy saving equipment in own house, need to pay higher energy prices; attitude towards the EU setting a minimum percentage for energy coming from renewable sources; preferred measure of the national government to help people reduce energy consumption: provide more information on efficient energy use, adopt stricter efficiency standards for energy consuming equipment, subsidise energy efficient solutions; influence of energy efficiency on purchasing decision with regard to household appliances; attitude towards selected kinds of support of the national government for the development of cleaner energy technologies and products: public funding, tax incentives, prohibition of products that are not up to energy efficiency standards, no support; preferred level of action to tackle energy-related issues: national, EU; attitude towards the use of nuclear energy: increase use as nuclear energy is environmentally friendly, decrease use due to safety problems; attitude towards the introduction of common EU high safety requirements for nuclear installations; preferred level for negotiations on oil and gas prices on international markets: EU, national; preferred measures in case of oil or gas shortages in selected EU member states: affected member state has to rely on own reserves, affected member state should be able to rely on reserves of other member states; importance to be able to choose electricity or gas supplier and reasons. Demography: sex; age; age at end of education; occupation; professional position; type of community; number of fixed phone lines in the household; number of fixed phone lines in the household listed in telephone directory; number of fixed phone lines in the household used exclusively for business purposes or for electronic equipment; number of fixed phone lines in the household listed in telephone directory and used exclusively for business purposes or for electronic equipment. Additionally coded was: respondent ID; interviewer ID; language of the interview; country; date of interview; time of the beginning of the interview; duration of the interview; region; weighting factor. Meinung zum Klimawandel. Einstellung zu Maßnahmen der EU sowie der Regierung im Herkunftsland hinsichtlich des Klimawandels. Einstellung zu erneuerbaren Energien. Einstellung zur Kernenergie. Meinung zur Öl- und Gasversorgung. Themen: Beunruhigung durch Klimawandel und globale Erwärmung; Einschätzung des Einflusses des Energieverbrauchs des Herkunftslands auf den Klimawandel und die globale Erwärmung; Einfluss der Klimaveränderungen auf künftigen eigenen Energieverbrauch; Zustimmung zu einem durch die EU festgelegten Anteil an erneuerbaren Energien; präferierte Maßnahmen der Regierung des Herkunftslandes zur Reduzierung des Energieverbrauchs der Bürger; eigene Orientierung an der Energieeffizienz beim Kauf eines Haushaltsgeräts; Zustimmung zur Unterstützung der Regierung des Herkunftslandes für die Entwicklung von neuen und sauberen Energietechnologien und Produkten; Präferenz für eine nationale oder europäische Behandlung von Energiefragen; Einstellung zur Kernenergie; Zustimmung zur Einführung hoher Sicherheitsanforderungen für alle Nuklearanlagen innerhalb der EU durch die EU; Präferenz für europäische oder nationale Sicherung günstiger Öl- bzw. Gaspreise; Unterstützung eines EU-Landes bei Öl- und Gasknappheit durch ein anderes EU-Mitgliedsland; Wichtigkeit der Wahlmöglichkeit bei der Auswahl eines Öl- und Gaslieferanten; Gründe für die Bevorzugung einer Wahlmöglichkeit bei der Auswahl des Anbieters. Demographie: Geschlecht; Alter; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Urbanisierungsgrad; Anzahl der Festnetzanschlüsse im Haushalt; Anzahl der im Telefonbuch verzeichneten Festnetzanschlüsse im Haushalt; Anzahl der Festnetzanschlüsse für geschäftliche Nutzung bzw. für elektronische Anlagen wie Fax und Computer; Anzahl der im Telefonbuch verzeichneten Festnetzanschlüsse für geschäftliche Nutzung bzw. für elektronische Anlagen wie Fax und Computer. Zusätzlich verkodet wurde: Befragten-ID; Interviewer-ID; Interviewsprache; Land; Interviewdatum; Interviewdauer (Interviewbeginn und Interviewende); Region; Gewichtungsfaktor.

Do web surveys still yield lower response rates compared with other survey modes? To answer this question, we replicated and extended a meta-analysis done in 2008 which found that, based on 45 experimental comparisons, web surveys had an 11 percentage points lower response rate compared with other survey modes. Fundamental changes in internet accessibility and use since the publication of the original meta-analysis would suggest that people’s propensity to participate in web surveys has changed considerably in the meantime. However, in our replication and extension study, which comprised 114 experimental comparisons between web and other survey modes, we found almost no change: web surveys still yielded lower response rates than other modes (a difference of 12 percentage points in response rates). Furthermore, we found that prenotifications, the sample recruitment strategy, the survey’s solicitation mode, the type of target population, the number of contact attempts, and the country in which the survey was conducted moderated the magnitude of the response rate differences. These findings have substantial implications for web survey methodology and operations.

This table includes all price index numbers calculated according to the Harmonised consumer price index (HICP) for the Netherlands, the Euro area and the European Union (EU). In all member states of the EU, these indices are compiled in a similar manner to facilitate comparison between the various EU countries.

The table also includes the harmonised consumer price index for the Euro area. This index figure reflects the average price increase/decrease in the countries which have adopted the euro as their currency. The table also includes the European consumer price index, i.e. the harmonised consumer price index for the member states of the European Union.

HICP figures are published every month. In addition, an annual figure is published at the end of the year. The HICP of a calendar year is calculated as the average of the indices of the twelve months of that year.

Data available from: January 1996.

Status of the figures: The HICP results for the Netherlands in this table are in most cases final immediately upon publication. At that time, the results for the euro area are still based on the flash estimate and are characterized as provisional. A month later, these figures become final.

The results of the HICP are only marked as provisional if it is already known at the time of publication that data are still incomplete, a revision is expected in a later month, or in special circumstances such as the corona crisis.

In most cases, all requested price information is known to Statistics Netherlands when the results are published and no adjustment is made later. However, sometimes certain price information is not available in time and the outcome can be adjusted later. HICP results can then always be revised together with the CPI results, even if they were not published as provisional in the previous month. CPI results are marked as provisional when the index figures are first published, the figures are final the following month.

Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented.

When will new figures be published? New figures will usually be published between the first and second Thursday of the month following on the reporting month.

All CPI and HICP publications are announced on the publication calendar.

This data package includes the underlying data files to replicate the data and charts presented in The Inflation Surge in Europe by Patrick Honohan, PIIE Policy Brief 24-2.

If you use the data, please cite as: Honohan, Patrick. 2024. The Inflation Surge in Europe. PIIE Policy Brief 24-2. Washington, DC: Peterson Institute for International Economics.

Inflation Rate In the Euro Area remained unchanged at 2 percent in July. This dataset provides the latest reported value for - Euro Area Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Attitude towards the EU and the euro. Topics: assessment of the own country’s membership in the EU as a good thing; having the euro is a good thing for the own country and for Europe; changes in feeling European due to the euro; difficulty to distinguish and handle euro bank notes and specific coins; opinion about the number of existing coins and which euro coin denominations should be removed; conversion from the price in euro to the national currency when it comes to exceptional and common purchases; assessment of dual price displays as useful (only in SI, MT, CY); prices increased during the changeover period (only in SI, MT, CY); development of the inflation rate compared with the situation before the introduction of the euro; travels outside the own country at least once a year; impact of the euro introduction: easier traveling, easier price comparisons with other countries, reduction of cross-border banking charges; state of national budget in 2007: surplus, deficit, balance; awareness of the ´Stability and Growth Pact´; need for significant reforms to improve economy; successful reforms in other euro area countries put pressure on national government to reform; governments need to save for the ageing populations; taxes should be increased to finance economic reforms; expenditures should be reduced to finance economic reforms; EU should play an active role in national reforms; importance of reforms in the areas: labour market, health system, pension system, social security system, market reforms, taxation, education systems, reforms in general, reforms in other areas; personally affected by the aforementioned reforms; expected impact of the reforms on national economy; inflation rate in the own country last year; expectations regarding the inflation rate in the current year; development of household income since last year and expectations for current year. Demography: sex; age; age at end of education; occupation; professional position; type of community. Additionally coded was: respondent ID; interviewer ID; language of the interview; country; date of interview; time of the beginning of the interview; duration of the interview; type of phone line; region; weighting factor. Einstellung zur Europäischen Union und zur Euro-Einführung. Wirtschaftliche Situation. Themen: EU-Mitgliedschaft ist eine gute Sache; der Euro ist eine gute Sache für das Befragungsland sowie für Europa; Veränderung des Identifikationsgefühls als Europäer durch den Euro; Schwierigkeiten mit dem Unterscheiden der Euro-Münzen und Banknoten sowie Nennung der Münzen, die Schwierigkeiten bereiten; Zufriedenheit mit der Menge der unterschiedlichen Münzarten; Münzen, die abgeschafft werden sollten; Umrechnen in die alte nationale Währung bei täglichen oder außergewöhnlichen Einkäufen; Inflationsschub durch die Euro-Einführung; Auslandsreisen; Vorteile durch den Euro: kostengünstigeres Reisen, leichterer Preisvergleich, Verringerung der grenzüberschreitenden Bankgebühren; Kenntnis eines Überschusses des Staatshaushalts im Befragungsland; Kenntnis des Stabilitäts- und Wachstumspakts; Zustimmung zu Reformen: zur Leistungssteigerung der Wirtschaft, Reformen in anderen Euro-Ländern üben Druck auf das eigene Land aus, Notwendigkeit des Sparens zur Vorbereitung auf Auswirkungen des demographischen Wandels, Steuererhöhung zur Finanzierung von Reformen, Reduzierung der Sozialausgaben zur Finanzierung von Reformen, Wunsch nach aktiver Rolle der EU beim Reformprozess im Befragungsland; wichtigste Reformbereiche; eigene Betroffenheit von genannten Reformbereichen; positive oder negative Wirkung der Reformen auf die nationale Wirtschaft; Inflationsrate im letzten Jahr im Befragungsland; erwartete Inflationsrate; Entwicklung des Haushaltseinkommens des Befragten im letzten Jahr sowie erwartete zukünftige Entwicklung. Demographie: Geschlecht; Alter; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Urbanisierungsgrad. Zusätzlich verkodet wurde: Befragten-ID; Interviewer-ID; Interviewsprache; Land; Interviewdatum; Interviewdauer (Interviewbeginn und Interviewende); Interviewmodus (Mobiltelefon oder Festnetz); Region; Gewichtungsfaktor.

Analysis of ‘Tax rate on low wage earners - Unemployment trap’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://data.europa.eu/data/datasets/29rqta57aajujyiwc8yffq on 30 September 2021.

--- Dataset description provided by original source is as follows ---

The 'unemployment trap' measures what percentage of the gross earnings (after moving into employment) is 'taxed away' by the combined effects of the withdrawal of benefits and higher tax and social security contributions.

--- Original source retains full ownership of the source dataset ---

Dataset to "Hydride Formation Diminishes CO2 Reduction Rate on Palladium" as published in ChemPhysChem, 20 (2019), 1398-1403