Graph and download economic data for Economic Policy Uncertainty Index for Europe (EUEPUINDXM) from Jan 1987 to Jul 2025 about Spain, uncertainty, academic data, Italy, France, Germany, United Kingdom, Europe, and indexes.

Graph and download economic data for World Uncertainty Index: Europe (WUIEUROPE) from Q1 1990 to Q2 2025 about uncertainty, World, Europe, and indexes.

In a 2023 survey, ** percent of percent of respondents considered the impact of economic uncertainty on cloud usage and spend in Europe to be somewhat higher than planned, and ** percent saw it as significantly higher than planned. On the other hand, economic uncertainty did not seem to have a particular impact on cloud usage and spend in Europe for ** percent of respondents.

European Union Uncertainty Indicator (UI): Consumer: EU27 data was reported at -9.100 % Point in Mar 2025. This records an increase from the previous number of -10.500 % Point for Feb 2025. European Union Uncertainty Indicator (UI): Consumer: EU27 data is updated monthly, averaging -10.150 % Point from Apr 2019 (Median) to Mar 2025, with 72 observations. The data reached an all-time high of 6.300 % Point in Oct 2022 and a record low of -25.100 % Point in Oct 2019. European Union Uncertainty Indicator (UI): Consumer: EU27 data remains active status in CEIC and is reported by European Commission's Directorate-General for Economic and Financial Affairs. The data is categorized under Global Database’s European Union – Table EU.S010: DG ECFIN: Uncertainty Indicator: Consumer: NACE Revision 2.0.

United States - World Uncertainty : Europe was 42431.81000 Index in January of 2025, according to the United States Federal Reserve. Historically, United States - World Uncertainty : Europe reached a record high of 67015.99000 in October of 2019 and a record low of 312.38160 in January of 1985. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - World Uncertainty : Europe - last updated from the United States Federal Reserve on July of 2025.

This replication package contains the data and the code for the decomposition of the Economic Policy Uncertainty index (EPU) on the common and country-specific components using the dynamic connectedness (EPU_disaggregation), and for estimation of the effects of shocks into both components on economic activity, inflation and monetary policy setting (PVAR). The estimates of shocks are conducted using panel VAR identified with zero and sign restrictions. We show that only synchronized uncertainty shocks significantly affect Europe’s macroeconomy, while purely national shocks have limited impact.

UI: Consumer: EA data was reported at -10.100 % Point in Mar 2025. This records an increase from the previous number of -10.700 % Point for Feb 2025. UI: Consumer: EA data is updated monthly, averaging -10.200 % Point from Apr 2019 (Median) to Mar 2025, with 72 observations. The data reached an all-time high of 6.400 % Point in Oct 2022 and a record low of -19.100 % Point in Feb 2020. UI: Consumer: EA data remains active status in CEIC and is reported by European Commission's Directorate-General for Economic and Financial Affairs. The data is categorized under Global Database’s European Union – Table EU.S010: DG ECFIN: Uncertainty Indicator: Consumer: NACE Revision 2.0.

We examine how trade and policy uncertainty affect shipping freight rates, using a Bayesian Vector Autoregression (BVAR) model. Trade uncertainty has a strong effect on shipping costs, even though the effects become insignificant within a year. On the other hand, policy uncertainty has a slightly smaller initial effect but tends to have longer-lasting effects on shipping costs. Trade uncertainty tends to benefit European stocks, perhaps as investors may believe that consumers will shift to local companies, with the impact on US stocks also being (mildly) positive, despite the (lagged) deterioration in economic activity. Trade uncertainty tends to have a longer-lasting impact on GDP than policy uncertainty, given then known merits of comparative advantage, while the effect of policy uncertainty is higher in the European markets compared to the US ones.

In 2024 the gross domestic product of the European Union amounted to approximately 17.9 trillion euros. GDP is the total value of all goods and services produced in a country within a year. It is an important indicator of the economic strength of a country. The financial crisis and its aftermath The European Union is a union made up of 27 states located within and around Europe, including several of the world’s largest economies. Since its inception in 1993, the European Union has displayed the benefits of uniting several countries together, however have also showed possible consequences. The majority of European countries felt the aftermath of the 2008 global financial crisis and afterwards the Eurozone crisis, which has had a severe and continuous effect on the general European economy. Additionally, due to the close association between all the countries, several banks around different European countries were forced to shut down. A generally lower standard of life in the EU, particularly around 2009 during the prime of both economical disasters, led to doubt and uncertainty about the future of many European families and consumers. However, as the economic situation all around the world slowly improved, so did the outlook on the future for most consumers. Struggles around Europe resulted in a larger need to stimulate the economy, which was only possible by borrowing and spending more money. As a result, national debt soared. It was also necessary for more economically successful countries to help finance countries that were deep in the crisis, such as Greece.

Global climate policy uncertainty was assessed using approximately 11.27 million news articles from 2000 to 2023 in twelve countries of the G20 spanning six continents: Asia (China, Japan, Korea, and India), Africa (South Africa), North America (the US and Canada), South America (Brazil), Europe (the UK, France, and Germany), and Australasia (Australia). The data is categorized into global and national indices with different frequencies including daily, weekly, and monthly intervals.Citation:Ma, D., Zhang, D., Guo, K., & Ji, Q. (2024). Coupling between global climate policy uncertainty and economic policy uncertainty. Finance Research Letters, 69, 106180.Ji, Q., Ma, D., Zhai, P., Fan, Y., & Zhang, D. (2024). Global climate policy uncertainty and financial markets. Journal of International Financial Markets, Institutions and Money, 102047.

This dataset was prepared by TNO as a contribution to the H2020 project CHE and the H2020 project VERIFY. The basis is a high-resolution (~1x1 km) emission inventory providing CO₂ and CO (from fossil fuels and biofuels separately) over western Europe (2ºW - 19ºE, 47ºN - 56ºN). The reported emissions by European countries to UNFCCC (CO₂) and to EMEP/CEIP (CO) have been used and where needed gap-filled or replaced with emission data from the GAINS model. These country-level emissions are disaggregated in space using a consistent spatial distribution methodology, whereas large point sources are listed with their exact locations. This approach is similar to the one described by Kuenen et al., (ACP, 2014). Emissions are reported per GNFR sector, with an extra split for road transport.

The emission grids that are part of this dataset are a variation on the base grid, representing the uncertainty in the emission data. Each grid is equally plausible. The grids have been created using a Monte Carlo approach. The uncertainties in the underlying data used to create the base grid (emissions: activity data and emission factors, spatial proxies) have been collected (either from country reports or based on expert judgement). Through the Monte Carlo simulation these uncertainties, taking into account error correlations between some sub-sectors, are combined to create ten new emission grids. The spread in emissions between these emission maps gives an indication of the uncertainty in the emissions.

The grid files (in .csv and .nc format) contain annual total emissions per grid cell for the year 2015. A separate file has been prepared for each ensemble member in the Monte Carlo simulation (indicated with M). The unit in the files is kg/yr.

A detailed description of the Monte Carlo simulation is presented in:

Super, I., Dellaert, S. N. C., Visschedijk, A. J. H., and Denier van der Gon, H. A. C.: Uncertainty analysis of a European high-resolution emission inventory of CO₂ and CO to support inverse modelling and network design, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-696, in review, 2019.

N.B. It is important to note that 10 maps are not sufficient to describe the sometimes complex uncertainty structures, for example in the case of lognormal uncertainty distributions. The interpretation of the uncertainty based on these 10 maps should therefore be done with care.

NB. Despite efforts to prevent negative emissions to occur in the grid maps, some negative values are still present. In local studies this might cause some issues, and we recommend to set negative emissions to zero in those cases.

This paper explores how changes in macroeconomic uncertainty have affected the decision to reply to the European Central Bank’s Survey of Professional Forecasters (ECB’s SPF). The results suggest that higher (lower) aggregate uncertainty increases (reduces) non-response to the survey. This effect is statistically and economically significant. Therefore, the assumption that individual ECB’s SPF data are missing at random may not be appropriate. Moreover, the forecasters that perceive more individual uncertainty seem to have a lower likelihood of replying to the survey. Consequently, measures of uncertainty computed from individual ECB’s SPF data could be biased downwards.

The statistic shows an index of consumer confidence in the European Union from February 2024 to January 2025. According to the source, consumer confidence is shown by seasonally adjusted numerical averages, converted from answers to selected questions that are closely related to the reference variables being tracked (e.g. expectations around personal financial situation in the next twelve months, savings, prosperity to buy). A positive value indicates that the consumers are optimistic about the economic situation; a negative value indicates pessimism. In January 2025, the consumer confidence in the EU amounted to ***** points.

spreads for EU countries

In 2023 the gross domestic product of the European Union amounted to approximately 17.1 trillion euros. GDP is the total value of all goods and services produced in a country within a year. It is an important indicator of the economic strength of a country. The financial crisis and its aftermath The European Union is a union made up of 27 states located within and around Europe, including several of the world’s largest economies. Since its inception in 1993, the European Union has displayed the benefits of uniting several countries together, however have also showed possible consequences. The majority of European countries felt the aftermath of the 2008 global financial crisis and afterwards the Eurozone crisis, which has had a severe and continuous effect on the general European economy. Additionally, due to the close association between all the countries, several banks around different European countries were forced to shut down. A generally lower standard of life in the EU, particularly around 2009 during the prime of both economical disasters, led to doubt and uncertainty about the future of many European families and consumers. However, as the economic situation all around the world slowly improved, so did the outlook on the future for most consumers. Struggles around Europe resulted in a larger need to stimulate the economy, which was only possible by borrowing and spending more money. As a result, national debt soared. It was also necessary for more economically successful countries to help finance countries that were deep in the crisis, such as Greece.

This dataset provides all output data generated in the standard settings of HANZE v2.0 model. The 100-m pan-European maps (GeoTIFF) provide gridded totals of five variables for years 1870-2020 for 42 countries. The rasters are group in five ZIP files:

• CLC: land cover/use (Corine Land Cover classification; legend files are included in a separate ZIP)

• Pop: population

• GDP: gross domestic product (2020 euros)

• FA: fixed asset value (2020 euros)

• imp: imperviousness density (%)

Two additional CSV files contain uncertainty estimates of population, GDP and fixed asset value per NUTS3 region and flood hazard zone. The files provide 5th, 20th, 50th, 80th and 95th percentile for all timesteps, separately for coastal and riverine floods.

Two further Excel files contain subnational and national-level statistical data on population, land use and economic variables.

For detailed description of the files, see the documentation provided with the code.

This version replaces the airport list, which was previously incorrectly taken from HANZE v1, and adds land cover/use legend files for ArcGIS and QGIS.

We estimate the impact of increased policy uncertainty from Brexit on UK trade inservices. We apply an uncertainty-augmented gravity equation to UK services tradewith the European Union at the industry level from 2016Q1 to 2018Q4. By exploitingthe variation in the probability of Brexit from prediction markets interacted with anew trade policy risk measure across service industries we identify a significant negativeimpact of the threat of Brexit on trade values and participation. The increasedprobability of Brexit in this period lowered services exports by at least 20 log points.

This paper introduces a hitherto unused source of information to evaluate the importance of uncertainty in driving demand for particular types of entertainment events. We use web searches via Google, and consider various dimensions of uncertainty of outcome in sporting events. Most saliently, we consider whether the complete removal of uncertainty surrounding the winner of a competition, something that often happens before European football leagues have completed, reduces interest. We find that the decrease in interest is significant, but that it is mitigated by the existence of multiple objectives (secondary prizes), such as qualifying for European competitions and avoiding relegation, which expands the fan interest in these leagues. We conclude by affirming that such a diversified structure of competition, replete with an open structure of promotion and relegation, is desirable in the context of league competitions such as those in Europe that do not have a prominent play-off system to conclude the season.

Combining proxy information and climate model simulations reconciles these sources of information about past climates. This, in turn, strengthens our understanding of past climatic changes. The analogue or proxy surrogate reconstruction method is a computationally cheap data assimilation approach, which searches in a pool of simulated climate states the best fit to proxy data. We use the approach to reconstruct European summer mean temperature from the 13th century until present using the Euro 2k set of proxy-records and a pool of global climate simulation output fields. Our focus is on quantifying the uncertainty of the reconstruction, because previous applications of the analogue method rarely provided uncertainty ranges. We show several ways of estimating reconstruction uncertainty for the analogue method, which take into account the non-climate part of the variability in each proxy record. In general, our reconstruction agrees well at multi-decadal timescales with the Euro 2k reconstruction, which was conducted with two different statistical methods and no information from model simulations. In both methodological approaches, the decades around year 1600 CE were the coldest. However, the approaches disagree on the warmest preindustrial periods. The reconstructions from the analogue method also represent the local variations of the observed proxies. The diverse uncertainty estimates obtained from our analogue approaches can be locally larger or smaller than the estimates from the Euro 2k effort. Local uncertainties of the temperature reconstructions tend to be large in areas that are poorly covered by the proxy records. Uncertainties highlight the ambiguity of field based reconstructions constrained by a limited set of proxies.

In a dynamic model with sunk export costs, a firm's export investment is lower under trade policy uncertainty, and credible preferential trade agreements (PTAs) increase trade even if current tariffs are low. Exploring Portugal's accession to the European Community as a policy uncertainty shock we find that the trade reform accounted for a large fraction of Portuguese exporting firms' entry and sales; the accession removed uncertainty about future EC trade policies; and this uncertainty channel accounted for a large fraction of the predicted growth. Our approach can be applied to other PTAs and sources of policy uncertainty. (JEL D22, F12, F14, F15, G31, L11)