This statistic shows the results of a survey conducted among American companies in China on the perceived impact on their businesses of the U.S.-China trade tariffs as of September 2018. During the survey period, **** percent of the surveyed American companies in China responded that the second round of 60 billion U.S. dollars tariffs imposed by China on the U.S. imports had a strong negative impact on their business.

Passing on the increased costs was the leading measure businesses in Japan decided on in response to the Trump administration's tariffs. In a survey conducted in April 2025, only **** percent of respondents stated that the tariff policy would impel their businesses to shift production to the United States.

Name: Measuring the flexibility achieved by a change of tariff.

Summary: This dataset contains the results of a survey carried out by the Spanish electricity retailer GoiEner to assess the impact that a change of from a "flat rate" tariff towards a "time of use" tariff have. Two files are provided:

The results of the survey merged with a summary of the energy consumption of the clients before and after the change of tariff.

The questions of the survey.

License: CC-BY-SA

Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.

Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European Commission (EC). EASME or the EC are not responsible for any use that may be made of the information contained therein.

Collection Date:

Publication Date: December 1, 2022.

DOI: 10.5281/zenodo.7382924

Other repositories: None.

Author: GoiEner, University of Deusto.

Objective of collection: The objective of the data collected is to assess the impact that the change of tariff has on the clients of GoiEner. In particular, the following questions wanted to be answered:

How much energy conservation can trigger a change of tariff?

How much time flexibility could be trigger with a Time of Use Tariff?

What are the main barriers to change the behavoiur?

Are there any differences on behaviour depending on the socio-cultural-psycological profile of the consumers?

Description: The meaning of each column is described next:

Qx_y: Answers to the survey. See the survey file attached (in Spanish) for details.

X.z: Answers to question "¿En qué rango de horas se realizan las siguientes acciones en el domicilio?". Sorted from left to right, top to bottom.

Idioma: Languaje used to answer the survey.

Desea.dar.su.CU: data used to de-anonymize the answers.

{P,F,V}{19,20,21}: total energy consumed during the peak, flat and valley period of the day between 6/19 to 5/20 (19), 6/20 to 5/21 (20) and 6/21 to 5/22 (21).

T{19,20,21}: total energy consumed between 6/19 to 5/20 (19), 6/20 to 5/21 (20) and 6/21 to 5/22 (21).

kpi2_abs: T20 - T21

kpi2_rel: kpi2_abs / T20

kpi1_{P,F,V}{19,20,21}: {P,F,V}{19,20,21} / T{19,20,21}

kpi1_{P,F,V}diff: kpi1_{P,F,V}19 - kpi1_{P,F,V}21

T20DHS{19,20,21}: Cost of the energy of during the different periods using the last tariff.

T20TD{19,20,21}: Cost of the energy of during the different periods using the new tariff.

POWER_TARGET: Energy powerty rist indicator (https://powerpoor.eu/sites/default/files/2022-09/POWERPOOR%20D2.2%20POWER%20TARGET%20v1.0.pdf)

TotalEnergyBudget: Self assessment of the energy budget of the house in Euros.

Invoices20{20,21,22}: Amount of all the invoices for the house during the different periods.

min30{in,pre,pst}: Cluster assigned depending on its electric behaviour pre-COVID, during the COVID lockdowns and post COVID lockdowns. See 10.5281/zenodo.7382818 for details.

5 star: ⭐⭐⭐

Preprocessing steps: Data integration (from different sources from GoiEner services); data transformation (anonymization, unit conversion, metadata generation).

Reuse: This dataset is related to datasets:

"A database of features extracted from different electricity load profiles datasets" (DOI 10.5281/zenodo.7382818), where time series feature extraction has been performed.

Update policy: There might be a single update in mid-2023 with a repetition of the survey as there have been another change of tariff in Spain.

Ethics and legal aspects: The data provided by GoiEner contained values of the CUPS (Meter Point Administration Number), which are personal data. A pre-processing step has been carried out to replace the CUPS by random 64-character hashes.

Technical aspects: the survey is provided as a PDF file and the data as a CSV file compressed with zstandard.

Other: None.

Businesses in Japan sought various forms of support to deal with the Trump administration's tariffs, according to a survey conducted in April 2025. Almost ** percent of respondents stated that they desired updates for the latest information on tariff measures, while *************** sought governmental financing and fundraising.

Monthly response rates for the UK Monthly Business Survey (production) by turnover and questionnaire.

As of April 2025, just over **** of respondents to a survey on U.S. tariffs against the UK would support the government seeking a trade deal to reduce barriers, compared with ** percent who thought the UK should immediately introduce retaliatory tariffs.

According to a 2025 survey, over one-quarter of Americans were planning on making electronics purchases because they expect prices to increase across the country as a result of Trump's proposed tariffs on all imported goods. Of those, 42 percent were between the age of 18 and 24, compared to only 12 percent 55 and older.

In response to President Trump’s escalation of trade relations, China countered by issuing tariffs on over 6,000 products worth over $110 billion in U.S. exports. We explore whether China’s tariffs reflected a strategy to apply counter-pressure by hurting political support for Republicans, assess the strategy’s impact on the 2018 mid-term elections, and examine the mechanism underlying the resulting electoral shift. We find strong evidence that Chinese tariffs systematically targeted U.S. goods whose production is concentrated in Republican-supporting counties, particularly when located in closely contested Congressional districts. This apparent strategy was successful: targeted areas were more likely to turn against Republican candidates. Using data on campaign communications, local search patterns online and an original national survey, we find evidence that voters residing in areas vulnerable to the tariffs were more likely to learn about the trade war, recognize its adverse impact, and assign the Republicans responsibility for the escalating situation.

This statistic shows the results of a survey conducted among American companies in China on the perceived impact on their businesses of the U.S.-China trade tariffs as of September 2018. During the survey period, 64.6 percent of the surveyed American companies in China said that they had no plans to relocate their manufacturing facilities due to the U.S.-China trade tariffs and trade relations, however, 18.5 percent of the companies reported to have relocated or considering relocating their manufacturing facilities to Southeast Asia.

Monthly comparison of response rates for the Monthly Business Survey (services) by turnover and questionnaire, UK.

This paper characterizes the trade-off between the income gains and the inequality costs of trade using survey data for 54 developing countries. Tariff data on agricultural and manufacturing goods are combined with household survey data on detailed income and expenditure patterns to estimate the first-order effects of the elimination of import tariffs on household welfare. The paper assesses how these welfare effects vary across the distribution by estimating impacts on the consumption of traded goods, wage income, farm and non-farm family enterprise income, and government transfers. For each country, the income gains and the inequality costs of trade liberalization are quantified and the trade-offs between them are assessed using an Atkinson social welfare index. The analysis finds average income gains from import tariff liberalization in 45 countries and average income losses in nine countries. Across countries in the sample, the gains from trade are 1.9 percent of real household expenditure on average. We find overwhelming evidence of a trade-off between the income gains (losses) and the inequality costs (gains), which arise because trade tends to exacerbate income inequality: 45 countries face a trade-off, while only nine do not. The income gains typically more than offset the increase in inequality. In the majority of developing countries, the prevailing tariff structure thus induces sizable welfare losses

During a February online survey among buy-side advertising decision-makers handling annual ad budgets of at least 250 thousand U.S. dollars in the United States, 41 percent of participants anticipated cuts in social media ad spending that year due to tariffs. Gaming and linear TV followed, each mentioned by 24 percent. Approximately 43 percent of respondents cited other traditional media.

Characteristics of the United States housing inventory listed in this file include the age, size, and type of living quarters, property values, and the presence of commercial establishments on the property. Additional data focus on the presence and condition of kitchen and plumbing facilities and the type and cost of utilities, as well as housing expenses, property repair or alteration, and insurance costs. Many of the same characteristics are given for housing previously occupied by recent movers. Information on age, sex, race, marital status, and income is provided for each household member, with additional data on education, Spanish origin, and household tenure for the head of household. Indicators provided for housing quality include privacy and structural condition. For neighborhood quality, indicators assess noise, crime, air quality, and the presence of abandoned structures, along with the adequacy of neighborhood services such as police protection, parks, health care, and public transportation.

This dataset contains individual level data on British energy bill payers collected from an online survey of a nationally representative sample of market research panel participants in 2014. It contains demographic data as well as data on their electrical appliance ownership (electric vehicles, washing machines, tumble dryers, dishwashers) as well as their occupancy patterns. It also contains a measure of willingness to switch to a smart time of use tariff designed to be commercially viable in 2020. The data was collected as part of a trial to determine the impact of message framing (loss, gain, environmental) on willingness to switch to this tariff) as well as how willingness to switch is affected by demographics and electrical appliance ownership. No personal data is contained in the dataset.

Abstract

The Ghana Millennium Development Authority's (MiDA) Agriculture Project within the Government of Ghana's Compact with the Millennium Challenge Corporation is design to improve farming in a number of areas. Under the Agricultural Project being implemented by (MiDA) some feeder roads are to be rehabilitated or reconstructed to promote development in the sector. In the first phase, about 336 km of feeder roads in eight (8) districts in two intervention zones are to be rehabilitated to reduce transportation costs and time, and increase access to major domestic and international markets. The feeder roads activity will also facilitate transportation linkages from rural areas to social service networks (including hospitals, clinics and schools).

The purpose of this project is to conduct an impact evaluation of the MiDA's Feeder Roads Activity. As stated in the Terms of Reference of the request for proposals, "the primary data for the impact evaluation will be a series of surveys similar in scope to the Consumer Price Index (CPI) survey, examining changes in prices over time Findings from the market surveys will contribute to the overall impact evaluation conducted by the Institute of Statistical, Social and Economic Research (ISSER). The Ghana Living Standards Survey (GLSS) 5+ is the primary instrument used in the overall evaluation, and 'Difference in Difference' is the proposed method of evaluation of data."

Thus, this study focuses on how prices of goods sold at local markets (that are transported on improved roads) change over time. It is also to document the changes in goods transport tariffs and passenger fares to market places served by the feeder roads.

The sample design uses a carefully tailored algorithm employed to match 154 localities that will benefit from the road improvements with an identical number of control localities that are comparatively far from the improvements. The sample size is sufficient to provide robust estimates of price effects associated with the road improvements. The minimum population for a locality to be included in the sample is 1,000, a condition imposed to help ensure that most designated items could be found in most localities.

Beginning in August 2009 interviewers visited the sample localities to obtain three price observations for each item in the defined "basket" of goods and transportation services. The final "basket" contains 39 fresh food items, 24 packaged food items, 19 non food items and 6 transportation tariffs-3 for the locality's residents' most frequent passenger destinations and 3 for the most frequent freight destinations.

Geographic coverage

308 localities in Ghana - 154 localities that will benefit from the road improvements with an identical number of control localities that are comparatively far from the improvements.

Analysis unit

The main unit of analysis is a market. Within each market, we priced the following items at up to three different vendors: 39 fresh food items, 24 packaged food items, 19 non food items and 6 transportation tariffs-3 for the locality's residents' most frequent passenger destinations and 3 for the most frequent freight destinations.

Universe

The data is only meant to represent the 308 localities surveyed. The results cannot be generalized to a larger population. The objective was not to produce estimates of national means and totals, but to estimate the parameters of an analytical model of program impact.

Kind of data

Sample survey data [ssd]

Sampling procedure

In the present application, the approach that is being used, in lieu of randomization, to select a control sample is statistical matching. A matched-pairs design was used, matching 174 (154 plus 20 replacements) treatment localities to 174 control localities using nearest-neighbor matching. Sampling was restricted, as mentioned earlier, to localities having population 1,000 or more (according to the 2000 Census) and to the 20 largest localities in each district.

The treatment population included all localities within 120 minutes estimated travel time of the nearest MiDA program road, and the control population included all localities located more than 120 minutes estimated travel time from the nearest MiDA program road. (The estimated travel times were calculated using a GIS model of the Ghana road network (documented separately).) This resulted in resulted in population sizes of 675 treatment units and 848 control units. Sampling was restricted to all of the country except Western Region.

Matching was based on a number of variables, including population, travel time to Accra, travel time to the nearest MiDA feeder road, and physiographic data.

The sample localities occur at all distances from the program roads, since it was desired to have substantial variation in the travel time to the program roads.

Because of the sample design process, the sample has reasonable spread, balance and orthogonality for a large number of design variables. Also, the sample includes a control sample for which the units are individually matched to units in the treatment sample. The sample will be a very good one for use in estimating an analytical model showing the relationship of program impact (price changes) to the Ghana MiDA feeder-road improvements, and for estimating a double-difference estimate of program impact.

Sampling deviation

Of the 308 sampled localities only one locality was removed from the sample because we were unable to locate it. This locality, Choo #0155, was not located and was removed along with its matching pair, Sabiye #0159. These localities were replaced with Suame #0812 and Ogbodzo #1264. All other localities were located and surveyed.

Mode of data collection

Face-to-face [f2f]

Research instrument

During the initial visit the NORC FM identified a subset of items on the GLSS surveys to identify and price in the market. This initial pricing and observation allowed for a detailed understanding of the impediments interviewers may encounter during data collection. After observing local conditions the NORC FM met with his counterparts on the local subcontractor team (Pentax Management and Consulting) to carry out an item by item review of the GLSS survey. Through this review NORC and Pentax were able to refine the GLSS survey to meet the needs of the current study. Standard weights and product types were identified for the majority of products, non important items were deleted in order to reduce the time of the survey, and possible fielding issues were discussed with resolutions identified.

The three questionnaires are attached to this document - one for the pricing of goods, one for the pricing of tariff and passenger costs, and one for collecting information on the locality.

Cleaning operations

Data editing was done in the field by supervisors, and double data entry was carried out by Pentax. After receiving data from Pentax, NORC assisted with reconciliation between the first and second entries. After reconciling the data, NORC carried out significant data cleaning, including some imputation of values for missing observations. For a detailed explanation of data editing and cleaning, please refer to the attached “Phase 1, Baseline Findings” report. For the raw dataset receioved by NORC from Pentax, see the attached "Raw Data". For SPSS scripts detailing cleaning done on the dataset, see "SPSS Scripts".

German-speaking resident population aged 16 and over

The majority of businesses in Japan were affected by the tariffs announced by the Trump administration, according to a survey conducted in April 2025. The ***********tariffs, or ******** tariffs, showed the highest impact among responding entities. Among the tariffs not directly targeting Japan, the ********************* were considered the most challenging, with ** percent stating that it either already had or might have an impact in the future.

This data collection provides information on the characteristics of a national sample of housing units. Data include year the structure was built, type and number of living quarters, occupancy status, access, number of rooms, presence of commercial establishments on the property, and property value. Additional data focus on kitchen and plumbing facilities, types of heating fuel used, source of water, sewage disposal, heating and air-conditioning equipment, and major additions, alterations, or repairs to the property. Information provided on housing expenses includes monthly mortgage or rent payments, cost of services such as utilities, garbage collection, and property insurance, and amount of real estate taxes paid in the previous year. Also included is information on whether the household received government assistance to help pay heating or cooling costs or for other energy-related services. Similar data are provided for housing units previously occupied by respondents who had recently moved. Additionally, indicators of housing and neighborhood quality are supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, breakdowns of plumbing facilities and equipment, and overall opinion of the structure. For quality of neighborhood, variables include use of exterminator services, existence of boarded-up buildings, and overall quality of the neighborhood. In addition to housing characteristics, some demographic data are provided on household members, such as age, sex, race, marital status, income, and relationship to householder. Additional data provided on the householder include years of school completed, Spanish origin, length of residence, and length of occupancy.

This data collection provides information on the characteristics of the housing inventory in 12 Standard Metropolitan Statistical Areas (SMSAs). Data include year the structure was built, type and number of living quarters, occupancy status, presence of commercial establishments on the property, presence of a garage, and property value. Additional data focus on kitchen and plumbing facilities, type of heating fuel used, source of water, sewage disposal, and heating and air conditioning equipment. Information about housing expenses includes mortgage or rent payments, utility costs, garbage collection fees, property insurance, and real estate taxes as well as repairs, additions, or alterations to the property. Similar data are provided for housing units previously occupied by respondents who had recently moved. Indicators of housing and neighborhood quality are also supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, presence of cracks or holes in walls, ceilings, or floor, reliability of plumbing and heating equipment, and concealed electrical wiring. The presence of storm doors and windows and insulation was also noted. Neighborhood quality variables indicate presence of and objection to street noise, odors, crime, litter, and rundown and abandoned structures, as well as the adequacy of street lighting, public transportation, public parks, schools, shopping facilities, and police and fire protection. Extensive information on the ability of handicapped persons to move around their homes is also provided. Respondents were asked if they needed special equipment, or the help of another person to move around. They were also asked about the presence or need for housing features to aid their movement, such as ramps, braille lettering, elevators, and extra wide doors. In addition to housing characteristics, demographic data for household members are provided, including sex, age, race, income, marital status, and household relationship. Additional data are available for the household head, including Hispanic origin, length of residence, and travel-to-work information.

United States CES Collection Rates: Third Final Sample Based-Release data was reported at 93.700 Number in Feb 2025. This records a decrease from the previous number of 93.800 Number for Jan 2025. United States CES Collection Rates: Third Final Sample Based-Release data is updated monthly, averaging 90.200 Number from Jan 1981 (Median) to Feb 2025, with 529 observations. The data reached an all-time high of 97.300 Number in Oct 2014 and a record low of 72.400 Number in Mar 2003. United States CES Collection Rates: Third Final Sample Based-Release data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G093: Current Employment Statistics: Collection Rates.