The Quarterly Fuels Inquiry (QFI) is a quarterly survey among a panel of manufacturing plants. The survey is managed by ONS on behalf of the Department of Energy and Climate Change (DECC). Using this it is possible to calculate sectorial energy prices which are reported in publications such as Quarterly Energy Prices. This is the first external research study which had access to this data at micro level. It spans a period from 1993 to 2015. The inquiry is statutory, being conducted under Section 1 of the Statistics of Trade Act 1947.

The aim of the inquiry is to collect data on the prices paid by small, medium and large industrial fuel consumers. The data by consumption band, available through QFI, are the only data of their kind available for the UK energy market. As such it allows cost comparisons to be made between various fuel types and between large and small users. These data are important in determining whether price movements for large and small users have been comparable and therefore providing data to examine any possible price discrimination in the fuel products. QFI data are widely used both within government and industry and are considered a vital source of data. The survey is used by officials and ministers to monitor trends in industrial prices, particularly for gas and electricity, and by companies as price escalators in fuel purchasing contracts.

Linking to other business studies
These data contain Inter-Departmental Business Register reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Latest edition information
For the fourth edition (May 2024), a new data file covering the period quarter 1 2015 to quarter 3 2023 was added to the study. A data dictionary covering the same time period was also added, along with the latest version of the Industrial Price Statistics - Data Sources and Methodologies documentation file.

Abstract copyright UK Data Service and data collection copyright owner. The Quarterly Fuels Inquiry (QFI) is a quarterly survey among a panel of manufacturing plants. The survey is managed by ONS on behalf of the Department of Energy and Climate Change (DECC). Using this it is possible to calculate sectorial energy prices which are reported in publications such as Quarterly Energy Prices. This is the first external research study which had access to this data at micro level. It spans a period from 1993 to 2015. The inquiry is statutory, being conducted under Section 1 of the Statistics of Trade Act 1947. The aim of the inquiry is to collect data on the prices paid by small, medium and large industrial fuel consumers. The data by consumption band, available through QFI, are the only data of their kind available for the UK energy market. As such it allows cost comparisons to be made between various fuel types and between large and small users. These data are important in determining whether price movements for large and small users have been comparable and therefore providing data to examine any possible price discrimination in the fuel products. QFI data are widely used both within government and industry and are considered a vital source of data. The survey is used by officials and ministers to monitor trends in industrial prices, particularly for gas and electricity, and by companies as price escalators in fuel purchasing contracts. Linking to other business studies These data contain Inter-Departmental Business Register reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.Latest edition informationFor the fourth edition (May 2024), a new data file covering the period quarter 1 2015 to quarter 3 2023 was added to the study. A data dictionary covering the same time period was also added, along with the latest version of the Industrial Price Statistics - Data Sources and Methodologies documentation file.

Quarterly panel survey of industrial consumers for purchased volume & value of gas, electricity, gasoil, coal and heavy fuel oil.

This table contains 10 series, with data for years 1999 - 2003 (not all combinations necessarily have data for all years), and was last released on 2007-07-20. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada excluding Territories ...), Type of vehicle (5 items: Total; all vehicles; Trucks 4.5 tonnes to 14.9 tonnes; Trucks 15 tonnes and over; Vehicles up to 4.5 tonnes ...), Type of fuel (2 items: Gasoline; Diesel ...).

Survey of gas sold by companies operating in the UK

This table contains 10 series, with data for years 1999 - 2003 (not all combinations necessarily have data for all years), and was last released on 2007-07-20. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada excluding Territories ...), Type of vehicle (5 items: Total; all vehicles; Trucks 4.5 tonnes to 14.9 tonnes; Trucks 15 tonnes and over; Vehicles up to 4.5 tonnes ...), Type of fuel (2 items: Gasoline; Diesel ...).

This table contains 56 series, with data for years 2004 - 2009 (not all combinations necessarily have data for all years), and was last released on 2014-06-19. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada excluding Territories ...), Type of vehicle (4 items: Total; all vehicles; Trucks 15 tonnes and over; Vehicles up to 4.5 tonnes; Trucks 4.5 tonnes to 14.9 tonnes ...), Type of vehicle body (10 items: Total; all vehicles body types; Car; Station wagon; Van ...), Type of fuel (2 items: Gasoline; Diesel ...).

United States QSS: Revenue: Utilities: Natural Gas Distribution data was reported at 41.636 USD bn in Mar 2018. This records an increase from the previous number of 31.332 USD bn for Dec 2017. United States QSS: Revenue: Utilities: Natural Gas Distribution data is updated quarterly, averaging 23.797 USD bn from Mar 2010 (Median) to Mar 2018, with 33 observations. The data reached an all-time high of 46.908 USD bn in Mar 2014 and a record low of 15.551 USD bn in Sep 2012. United States QSS: Revenue: Utilities: Natural Gas Distribution data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H019: Quarterly Services Survey.

A cells polygon feature class was created by the U. S. Geological Survey (USGS) to illustrate the degree of exploration, type of production, and distribution of production in the State of Pennsylvania. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. Data were retrieved from the Pennsylvania Internet Record Imaging System (PA*IRIS). Cells were developed as a graphic solution to overcome the problem of displaying proprietary well data. No proprietary data are displayed or included in the cell maps. The data are current as of 2006.

This table contains 20 series, with data for years 2009 (not all combinations necessarily have data for all years), and was last released on 2014-06-19. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada excluding Territories ...), Type of vehicle (5 items: Total; all vehicles; Trucks 4.5 tonnes to 14.9 tonnes; Trucks 15 tonnes and over; Vehicles up to 4.5 tonnes ...), Type of fuel (4 items: Total; all fuel types; Gasoline; Other fuel type; Diesel ...).

Abstract

Because of the importance of the household sector and due to it's large contribution to energy consumption in the Palestinian Territory, PCBS decided to conduct a special household energy survey to cover energy indicators in the household sector. To achieve this, a questionnaire was attached to the Labor Force Survey.

This survey aimed to provide data on energy consumption in the household and to provide data on energy consumption behavior in the society by type of energy.

The survey presents data on various energy households indicators in the Palestinian Territory, and presents statistical data on electricity and other fuel consumption for the household, using type of fuel by different activities (cooking, baking, conditioning, lighting, and water Heating).

Analysis unit

Households

Universe

The target population was all Palestinian households living in West Bank and Gaza.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame The sampling frame consists of all the enumeration areas enumerated in 2007: each enumeration area consists of buildings and housing units with an average of around 124 households. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection.

Sample size The estimated sample size is 3,184 households.

Sampling Design: The sample of this survey is a part of the main sample of the Labor Force Survey (LFS), which is implemented quarterly (distributed over 13 weeks) by PCBS since 1995. This survey was attached to the LFS in the third quarter of 2013 and the sample comprised six weeks, from the eighth week to the thirteen week of the third round of the Labor Force Survey of 2013. The sample is two-stage stratified cluster sample:

First stage: selection of a stratified systematic random sample of 206 enumeration areas for the semi-round.

Second stage: selection of a random area sample of an average of 16 households from each enumeration area selected in the first stage.

Sample strata The population was divided by: 1. Governorate (16 governorates) 2. Type of locality (urban, rural, refugee camps)

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire for the Household Energy Survey was based on the experiences of similar countries as well as on international standards and recommendations for the most important indicators, taking into account the special situation of the Palestinian Territory.

Cleaning operations

The data processing stage consisted of the following operations: - Editing and coding prior to data entry: all questionnaires were edited and coded in the office using the same instructions adopted for editing in the field. - Data entry: The household energy survey questionnaire was programmed onto handheld devices and data were entered directly using these devices in the West Bank. With regard to Jerusalem J1 and the Gaza Strip, data were entered into the computer in the offices in Ramallah and Gaza. At this stage, data were entered into the computer using a data entry template developed in Access. The data entry program was prepared to satisfy a number of requirements: · To prevent the duplication of questionnaires during data entry. · To apply checks on the integrity and consistency of entered data. · To handle errors in a user friendly manner. · The ability to transfer captured data to another format for data analysis using statistical analysis software such as SPSS.

Response rate

During fieldwork 3,184 families were visited in the Palestinian Territory. There are 2,692 complete questionnaires, which in percentage was about 85%.

Sampling error estimates

Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences are anticipated in comparison with the real values obtained through censuses. The variance was calculated for the most important indicators: the variance table is attached with the final report. There is no problem in the dissemination of results at national and regional level (North, Middle, South of West Bank, Gaza Strip) and by locality. However, the indicator of averages of household consumption for certain fuels by region show a high variance.

Non Sampling Errors The implementation of the survey encountered non-response where the household was not present at home during the field work visit and where the housing unit was vacant: these made up a high percentage of the non-response cases. The total non-response rate was 10.8%, which is very low when compared to the household surveys conducted by PCBS. The refusal rate was 3.3%, which is very low compared to the household surveys conducted by PCBS and may be attributed to the short and clear questionnaire.

The survey sample consisted of around 3,184 households, of which 2,692 households completed the interview: 1,757 households from the West Bank and 935 households in the Gaza Strip. Weights were modified to account for the non-response rate. The response rate in the West Bank was 86.8 % while in the Gaza Strip it was 94.3%.

Non-Response Cases

No. of cases non-response cases
2,692 Household completed 35 Household traveling 17 Unit does not exist 111 No one at home
102 Refused to cooperate
152 Vacant housing unit 5 No available information
70 Other
3,184 Total sample size

Response and non-response formulas:

Percentage of over coverage errors = Total cases of over coverage x 100% Number of cases in original sample = 5.3%

Non response rate = Total cases of non response x 100% Net Sample size = 10.8%

Net sample = Original sample - cases of over coverage Response rate = 100% - non-response rate = 89.2%

Treatment of non-response cases using weight adjustment

Where

the primary weight before adjustment for the household i g: adjustment group by ( governorate, locality type ). fg: weight adjustment factor for the group g. : Total weights in group g
cases : Total weights of over coverage : Total weights of response cases

We calculate fg for each group ,and final we obtain the final household weight () by using the following formula:

Comparability The data of the survey are comparable geographically and over time by comparing data from different geographical areas to data of previous surveys and the 2007 census.

Data quality assurance procedures Several procedures were undertaken to ensure appropriate quality control in the survey. Field workers were trained on the main skills prior to data collection, field visits were conducted to field workers to ensure the integrity of data collection, editing of questionnaires took place prior to data entry and a data entry application was used that prevents errors during the data entry process, then the data were reviewed. This was done to ensure that data were error free, while cleaning and inspection of anomalous values were carried out to ensure harmony between the different questions on the questionnaire.

Technical notes The following are important technical notes on the indicators presented in the results of the survey: · Some households were not present in their houses and could not be seen by interviewers. · Some households were not accurate in answering the questions in the questionnaire.
· Some errors occurred due to the way the questions were asked by interviewers. · Misunderstanding of the questions by the respondents. · Answering questions related to consumption based on estimations. · In all calculations related to gasoline, the average of all available types of gasoline was used. · In this survey, data were collected about the consumption of olive cake and coal in households, but due to lack of relevant data and fairly high variance, the data were grouped with others in the statistical tables. · The increase in consumption of electricity and the decrease in the consumption of the other types of fuel in the Gaza Strip reflected the Israeli siege imposed on the territory.

Data appraisal

The data of the survey is comparable geographically and over time by comparing the data between different geographical areas to data of previous surveys.

This table contains 10 series, with data for years 1999 - 2003 (not all combinations necessarily have data for all years), and was last released on 2007-07-20. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada excluding Territories ...), Type of vehicle (5 items: Total; all vehicles; Trucks 4.5 tonnes to 14.9 tonnes; Trucks 15 tonnes and over; Vehicles up to 4.5 tonnes ...), Type of fuel (2 items: Gasoline; Diesel ...).

Germany RS: Fuel: Lack of Qualified Employees data was reported at 0.000 % in Jun 2021. This records a decrease from the previous number of 5.000 % for Mar 2021. Germany RS: Fuel: Lack of Qualified Employees data is updated quarterly, averaging 5.000 % from Jun 2006 (Median) to Jun 2021, with 61 observations. The data reached an all-time high of 25.800 % in Dec 2019 and a record low of 0.000 % in Jun 2021. Germany RS: Fuel: Lack of Qualified Employees data remains active status in CEIC and is reported by Ifo Institute - Leibniz Institute for Economic Research at the University of Munich. The data is categorized under Global Database’s Germany – Table DE.S044: Quarterly Business Survey: Retailing: IFO Institute: WZ 2008.

Germany RS: Fuel: Inadequate Equipment data was reported at 0.000 % in Jun 2021. This stayed constant from the previous number of 0.000 % for Mar 2021. Germany RS: Fuel: Inadequate Equipment data is updated quarterly, averaging 0.000 % from Jun 2006 (Median) to Jun 2021, with 61 observations. The data reached an all-time high of 9.600 % in Mar 2009 and a record low of 0.000 % in Jun 2021. Germany RS: Fuel: Inadequate Equipment data remains active status in CEIC and is reported by Ifo Institute - Leibniz Institute for Economic Research at the University of Munich. The data is categorized under Global Database’s Germany – Table DE.S044: Quarterly Business Survey: Retailing: IFO Institute: WZ 2008.

Germany RS: Fuel: Other Obstructive Factors data was reported at 23.500 % in Jun 2021. This records an increase from the previous number of 11.900 % for Mar 2021. Germany RS: Fuel: Other Obstructive Factors data is updated quarterly, averaging 9.500 % from Jun 2006 (Median) to Jun 2021, with 61 observations. The data reached an all-time high of 47.900 % in Sep 2008 and a record low of 0.000 % in Sep 2020. Germany RS: Fuel: Other Obstructive Factors data remains active status in CEIC and is reported by Ifo Institute - Leibniz Institute for Economic Research at the University of Munich. The data is categorized under Global Database’s Germany – Table DE.S044: Quarterly Business Survey: Retailing: IFO Institute: WZ 2008.

A cells polygon feature class was created by the U. S. Geological Survey (USGS) to illustrate the degree of exploration, type of production, and distribution of production in the State of Indiana. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. Cells were developed as a graphic solution to overcome the problem of displaying proprietary well data. No proprietary data are displayed or included in the cell maps. The data are current as of 2006.

Germany RS: Fuel: Obstruction of Business Volume data was reported at 72.500 % in Jun 2021. This records a decrease from the previous number of 86.100 % for Mar 2021. Germany RS: Fuel: Obstruction of Business Volume data is updated quarterly, averaging 46.500 % from Jun 2006 (Median) to Jun 2021, with 61 observations. The data reached an all-time high of 86.100 % in Mar 2021 and a record low of 17.300 % in Dec 2006. Germany RS: Fuel: Obstruction of Business Volume data remains active status in CEIC and is reported by Ifo Institute - Leibniz Institute for Economic Research at the University of Munich. The data is categorized under Global Database’s Germany – Table DE.S044: Quarterly Business Survey: Retailing: IFO Institute: WZ 2008.

Germany RS: Fuel: Lack of Space & Real Estate data was reported at 5.900 % in Jun 2021. This records an increase from the previous number of 0.000 % for Mar 2021. Germany RS: Fuel: Lack of Space & Real Estate data is updated quarterly, averaging 0.000 % from Jun 2006 (Median) to Jun 2021, with 61 observations. The data reached an all-time high of 6.700 % in Dec 2018 and a record low of 0.000 % in Mar 2021. Germany RS: Fuel: Lack of Space & Real Estate data remains active status in CEIC and is reported by Ifo Institute - Leibniz Institute for Economic Research at the University of Munich. The data is categorized under Global Database’s Germany – Table DE.S044: Quarterly Business Survey: Retailing: IFO Institute: WZ 2008.

This table contains 25 series, with data for years 1999 - 2009 (not all combinations necessarily have data for all years), and was last released on 2014-06-19. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Type of vehicle (5 items: Total; all vehicles; Vehicles up to 4.5 tonnes; Trucks 4.5 tonnes to 14.9 tonnes; Trucks 15 tonnes and over ...), Type of fuel (5 items: Total; all fuel types; Other fuel type; Gasoline; Diesel ...).

Quarterly survey of tariff details and customer numbers for the 6 major domestic electricity and gas suppliers, plus a few independents.