Data analysis raw data in a PDF file

Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demographic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor characteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Expenditure and Income survey sample for 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the country. Jordan is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map.

It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

• General form
• Expenditure on food commodities form
• Expenditure on non-food commodities form

Cleaning operations

Raw Data: - Organizing forms/questionnaires: A compatible archive system was used to classify the forms according to different rounds throughout the year. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms were back to the archive system. - Data office checking: This phase was achieved concurrently with the data collection phase in the field where questionnaires completed in the field were immediately sent to data office checking phase. - Data coding: A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were used, while for the rest of the questions, coding was predefined during the design phase. - Data entry/validation: A team consisting of system analysts, programmers and data entry personnel were working on the data at this stage. System analysts and programmers started by identifying the survey framework and questionnaire fields to help build computerized data entry forms. A set of validation rules were added to the entry form to ensure accuracy of data entered. A team was then trained to complete the data entry process. Forms prepared for data entry were provided by the archive department to ensure forms are correctly extracted and put back in the archive system. A data validation process was run on the data to ensure the data entered is free of errors. - Results tabulation and dissemination: After the completion of all data processing operations, ORACLE was used to tabulate the survey final results. Those results were further checked using similar outputs from SPSS to ensure that tabulations produced were correct. A check was also run on each table to guarantee consistency of figures presented, together with required editing for tables' titles and report formatting.

Harmonized Data: - The Statistical Package for Social Science (SPSS) was used to clean and harmonize the datasets. - The harmonization process started with cleaning all raw data files received from the Statistical Office. - Cleaned data files were then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program was generated for each dataset to generate/compute/recode/rename/format/label harmonized variables. - A post-harmonization cleaning process was run on the data. - Harmonized data was saved on the household as well as the individual level, in SPSS and converted to STATA format.

Raw data and descriptive statistic data of the market survey performed with the Add-In XLSTAT 2009.1.02 is provided as Excel-file (CSV). The data include file name, sample name, area, calculated N2O amounts, test result and statistical values.

Various population statistics, including structured demographics data.

dataset and Octave/MatLab codes/scripts for data analysis Background: Methods for p-value correction are criticized for either increasing Type II error or improperly reducing Type I error. This problem is worse when dealing with thousands or even hundreds of paired comparisons between waves or images which are performed point-to-point. This text considers patterns in probability vectors resulting from multiple point-to-point comparisons between two event-related potentials (ERP) waves (mass univariate analysis) to correct p-values, where clusters of signiticant p-values may indicate true H0 rejection. New method: We used ERP data from normal subjects and other ones with attention deficit hyperactivity disorder (ADHD) under a cued forced two-choice test to study attention. The decimal logarithm of the p-vector (p') was convolved with a Gaussian window whose length was set as the shortest lag above which autocorrelation of each ERP wave may be assumed to have vanished. To verify the reliability of the present correction method, we realized Monte-Carlo simulations (MC) to (1) evaluate confidence intervals of rejected and non-rejected areas of our data, (2) to evaluate differences between corrected and uncorrected p-vectors or simulated ones in terms of distribution of significant p-values, and (3) to empirically verify rate of type-I error (comparing 10,000 pairs of mixed samples whit control and ADHD subjects). Results: the present method reduced the range of p'-values that did not show covariance with neighbors (type I and also type-II errors). The differences between simulation or raw p-vector and corrected p-vectors were, respectively, minimal and maximal for window length set by autocorrelation in p-vector convolution. Comparison with existing methods: Our method was less conservative while FDR methods rejected basically all significant p-values for Pz and O2 channels. The MC simulations, gold-standard method for error correction, presented 2.78±4.83% of difference (all 20 channels) from p-vector after correction, while difference between raw and corrected p-vector was 5,96±5.00% (p = 0.0003). Conclusion: As a cluster-based correction, the present new method seems to be biological and statistically suitable to correct p-values in mass univariate analysis of ERP waves, which adopts adaptive parameters to set correction.

Information on data sources for field analyzer manuscript calculations. This dataset is not publicly accessible because: This data was not generated by EPA, but rather used by EPA researchers to calculate basic statistics (R square and slope), as part of this literature review. It can be accessed through the following means: These two old conference proceedings are available in book volumes that can be found in libraries, with page numbers as specified below: - Argent, V.A., Southall, J.M. and D'Costa, E. (1994) Analysis of water for lead and copper using disposable sensor technology. American Water Works Association – Annual Conference, pp. 43-54, New York, New York. - Wiese, P.M. (1989) Monitoring method for lead in first-draw drinking water samples. American Water Works Association - Annual Conference and Exposition, pp. 1309-1313, Los Angeles, California. Format: Data from three tables in two old conference proceedings were used to calculate basic statistics (R square and slope): - Table 2 and 4 in Proceeding "Argent, V.A., Southall, J.M. and D'Costa, E. (1994) Analysis of water for lead and copper using disposable sensor technology. American Water Works Association – Annual Conference, pp. 43-54, New York, New York." - Table 2 in Proceeding "Wiese, P.M. (1989) Monitoring method for lead in first-draw drinking water samples. American Water Works Association - Annual Conference and Exposition, pp. 1309-1313, Los Angeles, California.".

This dataset is associated with the following publication: Dore, E., D. Lytle, L. Wasserstrom, J. Swertfeger, and S. Triantafyllidou. Field Analyzers for Lead Quantification in Drinking Water Samples. CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY. CRC Press LLC, Boca Raton, FL, USA, 50(20): 999-999, (2020).

Germany Exports: Trade Industry: Raw Materials data was reported at 941.243 EUR mn in Dec 2024. This records an increase from the previous number of 926.710 EUR mn for Nov 2024. Germany Exports: Trade Industry: Raw Materials data is updated monthly, averaging 936.184 EUR mn from Jan 2000 (Median) to Dec 2024, with 300 observations. The data reached an all-time high of 2,209.876 EUR mn in Mar 2022 and a record low of 440.000 EUR mn in Sep 2001. Germany Exports: Trade Industry: Raw Materials data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.JA001: Trade Statistics: Value.

A csv file containing the tidal frequencies used for statistical analyses in the paper "Estimating Freshwater Flows From Tidally-Affected Hydrographic Data" by Dan Pagendam and Don Percival.

ml_admin_stats_raw: Contains administrative statistics from the 2011 census and aser surveys used in online Appendix Table 1 in the paper; this is merged with some of the survey data to create ml_admin_stats

United States Exports: 3-Digit: MX: Furskins and Raw data was reported at 0.000 USD mn in May 2018. This stayed constant from the previous number of 0.000 USD mn for Apr 2018. United States Exports: 3-Digit: MX: Furskins and Raw data is updated monthly, averaging 0.000 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 0.506 USD mn in Jan 2016 and a record low of 0.000 USD mn in May 2018. United States Exports: 3-Digit: MX: Furskins and Raw data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA099: Trade Statistics: Mexico: Exports: FAS: SITC.

Abstract

The basic goal of this survey is to provide the necessary database for formulating national policies at various levels. It represents the contribution of the household sector to the Gross National Product (GNP). Household Surveys help as well in determining the incidence of poverty, and providing weighted data which reflects the relative importance of the consumption items to be employed in determining the benchmark for rates and prices of items and services. Generally, the Household Expenditure and Consumption Survey is a fundamental cornerstone in the process of studying the nutritional status in the Palestinian territory.

The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality. Data is a public good, in the interest of the region, and it is consistent with the Economic Research Forum's mandate to make micro data available, aiding regional research on this important topic.

Geographic coverage

The survey data covers urban, rural and camp areas in West Bank and Gaza Strip.

Analysis unit

1- Household/families. 2- Individuals.

Universe

The survey covered all Palestinian households who are usually resident in the Palestinian Territory during 2010.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample and Frame:

The sampling frame consists of all enumeration areas which were enumerated in 2007, each numeration area consists of buildings and housing units with average of about 120 households in it. These enumeration areas are used as primary sampling units PSUs in the first stage of the sampling selection.

Sample Design:

The sample is a stratified cluster systematic random sample with two stages: First stage: selection of a systematic random sample of 192 enumeration areas. Second stage: selection of a systematic random sample of 24 households from each enumeration area selected in the first stage.

Note: in Jerusalem Governorate (J1), 13 enumeration areas were selected; then in the second phase, a group of households from each enumeration area were chosen using census-2007 method of delineation and enumeration. This method was adopted to ensure household response is to the maximum to comply with the percentage of non-response as set in the sample design.Enumeration areas were distributed to twelve months and the sample for each quarter covers sample strata (Governorate, locality type) Sample strata:

The population was divided by:

1- Governorate 2- Type of Locality (urban, rural, refugee camps)

Sample Size:

The calculated sample size for the Expenditure and Consumption Survey in 2010 is about 3,757 households, 2,574 households in West Bank and 1,183 households in Gaza Strip.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire consists of two main parts:

First: Survey's questionnaire

Part of the questionnaire is to be filled in during the visit at the beginning of the month, while the other part is to be filled in at the end of the month. The questionnaire includes:

Control sheet: Includes household’s identification data, date of visit, data on the fieldwork and data processing team, and summary of household’s members by gender.

Household roster: Includes demographic, social, and economic characteristics of household’s members.

Housing characteristics: Includes data like type of housing unit, number of rooms, value of rent, and connection of housing unit to basic services like water, electricity and sewage. In addition, data in this section includes source of energy used for cooking and heating, distance of housing unit from transportation, education, and health centers, and sources of income generation like ownership of farm land or animals.

Food and Non-Food Items: includes food and non-food items, and household record her expenditure for one month.

Durable Goods Schedule: Includes list of main goods like washing machine, refrigerator,TV.

Assistances and Poverty: Includes data about cash and in kind assistances (assistance value,assistance source), also collecting data about household situation, and the procedures to cover expenses.

Monthly and annual income: Data pertinent to household’s income from different sources is collected at the end of the registration period.

Second: List of goods

The classification of the list of goods is based on the recommendation of the United Nations for the SNA under the name Classification of Personal Consumption by purpose. The list includes 55 groups of expenditure and consumption where each is given a sequence number based on its importance to the household starting with food goods, clothing groups, housing, medical treatment, transportation and communication, and lastly durable goods. Each group consists of important goods. The total number of goods in all groups amounted to 667 items for goods and services. Groups from 1-21 includes goods pertinent to food, drinks and cigarettes. Group 22 includes goods that are home produced and consumed by the household. The groups 23-45 include all items except food, drinks and cigarettes. The groups 50-55 include durable goods. The data is collected based on different reference periods to represent expenditure during the whole year except for cars where data is collected for the last three years.

Registration form

The registration form includes instructions and examples on how to record consumption and expenditure items. The form includes columns: 1.Monetary: If the good is purchased, or in kind: if the item is self produced. 2.Title of the service of the good 3.Unit of measurement (kilogram, liter, number) 4. Quantity 5. Value

The pages of the registration form are colored differently for the weeks of the month. The footer for each page includes remarks that encourage households to participate in the survey. The following are instructions that illustrate the nature of the items that should be recorded: 1. Monetary expenditures during purchases 2. Purchases based on debts 3.Monetary gifts once presented 4. Interest at pay 5. Self produced food and goods once consumed 6. Food and merchandise from commercial project once consumed 7. Merchandises once received as a wage or part of a wage from the employer.

Cleaning operations

Raw Data

Data editing took place through a number of stages, including: 1. Office editing and coding 2. Data entry 3. Structure checking and completeness 4. Structural checking of SPSS data files

Harmonized Data

• The Statistical Package for Social Science (SPSS) is used to clean and harmonize the datasets.
• The harmonization process starts with cleaning all raw data files received from the Statistical Office.
• Cleaned data files are then all merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/compute/recode/rename/format/label harmonized variables.
• A post-harmonization cleaning process is run on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and converted to STATA format.

Response rate

The survey sample consisted of 4,767 households, which includes 4,608 households of the original sample plus 159 households as an additional sample. A total of 3,757 households completed the interview: 2,574 households from the West Bank and 1,183 households in the Gaza Strip. Weights were modified to account for the non-response rate. The response rate in the Palestinian Territory 28.1% (82.4% in the West Bank was and 81.6% in Gaza Strip).

Sampling error estimates

The impact of errors on data quality was reduced to a minimum due to the high efficiency and outstanding selection, training, and performance of the fieldworkers. Procedures adopted during the fieldwork of the survey were considered a necessity to ensure the collection of accurate data, notably: 1) Develop schedules to conduct field visits to households during survey fieldwork. The objectives of the visits and the data collected on each visit were predetermined. 2) Fieldwork editing rules were applied during the data collection to ensure corrections were implemented before the end of fieldwork activities. 3) Fieldworkers were instructed to provide details in cases of extreme expenditure or consumption by the household. 4) Questions on income were postponed until the final visit at the end of the month. 5) Validation rules were embedded in the data processing systems, along with procedures to verify data entry and data edit.

This dataset contains raw data and statistical summaries that reflect use of keywords in LTER Datasets in May 2018 and 2006. Specific summaries include: Number of uses and number sites by keyword (LTERVocabKeywordSummary.csv), Summary of keyword use by data package (LTERVocabDataPackageSummary.csv), Summary of Keyword Use by LTER Site in 2018(LTERVocabSiteSummary.csv), Summary of Keyword Use by LTER Site in 2006(KeyStats2006.csv). Raw data includes XML files containing the US LTER Thesaurus in Moodle format and the ResultSet containing the information for each dataset from the Environmental Data Initiative PASTA repository.

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

Luxembourg Imports from United States of Peanuts (ground-nuts), raw was US$407.37 Thousand during 2023, according to the United Nations COMTRADE database on international trade. Luxembourg Imports from United States of Peanuts (ground-nuts), raw - data, historical chart and statistics - was last updated on March of 2025.

Dominican Republic Imports from Mexico of Peanuts (ground-nuts), raw was US$3.41 Thousand during 2014, according to the United Nations COMTRADE database on international trade. Dominican Republic Imports from Mexico of Peanuts (ground-nuts), raw - data, historical chart and statistics - was last updated on March of 2025.

United States Imports: 3-Digit: JP: Furskins and Raw data was reported at 0.000 USD mn in May 2018. This stayed constant from the previous number of 0.000 USD mn for Apr 2018. United States Imports: 3-Digit: JP: Furskins and Raw data is updated monthly, averaging 0.000 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 0.027 USD mn in Feb 1997 and a record low of 0.000 USD mn in May 2018. United States Imports: 3-Digit: JP: Furskins and Raw data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA094: Trade Statistics: Japan: Imports: Customs: SITC.

The source of the data in this table are municipalities and CBS offers them as a service as open data.

Statistics Netherlands (CBS) receives data from municipalities as part of the Information for Third Parties (Iv3) reports. The data in the table have not been edited by Statistics Netherlands. This type of data is also referred to as 'unprocessed data'. CBS bears no responsibility for the quality of the data. The data in Statistics Netherlands' own publications do not have to be traced back one-on-one to the data in this table.

The table contains raw Iv3 data from all reporting types of one reporting year. The types of reports are the budget, the four quarters and the annual accounts. If a municipality has not provided Iv3 data for a report type, then this municipality is included in the table, but each cell has the value '.', in the sense of missing. This is particularly the case for the quarterly accounts of municipalities with fewer than 20 thousand inhabitants (size classes 6.7 and 8), as they are not obliged to supply them to Statistics Netherlands.

The codes used in the table for the categories on the one hand and the task fields and balance sheet items on the other hand, as well as their meaning, are derived from the 'Decree on the budget and accountability of provinces and municipalities' (BBV) of the Ministry of the Interior and Kingdom Relations. The BBV contains, among other things, the regulations for the deliveries of Iv3 data to CBS.

For each type of report, all reports received so far are published at the same time at two points in time. The reason for placing the data a second time is that CBS gives municipalities the opportunity to provide an improved Iv3 dataset. The data that is placed the first time has the value '1st placement' in the topic 'Place'. The data that is placed the second time has the value '2nd placement'.

Data available from: 2025.

Status of figures The figures in this table are final upon publication (i.e., subject to exceptions, once published data are no longer updated).

Changes as of 2 December 2024: None, this is a new table. Figures for the first allocation of the 2025 budget are included.

When will there be new figures? The time of publication of new figures for a type of report depends on the deadline for submission to Statistics Netherlands that applies to the type of report in question. For budgets for year j, the deadline for submission is 14 November in the year preceding the budget year (j-1). For quarterly data for the first, second and third quarters of year j, this is one month after the end of the quarter. For submission of the fourth quarter of year j, a deadline of 14 February in the year following the reporting year (j+1) applies. Finally, for the annual accounts for year j, this date is 14 July in the year following the reporting year (j+1). All reports received for a report type are published at the same time. This publication happens twice. The first time is 10 days after the submission deadline. If this day falls on the weekend or on a public holiday, the dates will be published on the next working day. With this placement, the most recent report received by each reporter will be published and received no later than 5 days after the deadline for submission. The second time is 70 days after the submission deadline. If this day falls on the weekend or on a public holiday, the dates will be published on the next working day. With this placement, the most recent report received by each reporter will be published and received no later than two months after the deadline for submission. The second allocation of the budget will (possibly) take place in phases: the figures of municipal reclassifications that were missing due to postponement in the regular second placement will be published by the end of May at the latest. The distinction between the first and the second placement can be seen in the subject.

The source of the data in this table are provinces and CBS offers this as a service as open data. Statistics Netherlands receives data from provinces in the context of the Information for Third Parties (Iv3) reports. The data in the table has not been processed by Statistics Netherlands. This type of data is also referred to as 'raw data'. Statistics Netherlands is not responsible for the quality of the data. The data in Statistics Netherlands' own publications need not be traceable one-to-one to the data in this table. The table contains raw Iv3 data of all reporting types from one reporting year. The report types are the budget, the four quarters and the annual accounts. If a province has not provided Iv3 data for a report type, then this province is included in the table, but each cell has the value '.' (missing). The coding used in the table for the categories on the one hand and the task fields and balance sheet items on the other hand, as well as their meaning, are derived from the 'Decree on the budget and accountability of provinces and municipalities' (BBV) of the Ministry of the Interior and Kingdom Relations. The BBV contains, among other things, the regulations for the supply of Iv3 data to Statistics Netherlands. For each type of report, all reports received so far are published simultaneously at two times. The reason for posting the data a second time is that CBS is giving the provinces the opportunity to provide an improved Iv3 dataset. The data that is placed for the first time has the value '1st placement' in the subject 'Placement'. The data that is placed the second time has the value '2nd placement'. Data available from: 2021. Status of the figures The figures in this table are final upon publication (i.e., barring exceptions, data once published will no longer be updated). Changes as of September 22, 2022: The figures of the second placement of the year 2021 have been included. When will new numbers come out? The time of publication of new figures for a type of report depends on the deadline for submission to Statistics Netherlands that applies to the relevant type of report. For budgets for year j, the submission deadline is 14 November in the year preceding budget year (y-1). For the quarterly data of the first, second and third quarters of year j, it is one month after the end of the quarter. For submission of the fourth quarter of year j, a final submission date of 14 February in the year following the year under review (j+1) applies. Finally, for the annual accounts of year j, this date is 14 July in the year following the year under review (j+1). All reports received for a report type are published at the same time. This publishing happens twice. The first time is 10 days after the submission deadline. If this day falls on a weekend or public holiday, the data will be published on the next working day. With this posting, the most recently received report is published for each reporter, which was received no later than 5 days after the submission deadline. The second time is 70 days after the submission deadline. If this day falls on a weekend or public holiday, the data will be published on the next working day. With this posting, the most recently received report is published for each reporter, which was received no later than two months after the submission deadline. The distinction between the first and second placement can be seen in the subject.

Abstract copyright UK Data Service and data collection copyright owner.The People and Nature Survey for England is one of the main sources of data and statistics on how people experience and think about the environment. It began collecting data in April 2020 and has been collecting data since. The survey builds on the Monitor of Engagement with the Natural Environment (MENE) survey which ran from 2009 to 2019. Data from the People and Nature Survey for England enables users to:understand how people use, enjoy, and are motivated to protect the natural environmentmonitor changes in use of the natural environment over time, at a range of different spatial scales and for key groups within the populationunderstand how being in the natural environment can influence wellbeingunderstand environmental attitudes and the actions people take at home, in the garden and in the wider community to protect the environmentThis data contributes to Natural England’s delivery of statutory duties, informs Defra policy and natural capital accounting, and contributes to the outcome indicator framework for the 25 Year Environment Plan.Different versions of the People and Nature Survey for England are available from the UK Data Archive under Open Access (SN 9092) conditions, End User Licence (SN 9093), and Secure Access (SN 9094). The Secure Access version includes the same data as the End User Licence version, but includes more detailed variables including:age as a continuous variablesexwhether gender is the same as at birthsexual orientationmore detailed ethnicitywhere journey to recent visit to green and natural space started fromvisit datedetailed home geography, including local authority district, urban/rural area, and Index of Multiple Deprivationa number of variables that have not been top-coded, including number of children and number of children in household, food and drink expenditure, and incomeThe Open Access version includes the same data as the End User Licence version, but does not include the following variables:age bandgender identitymarital statusnumber of children living in householdnumber of childrenwork statusstudent working statusincomequalificationethnicity and consent to answer ethnicity questionnumber of vehiclespresence of dog in householdphysical activityvarious health dataResearchers are advised to review the Open Access and/or the End User Licence versions to determine if these are adequate prior to ordering the Secure Access version.Accredited official statistics are called National Statistics in the Statistics and Registration Service Act 2007. An explanation can be found on the Office for Statistics Regulation website.Natural England's statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to. These accredited official statistics were independently reviewed by the Office for Statistics Regulation in January 2023. They comply with the standards of trustworthiness, quality and value in the Code of Practice for Statistics and should be labelled ‘accredited official statistics’.Users are welcome to contact Natural England directly at people_and_nature@naturalengland.org.uk with any comments about how they meet these standards. Alternatively, users can contact OSR by emailing regulation@statistics.gov.uk or via the OSR website.Since the latest review by the Office for Statistics Regulation, Natural England have continued to comply with the Code of Practice for Statistics, and have made the following improvements:Published a development plan with timetables for future work, which will be updated annuallyEnsured that users have opportunities to contribute to development planning through their biannual Research User GroupEnabled wider access to the data by publishing raw data sets through the UK Data ServiceProvided users with guidance on how statistics from their products can be compared with those produced in the devolved nationsPublished guidance on the differences between PaNS and MENEImproved estimates of the percentage of people visiting nature in the previous 14 days by reducing the amount of respondents answering ‘don’t know’.These data are available in Excel, SPSS, as well as Open Document Spreadsheet (ODS) formats. For the fifth edition (June 2024), data for October to December 2023 have been added. Main Topics: