Weekly Economic Calendar shows future release dates of key economic data and publications used by NSW Treasury for monitoring and analysis.

Weekly Economic Calendar shows future release dates of key economic data and publications used by NSW Treasury for monitoring and analysis.

Retail Sales in the United States increased 0.60 percent in June of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Weekly Economic Calendar shows future release dates of key economic data and publications used by NSW Treasury for monitoring and analysis.

Key Table Information.Table Title.Selected Sectors: Industry by Products for the U.S. and States: 2022.Table ID.ECNNAPCSIND2022.EC2200NAPCSINDPRD.Survey/Program.Economic Census.Year.2022.Dataset.ECN Multi-Sector Statistics Product Statistics.Source.U.S. Census Bureau, 2022 Economic Census.Release Date.2025-05-29.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of establishmentsQuantity produced for the NAPCS collection code (sectors 21 and 31-33 only, units defined by Unit of Measurement column)Quantity shipped for the NAPCS collection code (sectors 21 and 31-33 only, units defined by Unit of Measurement column)Sales, value of shipments, or revenue of NAPCS collection code ($1,000)NAPCS collection code sales, value of shipments, or revenue as % of industry sales, value of shipments, or revenue (%)NAPCS collection code sales, value of shipments, or revenue as % of total sales, value of shipments, or revenue of establishments with the NAPCS collection code (%)Number of establishments with NAPCS collection code as % of industry establishments (%)Coefficient of variation for number of establishments (%)Coefficient of variation for quantity produced for the NAPCS collection code (%)Coefficient of variation for quantity shipped for the NAPCS collection code (%)Coefficient of variation for NAPCS collection code sales, value of shipments, or revenue (%)Standard error of NAPCS collection code sales, value of shipments, or revenue as % of industry sales, value of shipments, or revenue (%)Standard error of NAPCS collection code sales, value of shipments, or revenue as % of total sales, value of shipments, or revenue of establishments with the NAPCS collection code (%)Standard error of number of establishments with NAPCS collection code as % of industry establishments (%)Range indicating imputed percentage of total NAPCS collection code sales, value of shipments, or revenueDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S. level for all sectors and at the U.S. and state levels for sectors 44-45, 61, 62, 71, 72, and 81. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 6-digit 2022 NAICS code levels for all sectors except Agriculture and for selected 7- and 8-digit 2022 NAICS-based code levels for various sectors. For information about NAICS, see Economic Census Code Lists..Business Characteristics.For Wholesale Trade (42), data are presented by Type of Operation (All establishments; Merchant Wholesalers, except Manufacturers’ Sales Branches and Offices; and Manufacturers’ Sales Branches and Offices).For selected Services sectors, data are presented by Tax Status (All establishments, Establishments subject to federal income tax, and Establishments exempt from federal income tax)..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For some data on this table, estimates come only from the establishments selected into the sample. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review ...

Wholesale Inventories in the United States decreased 0.30 percent in May of 2025 over the previous month. This dataset provides - United States Wholesale Inventories - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Initial Jobless Claims in the United States decreased to 221 thousand in the week ending July 12 of 2025 from 228 thousand in the previous week. This dataset provides the latest reported value for - United States Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Datasource: Statistics Canada. 2008. Profile for Canada, Provinces, Territories, Census Divisions and Census Subdivisions, 2006 Census (table). Cumulative Electronic Profiles. Statistics Canada Catalogue no. 95F0495XCB01001. Ottawa. May 01, 2008. Version modified July 29,2008. http://www12.statcan.ca/english/census06/data/profiles/release/ListProdu... (accessed November 7, 2008). Statistics Canada. 2009. 2006 Semi-custom Profile of Yukon CSD Aggregations, 2006 Census (table). CRO0104245. Ottawa. March 17, 2009. Footnotes: A value of 0 in any given cell represents one of the following: 1) value is actually zero; 2) value may be random rounded to zero; or 3) value is more than zero but is suppressed for confidentiality reasons. This table is based on 20% data. Values have been subjected to a confidentiality procedure known as random rounding. For Statistics Canada's definition of terms, http://www12.statcan.ca/english/census06/reference/dictionary/atoz.cfm.

Abstract

The World Bank and UNHCR in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley are conducting the Kenya COVID-19 Rapid Response Phone Survey to track the socioeconomic impacts of the COVID-19 pandemic, the recovery from it as well as other shocks to provide timely data to inform a targeted response. This dataset contains information from eight waves of the COVID-19 RRPS, which is part of a panel survey that targets refugee household and started in May 2020. The same households were interviewed every two months for five survey rounds, in the first year of data collection, and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques. The sample aims to be representative of the refugee and stateless population in Kenya. It comprises five strata: Kakuma refugee camp, Kalobeyei settlement, Dadaab refugee camp, urban refugees, and Shona stateless. Waves 1-7 of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge. Wave 8 focused on how households were exposed to shocks, in particular adverse weather shocks and the increase in the price of food and fuel, but also included parts of the previous modules on household background, service access, employment, food security, income loss, and subjective wellbeing. The data is uploaded in three files. The first is the hh file, which contains household level information. The 'hhid', uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the 'adult_id'. The third file is the child level file, available only for waves 3-7, which contains information for every child in the household. Each child in a household is uniquely identified by the 'child_id'. The duration of data collection and sample size for each completed wave was: Wave 1: May 14 to July 7, 2020; 1,328 refugee households Wave 2: July 16 to September 18, 2020; 1,699 refugee households Wave 3: September 28 to December 2, 2020; 1,487 refugee households Wave 4: January 15 to March 25, 2021; 1,376 refugee households Wave 5: March 29 to June 13, 2021; 1,562 refugee households Wave 6: July 14 to November 3, 2021; 1,407 refugee households Wave 7: November 15, 2021, to March 31, 2022; 1,281 refugee households Wave 8: May 31 to July 8, 2022: 1,355 refugee households The same questionnaire is also administered to nationals in Kenya, with the data available in the WB microdata library: https://microdata.worldbank.org/index.php/catalog/3774

Geographic coverage

National coverage covering rural and urban areas

Analysis unit

Individual and Household

Universe

All persons of concern for UNHCR

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample aims to be representative of the refugee and stateless population in Kenya. It comprises five strata: Kakuma refugee camp, Kalobeyei settlement, Dadaab refugee camp, urban refugees, and Shona stateless, where sampling approaches differ across strata. For refugees in Kakuma and Kalobeyei, as well as for stateless people, recently conducted Socioeconomic Surveys (SES), were used as sampling frames. For the refugee population living in urban areas and the Dadaab camp, no such household survey data existed, and sampling frames were based on UNHCR's registration records (proGres), which include phone numbers. For Kakuma, Kalobeyei, Dadaab and urban refugees, a two-step sampling process was used. First, 1,000 individuals from each stratum were selected from the corresponding sampling frames. Each of these individuals received a text message to confirm that the registered phone was still active. In the second stage, implicitly stratifying by sex and age, the verified phone number lists were used to select the sample. Until wave 7 sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. In wave 8 only households that had previously participated in the survey were contacted for interview. The “wave” variable represents in which wave the households were interviewed in. For the stateless population, all the participants of the Shona socioeconomic survey (n=400) were included in the RRPS, because of limited sample size. The sampling frames for the refugee and Shona stateless communities are thus representative of households with active phone numbers registered with UNHCR.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire included 12 sections Section 1: Introduction Section 2: Household background Section 3: Travel patterns and interactions Section 4: Employment Section 5: Food security Section 6: Income Loss Section 7: Transfers Section 8: Subjective welfare (50% of sample) Section 9: Health Section 10: COVID Knowledge Section 11: Household and Social Relations (50% of sample) Section 12: Conclusion

Cleaning operations

Variable names were kept constant across survey waves. For questions that remained exactly the same across survey waves, data points for all waves can be found under one variable name. For questions where the phrasing changed (even in a minimal way) across waves, variable names were also changed to reflect the change in phrasing. Extended missing values are used to indicate why a value is missing for all variables. The following extended missing values are used in the dataset: · .a for 'Don't know' · .b for 'Refused to respond' · .c for 'Outliers set to missing' · .d for 'Inconsistency set to missing' (used for employment data as explained below) · .e for 'Field Skipped' (where an error in the survey tool caused the question to be missed) · .z for 'Not administered' (as the variable was not relevant to the observation) More detailed data on children was collected between waves 3 and 7, compared to waves 1, 2 and 8. In waves 1 and 2, data on children, e.g. on their learning activities, was collected for all children in a household with one question. Therefore, variables related to children are part of the 'hh' data for waves 1 and 2. Between waves 3 and 7, questions on children in the household were asked for specific children. Some questions covered all children, while others were only administered to one randomly selected child in the household. This approach allows to disaggregate data at the level of the child household members, and the data can be found in the 'child' data set. The household level weights can be used for analysis of the children's data. In wave 8, detailed information on children was dropped, as the questionnaire focused on other topics. The education status of household members, except for the respondent, was imputed for rounds 1 and 2. For rounds 1 and 2, only the education status of the respondent was elicited, while for later rounds the education status for each household member was asked. In order to evaluate outcomes by the household member's education status, information on education was imputed for waves 1 and 2, using the information provided for all household members in waves 3, 4, and 5. This resulted in additional information on the education status for household members in round 1 and 2, which was not yet available for earlier versions of this data. Some questions are not asked repeatedly across waves such that their values were imputed. For some questions, answers are not possible or unlikely to change within two months between survey waves such that households were not asked about them in all waves. The questions on assets owned before March 2020 were only asked to households when they are interviewed for the first time. The questions on the dwelling's wall and floor material as well as the household's connection to the power grid was not asked for all households in wave 2 and 3, where only new households and those who moved were covered by these questions. Questions on the main source of electricity in the households and types of assets owned were not asked in wave 8. The missing values those variables have when they were not asked, are imputed from the answers given in earlier waves. Improved quality insurance algorithms lead to minor revisions to wave 1 to 5 data. Based on additional data checks, the team has made minor refinements to wave 1 to 5 data. The identification of the household members that were the respondent or the household head was refined in the rare cases where it was not possible to interview the same respondent as in previous waves for a given household such that another adult was interviewed. For this reason, for about 2 percent of observations the household head status was assigned to an incorrect household member, which was corrected. For <1 percent of households the respondent did not appear in adult level dataset. For about 1 percent of observations in wave 5 the respondent appeared twice in the adult level dataset. Data from questions on COVID-19 vaccinations from wave 7 was dropped from the dataset. Due to significantly higher self-reported vaccination rates compared to official administrative records, data on vaccinations was deemed unreliable, most likely due to social desirability bias. Consequently, questions on vaccination status and questions using the vaccination data as a validation criterion were dropped from the datasets.

Datasource: Statistics Canada. 2008. Profile for Canada, Provinces, Territories, Census Divisions and Census Subdivisions, 2006 Census (table). Cumulative Electronic Profiles. Statistics Canada Catalogue no. 95F0495XCB01001. Ottawa. May 01, 2008. Version modified July 29,2008. http://www12.statcan.ca/english/census06/data/profiles/release/ListProdu... (accessed November 7, 2008). Statistics Canada. 2009. 2006 Semi-custom Profile of Yukon CSD Aggregations, 2006 Census (table). CRO0104245. Ottawa. March 17, 2009. Footnotes: A value of 0 in any given cell represents one of the following: 1) value is actually zero; 2) value may be random rounded to zero; or 3) value is more than zero but is suppressed for confidentiality reasons. This table is based on 20% data. Values have been subjected to a confidentiality procedure known as random rounding. For Statistics Canada's definition of terms, http://www12.statcan.ca/english/census06/reference/dictionary/atoz.cfm.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. The dataset consists of following shapefiles: a) Alluvium_Bores_with_Water_Source_Areas.shp: consists of all alluvial bores with information related to the water source they are extracting water from b) AssginingWaterSourcetoSWPoints.shp: consists of all surface water elements (extraction points) located within the zone of potential hydraulic change (ZoPHC) along with the water source areas c) GLO_GWMgmtzones.shp & SW_Elements_WaterSourceArea.shp: shapefiles representing the management and water source areas intersecting the Gloucester subregion d) GLO_ZoPHC_footprint_20161117.shp: shapefile representing the zone of potential hydraulic change. e) GM_GLO_ElementList_poly_SW.shp: shapefile repenting the surface water polygon elements in the Gloucester Assets database f) GM_GLO_ElementList_pt_GW.shp & GM_GLO_ElementList_pt_SW.shp: shapefiles representing the groundwater bores as well as surface water extraction points as in the Gloucester Assets database. g) GWBores_in_Gloucester_Basin_GMA.shp: & GWBores_in_New_England_Fold_Belt_Coast_GMA.shp: shapefile representing the bores located in the Gloucester basin and New England Fold Belt Coast groundwater management areas of the North Coast Fractured and Porous Rock Groundwater Sources 2016 Water Sharing Plan Dataset History This dataset consists of economic elements extracted from the Gloucester Assets Database and intersecting the zone of potential hydraulic changes for the Gloucester subregion. The shapefiles were created after created a set of queries defined in ARCGIS software for shapefile datasets in the GIS folder of the Assets database. The groundwater management areas intersecting the zone of potential hydraulic change were extracted from the Management zones dataset and a shapefile was created by developing a query in the ARCGIS software. Dataset Citation Bioregional Assessment Programme (2017) GLO Economic Elements ZoPHC v01. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/d4c64d64-6646-4188-a10f-9525743dd9c1. Dataset Ancestors Derived From Standard Instrument Local Environmental Plan (LEP) - Heritage (HER) (NSW) Derived From NSW Office of Water GW licence extract linked to spatial locations - GLO v5 UID elements 27032014 Derived From Asset database for the Gloucester subregion on 21 August 2015 Derived From Gloucester digitised coal mine boundaries Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014 Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv4 UID 14032014 Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Asset database for the Gloucester subregion on 12 September 2014 Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008 Derived From National Groundwater Information System (NGIS) v1.1 Derived From Groundwater Entitlement Data GLO NSW Office of Water 20150320 PersRemoved Derived From Asset database for the Gloucester subregion on 29 October 2015 Derived From Geofabric Surface Cartography - V2.1 Derived From Groundwater Entitlement Data Gloucester - NSW Office of Water 20150320 Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From EIS Gloucester Coal 2010 Derived From Report for Director Generals Requirement Rocky Hill Project 2012 Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From Asset database for the Gloucester subregion on 12 February 2016 Derived From Asset database for the Gloucester subregion on 28 May 2015 Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv3 12032014 Derived From EIS for Rocky Hill Coal Project 2013 Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From Asset database for the Gloucester subregion on 8 April 2015 Derived From Gloucester - Additional assets from local councils Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From Asset database for the Gloucester subregion on 29 August 2014 Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From Groundwater Modelling Report for Stratford Coal Mine Derived From National Groundwater Management Zones BOM 20150730 Derived From Groundwater Economic Assets GLO 20150326 Derived From NSW Office of Water Groundwater Licence Extract Gloucester - Oct 2013 Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From Freshwater Fish Biodiversity Hotspots Derived From NSW Office of Water Groundwater licence extract linked to spatial locations GLOv2 19022014 Derived From Australia - Species of National Environmental Significance Database Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From NSW Office of Water Groundwater Entitlements Spatial Locations Derived From GLO Receptors 20150828 Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release) Derived From Asset database for the Gloucester subregion on 16 September 2015

Central Bank Balance Sheet In the Euro Area decreased to 6118884 EUR Million in July 18 from 6137098 EUR Million in the previous week. This dataset provides - Euro Area Central Bank Balance Sheet - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Retail Sales in the United Kingdom decreased 2.70 percent in May of 2025 over the previous month. This dataset provides the latest reported value for - United Kingdom Retail Sales MoM - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Denmark - Youth employment rate, age group 20-29 was 74.10% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Denmark - Youth employment rate, age group 20-29 - last updated from the EUROSTAT on July of 2025. Historically, Denmark - Youth employment rate, age group 20-29 reached a record high of 78.20% in December of 2007 and a record low of 66.10% in December of 2013.

Interbank Rate in Taiwan remained unchanged at 1.68 percent on Thursday July 24. This dataset provides - Taiwan Two to Six Month Interbank Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Canada's main stock market index, the TSX, rose to 27416 points on July 23, 2025, gaining 0.19% from the previous session. Over the past month, the index has climbed 2.61% and is up 21.10% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Canada. Canada Stock Market Index (TSX) - values, historical data, forecasts and news - updated on July of 2025.

Fixed Asset Investment in China decreased to 2.80 percent in June from 3.70 percent in May of 2025. This dataset provides - China Fixed Asset Investment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Interbank Rate in Hong Kong decreased to 1.76 percent on Thursday July 24 from 1.78 in the previous day. This dataset provides - Hong Kong Three Month Interbank Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Consumer Confidence in Ireland increased to 62.50 points in June from 60.80 points in May of 2025. This dataset provides the latest reported value for - Ireland Consumer Confidence - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.