The General Social Surveys (GSS) have been conducted by the National Opinion Research Center (NORC) annually since 1972, except for the years 1979, 1981, and 1992 (a supplement was added in 1992), and biennially beginning in 1994. The GSS are designed to be part of a program of social indicator research, replicating questionnaire items and wording in order to facilitate time-trend studies. This data file has all cases and variables asked on the 2022 GSS.

The 2022 cross-sectional General Social Survey has been updated to Release Version 3a as of May 2024. This Release includes the addition of an oversample of minorities (based on the AmeriSpeak® Panel), household composition and respondent selection data, and post-stratified weights for all years of the GSS.

To download syntax files for the GSS that reproduce well-known religious group recodes, including RELTRAD, please visit the "/research/syntax-repository-list" Target="_blank">ARDA's Syntax Repository.

Abstract

The 2022 Ghana Demographic and Health Survey (2022 GDHS) is the seventh in the series of DHS surveys conducted by the Ghana Statistical Service (GSS) in collaboration with the Ministry of Health/Ghana Health Service (MoH/GHS) and other stakeholders, with funding from the United States Agency for International Development (USAID) and other partners.

The primary objective of the 2022 GDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the GDHS collected information on: - Fertility levels and preferences, contraceptive use, antenatal and delivery care, maternal and child health, childhood mortality, childhood immunisation, breastfeeding and young child feeding practices, women’s dietary diversity, violence against women, gender, nutritional status of adults and children, awareness regarding HIV/AIDS and other sexually transmitted infections, tobacco use, and other indicators relevant for the Sustainable Development Goals - Haemoglobin levels of women and children - Prevalence of malaria parasitaemia (rapid diagnostic testing and thick slides for malaria parasitaemia in the field and microscopy in the lab) among children age 6–59 months - Use of treated mosquito nets - Use of antimalarial drugs for treatment of fever among children under age 5

The information collected through the 2022 GDHS is intended to assist policymakers and programme managers in designing and evaluating programmes and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men aged 15-59, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

To achieve the objectives of the 2022 GDHS, a stratified representative sample of 18,450 households was selected in 618 clusters, which resulted in 15,014 interviewed women age 15–49 and 7,044 interviewed men age 15–59 (in one of every two households selected).

The sampling frame used for the 2022 GDHS is the updated frame prepared by the GSS based on the 2021 Population and Housing Census.1 The sampling procedure used in the 2022 GDHS was stratified two-stage cluster sampling, designed to yield representative results at the national level, for urban and rural areas, and for each of the country’s 16 regions for most DHS indicators. In the first stage, 618 target clusters were selected from the sampling frame using a probability proportional to size strategy for urban and rural areas in each region. Then the number of targeted clusters were selected with equal probability systematic random sampling of the clusters selected in the first phase for urban and rural areas. In the second stage, after selection of the clusters, a household listing and map updating operation was carried out in all of the selected clusters to develop a list of households for each cluster. This list served as a sampling frame for selection of the household sample. The GSS organized a 5-day training course on listing procedures for listers and mappers with support from ICF. The listers and mappers were organized into 25 teams consisting of one lister and one mapper per team. The teams spent 2 months completing the listing operation. In addition to listing the households, the listers collected the geographical coordinates of each household using GPS dongles provided by ICF and in accordance with the instructions in the DHS listing manual. The household listing was carried out using tablet computers, with software provided by The DHS Program. A fixed number of 30 households in each cluster were randomly selected from the list for interviews.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Face-to-face computer-assisted interviews [capi]

Research instrument

Four questionnaires were used in the 2022 GDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Ghana. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.

The GSS organized a questionnaire design workshop with support from ICF and obtained input from government and development partners expected to use the resulting data. The DHS Program optional modules on domestic violence, malaria, and social and behavior change communication were incorporated into the Woman’s Questionnaire. ICF provided technical assistance in adapting the modules to the questionnaires.

Cleaning operations

DHS staff installed all central office programmes, data structure checks, secondary editing, and field check tables from 17–20 October 2022. Central office training was implemented using the practice data to test the central office system and field check tables. Seven GSS staff members (four male and three female) were trained on the functionality of the central office menu, including accepting clusters from the field, data editing procedures, and producing reports to monitor fieldwork.

From 27 February to 17 March, DHS staff visited the Ghana Statistical Service office in Accra to work with the GSS central office staff on finishing the secondary editing and to clean and finalize all data received from the 618 clusters.

Response rate

A total of 18,540 households were selected for the GDHS sample, of which 18,065 were found to be occupied. Of the occupied households, 17,933 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 15,317 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 15,014 women, yielding a response rate of 98%. In the subsample of households selected for the male survey, 7,263 men age 15–59 were identified as eligible for individual interviews and 7,044 were successfully interviewed.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Ghana Demographic and Health Survey (2022 GDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 GDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results. A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 GDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the GDHS 2022 is an SAS program. This program used the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Age distribution of eligible and interviewed women
• Age distribution of eligible and interviewed men
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Standardisation exercise results from anthropometry training
• Height and weight data completeness and quality for children
• Height measurements from random subsample of measured children
• Interference in height and weight measurements of children
• Interference in height and weight measurements of women and men
• Heaping in anthropometric measurements for children (digit preference)
• Observation of mosquito nets
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Number of

Genetic Signatures reported 42.7 in PE Price to Earnings for its fiscal semester ending in June of 2022. Data for Genetic Signatures | GSS - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last July in 2025.

The Graduate Students and Postdoctorates in Science and Engineering survey is an annual census of all U.S. academic institutions granting research-based master's degrees or doctorates in science, engineering, and selected health fields as of fall of the survey year. The survey, sponsored by the National Center for Science and Engineering Statistics within the National Science Foundation and by the National Institutes of Health, collects the total number of master's and doctoral students, postdoctoral appointees, and doctorate-level nonfaculty researchers by demographic and other characteristics such as source of financial support. Results are used to assess shifts in graduate enrollment and postdoc appointments and trends in financial support. This dataset includes GSS assets for 2022.

This map is the lesson start map for the interpolate elevation values activity in the Learn ArcGIS Lesson Interpolate Data, a TeachGIS lesson.The lesson introduces the basics of interpolating patterns from point data.

Wellbeing statistics: 2021 (supplementary) presents supplementary data from the 2021 General Social Survey (GSS), adding to the data released in Wellbeing statistics: 2021 in July 2022.

As of the end of 2022, Brazil had the highest number of issuers of green, social, and sustainable (GSS) bonds among countries in Latin America and the Caribbean, totaling around 133. Green, social, and sustainable (GSS) bonds are fixed-income instruments which finance projects that have a positive impact on environmental and social sustainability.

Global issuance of green, social and sustainability (GSS) bonds amounted to approximately 850 billion U.S. dollars in 2023, after exceeding one trillion in 2021. Green bonds accounted for nearly two thirds of the total GSS bonds issued in 2022.

This study utilizes data from the General Social Survey (GSS), a nationally representative, repeated cross-sectional survey administered by NORC at the University of Chicago. The GSS is one of the most authoritative sources of longitudinal public opinion data in the United States, tracking American attitudes, beliefs, and behaviors across a wide range of social, political, and economic domains since 1972.

For the purposes of this analysis, the dataset was restricted to survey waves from 2000 to 2022, to capture contemporary patterns of polarization around economic redistribution and party identity, particularly during the post-9/11 and post-2016 political realignments. Data were accessed and downloaded through the GSS Data Explorer (https://gssdataexplorer.norc.org/), using the platform’s variable filtering and trend tools.

Key variables used in the analysis include:

• Dependent variable: Support for redistribution, measured by agreement with the statement “The government should reduce income differences between the rich and the poor.”

• Independent variables:

  • Party identification (Democrat, Republican, Independent/Other)

  • Racial resentment indicators, including agreement with items such as “Blacks should work their way up without special favors”

  • Year (centered for interaction and trend modeling)

  • Demographic controls: age, gender, income, education, and geographic region

The analytic sample includes respondents with valid responses to all core variables, totaling 5,483 observations after listwise deletion and multiple imputation for missing attitudinal items. All analyses were conducted using R and Python, with appropriate statistical methods for logistic regression, rolling OLS estimation, and interaction modeling. Attempts to estimate a Markov Switching model encountered convergence issues and are excluded from the final analysis.

The GSS sampling design includes multistage area probability sampling and post-stratification weights to ensure representativeness of the U.S. adult population. All interpretations in this study are based on weighted data unless otherwise noted.

Abstract

The Ghana International Merchandise Trade Data 2022 serves a comprehensive repository of information detailing Ghana's imports and exports for the year 2022. The dataset's primary objectives include informing evidence-based policies and facilitating comprehensive research on trade dynamics. Sourced from the Customs Division of the Ghana Revenue Authority, the dataset follows the International Merchandise Trade Statistics Manual of the United Nations, ensuring methodological rigor. This structured dataset containing key indicators like trade values, partner countries, and commodity codes, presents an invaluable resource for understanding Ghana's trade patterns.

Geographic coverage

National coverage of all international trade with the rest of the world

Analysis unit

Countries, commodities

Universe

All exports and imports under the general trade system, as outlined in the International Merchandise Trade Statistics Manual of the United Nations.

Mode of data collection

Other [oth]

Abstract

The Annual Household Income and Expenditure Survey (AHIES) is the first nationally representative high-frequency household panel survey in Ghana. The AHIES is being conducted to obtain quarterly and annual data on household final consumption expenditure and a wide scope of demographic, economic and welfare variables including statistics on labour, food security, multi-dimensional poverty and health status for research, policy, and planning. Some of the key macroeconomic indicators to be generated include quarterly GDP, regional GDP, quarterly unemployment, underemployment, inequality, consumption expenditure poverty, multidimensional poverty and food security. The data from the AHIES is classified, tabulated and disseminated so that researchers, administrators, policy makers and development partners can use the information in formulating and implementing various development programs at the national and community levels and also to monitor targets under the Sustainable Development Goals.

Geographic coverage

Nation - Wide

Analysis unit

Individuals, Households

Universe

The universe covers the population living within individual households in Ghana. However, such population which is defined as institutionalised population as persons living at elderly houses, rest homes, correction facilities, military baracks, and hospitals with special characteristics, nursery,and also nomadic population are excluded.

Sampling procedure

With the sampling procedure, 10,800 households in 600 EAs, consisting of 304 (50.67%) urban and 296 (49.33%) rural households were drawn from the 2021 Population and Housing Census listing frame to form the secondary sampling units. A random sampling methodology was adopted to select eighteen (18) households per selected EAs in all regions to form the full sample for the fieldwork to be able to produce regionally representative expenditures for GDP.

Mode of data collection

Computer Assisted Personal Interview [CAPI]

Background
The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS) (held at the UK Data Archive under GN 33246), all of its associated LFS boosts and the APS boost. Thus, the APS combines results from five different sources: the LFS (waves 1 and 5); the English Local Labour Force Survey (LLFS), the Welsh Labour Force Survey (WLFS), the Scottish Labour Force Survey (SLFS) and the Annual Population Survey Boost Sample (APS(B) - however, this ceased to exist at the end of December 2005, so APS data from January 2006 onwards will contain all the above data apart from APS(B)). Users should note that the LLFS, WLFS, SLFS and APS(B) are not held separately at the UK Data Archive. For further detailed information about methodology, users should consult the Labour Force Survey User Guide, selected volumes of which have been included with the APS documentation for reference purposes (see 'Documentation' table below).

The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples such as the WLFS and SLFS, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.

Secure Access APS data
Secure Access datasets for the APS include additional variables not included in the standard End User Licence (EUL) versions (see under GN 33357). Extra variables that typically can be found in the Secure Access version but not in the EUL versions relate to:

• geography
  
• types of benefits claimed
• qualifications, education and training, including level of highest qualification, qualifications below highest level, class of first degree, single subject of degree, qualifications from Government schemes, number of O-levels/GCSEs etc passed, type of 'other qualification', type of other work-related or vocational qualifications, qualifications related to work, sources of qualifications, qualifications from school, level of Welsh baccalaureate
• frequency of Welsh speaking
• casual/holiday work
• regular/normal work pattern
• reasons not in work or for leaving work, reasons not looking for work
• payment of own National Insurance and tax
• smoking habits
• single year of age
• health issues
• learning difficulty/disability
• number of bedrooms
• serving in armed forces
• marital status
• main reason for coming to the UK

The EUL version contains less detailed variables. For example, the lowest geography is Government Office Region, only banded age is available, only 3-digit SOC is available for main, second and last job, and only industry division for main, second and last job.

Prospective users of the Secure Access version of the APS will need to fulfil additional requirements, commencing with the completion of extra application forms to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access data users must also complete face-to-face training and agree to the Secure Access User Agreement and Licence Compliance Policy (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL version of the data prior to ordering the Secure Access. Further details and links to all APS studies available from the UK Data Archive can be found via the APS Key Data series webpage.

Documentation and coding frames
The APS is compiled from variables present in the LFS. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation (e.g. coding frames for education, industrial and geographic variables, which are held in LFS User Guide Vol.5, Classifications), users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.

Disability variables from 2013 onwards - LFS and APS
ONS have provided some information on changes since 2013 to the disability variables available on the LFS and APS. The Disability Discrimination Act (DDA) disabled (current disability) category within the historic DISCURR variable no longer corresponds with the advised legal definition of 'current disability'. DISCURR should only be available on LFS microdata from Spring 1998 to January-March 2013 (JM13); beyond that point users should ignore or delete it. In addition, the same 'DDA disabled (current disability)' category within variable DISCURR13 is also not the most appropriate variable to use because a) it is not comparable to the corresponding category in variable DISCURR due to question changes, and b) it no longer measures either the DDA definition of disability or the latest Equality Act definition of disability. However, DISCURR13 is available from the April-March 2013 quarter (AJ13) onwards and was introduced to demonstrate that the variables used to compile DISCURR had also changed from that quarter. Therefore, users are advised to use the disability variable DISEA from AJ13 onwards, which reflects the Equality Act 2010 legal definition of 'disabled', measured according to the GSS Harmonised Standard on health conditions and illnesses. The harmonised disability variables DISEA and DISCURR13 should both be present on the APS person microdata from April 2013-March 2014 (A13M14) onwards. This ensures that APS users have a complete 12 months' data on which to base analysis of the variables. DISCURR should only be present on APS microdata up to and including April 2012-March 2013 (A12M13).

Variables DISEA and LNGLST
Dataset A08 (Labour market status of disabled people) which ONS suspended due to an apparent discontinuity between April to June 2017 and July to September 2017 is now available. As a result of this apparent discontinuity and the inconclusive investigations at this stage, comparisons should be made with caution between April to June 2017 and subsequent time periods. However users should note that the estimates are not seasonally adjusted, so some of the change between quarters could be due to seasonality. Further recommendations on historical comparisons of the estimates will be given in November 2018 when ONS are due to publish estimates for July to September 2018.

An article explaining the quality assurance investigations that have been conducted so far is available on the ONS Methodology webpage. For any queries about Dataset A08 please email Labour.Market@ons.gov.uk.

Occupation data for 2021 and 2022 data files
The ONS have identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. For further information on this issue, please see: https://www.ons.gov.uk/news/statementsandletters/occupationaldatainonssurveys.

Latest edition information:
For the thirty-first edition (April 2025), a data file for July 2022 to June 2023 has been added to the study.

Nuo 2021 m. sausio 1 d. vietos ir regionų valdžios institucijos ir viešosios institucijos turi parengti metinę bendrą socialinę ataskaitą (SSR) už praėjusius metus.

Šioje ataskaitoje pateikiami rodikliai, susiję su žmogiškųjų išteklių politika, pagal šias temas: užimtumas, įdarbinimas, karjeros galimybės, mokymas, darbo užmokestis, darbuotojų sveikata ir sauga ir kt.

Ši byla yra 2022 m. GSS santrauka, kurią Departamentas parengė remdamasis 2022 m. gruodžio 31 d. socialinių duomenų bazės (BDS) duomenimis. Kartu su Centre Départemental de Gestion de la Seine-Maritime parengta duomenų bazės santrauka (žr. metaduomenis) ir duomenų bazės CSV rinkmenomis, taip pat paskelbtomis svetainėje, ji sudaro Bendrąją socialinę ataskaitą.

Tada RSU yra valdymo gairių, pagal kurias viešojo sektoriaus darbdaviai gali įforminti arba atnaujinti savo daugiametę žmogiškųjų išteklių valdymo strategiją, rengimo pagrindas.

Metaduomenys Nuoroda į metaduomenis

Papildomi ištekliai * Légifrance interneto svetainė: https://www.legifrance.gouv.fr/loda/id/JORFTEXT000044930851/

Viešosios teisės sklaidos tarnybos interneto svetainėje siūloma atsisiųsti 2021 m. gruodžio 10 d. įsakymo konsoliduotą redakciją, kurioje pateikiami socialinės duomenų bazės rodikliai (BDS, žr. apibrėžtį metaduomenyse).

• ** Unikalių socialinių santykių pavyzdžiai**: https://www.paris.fr/pages/le-rapport-social-unique-22259 ir https://donnees-sociales.fr/dossier-de-presse-2-2/

  Palyginimui internete galima rasti įvairius RSU (žr. apibrėžtį metaduomenyse), kuriuos galima atsisiųsti .pdf formatu. Pridedamos nuorodos į Paryžiaus miesto RSU ir metinę nacionalinę santrauką, kurią galima rasti valdymo centrų svetainėje données-sociales.fr (žr. apibrėžtį metaduomenyse).

• ĮRAŠYKITE tinklalapį: https://www.insee.fr/information/2407785

Nacionalinio statistikos ir ekonomikos studijų instituto (INSEE) interneto svetainėje yra puslapis, skirtas visų darbuotojų bazei (BTS), remiantis registruotomis socialinėmis deklaracijomis (DSN), kurias pagal Socialinės apsaugos kodeksą ir Bendrąjį mokesčių kodeksą turi pateikti bet kuri bendrovė, kurioje dirba darbuotojai.

Nuo 2009 m. jos taikymo sritis išplėsta įtraukiant tris viešąsias funkcijas (valstybines, teritorines ir ligonines) ir privačių darbdavių darbuotojus, taigi ji apima visus Prancūzijos ekonomikos sektoriaus darbuotojus.

Šioje duomenų bazėje pateikiami išsamūs užimtumo duomenys (darbo vietų, išreikštų visos darbo dienos ekvivalentais, skaičius, bruto ir neto darbo užmokestis, darbo kvalifikacija, darbo sutarties rūšis, dirbtos valandos pagal lytį ir kvalifikaciją ir kt.), kai kurie iš jų yra nemokami. Nuo 2021 m. sausio 1 d. vietos ir regionų valdžios institucijos ir viešosios institucijos turi parengti metinę bendrą socialinę ataskaitą (SSR) už praėjusius metus.

Šioje ataskaitoje pateikiami rodikliai, susiję su žmogiškųjų išteklių politika, pagal šias temas: užimtumas, įdarbinimas, karjeros galimybės, mokymas, darbo užmokestis, darbuotojų sveikata ir sauga ir kt. Ši byla yra 2022 m. GSS santrauka, kurią Departamentas parengė remdamasis 2022 m. gruodžio 31 d. socialinių duomenų bazės (BDS) duomenimis. Kartu su Centre Départemental de Gestion de la Seine-Maritime parengta duomenų bazės santrauka (žr. metaduomenis) ir duomenų bazės CSV rinkmenomis, taip pat paskelbtomis svetainėje, ji sudaro Bendrąją socialinę ataskaitą.

Tada RSU yra valdymo gairių, pagal kurias viešojo sektoriaus darbdaviai gali įforminti arba atnaujinti savo daugiametę žmogiškųjų išteklių valdymo strategiją, rengimo pagrindas.

Metaduomenys Nuoroda į metaduomenis

Papildomi ištekliai

• Légifrance interneto svetainė: https://www.legifrance.gouv.fr/loda/id/JORFTEXT000044930851/

Viešosios teisės sklaidos tarnybos interneto svetainėje siūloma atsisiųsti 2021 m. gruodžio 10 d. įsakymo konsoliduotą redakciją, kurioje pateikiami socialinės duomenų bazės rodikliai (BDS, žr. apibrėžtį metaduomenyse).

• ** Unikalių socialinių santykių pavyzdžiai**: https://www.paris.fr/pages/le-rapport-social-unique-22259 ir https://donnees-sociales.fr/dossier-de-presse-2-2/

Palyginimui internete galima rasti įvairius RSU (žr. apibrėžtį metaduomenyse), kuriuos galima atsisiųsti .pdf formatu. Pridedamos nuorodos į Paryžiaus miesto RSU ir metinę nacionalinę santrauką, kurią galima rasti v

Australia GSS: RBA Holdings: Semis data was reported at 60,756.000 AUD mn in Apr 2025. This records a decrease from the previous number of 61,068.000 AUD mn for Mar 2025. Australia GSS: RBA Holdings: Semis data is updated monthly, averaging 28,732.000 AUD mn from Jan 2017 (Median) to Apr 2025, with 100 observations. The data reached an all-time high of 67,718.000 AUD mn in Feb 2022 and a record low of 2,164.000 AUD mn in Oct 2019. Australia GSS: RBA Holdings: Semis data remains active status in CEIC and is reported by Reserve Bank of Australia. The data is categorized under Global Database’s Australia – Table AU.Z010: Government Securities Holdings by Reserve Bank of Australia.

GSS：澳大利亚储备银行控股公司：澳大利亚政府债券在04-01-2025达216,127.000百万澳大利亚元，相较于03-01-2025的236,320.000百万澳大利亚元有所下降。GSS：澳大利亚储备银行控股公司：澳大利亚政府债券数据按月更新，01-01-2017至04-01-2025期间平均值为140,565.000百万澳大利亚元，共100份观测结果。该数据的历史最高值出现于06-01-2022，达288,127.000百万澳大利亚元，而历史最低值则出现于02-01-2018，为975.000百万澳大利亚元。CEIC提供的GSS：澳大利亚储备银行控股公司：澳大利亚政府债券数据处于定期更新的状态，数据来源于Reserve Bank of Australia，数据归类于全球数据库的澳大利亚 – Table AU.Z010: Government Securities Holdings by Reserve Bank of Australia。