Youth living in remote rural communities face significant geographic barriers to college access. Even those living near to a postsecondary institution may not have the means for, or may not see the value of, pursuing a college degree within their local economy. This study uses 18 years of national county-level data to ask how local economic shocks affect the postsecondary enrollment and attainment of rural students, as compared to students in metropolitan and metropolitan-adjacent regions. Results from an instrumental variables analysis indicate that each 1 percentage point increase in local unemployment increases local college enrollment by 10.0 percent in remote rural areas, as compared to a 5.2 percent increase in metropolitan-adjacent areas and no detectable increase in metropolitan areas. The rise in rural college enrollment is driven primarily by students enrolling in or continuing in associate degree programs, and by students transferring from two-year to four-year programs.

Unemployment Rate in China increased to 5.40 percent in February from 5.20 percent in January of 2025. This dataset provides - China Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract copyright UK Data Service and data collection copyright owner. The aim of the research is to investigate the nature and causes of the substantial increase in the number of men of working age in the UK who are no longer either employed or unemployed - i.e. 'economically inactive'. These include, among others, very large numbers who are now recorded as 'permanently sick' and 'early retired'. The research will assess the extent which this withdrawal from the labour market is related to individual and family circumstances and to local economic conditions. In particular, the research will investigate the extent to which inactivity 'disguises' unemployment, especially in the most disadvantaged labour markets. The survey took place in three towns with contrasting local economic conditions (Barnsley, Chesterfield and Northampton) and four rural areas (South Shropshire, North Norfolk, North Yorkshire and West Cumbria). Main Topics: Subjects covered by the questionnaire include current economic status of the respondent; age; marital status; social class/occupation; qualifications; housing status; experience of and duration of previous periods of non-employment; work history; reasons for last three jobs ending; job aspirations and perceived obstacles to re-employment; health limitations in respect of ability to work; benefits status of respondent; whether in receipt of pension income. Also included is the household composition and economic status of other household members. For the rural areas surveyed, additional questions were also asked about how long the respondent had lived in the area; availability of vehicles in the household and whether these would be available if needed to travel to work; difficulties of travelling to work or college to acquire further qualifications. Standard Measures Standard Occupational Classifications (major groups); Social Class based on Occupation. Multi-stage stratified random sample Face-to-face interview

Ecuador Unemployment Rate: Rural: Hidden data was reported at 0.636 % in Jun 2019. This records an increase from the previous number of 0.552 % for Mar 2019. Ecuador Unemployment Rate: Rural: Hidden data is updated quarterly, averaging 0.552 % from Dec 2013 (Median) to Jun 2019, with 23 observations. The data reached an all-time high of 1.356 % in Mar 2014 and a record low of 0.216 % in Jun 2017. Ecuador Unemployment Rate: Rural: Hidden data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Ecuador – Table EC.G027: ENEMDU: Unemployment Rate: Rural.

The Labour Force Survey (LFS) is a household survey carried out monthly by Statistics Canada. Since its inception in 1945, the objectives of the LFS have been to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these categories. Data from the survey provide information on major labour market trends such as shifts in employment across industrial sectors, hours worked, labour force participation and unemployment rates, employment including the self-employed, full and part-time employment, and unemployment. It publishes monthly standard labour market indicators such as the unemployment rate, the employment rate and the participation rate. The LFS is a major source of information on the personal characteristics of the working-age population, including age, sex, marital status, educational attainment, and family characteristics. Employment estimates include detailed breakdowns by demographic characteristics, industry and occupation, job tenure, and usual and actual hours worked. This dataset is designed to provide the user with historical information from the Labour Force Survey. The tables included are monthly and annual, with some dating back to 1976. Most tables are available by province as well as nationally. Demographic, industry, occupation and other indicators are presented in tables derived from the LFS data. The information generated by the survey has expanded considerably over the years with a major redesign of the survey content in 1976 and again in 1997, and provides a rich and detailed picture of the Canadian labour market. Some changes to the Labour Force Survey (LFS) were introduced which affect data back to 1987. There are three reasons for this revision: The revision enables the use of improved population benchmarks in the LFS estimation process. These improved benchmarks provide better information on the number of non-permanent residents. There are changes to the data for the public and private sectors from 1987 to 1999. In the past, the data on the public and private sectors for this period were based on an old definition of the public sector. The revised data better reflects the current public sector definition, and therefore result in a longer time series for analysis. The geographic coding of several small Census Agglomerations (CA) has been updated historically from 1996 urban centre boundaries to 2001 CA boundaries. This affects data from January 1987 to December 2004. It is important to note that the changes to almost all estimates are very minor, with the exception of the public sector series and some associated industries from 1987 to 1999. Rates of unemployment, employment and participation are essentially unchanged, as are all key labour market trends. The article titled Improvements in 2006 to the LFS (also under the LFS Documentation button) provides an overview of the effect of these changes on the estimates. The seasonally-adjusted tables have been revised back three years (beginning with January 2004) based on the latest seasonal output.

The Inflation Reduction Act of 2022 (IRA) became law on August 8, 2022. Under the law, new qualifying renewable and/or carbon-free electricity generation projects constructed in certain areas of the US, called energy communities, are eligible for bonus worth an additional 10% to the value of the production tax credit or a 10 percentage point increase in the value of the investment tax credit. The IRA does not explicitly map or list these specific communities. Instead, eligible communities are defined by a series of qualifications:

1. a brownfield site,
2. a metropolitan statistical area (MSA) or non-metropolitan statistical area with either (a) 0.17% or greater employment or (b) 25% or greater local tax revenues related to the extraction, processing, transport, or storage of coal, oil, or natural gas; and an unemployment rate at or above the national average for the previous year, or
3. a census tract containing or adjacent to (a) a coal mine closed after December 31, 1999 or (b) a coal-fired electric generating unit retired after December 31, 2009.

These maps and data layers contain GIS data for coal mines, coal-fired power plants, fossil energy related employment, and brownfield sites. Each record represents a point, tract or metropolitan statistical area and non-metropolitan statistical area with attributes including plant type, operating information, GEOID, etc. The input data used includes:

1. Brownfields – Source: EPA. No analysis was performed on this data layer. However, tract polygon layers have a column denoting brownfield presence (0 for no brownfield site, 1 if the tract contains a brownfield somewhere within the polygon).
2. Eligible Employment MSAs (“Final_Employment_Qualifying_MSAs”) – Source: US Census County Business Patterns. MSAs and non-MSA regions with employment over 0.17% in the fossil fuel industry (defined here as NAICS codes 211, 2121, 213, 23712, 324, 4247, and 486) and unemployment greater than or equal to 3.9% (the average national unemployment rate in 2021, according to the Bureau of Labor Statistics).

--Possibly Eligible MSAs (“FossilFuel_Employment_Qualifying_MSAs”) are MSA and non-MSA regions that meet or exceed the 0.17% employment in the fossil fuel industry threshold but do not exceed the unemployment threshold.

--Relevant columns include:

  a) SUM\_nhgis0: Total employment in 2020.


  b) SUM\_nhgis1: Total unemployment in 2020.


  c) P\_Unemp: Percent unemployment in 2020.


  d) Q\_Unemp: Boolean column indicating if the MSA or non-MSA’s unemployment rate is at or above the national average of 3.9%.


  e) FF\_Qual: Boolean column indicating if the MSA or non-MSA had employment in the fossil fuel industry at or above 0.17% in the past 11 years.


  f) final\_Qual: Boolean column indicating if an MSA or non-MSA qualifies for both unemployment rate and fossil fuel employment under the IRA.

1. Retired Power Plants – Source: EIA via HFLID. Qualifying power plants were selected by use of coal in at least one generator, and if they were retired (RET_DATE) on or after January 1, 2010. This data goes through December 2021.

--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.

1. Abandoned Coal Mines – Source: MSHA. Mines labeled “Abandoned”, “Abandoned and Sealed” or “NonProducing” between January 1, 2000 and September 2022.

--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.

5) US State Borders– Source: IPUMS NHGIS.

Also included here are polygon shapefiles for Onshore Wind and Solar Candidate Project Areas from Princeton REPEAT. These files have been updated to include columns related to the energy communities.

New columns include:

1. CoalPlantTract: Boolean column indicating if the CPA is within a tract that qualifies because of a retired coal plant.
2. CoalMineTract: Boolean column indicating if the CPA is within a tract that qualifies because of a closed coal mine.
3. FossilFuelEmp: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.
4. UnempQualification: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.
5. MSA_non_to: The code of the MSA or non-MSA area that contains the CPA.
6. P_Unemp: The percent unemployment of the MSA or non-MSA that contains the CPA in 2021.

Abstract

In Chad, COVID-19 is expected to affect households in many ways. First, governments might reduce social transfers to households due to the decline in revenue arising from the potential COVID-19 economic recession. Second households deriving income from vulnerable sectors such as tourism and related activities will likely face risk of unemployment or loss of income. Third an increase in prices of imported goods can also negatively impact household welfare, as a direct consequence of the increase of these imported items or as indirect increase of prices of local good manufactured using imported inputs. In this context, there is a need to produce high frequency data to help policy makers in monitoring the channels by which the pandemic affects households and assessing its distributional impact. To do so, the sample of the longitudinal survey will be a sub-sample of the 2018/19 Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (Ecosit 4) in Chad.

This has the advantage of conducting cost effectively welfare analysis without collecting new consumption data. The 30 minutes questionnaires covered many modules, including knowledge, behavior, access to services, food security, employment, safety nets, shocks, coping, etc. Data collection is planned for four months (four rounds) and the questionnaire is designed with core modules and rotating modules.

The main objectives of the survey are to: • Identify type of households directly or indirectly affected by the pandemic; • Identify the main channels by which the pandemic affects households; • Provide relevant data on income and socioeconomic indicators to assess the welfare impact of the pandemic.

Geographic coverage

National coverage, including Ndjamena (Capital city), other urban and rural

Analysis unit

• Households
• Individuals

Universe

The survey covered only households of the 2018/19 Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (ECOSIT 4) which excluded populations in prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Chad COVID-19 impact monitoring survey is a high frequency Computer Assisted Telephone Interview (CATI). The survey’s sample was drawn from the Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (Ecosit 4) which was conducted in 2018-2019. ECOSIT 4 is a survey with a sample size of 7,493 household’s representative at national, regional and by urban/rural. During the survey, each household was asked to provide a phone number of at least one member or a non-household member (e.g. friends or neighbors) so that they can be contacted for follow-up questions. The sampling of the high frequency survey aimed at having representative estimates by national and area of residence: Ndjamena (capital city), other urban and rural area. The minimum sample size was 2,000 for which 1,748 households (87.5%) were successfully interviewed at the national level. To account for non-response and attrition and given that this survey was the first experience of INSEED, 2,833households were initially selected, among them 1,832 households have been reached. The 1,748 households represent the final sample and will be contacted for the next three rounds of the survey.

Sampling deviation

None

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire is in French and has been administrated in French and local languages. The length of an interview varies between 20 and 30 minutes. The questionnaires consisted of the following sections: 1- Household Roster 2- Knowledge of COVID-19 3- Behavior and Social Distancing 4- Access to Basic Services 5- Employment and Income 6- Prices and Food Security 7- Other Impacts of COVID-19 8- Income Loss 9- Coping/Shocks 10- Social Safety Nets 11- Fragility 12. Gender based Violence (for the fourth wave) 13. Vaccine (for the fourth wave)

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the INSEED with the support of the WB team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Response rate

The minimum sample expected is 2,000 households covering Ndjamena, other urban and rural areas. Overall, the survey has been completed for 1,748 households that is about 87.5 % of the expected minimal sample size at the national level. This provide reliable estimates at national and area of residence level.

Ecuador Unemployment Rate: Rural: 35 to 44 Years Old data was reported at 1.558 % in Jun 2019. This records an increase from the previous number of 1.174 % for Mar 2019. Ecuador Unemployment Rate: Rural: 35 to 44 Years Old data is updated quarterly, averaging 1.545 % from Dec 2013 (Median) to Jun 2019, with 23 observations. The data reached an all-time high of 2.090 % in Jun 2016 and a record low of 0.655 % in Sep 2018. Ecuador Unemployment Rate: Rural: 35 to 44 Years Old data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Ecuador – Table EC.G027: ENEMDU: Unemployment Rate: Rural.

Moldova Unemployment Rate: Annual: Rural data was reported at 2.700 % in 2017. This records an increase from the previous number of 2.600 % for 2016. Moldova Unemployment Rate: Annual: Rural data is updated yearly, averaging 3.900 % from Dec 1999 (Median) to 2017, with 19 observations. The data reached an all-time high of 5.800 % in 2006 and a record low of 2.600 % in 2016. Moldova Unemployment Rate: Annual: Rural data remains active status in CEIC and is reported by National Bureau of Statistics of the Republic of Moldova. The data is categorized under Global Database’s Moldova – Table MD.G007: Unemployment Rate.

Introduced in 1993, the Empowerment Zone (EZ), Enterprise Community (EC), and Renewal Community (RC) Initiatives sought to reduce unemployment and generate economic growth through the designation of Federal tax incentives and award of grants to distressed communities. Local, Tribal, and State governments interested in participating in this program were required to present comprehensive plans that included the following principles: Strategic Visions for Change, Community-Based Partnerships, Economic Opportunities, and Sustainable Community Development. Communities selected to participate in this program embraced these principles and led projects that promoted economic development in their distressed communities. The EZ/EC initiative was implemented in the form of three competitions authorized by Congress in 1994 (round I), 1998 (round II), and 2001 (round III). The EC designation expired in 2004 and EZ and RC designations generally expired at the end of 2009. However, the Tax Relief, Unemployment Insurance Reauthorization, and Job Creation Act of 2010, Pub. L. No. 111-312 extended the Empowerment Zone and DC Enterprise Zone designations to December 31, 2011. Following the end of the first EZ designation extension on December 31, 2011, the American Taxpayer Relief Act (ATRA) of 2012, signed into law by President Obama on January 2, 2013, provided for an extension of the Empowerment Zone designations for Empowerment Zone Tax Credit purposes only until December 31, 2013. The ATRA of 2012 did not extend the designation of the DC Enterprise Zone. The third retroactive extension of the Empowerment Zone designation, for the purpose claiming EZ tax credits only, was the Tax Increase Prevention Act of 2014 (TIPA 2014). TIPA 2014 was signed into law by President Obama on December 19, 2014 and extended the EZ designation for the purpose of businesses and entities claiming EZ tax incentives until December 31, 2014. TIPA 2014 did not extend the designation of the DC Enterprise Zone. To learn more about Empowerment Zones Renewal and Enterprise Communities (EZRC) visit: https://www.hud.gov/hudprograms/empowerment_zones, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Empowerment Zones Renewal and Enterprise Communities

Date of Coverage: Through 2014

This dataset was calculated using American Forests’ Tree Equity Score Methodology. Tree canopy (2015) and heat severity (2013-2015) data provided by Pima Association of Governments.Census data (2019 ACS) was obtained using ESRI’s Enrich tool, which estimates census variables on irregular polygons. The scores are a metric that informs the city on how well we are delivering equitable tree cover to all our residents. The score combines "measures of tree canopy cover need and priority for trees in urban neighborhoods. It is derived from tree canopy cover, climate, demographic and socioeconomic data." (American Forests, 2020) Definitions: Tree Equity Score (0-100): A score of 100 means tree equity has been achieved in this neighborhood. Lower scores indicate neighborhoods in greatest need of improved canopy. This metric is only calculated in populated neighborhoods. Priority Index (0-1): Higher scores indicate higher vulnerability. Includes 5 equally weighted variables: Income: Percentage of population below 200% of poverty Employment: Unemployment rate Race: Percentage of people who are not white non-Hispanic Age: Ratio of seniors and children to working-age adults Climate: Urban Heat Island severity Heat Severity (0.43-9.26): Indicates the deviance from mean surface temperatures in urbanized areas. i.e., heat severity 9 indicates the neighborhood is, on average, 9°F hotter than the mean surface temperature in Tucson. Purpose This layer was developed to assist the Green Stormwater Infrastructure identify vulnerable neighborhoods and focus GSI projects in those neighborhoods. Dataset Classification Level 0 - Open Known Uses Tree Equity Scores Dashboard https://arcg.is/1598H Known ErrorsCensus data used in the calculation of the Tree Equity Scores were derived from ESRI's Enrich tool, which creates an estimate of census It was noted upon on the creation of this layer that there are several neighborhoods with inaccurate estimations of population count. Data ContactInformation Technology Department IT_GIS@tucsonaz.gov Publisher ContactInformation Technology Department IT_GIS@tucsonaz.gov Update FrequencyAs new tree canopy data is released (rarely)

Morocco Unemployment Rate: Male: Rural data was reported at 4.300 % in Sep 2018. This records an increase from the previous number of 3.600 % for Jun 2018. Morocco Unemployment Rate: Male: Rural data is updated quarterly, averaging 4.800 % from Mar 1999 (Median) to Sep 2018, with 78 observations. The data reached an all-time high of 7.600 % in Sep 1999 and a record low of 2.900 % in Dec 2004. Morocco Unemployment Rate: Male: Rural data remains active status in CEIC and is reported by High Commission for Planning. The data is categorized under Global Database’s Morocco – Table MA.G015: Unemployment Rate.

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN FOR ROUND 4 OF THE GLSS A nationally representative sample of households was selected in order to achieve the survey objectives.

Sample Frame For the purposes of this survey the list of the 1984 population census Enumeration Areas (EAs) with population and household information was used as the sampling frame. The primary sampling units were the 1984 EAs with the secondary units being the households in the EAs. This frame, though quite old, was considered inadequate, it being the best available at the time. Indeed, this frame was used for the earlier rounds of the GLSS.

Stratification In order to increase precision and reliability of the estimates, the technique of stratification was employed in the sample design, using geographical factors, ecological zones and location of residence as the main controls. Specifically, the EAs were first stratified according to the three ecological zones namely; Coastal, Forest and Savannah, and then within each zone further stratification was done based on the size of the locality into rural or urban.

SAMPLE SELECTION EAs A two-stage sample was selected for the survey. At the first stage, 300 EAs were selected using systematic sampling with probability proportional to size method (PPS) where the size measure is the 1984 number of households in the EA. This was achieved by ordering the list of EAs with their sizes according to the strata. The size column was then cumulated, and with a random start and a fixed interval the sample EAs were selected.

It was observed that some of the selected EAs had grown in size over time and therefore needed segmentation. In this connection, such EAs were divided into approximately equal parts, each segment constituting about 200 households. Only one segment was then randomly selected for listing of the households.

Households At the second stage, a fixed number of 20 households was systematically selected from each selected EA to give a total of 6,000 households. Additional 5 households were selected as reserve to replace missing households. Equal number of households was selected from each EA in order to reflect the labour force focus of the survey.

NOTE: The above sample selection procedure deviated slightly from that used for the earlier rounds of the GLSS, as such the sample is not self-weighting. This is because, 1. given the long period between 1984 and the GLSS 4 fieldwork the number of households in the various EAs are likely to have grown at different rates. 2. the listing exercise was not properly done as some of the selected EAs were not listed completely. Moreover, it was noted that the segmentation done for larger EAs during the listing was a bit arbitrary.

Mode of data collection

Face-to-face [f2f]

Malaysia Unemployment Rates: Rural: Tertiary data was reported at 6.700 % in 2017. This records an increase from the previous number of 5.900 % for 2016. Malaysia Unemployment Rates: Rural: Tertiary data is updated yearly, averaging 6.100 % from Dec 2012 (Median) to 2017, with 6 observations. The data reached an all-time high of 6.700 % in 2017 and a record low of 5.900 % in 2016. Malaysia Unemployment Rates: Rural: Tertiary data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.G047: Labour Force Survey: Unemployment Rates: By Education Level, Strata & Sex.

Ecuador Unemployment Rate: Rural: Open: 15 to 24 Years Old data was reported at 4.026 % in Jun 2019. This records an increase from the previous number of 3.537 % for Mar 2019. Ecuador Unemployment Rate: Rural: Open: 15 to 24 Years Old data is updated quarterly, averaging 3.433 % from Dec 2013 (Median) to Jun 2019, with 23 observations. The data reached an all-time high of 5.043 % in Mar 2014 and a record low of 2.139 % in Dec 2018. Ecuador Unemployment Rate: Rural: Open: 15 to 24 Years Old data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Ecuador – Table EC.G027: ENEMDU: Unemployment Rate: Rural.

Zambia Unemployment Rate: Rural: 25-29 data was reported at 4.400 % in 2014. This records an increase from the previous number of 3.600 % for 2012. Zambia Unemployment Rate: Rural: 25-29 data is updated yearly, averaging 4.000 % from Dec 2012 (Median) to 2014, with 2 observations. The data reached an all-time high of 4.400 % in 2014 and a record low of 3.600 % in 2012. Zambia Unemployment Rate: Rural: 25-29 data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Zambia – Table ZM.G020: Unemployment Rate by Age Group, Gender and Settlement Type.

Zambia Unemployment Rate: Rural: Female: 15-19 data was reported at 9.400 % in 2014. This records an increase from the previous number of 6.100 % for 2012. Zambia Unemployment Rate: Rural: Female: 15-19 data is updated yearly, averaging 7.750 % from Dec 2012 (Median) to 2014, with 2 observations. The data reached an all-time high of 9.400 % in 2014 and a record low of 6.100 % in 2012. Zambia Unemployment Rate: Rural: Female: 15-19 data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Zambia – Table ZM.G020: Unemployment Rate by Age Group, Gender and Settlement Type.

Vietnam Unemployment Rate: RR: Rural: Age 65 and Over data was reported at 0.110 % in 2022. This records an increase from the previous number of 0.090 % for 2021. Vietnam Unemployment Rate: RR: Rural: Age 65 and Over data is updated yearly, averaging 0.125 % from Dec 2011 (Median) to 2022, with 12 observations. The data reached an all-time high of 0.770 % in 2015 and a record low of 0.000 % in 2014. Vietnam Unemployment Rate: RR: Rural: Age 65 and Over data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.G043: Unemployment Rate: By Age Group: By Provinces: Annual.

Romania Unemployment Rate: Rural data was reported at 4.500 % in Sep 2018. This records an increase from the previous number of 4.300 % for Jun 2018. Romania Unemployment Rate: Rural data is updated quarterly, averaging 4.900 % from Mar 1996 (Median) to Sep 2018, with 91 observations. The data reached an all-time high of 8.100 % in Mar 2002 and a record low of 2.100 % in Sep 2001. Romania Unemployment Rate: Rural data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Romania – Table RO.G015: Unemployment Rate.

Jordanian Unemployment Rate: Rural: Female data was reported at 33.800 % in May 2018. This records an increase from the previous number of 30.600 % for Feb 2018. Jordanian Unemployment Rate: Rural: Female data is updated quarterly, averaging 27.300 % from Feb 2000 (Median) to May 2018, with 70 observations. The data reached an all-time high of 43.100 % in Aug 2005 and a record low of 16.800 % in May 2001. Jordanian Unemployment Rate: Rural: Female data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Jordan – Table JO.G017: Jordanian Unemployment Rate: by Region.