The Department of Economic Research Data Index provides information about the economy and labor market in Massachusetts. It includes links to data tools, analytic reports, data visualizations, dashboards and other resources in the following areas:

• Unemployment and Labor Force Data
• Employment Information by Industry
• Employment Information by Occupation
• Information on Massachusetts Employers
• Location Data

A collection of online resources where one can obtain different Labour Market Information.

The data contain a sample of 947,253 vacancies from HeadHunter database covering 2015-2021 period. Each row represents one vacancy. The columns descriptions are the following:

firm - the firm identifier that published a vacancy; occupation - the first mentioned occupation code in a vacancy according to HeadHunter classification (https://api.hh.ru/specializations); year_mon - year and month of vacancy published date; columns from ai to data - binary variables (skill groups) indicating the inclusion of specified skills into a particular skill group (according to Deming, D., & Noray, K. (2020). Earnings dynamics, changing job skills, and STEM careers. The Quarterly Journal of Economics, 135(4),1965–2005); wage - monthly wage suggested in a vacancy (in Rubles); region - the integer indicator of a vacancy posting region (Russian community zone) according to HeadHuter platform; firm_size - firm size 5-level categorical variable indicating the number of workers: "micro", 1–15 workers; "small", 16–100 workers; "medium", 101–250 workers; "large", 251–1,000 workers; "huge", more than 1,000 workers; industry - firm industry code according to Russian Classification of Economic Activities (OKVED); firm_ai - the measure of the AI skills demand (calculated based on Alekseeva, L., Azar, J., Gine, M., Samila, S., & Taska, B. (2021). The demand for AI skills in the labor market. Labour Economics, 71, 102002).

Tired of guessing what's happening in the job market? Xverum's 13M+ job data gives you real-time insights into the dynamic world of work, empowering you to make data-driven decisions and stay ahead of the curve.

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This dataset contains Saudi Arabia Labor Market Administrative Records Data. Data from General Authority for Statistics. Follow datasource.kapsarc.org for timely data to advance energy economics research.- It is data and information registered and updated with government agencies related to the labor market and generated through the official electronic registration and documentation processes followed in these agencies, which include all residents of the Kingdom of Saudi Arabia. Ministry of Human Resources and Social Development, General Organization for Social Insurance, and the Center of national information periodically provide the General Authority for Statistics with the data registered with it (data of administrative records are assigned to the last day of the Gregorian quarter of each year).The following table shows the type of data provided by each entity from the labor market statistics sources:Data sourceData and indicatorsGeneral Organization of Social InsuranceParticipants on the job who are subject to social insurance laws and regulations New subscribers subject to social insurance laws and regulationsMinistry of Human Resources and Social DevelopmentEmployees on the job Subject to the rules and regulations of the Civil Service Work visas for private sectorNational Information CentreDomestic workers work visas issued for government sector and for individuals sector

• Administrative records data has several implications for the labor market, but it is not used statistically to measure unemployment, employment or labor force participation rates

  Concepts related to administrative records available at government agencies:

  Workers (based on the administrative records):

  All working individuals subjected to approved regulations and laws from the regulatory entities of labor market and are registered in the administrative records. On the other hand, workers can be classified in the administrative records based on the regulations and laws they are subjected to as follows:

  1- Saudi workers subjected to the laws and regulations of the civil services and working at all governmental institutions and bodies, in other words, workers who hold jobs that are considered within the general budget of the country, also subjected to the civil retirement system (males or females) employees, as well as non-Saudis contractors who fill these positions in accordance the regulations of non-Saudis employment. 2- Participants on the job who are subject and regulations of social insurance as well as labor system, which includes Saudis and non-Saudis. 3- Domestic workers: non-Saudis workers from both genders who work in houses, including servants, cleaners, cooks, waiters, drivers, guards, nurses, and private teachers.

  Administrative data on the labour market do not include the following categories:

  1- Workers of military and security sectors 2- Workers who are not registered in the civil service and social insurance records, which include: - Saudis working for their own businesses and are not subjected to the labor regulations, also, not registered in social insurance, such as: those who work in delivery through electronic apps - Saudi employers who work in establishments and not registered in the social insurance - Non-Saudi staff working in foreign international, political or military missions 3- Non-Saudi employees who come to the Kingdom for work that normally takes less than three months to be completed.

  The National Classification of the Economic Activities:

  It is a statistical classification based on ISIC4 which is the reference of the productive activities.

  Saudi classification of professions:

  It is a statistical classification which is based on ISCO that provides a system to classify and collect professions’ information where they can be obtained by statistical surveys, census and administrative registers.

  Saudi classification for majors and educational levels:

  It is a statistical classification that is based on ISCED which is the reference for organizing educational programs and related qualifications based on the education levels and fields.

  Domestic worker data from NICThe public sector includes those subject to civil service regulations and government employees subject to insurance regulations (GOSI)There are cases for subscribers working on more than one job in different professions, so they may be counted more than once depending on the subscription, not the Participant.

  (1) Primary includes: a one-year diploma after primary school, one year and six months diploma after primary school, and a two-year diploma after primary school.

  (2) Diploma after Intermediate includes: diploma after intermediate , one year diploma after intermediate , one and a half years diploma after intermediate , two years diploma after intermediate , two and a half years diploma after intermediate , and a 3-year diploma after intermediate .

  (3) Diploma under University includes: diploma after high school, diploma one year after high school, two years diploma after high school, two years and six months after high school diploma, three years diploma after high school and one year and six months diploma after high school.

  (4) The undergraduate level includes: a three-year and six-month diploma after high school and a bachelor's.

  (5) Higher Diploma / Master Degree includes: diploma one year after university and diploma in one year and six months after university , two years diploma after university and A two-year and six-month diploma after university and a three-year diploma after university and masters.

  Others include: Diploma after Primary, post-university diploma, fellowship, and diploma after masters.

  Data do not include employees in the security and military sectors and non-registered in the records of GOSI, MHRSD

• Data of the GOSI , MHRSD is preliminary data

China City Labor Market: Demand data was reported at 4,687.000 Person th in Sep 2022. This records an increase from the previous number of 4,492.000 Person th for Jun 2022. China City Labor Market: Demand data is updated quarterly, averaging 4,788.862 Person th from Mar 2001 (Median) to Sep 2022, with 86 observations. The data reached an all-time high of 6,682.486 Person th in Sep 2010 and a record low of 856.007 Person th in Mar 2001. China City Labor Market: Demand data remains active status in CEIC and is reported by Ministry of Human Resources and Social Security. The data is categorized under China Premium Database’s Labour Market – Table CN.GJ: City Labor Market: Demand of Labour.

China City Labor Market: Demand: Male or Female data was reported at 2,271.953 Person th in Mar 2014. This records an increase from the previous number of 1,825.389 Person th for Dec 2013. China City Labor Market: Demand: Male or Female data is updated quarterly, averaging 1,119.227 Person th from Mar 2001 (Median) to Mar 2014, with 53 observations. The data reached an all-time high of 2,357.925 Person th in Sep 2010 and a record low of 287.858 Person th in Mar 2001. China City Labor Market: Demand: Male or Female data remains active status in CEIC and is reported by Ministry of Human Resources and Social Security. The data is categorized under China Premium Database’s Labour Market – Table CN.GJ: City Labor Market: Demand of Labour.

Various studies have indicated the disadvantaged positions of refugees on the labor market and studied various characteristics explaining this. Yet, little is known about the impact of settlement policy characteristics on recent arrivals' labor market participation, despite them being heavily subject to such policies. We argue such policies, next to individual characteristics, can serve as a means to gather resources relevant to the host country and consequently labor market positions, but can also serve as a post-migration stressor obstructing this. Using the Netherlands as an example, we contribute to studies on the refugee gap and provide insight into key policy characteristics explaining recently arrived refugees' (finding) employment. We use two-wave panel data of 2,379 recently arrived Syrian refugees in the Netherlands, including data on key policy and individual characteristics combined with administrative data. Employing a hybrid model, we show both within- and between-person variation. Results indicate policy matters: short and active stays in reception, complying with the civic integration obligation and a lower unemployment rate in the region refugees are randomly assigned to are beneficial for Syrians' (finding) employment. Like for other migrants, various forms of individual human capital also play a role.

This dataset provides a comprehensive view of the job market in California, highlighting companies and cities with the highest number of job opportunities. Created by JoPilot, it contains valuable information for anyone interested in the employment landscape across different industries and regions. It includes key information such as:

• Company name • City • State • Number of active jobs

For job seekers, employers, and researchers, this resource can be particularly useful in several ways:

1. Identifying hot job markets: The data highlights cities with the highest number of job openings, helping job seekers focus their search on areas with more opportunities.
2. Company targeting: By showing which companies have the most active job listings, the dataset allows job seekers to target their applications to organizations that are actively hiring.
3. Industry trends: The information can reveal which industries or sectors are experiencing growth in California, guiding career decisions and educational pursuits.
4. Regional comparisons: Users can compare job markets across different California cities and regions, which is valuable for those considering relocation or analyzing economic trends.
5. Skill alignment: While the dataset doesn't directly provide skill requirements, it can be used alongside other resources to align job seekers' skills with in-demand positions.

For a more comprehensive job search strategy, consider complementing this dataset with additional resources such as the California Labor Market Information tools, which offer detailed insights into wages, employment projections, and industry-specific data.

The Egypt Labor Market Panel Survey, carried out by the Economic Research Forum (ERF) in cooperation with Egypt’s Central Agency for Public Mobilization and Statistics (CAPMAS) since 1998, has become the mainstay of labor market and human resource development research in Egypt, being the first and most comprehensive source of publicly available micro data on the subject. The 2012 round of the survey provides a unique opportunity to ascertain the impact of the momentous events accompanying the January 25th revolution on the Egyptian economy and labor market and on the lives of Egyptian workers and their families. The Egypt Labor Market Panel Survey of 2012 (ELMPS 2012) is the third round of this longitudinal survey, which was also carried out in 2006. The ELMPS is a wide-ranging, nationally representative panel survey that covers topics such as parental background, education, housing, access to services, residential mobility, migration and remittances, time use, marriage patterns and costs, fertility, women’s decision making and empowerment, job dynamics, savings and borrowing behavior, the operation of household enterprises and farms, besides the usual focus on employment, unemployment and earnings in typical labor force surveys. In addition to the survey’s panel design, which permits the study of various phenomena over time, the survey also contains a large number of retrospective questions about the timing of major life events such as education, residential mobility, jobs, marriage and fertility. The survey provides detailed information about place of birth and subsequent residence, as well information about schools and colleges attended at various stages of an individual’s trajectory, which permit the individual records to be linked to information from other data sources about the geographic context in which the individual lived and the educational institutions s/he attended." (Assaad and Krafft, 2013) The data may be accessed through the ERF Data Portal: http://www.erfdataportal.com/index.php/catalog/161

Replication Data for: "The Impact of NAFTA on U.S. Local Labor Market Employment", Journal of Human Resources.

Abstract

The Sudan Labor Market Panel Survey 2022 (SLMPS 2022) is the first wave of a planned longitudinal study of the Sudanese labor market designed to elucidate the way in which human resources are developed and deployed in the Sudanese economy. The SLMPS 2022 is a nationally-representative household survey on a panel of about 5,000 households planned to be repeated every six years. The focus of the survey is to understand key relationships between labor market processes and outcomes and other socio-economic processes such as education, training, family formation and fertility, internal and international migration, gender equality and women's empowerment, enterprise development, housing acquisition, and equality of opportunity and intergenerational mobility.

The SLMPS 2022 is modeled on similar surveys carried out in Egypt in 1998, 2006, 2012, and 2018 in Jordan in 2010 and 2016, and in Tunisia in 2014. All of these surveys started out with a sample of 5,000 households in the first wave and then the sample grew as a results of household splits and the addition of a refresher sample in every new wave. The SLMPS 2022 also includes modules from the Living Standards Measurement Study Plus (LSMS+) surveys that focus on gender disaggregated asset, employment, and entrepreneurship data. Given the level of detail desired in the individual level information, it is crucial in this survey that the information be collected from the individual him or herself rather than from any informant in the household. Therefore, the survey design calls for a number of visits to the same household to make sure that each individual aged five and older can be interviewed in person.

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For details on the key characteristics of the SLMPS 2022, see: Krafft C., Assaad R., and Cheung R.(2023). Introducing the Sudan Labor Market Panel Survey 2022. Economic Research Forum Working Paper No. 1647

https://erf.org.eg/publications/introducing-the-sudan-labor-market-panel-survey-2022/

Geographic coverage

The sample was designed to provide estimates of the indicators at the national level, for urban and rural areas, and for all regions.

For detailed information on the regions and governorates used in the SLMPS 2022 Sample, see: Krafft C., Assaad R., and Cheung R.(2023). Introducing the Sudan Labor Market Panel Survey 2022. Economic Research Forum Working Paper No. 1647

https://erf.org.eg/publications/introducing-the-sudan-labor-market-panel-survey-2022/

Analysis unit

1- Households. 2- Individuals. 3- Household Enterprises.

Universe

The survey covered a national sample of households and all household's members aged five and above. In addition, the survey covered enterprises operated by the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

A fundamental challenge when designing the SLMPS sample was the lack of a recent, nationally representative sample frame. The last national population census in Sudan was in 2008, before the secession of South Sudan. There had also been limited updating of administrative borders and maps. The first level of administrative geography in Sudan is the state (wilaya), and there are 18 states in Sudan. The second level of administrative geography in Sudan is the locality (mahaliya), and CBS had updated the borders of localities in 2017 to 189 distinct geographies (each locality nested within a single state).). The principal investigators (C. Krafft and R. Assaad) used the updated borders combined with 2020 population estimates based on remote sensing data to create our sampling frame and draw our sample. These sources were supplemented with additional data to identify refugee and IDP camps and areas for our strata. The planned sample design was a random stratified cluster sample made up of 5,000 households sub-divided into 250 primary sampling units (PSUs). The strata represented in the sample are: (i) refugee camps, (ii) refugee areas (areas with non-camp refugee settlements), (iii) IDP camps, (iv) IDP areas (areas with non-camp IDP settlements), (v) other (non-refugee/non-IDP) rural areas,

(vi) other urban areas.

For details on the sampling of the SLMPS 2022, see: Krafft C., Assaad R., and Cheung R.(2023). Introducing the Sudan Labor Market Panel Survey 2022. Economic Research Forum Working Paper No. 1647

https://erf.org.eg/publications/introducing-the-sudan-labor-market-panel-survey-2022/

Sampling deviation

The realities of the sample frame and the logistics of fielding led to a number of deviations from the planned sample in fielding. While the initial sample was estimated to have a reasonable number of households in each PSU based on satellite imaging and population projections, there were cases where a PSU did not, in fact, have any or many households. All PSU locations were reviewed first in the CBS offices to identify locations that were empty or where there appeared to be five or fewer households and these locations were replaced with backup PSUs. There were a variety of reasons why a PSU might have few or no households, including that it consisted of industrial/commercial (not residential) buildings, that it was a mine or grain storage area, or that it had rocks or grain silos that looked like residences. When office review determined there were at least five or more potential households on the satellite maps, fielding was attempted. However, a number of issues arose in the field as well. Upon visiting, buildings were determined to be non-residential, or were abandoned. Furthermore, a number of locations were determined to be unsafe to field, a status that even changed and fluctuated frequently during the fieldwork. Persistent sandstorms also prevented fielding in specific localities. The rainy season likewise made some locations inaccessible for fielding. Backup samples were created; initially one urban and one rural backup were provided per state, and further backups were drawn as needed to replace PSUs that could not be fielded. Backups were, if possible, from the same strata and always from the same state. When possible, additional backups were also drawn from the same locality in an attempt to minimize bias. However, there were cases when an entire locality became inaccessible. Ultimately, 152 PSUs from the original sample of 250 were fielded in the initially planned locations. Nine of the initially planned backups were used. For the remainder, 24 were replaced by the first replacement given, 17 by the second, 17 by the third, 9 by the fourth, 6 by the fifth, 4 by the seventh, and the remaining 12 by various higher order replacements. Repeated replacements tended to occur in localities with a high share of buildings (e.g. mines, grain storage) that the population estimates likely mistook for residences.

===============================================================================================

For details on the sampling of the SLMPS 2022, see: Krafft C., Assaad R., and Cheung R.(2023). Introducing the Sudan Labor Market Panel Survey 2022. Economic Research Forum Working Paper No. 1647

https://erf.org.eg/publications/introducing-the-sudan-labor-market-panel-survey-2022/

Mode of data collection

Face-to-face [f2f]

Research instrument

The SLMPS questionnaires consist of a household questionnaire and an individual questionnaire, with modules. The modules built on and ensured substantial comparability with other LMPSs. The household questionnaire includes: (i) identifiers and household location (ii) roster of household members (iii) housing conditions and durable assets (iv) current household member migrants abroad (v) remittances (vi) other income and transfers (vii) shocks and coping mechanisms (viii) non-agricultural enterprises, including information on characteristics, employment of household members and others, assets, expenditures, and revenue (ix) agricultural assets, land and parcels, capital equipment, livestock, crops, and other agricultural income. The individual questionnaire collects data from all individuals 5 and older (children under five are captured in the household roster). The individual questionnaire elicits information about (i) residential mobility (ii) father's, mother's and sibling characteristics (including siblings abroad) (iv) health (v) education level and detailed educational history (vi) training experiences (vii) skills (viii) current employment and unemployment (viii) job characteristics for the primary and secondary job (ix) labor market history (x) costs and characteristics of marriage (ix) fertility (xii) women's employment (xiii) wages from primary and any secondary jobs (xiv) return migration, refugee, and IDP experiences for Sudanese respondents (xv) modules for immigration and refugees for non-Sudanese respondents (xvi) information technology (xvi) savings and borrowing (xvii) attitudes (xviii) time use (a full 24 hour diary for adults and a shorter module for children) and (xix) a series of questions on rights to parcels, livestock, and durables.

For more details, see the questionnaires in the documentation.

Response

This dataset uses seasonally adjusted data from the US Bureau of Labor Statistics to present information on Maryland's labor force participation rate, employment rate, and unemployment rate.

The primary purpose of the five sets of surveys that comprise the National Longitudinal Surveys is the collection of data on the labor force experience of specific age-sex groups of Americans: Older Men aged 45-59 in 1966, Mature Women aged 30-44 in 1967, Young Men aged 14-24 in 1966, Young Women aged 14-24 in 1968, and Youth aged 14-21 in 1979. Each of the 1960s cohorts has been surveyed 12 or more times over the years, and the Youth cohort has been surveyed yearly since 1979. The major topics covered within the surveys of each cohort include: (1) labor market experience variables (including labor force participation, unemployment, job history, and job mobility), (2) socioeconomic and human capital variables (including education, training, health and physical condition, marital and family characteristics, financial characteristics, and job attitudes), and (3) selected environmental variables (size of labor force and unemployment rates for local area). While the surveys of each cohort have collected data on the above core sets of variables, cohort-specific data have been gathered over the years focusing on the particular stage of labor market attachment that each group was experiencing. Thus, the surveys of young people have collected data on their educational goals, high school and college experiences, high school characteristics, and occupational aspirations and expectations, as well as military service. The surveys of women have gathered data on topics such as fertility, child care, responsibility for household tasks, care of parents, volunteer work, attitudes towards women working, and job discrimination. As the older-aged cohorts of men and women approached labor force withdrawal, surveys for these groups collected information on their retirement plans, health status, and pension benefits. Respondents within the 1979 Youth cohort have been the focus of a number of special surveys, including the collection of data on: (1) last secondary school attended, including transcript information and selected aptitude/intelligence scores, (2) test scores from the Armed Services Vocational Aptitude Battery (ASVAB), (3) illegal activities participation including police contacts, and (4) alcohol use and substance abuse. Finally, the 1986 and 1988 surveys of the Youth cohort included the administration of a battery of cognitive-socioemotional assessments to the approximately 7,000 children of the female 1979 Youth respondents. Data for the five cohorts are provided within main file releases, i.e., Mature Women 1967-1989, Young Women 1968-1991, Young Men 1966-1981, Older Men 1966-1990, and NLSY (Youth) 1979-1992. In addition, the following specially constructed data files are available: (1) a file that specifies the relationships among members of the four original cohorts living in the same household at the time of the initial surveys, i.e., husband-wife, mother-daughter, brother-sister, etc., (2) an NLSY workhistory tape detailing the week-by-week labor force attachment of the youth respondents from 1978 through the most current survey date, (3) an NLSY child-mother file linking the child assessment data to other information on children and mothers within the NLSY, (4) a supplemental NLSY file of constructed and edited fertility variables, (5) a women's support network tape detailing the geographic proximity of the relatives, friends, and acquaintances of 6,308 female NLSY respondents who were interviewed during the 1983-1985 surveys, and (6) two 1989 Mature Women's pension file detailing information on pensions and other employer-provided benefits.

EIA07 - Employee Series from Administrative Data Sources. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Employee Series from Administrative Data Sources...

China City Labor Market: Demand-Supply Ratio: Xi'an data was reported at 1.690 NA in Sep 2021. This records a decrease from the previous number of 2.330 NA for Jun 2021. China City Labor Market: Demand-Supply Ratio: Xi'an data is updated quarterly, averaging 0.980 NA from Sep 2001 (Median) to Sep 2021, with 54 observations. The data reached an all-time high of 2.330 NA in Jun 2021 and a record low of 0.350 NA in Sep 2001. China City Labor Market: Demand-Supply Ratio: Xi'an data remains active status in CEIC and is reported by Ministry of Human Resources and Social Security. The data is categorized under China Premium Database’s Labour Market – Table CN.GJ: City Labor Market: Demand-Supply Ratio.

The survey covers the Taiwan region and includes individuals aged 15 and above residing in households and collective business units within the region who are engaged in economic activities, excluding armed labor force and regulated population. The survey is conducted once a month, based on the week containing the 15th day of each month (Sunday to Saturday), and captures events occurring during that standard week for data collection.

China City Labor Market: Demand-Supply Ratio data was reported at 1.460 NA in Dec 2022. This records an increase from the previous number of 1.310 NA for Sep 2022. China City Labor Market: Demand-Supply Ratio data is updated quarterly, averaging 1.055 NA from Mar 2001 (Median) to Dec 2022, with 88 observations. The data reached an all-time high of 1.620 NA in Mar 2020 and a record low of 0.650 NA in Mar 2001. China City Labor Market: Demand-Supply Ratio data remains active status in CEIC and is reported by Ministry of Human Resources and Social Security. The data is categorized under China Premium Database’s Labour Market – Table CN.GJ: City Labor Market: Demand-Supply Ratio.

EIA06 - Employee Series from Administrative Data Sources. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Employee Series from Administrative Data Sources...