Introduction

The Job Market Insights Dataset offers a comprehensive view of job postings worldwide, providing critical data on job roles, salaries, qualifications, locations, and company profiles. This dataset serves as a valuable resource for understanding global employment trends and patterns in various industries.

Objective

The primary objective of analyzing this dataset is to gain actionable insights into job market dynamics, including in-demand skills, salary ranges by role, preferred qualifications, and geographical job distributions. This analysis can empower job seekers, recruiters, and businesses to make informed decisions.

Key Features

1. Diverse Job Roles: Includes details for various professions like Network Engineers, Software Testers, UX/UI Designers, and more.
2. Global Scope: Covers jobs from diverse locations, spanning countries and industries worldwide.
3. Comprehensive Data Points: Provides salary ranges, qualifications, job types, company profiles, and benefits offered.
4. Temporal Data: Captures job posting dates to understand trends over time.
5. Skills and Responsibilities: Details required skills and responsibilities, aiding in understanding role-specific requirements.

Benefits for Data Science

• Predictive Modeling: Build models to predict salaries, skill demands, or the probability of job fulfillment.
• Trend Analysis: Identify trends in job roles, qualifications, and compensation.
• Geospatial Analysis: Map job distributions to uncover opportunities in specific regions.
• Clustering & Segmentation: Segment jobs by industry, role, or qualifications for targeted insights.
• Skill Gap Identification: Analyze skill requirements to identify gaps between current offerings and market demands.

This dataset is a goldmine for extracting insights that can optimize recruitment strategies, guide career planning, and inform educational initiatives.

Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

Choose your preferred dataset delivery options for convenience:

Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.

Why Choose Oxylabs Job Posting Datasets:

1. Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.

2. Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.

3. Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.

The global market size of Labor is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.
Global Labor Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global Labor industry. The key insights of the report:
1.The report provides key statistics on the market status of the Labor manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.
2.The report provides a basic overview of the industry including its definition, applications and manufacturing technology.
3.The report presents the company profile, product specifications, capacity, production value, and 2013-2018 market shares for key vendors.
4.The total market is further divided by company, by country, and by application/type for the competitive landscape analysis.
5.The report estimates 2019-2024 market development trends of Labor industry.
6.Analysis of upstream raw materials, downstream demand, and current market dynamics is also carried out
7.The report makes some important proposals for a new project of Labor Industry before evaluating its feasibility.
There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment.
For competitor segment, the report includes global key players of Labor as well as some small players.
The information for each competitor includes:
* Company Profile
* Main Business Information
* SWOT Analysis
* Sales, Revenue, Price and Gross Margin
* Market Share

For product type segment, this report listed main product type of Labor market
* Product Type I
* Product Type II
* Product Type III

For end use/application segment, this report focuses on the status and outlook for key applications. End users sre also listed.
* Application I
* Application II
* Application III

For geography segment, regional supply, application-wise and type-wise demand, major players, price is presented from 2013 to 2023. This report covers following regions:
* North America
* South America
* Asia & Pacific
* Europe
* MEA (Middle East and Africa)
The key countries in each region are taken into consideration as well, such as United States, China, Japan, India, Korea, ASEAN, Germany, France, UK, Italy, Spain, CIS, and Brazil etc.

Reasons to Purchase this Report:
* Analyzing the outlook of the market with the recent trends and SWOT analysis
* Market dynamics scenario, along with growth opportunities of the market in the years to come
* Market segmentation analysis including qualitative and quantitative research incorporating the impact of economic and non-economic aspects
* Regional and country level analysis integrating the demand and supply forces that are influencing the growth of the market.
* Market value (USD Million) and volume (Units Million) data for each segment and sub-segment
* Competitive landscape involving the market share of major players, along with the new projects and strategies adopted by players in the past five years
* Comprehensive company profiles covering the product offerings, key financial information, recent developments, SWOT analysis, and strategies employed by the major market players
* 1-year analyst support, along with the data support in excel format.
We also can offer customized report to fulfill special requirements of our clients. Regional and Countries report can be provided as well.

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

The global Labor Market Intelligence Platform market is projected to grow from USD 3,455.3 million in 2025 to USD 10,842.8 million by 2033, exhibiting a CAGR of 16.5% from 2025 to 2033. The market growth is primarily driven by the increasing demand for data-driven insights into labor market trends, skills gaps, and talent availability. The rising adoption of cloud-based platforms, the growing need for talent management and workforce planning, and government initiatives to enhance labor market efficiency are further contributing to market expansion. North America is expected to dominate the Labor Market Intelligence Platform market throughout the forecast period, owing to the presence of well-established vendors, early adoption of technology, and a large pool of skilled professionals. Asia Pacific is anticipated to witness significant growth in the coming years, driven by the increasing demand for talent in emerging economies and government initiatives to improve labor market information systems. The key players in the market include LinkedIn, Lightcast, Claro Analytics, Coresignal, Horsefly, Chmura, Talismatic, Textkernel, WageScape, Liepin, BOSS Zhipin, 51Job, and Zhilian Zhaopin. These companies offer a range of solutions to meet the diverse needs of organizations, including data-driven insights, talent management tools, and recruitment solutions.

Artificial Intelligence (AI) is rapidly transforming the labor market, reshaping how industries operate and how work is performed. As a driving force behind automation, AI enhances productivity, reduces operational costs, and improves decision-making processes across various sectors-including healthcare, manufacturi

The revenue is forecast to experience significant growth in all segments in 2029. The trend observed from 2024 to 2029 remains consistent throughout the entire forecast period. There is a continuous increase in the indicator across all segments. Notably, the Other IT Outsourcing segment achieves the highest value of 1.1 billion U.S. dollars at 2029. Find other insights concerning similar markets and segments, such as a comparison of revenue in Finland and a comparison of revenue in Portugal. The Statista Market Insights cover a broad range of additional markets.

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.

Why Xverum's employee data?

➨ Real-time intelligence: Get a pulse on the job market with daily updates from over 13 million job ads, revealing the latest trends and opportunities.

➨ Unrivaled data breadth: Access rich datasets, including employee data, job market data, recruiting data, and even Indeed data, giving you a comprehensive picture of the job landscape.

Actionable insights: Use Xverum's data to:

➨ Optimize your HR strategy: Identify in-demand skills, salary expectations, and talent pools to attract the best talent.

➨ Validate your B2B leads: Target companies actively hiring for your ideal clients, maximizing your marketing ROI.

➨ Unlock HR intelligence: Gain deeper insights into employee demographics, industry trends, and competitor hiring practices.

➨ Optimize talent acquisition: Attract the right talent with precision, ensuring your recruitment efforts are effective and efficient.

➨ Conduct in-depth labor market research: Analyze specific industries, regions, and job categories to inform your business strategies.

➨ Effortless integration: Our industry-standard CSV formats seamlessly integrate with your existing systems and tools for easy analysis.

➨ Historical data at your fingertips: Access past job trends, using 3 years of historical job data, to understand how the market has evolved and anticipate potential future opportunities.

Xverum's global HR data is your secret weapon for success in the dynamic job market. Contact us today to learn how it can transform your business!

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 offers a curated collection of job listings from LinkedIn, captured over the first half of August 2023. The dataset is designed to provide insights into job market trends, employer demands, and the types of roles being offered during this period. The data is presented in a structured CSV format, making it accessible for various analytical purposes.

Key Features:

Date Range: August 1st, 2023 - August 15th, 2023 Platform: LinkedIn File Format: CSV Potential Use Cases:

Analyzing job market trends and the demand for specific skills Building job recommendation systems based on role and industry Conducting research on hiring patterns and employer preferences Exploring geographic and industry-specific job availability Dataset Contents: The dataset likely includes details such as job title, company, location, job type (e.g., full-time, part-time, remote), industry, posting date, and possibly job descriptions. The exact structure of the dataset can be explored within the CSV file.

Usage: This dataset is ideal for data analysts, HR professionals, and researchers interested in understanding job market dynamics during early August 2023. It can also be used by those developing applications or models related to job matching, skill assessments, or labor market predictions.

Download here: https://app.datastock.shop/?site_name=linkedin_Jobs_Listing_1st_Aug_2023_-_15th_Aug_2023 For custom web scraping, https://www.promptcloud.com/web-scraping-services/

Arellano-Bover, Jaime, (2022) “The Effect of Labor Market Conditions at Entry on Workers' Long-Term Skills.” Review of Economics and Statistics 104:5, 1028–1045.

Xverum’s Global Job Market & Job Postings Data offers one of the largest datasets available, featuring 280B+ records updated daily. Covering 13M+ daily job postings, employee insights, and recruiting trends, our dataset provides a comprehensive view of global labor market dynamics. Designed to empower workforce analytics, talent acquisition, economic forecasting, and AI & ML model training, it’s an essential resource for data-driven decision-making.

Key Features:

1️⃣ Extensive Job Postings Data: Access 13M+ job postings daily from multiple industries and geographies. Detailed attributes include job titles, descriptions, locations, industries, and application requirements.

2️⃣ Real-Time Updates: Data refreshed daily ensures relevance and accuracy for live applications.

3️⃣ Global Coverage: One of the most extensive datasets available, with hiring activity tracked in every country worldwide.

4️⃣ GDPR-Compliant and Secure: Fully compliant with GDPR and CCPA regulations, ensuring ethical and safe data usage.

Primary Use Cases:

✳️ Workforce Analytics: Monitor job demand and labor market trends for strategic workforce planning.

✳️ Talent Acquisition and Recruiting: Analyze hiring activity to identify recruiting trends and optimize talent strategies.

✳️ Economic Forecasting: Use job postings data as an economic indicator to track industry growth and market opportunities.

✳️ Market Research: Gain insights into hiring activity across industries and regions to understand market dynamics.

✳️ Competitive Intelligence: Track competitor hiring patterns and job postings to benchmark market positioning.

✳️ AI/ML Model Training: Train predictive models for job matching, labor trend forecasting, and workforce optimization.

Why Choose Xverum’s Job Market Data? ✅ Massive Scale: 13M+ daily job postings and 280B+ records ensure unparalleled depth and global reach. ✅ Real-Time Updates: Daily refreshes ensure the latest job data for actionable insights. ✅ Comprehensive Coverage: Spanning industries, and geographies worldwide. ✅ GDPR-Compliant: Secure and ethically sourced data for peace of mind.

Key Data Attributes: 📎 Job title, description, and location. 📎 Industry classification and hiring organization. 📎 Posting date, application deadline, and employment type (e.g., full-time, remote).

Request a sample dataset today or contact us to tailor your job market data solution. Empower your business with Xverum’s Job Market & Job Postings Data for smarter, data-driven decision-making.

The Temporary Labor Market size was valued at USD 491.52 billion in 2023 and is projected to reach USD 758.81 billion by 2032, exhibiting a CAGR of 6.4 % during the forecasts period. This growth is driven by a number of factors, including the increasing demand for flexible staffing solutions, the rising cost of permanent employment, and the growing number of businesses outsourcing their HR functions. Temporary labor provides businesses with a number of advantages, including the ability to quickly and easily scale their workforce up or down, the flexibility to meet fluctuating demand, and the reduced cost of recruitment and training. Temporary labor, often referred to as temp work or temporary employment, plays a crucial role in today's workforce dynamics. It involves hiring individuals for short-term assignments, typically through agencies that specialize in staffing solutions. Temp workers provide flexibility for businesses, especially during peak seasons or special projects, without the long-term commitment of permanent hires.

The size and share of this market is categorized based on Industrial Staffing (Manufacturing, Construction, Logistics, Warehousing, Transportation) and Office Staffing (Administrative, Clerical, Customer Service, Human Resources, Finance and Accounting) and Healthcare Staffing (Nursing, Allied Health, Physician Staffing, Home Health Aides, Pharmacy Staffing) and IT Staffing (Software Development, Network Administration, Cybersecurity, Data Science, Technical Support) and Event Staffing (Event Coordination, Promotional Staff, Security Personnel, Catering Staff, Technical Support) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

This dataset provides a comprehensive overview of employment trends in Canada. The data is sourced from the Survey of Employment, Payrolls and Hours (SEPH), offering a detailed breakdown of employment levels across various industrial sectors. The dataset is structured to include key metrics such as geographical location, industry classification, and employment estimates, providing a robust foundation for analyzing employment dynamics within the country.

Key Features

• Employment Metrics: The dataset includes employment estimates for all employees, providing a complete picture of workforce dynamics across different industries.
• Industrial Classification: Data is categorized by the North American Industry Classification System (NAICS), offering insights into employment trends within specific sectors such as industrial aggregate, goods producing industries, and more.
• Geographical Focus: Data is specific to Canada, providing insights into national employment trends and patterns.
• Unit of Measurement: Information is presented in units of persons, allowing for straightforward analysis and comparison.
• Temporal Precision: The data is time-stamped for January 2001, ensuring relevance and accuracy for temporal analysis.

Potential Uses

• Labor Market Analysis: Assist in understanding the employment dynamics in Canada, which is crucial for labor market forecasting and planning.
• Human Resource Management: Provide insights into optimal workforce management practices for various industries.
• Economic Policy: Support policymakers in monitoring and ensuring compliance with labor market trends and economic standards.
• Industry-Specific Insights: Evaluate the impact of employment trends on specific industries and potential growth or decline areas.
• Strategic Planning: Inform strategic planning for businesses and policymakers by providing a clear snapshot of current employment levels and trends.

Note: Updates to this data product are discontinued. County boundaries do not always accurately define local economies. Commuting zones and Labor Market Areas combine counties into units intended to more closely reflect the geographic interrelationships between employers and labor supply.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Data download page For complete information, please visit https://data.gov.

Abstract

"As part of its series of comprehensive labor market panel surveys, the Economic Research Forum had conducted a survey in Jordan in 2010, the Jordan Labor Market Panel Survey of 2010 (JLMPS 2010) and had planned to conduct a new wave after six years. The JLMPS 2016 thus comes at an opportune time to allow for an in-depth assessment of critical social and economic developments in Jordan's recent history.
The JLMPS is part of a series of labor market panel surveys carried out by the Economic Research Forum (ERF) in several Arab countries since 1998 and whose microdata are available for public use through the ERF data portal. These surveys have, so far, been carried out in Egypt (1998, 2006, 2012), Jordan (2010, 2016) and Tunisia (2014). The ERF Labor Market Panel Surveys (LMPSs) are carried out in cooperation with the national statistical office of each country. Accordingly, the JLMPS 2016 was carried out in cooperation with the Jordanian Department of Statistics (DoS), which had preserved the personally identifiable information (PII) of the sample from the previous wave, supplied a refresher sample based on the design provided by ERF researchers, and implemented all data collection activities using tablet computers.
As part of a longitudinal survey, the 2016 wave of JLMPS was designed to follow an existing population over time. However, the 2016 wave was also designed to capture the implications of the large influx of new populations, both refugee and migrant worker flows, into Jordan during the intervening period. To this end, the survey design team decided to add a large refresher sample of 3,000 households that over-sampled neighborhoods in Jordan that had high proportions of non-Jordanian households, including refugee camps, as ascertained by the 2015 Population Census. New modules were also added to the questionnaire to inquire about the in-migration of non-Jordanians, food security, and household exposure to shocks and coping strategies. We assume in this paper that the 2015 Census population counts of various nationality groups are appropriate for our sample and reproduce these counts by means of the appropriate ex-post weights" (Krafft and Assaad, 2018).

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.

Analysis unit

1- Households. 2- Individuals.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

"As the second wave of the JLMPS longitudinal study, the JLMPS 2016 both followed the 2010 panel and added a refresher sample. For the panel component of the data, we attempted to recontact all households that were included in the 2010 wave. Among the households that were found, we also followed any split households. Split households occur when one or more individuals from 2010 leave their 2010 household to form a new household. For example, an individual who was the son of the household head in 2010 might marry and form a new household. The entire new household is included in our sample, including members who were not part of the 2010 sample. The refresher sample over-sampled neighborhoods in Jordan that, as of the 2015 Census, had a high proportion of non-Jordanians. The final JLMPS 2016 sample is made up of 7,229 households, including 3,058 that were part of the original 2010 sample, 1,221 split households and 2,950 refresher households. The JLMPS 2016 sample captured a total of 33,450 individuals. We discuss the sampling strategy and the creation of the sampling and attrition weights in detail below.

----> 2010 sample

The 2010 sample was a nationally-representative sample designed to represent urban and rural areas in the three regions of Jordan: North, Middle, and South. For sampling purposes, the sample was stratified into 30 strata based on a combination of the 12 governorates of Jordan and five different location classifications within them: (1) basic urban (2) rural (3) large central city urban in Amman, Zarqa, and Irbid governorates (4) suburban Amman and Zarqa and (5) exurban Amman. The 2010 sample captured 5,102 households and 25,953 individuals.

----> Refresher sample

The refresher sample in 2016 was designed to over-sample neighborhoods with high proportions of non-Jordanians. The prior, 2010 wave, was just before the Arab Spring and subsequent conflicts in the region. Although Jordan itself did not have internal conflict, its neighbors of Iraq and Syria did. Jordan now hosts a large number of refugees from these conflicts. Based on the Jordanian Census of 2015, there were 9.5 million individuals in Jordan, of whom 6.6 million were Jordanian and 1.3 million were Syrian (Department of Statistics. Jordan also hosts a large population of migrant workers, including 636,000 Egyptians as of 2015. UNHCR's estimate of the number of registered Syrian refugees in Jordan as of September 2017 was 654,000. Jordan also hosts a number of Palestinians, with substantial waves of arrivals around 1948 and 1967. Individuals of Palestinian origin are mostly naturalized and therefore counted in the Jordanian population. However, non-nationalized Palestinians were the third largest group after Syrians and Egyptians in Jordan, at around 634,000 individuals in 2015. There were also around 131,000 Iraqis and smaller populations from numerous other countries. Altogether, these non-Jordanians play a large and increasing role in the Jordanian economy. The refresher sample was designed to over-sample these groups in order to ensure national representativeness in the JLMPS 2016, as well as sufficient observations for analysis of different groups, such as Syrian refugees. The sampling frame for the refresher sample was Jordan's 2015 Population and Housing Census. The census was fielded in late November of 2015. There were 6.6 million Jordanians in 1.4 million households, including Palestinians with Jordanian citizenship. There were 1.3 million Syrians in 0.2 million households at the time of the Census. There were 0.6 million Egyptians and 0.8 million Other Arabs. Other Arabs are primarily Palestinians who are not citizens, historically from Gaza, more recently some are Palestinians from Syria. There are also 0.2 million individuals of other nationalities. In total, there were 1.9 million households and 9.5 million individuals. These census data (geographically disaggregated, as discussed below) are also the source of our expansion factors for the JLMPS weights.

In order to over-sample areas with high proportions of non-Jordanians, we examined the distribution of households with non-Jordanian heads (hereafter referred to as non-Jordanian households). Our goal was to create two strata, one with a high proportion of non-Jordanian households and one with a low proportion of non-Jordanian households in order to oversample the former. The information on household nationality was assessed at the lowest geographical level possible, the neighborhood (hayy). This is the cluster or primary sampling unit (PSU) level we used for drawing our refresher sample. To draw the sample, we identified the 90th percentile of neighborhoods as having 45.7% non-Jordanian households. Thus, 45.7% and higher shares of non-Jordanian household are our "high" non-Jordanian strata and shares lower than 45.7% are our "low" non-Jordanian strata. We further stratified our refresher sample along two dimensions: governorate and location (urban, rural, or refugee camps). The camps were the two official camps in Jordan: Zaatari refugee camp, in the Mafraq governorate, and Azraq refugee camp, in the Zarqa governorate. The high non-Jordanian and camps strata were both over-sampled in order to provide a sufficient number of observations for research and analysis. This over-sampling strategy is accounted for in our weights, discussed below. Across the strata, a total of 200 PSUs (neighborhoods) were selected. Within each PSU, the plan was to sample 15 households.

----> Sample attrition

For the panel data, tracking households from 2010 to 2016, a key issue is sample attrition. There are two points in time when attrition can occur: between the 2010 round and 2016 enumeration and between 2016 enumeration and 2016 fielding. There are also two types of attrition that can occur: Type I attrition occurs when we cannot locate a 2010 household at all, while Type II attrition occurs when we can locate a 2010 household, it has a split, and we cannot locate the split household. This section discusses the patterns of the two different types of attrition and then presents the models predicting attrition that are used as inputs into generating the sample weights.

    -----> Attrition of entire households (Type I attrition)

In undertaking the enumeration and fieldwork, a key goal was to relocate as many 2010 households as possible. At the enumeration stage, from the original 2010 sample of 5,102 households, 3,427 were re-located. In the cases when households were not located, if possible, data were collected on the status of the household or the reason they were not present. During enumeration, there were 81 households that had left the country entirely (all members left) and 44 households that had all died (all members died). We refer to these cases of all the members leaving or dying as "natural attrition." We do not include cases of natural attrition in our calculation of attrition rates or our attrition models, as these households do not exist (in our sampling frame) in 2016. At the enumeration stage, we were unable to locate 1,481 households and 69 households refused (both these results are forms of

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