See notice below about this dataset

This dataset provides the average annual earnings by industry per district.

Wage records are obtained from the Massachusetts Department of Unemployment Assistance (DUA) using a secure, anonymized matching process with limitations. For details on the process and suppression rules, please visit the Employment and Earnings of High School Graduates dashboard.

This dataset is one of three containing the same data that is also published in the Employment and Earnings of High School Graduates dashboard: Average Earnings by Student Group Average Earnings by Industry College and Career Outcomes

List of Industries

• 00 - All Students
• 11 - Agriculture, Forestry, Fishing and Hunting
• 21 - Mining, Quarrying, and Oil and Gas Extraction
• 22 - Utilities
• 23 - Construction
• 31 - Manufacturing
• 42 - Wholesale Trade
• 44 - Retail Trade
• 48 - Transportation and Warehousing
• 51 - Information
• 52 - Finance and Insurance
• 53 - Real Estate and Rental and Leasing
• 54 - Professional, Scientific, and Technical Services
• 55 - Management of Companies and Enterprises
• 56 - Administrative and Support and Waste Management and Remediation Services
• 61 - Educational Services
• 62 - Health Care and Social Assistance
• 71 - Arts, Entertainment, and Recreation
• 72 - Accommodation and Food Services
• 81 - Other Services (except Public Administration)
• 92 - Public Administration
• 0 - No Industry Reported

The data link between high school graduates and future earnings makes it possible to follow students beyond high school and college into the workforce, enabling long-term evaluation of educational programs using workforce outcomes.

While DESE has published these data in the past, as of June 2025 we are temporarily pausing updates due to an issue conducting the link that was brought to our attention in 2023 by a team of researchers. The issue impacts the earnings information for students who never attended a postsecondary institution or who only attended private or out-of-state colleges or universities, beginning with the 2017 high school graduation cohort, with growing impact in each successive high school graduation cohort.

The issue does not impact the earnings information for students who attended a Massachusetts public institution of higher education, and earnings data for those students will continue to be updated.

Once a solution is found, the past cohorts of data with low match rates will be updated. DESE and partner agencies are exploring linking strategies to maximize the utility of the information.

More detailed information can be found in the attached memo provided by the research team from the Annenberg Institute. We thank them for calling this issue to our attention.

As of the first quarter of 2025, some ** percent of the employed population in Morocco worked in the agriculture, forestry, and fisheries sector. In urban areas, services constituted the primary source of employment, with about **** percent employed in commerce, some *** percent in transport, warehousing, and communication, and **** percent in public administration and community social services. On the other hand, in rural regions, the agricultural, forestry, and fishery sectors employed the highest share of working population, approximately ** percent. This contrast indicates the disparity in development across different regions in the country. Unemployment among the youth Youth unemployment remains a challenge in the country, standing at ***** in 2024. This divide is more pronounced in the 15-24 years age group, where the unemployment rate reached **** percent in 2024. These figures highlight the lack of job opportunities for young individuals entering the workforce, and the need for investments in job creation initiatives. Unemployment gender disparity in North Africa In the North African region, women are facing significantly higher unemployment rates than men. In fact, around **** percent of women in the region were expected to be unemployed as of 2024, compared to **** percent for men. This suggests difficulties accessing the labor market for North African women and calls for a region-wide improvement in the sector.

What is the COVID-19 Economic Vulnerability Index?The COVID-19 Vulnerability Index (CVI) is a measurement of the negative impact that the coronavirus (COVID-19) crisis can have on employment based upon a region's mix of industries. For example, accommodation and food services are projected to lose more jobs as a result of the coronavirus (in the neighborhood of 50%) compared with utilities and healthcare (with none or little expected job contraction).This updated dataset contains 116 jobs attributes including the 10 most likely jobs to be impacted for each county, the total employment and employment by sector. An attribute list is included below.An average Vulnerability Index score is 100, representing the average job loss expected in the United States. Higher scores indicate the degree to which job losses may be greater — an index score of 200, for example, means the rate of job loss can be twice as large as the national average. Conversely, an index score of 50 would mean a possible job loss of half the national average. Regions heavily dependent on tourism with relatively high concentrations of leisure and hospitality jobs, for example, are likely to have high index scores. The Vulnerability Index only measures the impact potential related to the mix of industry employment. The index does not take into account variation due to a region’s rate of virus infection, nor does it factor in local government's policies in reaction to the virus. For more detail, please see this description.MethodologyThe index is based on a model of potential job losses due to the COVID-19 outbreak in the United States. Expected employment losses at the subsector level are based upon inputs which include primary research on expert testimony; news reports for key industries such as hotels, restaurants, retail, and transportation; preliminary release of unemployment claims; and the latest job postings data from Chmura's RTI database. The forecast model, based on conditions as of March 23, 2020, assumes employment in industries in each county/region would change at a similar rate as employment in national industries. The projection estimates that the United States could lose 15.0 million jobs due to COVID-19, with over half of the jobs lost in hotels, food services, and entertainment industries. Contact Chmura for further details.Attribute ListFIPSCounty NameStateTotal JobsWhite Collar JobsBlue Collar JobsService JobsWhite Collar %Blue Collar %Service %Government JobsGovernment %Primarily Self-Employed JobsPrimarily Self-Employed %Job Change, Last Ten YearsIndustry 1 NameIndustry 1 EmplIndustry 1 %Industry 2 NameIndustry 2 EmplIndustry 2 %Industry 3 NameIndustry 3 EmplIndustry 3 %Industry 4 NameIndustry 4 EmplIndustry 4 %Industry 5 NameIndustry 5 EmplIndustry 5 %Industry 6 NameIndustry 6 EmplIndustry 6 %Industry 7 NameIndustry 7 EmplIndustry 7 %Industry 8 NameIndustry 8 EmplIndustry 8 %Industry 9 NameIndustry 9 EmplIndustry 9 %Industry 10 NameIndustry 10 EmplIndustry 10 %All Other IndustriesAll Other Industries EmplAll Other Industies %Agriculture, Food & Natural Resources EmplArchitecture and Construction EmplArts, A/V Technology & Communications EmplBusiness, Management & Administration EmplEducation & Training EmplFinance EmplGovernment & Public Administration EmplHealth Science EmplHospitality & Tourism EmplHuman Services EmplInformation Technology EmplLaw, Public Safety, Corrections & Security EmplManufacturing EmplMarketing, Sales & Service EmplScience, Technology, Engineering & Mathematics EmplTransportation, Distribution & Logistics EmplAgriculture, Food & Natural Resources %Architecture and Construction %Arts, A/V Technology & Communications %Business, Management & Administration %Education & Training %Finance %Government & Public Administration %Health Science %Hospitality & Tourism %Human Services %Information Technology %Law, Public Safety, Corrections & Security %Manufacturing %Marketing, Sales & Service %Science, Technology, Engineering & Mathematics %Transportation, Distribution & Logistics %COVID-19 Vulnerability IndexAverage Wages per WorkerAvg Wages Growth, Last Ten YearsUnemployment RateUnderemployment RatePrime-Age Labor Force Participation RateSkilled Career 1Skilled Career 1 EmplSkilled Career 1 Avg Ann WagesSkilled Career 2Skilled Career 2 EmplSkilled Career 2 Avg Ann WagesSkilled Career 3Skilled Career 3 EmplSkilled Career 3 Avg Ann WagesSkilled Career 4Skilled Career 4 EmplSkilled Career 4 Avg Ann WagesSkilled Career 5Skilled Career 5 EmplSkilled Career 5 Avg Ann WagesSkilled Career 6Skilled Career 6 EmplSkilled Career 6 Avg Ann WagesSkilled Career 7Skilled Career 7 EmplSkilled Career 7 Avg Ann WagesSkilled Career 8Skilled Career 8 EmplSkilled Career 8 Avg Ann WagesSkilled Career 9Skilled Career 9 EmplSkilled Career 9 Avg Ann WagesSkilled Career 10Skilled Career 10 EmplSkilled Career 10 Avg Ann Wages

The US Public Sector Consulting And Advisory Services Market report segments the industry into By Type (Policy Analysis Services, Bond Issuance Services, Major Project Advisory Services, Program Evaluation Services, Financial Management Advisory Services, Other Types), By Applications (Central, State, Urban Local Bodies, Other Applications), and By Project Size (Large Scale Projects, Mid-small Scale Projects).

The North American Public Sector Consulting and Advisory Services market is experiencing robust growth, projected to reach a valuation of $13.98 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 6.19% from 2019 to 2033. This expansion is driven by several key factors. Increasing government initiatives focused on infrastructure development and modernization necessitate specialized consulting expertise in areas like major project advisory, bond issuance, and financial management. Furthermore, the growing complexity of public sector operations, coupled with the need for efficient resource allocation and improved service delivery, fuels demand for policy analysis and program evaluation services. The market's segmentation reflects this diversity, with significant contributions from services catering to large-scale projects across central, state, and urban local bodies. Leading consulting firms like EY, Deloitte, McKinsey, PwC, KPMG, and others are key players, capitalizing on the market's growth trajectory through their extensive experience and comprehensive service portfolios. The United States, as the largest economy in North America, constitutes a major share of this market, followed by Canada and Mexico. The market's continued expansion is expected to be influenced by factors such as increasing government budgets allocated to infrastructure, the adoption of advanced technologies in public administration, and a growing focus on data-driven decision-making within the public sector. The forecast period (2025-2033) promises continued growth, with the market projected to benefit from ongoing digital transformation initiatives within government agencies and a sustained demand for expertise in areas like sustainable development and climate change mitigation. While potential restraints could include budgetary constraints and fluctuating economic conditions, the long-term outlook remains positive, driven by the fundamental need for efficient and effective public sector management. The market will likely witness further consolidation among leading firms and increased competition, leading to innovation and service diversification to meet evolving client needs. This competitive landscape will drive innovation and specialization within the public sector consulting sector, fostering an environment of continuous improvement and enhanced service delivery for government entities. Recent developments include: April 2024: Deloitte, in partnership with Google Public Sector, introduced the EDGE platform. This cutting-edge solution, backed by Google Cloud's generative AI, is set to revolutionize how government agencies provide information and services to their constituents., June 2023: Accenture inked a deal to purchase Anser Advisory, a US-based firm specializing in advisory and management services for infrastructure projects.. Key drivers for this market are: Consulting Firms Drive Government Tech Integration with AI, Big Data, and Blockchain, Safeguarding Government Agencies Against Cyber Threats. Potential restraints include: Consulting Firms Drive Government Tech Integration with AI, Big Data, and Blockchain, Safeguarding Government Agencies Against Cyber Threats. Notable trends are: Consulting Firms Drive Government Tech Integration with AI, Big Data, and Blockchain.

The trade, hotels, transport, and communication industries had the highest GVA growth rate of ** percent among all other industries in India in the financial year 2022. Overall, the services sector registered the highest growth compared to the agriculture and industry sectors. Public administration, defense and other services industries were expected to have a GVA growth of over **** percent in the financial year 2025.
What is GVA? GVA or gross value added is the value of goods and services produced by an industry, sector, manufacturer, or region in an economy and is used to calculate the GDP of a country. GDP combines all GVA values across industries, levies taxes, and subsidies. While GDP calculates an overall number of goods produced by a nation, GVA measures the value added to the product. It is the difference between gross and net production. The sectoral analysis provided by GVA helps policymakers create sector-specific policies and make decisions regarding incentives. The National Statistical Office (NSO) publishes estimates of GVA in India on a quarterly and annual basis, elaborating on eight main types of commodities. Services sector In India India’s services sector covers a wide range of industries including trade, hotels, restaurants, IT-BPM, storage, communication, financing, insurance, real estate, business services, etc. Numerous government projects like Smart Cities, Clean Cities, and Digital India are strengthening the growth of the services sector. The sector also attracts significant foreign direct investment and contributes massively to exports, although agriculture accounts for the majority of the employed population.

What is the COVID-19 Economic Vulnerability Index?The COVID-19 Vulnerability Index (CVI) is a measurement of the negative impact that the coronavirus (COVID-19) crisis can have on employment based upon a region's mix of industries. For example, accommodation and food services are projected to lose more jobs as a result of the coronavirus (in the neighborhood of 50%) compared with utilities and healthcare (with none or little expected job contraction).This updated dataset contains 116 jobs attributes including the 10 most likely jobs to be impacted for each county, the total employment and employment by sector. An attribute list is included below.An average Vulnerability Index score is 100, representing the average job loss expected in the United States. Higher scores indicate the degree to which job losses may be greater — an index score of 200, for example, means the rate of job loss can be twice as large as the national average. Conversely, an index score of 50 would mean a possible job loss of half the national average. Regions heavily dependent on tourism with relatively high concentrations of leisure and hospitality jobs, for example, are likely to have high index scores. The Vulnerability Index only measures the impact potential related to the mix of industry employment. The index does not take into account variation due to a region’s rate of virus infection, nor does it factor in local government's policies in reaction to the virus. For more detail, please see this description.MethodologyThe index is based on a model of potential job losses due to the COVID-19 outbreak in the United States. Expected employment losses at the subsector level are based upon inputs which include primary research on expert testimony; news reports for key industries such as hotels, restaurants, retail, and transportation; preliminary release of unemployment claims; and the latest job postings data from Chmura's RTI database. The forecast model, based on conditions as of March 23, 2020, assumes employment in industries in each county/region would change at a similar rate as employment in national industries. The projection estimates that the United States could lose 15.0 million jobs due to COVID-19, with over half of the jobs lost in hotels, food services, and entertainment industries. Contact Chmura for further details.Attribute ListFIPSCounty NameStateTotal JobsWhite Collar JobsBlue Collar JobsService JobsWhite Collar %Blue Collar %Service %Government JobsGovernment %Primarily Self-Employed JobsPrimarily Self-Employed %Job Change, Last Ten YearsIndustry 1 NameIndustry 1 EmplIndustry 1 %Industry 2 NameIndustry 2 EmplIndustry 2 %Industry 3 NameIndustry 3 EmplIndustry 3 %Industry 4 NameIndustry 4 EmplIndustry 4 %Industry 5 NameIndustry 5 EmplIndustry 5 %Industry 6 NameIndustry 6 EmplIndustry 6 %Industry 7 NameIndustry 7 EmplIndustry 7 %Industry 8 NameIndustry 8 EmplIndustry 8 %Industry 9 NameIndustry 9 EmplIndustry 9 %Industry 10 NameIndustry 10 EmplIndustry 10 %All Other IndustriesAll Other Industries EmplAll Other Industies %Agriculture, Food & Natural Resources EmplArchitecture and Construction EmplArts, A/V Technology & Communications EmplBusiness, Management & Administration EmplEducation & Training EmplFinance EmplGovernment & Public Administration EmplHealth Science EmplHospitality & Tourism EmplHuman Services EmplInformation Technology EmplLaw, Public Safety, Corrections & Security EmplManufacturing EmplMarketing, Sales & Service EmplScience, Technology, Engineering & Mathematics EmplTransportation, Distribution & Logistics EmplAgriculture, Food & Natural Resources %Architecture and Construction %Arts, A/V Technology & Communications %Business, Management & Administration %Education & Training %Finance %Government & Public Administration %Health Science %Hospitality & Tourism %Human Services %Information Technology %Law, Public Safety, Corrections & Security %Manufacturing %Marketing, Sales & Service %Science, Technology, Engineering & Mathematics %Transportation, Distribution & Logistics %COVID-19 Vulnerability IndexAverage Wages per WorkerAvg Wages Growth, Last Ten YearsUnemployment RateUnderemployment RatePrime-Age Labor Force Participation RateSkilled Career 1Skilled Career 1 EmplSkilled Career 1 Avg Ann WagesSkilled Career 2Skilled Career 2 EmplSkilled Career 2 Avg Ann WagesSkilled Career 3Skilled Career 3 EmplSkilled Career 3 Avg Ann WagesSkilled Career 4Skilled Career 4 EmplSkilled Career 4 Avg Ann WagesSkilled Career 5Skilled Career 5 EmplSkilled Career 5 Avg Ann WagesSkilled Career 6Skilled Career 6 EmplSkilled Career 6 Avg Ann WagesSkilled Career 7Skilled Career 7 EmplSkilled Career 7 Avg Ann WagesSkilled Career 8Skilled Career 8 EmplSkilled Career 8 Avg Ann WagesSkilled Career 9Skilled Career 9 EmplSkilled Career 9 Avg Ann WagesSkilled Career 10Skilled Career 10 EmplSkilled Career 10 Avg Ann Wages

The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society.
The survey is created for both individuals and businesses.
It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.

The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)

***Description of the data in this data set: structure of the survey and pre-defined answers (if any)***
1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed}
2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high
3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question)
4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility}
5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available
6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
8. How would you assess the value of the following data categories?
8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question
10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question
11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question
12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)}
13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable
14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)}
15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company
16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company}
17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”}
18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}

***Format of the file***
.xls, .csv (for the first spreadsheet only), .odt

***Licenses or restrictions***
CC-BY

Explore the Saudi Arabia World Development Indicators dataset , including key indicators such as Access to clean fuels, Adjusted net enrollment rate, CO2 emissions, and more. Find valuable insights and trends for Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, and India.

Indicator, Access to clean fuels and technologies for cooking, rural (% of rural population), Access to electricity (% of population), Adjusted net enrollment rate, primary, female (% of primary school age children), Adjusted net national income (annual % growth), Adjusted savings: education expenditure (% of GNI), Adjusted savings: mineral depletion (current US$), Adjusted savings: natural resources depletion (% of GNI), Adjusted savings: net national savings (current US$), Adolescents out of school (% of lower secondary school age), Adolescents out of school, female (% of female lower secondary school age), Age dependency ratio (% of working-age population), Agricultural methane emissions (% of total), Agriculture, forestry, and fishing, value added (current US$), Agriculture, forestry, and fishing, value added per worker (constant 2015 US$), Alternative and nuclear energy (% of total energy use), Annualized average growth rate in per capita real survey mean consumption or income, total population (%), Arms exports (SIPRI trend indicator values), Arms imports (SIPRI trend indicator values), Average working hours of children, working only, ages 7-14 (hours per week), Average working hours of children, working only, male, ages 7-14 (hours per week), Cause of death, by injury (% of total), Cereal yield (kg per hectare), Changes in inventories (current US$), Chemicals (% of value added in manufacturing), Child employment in agriculture (% of economically active children ages 7-14), Child employment in manufacturing, female (% of female economically active children ages 7-14), Child employment in manufacturing, male (% of male economically active children ages 7-14), Child employment in services (% of economically active children ages 7-14), Child employment in services, female (% of female economically active children ages 7-14), Children (ages 0-14) newly infected with HIV, Children in employment, study and work (% of children in employment, ages 7-14), Children in employment, unpaid family workers (% of children in employment, ages 7-14), Children in employment, wage workers (% of children in employment, ages 7-14), Children out of school, primary, Children out of school, primary, male, Claims on other sectors of the domestic economy (annual growth as % of broad money), CO2 emissions (kg per 2015 US$ of GDP), CO2 emissions (kt), CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion), CO2 emissions from transport (% of total fuel combustion), Communications, computer, etc. (% of service exports, BoP), Condom use, population ages 15-24, female (% of females ages 15-24), Container port traffic (TEU: 20 foot equivalent units), Contraceptive prevalence, any method (% of married women ages 15-49), Control of Corruption: Estimate, Control of Corruption: Percentile Rank, Upper Bound of 90% Confidence Interval, Control of Corruption: Standard Error, Coverage of social insurance programs in 4th quintile (% of population), CPIA building human resources rating (1=low to 6=high), CPIA debt policy rating (1=low to 6=high), CPIA policies for social inclusion/equity cluster average (1=low to 6=high), CPIA public sector management and institutions cluster average (1=low to 6=high), CPIA quality of budgetary and financial management rating (1=low to 6=high), CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high), Current education expenditure, secondary (% of total expenditure in secondary public institutions), DEC alternative conversion factor (LCU per US$), Deposit interest rate (%), Depth of credit information index (0=low to 8=high), Diarrhea treatment (% of children under 5 who received ORS packet), Discrepancy in expenditure estimate of GDP (current LCU), Domestic private health expenditure per capita, PPP (current international $), Droughts, floods, extreme temperatures (% of population, average 1990-2009), Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative), Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative), Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative), Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative), Electricity production from coal sources (% of total), Electricity production from nuclear sources (% of total), Employers, total (% of total employment) (modeled ILO estimate), Employment in industry (% of total employment) (modeled ILO estimate), Employment in services, female (% of female employment) (modeled ILO estimate), Employment to population ratio, 15+, male (%) (modeled ILO estimate), Employment to population ratio, ages 15-24, total (%) (national estimate), Energy use (kg of oil equivalent per capita), Export unit value index (2015 = 100), Exports of goods and services (% of GDP), Exports of goods, services and primary income (BoP, current US$), External debt stocks (% of GNI), External health expenditure (% of current health expenditure), Female primary school age children out-of-school (%), Female share of employment in senior and middle management (%), Final consumption expenditure (constant 2015 US$), Firms expected to give gifts in meetings with tax officials (% of firms), Firms experiencing losses due to theft and vandalism (% of firms), Firms formally registered when operations started (% of firms), Fixed broadband subscriptions, Fixed telephone subscriptions (per 100 people), Foreign direct investment, net outflows (% of GDP), Forest area (% of land area), Forest area (sq. km), Forest rents (% of GDP), GDP growth (annual %), GDP per capita (constant LCU), GDP per unit of energy use (PPP $ per kg of oil equivalent), GDP, PPP (constant 2017 international $), General government final consumption expenditure (current LCU), GHG net emissions/removals by LUCF (Mt of CO2 equivalent), GNI growth (annual %), GNI per capita (constant LCU), GNI, PPP (current international $), Goods and services expense (current LCU), Government Effectiveness: Percentile Rank, Government Effectiveness: Percentile Rank, Lower Bound of 90% Confidence Interval, Government Effectiveness: Standard Error, Gross capital formation (annual % growth), Gross capital formation (constant 2015 US$), Gross capital formation (current LCU), Gross fixed capital formation, private sector (% of GDP), Gross intake ratio in first grade of primary education, male (% of relevant age group), Gross intake ratio in first grade of primary education, total (% of relevant age group), Gross national expenditure (current LCU), Gross national expenditure (current US$), Households and NPISHs Final consumption expenditure (constant LCU), Households and NPISHs Final consumption expenditure (current US$), Households and NPISHs Final consumption expenditure, PPP (constant 2017 international $), Households and NPISHs final consumption expenditure: linked series (current LCU), Human capital index (HCI) (scale 0-1), Human capital index (HCI), male (scale 0-1), Immunization, DPT (% of children ages 12-23 months), Import value index (2015 = 100), Imports of goods and services (% of GDP), Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24), Incidence of HIV, all (per 1,000 uninfected population), Income share held by highest 20%, Income share held by lowest 20%, Income share held by third 20%, Individuals using the Internet (% of population), Industry (including construction), value added (constant LCU), Informal payments to public officials (% of firms), Intentional homicides, male (per 100,000 male), Interest payments (% of expense), Interest rate spread (lending rate minus deposit rate, %), Internally displaced persons, new displacement associated with conflict and violence (number of cases), International tourism, expenditures for passenger transport items (current US$), International tourism, expenditures for travel items (current US$), Investment in energy with private participation (current US$), Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate), Development

Saudi Arabia, Bahrain, Kuwait, Oman, Qatar, China, India Follow data.kapsarc.org for timely data to advance energy economics research..

The statistic shows the distribution of the workforce across economic sectors in China from 2014 to 2024. In 2024, around 22.2 percent of the workforce were employed in the agricultural sector, 29 percent in the industrial sector and 48.8 percent in the service sector. In 2022, the share of agriculture had increased for the first time in more than two decades, which highlights the difficult situation of the labor market due to the pandemic and economic downturn at the end of the year. Distribution of the workforce in China In 2012, China became the largest exporting country worldwide with an export value of about two trillion U.S. dollars. China’s economic system is largely based on growth and export, with the manufacturing sector being a crucial contributor to the country’s export competitiveness. Economic development was accompanied by a steady rise of labor costs, as well as a significant slowdown in labor force growth. These changes present a serious threat to the era of China as the world’s factory. The share of workforce in agriculture also steadily decreased in China until 2021, while the agricultural gross production value displayed continuous growth, amounting to approximately 7.8 trillion yuan in 2021. Development of the service sector Since 2011, the largest share of China’s labor force has been employed in the service sector. However, compared with developed countries, such as Japan or the United States, where 73 and 79 percent of the work force were active in services in 2023 respectively, the proportion of people working in the tertiary sector in China has been relatively low. The Chinese government aims to continue economic reform by moving from an emphasis on investment to consumption, among other measures. This might lead to a stronger service economy. Meanwhile, the size of the urban middle class in China is growing steadily. A growing number of affluent middle class consumers could promote consumption and help China move towards a balanced economy.

The Professional Services subdivision's performance is largely linked to overall economic conditions, which often determine business confidence and capital expenditure. Over the past few years, greater business profit and rising capital expenditure by the public sector have supported subdivision demand. However, construction projects that were delayed or cancelled because of surging construction expenses and labour scarcity adversely impacted several industries, including architectural services and engineering consulting. Many firms have faced scrutiny over conflicts of interest, transparency and public sector expenditures, which has led them to restructure operations and enhance compliance procedures in order to re-establish confidence. These factors have dampened overall subdivision performance, contributing to revenue weakening by an annualised 0.2% through the end of 2024-25 to $305.7 billion. This trend includes a 1.9% dip in 2024-25 as the advisory market faces a significant downturn. The Professional Services subdivision is shifting towards tech-oriented strategies. Service providers are incorporating advanced tech solutions like AI-assisted data analysis and GenAI into operations. This technological integration improves efficiency and service delivery and facilitates innovation. GenAI has also enabled higher precision in services like design, consulting and accounting, redefining service delivery. High-value, tech-oriented services command premium pricing and have supported revenue. However, these come with inherent challenges. Requiring specialised skills leads to increased operational costs, including training expenses and investments in technology. The increased remuneration needed to attract and retain talent has escalated costs and exerted pressure on profit margins over the past few years. The Professional Services subdivision is forecast to expand over the next few years, driven by sustainability trends and enhanced regulations. As the focus on renewable energies intensifies, demand for engineering consultants equipped with specialist knowledge is set to accelerate. The need for mandatory climate disclosures and ESG compliance also presents growth potential for accounting and advisory firms. Technology is another pivotal factor that will influence the operations, quality and variety of service offerings. As demand for tech-oriented solutions intensifies, service providers will be more inclined to invest in tech-related expertise, adding value to their service offerings and enhancing their competitive edge in the market. These forces are why subdivision revenue is forecast to expand at an annualised 2.2% through the end of 2029-30 to $340.1 billion.

Abstract

This survey was conducted as part of a review of the different civil service reform tools in Ethiopia, to assess what has been achieved, and what to consider next. The review aimed to take stock of what has been done, identify remaining and potential new challenges, and draw lessons, as well as suggest recommendations on how to move further ahead in the coming years to foster a fair, responsible, efficient, ethical, and transparent civil service. A survey of civil servants at the Federal, Regional and Woreda levels was implemented that focused on five sectors, namely, agriculture, education, health, revenue administration, and trade.

The aim of the Ethiopia Civil Servant Survey was to gather micro-level data on the perceptions and experiences of civil servants, and on the key restraints to civil servants performing their duties to the best of their abilities, and to the provision of public goods. This civil servant survey aimed to contribute to the development of diagnostic tools which would allow to better understand the incentive environments which lead to different types of behavior and the determinants of service delivery in the civil service.

Report available at: https://documents.worldbank.org/en/publication/documents-reports/documentdetail/981311547566282423/moving-further-on-civil-service-reforms-in-ethiopia-findings-and-implications-from-a-civil-service-survey-and-qualitative-analysis

Geographic coverage

At the Federal level 330 individuals were planned to be interviewed; 550 at the Region level (Harar, Afar, SNNPR, Oromiya, Amhara, Dire Dawa, Addis Ababa, Benishangul, Somali, Tigray, Gambella); and 1615 at the Woreda (66 Woredas) level. Within each region 50 individuals were targeted to be interviewed, except in Addis Ababa, where the target was 40 due to not having an agriculture bureau, and except in Oromiya, where, due to additional funds becoming available, the target became 60. Within each Woreda, 25 individuals were planned to be sampled.

Analysis unit

Public servants, including managers and non-managers at the Federal, Regional and Woreda levels.

Kind of data

Aggregate data [agg]

Sampling procedure

To provide a large sample for statistical analysis, while remaining within budget, the Ethiopian civil servants survey focused on the three major policy making tiers of government: Federal; Regional; and Woreda. The Ministry of Public Sector and Human Resource Development identified the 5 core sectors that the survey should include: agriculture, education, health, revenue, and trade. The decision was made then to plan to interview a sufficient number of individuals from each of those tiers and allocate the remaining funds to Woreda-level interviews. With this methodology, with the funds available, 70 Woredas were included in the target sample at the planning stage. At the Federal level 330 individuals were planned to be interviewed; 550 at the Region level; and 1615 at the Woreda level. Within each region 50 individuals were targeted to be interviewed, except in Addis Ababa, where the target was 40 due to not having an agriculture bureau, and except in Oromiya, where, due to additional funds becoming available, the target became 60. Within each Woreda, 25 individuals were planned to be sampled.

Stratified randomization was conducted to select 70 Woredas from the 9 regional states in a way that is proportional to the size of the region (in terms of number of Woredas as per the 2007 census). However, 4 Woredas were dropped due to security challenges.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey questionnaire comprises following modules: 1- Cover page, 2- Demographic and work history information, 3- Management practices, 4- Turnover, 5- Recruitment and selection, 6- Attitude, 7- Time use and bottlenecks, 8- Information, 9- Information technology, 10- Stakeholder engagement, 11- Reforms, and 12- Woreda and city benchmarking.

The questionnaire was prepared in English and Amharic.

Response rate

Response rate was 88%.

The United States human resource (HR) technology market size reached USD 11.0 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 29.4 Billion by 2033, exhibiting a growth rate (CAGR) of 11.60% during 2025-2033.

IMARC Group provides an analysis of the key trends in each segment of the United States human resource (HR) technology market report, along with forecasts at the country and regional levels from 2025-2033. Our report has categorized the market based on application, type, end use industry and company size.

The global digital government services market is experiencing robust growth, driven by increasing government initiatives to enhance citizen engagement, improve service delivery, and optimize operational efficiency. The market, estimated at $50 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $150 billion by 2033. Key drivers include the rising adoption of cloud computing, big data analytics, and artificial intelligence (AI) to improve data management, personalize citizen services, and enhance decision-making. The shift towards open data initiatives, fostering transparency and citizen participation, is further fueling market expansion. Government data asset management and government big data governance are prominent segments, accounting for a significant portion of market revenue. North America currently holds the largest market share, followed by Europe and Asia Pacific, but Asia Pacific is expected to exhibit the fastest growth rate due to increasing digitalization efforts and substantial government investments in digital infrastructure across countries like India and China. Challenges remain, including data security concerns, legacy system integration difficulties, and the need for skilled personnel to manage complex digital systems. However, ongoing technological advancements and increasing government investments are expected to mitigate these challenges, sustaining the market's strong growth trajectory. The market segmentation reveals a strong demand for solutions across various government levels – central, local, and other agencies. The "other" application segment includes various specialized government bodies adopting digital transformation strategies. Similarly, within the types segment, "Other" encompasses emerging technologies and services contributing to the overall growth. Leading companies like Granicus, Accenture, and NEC Corporation are actively shaping the market landscape through innovative solutions and strategic partnerships. The competitive landscape is characterized by a mix of established players and emerging technology providers, leading to continuous innovation and the development of more sophisticated and integrated digital government solutions. Regional variations in digital infrastructure and government policies influence adoption rates, creating opportunities for tailored solutions and market penetration strategies. Continued focus on improving cybersecurity, data privacy, and interoperability will be critical for maintaining market trust and sustained growth.

The Vietnam Facility Management Market Report is Segmented by Service Type (Hard Services, Soft Services), Offering Type (In-House, Outsourced), End-User Industry (Commercial, Hospitality, Institutional and Public Infrastructure, Healthcare, Industrial and Process, Other End-User Industries), and by Geography. The Market Forecasts are Provided in Terms of Value (USD).

The UK IT Services Market Report is Segmented by Service Type (IT Consulting and Implementation, IT Outsourcing, Business Process Outsourcing, and More), End-User Enterprise Size (Small and Medium Enterprises, and Large Enterprises), Deployment Model (Onshore Delivery, Nearshore Delivery, and More), and End-User Vertical (BFSI, Government and Public Sector, and More). The Market Forecasts are Provided in Terms of Value (USD).

The arts, entertainment and recreation sector has experienced a significant upswing in recent years, primarily fueled by a surge in tourism. With destinations like Disney parks drawing nearly 50 million visitors a year, the theme park industry is seeing robust growth in attendance and revenue. This trend underscores a broader consumer shift towards experiential and immersive activities as people increasingly prioritize memorable experiences over tangible goods. High-profile attractions and events continue to entice domestic and international visitors, reinforcing the sector's role as a dominant player in the global entertainment market. As people seek out these memorable experiences, whether at amusement parks or immersive art installations, it emphasizes the dynamic industries present in the sector. Revenue expanded at a CAGR of 11.8% to $521.3 billion in 2025, including a boost of 3.3% in the same year. Over the past few years, the sector has embraced the rising demand for exclusive and premium experiences. VIP packages at music festivals and major sports events have become increasingly popular, offering consumers unique benefits that justify more lavish spending. Meanwhile, the arts sector has witnessed a surge in immersive installations like The Van Gogh Experience and Luna Luna: Forgotten Fantasy, highlighting consumers' desire for engagement and novelty. Despite federal budget cuts impacting cultural institutions and national parks, the arts and entertainment industries have thrived thanks to strategic pivots and robust consumer demand. The ongoing willingness of individuals to spend generously on concerts and experiential events, even amid rising ticket prices, also speaks to a cultural shift towards prioritizing shared moments and digital engagement over material acquisitions. Arts, entertainment and recreation industries will see continued growth over the next few years, driven by greater disposable incomes and a strong alignment with consumer preferences. With high-profile tours and immersive events maintaining their allure, concert attendance will remain high, even as ticket prices climb. As more people travel to concerts in different cities, this burgeoning trend will bolster the industry's reach and economic impact. Despite challenges posed by automation and AI, the demand for unique, human-driven talent will persist, ultimately sustaining the sector's dynamic trajectory. Revenue is expected to climb at a CAGR of 1.9% to an estimated $573.5 billion over the years to 2030.

In 2021, there were approximately ****** more jobs in the City of Marseille than in 2008. The number of jobs had increased in several sectors, including public administration, education, health, and social work, for which there were nearly ******* jobs in 2008, compared to more than ******* in 2021, or trade, transport, and other services, for which there were around ****** more jobs in 2021 than in 2008. By contrast, the number of jobs in the agriculture sector decreased during this period.

The India Facility Management Market Report is Segmented by Service Type (Hard Services, Soft Services), Offering Type (In-House, Outsourced), and End-User Industry (Commercial, Hospitality, Institutional and Public Infrastructure, Healthcare, Industrial and Process, Other End-User Industries). The Market Forecasts are Provided in Terms of Value (USD).

There were around ******* people working as in the civil service in the United Kingdom as of the first quarter of 2025. Between 2011 and 2016, the number of civil servants fell significantly, with a recent uptick noticeable from 2018 onwards.