A survey conducted in 2022 found that members of Generation Z were the least likely to say they were just as productive when working from home versus working in the office. In contrast, nearly ***** times the number of Baby Boomers said they were just as productive working from home versus the office.

Graph and download economic data for Nonfarm Business Sector: Labor Productivity (Output per Hour) for All Workers (OPHNFB) from Q1 1947 to Q1 2025 about per hour, output, headline figure, sector, nonfarm, business, real, persons, and USA.

In 2024, over 68 percent of respondents among U.S. workers reported experiencing at least one form of electronic monitoring on the job. Technology monitoring, including tracking of work-related smartphones, tablets, and computers, was the most common at around 52 percent. Camera monitoring followed at 45 percent, while productivity and location tracking were reported by 37 percent and 27 percent of workers, respectively.

Nonfarm Productivity QoQ in the United States decreased to -1.50 percent in the first quarter of 2025 from 1.70 percent in the fourth quarter of 2024. This dataset includes a chart with historical data for the United States Nonfarm Productivity Qoq.

Background: Human-capital based lifetime productivity estimates are frequently used in cost-of-illness (COI) analyses and, less commonly, in cost-effectiveness analyses (CEAs). Previous US estimates assumed that labor productivity and real earnings both grow by 1% per year. Objectives: This study presents estimates of annual and lifetime productivity for 2016 using data from the American Community Survey, the American Time Use Survey, and the Current Population Survey, and with varying assumptions about real earnings growth. Methods: The sum of market productivity (gross annual personal labor earnings adjusted for employer-paid benefits) and the imputed value of non-market time spent in household, caring, and volunteer services was estimated. The present value of lifetime productivity at various ages was calculated for synthetic cohorts using annual productivity estimates, life tables, discount rates, and assumptions about future earnings growth rates. Results: Mean annual productivity was $57,324 for US adults in 2016, including $36,935 in market and $20,389 in non-market productivity. Lifetime productivity at birth, using a 3% discount rate, is roughly $1.5 million if earnings grow by 1% per year and $1.2 million if future earnings growth averages 0.5% per year. Conclusions: Inclusion of avoidable productivity losses in societal-perspective CEAs of health interventions is recommended in new US cost-effectiveness guidelines. However, estimates vary depending on whether analysts choose to estimate total productivity or just market productivity, and on assumptions made about growth in future productivity and earnings.

In 1870, labor productivity in Western Europe was approximately 70 percent of labor productivity in the United States. This rate gradually fell over the next 80 years, to less than half of the U.S. rate by 1950. In Southern and Eastern Europe, productivity was less than half of Western Europe's rate and more than half a century behind. Over this period, much of Western Europe was devastated by two world wars; while the U.S.' industrial development was not hindered in the same way and structurally, the U.S. was largely untouched by the war. By the end of the century, Western Europe had mostly caught up with the U.S.. Although overall productivity in the EU in 1990 was 90 percent of the U.S.' rate, there were some individual countries, such as Luxembourg, the Netherlands, and France, where GDP per capita or productivity were actually higher than in the U.S..

The more powerful the artificial intelligence (AI) used, the more impressive the benefits on labor productivity will be. This is consistent for those companies adopting AI over a ** year period, with ** year and ** year periods all both demonstrating a far slower increase in productivity. The displacement of labor is far less drastic if companies are willing to employ more powerful AI models.

Graph and download economic data for Average Weekly Earnings of Production and Nonsupervisory Employees, Total Private (CES0500000030) from Jan 1964 to Jun 2025 about nonsupervisory, earnings, establishment survey, production, private, employment, and USA.

This statistic portrays the number of production workers in U.S. motor vehicle parts manufacturing from 1990 through 2020. In 2020, there were about 387,900 production workers in U.S. motor vehicle parts manufacturing, 59,100 less than the previous year.

United States US: Agriculture Value Added per Worker: 2010 Price data was reported at 80,537.733 USD in 2015. This records an increase from the previous number of 76,456.524 USD for 2014. United States US: Agriculture Value Added per Worker: 2010 Price data is updated yearly, averaging 56,668.409 USD from Dec 1997 (Median) to 2015, with 19 observations. The data reached an all-time high of 80,537.733 USD in 2015 and a record low of 33,967.250 USD in 1998. United States US: Agriculture Value Added per Worker: 2010 Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Agricultural Production and Consumption. Agriculture value added per worker is a measure of agricultural productivity. Value added in agriculture measures the output of the agricultural sector (ISIC divisions 1-5) less the value of intermediate inputs. Agriculture comprises value added from forestry, hunting, and fishing as well as cultivation of crops and livestock production. Data are in constant 2010 U.S. dollars.; ; Derived from World Bank national accounts files and Food and Agriculture Organization, Production Yearbook and data files.; Weighted average;

Snapshot img

Employee Engagement Software Market Size 2024-2028

The employee engagement software market size is forecast to increase by USD 325.9 million, at a CAGR of 6.8% between 2023 and 2028.

The market is driven by the increasing need for workforce diversity management and the rising adoption of digital Human Resource (HR) technology. Companies are recognizing the importance of fostering an inclusive work environment and are turning to employee engagement software solutions to manage diversity initiatives, track progress, and promote equal opportunities. Additionally, the shift towards digital HR technology is gaining momentum, as organizations seek to streamline processes, enhance productivity, and improve employee experiences. However, this market also faces challenges.
Technical constraints, such as data security and privacy concerns, can hinder the adoption of employee engagement software. Moreover, poor customer service can negatively impact user experience and hinder the market's growth. To capitalize on opportunities and navigate these challenges effectively, companies must prioritize addressing these issues, ensuring robust data security measures and delivering exceptional customer service to maintain a competitive edge.

What will be the Size of the Employee Engagement Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
Request Free Sample

The market continues to evolve, with dynamic market activities shaping its landscape. Employee journey mapping tools enable organizations to track and optimize the employee experience, while productivity tracking software ensures efficient workflows. Integrated employee experience platforms offer mobile engagement, peer-to-peer feedback, goal setting, and performance review functionalities. performance management systems, pulse survey software, and employee recognition programs foster continuous employee feedback and engagement. Knowledge sharing platforms, virtual recognition awards, and workplace collaboration tools promote a culture of innovation and learning. Culture building initiatives, HR analytics dashboards, and employee wellbeing platforms prioritize employee satisfaction and retention. Employee training platforms, team communication tools, talent management systems, and internal communications software streamline work processes and improve team coordination.

Engagement survey tools, employee onboarding systems, employee voice platforms, gamified engagement platforms, leadership development programs, and employee sentiment analysis tools further enhance the employee experience. These solutions adapt to the ever-changing needs of various sectors, ensuring a seamless and engaging employee journey. The integration of these tools fosters a productive and collaborative work environment, ultimately contributing to the overall success of an organization.

How is this Employee Engagement Software Industry segmented?

The employee engagement software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud-based
  On-premises


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.

Cloud-based employee engagement software is experiencing significant growth due to its ability to provide a unified platform for gathering, storing, and accessing employee data from anywhere in the world. This includes features such as productivity tracking, goal setting, performance reviews, peer-to-peer feedback, and employee recognition programs. The use of cloud technology enables enterprises to accommodate unique HR requirements, ensure better reliability, and improve visibility into employee engagement metrics. Cloud-based solutions also offer advantages in terms of cost and flexibility. Instead of large, one-time investments and periodic expenses for maintenance and updates associated with on-premises software, cloud-based applications require regular payments.

This business model allows enterprises to allocate resources more effectively and adapt to changing needs. Additionally, cloud-based employee engagement software supports various tools and platforms, such as pulse surveys, knowledge sharing, team communication, and talent management systems. These tools contribute to a more immersive and harmonious employee experience, fostering a culture of collaboration, continuous learning, and open communication. Moreover, cloud-based solutions facilitate culture building initiatives, employee wellbe

Quarterly series on labour productivity growth and related variables have been published for the first time on December 20th, 2000. These statistical series go back to the first quarter of 1981. The data are published two months after the reference quarter. The quarterly productivity measures are meant to assist in the analysis of the short-run relationship between the fluctuations of output, employment, compensation and hours worked. This measure is fully comparable with the United States quarterly measure. The quarterly estimations of this table are limited to the overall business sector. This aggregate excludes government and non-profit institutions expenditures on primary factors as well as the output of households (including the rental value of owner-occupied dwellings). Corresponding exclusions are also made to labour compensation and hours worked to make output and labour input data consistent with one another. The real output of the business sector is constructed using a Fisher-chained index, after excluding from GDP at market prices the real gross value added of the government sector, of the non-profit institutions and of households (including the rental value of owner-occupied dwellings). This approach is similar to that used for the quarterly productivity of the business sector in the United States. The estimate of the total number of jobs covers four main categories: employee jobs, work owner of an unincorporated business, own account self-employment, and unpaid family jobs. This last category is found mainly in sectors where family firms are important (agriculture and retail trade, in particular). Jobs data are consistent with the System of National Accounts. This is the quarterly average of hours worked for jobs in all categories. The number of hours worked in all jobs is the quarterly average for all jobs times the annual average hours worked in all jobs. According to the retained definition, hours worked means the total number of hours that a person spends working, whether paid or not. In general, this includes regular and overtime hours, breaks, travel time, training in the workplace and time lost in brief work stoppages where workers remain at their posts. On the other hand, time lost due to strikes, lockouts, annual vacation, public holidays, sick leave, maternity leave or leave for personal needs are not included in total hours worked. Labour productivity is a measure of real gross domestic product (GDP) per hour worked. The ratio between total compensation for all jobs, and the number of hours worked. The term hourly compensation" is often used to refer to the total compensation per hour worked." This measures the cost of labour input required to produce one unit of output, and equals labour compensation in current dollars divided by the real output. It is often calculated as the ratio of labour compensation per hour worked and labour productivity. Unit labour cost increases when labour compensation per hour worked increases more rapidly than labour productivity. It is widely used to measure inflation pressures arising from wage growth. Unit non-labour payments are the non-labour payments associated with each unit of output of goods and services, and they are calculated as the non-labour payments divided by the real output. The implicit price deflator is equal to current-dollar output, divided by real output. The output measure is consistent with the Quarterly Income and Expenditure Accounts, prepared by the National Economic Accounts Division. Labor share is equal to the labour compensation divided by current dollar output. The output measure is consistent with the Quarterly Income and Expenditure Accounts, prepared by the National Economic Accounts Division. Current-dollar gross domestic product (GDP) in business sector equals current-dollar GDP in the economy less the gross value added of government, nonprofit institutions, households, and the rental of owner-occupied-dwellings. The output measure is consistent with the Quarterly Income and Expenditure Accounts. The total compensation for all jobs consists of all payments in cash or in kind made by domestic producers to workers for services rendered. It includes wages and salaries and employer's social contributions of employees, plus an imputed labour income for self-employed workers. Non-labour payments are the excess of current-dollar output in the business sector over corresponding labour compensation, and include non-labour costs as well as corporate profits and the profit-type income of proprietors. Non-labour costs include interest, depreciation, rent, and indirect business taxes. Unit labour cost in United States dollars is the equivalent of the ratio of Canadian unit labour cost to the exchange rate. This latter corresponds to the United States dollar value expressed in Canadian dollars.

Release Date: 2021-12-09.Release Schedule:.The data in this file come from the 2020 Annual Survey of Manufactures data files released in December 2021. For more information about the Annual Survey of Manufactures data, see About: Annual Survey of Manufactures...Key Table Information:.Includes only establishments of firms with payroll..Data may be subject to employment- and/or sales-size minimums that vary by industry...Data Items and Other Identifying Records: .First-quarter payroll ($1,000).Relative standard error for estimate of first-quarter payroll (%).Number of employees.Relative standard error for estimate of number of employees (%).First-quarter Production workers wages ($1,000) .Relative standard error for estimate of first-quarter Production workers wages (%) .Production workers for pay period including March 12.Relative standard error for estimate of production workers for pay period including March 12 (%).Second-quarter Production workers wages ($1,000) .Relative standard error for estimate of second-quarter Production workers wages (%) .Production workers for pay period including June 12.Relative standard error for estimate of production workers for pay period including June 12 (%).Third-quarter Production workers wages ($1,000) .Relative standard error for estimate of third-quarter Production workers wages (%) .Production workers for pay period including September 12.Relative standard error for estimate of production workers for pay period including September 12 (%).Fourth-quarter Production workers wages ($1,000) .Relative standard error for estimate of fourth-quarter Production workers wages (%) .Production workers for pay period including December 12.Relative standard error for estimate of production workers for pay period including December 12 (%)..Geography Coverage:.The data are shown for employer establishments and firms for the U.S. and State levels that vary by industry..For information about 2020 Annual Survey of Manufactures, see About: Annual Survey of Manufactures...Industry Coverage:.The data are shown at the 2-through 6-digit 2017 NAICS code levels for the U.S. and at the 2-digit 2017 NAICS code level for States. For information about NAICS, see Annual Survey of Manufactures (ASM): Technical Documentation: ASM Product Class Codes and Descriptions...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/asm/data/2020/AM1831BASIC04.zip..API Information:.Annual Survey of Manufactures API data are housed in the Census Bureau API. For more information, see ASM API..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only..To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Annual Survey of Manufactures (ASM): Technical Documentation: Annual Survey of Manufactures Methodology...Symbols:.D - Withheld to avoid disclosing data of individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised (represented as a superscript).s - Relative standard error is 40 percent or more and less than 100 percent (data variable displayed as a superscript).For a complete list of all economic programs symbols, see the Economic Census: Technical Documentation: Data Dictionary...Source:.U.S. Census Bureau, 2020 Annual Survey of Manufactures (ASM).For information about the Annual Survey of Manufactures (ASM), see Business and Economy: Annual Survey of Manufactures (ASM)..Contact Information:.U.S. Census Bureau.For general inquiries:.(800) 242-2184/ (301) 763-5154.ewd.surveys@census.gov.For specific data questions:.(844) 303-7713.For additional contacts, see Annual Survey of Manufactures (ASM): About: Contact Us

Introduction

Wasting Time At the Workplace Statistics: Wasting time at the workplace is a widespread issue with significant implications for productivity and financial performance. Recent statistics reveal that 89% of employees admit to wasting at least 30 minutes daily, with 18.5% acknowledging they spend three or more hours on non-work-related activities each day.

The average employee spends approximately 2.9 hours per workday on non-work activities, including internet browsing, social media, and personal tasks. This time loss translates into substantial financial costs; U.S. companies lose about USD 1.7 million annually for every 100 employees due to inefficiencies.

Meetings are a significant contributor to time wastage, with employees spending an average of 21.5 hours per week in meetings, nearly half of which are deemed unproductive. Unnecessary meetings alone cost U.S. businesses approximately USD 37 billion annually.

Email management is another area of concern; employees check their emails an average of 121 times per day, consuming about 28% of the workweek. Additionally, 47% of employees consider meetings the biggest time-waster, and 53% believe that taking regular breaks enhances work quality.

Understanding these statistics underscores the need for organizations to implement effective time management strategies and streamline workflows to enhance productivity and reduce financial losses associated with time wastage. But how common is wasting time at work, and what kind of impact does it have? Statistics tell a surprising story about how much time is lost and why it happens.

Release Date: 2021-12-09.Release Schedule:.The data in this file come from the 2020 Annual Survey of Manufactures data files released in December 2021. For more information about the Annual Survey of Manufactures data, see About: Annual Survey of Manufactures...Key Table Information:.Includes only establishments of firms with payroll..Data may be subject to employment- and/or sales-size minimums that vary by industry...The average days closed for an industry are estimated based on those establishments in the industry reporting days closed. Simple weighted estimates of the days closed are formed by applying the establishment's sample weight to its respective values and adding these weighted values across the reporting establishments. The average is formed as the ratio of the days closed weighted sum to the sum of the weights for the reporting establishments...Data Items and Other Identifying Records: .First-quarter payroll ($1,000).Standard error for estimate of first-quarter payroll .Number of employees.Standard error for estimate of number of employees .First-quarter Production workers wages ($1,000) .Standard error for estimate of first-quarter Production workers wages .Production workers for pay period including March 12.Standard error for estimate of production workers for pay period including March 12 .Second-quarter Production workers wages ($1,000) .Standard error for estimate of second-quarter Production workers wages .Production workers for pay period including June 12.Standard error for estimate of production workers for pay period including June 12 .Third-quarter Production workers wages ($1,000) .Standard error for estimate of third-quarter Production workers wages .Production workers for pay period including September 12.Standard error for estimate of production workers for pay period including September 12 .Fourth-quarter Production workers wages ($1,000) .Standard error for estimate of fourth-quarter Production workers wages .Production workers for pay period including December 12.Standard error for estimate of production workers for pay period including December 12 ..Geography Coverage:.The data are shown for employer establishments and firms for the U.S. and State levels that vary by industry..For information about 2020 Annual Survey of Manufactures, see About: Annual Survey of Manufactures...Industry Coverage:.The data are shown at the 2-through 6-digit 2017 NAICS code levels for the U.S. and at the 2-digit 2017 NAICS code level for States. For information about NAICS, see Annual Survey of Manufactures (ASM): Technical Documentation: ASM Product Class Codes and Descriptions...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/asm/data/2020/AM1831BASIC04.zip..API Information:.Annual Survey of Manufactures API data are housed in the Census Bureau API. For more information, see ASM API..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only..To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Annual Survey of Manufactures (ASM): Technical Documentation: Annual Survey of Manufactures Methodology...Symbols:.D - Withheld to avoid disclosing data of individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised (represented as a superscript).s - Relative standard error is 40 percent or more and less than 100 percent (data variable displayed as a superscript).For a complete list of all economic programs symbols, see the Economic Census: Technical Documentation: Data Dictionary...Source:.U.S. Census Bureau, 2020 Annual Survey of Manufactures (ASM).For information about the Annual Survey of Manufactures (ASM), see Business and...

US Onsite/Near-site Healthcare Market size was valued at USD 8.95 Billion in 2024 and is projected to reach USD 15.7 Billion by 2032, growing at a CAGR of 7.3% from 2026 to 2032.

US Onsite/Near-site Healthcare Market Drivers

1. An increase in employers' attention to worker productivity and health Businesses are realising more and more the return on investment (ROI) of healthier workforces due to less turnover, increased productivity, and absenteeism. In or near the workplace, onsite and near-site clinics support wellness initiatives, enhance preventive care, and assist in managing chronic disorders.

2. Rising Medical Expenses By lowering ER visits and hospital stays, employers are using onsite and near-site solutions to combat the rising costs of healthcare.

Projected changes in fraction of jobs that can be performed remotely by 2-digit industry sector.

This table replaces tables 36-10-0214 and 36-10-0215, which are now archived. For concepts, methods and sources, see http://www.statcan.gc.ca/imdb-bmdi/5103-eng.htm. Data by industry included in this table correspond to the Canadian System of Macroeconomic Accounts input-output detailed level of aggregation. The table is built around the Input-Output Industry Classification (IOIC). This one identifies both Institutional Sectors and Industries based on the North American Industry Classification System (NAICS). The alphanumeric codes appearing in square brackets besides each industry title represent the IOIC identification code. The first two characters of the IOIC alphanumeric codes represent the sector. IOIC codes beginning with a BS represent Business Sector industries, codes beginning with an NP represent Non-Profit Institutions Serving Household (NPISH) Sector industries, and codes beginning with a GS represent Government Sector industries. The IOIC is a hierarchical classification. IOIC codes consisting of four alpha-numeric characters represent industries at the Summary (S) level of aggregation, IOIC codes consisting of five or six alpha-numeric characters represent industries at the Medium (M) and IOIC codes consisting of eight alpha-numeric characters represent industries at the Detailed (D) level of aggregation. The classifications of the Input-Output tables can be found at the following link http://www.statcan.gc.ca/nea-cen/hr2012-rh2012/data-donnees/aggregation-agregation/aggregation-agregation-eng.htm. Provincial and territorial data are available from 1997. For Northwest Territories including Nunavut, statistics are available until 1998 inclusively. Starting in 1999, data for Northwest Territories and Nunavut are presented separately. The estimate of the total number of jobs covers two main categories: employee jobs and self-employed jobs. The number of hours worked in all jobs is the annual average for all jobs times the annual average hours worked in all jobs. According to the retained definition, hours worked means the total number of hours that a person spends working, whether paid or not. In general, this includes regular and overtime hours, breaks, travel time, training in the workplace and time lost, in brief, work stoppages where workers remain at their posts. On the other hand, time lost due to strikes, lockouts, annual vacation, public holidays, sick leave, maternity leave or leave for personal needs are not included in total hours worked. This is the annual average of hours worked per job in all categories of jobs. The total compensation for all jobs consists of all payments in cash or in kind made by domestic producers to workers for services rendered. It includes wages and salaries and employer's social contributions of employees, plus an imputed labour income for self-employed workers. For a given industry, value added is equal to its gross production (mainly sales) less its intermediate consumption (energy, raw materials, services) stemming from other industries. The value added corresponds to Gross domestic product (GDP) at basic prices which corresponds to the GDP at market prices excluding net taxes on products. Real value added is evaluated in 2017 chained dollars. A double-deflation procedure is used to measure real value added: real intermediate inputs are subtracted from real gross output. For productivity measurement, a real value added Fisher chain index is used for each industry. Chain indexes are calculated for consecutive periods to determine variation of quantities from one period to another. The chain indexes offer the advantage of reducing the variation in the values recorded by the various fixed-base indexes. Labour productivity is the ratio between real value added and hours worked. Real value added for each industry and each aggregate is constructed from a Fisher chain index. The ratio between total compensation for all jobs, and the number of hours worked. The term hourly compensation" is often used to refer to the total compensation per hour worked." This is the labour cost per unit of output, and it equals labour compensation divided by real value added. It is also equal to the ratio of labour compensation per hour worked and labour productivity. Unit labour cost increases when labour compensation per hour worked increases more rapidly than labour productivity. It is widely used to measure long-term inflation pressures arising from wage growth. This is the unit labour cost expressed in US dollars. This is obtained by dividing the unit labour cost by the exchange rate between Canada and the United States. Labour share corresponds to the ratio of total compensation as a percentage to the nominal value added. The North American Industry Classification System (NAICS) is an industry classification system triggered by the North American Free Trade Agreement, that was developed by the statistical agencies of Canada, Mexico and the United States. It is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply side or production oriented principles, to ensure that industrial data, classified to NAICS, is suitable for the analysis of production related issues such as industrial performance. Since 1997, the industry classification system of the Canadian System of Macroeconomic Accounts input-output tables is based on NAICS. In the Macroeconomic Accounts industries, the levels of the different classification systems were chosen so as to provide the most detail possible in order to maximize continuity with the previous classification systems developed by Statistics Canada since 1961. For more details, see http://www.statcan.gc.ca/imdb-bmdi/5103-eng.htm. Total economic activities that have been realized within the country. This includes both business and non-business sectors. This combines the business establishments of the North American Industry Classification System (NAICS) codes 11-81, with the exception of owner occupied dwellings industry. This combines the business establishments of the North American Industry Classification System (NAICS) codes 11, 21, 22, 23, 31-33. This combines the business establishments of the North American Industry Classification System (NAICS) code 11. Starting in 2014, the crop production industry incorporates the activities related to cannabis. This combines the business establishments for the North American Industry Classification System (NAICS) codes 111, 112. This combines the business establishments for the North American Industry Classification System (NAICS) code 111 excluding 1114. Starting in 2014, the crop production industry incorporates the activities related to illegal cannabis. This combines the business establishments for the North American Industry Classification System (NAICS) code 112, excluding 1125 This combines the business establishments for the North American Industry Classification System (NAICS) codes 1151, 1152. This combines the business establishments for the North American Industry Classification System (NAICS) codes 212393, 212394, 212395, 212397, 212398. This combines the business establishments for the North American Industry Classification System (NAICS) codes 213111, 213118. This combines the business establishments for the North American Industry Classification System (NAICS) codes 213117, 213119. This combines the business establishments for the North American Industry Classification System (NAICS) codes 2212, 2213. Special hybrid: corresponds to sections of the North American Industry Classification System (NAICS) code 23. This combines the business establishments of the North American Industry Classification System (NAICS) codes 311-316, 321-327, 331-337, 339. This combines the business establishments for the North American Industry Classification System (NAICS) codes 3112, 3118, 3119. This combines the business establishments for the North American Industry Classification System (NAICS) codes 31213, 31214. This combines the business establishments for the North American Industry Classification System (NAICS) codes 313, 314. This combines the business establishments for the North American Industry Classification System (NAICS) codes 315, 316. This combines the business establishments for the North American Industry Classification System (NAICS) code 324, excluding 32411. This combines the business establishments for the North American Industry Classification System (NAICS) codes 3255, 3256, 3259. This combines the business establishments for the North American Industry Classification System (NAICS) code 327, excluding 3273. This combines the business establishments for the North American Industry Classification System (NAICS) codes 3322, 3329. This combines the business establishments for the North American Industry Classification System (NAICS) codes 3332, 3333. This combines the business establishments for the North American Industry Classification System (NAICS) codes 3343, 3345, 3346. This combines the business establishments of the North American Industry Classification System (NAICS) codes 41, 44-45, 48-49, 51, 52, 53, 54, 55, 56, 61, 62, 71, 72, 81 with the exception of owner occupied dwelling industry. This combines the business establishments for the North American Industry Classification System (NAICS) codes 485, 487. This combines the business establishments for the North American Industry Classification System (NAICS) codes 4852, 4854, 4855, 4859, 487. This combines the business establishments for the North American Industry Classification System (NAICS) codes 4861, 4869. This combines the business establishments for the North American Industry Classification System (NAICS) codes 491, 492. This combines the business establishments for the North American Industry Classification System (NAICS) codes 51112, 51113, 51114, 51119. This combines the business

Projected change in fraction of jobs susceptible to automation by 4-digit industry.

Projected changes in percent of workforce holding each degree group (shown as a percentage).