United States US: Employment In Industry: Modeled ILO Estimate: Female: % of Female Employment data was reported at 8.347 % in 2017. This records an increase from the previous number of 8.251 % for 2016. United States US: Employment In Industry: Modeled ILO Estimate: Female: % of Female Employment data is updated yearly, averaging 9.952 % from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 13.606 % in 1991 and a record low of 7.916 % in 2010. United States US: Employment In Industry: Modeled ILO Estimate: Female: % of Female Employment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Employment and Unemployment. Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

This statistic gives the industrial consumer price estimates for natural gas from 1970 to 2009 in the United States. In 2001, this estimate came to 5.71 U.S. dollars per million British thermal units.

This table contains 292 series, with data for years 1961 - 1983 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Unit of measure (1 items: Employees ...), Geography (12 items: Canada;Nova Scotia;Newfoundland and Labrador;Prince Edward Island ...), Seasonal adjustment (2 items: Unadjusted;Seasonally adjusted ...), Employees by type of industry (13 items: Total non-agricultural industries;Fishing and trapping industries;Mining industries; including milling;Forestry industry ...).

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Tables for Workplace Geography are only available for States; Counties; Places; County Subdivisions in selected states (CT, ME, MA, MI, MN, NH, NJ, NY, PA, RI, VT, WI); Combined Statistical Areas; Metropolitan and Micropolitan Statistical Areas, and their associated Metropolitan Divisions and Principal Cities; Combined New England City and Town Areas; New England City and Town Areas, and their associated Divisions and Principal Cities. Tables B08601, B08602, B08603, and B08604 are also available for Place parts and County Subdivision parts for the 5-year ACS datasets..These tabulations are produced to provide estimates of workers at the location of their workplace. Estimates of counts of workers at the workplace may differ from those of other programs because of variations in definitions, coverage, methods of collection, reference periods, and estimation procedures. The ACS is a household survey which provides data that pertains to individuals, families, and households..Workers include members of the Armed Forces and civilians who were at work last week..Industry titles and their 4-digit codes are based on the North American Industry Classification System (NAICS). The Census industry codes for 2018 and later years are based on the 2017 revision of the NAICS. To allow for the creation of multiyear tables, industry data in the multiyear files (prior to data year 2018) were recoded to the 2017 Census industry codes. We recommend using caution when comparing data coded using 2017 Census industry codes with data coded using Census industry codes prior to data year 2018. For more information on the Census industry code changes, please visit our website at https://www.census.gov/topics/employment/industry-occupation/guidance/code-lists.html..Several means of transportation to work categories were updated in 2019. For more information, see: Change to Means of Transportation..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient...

About this dataset

Context

The dataset tabulates the Industry population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Industry across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Industry was 282, a 1.81% increase year-by-year from 2022. Previously, in 2022, Industry population was 277, an increase of 1.47% compared to a population of 273 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Industry decreased by 31. In this period, the peak population was 356 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Industry is shown in this column.
• Year on Year Change: This column displays the change in Industry population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Industry Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Industry population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Industry. The dataset can be utilized to understand the population distribution of Industry by age. For example, using this dataset, we can identify the largest age group in Industry.

Key observations

The largest age group in Industry, CA was for the group of age 5-9 years with a population of 28 (11.48%), according to the 2021 American Community Survey. At the same time, the smallest age group in Industry, CA was the 85+ years with a population of 0 (0.00%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Industry is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Industry total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Industry Population by Age. You can refer the same here

United States US: Total Business Enterprise R&D Personnel: Per Thousand Employment In Industry data was reported at 17.169 Per 1000 in 2020. This records an increase from the previous number of 15.152 Per 1000 for 2019. United States US: Total Business Enterprise R&D Personnel: Per Thousand Employment In Industry data is updated yearly, averaging 13.282 Per 1000 from Dec 2011 (Median) to 2020, with 10 observations. The data reached an all-time high of 17.169 Per 1000 in 2020 and a record low of 12.478 Per 1000 in 2012. United States US: Total Business Enterprise R&D Personnel: Per Thousand Employment In Industry data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual.

Definition of MSTI variables 'Value Added of Industry' and 'Industrial Employment':

R&D data are typically expressed as a percentage of GDP to allow cross-country comparisons. When compiling such indicators for the business enterprise sector, one may wish to exclude, from GDP measures, economic activities for which the Business R&D (BERD) is null or negligible by definition. By doing so, the adjusted denominator (GDP, or Value Added, excluding non-relevant industries) better correspond to the numerator (BERD) with which it is compared to.

The MSTI variable 'Value added in industry' is used to this end:

It is calculated as the total Gross Value Added (GVA) excluding 'real estate activities' (ISIC rev.4 68) where the 'imputed rent of owner-occupied dwellings', specific to the framework of the System of National Accounts, represents a significant share of total GVA and has no R&D counterpart. Moreover, the R&D performed by the community, social and personal services is mainly driven by R&D performers other than businesses.

Consequently, the following service industries are also excluded: ISIC rev.4 84 to 88 and 97 to 98. GVA data are presented at basic prices except for the People's Republic of China, Japan and New Zealand (expressed at producers' prices).In the same way, some indicators on R&D personnel in the business sector are expressed as a percentage of industrial employment. The latter corresponds to total employment excluding ISIC rev.4 68, 84 to 88 and 97 to 98.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Industry titles and their 4-digit codes are based on the North American Industry Classification System (NAICS). The Census industry codes for 2018 and later years are based on the 2017 revision of the NAICS. To allow for the creation of multiyear tables, industry data in the multiyear files (prior to data year 2018) were recoded to the 2017 Census industry codes. We recommend using caution when comparing data coded using 2017 Census industry codes with data coded using Census industry codes prior to data year 2018. For more information on the Census industry code changes, please visit our website at https://www.census.gov/topics/employment/industry-occupation/guidance/code-lists.html..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Gross of Depreciation: Livestock and Plants, Gross of Depreciation: Producers' Durable Equipment, Gross of Depreciation: Structures other than Buildings, Gross of Depreciation: Non-residential Buildings, Gross of Depreciation: Total, Net of Depreciation: Livestock and Plants, Net of Depreciation: Producers' Durable Equipment, Net of Depreciation: Structures other than Buildings, Net of Depreciation: Non-residential buildings, Net of Depreciation: Total

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

Cost Estimating Software Market Outlook

The global cost estimating software market size is anticipated to witness substantial growth, with projections indicating an escalation from USD 1.5 billion in 2023 to USD 2.9 billion by 2032, reflecting a robust CAGR of 7.4% during the forecast period. This growth is primarily driven by the increasing adoption of digital tools for precision in budgeting and cost management across various industries. The need for efficiency and accuracy in project cost management, coupled with the integration of advanced technologies like artificial intelligence (AI) and machine learning (ML), are the key factors propelling the market growth. Additionally, the surge in construction and infrastructural development projects worldwide has significantly contributed to the demand for sophisticated cost estimating solutions.

The burgeoning construction sector, particularly in emerging economies, is a notable growth factor driving the cost estimating software market. With urbanization and modernization, there is an increased requirement for precise cost estimation tools to manage extensive construction projects efficiently. These tools help in mitigating risks associated with cost overrun, ensuring projects are completed within the stipulated budgets. The advent of cloud computing and advanced analytics has further enhanced the capabilities of these software, making them more accessible and versatile for users across various segments. As industries continue to digitize, the reliance on cost estimating software is expected to grow, further driving market expansion.

Another significant factor contributing to the market's growth is the increasing complexity of projects across industries such as manufacturing, healthcare, and IT. In manufacturing, the need for precise cost estimation is critical to manage resources and optimize production processes. In healthcare, accurate cost estimation is essential for budgeting and managing the financial aspects of healthcare facilities and services. Similarly, in the IT and telecommunications sector, cost estimating software plays a vital role in budgeting for new technologies and infrastructure projects. As these industries continue to evolve, the demand for advanced cost estimating solutions is expected to rise, fostering market growth.

The integration of advanced technologies such as AI and ML in cost estimating software is another pivotal growth driver. These technologies help enhance the accuracy and efficiency of cost estimation processes by analyzing vast amounts of data and providing predictive analytics. AI and ML enable the software to learn from past projects, improving future cost estimates and aiding in decision-making. This technological advancement has expanded the application of cost estimating software, making it an invaluable tool for businesses aiming to enhance operational efficiency and project profitability. As technological innovation continues to advance, the adoption of AI-driven cost estimating solutions is projected to rise, further contributing to market growth.

Residential Remodeling Estimating Software is becoming increasingly vital as the demand for home renovations and improvements continues to rise. This type of software provides contractors and remodelers with precise tools to estimate costs associated with residential projects, from materials and labor to permits and design fees. By utilizing such software, professionals can streamline their workflow, reduce errors, and enhance client satisfaction through transparent and accurate cost projections. As the housing market remains dynamic, the adoption of Residential Remodeling Estimating Software is expected to grow, supporting the broader trend of digital transformation in the construction industry.

Regionally, North America dominates the cost estimating software market, with a significant market share attributed to the high adoption rate of advanced technologies and the presence of major industry players. The region's well-established infrastructure and increasing investments in construction and IT projects are key factors driving market growth. Europe also holds a substantial share in the market, supported by the region's focus on infrastructure development and manufacturing. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, driven by rapid industrialization and urbanization in countries such as China and India. The rise in construction activities and government initiatives to promote digitalization in

Saint Lucia LC: Employment In Industry: Modeled ILO Estimate: % of Total Employment data was reported at 17.302 % in 2017. This records an increase from the previous number of 17.248 % for 2016. Saint Lucia LC: Employment In Industry: Modeled ILO Estimate: % of Total Employment data is updated yearly, averaging 19.224 % from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 25.605 % in 1995 and a record low of 16.876 % in 2014. Saint Lucia LC: Employment In Industry: Modeled ILO Estimate: % of Total Employment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s St. Lucia – Table LC.World Bank: Employment and Unemployment. Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).; ; International Labour Organization, ILOSTAT database. Data retrieved in September 2018.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

Nepal NP: Employment In Industry: Modeled ILO Estimate: % of Total Employment data was reported at 8.109 % in 2017. This records an increase from the previous number of 7.787 % for 2016. Nepal NP: Employment In Industry: Modeled ILO Estimate: % of Total Employment data is updated yearly, averaging 7.384 % from Dec 1991 (Median) to 2017, with 27 observations. The data reached an all-time high of 13.393 % in 2001 and a record low of 2.760 % in 1991. Nepal NP: Employment In Industry: Modeled ILO Estimate: % of Total Employment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nepal – Table NP.World Bank: Employment and Unemployment. Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted average; Data up to 2016 are estimates while data from 2017 are projections.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Industry. Based on the latest 2018-2022 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Industry. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2022

In terms of income distribution across age cohorts, in Industry, the median household income stands at $94,814 for householders within the 25 to 44 years age group, followed by $74,189 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $53,798.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Industry median household income by age. You can refer the same here

Estimates of the markup for the Primary Foods industry (comprised of agriculture, hunting, fishing, and logging) using the De Loecker et al. (2020) methodology. Instead of using micro-based, firm-level data, we calculate the markups using aggregate, macro-data. The database contains information on 170 countries for the years 1995 - 2015.

Our sources of data are two-fold; the first is the EORA input-output database, and the second is the UN FAO-Stat database. Our paper Rodriguez del Valle and Fernández-Vázquez (2024), explains in more detail the estimation technique based on Generalized Maximum Entropy employed to derive these estimates.

The dataset can be used to explore and research a myriad of topics, including the impact globalization has on markups, the role of institutional quality, and even climate. We have found strong evidence that the percentage share of value added required for production originating from abroad is significantly connected to lower markups. We have also found compelling empirical evidence that institutional quality can impact the evolution of markups.

Please note that we make a strong assumption that each industry is produced by one "firm" consistent with input-output theory. For this industry, we believe the assumption will not bias results too strongly, particularly when analyzing developing countries. Results may be biased in countries where large farms exist.

The revenue of the global fintech industry increased sharply between 2017 and 2023. In 2023, the total revenue of the industry was estimated at ***** billion U.S. dollars. According to Statista Market Insights, the revenue of the global fintech sector is forecast to increase further in the coming years, exceeding ****** billion U.S. dollars in 2028.

Construction Estimating Software Market Outlook

The global construction estimating software market size was valued at approximately $2.5 billion in 2023 and is projected to reach around $6.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% during the forecast period. This notable growth can be attributed to several key factors including the increasing adoption of digital solutions in the construction industry, the need for more accurate and efficient cost estimation processes, and the rising demand for automation in project management.

One of the primary growth factors driving the construction estimating software market is the increasing complexity of construction projects. As projects become larger and more intricate, the need for precise and efficient cost estimation becomes critical. Traditional methods of cost estimating, which often involve spreadsheets and manual calculations, are not only time-consuming but also prone to errors. Construction estimating software offers a solution by automating the process, reducing errors, and providing more accurate and reliable estimates. This not only helps in better budget management but also in avoiding cost overruns, thus making the software an essential tool for construction companies.

Another significant factor contributing to the market growth is the rapid advancement in technology, particularly in areas such as artificial intelligence (AI) and machine learning (ML). These technologies are being integrated into construction estimating software to enhance its capabilities. AI and ML can analyze large datasets to predict costs more accurately, identify potential risks, and suggest optimal resource allocation. This technological advancement is expected to drive the adoption of construction estimating software further, as it enables construction firms to improve efficiency, reduce costs, and enhance overall project outcomes.

Additionally, the growing trend of Building Information Modeling (BIM) in the construction industry is playing a crucial role in the adoption of construction estimating software. BIM allows for the creation of detailed 3D models of buildings and infrastructure, which can be used to generate accurate cost estimates. The integration of BIM with construction estimating software enables more precise and comprehensive cost analysis, facilitating better project planning and execution. This synergy between BIM and estimating software is anticipated to fuel market growth over the forecast period.

Contenting Software is becoming increasingly vital in the construction industry as companies strive to manage and disseminate vast amounts of information efficiently. This type of software facilitates the organization and distribution of content across various platforms, ensuring that all stakeholders have access to the latest project details and updates. By streamlining communication and documentation processes, Contenting Software helps construction firms maintain consistency and accuracy in their project data. This is particularly important in large-scale projects where multiple teams and contractors are involved, as it minimizes the risk of miscommunication and errors. As the construction industry continues to embrace digital transformation, the role of Contenting Software is expected to grow, offering enhanced collaboration and information management capabilities.

From a regional perspective, North America holds a significant share of the construction estimating software market, driven by the presence of a large number of construction companies and the early adoption of advanced technologies. The Asia Pacific region is expected to witness the highest growth rate, primarily due to rapid urbanization, infrastructure development, and increasing investment in the construction sector. Europe, Latin America, and the Middle East & Africa also present substantial growth opportunities, supported by ongoing construction activities and the need for efficient project management solutions.

Component Analysis

In terms of components, the construction estimating software market is bifurcated into software and services. The software segment is further divided into cloud-based and on-premises solutions. The software component holds the major share of the market due to its crucial role in automating the cost estimation process. Companies are incre

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