These market data provide a comprehensive set of measures of changes in economic activity throughout the coastal regions of the United States. In regard to the sources of data, establishments, employment, and wages are taken from the Quarterly Census of Employment and Wages (QCEW). The data series also is known as the ES-202 data. These data are based on the quarterly reports of nearly all employers in the United States. These reports are filed with each state's employment or labor department, and each state then transmits the data to the Bureau of Labor Statistics (BLS), where the national databases are maintained. The data for the Coastal Economies have been taken from the national databases at BLS (except in the case of Massachusetts). Gross State Product (GSP) data are taken from the Bureau of Economic Analysis (BEA), which develops the estimates of GSP from a number of sources. In regard to "employment", data are reported by employers, not employees, and does not contain any information about age. There is no difference between "employed" and "employment". The source is known as the payroll survey, a survey filed by employers every 3 months showing the number of people employed at each establishment in each of the preceding 3 months. Detailed information on the geographies the data are available for can be found here: https://coast.noaa.gov/htdata/SocioEconomic/CoastalEconomy/CoastalEconomy_DataDescription.pdf

This data collection is comprised of responses from two sets of survey questionnaires, the basic Current Population Survey (CPS) and a survey administered as a supplement to the February CPS questionnaire on the topic of displaced workers, employee tenure, and occupational mobility in the United States.The CPS, administered monthly, collects labor force data about the civilian noninstitutional population living in the United States. Moreover, the CPS provides current estimates of the economic status and activities of this population which includes estimates of total employment (both farm and nonfarm), nonfarm self-employed persons, domestics, and unpaid helpers in nonfarm family enterprises, wage and salaried employees, and estimates of total unemployment. Data from the CPS are provided for the week prior to the administration of the survey.All persons eligible for the labor force items of the basic CPS were also eligible for the supplement. The supplement was designed to be a proxy response supplement, meaning a single respondent could provide answers for all eligible household members, provided the respondent was a household member 15 years of age or older. Persons 20 years of age and older, who lost or left a job in the last 3 years for selected reasons, were eligible for the first part of the supplement, which consists of the displaced workers items. Employed persons 15 years of age and older were eligible for the second part of the supplement, which consists of the employee tenure and occupational mobility items.Respondents were queried on involuntary job loss within the last three years based on operating decisions of a firm, plant, or business, reasons for job displacement, industry and occupation of the former job, group health insurance coverage, job tenure, and weekly earnings. Additional data refer to periods of unemployment as well as number of jobs held, use of unemployment benefits, whether residence was changed to seek work in another area, and current health insurance coverage.Demographic variables include age, sex, race, Hispanic origin, marital status, veteran status, educational attainment, family relationship, occupation, and income.

This data collection is comprised of responses from two sets of survey questionnaires, the basic Current Population Survey (CPS) and a survey on the topics of School Enrollment and Internet Use in the United States, which was administered as a supplement to the 2010 October CPS. The Census Bureau and the National Center for Education Statistics jointly sponsored the supplemental questions for October.The CPS, administered monthly, is a labor force survey providing current estimates of the economic status and activities of the population of the United States, for the week prior to the survey. Specifically, the CPS provides estimates of total employment (both farm and nonfarm), nonfarm self-employed persons, domestics, and unpaid helpers in nonfarm family enterprises, wage and salaried employees, and estimates of total unemployment.The October 2010 supplemental survey queried respondents on school enrollment for all persons in the household aged three years and over. Supplement data includes information collected on current grade at public or private school, whether currently attending college full- or part-time at a two- or four-year institution, year last attended a regular school, year graduated from high school, grade retention, and whether any business, vocational, technical, trade, or correspondence courses were ever taken. Respondents were also queried on Internet and computer use, particularly if members of the household use the Internet, and how access to the Internet is obtained. Demographic variables include age, sex, race, Hispanic origin, marital status, veteran status, educational attainment, occupation, and income.

US Furniture Market size was valued at USD 240 Billion in 2024 and is projected to reach USD 330 Billion by 2032, growing at a CAGR of 4% from 2025 to 2032.

Key Market Drivers: Increasing Home Ownership and Real Estate Development: The rise in homeownership rates and new dwelling buildings is fueling furniture demand. According to the United States Census Bureau, new privately owned housing starts totaled 1.46 million units in December 2023, a 7.6% increase over the prior year. Furthermore, the National Association of Realtors reported that first-time homeowners accounted for 32% of all house purchases in 2023, indicating a sizable pool of potential furniture buyers.

Increasing E-commerce Furniture Sales: The move to internet furniture shopping has advanced considerably. According to the US Department of Commerce, e-commerce furniture and home furnishing sales will reach $118.3 billion in 2023, a 24.5% rise over 2022 levels. The American Home Furnishings Alliance (AHFA) reports that over 35% of all furniture purchases in the United States are now made online, compared to just 15% in 2019.

Increased Focus on Home Office Furniture: The continued adoption of remote and hybrid work arrangements is driving up demand for home office furniture. According to the United States Bureau of Labor Statistics, as of December 2023, 27.8% of employed people teleworked due to the epidemic, resulting in continued demand for home office equipment. In addition, the American Society of Interior Designers (ASID) reports that home office renovations and furniture upgrades will increase by 43% in 2023 compared to pre-pandemic levels.

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad,

Analysis of ‘Broadband Adoption and Computer Use by year, state, demographic characteristics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/720f8c4b-7a1c-415c-9297-55904ba24840 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad,

--- Original source retains full ownership of the source dataset ---

The contents of the dataset are related to the economic indicators of companies in the province of Trento and can be viewed at the URL of the Employment Agency.

The data, which come from various sources, were drawn up by the Labour Market and Policy Studies Office for the preparation of the Annual Employment Report in the province of Trento, available as content open to the URL: https://www.agenzialavoro.tn.it/Open-Data/Other-content-available

The dataset, including resources in PDF format, is also available on the Employment Agency’s Open Data Portal at the URL: https://www.agenzialavoro.tn.it/Open-Data/I-dataset-available/Economy-and-finance/Economic structure/Economic indicators/Year-2015

The “time coverage” metadata refers to the time interval taken into account by the Historical Series that are identified in the file name with the suffix _ST.

The data released in CSV format are: Machine Readable, identified in the file name with the suffix _MR and validated with the Good Tables library. https://okfnlabs.org/blog/2015/02/20/introducing-goodtables.html

ATTRIBUTION: data processed by the Office for the Study of Policies and Labour Market based on data from the Chamber of Commerce of Trento.

According to Cognitive Market Research, the global Automated Truck Loading System market size will be USD 2954.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 9.70% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 1181.6 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 886.2 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 679.4 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.7% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 147.7 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.1% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 59.0 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.4% from 2024 to 2031.
The Flush Dock Loading Dock Type held the highest Automated Truck Loading System market revenue share in 2024.

Market Dynamics of Automated Truck Loading System Market

Key Drivers for Automated Truck Loading System Market

Increasing Industrialization across the Globe to Increase the Demand Globally

The global growth of industrialization is expected to drive the expansion of the automated truck-loading system market, fueled by ongoing development and urbanization worldwide. According to the Federal Reserve, industrial production increased by 0.6 percent in June 2024, following a 0.9 percent rise in May 2024. For the second quarter of 2024, industrial production grew at an annual rate of 4.3 percent. Manufacturing output rose by 0.4 percent in June and saw a 3.4 percent increase annually for the second quarter. Mining and utilities indexes also gained 0.3 percent and 2.8 percent, respectively. With total industrial production in June reaching 104 percent of its 2017 average, it was 1.6 percent higher than the previous year. The focus on improved workplace safety and the ongoing escalation of industrialization are key factors driving the growth of the automated truck-loading system market. (Source: https://www.federalreserve.gov/releases/g17/current/

Growth in E-commerce Sector to Propel Market Growth

The surge in e-commerce has significantly increased the volume of goods transported. In 2023, e-commerce accounted for 22.0% of total retail sales in the U.S., up from 21.2% in 2022, according to Digital Commerce 360’s analysis of U.S. Department of Commerce data. E-commerce sales grew from approximately $1.040 trillion in 2022 to about $1.119 trillion in 2023, marking a 7.6% increase. In the first quarter of 2024, e-commerce sales rose by 8.6% (±1.1%) compared to the same period in 2023, while total retail sales grew by 1.5% (±0.5%). E-commerce sales represented 15.9% of total sales in the first quarter of 2024. Automated systems are essential for efficiently managing this increased volume, ensuring rapid and accurate loading and unloading of goods. E-commerce companies require faster fulfillment and shipping processes, and automated truck loading systems meet these needs by accelerating the loading process and helping maintain competitive delivery times. (Source: https://www.digitalcommerce360.com/article/us-ecommerce-sales/)

Restraint Factor for the Automated Truck Loading System Market

Availability of Cheap Labor in Developing Nations to Limit the Sales

The availability of inexpensive labor in developing countries, such as India, Bangladesh, Sri Lanka, the UAE, Argentina, and Indonesia, has reduced the demand for automated trucks in these regions. Many Asian nations have large populations, which increases the availability of human labor. For example, China and India each have populations exceeding 1 billion, suggesting a high potential for labor availability. These factors are expected to diminish the need for automated trucks in developing countries. According to the Bureau of Labor Statistics, approximately 3.40 million people were employed in the warehousing and storage industry in 2019. Additionally, as many countries continue to develop, the disparity between urban and rural areas remains significant. Migrat...

The contents of the dataset are related to the economic indicators of companies in the province of Trento and can be viewed at the URL of the Employment Agency.

The data, which come from various sources, were drawn up by the Labour Market and Policy Studies Office for the preparation of the Annual Employment Report in the province of Trento, available as content open to the URL: https://www.agenzialavoro.tn.it/Open-Data/Other-content-available

The dataset, including resources in PDF format, is also available on the Employment Agency’s Open Data Portal at the URL: https://www.agenzialavoro.tn.it/Open-Data/I-dataset-available/Economy-and-finance/Economic structure/Economic indicators/Year-2017

The “time coverage” metadata refers to the time interval taken into account by the Historical Series that are identified in the file name with the suffix _ST.

The data released in CSV format are: Machine Readable, identified in the file name with the suffix _MR and validated with the Good Tables library. https://okfnlabs.org/blog/2015/02/20/introducing-goodtables.html

ATTRIBUTION: data processed by the Office for the Study of Policies and Labour Market based on data from the Chamber of Commerce of Trento.

Abstract (en): This data collection supplies standard monthly labor force data for the week prior to the survey. Comprehensive information is given on the employment status, occupation, and industry of persons 14 years old and older. Additional data are available concerning weeks worked and hours per week worked, reason not working full-time, total income and income components, and residence. Besides the CPS core questions, this survey gathered additional data on respondents' premium pay, number of days and hours per week usually worked, whether they worked a shift or flextime schedule, time of day that workers started and ended work, and union membership status. Supplemental questions on multiple job holding were asked of one-fourth of sample households. Questions asked of dual job-holders include the reason for working at a second job, the number of hours worked at this job, and whether they were on layoff from their primary job. Statistics on adult education participation by persons aged 16 years and older are also provided. For each course taken, data are included on subject area, reason for taking the course, amount paid for the course, and source of payment. Information on demographic characteristics, such as age, sex, race, marital status, veteran status, household relationship, educational level, and Hispanic origin, is available for each person in the household enumerated. All persons in the civilian noninstitutionalized population of the United States living in housing units. A national probability sample was used in selecting housing units. Approximately 77,000 households were sampled.

The contents of the dataset are related to the economic indicators of companies in the province of Trento. The data, which come from various sources, were compiled by the Labour Market and Policy Studies Office for the drafting of the Annual Employment Report in the province of Trento, available as open content at the URL: https://www.agenzialavoro.tn.it/Open-Data/Other-content-available The dataset, including resources in PDF format, is also available on the Employment Agency’s Open Data Portal at the URL: https://www.agenzialavoro.tn.it/Open-Data/I-dataset-available/Economics-and-finance/Economic structure/Economic indicators/Year-2019 The “time coverage” metadata refers to the time interval taken into account by the Historical Series that are identified in the file name with the suffix _ST. The data released in CSV format are: Machine Readable, identified in the file name with the suffix _MR and validated. ATTRIBUTION: data processed by the Office for the Study of Policies and Labour Market based on data from the Chamber of Commerce of Trento.