Owing to its financial attractiveness and availability of staff and skills, India was considered the most attractive destination to offshore business services, with a score of **** and **** respectively, according to this survey conducted in 2021. The field in which India was not as attractive though was digital resonance scoring ****. For comparison, the United States scored **** for its digital resonance.

This statistic shows the likelihood of offshoring or nearshoring to change in the next two years worldwide from 2014 to 2018, by function and growth rate. According to the 2018 survey, procurement offshoring/ nearshoring is expected to increase by **** percent.

Following Grossman and Rossi-Hansberg (2008) we present a model in which tasks of varying complexity are matched to workers of varying skill in order to develop and test predictions regarding the effects of immigration and offshoring on US native-born workers. We find that immigrant and native-born workers do not compete much due to the fact that they tend to perform tasks at opposite ends of the task complexity spectrum, with offshore workers performing the tasks in the middle. An effect of offshoring and a positive effect of immigration on native-born employment suggest that immigration and offshoring improve industry efficiency.

Replication package for Reijnders, L. S. M., and G. J. de Vries (2018). “Technology, Offshoring, and the Rise of non-Routine Jobs”, Journal of Development Economics, vol 135, pp. 412-432. https://doi.org/10.1016/j.jdeveco.2018.08.009

Kovak, Brian K., Oldenski, Lindsay, and Sly, Nicholas, (2021) “The Labor Market Effects of Offshoring by U.S. Multinational Firms.” Review of Economics and Statistics 103:2, 381–396.

We study the implications of offshoring on innovation, technology, and wage inequality in a Ricardian model with directed technical change. Profit maximization determines both the extent of offshoring and the direction of technological progress. A fall in the offshoring cost induces technical change with an ambiguous factor bias. When the initial cost of offshoring is high, an increase in offshoring opportunities causes a fall in the real wages of unskilled workers in industrial countries, skill-biased technical change and rising skill premia. When the offshoring cost is sufficiently low, instead, offshoring induces technical change biased in favor of the unskilled workers. (JEL J24, J31, L24, O33)

In 2022, the offshore execution value of manufacturing services outsourcing in China amounted to over ** billion U.S. dollars. China's total outsourcing industry received revenues of ****** billion U.S. dollars from foreign markets that year.

This data set contains Offshore Statistics by Water Depth.

Online markets for remote labor services allow workers and firms to contract with each other directly. Despite this, intermediaries -- called outsourcing agencies -- have emerged in these markets. This paper shows that agencies signal to employers that inexperienced workers are high quality. Workers affiliated with an agency have substantially higher job-finding probabilities and wages at the beginning of their careers compared to similar workers without an agency affiliation. This advantage declines after high-quality non-affiliated workers receive good public feedback scores. The results indicate that intermediaries have arisen endogenously to permit a more efficient allocation of workers to jobs.

This paper proposes a Ricardian model to understand the short-run and long-run aggregate effects of increased fragmentation and offshoring on rich and poor countries. The short-run analysis shows that, when offshoring is sufficiently high, further increases in offshoring benefit the poor country and hurt the rich country. But these effects may be reversed in the long run as countries adjust their research efforts in response to increased offshoring. In particular, in the long run, the rich country always gains from increased offshoring, whereas poor countries see their static gains partially eroded by a decline in their research efforts. (JEL F12, F23, L24, M16)

The statistic shows the revenue generated in the outsourcing industry by service type from 2010 to 2019. In 2019, ** billion U.S. dollars was generated through business process outsourcing (BPO). Global outsourcing industry - additional information Outsourcing is the contracting out of processes to external parties. Business process outsourcing (BPO), which generated ** billion U.S. dollars worldwide in 2019, involves transferring business processes to service providers outside of an organization. This process often requires offshoring. In 2019, India was the best country for offshoring when its financial attractiveness, the skills and availability of its people and its business environment are considered together. Information technology outsourcing (ITO) is closely related to business process outsourcing as many business processes are technology based. In 2019, global ITO revenue was **** billion U.S. dollars. The global revenue of business process outsourcing and information technology outsourcing amounted to around **** billion U.S. dollars in 2019, of which more than half was generated in the Americas. Southern Europe was the region with the largest share of businesses practicing, or planning to practice, business process outsourcing. Latin America and South Africa also had high proportions. Business process outsourcing includes delegating back-office or internal functions, like HR and accounting, and front-office or customer-related functions, such as those that would be passed on to call centers. In 2016, most companies used outsourcing services as a cost cutting tool while communication was a main driver of a successful outsourcing experience among mid-market business leaders worldwide.

The STATA dataset as well as the corresponding do-file allow for a replication of all results in the main empirical analysis of Kohler/Kukharskyy (2019) "Offshoring under Uncertainty".

The data and programs replicate tables and figures from "The impact of service and goods offshoring on employment: Firm-level evidence", by Ornaghi,Van Beveren and Vanormelingen. Please see the ReadMe file for additional details.

We show that the observed polarization of employment toward the high- and low-skill occupations disappears when only native workers are considered. Instead, low-skilled immigration explains employment growth at the low tail of the skill distribution. Moreover, while employment rose, wages remained subdued in low-skill occupations. A data-disciplined structural model accounts for this evidence: Offshoring and automation negatively affect middle-skill occupations but enhance employment and wages for the high-skilled. Low-skill employment is sheltered from offshoring and automation, as it consists of manual, non-tradable services. However, low-skilled immigration depresses low-skill wages and encourages native workers to move up along the skill ladder through training.

The 2023 National Offshore Wind data set (NOW-23) is the latest wind resource data set for offshore regions in the United States, which supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published about a decade ago and is currently one of the primary resources for stakeholders conducting wind resource assessments in the continental United States. The NOW-23 data set was produced using the Weather Research and Forecasting Model (WRF) version 4.2.1. A regional approach was used: for each offshore region, the WRF setup was selected based on validation against available observations. The WRF model was initialized with the European Centre for Medium Range Weather Forecasts 5 Reanalysis (ERA-5) data set, using a 6-hour refresh rate. The model is configured with an initial horizontal grid spacing of 6 km and an internal nested domain that refined the spatial resolution to 2 km. The model is run with 61 vertical levels, with 12 levels in the lower 300m of the atmosphere, stretching from 5 m to 45 m in height. The MYNN planetary boundary layer and surface layer schemes were used the North Atlantic, Mid Atlantic, Great Lakes, Hawaii, and North Pacific regions. On the other hand, using the YSU planetary boundary layer and MM5 surface layer schemes resulted in a better skill in the South Atlantic, Gulf of Mexico, and South Pacific regions. A more detailed description of the WRF model setup can be found in the WRF namelist files linked at the bottom of this page. For all regions, the NOW-23 data set coverage starts on January 1, 2000. For Hawaii and the North Pacific regions, NOW-23 goes until December 31, 2019. For the South Pacific region, the model goes until 31 December, 2022. For all other regions, the model covers until December 31, 2020. Outputs are available at 5 minute resolution, and for all regions we have also included output files at hourly resolution. The NOW-23 data are provided here as HDF5 files. Examples of how to use the HSDS Service to Access the NOW-23 files are linked below. A list of the variables included in the NOW-23 files is also linked below. No filters have been applied to the raw WRF output.

The data and programs replicate tables and figures from "Downstream offshoring and firm-level employment", by Merlevede and Michel. Please see the ReadMe file for additional details.

This paper documents how US firms organize goods production across firm and country boundaries. Most US firms that perform physical transformation tasks in-house using foreign manufacturing plants in 2007 also own US manufacturing plants; moreover manufacturing comprises their main domestic activity. By contrast, “factoryless goods producers” outsource all physical transformation tasks to arm's-length contractors, focusing their in-house efforts on design and marketing. This distinct firm type is missing from standard analyses of manufacturing, growing in importance, and increasingly reliant on foreign suppliers. Physical transformation “within-the-firm” thus coincides with substantial physical transformation “within-the-country,” whereas its performance “outside-the-firm” often also implies “outside-the-country.” Despite these differences, factoryless goods producers and firms with foreign and domestic manufacturing plants both employ relatively high shares of US knowledge workers. These patterns call for new models and data to capture the potential for foreign production to support domestic innovation, which US firms leverage around the world.

Key Table Information.Table Title.Mining: Detailed Statistics by Type of Operation for the U.S., States and Offshore Areas: 2022.Table ID.ECNTYPE2022.EC2221TYPE.Survey/Program.Economic Census.Year.2022.Dataset.ECN Sector Statistics Mining: Detailed Statistics by Type of Operation for the U.S., States, and Offshore Areas.Source.U.S. Census Bureau, 2022 Economic Census, Sector Statistics.Release Date.2024-12-05.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Number of firmsNumber of establishmentsSales, value of shipments, and revenue ($1,000)Annual payroll ($1,000)First-quarter payroll ($1,000)Number of employeesProduction and/or development and exploration workers annual wages ($1,000)Production and/or development and exploration workers for pay period including March 12Construction, production and/or development and exploration workers annual hours ($1,000)Other employees annual wages ($1,000)Other employees for pay period including March 12Value added ($1,000)Range indicating imputed percentage of total sales, value of shipments, or revenueRange indicating imputed percentage of total annual payrollRange indicating imputed percentage of total employeesDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S., State, and Offshore Area levels that vary by industry. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 6-digit 2022 NAICS code levels for the U.S and at the 2- through 3-digit 2022 NAICS code levels for States and Offshore Areas. For information about NAICS, see Economic Census Code Lists..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector21/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual c...

Offshore Software Development Market size was valued at USD 122 Billion in 2024 and is projected to reach USD 283 Billion by 2031, growing at a CAGR of 10.13% from 2024 to 2031.

Offshore Software Development Market Drivers

Cost Efficiency: One of the primary drivers of offshore software development is cost savings. Offshore development can significantly reduce labor costs compared to hiring onshore developers. Companies can leverage the lower wage rates in developing countries while maintaining high-quality standards.

Access to a Global Talent Pool: Offshore development allows companies to access a vast pool of skilled professionals worldwide. This enables businesses to tap into specialized expertise and technologies that may not be readily available locally.

Focus on Core Business Activities: By outsourcing software development, companies can focus more on their core competencies and strategic activities. Offshore development partners can handle the technical aspects, allowing businesses to allocate more resources to areas such as marketing, sales, and customer service.

..Table Name.Mining: Geographic Area Series: Industry Statistics by Type of Operation for the State or Offshore Areas: 2012....ReleaseSchedule.Data are scheduled for release in September 2015.......Universe.The universe includes all establishments classified in mining sector 21 with one or more paid employee at any time during the year......GeographyCoverage.Data are shown at the state and offshore area levels.....IndustryCoverage.Data are shown at the two- and three-digit North American Industry Classification System (NAICS) levels.....Data ItemsandOtherIdentifyingRecords.This file contains data on:..Number of establishments.Establishments with 20 employees or more.Number of employees.Annual payroll ($1,000).Production, development, and exploration workers for pay period including March 12.Production, development, and exploration workers annual hours (1,000).Production, development, and exploration workers annual wages ($1,000).Total value of shipments and receipts for services ($1,000).Value added ($1,000)......Sort Order.Data are presented in state or offshore area by ascending NAICS code sequence.....FTP Download.Download the entire table at https://www2.census.gov/econ2012/EC/sector21/EC1221A3.zip....ContactInformation. U.S. Census Bureau, Economy Wide Statistics Division. Data User Outreach and Education Staff . Washington, DC 20233-6900. Tel: (800) 242-2184 . Tel: (301) 763-5154. ewd.outreach@census.gov. ..For information on economic census geographies, including changes for 2012, see the economic census Help Center..Data based on the 2012 Economic Census. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Methodology. Data in this table represent those available when this file was created; data may not be available for all NAICS industries or geographies. Data in this table may be subject to employment- and/or sales-size minimums that vary by industry..Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableFor a complete list of all economic programs symbols, see the Symbols Glossary.Source: U.S. Census Bureau, 2012 Economic Census.Note: The data in this file are based on the 2012 Economic Census. 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 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. For the full technical documentation, see Methodology link in above headnote.