Frequently Asked Questions

The 2026 NYC Tech Ecosystem Report is an economic research study commissioned by Tech:NYC and conducted by HR&A Advisors. It measures the size, composition, growth, and economic significance of New York City’s tech economy, defined broadly to include not just workers at tech companies, but tech-skilled workers embedded across all industries. The study also examines the demographic composition of the Ecosystem’s workforce and compares NYC’s performance against peer metros. HR&A has conducted three prior editions of this study and updates the methodology and definitions as data availability evolves.

HR&A Advisors, Inc. is an employee-owned consulting firm advising public, private, non-profit, and philanthropic clients on real estate, economic development, and public policy. HR&A has authored hundreds of economic impact studies for major institutions including Cornell Tech, Lincoln Center, Con Edison, and NYU, as well as prior editions of the NYC Tech Ecosystem Study. The firm was founded in 1976 and has served as economic development advisors for over 200 cities.

Employment and wage estimates reflect conditions as of 2026, using the most recent available data from Lightcast. Growth comparisons are made against two benchmarks: 2021 (five-year) and 2016 (ten-year). Wage comparisons use inflation-adjusted figures expressed in 2026 dollars, using the Consumer Price Index for urban wage earners. Startup investment data is drawn from Crunchbase and covers the period 2016–2026. Economic and fiscal impact estimates use 2026 employment and wage data as IMPLAN inputs.

The primary geographic unit of analysis is New York City (all five boroughs as defined by their respective counties). Economic and fiscal impacts are estimated for two geographies: New York City and New York State, reflecting that different tax instruments are administered at different governmental levels. National competitiveness comparisons are made against three peer metros: the San Francisco Bay Area (Alameda, Contra Costa, Marin, San Francisco, San Mateo, and Santa Clara Counties), Austin (Travis County), and Miami (Miami-Dade County). These metros were selected because they are commonly cited competitors for tech talent and investment.

HR&A defines the Tech Ecosystem using both industry classifications and occupation classifications. A purely industry-based definition misses the large and growing population of tech-skilled workers employed outside of tech companies (for example, software developers at banks or hospitals). A purely occupation-based definition misses the non-tech workers who support tech companies’ operations (for example, accountants and HR professionals at Google). Combining both captures the full range of workers who directly enable, produce, or facilitate technological activity in New York City.

The Ecosystem is defined by 15 tech industries (identified by six-digit NAICS codes) and 51 tech occupations (identified by six-digit SOC codes). The industry list was developed in consultation with industry practitioners and reflects sectors where technological activity is central to the business model, such as software publishing, computer systems design, data processing, and internet services. The occupation list was drawn from standard federal classifications of technology-related roles, covering fields such as software development, computer and information systems management, data science, cybersecurity, and network administration. Full lists of included industries and occupations are provided below.

HR&A segments Ecosystem workers into three mutually exclusive groups based on whether their job falls in a tech industry, a tech occupation, or both:

• Tech in Tech:Workers in tech occupations at tech companies. This is the most commonly understood “tech worker,” such as a software engineer at a tech firm.
• Non-Tech in Tech: Workers in non-tech occupations at tech companies, such as an HR professional at a tech firm.
• Tech in Non-Tech: Workers in tech occupations at non-tech companies, such as a data scientist at a hospital.

These segments are analytically useful because they have different wage profiles, demographic compositions, and growth dynamics. Reporting them separately allows policymakers and industry stakeholders to understand where the Ecosystem is growing, who it employs, and where workforce gaps are concentrated.

HR&A’s approach uses a unique mix of industry, occupation, and job definitions to present the full tech “Ecosystem,” thereby counting more overall employment than other reports that focus only on the tech “industry.” An example of a job that is included in HR&A’s count but not in comparable reports is an HR professional that works at a tech firm. Additionally, HR&A includes self-employed and gig workers to fully represent employment in the ecosystem while other reports focus solely on official employment.

Self-employed workers and gig workers are included in the Ecosystem count. Lightcast estimates self-employment by occupation and geography using American Community Survey (ACS) microdata, which captures workers who report self-employment as their primary work arrangement. This is an important methodological choice because a meaningful share of tech workers operate as independent contractors or freelancers. Excluding them would systematically undercount the Ecosystem’s true size.

Lightcast (formerly EMSI Burning Glass) is a leading labor market analytics firm whose employment database is widely used by researchers, government agencies, and consulting firms. Lightcast constructs employment estimates by integrating multiple official data sources such as the Quarterly Census of Employment and Wages (QCEW), produced jointly by the U.S. Bureau of Labor Statistics and state agencies, which covers workers in firms that pay into unemployment insurance. Lightcast supplements QCEW data with estimates for workers not covered by unemployment insurance, including the self-employed, certain agricultural workers, and some railroad workers. These supplemental estimates draw on the American Community Survey, the Current Population Survey, and Lightcast’s own proprietary models.

Lightcast wage estimates are derived from the Occupational Employment and Wage Statistics (OEWS) program, supplemented with ACS microdata for self-employed workers. Wages are reported as the median hourly wage for workers in a given occupation and geography. Lightcast uses a combination of interpolation and smoothing to produce stable estimates at the city level, where OEWS sample sizes for specific occupations can be small. For this study, HR&A reports median hourly wages, which are more representative of a typical worker’s earnings than averages, which can be skewed by very high earners.

Lightcast derives race and gender breakdowns for occupations from the Equal Employment Opportunity (EEO) tabulations published by the U.S. Census Bureau, which are based on American Community Survey microdata. These tabulations report the racial and gender composition of workers by detailed occupation at the national level. Lightcast applies these national occupation-level proportions to local employment counts to produce county-level demographic estimates.

HR&A uses IMPLAN’s input-output economic model to evaluate the Ecosystem’s total economic impact in New York City and New York State. The direct employment and associated wages of the Ecosystem’s workers serve as the model’s primary inputs. IMPLAN then traces how that spending ripples through the economy to businesses that supply the Ecosystem, and then to households that spend wages earned in those businesses to produce total estimates of jobs supported, economic output, and fiscal revenue. Leading public and private sector organizations across the United States use IMPLAN as an industry-standard tool for economic impact analysis.

IMPLAN (formerly IMpact Analysis for PLANning) is a widely recognized economic modeling tool that generates estimates of indirect and induced employment and economic output based on direct economic activity in a specific geography. IMPLAN traces the pattern of commodity purchases and sales between industries associated with each dollar’s worth of a product or service sold to a final customer. The model analyzes interactions among 528 industries for a specific geography, with assumptions about spending that takes place outside of that geography (known as “leakage”). In addition to economic output, the model produces estimates of the number of jobs supported and total employee compensation.

Economic impacts are comprised of three distinct components:

• Direct impactsare the immediate economic effects of the Tech Ecosystem’s own employment and spending. When a tech employer hires a worker and pays their salary, that is a direct impact. Direct impacts are derived from Lightcast employment and wage data.
• Indirect impactsarise from business-to-business spending stimulated by the Ecosystem’s direct activity. When a tech company purchases cloud services, office supplies, or legal counsel, those vendors experience indirect impacts — they hire workers and make their own purchases as a result.
• Induced impacts stem from household spending by workers employed in direct and indirect industries. When those employees use their earnings to pay rent, buy groceries, or dine at a restaurant, they generate induced impacts in the broader economy.

Total economic impacts represent the sum of all three categories.

Economic multipliers refer to the additional economic activity created in the economy as a result of direct economic activity in a particular region. An employment multiplier is the ratio of total jobs to direct jobs, quantifying how many total jobs are supported in the economy for each direct Ecosystem job. A spending multiplier works analogously by expressing how much additional output is generated in the economy for each dollar of direct spending. Multipliers are produced by the IMPLAN model based on the economic structure of the defined geography, in this case the five counties of New York City. They reflect the degree to which local industries are interconnected and the extent to which spending remains within the local economy rather than leaking out to national or international suppliers.

Multipliers are sensitive to the economic structure of the region being analyzed. Larger and more diversified economies like New York City tend to have higher multipliers because more of the supply chain is local: when a tech company buys services, it is more likely to purchase them from another NYC-based firm, keeping spending within the local economy. Smaller or less diversified economies tend to have lower multipliers because more spending leaks to external suppliers. IMPLAN calibrates its multipliers to the specific industry mix and trade flows of each geography, so the NYC multiplier is not directly comparable to multipliers used in studies of other geographies without accounting for these structural differences.

Fiscal impact estimates are derived by applying effective tax rates to the economic activity attributable to the Tech Ecosystem. HR&A uses IMPLAN’s output on total wages and output, combined with New York City and New York State tax databases, to estimate the portion of that economic activity that flows to government as tax revenue. The analysis focuses on two principal tax instruments: income taxes (personal income taxes paid by workers in the Ecosystem and its supply chain) and sales taxes (generated by consumer spending associated with induced economic activity). Property taxes and business taxes are not estimated in this analysis.

National competitiveness comparisons use Lightcast data for each peer metro, applying the same industry and occupation definitions used for New York City. This ensures that differences in counts across metros reflect actual differences in workforce composition rather than methodological inconsistency. The peer metros of San Francisco Bay Area, Austin, and Miami were selected because they are frequently cited competitors for tech talent and investment in policy and industry discussions.

Employment shares are calculated by dividing Tech Ecosystem employment in a given metro by Lightcast’s estimate of total employment in that metro. Growth rates compare employment in 2026 to a baseline year (2021 for five-year comparisons, 2016 for ten-year comparisons) using the same industry and occupation definitions throughout.