Hollywood’s diversity debate often focuses on who appears on screen. But the people behind the camera also shape which stories are told, how productions are made, and who gains access to some of the industry’s most influential career paths. Representation and pay can look very different across producing, directing, editing, camera, technical, and visual-effects roles.

To examine where behind-the-camera opportunity appears strongest, Giggster analyzed 50 large U.S. metropolitan areas and 13 film-production-related occupations. The study combines national data on workforce representation and weekly earnings with local employment, wages, regional prices, and population.

Using the latest available data, the report ranks metros separately for women, Black workers, Asian workers, and Hispanic or Latino workers. Each ranking considers representation implied by the local job mix, a pay-adjusted wage benchmark, and population-adjusted job access.

View our full methodology and breakdown here.

Key Takeaways

• No metro leads every ranking. San Francisco and Washington are the only metros in the Top 10 for all four groups. Houston is the only metro in the Bottom 10 across all four, scoring from 7.5 points for Black workers to 27.3 for Hispanic or Latino workers.

• California performs strongly, but not universally. San Francisco leads for women at 98.6 points and Asian workers at 95.4. Los Angeles ranks second for women, Black workers, and Asian workers, but Las Vegas leads the Hispanic or Latino ranking at 76.2.

• Noncoastal metros also stand out. Atlanta scores 83.8 points for women and 83.4 for Black workers. Chicago reaches 85.8 for Asian workers and 71.1 for Hispanic or Latino workers.

• Higher national earnings do not always mean broader access. Asian workers earn 127.2% of White workers’ weekly earnings nationally but remain below representation parity in all four occupation groups. Women, Black workers, and Hispanic or Latino workers face national earnings gaps of 17.9%, 19.9%, and 22.7%, respectively.

San Francisco Leads Women’s Behind-the-Camera Opportunity

What the data says:

• San Francisco dominates the ranking, with nearly a 93-point spread between the first and last place, showing how sharply modeled behind-the-camera opportunity varies across eligible metros.

• Los Angeles has the highest modeled job access in the table, at 1,663 jobs per one million residents. Boston has the highest women’s share implied by its job mix, at 44%, while San Francisco records the strongest pay-adjusted wage benchmark, at $97K.

• San Antonio records the weakest result, with an index score of 6, a 28% women’s share implied by its job mix, and 125 modeled jobs per one million residents. 

New York Leads Black Behind-the-Camera Opportunity-Atlanta Breaks Into the Top Five

What the data says:

• New York leads because it performs strongly across all three measures, rather than dominating a single one. It scores 96 points, ahead of Los Angeles at 91 and Boston at 85. 

• Los Angeles has the highest modeled job access, at 448 jobs per one million residents. San Francisco records the strongest pay-adjusted wage benchmark, at $86.7K. Charlotte has the highest Black workers’ share implied by its job mix, at 11.2%, despite ranking ninth overall.

• Houston records the weakest eligible result, with an index score of 8, a $48.9K pay-adjusted wage benchmark, and 37 modeled jobs per one million residents. Cleveland follows at 13 points, while San Antonio ranks third-lowest at 17.

San Francisco and Los Angeles Lead Asian Opportunity while Chicago and Orlando Break the Coastal Pattern

What the data says:

• The West Coast dominates the top tier. San Francisco ranks first at 95 points, followed by Los Angeles and Seattle at 91. San Diego also places eighth at 75.

• The strongest-to-weakest gap reaches 86 points. San Francisco scores 95, compared with 9 for Kansas City. Los Angeles also has nearly 10 times Kansas City’s modeled job access: 236 versus 25 jobs per one million residents.

• Chicago and Orlando break the coastal pattern. Chicago ranks fourth at 86 points, while Orlando places sixth at 76. Chicago leads on the wage benchmark at $106.4K, while Orlando has stronger job access at 118 per one million residents.

Las Vegas and Orlando Beat the Coastal Giants for Hispanic or Latino Opportunity

What the data says:

• The strongest-to-weakest gap reaches 60 points. Las Vegas scores 76, compared with 16 for Memphis. Las Vegas also has nearly four times the modeled job access - 307 versus 79, while their shares from job mix are much closer, at 16.2% and 15.4%.

• Traditional coastal production hubs miss the top three. San Francisco places eighth despite having the highest wage benchmark, at $81.7K, and strongest job access, at 433 per million residents.

• Southern metros appear at both ends of the ranking. Orlando ranks second at 75 points and Nashville seventh at 69. Memphis ranks last at 16, while Tampa, Birmingham, Houston, and Jacksonville also appear in the Bottom 10.

Key Terms

Share Implied by Job Mix

A modeled estimate created by applying national demographic occupation shares to each metro’s local mix of selected jobs. It is not the observed percentage of local workers who belong to that demographic group.

Pay-Adjusted Wage Benchmark

A modeled local wage benchmark that combines the metro’s occupation mix, all-worker wages, regional price levels, and the demographic group’s national pay ratio. It is not the amount that women, Black, Asian, or Hispanic or Latino workers actually earn in that metro.

Job Access per 1 Million Residents

Modeled demographic employment divided by the metro population and expressed per one million residents. It allows metros of different sizes to be compared but is not a BLS-published demographic job count.

Opportunity Index

A metro score combining share implied by job mix at 35%, the pay-adjusted wage benchmark at 35%, and job access at 30%. Results are index points, not percentages, and scores should be compared only within the same demographic ranking.

Eligible Metro

A metro with sufficient employment and wage coverage to receive an Opportunity Index. Metros with incomplete coverage remain in the research but are not assigned zero points or placed below the lowest-ranked eligible metro.

Methodology

This study examined gender and racial disparities across selected behind-the-camera, film-production-related occupations in 50 large U.S. metropolitan areas. The unit of analysis was the metropolitan statistical area, not the city proper. City names such as Los Angeles, New York, San Francisco, and Washington are used as shorter editorial labels for their corresponding metro areas.

The research combines two different types of evidence:

• Actual national data on demographic representation and median weekly earnings.

• Modeled metro opportunity rankings based on national occupation patterns and local employment, wages, regional prices, and population.

The national figures show how representation and weekly earnings differ across demographic groups. The metro rankings estimate where local occupational conditions may provide stronger modeled opportunity for women, Black workers, Asian workers, and Hispanic or Latino workers.

The metro results do not measure actual race- or gender-specific wages, hiring rates, or workforce shares within individual metros.

The metro study universe and population-adjusted job-access calculations use the U.S. Census Bureau’s 2025 Metropolitan and Micropolitan Statistical Area Population Estimates.

The report contains four separate demographic rankings:

• Women’s Opportunity Index

• Black Opportunity Index

• Asian Opportunity Index

• Hispanic or Latino Opportunity Index

Each demographic group is ranked separately. The four index scores are not combined into one overall diversity score.

Occupations included

The study uses the May 2025 BLS Occupational Employment and Wage Statistics for 13 detailed occupations, grouped into five broader Current Population Survey categories.

Producers and Directors

• 27-2012 — Producers and Directors

Camera Operators and Film/Video Editors

• 27-4031 — Camera Operators, Television, Video, and Film

• 27-4032 — Film and Video Editors

Broadcast, Sound, and Lighting Technicians

• 27-4011 — Audio and Video Technicians

• 27-4012 — Broadcast Technicians

• 27-4014 — Sound Engineering Technicians

• 27-4015 — Lighting Technicians

Artists and Visual Effects Workers

• 27-1011 — Art Directors

• 27-1012 — Craft Artists

• 27-1013 — Fine Artists, Including Painters, Sculptors, and Illustrators

• 27-1014 — Special Effects Artists and Animators

• 27-1019 — Artists and Related Workers, All Other

Media and Communication Equipment Workers, All Other

• 27-4099 — Media and Communication Equipment Workers, All Other

The first four categories have publishable national demographic percentages and are used in the representation and metro opportunity models.

Media and Communication Equipment Workers, All Other remains in the broader OEWS wage and employment research but is excluded from the demographic model because its CPS race and sex percentages are not publishable.

The article uses the term film-production-related occupations rather than “film-industry workers.” OEWS is occupation-based and cross-industry, so workers in these roles may also be employed in broadcasting, advertising, corporate media, education, live entertainment, or other industries.

Measuring national representation

National representation data come from the BLS Current Population Survey’s Table 11: Employed People by Detailed Occupation, Sex, Race, and Hispanic or Latino Ethnicity.

For each reportable occupation group, the study records the percentage of workers who are:

• Women

• Black or African American

• Asian

• Hispanic or Latino

Each occupation-level percentage is compared with the demographic group’s share of the total U.S. employed workforce.

Representation gap

Representation Gap = Occupation demographic share − Total-workforce demographic share

A negative result means that the group represents a smaller share of the occupation than of the overall workforce.

For example, if women account for 30% of an occupation and 47% of the total workforce:

30% − 47% = −17 percentage points

The occupation has a 17-percentage-point representation gap for women.

Representation Index

Representation Index = Occupation demographic share ÷ Total-workforce demographic share × 100

The index is interpreted as follows:

• 100: Representation is proportional to the group’s share of the overall workforce.

• Below 100: The group is underrepresented.

• Above 100: The group is overrepresented.

• 50: The group’s occupation share is half its total-workforce share.

Separate Representation Index results are calculated for women, Black workers, Asian workers, and Hispanic or Latino workers.

The demographic groups are not combined into one diversity score. Hispanic or Latino is an ethnicity and may overlap with any race, so race and ethnicity percentages should not be added together.

Measuring national pay gaps

The national pay-gap analysis uses the BLS Current Population Survey’s Table 37: Median Weekly Earnings of Full-Time Wage and Salary Workers by Selected Characteristics.

These figures cover full-time wage-and-salary workers across all occupations. They are national benchmarks, not Hollywood-specific, film-occupation-specific, or metro-specific pay gaps.

The study calculates:

Pay Ratio = Comparison-group median weekly earnings ÷ Reference-group median weekly earnings × 100

Pay Gap = 100 − Pay Ratio

When the pay ratio is above 100%, the difference is reported as a premium, not as a negative gap.

The national factors used in the metro model are:

• Women vs. men: 82.1% pay ratio, equivalent to a 17.9% gap.

• Black workers vs. White workers: 80.1% pay ratio, equivalent to a 19.9% gap.

• Asian workers vs. White workers: 127.2% pay ratio, equivalent to a 27.2% premium.

• Hispanic or Latino workers vs. White workers: 77.3% pay ratio, equivalent to a 22.7% gap.

The article also includes national comparisons such as Black women versus White men and Hispanic or Latino women versus White men. These provide intersectional context but are not separate metro-ranking factors.

Detailed occupation-level earnings by sex come from BLS Table 39: Median Weekly Earnings by Detailed Occupation and Sex.

A detailed occupation pay ratio is calculated only when BLS publishes both the women’s and men’s medians. Where one value is suppressed, no pay gap is estimated.

In 2025, Producers and Directors is the only core study category with both values published. Women earned $1,802 per week, compared with $1,773 for men, equivalent to a 1.6% national premium.

The Table 37 and Table 39 figures are actual national BLS earnings comparisons. They do not show what demographic groups earn in individual metros.

Metro employment and wage data

Metro labor-market data come from the May 2025 BLS Occupational Employment and Wage Statistics.

The completed source table contains:

50 metros × 13 occupations = 650 metro-occupation records

For each metro and occupation, the study retains available data on:

• Total employment

• Employment per 1,000 local jobs

• Location quotient

• Mean hourly wage

• Mean annual wage

• Median hourly wage

• Median annual wage

• Employment relative standard error

• Mean-wage relative standard error

• Publication or suppression status

For an individual occupation, the published median annual wage is used as the primary wage measure.

When several occupations are combined into one broader category, the workbook calculates an employment-weighted wage-exposure benchmark. This is not described as a combined median because a true combined median cannot be reconstructed from separate occupation medians.

Suppressed or unavailable values remain blank. They are not treated as zero and are not estimated or imputed.

Adjusting wages for local prices

Metro wage data are adjusted using the Bureau of Economic Analysis’ 2024 Regional Price Parities.

Regional Price Parities compare local price levels with the U.S. average, where 100 represents the national price level.

RPP-Adjusted Wage = Nominal OEWS Wage ÷ (Metro RPP ÷ 100)

For example, if a metro has a nominal wage of $90,000 and an RPP of 110:

$90,000 ÷ 1.10 = $81,818

A metro with an RPP below 100 receives an upward adjustment because its overall price level is below the national average. A metro with an RPP above 100 receives a downward adjustment.

The result represents the purchasing-power value of the published all-worker wage.

It should be described as:

• RPP-adjusted all-worker wage

• Local-purchasing-power wage

It should not be described as:

• Cost-of-living-adjusted women’s wage

• Cost-of-living-adjusted Black workers’ wage

• Actual demographic pay in the metro

The RPP adjustment changes the value of the all-worker wage. It does not create a locally observed demographic wage.

Estimating demographic employment from the local job mix

OEWS reports metro employment by occupation but does not report workers’ sex, race, or ethnicity.

To create the metro model, the study applies each occupation’s national demographic share from BLS CPS Table 11 to the occupation’s local employment from BLS OEWS.

Modeled Demographic Employment = Metro occupation employment × National occupation demographic share

For example, if women account for 30% of an occupation nationally and a metro reports 1,000 workers in that occupation:

1,000 × 30% = 300 modeled jobs

This does not mean BLS counted 300 women in that metro occupation. It means that the metro’s occupation mix implies approximately 300 jobs under the national representation pattern.

The modeled estimate is used only to compare local occupation structures across metros.

Calculating demographic share from the job mix

For each metro and demographic group, modeled employment is combined across the four occupation categories with publishable CPS demographic percentages.

Demographic Share Implied by Job Mix = Total modeled demographic employment ÷ Total included OEWS employment × 100

This measure describes the representation implied by the metro’s mix of selected occupations.

Use:

Women’s share implied by the metro’s job mix is approximately 41%.

Avoid:

41% of the metro’s film workers are women.

The model applies national demographic percentages to local occupation employment. It does not observe the race, ethnicity, or sex of workers in the metro.

Calculating the pay-adjusted wage benchmark

The study first calculates the RPP-adjusted all-worker wage environment associated with the occupations in which each demographic group is modeled to be represented.

Base Wage Exposure = Sum of modeled demographic employment × RPP-adjusted all-worker wage ÷ Modeled demographic employment with publishable wage data

The applicable national factor from BLS CPS Table 37 is then applied:

Pay-Adjusted Wage Benchmark = Base Wage Exposure × National Pay Factor

The benchmark accounts for:

• The metro’s occupation mix

• Local all-worker wages

• Regional price levels

• The demographic group’s national pay ratio

It is a modeled wage environment, not the amount that women, Black workers, Asian workers, or Hispanic or Latino workers actually earn in that metro.

Because the same national pay factor is applied to every metro within a demographic group, it changes the displayed dollar benchmark but does not change the ordering of metros within that group’s wage component.

Calculating job access

Modeled demographic employment is adjusted for metro population using the U.S. Census Bureau’s 2025 Metropolitan Population Estimates.

Job Access per 1 Million Residents = Total modeled demographic employment ÷ Metro population × 1,000,000

This prevents the largest metros from automatically ranking first simply because they have more workers.

The result is a population-adjusted index input. It is not a BLS-published count of women, Black, Asian, or Hispanic or Latino workers.

Building the Metro Demographic Opportunity Index

A separate Opportunity Index is calculated for:

• Women

• Black or African American workers

• Asian workers

• Hispanic or Latino workers

The formula is:

Opportunity Index = 35% Share Score + 35% Pay-Adjusted Wage Score + 30% Job-Access Score

Share Score 35%

Measures how strongly the metro’s occupation mix implies representation for the demographic group.

Pay-Adjusted Wage Score 35%

Measures the local-purchasing-power wage environment after applying the group’s national BLS pay factor.

Job-Access Score 30%

Measures modeled demographic employment per one million residents.

Before the weights are applied, each raw measure is converted into a 0-100 percentile score among eligible metros within the same demographic group.

The result is reported in index points, not percentages.

A score of 95 does not mean that:

• 95% of workers belong to the demographic group.

• 95% of opportunities are equitable.

• The metro has eliminated representation or pay disparities.

It means that the metro performs near the top of the eligible comparison set under the study’s model.

The formula, weighting, and eligibility rules follow the framework documented in the Hollywood Wage Gap research workbook and outline.

Comparing the four demographic rankings

Each demographic group has its own ranking distribution.

Women are ranked against women’s modeled results in other metros. Black workers are ranked against Black workers’ results, and the same rule applies to Asian and Hispanic or Latino workers.

A valid comparison is:

Los Angeles ranks second in both the women’s and Black opportunity rankings.

An invalid comparison is:

Women have more opportunity than Black workers because the women’s index score is higher.

Raw scores should not be compared directly across demographic groups because each index is based on a separate within-group percentile distribution.

The four Opportunity Index results should not be averaged into one overall diversity score.

Metro eligibility

A metro receives an Opportunity Index and rank for a demographic group only when:

• All four CPS occupation categories used in the model are represented.

• Modeled employment coverage is at least 70%.

• Wage coverage is at least 80%.

• All three index inputs are available.

The number of eligible metros therefore varies by demographic group:

• Women: 46 eligible metros

• Black workers: 47 eligible metros

• Asian workers: 46 eligible metros

• Hispanic or Latino workers: 47 eligible metros

For the women’s and Asian rankings, Portland, Sacramento, Salt Lake City, and San Jose do not meet the required wage-coverage threshold.

For the Black and Hispanic or Latino rankings, Sacramento, Salt Lake City, and San Jose do not meet the threshold.

Unranked metros remain in the research workbook but are marked Not ranked - incomplete coverage. They are not assigned zero points and are not placed below the lowest eligible metro.

“Bottom 10” therefore means the 10 lowest-ranked eligible metros, not necessarily ranks 41 through 50.

Ranking and rounding rules

All eligibility decisions, component scores, Opportunity Index calculations, and rankings were based on the underlying unrounded values. Numbers were rounded only for presentation.

Where two metros display the same rounded score, their rank order is determined by the underlying unrounded result.

Things to keep in mind

The BLS Current Population Survey demographic shares are national. They do not directly measure the race, ethnicity, or sex of workers in individual metros.

The metro demographic shares, wage benchmarks, and job-access figures are modeled. They should not be presented as observed local representation, actual local demographic wages, or BLS-counted demographic employment.

OEWS is cross-industry and excludes self-employed workers. The analysis may not capture freelance and project-based workers who are common in film production.

Hispanic or Latino is an ethnicity and may overlap with any race. Race and ethnicity results should not be added together.

The Opportunity Index measures relative modeled opportunity among eligible metros. It does not directly measure hiring equity, discrimination, promotion rates, union access, project availability, career progression, or individual earnings.