Bill Overview
Title: Visa Transparency Anti-Trafficking Act of 2021
Description: This bill directs the Department of Homeland Security to build a searchable database with certain information about each temporary foreign nonimmigrant worker. The database shall include information such as (1) each worker's age, sex, and country of origin; (2) the type of visa used and the status of such visa; (3) where each worker is employed; and (4) each worker's occupation and the compensation received. The database shall only be available to (1) law enforcement, (2) service providers to human trafficking victims, (3) worker protection organizations, and (4) entities agreeing to use the information only for research purposes. The bill expands existing reporting requirements related to temporary foreign nonimmigrant workers to include additional information such as (1) the 10 employers that hired the most temporary foreign nonimmigrant workers, and (2) the 10 occupations with the most temporary foreign nonimmigrant workers.
Sponsors: Rep. Frankel, Lois [D-FL-21]
Target Audience
Population: Temporary foreign nonimmigrant workers
Estimated Size: 0
- The primary target population consists of temporary foreign nonimmigrant workers, who will be directly impacted by the data collection and reporting facilitated by this act.
- Secondary populations include law enforcement, human trafficking victim service providers, worker protection organizations, and researchers who will have access to this database.
- Indirectly, this may impact employers of nonimmigrant workers in the United States, as their data regarding employment practices will be monitored.
- The act could also influence policy makers who utilize the data for legislative purposes.
Reasoning
- This policy is targeted at temporary foreign nonimmigrant workers and organizations that have a vested interest in their wellbeing.
- The impact on US citizens is indirect, primarily affecting those in law enforcement and advocacy roles.
- Budgetary constraints limit extensive technological developments in the database, necessitating a focus on essential features and robust security measures.
- The wellbeing impacts will differ based on whether someone is a worker being tracked or an advocate using the data to improve worker conditions.
Simulated Interviews
Software Engineer (Los Angeles, CA)
Age: 30 | Gender: female
Wellbeing Before Policy: 7
Duration of Impact: 10.0 years
Commonness: 5/20
Statement of Opinion:
- I feel that this database could help improve transparency and ensure fair treatment.
- I'm concerned about privacy, but overall, the information could lead to better compliance by employers.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 8 | 7 |
| Year 2 | 8 | 7 |
| Year 3 | 8 | 7 |
| Year 5 | 8 | 7 |
| Year 10 | 8 | 7 |
| Year 20 | 8 | 7 |
Agricultural Worker (Miami, FL)
Age: 22 | Gender: male
Wellbeing Before Policy: 6
Duration of Impact: 5.0 years
Commonness: 4/20
Statement of Opinion:
- I'm hopeful that the act will protect workers like me from exploitation.
- It's important for authorities to have this data to find and help those in need.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 8 | 6 |
| Year 5 | 8 | 6 |
| Year 10 | 6 | 5 |
| Year 20 | 5 | 5 |
Policy Analyst (New York, NY)
Age: 45 | Gender: female
Wellbeing Before Policy: 5
Duration of Impact: 3.0 years
Commonness: 10/20
Statement of Opinion:
- This database is a step forward in understanding and improving nonimmigrant workers' conditions.
- Data privacy measures must be stringent to prevent misuse.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 5 |
| Year 2 | 6 | 5 |
| Year 3 | 6 | 5 |
| Year 5 | 5 | 5 |
| Year 10 | 5 | 5 |
| Year 20 | 5 | 5 |
HR Manager (Houston, TX)
Age: 40 | Gender: male
Wellbeing Before Policy: 6
Duration of Impact: 10.0 years
Commonness: 5/20
Statement of Opinion:
- This data could pressure employers to be more compliant with labor standards.
- However, companies may need to invest in systems to ensure data accuracy and compliance.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 7 | 6 |
| Year 5 | 7 | 6 |
| Year 10 | 7 | 6 |
| Year 20 | 7 | 6 |
Researcher (Chicago, IL)
Age: 35 | Gender: other
Wellbeing Before Policy: 5
Duration of Impact: 5.0 years
Commonness: 8/20
Statement of Opinion:
- This act allows for better research into the conditions of temporary workers.
- However, accessing detailed data while maintaining privacy is crucial.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 5 |
| Year 2 | 6 | 5 |
| Year 3 | 6 | 5 |
| Year 5 | 5 | 5 |
| Year 10 | 5 | 5 |
| Year 20 | 5 | 5 |
Non-Profit Worker (Phoenix, AZ)
Age: 50 | Gender: female
Wellbeing Before Policy: 4
Duration of Impact: 5.0 years
Commonness: 7/20
Statement of Opinion:
- Access to this database might help identify victims sooner.
- There is a risk of data being improperly accessed.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 5 | 4 |
| Year 2 | 6 | 4 |
| Year 3 | 6 | 4 |
| Year 5 | 5 | 4 |
| Year 10 | 5 | 4 |
| Year 20 | 4 | 4 |
Construction Worker (Seattle, WA)
Age: 28 | Gender: male
Wellbeing Before Policy: 5
Duration of Impact: 3.0 years
Commonness: 4/20
Statement of Opinion:
- I hope this move would discourage employers from exploiting workers.
- I don't want my personal details available, even to limited groups.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 5 |
| Year 2 | 6 | 5 |
| Year 3 | 5 | 5 |
| Year 5 | 5 | 5 |
| Year 10 | 5 | 5 |
| Year 20 | 5 | 5 |
Data Privacy Advocate (Boston, MA)
Age: 60 | Gender: female
Wellbeing Before Policy: 5
Duration of Impact: 10.0 years
Commonness: 12/20
Statement of Opinion:
- It's important to prioritize the security of this database.
- Even with good intentions, data breaches can have severe consequences.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 5 | 5 |
| Year 2 | 5 | 5 |
| Year 3 | 5 | 5 |
| Year 5 | 5 | 5 |
| Year 10 | 5 | 5 |
| Year 20 | 5 | 5 |
Tech Entrepreneur (San Francisco, CA)
Age: 33 | Gender: male
Wellbeing Before Policy: 6
Duration of Impact: 5.0 years
Commonness: 10/20
Statement of Opinion:
- This will add more oversight on employment practices, which could be beneficial.
- There may be additional administrative burdens.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 7 | 6 |
| Year 5 | 6 | 6 |
| Year 10 | 6 | 6 |
| Year 20 | 6 | 6 |
Freelancer (Austin, TX)
Age: 26 | Gender: female
Wellbeing Before Policy: 7
Duration of Impact: 2.0 years
Commonness: 15/20
Statement of Opinion:
- Transparency is key in addressing workforce issues.
- I'm concerned that the increased scrutiny could add hurdles for freelancers.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 7 |
| Year 2 | 6 | 7 |
| Year 3 | 6 | 7 |
| Year 5 | 6 | 7 |
| Year 10 | 6 | 7 |
| Year 20 | 6 | 7 |
Cost Estimates
Year 1: $20000000 (Low: $15000000, High: $25000000)
Year 2: $15000000 (Low: $12000000, High: $20000000)
Year 3: $15000000 (Low: $12000000, High: $20000000)
Year 5: $15000000 (Low: $12000000, High: $20000000)
Year 10: $15000000 (Low: $12000000, High: $20000000)
Year 100: $15000000 (Low: $12000000, High: $20000000)
Key Considerations
- Data privacy and security are critical due to the sensitive nature of the information to be stored.
- Ensuring the accuracy of data reported by employers is crucial to the effectiveness of the database.
- Interagency cooperation will be necessary to manage access to the database for authorized parties.
- The ability of law enforcement and other parties to utilize the data effectively will depend on adequate training and resources.