Bill Overview
Title: LGBTQI+ Data Inclusion Act
Description: I+ Data Inclusion Act This bill addresses federal data collection of voluntary, self-disclosed information on sexual orientation, gender identity, and variations in sex characteristics. Specifically, the bill requires federal agencies that collect information through a survey for statistical purposes that includes demographic data (where subjects self-report information or a proxy provides information about the subject or responds for all persons in a household) to review existing data sets to determine which data sets do not include information about sexual orientation, gender identity, and variations in sex characteristics. Such agencies must assess needed changes in survey methods related to asking questions on such matters. Agencies that publish reports relying on survey demographic data must include information on sexual orientation, gender identity, and variations in sex characteristics. Agencies may waive this publication requirement on a case-by-case basis if the confidentiality of the information cannot be maintained or if adding such information to the survey would impair the agency's ability to preserve the utility, accuracy, or objectivity of the survey while also generating relevant evidence about the LGBTQI+ community. The Government Accountability Office must report to Congress on the implementation of this bill's requirements by agencies.
Sponsors: Rep. Grijalva, Raúl M. [D-AZ-3]
Target Audience
Population: Individuals identifying as LGBTQI+ and demographic survey respondents
Estimated Size: 330000000
- The primary population impacted by the bill will include members of the LGBTQI+ community who will see an inclusion of their relevant data in federal statistical surveys.
- This bill will affect other federal statistical analysts and researchers who utilize demographic data, as the addition of LGBTQI+ identifiers may alter data collection methods and analyses.
- Various federal agencies conducting demographic surveys will also be impacted due to the increased requirements for data review and inclusion.
- The inclusion of this data could also affect policymakers and governmental bodies which use federal survey data to make informed decisions, particularly on issues affecting the LGBTQI+ population.
- Secondary effects might touch industries and services that rely on federal data to understand demographic trends and target community needs.
Reasoning
- The primary beneficiaries of the policy will be members of the LGBTQI+ community, who might experience increased visibility and potentially more accurate representation in government data, which could lead to better policies.
- Federal budget limits means not all agencies might implement changes immediately, potentially affecting the rate of integration and accuracy improvements.
- People whose professions rely on federal statistical data could experience shifts in the way data is handled and processed, affecting their workflow.
- General respondents to federal surveys may notice little to no change unless they are directly asked about LGBTQI+ data. Thus, the impact may be minimal for non-LGBTQI+ community members on a personal level.
Simulated Interviews
Software Engineer (Austin, TX)
Age: 32 | Gender: female
Wellbeing Before Policy: 6
Duration of Impact: 20.0 years
Commonness: 10/20
Statement of Opinion:
- I think it's high time our data is collected appropriately. It might just lead to better policies that benefit our community.
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 | 7 |
| Year 10 | 9 | 6 |
| Year 20 | 9 | 6 |
Federal Data Analyst (New York, NY)
Age: 45 | Gender: male
Wellbeing Before Policy: 5
Duration of Impact: 20.0 years
Commonness: 15/20
Statement of Opinion:
- Inclusion of this data will change the analytics we conduct, but it might lead to more comprehensive studies and insights.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 5 | 5 |
| Year 2 | 6 | 5 |
| Year 3 | 6 | 5 |
| Year 5 | 6 | 5 |
| Year 10 | 7 | 5 |
| Year 20 | 6 | 5 |
Marketing Specialist (San Francisco, CA)
Age: 27 | Gender: other
Wellbeing Before Policy: 7
Duration of Impact: 10.0 years
Commonness: 8/20
Statement of Opinion:
- It feels validating to have our identities recognized at a federal level. I hope this translates to real-world benefits.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 7 |
| Year 2 | 7 | 7 |
| Year 3 | 8 | 7 |
| Year 5 | 8 | 7 |
| Year 10 | 8 | 7 |
| Year 20 | 7 | 7 |
Retired (Rural Ohio)
Age: 60 | Gender: female
Wellbeing Before Policy: 4
Duration of Impact: 5.0 years
Commonness: 16/20
Statement of Opinion:
- I don't see the need for more data collecting on personal issues. It doesn't impact my life much.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 4 | 4 |
| Year 2 | 4 | 4 |
| Year 3 | 5 | 4 |
| Year 5 | 5 | 4 |
| Year 10 | 4 | 4 |
| Year 20 | 4 | 4 |
College Student (Seattle, WA)
Age: 21 | Gender: male
Wellbeing Before Policy: 6
Duration of Impact: 15.0 years
Commonness: 12/20
Statement of Opinion:
- I'm cautiously optimistic. It might pave the way for greater acceptance and better services in future.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 7 | 6 |
| Year 5 | 7 | 6 |
| Year 10 | 8 | 6 |
| Year 20 | 8 | 6 |
Market Researcher (Chicago, IL)
Age: 40 | Gender: female
Wellbeing Before Policy: 7
Duration of Impact: 10.0 years
Commonness: 14/20
Statement of Opinion:
- Incorporating more inclusive data makes my job both challenging and exciting as it opens new research possibilities.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 7 |
| Year 2 | 8 | 7 |
| Year 3 | 8 | 7 |
| Year 5 | 8 | 7 |
| Year 10 | 8 | 7 |
| Year 20 | 7 | 7 |
Elementary School Teacher (Birmingham, AL)
Age: 50 | Gender: male
Wellbeing Before Policy: 6
Duration of Impact: 5.0 years
Commonness: 11/20
Statement of Opinion:
- I believe every student has a story. This policy might help us understand them better, indirectly at least.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 6 |
| Year 2 | 6 | 6 |
| Year 3 | 6 | 6 |
| Year 5 | 6 | 6 |
| Year 10 | 6 | 6 |
| Year 20 | 6 | 6 |
Community Organizer (Los Angeles, CA)
Age: 29 | Gender: male
Wellbeing Before Policy: 8
Duration of Impact: 20.0 years
Commonness: 9/20
Statement of Opinion:
- Accurate data could mean more tailored programs and resources for the youth. It's a step forward.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 9 | 8 |
| Year 2 | 9 | 8 |
| Year 3 | 9 | 8 |
| Year 5 | 9 | 8 |
| Year 10 | 9 | 8 |
| Year 20 | 9 | 8 |
Healthcare Worker (New Orleans, LA)
Age: 38 | Gender: female
Wellbeing Before Policy: 5
Duration of Impact: 10.0 years
Commonness: 13/20
Statement of Opinion:
- Adding this data is a good idea, it will help us understand patient backgrounds more, though likely indirectly.
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 | 6 | 5 |
| Year 10 | 6 | 5 |
| Year 20 | 5 | 5 |
Freelancer (Miami, FL)
Age: 55 | Gender: other
Wellbeing Before Policy: 5
Duration of Impact: 10.0 years
Commonness: 17/20
Statement of Opinion:
- I hope this leads to more inclusive policies, but I haven't personally been affected by federal data collection before.
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 | 6 | 5 |
| Year 10 | 6 | 5 |
| Year 20 | 5 | 5 |
Cost Estimates
Year 1: $20000000 (Low: $15000000, High: $25000000)
Year 2: $19000000 (Low: $14000000, High: $24000000)
Year 3: $18000000 (Low: $13000000, High: $23000000)
Year 5: $17000000 (Low: $12000000, High: $22000000)
Year 10: $16000000 (Low: $11000000, High: $21000000)
Year 100: $15000000 (Low: $10000000, High: $20000000)
Key Considerations
- Protection of personal privacy and data confidentiality, particularly with sensitive information.
- Active engagement and training with agency staff to adjust survey methodologies and ensure data accuracy and utility.
- Monitoring and compliance by the Government Accountability Office (GAO) to ensure full implementation and address any issues in data reporting.
- Evaluation of long-term policy effectiveness facilitated by richer demographic insights.