As attention to the challenges facing boys and men grows, policymakers, nonprofits and local leaders are asking a natural next question: what do these issues look like for us? Luckily, measuring the wellbeing of boys and men doesn’t require building a research operation. Most of the data already exists in education agencies, health departments, workforce boards, and federal surveys. It just hasn’t been pulled together with a gender lens.
Why measure data on boys and men?
Understand the big picture. How are boys and men doing in your state compared to girls and women, and compared to other states? Where are the gaps widest? A baseline data portrait answers these questions and gives policymakers a shared set of facts.
Identify gaps in service delivery. Are men underrepresented in workforce programs? Are boys falling behind in reading unnoticed? Are fathers using paid leave at the rates you’d expect? Disaggregated data surfaces where existing systems might not be reaching men.
Build coalitions and political will. A data collection effort is itself a commitment device and an organizing tool. For example, asking agencies to pull numbers by gender creates interagency conversation. Or publishing a fact sheet or dashboard gives advocates, legislators, and the public something concrete to rally around. A shared baseline can be the first step to coordinated action.
This guide lays out four ways state and local leaders can assess and act on the challenges of boys and men. It ranges from what you can pull today to what requires deeper agency partnership, with recommendations for key metrics along the way.
Play 1: Get key metrics from public data
The fastest path to a baseline picture is federal public data. Many key metrics can be pulled readily using sources like the U.S. Census Bureau’s American Community Survey (ACS) and Current Population Survey (CPS), the CDC’s WISQARS/WONDER and the Behavioral Risk Factor Surveillance System (BRFSS), the National Assessment of Educational Progress (NAEP), and the Integrated Postsecondary Education Data System (IPEDS). Some of these sources, like CDC WISQARS and NAEP, have easily navigable dashboards or table-creation tools, requiring little to no data analysis experience. In many cases, the data pulled from these sources is enough to frame an initial conversation.
The table below surveys metrics that we often use at AIBM. But a measure alone is not meaningful; it needs to be contextualized. Depending on your region, interest and the topic you might do this differently:
Look at shares. Men make up 23% of public-school teachers and 80% of suicide deaths. These numbers tell you who is represented, or overrepresented, in a given outcome.
Compare outcomes by sex. In 2024, eighth-grade boys scored 253 in NAEP reading, compared with 263 for girls.
Track changes over time. Men’s share of bachelor’s degrees fell from 54% in 1976-77 to 42% in 2022-23. A single-year snapshot misses the trajectory.
Examine differences among boys and men. From 2008 to 2016, Black men had substantially lower overdose death rates than white men. That pattern reversed in 2019. Men and women are similarly lonely, but men without a college degree are much more lonely than those with one. When the data allow it, break results down by race, education, age, and place. The overall gender gap can hide very different patterns among men.
Compare across places. In 2024, young men’s NEET rate was 7% in Minnesota and 19% in Nevada; only Alaska was higher. National averages can mask wide state-level variation.
A note on comparisons: male/female comparisons and shares can be useful to identify gaps and contextualize trends, not to set up a competition. It is helpful to know that men are about 4x more likely to die by suicide, just as it is helpful to know that women are roughly 1.5 to 2x more likely to attempt suicide. The goal is not to frame this as men versus women, but to identify where gender-sensitive supports may be needed.
Table 1
Some patterns hold nationwide: Men account for roughly 80% of suicide deaths and about 70% of overdose deaths, with little variation in composition across states. Boys trail girls in reading proficiency almost everywhere. These patterns don’t diminish an issue’s importance, but show it is not confined to one place or driven by a single state’s circumstances.
There are other metrics where states diverge significantly. While the male share of suicide and overdose deaths are consistent, the rates are not. The male overdose death rate varies roughly eightfold: Washington, D.C. and West Virginia were highest, at about 63 deaths per 100,000 men, while Nebraska was lowest, at about 8. Male suicide rates varied almost fivefold, from about 47 deaths per 100,000 men in Wyoming and Alaska to about 10 in D.C. and New Jersey. College enrollment gaps range from modest to severe depending on the state and institution type. This is where state comparisons become useful because they show which states are worth learning from and which local conditions deserve an especially close look.
Play 2: Audit your state’s own data for gender gaps
Federal data can take you far, but state agencies hold administrative data on state programs as well as their own collections. State education results, program enrollment and completion data, vital records, and workforce participation data are often collected with a gender field but not routinely reported that way. For example, the California School Dashboard reports high school graduation rates across socioeconomic status and race. The rates by gender do exist in the underlying data, they are just not reported the same way.
The starting point is a simple audit: for each major state agency, what data is already published with a gender breakdown, where is gender data collected but not reported, and where is gender data not collected? Every state we’ve looked at has a version of this pattern: strong gender disaggregation in some domains (like Virginia’s health dashboards or the Illinois Report Card) and almost none in others (including public benefit usage, parental leave, education discipline data, CTE, and workforce programs).
Table 2
This list is not exhaustive. Add or subtract measures that reflect your state’s immediate concerns and your unique data assets.
A note on paid leave: For the states (plus Washington, D.C.) with paid family leave programs, take-up data by gender is one of the most policy-relevant metrics available. It’s also one of the least consistently reported. How many fathers are filing bonding claims? What’s the average duration of leave taken by men vs. women? If your state has a paid leave program, this should be a priority data ask.
Running the audit brings partners together around a question they may never have asked: “What do we know about boys and men in our state?” In some cases, the process generates more policy traction than the data itself. It creates a shared language and next steps across education, workforce, and health agencies that do not usually coordinate on gender.
When approaching a potential data partner, the request is generally not “give us everything by gender.” It’s “we’ve identified these three or four metrics that we think matter for understanding how boys and men are doing. Can you pull them disaggregated by gender, and ideally by gender and race?” Where state data infrastructure allows, looking at how gender interacts with race and class is particularly valuable. There are meaningful differences by race on various metrics, for example, Hispanic and Black men have lower college attendance rates, and white and Native American men have elevated suicide rates. And outcomes by education vary significantly too.
Play 3: Make it visible: dashboards, reports, and recurring measurement
An internal data pull identifies problems. Publishing the data signals commitment and creates accountability. And if you update it regularly, you can track whether anything is actually improving.
Options, from lowest to highest lift:
Add a men’s wellbeing page to existing websites. This doesn’t require new data collection, just curation and presentation of what already exists. This can be as simple as a dedicated page within a department website that pulls together a few gender-disaggregated statistics, as the CDC has done.
Publish a state data portrait or report. A one-time report can be a powerful tool for building buy-in and knowledge. This might be produced within government or with local partners – a community foundation, university center, or state policy institute, and cover topics that are important locally. This is what AIBM has done with Boston Indicators, and other organizations in Utah and Indiana have also produced similar reports. The report itself becomes a reference document for local stakeholders and media.
Build a live dashboard with recurring updates. A commission or task force can make dashboard maintenance part of its annual reporting mandate. Key features of good dashboards: they allow filtering by gender (not just overall or by race alone), include trend data, provide comparison points (state vs. national, male vs. female), and are housed on a main agency page, not buried in a report archive. North Dakota’s Men’s Health Dashboard is a good comprehensive example, along with the Virginia Injury Death Rate dashboard, which displays injury death rates by age, county, race, and sex.
Legislate a data requirement. Embedding data collection in statute creates durability beyond any single administration. Florida’s HB 7033 mandated a one-time report on educational gaps by gender, which also shared best practices across the state. Virginia’s new Boys and Men Advisory Commission (SB 447) includes annual reporting requirements.
Play 4: Pick your targets and act on them
A dashboard in isolation is just a website, and can be hard to maintain. The point of measuring is to translate that data into action via policy proposals, program redesigns, and budget asks. Once you have baseline data, the next step is to narrow your focus to a few metrics where the data is compelling, the issue aligns with state priorities, and there’s a plausible policy or programmatic lever. Then track as you go.
Some examples of how metrics connect to action:
A wide gender gap in higher education re-enrollment might mean your state’s tuition-free programs need targeted outreach to men. Michigan did this with Michigan Reconnect after discovering a 2:1 female-to-male re-enrollment ratio.
A high male suicide rate might mean investing in provider training through programs like Men in Mind, or launching a public-facing awareness effort like Massachusetts’ MassMen campaign.
Men making up less than a quarter of the HEAL workforce, in a state with acute labor shortages, might mean building apprenticeship pathways and rethinking recruitment messaging for HEAL careers.
Low father take-up of paid leave might mean redesigning program outreach and employer communications. Connecticut and Washington have seen relatively high male take-up and might serve as models.
Getting started
States, cities, and organizations are all at different stages in addressing these challenges. The plays above can be combined in different sequences depending on where you have traction:
If you’re just starting the conversation: Start with Play 1. A baseline fact sheet gives stakeholders something concrete to react to. “Our male suicide rate is 40% above the national average” opens doors that “we should pay more attention to boys and men” does not.
If you have executive or institutional buy-in: Combine Plays 1 and 2. Use the baseline data to frame the conversation, then run the agency audit to identify what’s already available and where the gaps are. Michigan, California, and Virginia have each done this differently ranging from specific asks to broad license to try to figure out what’s happening with men. In each case, the mandate created the space and the data gave agencies something concrete to focus on.
If you’re building a commission or advisory body: All four plays, sequenced over several months. The baseline data anchors your first meeting. The agency audit becomes an early deliverable and gives members something to do besides attend meetings. The dashboard or report makes the work visible. And picking two or three targets to act on will help bridge into action. However, you don’t need to overturn every data rock to start strategizing and acting, if you find something that feels like a problem – start to think of how to tackle it.
If you’re an agency head or program director: You may not need all four plays. The audit framework in Play 2 can be applied to a single agency. What data do you collect that could be disaggregated by gender? Can you add gender as a filter to existing dashboards? There may be a lot you can do on your own that may then inspire others.
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PolicyEducation & Skills, Employment, Health
Measures of a man: A practical guide for states tracking the wellbeing of boys and men
As attention to the challenges facing boys and men grows, policymakers, nonprofits and local leaders are asking a natural next question: what do these issues look like for us? Luckily, measuring the wellbeing of boys and men doesn’t require building a research operation. Most of the data already exists in education agencies, health departments, workforce boards, and federal surveys. It just hasn’t been pulled together with a gender lens.
Why measure data on boys and men?
This guide lays out four ways state and local leaders can assess and act on the challenges of boys and men. It ranges from what you can pull today to what requires deeper agency partnership, with recommendations for key metrics along the way.
Play 1: Get key metrics from public data
The fastest path to a baseline picture is federal public data. Many key metrics can be pulled readily using sources like the U.S. Census Bureau’s American Community Survey (ACS) and Current Population Survey (CPS), the CDC’s WISQARS/WONDER and the Behavioral Risk Factor Surveillance System (BRFSS), the National Assessment of Educational Progress (NAEP), and the Integrated Postsecondary Education Data System (IPEDS). Some of these sources, like CDC WISQARS and NAEP, have easily navigable dashboards or table-creation tools, requiring little to no data analysis experience. In many cases, the data pulled from these sources is enough to frame an initial conversation.
The table below surveys metrics that we often use at AIBM. But a measure alone is not meaningful; it needs to be contextualized. Depending on your region, interest and the topic you might do this differently:
A note on comparisons: male/female comparisons and shares can be useful to identify gaps and contextualize trends, not to set up a competition. It is helpful to know that men are about 4x more likely to die by suicide, just as it is helpful to know that women are roughly 1.5 to 2x more likely to attempt suicide. The goal is not to frame this as men versus women, but to identify where gender-sensitive supports may be needed.
Table 1
Some patterns hold nationwide: Men account for roughly 80% of suicide deaths and about 70% of overdose deaths, with little variation in composition across states. Boys trail girls in reading proficiency almost everywhere. These patterns don’t diminish an issue’s importance, but show it is not confined to one place or driven by a single state’s circumstances.
There are other metrics where states diverge significantly. While the male share of suicide and overdose deaths are consistent, the rates are not. The male overdose death rate varies roughly eightfold: Washington, D.C. and West Virginia were highest, at about 63 deaths per 100,000 men, while Nebraska was lowest, at about 8. Male suicide rates varied almost fivefold, from about 47 deaths per 100,000 men in Wyoming and Alaska to about 10 in D.C. and New Jersey. College enrollment gaps range from modest to severe depending on the state and institution type. This is where state comparisons become useful because they show which states are worth learning from and which local conditions deserve an especially close look.
Play 2: Audit your state’s own data for gender gaps
Federal data can take you far, but state agencies hold administrative data on state programs as well as their own collections. State education results, program enrollment and completion data, vital records, and workforce participation data are often collected with a gender field but not routinely reported that way. For example, the California School Dashboard reports high school graduation rates across socioeconomic status and race. The rates by gender do exist in the underlying data, they are just not reported the same way.
The starting point is a simple audit: for each major state agency, what data is already published with a gender breakdown, where is gender data collected but not reported, and where is gender data not collected? Every state we’ve looked at has a version of this pattern: strong gender disaggregation in some domains (like Virginia’s health dashboards or the Illinois Report Card) and almost none in others (including public benefit usage, parental leave, education discipline data, CTE, and workforce programs).
Table 2
This list is not exhaustive. Add or subtract measures that reflect your state’s immediate concerns and your unique data assets.
A note on paid leave: For the states (plus Washington, D.C.) with paid family leave programs, take-up data by gender is one of the most policy-relevant metrics available. It’s also one of the least consistently reported. How many fathers are filing bonding claims? What’s the average duration of leave taken by men vs. women? If your state has a paid leave program, this should be a priority data ask.
Running the audit brings partners together around a question they may never have asked: “What do we know about boys and men in our state?” In some cases, the process generates more policy traction than the data itself. It creates a shared language and next steps across education, workforce, and health agencies that do not usually coordinate on gender.
When approaching a potential data partner, the request is generally not “give us everything by gender.” It’s “we’ve identified these three or four metrics that we think matter for understanding how boys and men are doing. Can you pull them disaggregated by gender, and ideally by gender and race?” Where state data infrastructure allows, looking at how gender interacts with race and class is particularly valuable. There are meaningful differences by race on various metrics, for example, Hispanic and Black men have lower college attendance rates, and white and Native American men have elevated suicide rates. And outcomes by education vary significantly too.
Play 3: Make it visible: dashboards, reports, and recurring measurement
An internal data pull identifies problems. Publishing the data signals commitment and creates accountability. And if you update it regularly, you can track whether anything is actually improving.
Options, from lowest to highest lift:- Add a men’s wellbeing page to existing websites. This doesn’t require new data collection, just curation and presentation of what already exists. This can be as simple as a dedicated page within a department website that pulls together a few gender-disaggregated statistics, as the CDC has done.
- Publish a state data portrait or report. A one-time report can be a powerful tool for building buy-in and knowledge. This might be produced within government or with local partners – a community foundation, university center, or state policy institute, and cover topics that are important locally. This is what AIBM has done with Boston Indicators, and other organizations in Utah and Indiana have also produced similar reports. The report itself becomes a reference document for local stakeholders and media.
- Build a live dashboard with recurring updates. A commission or task force can make dashboard maintenance part of its annual reporting mandate. Key features of good dashboards: they allow filtering by gender (not just overall or by race alone), include trend data, provide comparison points (state vs. national, male vs. female), and are housed on a main agency page, not buried in a report archive. North Dakota’s Men’s Health Dashboard is a good comprehensive example, along with the Virginia Injury Death Rate dashboard, which displays injury death rates by age, county, race, and sex.
- Legislate a data requirement. Embedding data collection in statute creates durability beyond any single administration. Florida’s HB 7033 mandated a one-time report on educational gaps by gender, which also shared best practices across the state. Virginia’s new Boys and Men Advisory Commission (SB 447) includes annual reporting requirements.
Play 4: Pick your targets and act on them
A dashboard in isolation is just a website, and can be hard to maintain. The point of measuring is to translate that data into action via policy proposals, program redesigns, and budget asks. Once you have baseline data, the next step is to narrow your focus to a few metrics where the data is compelling, the issue aligns with state priorities, and there’s a plausible policy or programmatic lever. Then track as you go.
Some examples of how metrics connect to action:
Getting started
States, cities, and organizations are all at different stages in addressing these challenges. The plays above can be combined in different sequences depending on where you have traction:
If you’re just starting the conversation: Start with Play 1. A baseline fact sheet gives stakeholders something concrete to react to. “Our male suicide rate is 40% above the national average” opens doors that “we should pay more attention to boys and men” does not.
If you have executive or institutional buy-in: Combine Plays 1 and 2. Use the baseline data to frame the conversation, then run the agency audit to identify what’s already available and where the gaps are. Michigan, California, and Virginia have each done this differently ranging from specific asks to broad license to try to figure out what’s happening with men. In each case, the mandate created the space and the data gave agencies something concrete to focus on.
If you’re building a commission or advisory body: All four plays, sequenced over several months. The baseline data anchors your first meeting. The agency audit becomes an early deliverable and gives members something to do besides attend meetings. The dashboard or report makes the work visible. And picking two or three targets to act on will help bridge into action. However, you don’t need to overturn every data rock to start strategizing and acting, if you find something that feels like a problem – start to think of how to tackle it.
If you’re an agency head or program director: You may not need all four plays. The audit framework in Play 2 can be applied to a single agency. What data do you collect that could be disaggregated by gender? Can you add gender as a filter to existing dashboards? There may be a lot you can do on your own that may then inspire others.
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