When I became an executive director a colleague reached out to invite me to join the Asian Pacific Directors Coalition (APDC). I hadn’t really done a lot of work with the Asian community so it was a new experience to be surrounded by so many Asian leaders, many of whom had paved the way for me to be in my present job. At one meeting a colleague said “too often Asians are left out of conversations around race.”

At the recent Oscars the hashtag #OscarsSoWhite was trending because of the lack of diversity and several skits projecting stereotypes of Asians. Many in the Asian community hoped with the recent Supreme Court vacancy an Asian would be named to further diversify the court. Asians in the Seattle Metro Area are the second largest minority group and it continues to grow — this trend probably holds true in other parts of the United States, which means we are here and we cannot be made invisible.

Other racial groups are sometimes left out of data presentations, but for the purposes of this blog post I’m focusing on Asians because I am Asian and can speak to my Asian experience. I hope others will choose to share their perspectives as well, please email fakequity@gmail.com if you are willing to share.

“Hey, I’m missing from that chart.”
Recently I was looking at an education report, pretty standard stuff – charts on graduation rates, kindergarten readiness, etc. Where it got interesting was the chart labeled “opportunity gap.” I paused and started reading more carefully, but couldn’t make sense of the chart or the table. My colleague and I puzzled over it until we read the footnote and it became real. In the chart Asians were grouped with Whites in order to present their opportunity gap data. We went from “no way, they didn’t do that…” to “oh, shit they did…”

Several weeks later, in another meeting (for a separate organization) several charts were passed around to demonstrate how students of color are doing academically and where the ranking of the schools where students of color attend. These charts were much simpler to read so it took me only a few seconds to zoom in on the bar line labeled “White or Asian.” I could feel my blood pressure rising and the facilitator could see I was getting agitated. She graciously came over and asked what was going on. When the group reconvened I ‘soapboxed’ and passionately explained why grouping Asians with Whites is a bad practice. Several in the room nodded their heads, while others stared blankly or their eyes glazed over with confusion.

Invisible Asians — Can you see me?
Many Asians, myself included, receive the benefits passed on to us with Asian privilege. For the most part I don’t worry about safety and I’m not treated differently because of language or faith beliefs. That said I can’t ‘turn off’ how I look, or control how people perceive me because of my Asian background. There are still systemic and institutional barriers holding many Asians back. Disaggregated Asian data shows many Asians are still struggling.

Data is an important way to demonstrate the disparities that continue to exist for Asians. Data can either be used for good or as my colleague Dr. Jondou Chen describes as ‘weaponizing’ data against communities of color. Grouping Asians with Whites plays into the myth of the ‘model minority.’ While many Asians are doing well, many others still struggle or have to work twice as hard to find the same gains as our White counterparts.

As an example my organization just completed a big data project. Through our partners, we surveyed over 600 families including many East Asians. Two of the questions asked were:

• “How often have you received positive communication about your child?,” and
• “How often have you received negative communication about your child?”

When we looked at this question broken out by race, Asian families reported receiving more negative communications about their child than positive. Could this be because of the ‘myth’ that Asian students are expected to do well in school, or is it because of language barriers, or teacher biases? The data set showed us where we need to dig deeper and examine the systems involved and work with partners to close and improve the gaps. Had Asians been grouped with Whites we wouldn’t have this level of specificity and Asian student’s needs would have been lost.

Within the Asian race category are 48 distinct ethnic groups – each with their own histories and cultures, different languages, and unique migration story. We must honor these legacies and richness in order to understand opportunity and achievement gaps or other gaps in health care, justice systems, etc. At an event hosted by partners in the African American community a gentleman said “I cannot learn your song, until I learn to sing my own.” In this case we cannot expect to close gaps until we understand the Asian experience and recognize the richness and the needs of Asian communities.

Grouping Asian with White people  shifts the burden of closing gaps to Asians rather than identifying factors that continue to marginalize Asian communities. When we look at disaggregated data, such as this chart from the Washington State Commission on Asian Pacific American Affairs (CAPAA) we can clearly see not all Asians are academically achieving at high rates. If Asians were grouped with Whites this data would be invisible and we would continue to wonder and at worst blame Asians for not academically achieving. With this data we can begin to look more closely at the systems holding Asians back.

Making Asians and People of Color Visible
We need to adopt practices that intentionally makes visible Communities of Color. We need to ensure Communities of Color own and have a say in how data is presented and used, here are some suggestions:

• At a minimum stop grouping Asians with Whites, or other combinations. Disaggregated data is a best practice.
• Learn about different Asian experiences, recognizing each Asian experience is unique and we need to create space for multiple voices, stories, and truths.
• Listen to and work with communities of color on how data can be used to highlight needs and drive towards problems solving and resource sharing. Data use needs to build trust, not used against people of color.
• Allow communities of color ownership of their own data — only collect data in partnership with communities of color, honor how the communities want to have their data used, check with multiple people from communities on how they are experiencing their data.
• Continuously review data practices using a racial equity lens.

Many of these practices will benefit communities of color overall. We need to stop making communities of color invisible. We need to make visible Asian and people of color experiences and truths, to counter the narratives that play into myths and stereotypes around race. We need to work proactively with communities of color to identify what is working and where policy and community work is needed.

Posted by Erin Okuno, special thanks to Jondou Chen, PhD, James Hong, MEd, and Heidi Schillinger, MSW for background material and thought partnership.