A few weeks ago I was helping out with an in-service Pika CMS training for one of our special programs, and the experience was a sharp reminder of what has surfaced at all such trainings in the past, namely, how frequently and persistently all of us (myself included) misunderstand, misinterpret or are simply confused about the categories commonly used for tracking client race and ethnicity. I have written earlier about the threshold problem of client self-identification and how the data you get is directly affected by how it is solicited; and the related need to re-calibrate the institutional view of race and ethnicity data to conform to Federal funding requirements, to assure the data categories are understood as they are by the Census Bureau. The following is not an apologia on behalf of the Census categories. It is more a statement about the realities that intrude on organizations that select to track racial and ethnic data based on the client’s self-identification rather than the organization or its funding sources defining it.
Here’s what happened at the recent training:
It was my responsibility during the training to explain why LSNC had changed how it records race and ethnicity. The brief history lesson for the trainees was that the Pika defaults, like our prior case management system, were configured to track client race data using four long-used specific race categories (white, black, native american, asian/pacific islander), plus two additional generic catch-alls (multi-racial and other). I explained that, while these categories are still commonly used, even if incorrectly by some federal funding sources, they are not properly calibrated to conform to current Census race categories that have been in place since the 2000 Census. Among other things, the Census Bureau dropped “multi-racial” as a survey selected category years ago, instead opting to allow the individual to self-identify with one or more of the six Census-defined racial categories. I also explained that the Census Bureau categories include an all encompassing demographic view of the world such that all persons are either Hispanic/Latino or non-Hispanic/non-Latino. It took a few moments for the gathered crowd to wrap its collective head around these concepts, but at this initial level of abstraction no one voiced any concerns or confusion. They understood the basic but familiar racial categories, even if the particular labels had changed slightly, and the dichotomy of Hispanic/non-Hispanic made sense.
So far, so good. Then I walked them through how LSNC had redesigned the fields for tracking this information, with the initial question to the client being broader, i.e., whether he or she identifies as Hispanic or Non-Hispanic, followed by a more specific racial identity question, relying on the six basic Census categories:
OK, still so far, so good. But then we all danced our way, at times somewhat gingerly, through LSNC’s Pika CMS Race and Ethnicity Quiz-o-rama! Admittedly, several of the quiz questions are intentionally tricky, to challenge folks about their perceptions. And there was the rub. Without getting too much into particulars, several staffers were startled to confront what they assumed incorrectly (in terms of Census definitions) to be the racial or ethnic identity of some of their clients. And some were visibly uncomfortable with how some clients may self-identify. For example, for some it was news that some Latinos identify as “white.” Or that clients from two bordering countries like Iraq and Iran may or may not self-identify as “white” or “asian” or both, or even something else (“other”). And then there were the inevitable funding-driven concerns, along these lines: “Wait, you mean if a client from Morocco identifies herself as being Hispanic and/or white, we can’t record the person as African-American? But our funding source really wants to see that we’re serving Blacks/African Americans.” I answered, “Not if the client does not self-identify that way.” Not a popular answer, with some. And an instructive example of how organizations are prompted to perceive clients within categories driven, if not explicitly defined, by a funding source.
The problems with tracking racial data are compounded by the failure of many funding sources to catch-up with the prevailing Census race and ethnicity categories. I use here an example familiar to LSC-funded programs, but not to single out LSC. It does what a lot of funding sources currently do, including the California IOLTA program. But it is a good example of how race and ethnicity data can get muddled by confusing Hispanic ethnicity with the basic racial categories, however labeled.
Take a look at this example of the LSC Form G-4(a). Every LSC-funded field program has to fill this form out annually:
The problem with this form is that it demands totals of data categories that are of a different type, i.e., it lumps “Hispanic” ethnicity in with all the other categories which are racial, as the terms are understood by the Census Bureau. Tallied in this fashion, one cannot possibly get a statistically accurate total. Why? Because even if the first two categories (white and black) excluded those who identify as Hispanic, the three other racial groups (native american, asian or pacific islander and other) do not. And those groups are not Hispanic because…? Who says so? And if you say they may be, then if you total the “Hispanic origin” data with these three same groups, you are double-counting, no?
How should this been done properly? Take a look at HUD’s Race and Ethnic Data Collection Form (HUD Form 27061):
The difference here is that the form asks for numbers that correspond to the basic Census racial categories (or understandable but consistent variations of them) in the second column, but then parses out each of those rows with a third column to identify how many of each presents Hispanic/Latino ethnicity. Easier to understand. Consistent with the Census definitions and demographic data. And statistically accurate.
It is precisely that type of argument I am now making to varied funding sources, including most of our seniors funding grantors, to resolve inconsistencies between us and them and among themselves about the race and ethnicity data they demand.
