About This Case Study

The carceral system is enormous, but it is also, in a sense, invisible: the people held in it are dispersed across thousands of separate jails, prisons, and detention centers run by different jurisdictions that count and report in different ways. The system becomes legible at all only through data — through the work of assembling those scattered counts into a picture of scale, trend, and racial composition. This case study puts you inside that work. You will use two of the most widely relied-upon public data resources on American incarceration: the Vera Institute's Incarceration Trends, which compiles jail and prison figures for counties across the United States with demographic breakdowns where the underlying data allow, and the Prison Policy Initiative's Mass Incarceration: The Whole Pie, which consolidates the entire system — prisons, jails, detention, and more — into a single national portrait.

The exercise asks you to do two things in sequence. First, to profile one county or state — to describe its incarceration trend over time and the racial composition of the people it confines, working from the numbers themselves rather than from impression. Second, to audit those same numbers — to ask not only what they show but what they fail to show. Here the frame comes from Linny Kit Tong Ng, who documents that incarcerated Latinas are systematically undercounted: because many data systems record Latino or Latina identity inconsistently, often folding it into "white" or "other," a fast-growing population is rendered statistically faint or invisible. Ng's larger point is that an undercount is not a neutral gap. When a group is misclassified out of the data, its situation becomes hard to document, fund, or remedy: a failure of data becomes a failure of justice. The analytical move is to treat the categories a dataset uses not as a neutral container but as itself a racial decision — to ask what gets counted, how, and who disappears.

Before You Begin

Have ready:

• Vera Institute of Justice, Incarceration Trends — the dataset and county/state fact sheets (github.com/vera-institute/incarceration-trends, with accompanying fact sheets at vera.org) — the main data source.
• Prison Policy Initiative, "Mass Incarceration: The Whole Pie" — for the national picture and its racial and offense breakdowns.
• A note-taking surface, and a spreadsheet if you want to chart a trend — paper, a document, or a shared doc if you are working in a group.

A note on currency: both resources are updated over time, and the most recent figures will differ from earlier versions. Use the current data each resource presents, and record the year of any figure you cite so your profile is anchored in time.

Before you open the data, write down, in one or two sentences each:

• If someone asked you how many people are incarcerated in your county or state, and who they are, how would you find out — and how confident would you be in the answer?
• What would it mean for a count to be wrong not at random, but in a patterned way — undercounting some groups more than others? Who would be harmed, and how?

The Exercise

Phase 1: Orientation and Choosing a Jurisdiction (5–7 minutes)

Open the Vera Incarceration Trends resource. Spend a few minutes getting a sense of its shape: it lets you look at jail and prison incarceration for individual counties and states, track figures over time, and break them down by race and ethnicity where the data are available. Note what variables it offers and over what span of years.

Then choose one jurisdiction to profile. If you are not sure where to start, Bronx County, New York is the natural local choice; any county or state you have a connection to works as well. Larger and more populous jurisdictions tend to have richer data; very small counties may have sparse or suppressed figures. Record your choice and why you made it.

For groups: each member (or pair) takes a different jurisdiction, so the group can compare profiles later. Decide who takes what now.

Phase 2: Profiling the Jurisdiction (12–15 minutes)

Build a profile of your jurisdiction from the data. Pull specific figures and record the year for each; the aim is a precise portrait, not a general impression. Work through:

1. Scale. How many people are incarcerated in your jurisdiction, in jail and (where available) in prison? What is the incarceration rate — the number per 100,000 residents — and how does it compare to the national rate the Whole Pie reports?
2. Trend. How has incarceration in your jurisdiction changed over the span the data cover? Identify the shape of the trend — rising, falling, peaking and declining — and note any sharp turns and roughly when they occur.
3. Racial composition. Using the race and ethnicity breakdowns, describe who is incarcerated in your jurisdiction relative to the demographics of its overall population. Where are particular groups over- or under-represented? Pull the actual figures rather than describing them in general terms.

You will not have complete data on every point, and some jurisdictions are documented more fully than others. Note where figures are missing, suppressed, or ambiguous; those gaps are themselves data, and Phase 3 returns to them.

For groups: each member builds their own profile in this phase. Do not compare yet.

Phase 3: Auditing the Data (10–12 minutes)

Now turn from what the numbers show to what they leave out. Ng's argument, in compressed form: incarcerated Latinas are systematically undercounted because the systems that produce incarceration data record ethnicity inconsistently — Latino and Latina identity is often not captured as a distinct category, so people are absorbed into "white," "Black," or "other." The result is a population that grows rapidly while remaining statistically faint, and a count that cannot support the advocacy, funding, or policy that an accurate count would.

Hold your jurisdiction's data against this argument and work through:

• What the categories capture well. Which groups does your dataset count clearly and consistently? Where is the racial breakdown detailed and where does it inspire confidence?
• Where people disappear. Look specifically for the gaps Ng describes and others like them. How is Latino or Latina identity recorded in your data — as its own category, folded into another, or not at all? What about Indigenous people, people of Asian or Pacific Islander descent, or people of more than one race? Where in your profile would a person of one of these groups become hard to see?
• From data failure to justice failure. Take the most significant gap you found and follow it forward. If this group is undercounted, what becomes impossible to know about them? What advocacy, funding, or reform becomes harder to pursue? Make Ng's claim — that a data failure becomes a justice failure — concrete for your jurisdiction.

For groups: spend the first half comparing profiles across jurisdictions — do the same gaps recur everywhere, or does coverage differ by place? Spend the second half on the third question together.

Closing Reflection

In two or three sentences, complete this thought:

The incarceration data for the jurisdiction I profiled show __________. What the data cannot show — who disappears into its categories — is __________. The way a failure of counting becomes a failure of justice, in this case, is __________.

Write the most precise version you can given what you have just found. The value of the sentence is in the specificity of what you name — and in what you find the data unable to hold.

A Note on Modes

Solo mode. Work through the three phases in order, keeping your figures and their years in brief notes. The profile from Phase 2 and the audit from Phase 3 are the central artifacts; keep them for use with the unit's questions on Ng and on incarceration data.

Group mode (3–6 people). Designate a timekeeper. Members profile different jurisdictions individually in Phases 1 and 2, then compare in Phase 3 — the comparison across places is where the patterned nature of the gaps becomes visible. If group time is short, the closing reflection can be assigned as individual writing after the session.

After the Case Study

• The full Vera Incarceration Trends dataset can be downloaded for closer analysis; working with it directly — charting a county's trend, or comparing several counties — lets you ask quantitative questions a single fact sheet cannot answer.
• The Whole Pie's methodology section explains why a single national count is so difficult to produce, which connects this exercise to the unit's larger argument that what gets counted, and how, is itself a matter of racial politics.
• For the deep history of how crime and punishment statistics were racialized in the first place, see Khalil Gibran Muhammad, The Condemnation of Blackness (rev. ed., Harvard University Press, 2019), in the unit's Further Readings; it shows that the questions you raised here about who counts, and how, have a century-long history.