There were too many discussions circling around this central one to fit into one blog, so i decided to collect the extra tangents and related threads here in a follow-up.

The last article went out late at night and the last edits didn’t save before i pressed send. If the opening of my last email read like my cat walked across the keyboard—my apologies! The clean, readable version is live on the site now.

Here are a few “micro-essays” around “race realism”.

• Is there such a thing as race?

• Overlapping variance: the typical difference between individuals in the same ethnic group are far larger than the average between group difference

• Heritability (how within group heritability does not imply between group heritability)

• The Flynn Effect (how changing IQ over time demonstrates the strength of environmental effects on cognition)

• Population mixing (and its effect on reducing difference between groups)

• Evolution “above the neck” and the Parsimony Objection: a red herring

• What about Neanderthal genes?

I also add an appendix of further quotes from race realists, presenting their ideas in their own words.

Is there such a thing as race?

Race sits in an odd scientific position: it’s not a coherent biological taxonomic category, but it’s also not simply an illusion detached from any underlying reality — both things can be true at once.

The discussion can be confusing. In our day to day life in the west, we have no problem visually sorting people into races, and we will usually agree. We also know that we can do this because of where people’s ancestors come from and that the collections of physical traits tend to be hereditary. When some people in academia say that there is no such thing as race or that it is purely a “social construct”, that feels intuitively crazy… we know there is biology at play. We can argue about what goes along with that, but the truth of that feels intuitively obvious. We also have no problem conceptualizing “race mixing” or blended boundary cases between iconic examples, so that feels like some obtuse intellectual evasion to say that this nullifies the idea of races with heritable genetic traits.

I think we can all agree that it isn’t “exactly” right. From this starting place, i think we can shift the conversation from “is there sich a thing as race” to “when is the idea of race useful for understanding the world and when does a different way of looking at it (another “model”), help us understand the world better?”

There are several major problems with “race” in the context of evolutionary arguments. It does map loosely onto a specific kind of genetic clustering1. the problem is that the kinds of functional genes (genes that do something important) that are used in evolutionary models do not cluster along the same lines as race.

It helps to lay out three competing ways of thinking about human diversity.

• Model A: the egalitarian view - no subdivisions of humanity capture meaningful gene-based differences. We are all effectively pulled from the same random distribution of potentials

• Model B: the race view - social race labels track important functional genetic differences

• Model C: complex gene clustering view: genetic clustering of functional traits is complex and tied to specific historical environments and subsistence patterns in ways that vary dramatically by gene/trait, that do not line up with race categories

All models are simplifications of a complex reality, and as with any model comparison, the right one isn’t just whichever predicts best — ease of understanding matters too, which is part of why Model B’s simplicity keeps it in circulation despite its poor fit with the underlying biology and the fact that Model C is always a better fit. It’s also why Model A is a policy default. Even if the egalitarian view is not as precisely accurate as the race view, the race view quickly invites a decay into empirically ungrounded (inaccurate) racist assertions that are even further from the truth. This is exemplified when race realist researchers claim to have a basis for policy recommendations. While Model B might, properly applied with the data available, theoretically predict small, uncertain non-policy relevant variance in traits in across social race group labels, it invites the racist to use it in motivated reasoning to assert large, certain, policy relevant variance. It invites “scientistic” legitimization via warped application. This is largely why Woodley of Menie was suspended from Vrije Universiteit Brussel in June 2022 as his distorted misuse of theory was cited in the manifesto of the Buffalo supermarket shooter (Payton Gendron, who killed 10 Black people in a racist terrorist attack on May 14, 2022).

Model C is objectively the more accurate model, and is the consensus default for most empirical academics studying human evolution. It’s also more cumbersome to discuss and involves concepts that not everyone can follow without substantial time investment — which is part of why the marginal benefit of using it over Model A or B often isn’t enough to justify the cost in a given policy discussion. Which model to use depends on context, and determining that needs principles and care. Does the actual (usually small) effect size identified with the model imply a large enough policy consequence to warrant the extra energy and time needed to use it in policy discussions.

What does the data actually say relative to these models?

Part of the problem with “race” is that socially defined racial categories — the “Black,” “White,” “Asian,” “Hispanic” labels used in census data — treat enormous, genetically diverse continental populations as single biological units. Long and Kittles (2003) showed this directly using global allele-frequency data: the genetic lineages found in any non-African population are typically a subset of the much deeper and more diverse lineages found within Africa itself, since anatomically modern humans spent far longer diversifying within Africa before a relatively small subset migrated out. One of their most-cited results is that a San individual from Southern Africa and a Yoruba individual from West Africa are more genetically distant from each other than a European is from an East Asian — despite the fact that conventional racial schemes group the San and Yoruba together as “Black” while separating Europeans and East Asians into different categories. Long and Kittles used this to argue explicitly that biological races, in the classical taxonomic sense of clearly bounded, minimally-overlapping subspecies, do not exist in humans; what exists instead is a single, deeply nested, and predominantly African tree of lineages, with non-African populations representing a small, derived branch of it — hence the title of their highly cited paper, Human genetic diversity and the nonexistence of biological races.

Racial categories are unstable not just biologically but socially as labels. Campbell and Troyer (2007) examined how consistently individuals are racially classified across different observers and contexts — self-identification, interviewer classification, and others’ perception. While the overall finding was of consistency across different people classifying race, they also found substantial inconsistency: a meaningful share of people are classified differently depending on who is doing the classifying and by what method. This matters for the biological debate because it shows that even the social application of racial categories — which hereditarian arguments implicitly treat as a good proxy for ancestry — doesn’t hold still long enough to be a reliable stand-in for underlying genetic reality in many discussions or when talking about individuals.

The most important point from the perspective of looking at functional differences is that clustering of functional genes does not line up with racial categories. The genetic-ancestry side of the picture is often anchored to Rosenberg et al.’s 2002 clustering study, which analyzed genotypes from over 1,000 individuals across 52 populations worldwide and found that a clustering algorithm (STRUCTURE), given no information about sampling location, could sort individuals into groups that corresponded fairly well to major geographic regions — sub-Saharan Africa, Europe/Middle East/Central Asia, East Asia, Oceania, and the Americas. This is the result most often invoked as evidence that “race” corresponds to something real in the genome. ‘two problems with this: instability of clustering by analytic choice and different clustering patterns for different genes.

Templeton (2013) examined this kind of clustering evidence against the formal criteria evolutionary biologists actually use to define subspecies or races in other animals, and found humans fail those criteria: genetic differentiation between human groups is far too small and too clinal relative to the standard biologists apply elsewhere in taxonomy. Templeton also highlighted a methodological point critics raise about Rosenberg-style analyses more generally — the resulting “clusters” are sensitive to which populations are sampled and to the number of clusters (K) the researcher asks the algorithm to find. Choose a different reference set, or a different K, and different clusters emerge, which complicates the claim that these are objectively discovered natural categories rather than partly an artifact of study design.

The more accurate description, compatible with both Rosenberg’s clustering result and its critics — and consistent with Long and Kittles’ deeper point about nested African lineages — is that human populations form statistically distinguishable clusters with fuzzy, overlapping boundaries, sitting atop genetic variation that is fundamentally clinal, changing gradually across geography rather than in sharp jumps.

Functional genes

This becomes especially clear once you look at individual functional traits rather than aggregate ancestry. “Functional” genes are ones that do something useful for us. Most genes are functionless “marker” genes. Functional genes are the ones subject to directional natural selection. Others change randomly … through “drift”.

Functional genes will cluster in patterns caused by the specific environments generating those selection forces, not continental lines. Malaria resistance clusters around historically malarial regions like the Mediterranean and West Africa, cutting across conventional race lines. Amylase gene copy number, which affects starch digestion, tracks historical reliance on agriculture rather than racial ancestry. Blue eye color clusters through Central Asia while skipping large parts of Europe, again ignoring continental racial boundaries. This happens because Rosenberg-style clusters emerge from aggregating many neutral genetic markers genome-wide, which recovers broad ancestry patterns mainly through isolation-by-distance and historical migration bottlenecks — while individual functional traits are usually shaped by local selective pressures like diet, pathogens, altitude, or UV exposure that don’t respect those same population boundaries. This is especially true for genes with strong selection pressures, which are the central feature of race realist just-so stories.

So, as an example, above are photos of people from 4 local ethnic groups. We socially in the West would label the two on the right “Black” and the two on the right “white”, and we would do this consistently (what we call “high inter-rater reliability”). However, what happens when we look at a functional gene that clusters around historical selection pressures? Amylase allele copies are functionally important for digestion of grains, something that one would assume very large selection pressure for. Now, who has higher amylase allele copy number and who lower? The two above (Gambian and Finnish) have lower. The two underneath (San and Sardinian) have higher. The clustering of this functionally important genetic variation does not follow the western social category of “race”. If you look at different genes for different functional variants, they will cluster along yet different lines. To make things more complicated still, the function of a gene is just one reason why it might spread or disappear, in balance with demography.

Dropping for a moment the ungrounded “just-so” nature of the story, the popular “cold winters” hereditarian story about race and cognition, if it reflected a real selective history at all, would only illustrate this same point: local selection driven by climate, mapping onto specific local geography, not something that maps onto racial categories and continents as such.

Overlapping variance: within vs. between population variance in genetics

In 1972, Richard Lewontin studied blood and protein markers across human populations and found that about 85–90% of human genetic variation exists within any single population — two people from the same region are likely to be almost as genetically different from each other as two people from opposite sides of the world. Only 5–10% of variation shows up between populations in the same region, and just 6–10% distinguishes the broad continental groups people call “races.” This pattern has held up consistently in later, much larger genetic studies.

In 2003, A.W.F. Edwards pushed back, arguing this doesn’t mean genetic ancestry is undetectable — looking at one marker at a time hides patterns that show up once you look at many markers together. Rosenberg et al.’s 2002 study proved this in practice: computer clustering using hundreds of markers at once could sort people into groups matching continental ancestry fairly reliably, even though no single marker could do that alone. (This aligns with the intuition we have from our relatively well shared ability to identify “races” visually.)

A 2007 study by Witherspoon and colleagues showed both ideas are true at once: individuals from different continents are often genetically about as similar as two people from the same place, yet a computer using enough markers together can still guess someone’s ancestry accurately. These aren’t contradictions — similarity between individuals and the ability to classify populations are just different questions.

This matters because it shows race is a very rough, low-resolution label for genetic ancestry — real, detectable in aggregate, but capturing only a small sliver of total human variation. It also means you can’t assume traits differ between racial groups the way ancestry clusters do. Most traits, especially complex ones like disease risk or cognitive ability, are shaped by many genes interacting with environment and demographic history, and their distribution often doesn’t line up with the same geographic clusters ancestry markers reveal, as pointed out in the last section. So while ancestry can be statistically inferred from enough genetic data, that doesn’t tell you how a specific trait varies between groups — that requires separate, direct evidence for each trait rather than an assumption drawn from race classification. If clustering is revealed in a specific trait, there’s no good reason to assume a priori that those cluster lines won’t cross race lines.

Heritability

Heritability measures how much of trait variation is due to genetic differences, given that population’s actual environment. However, if one finds substantial heritability within a specific group in a specific environment, that doesn’t explain why two different populations might differ in their averages. Heritability found in studies within groups doesn’t imply that the difference between two groups is caused by genetics. Lewontin’s seed-corn analogy makes this clear: the cause of height variation within each of two plots of corn can be 100% genetic, while the average difference between plots is entirely due to soil quality. You can have the same exact genetic mix in the two plots, and the two plots will differ because of soil quality. That’s because heritability is specific to a given population and environment, not a fixed trait property, and inter-group differences can change if conditions change even with no genetic shift. To take this further, if you have two groups of corn seeds and one has a superior genetic mix but is put in particularly bad soil type, this group will do worse than the one with worse genetics. What this means for the race realism inter-racial cognition comparison is that even though black Americans score worse on average on IQ tests and we know there is genetic heritability in IQ test scoring, we can not rule out that black Americans are genetically pre-disposed to be higher IQ than whites, given how racism and the economic inequities inherited from historical racism give black Americans on average a more stressful environment in which to grow up and develop. Such intergroup comparisons tell us nothing about intergroup genetic differences, given the impossibility of controlling all environmental effects, even in cross-race adoption studies (see previous blog).

Applying within-group heritability to explain between-group gaps is a well-known inferential error that no academic studying the subject should be making. This matters for traits like cognitive ability, where some within-group heritability is well established but historical, economic, and educational differences between populations are strong, documented confounds. Group differences require direct causal evidence, not inference from within-group heritability numbers2 . Denying this well known inferential error in the context of arguing for racist-looking policy starts to look like the person making the argument is racist.

The Flynn Effect

The Flynn Effect, documented by James Flynn in the 1980s, refers to the substantial generational rise in average IQ scores seen across nearly every country with adequate data. Full-scale IQ scores rose roughly three points per decade across the twentieth century, accumulating to about thirty points over a century.3 Gains have also been stronger in middle-income countries than wealthy ones, and some wealthy nations have seen the effect stall or reverse recently.

This matters for hereditarian claims about race and cognition because it shows average IQ can shift dramatically — often more than the gaps hereditarians attribute to genetics — within a single population over one or two generations, with no genetic change involved. If purely environmental factors (nutrition, schooling, test familiarity, modernization) can move scores by 30 points, a smaller between-group gap can’t be assumed genetic by default. This is the within/between heritability problem again: documented environmental trends within groups undercut the assumption that group gaps must reflect underlying genetic differences. Flynn used this logic to argue that historical Black-white IQ gaps are better explained environmentally than genetically. Again, denying this while arguing for racist-looking policy or for factual claims that support it looks pretty racist.

Population mixing

One further piece explains why between-group variance stays low: ongoing gene flow. Human populations have never been fully isolated; migration via trade, conquest, and intermarriage has been continuous throughout history, and population genetics shows that even minimal migration (as few as one migrant per generation) is enough to prevent genetic divergence via drift between neighboring groups. This is also why functional adaptations rarely stay confined to one population for long — once a beneficial variant like lactase persistence or malaria resistance arises, migration and admixture tend to spread it beyond its origin through any local populations where it would be of benefit, which is part of why single-trait functional gene clusters cut across the broader racial/ancestry clusters discussed above. In short, both insufficient time and insufficient isolation keep human between-group genetic variance far below what’s seen in species with genuine long-term reproductive barriers.

Evolution “above the neck” and the Parsimony Objection

Even as the clustering of functional traits does not line up with race categories, the structure of that clustering still does sometimes imply a statistical difference in the functional genes between race groups. For example, this may be because the cluster is entirely a subset of one race group or the largest clusters of a gene are within one race group. Genetic evolution has thus been observed to create statistical differences in functional genes between race groups for physical features.

However race realists assert that ideologically motivated genetic researchers claim arbitrarily, by fiat that evolution “stops at the neck”… that evolution simply does not act on the brain or cognition. Race realism claims to challenge this ideological assertion.

Somewhat in parallel to this is the “parsimony objection” to race realism — the assertion that environmental causes have been shown to be fully capable of explaining existing variance in cognition between race groups, and that asserting a further genetic cause doesn’t explain anything additional and should therefore be rejected in favor of the simpler (more parsimonious) explanation of purely environmental effects. This complaint is valid to a point — there’s no a priori reason there shouldn’t be selection on cognition4.

However, what race realists refuse to acknowledge in such moments is that their claims have been rejected on other grounds, not on the basis of a fiat rule against the evolution of cognition. This is a bit of a red herring.

There are good reasons to think that evolution of cognition will be rarer. Given the delicacy of biological mechanisms of cognition, we document that areas of the human genome governing cognition are incredibly well preserved, showing much less signs of recent evolutionary change than other parts of the genome. Mutations in these parts of the genome have a much higher rate of being lethal or highly deleterious. Still, this is not a serious argument that there is no evolution on cognition. the rejection of race realism is on other grounds.

What about Neanderthal genes?

The admixture of Homo sapiens with Neanderthals out of Africa, and the legacy that leaves in our non-African genome, is a fascinating topic — and also something that has become a bit of a fetish among race realists trying to fit it into the cold-weather cognitive selection just-so story. Here’s a short, big-picture breakdown of what we know and what we don’t.

100,000 years ago, the planet had multiple subspecies of humans — real “races”/“subspecies” in the way we think about them in biology, not just local subpopulations with slight genetic variance. Very roughly speaking, the genetic variance between modern human “races” (mostly an accounting fiction) is about one-sixth of the variance between modern humans and Neanderthals or Denisovans (more like actual races or subspecies as biologists think of them).

Homo sapiens had been wandering occasionally out of Africa for a while, but about 70,000 to 50,000 years ago a bigger and more effective out-migration began. As they spread through West Asia, then East Asia and Europe, they encountered Neanderthals, and human things happened across many thousands of years of contact — cohabitation, interbreeding, and killing each other. Homo sapiens eventually completely replaced Neanderthals; mostly they were killed off, but to a small degree they became part of us. Approximately 2% of the genes of non-African populations today are Neanderthal in origin; Africans have effectively none. A similar, more localized story exists for Denisovans in East Asia and Oceania (about 0.4% of genes).

The people who migrated out of Africa were a small subpopulation, and their genes didn’t represent the full variance within Africa — remember, the variance between a Yoruba and a San person within Africa is far larger than the variance between a white European and an Asian. That small out-of-Africa subpopulation then mixed with Neanderthals as it spread, and in isolation from Africa, that new genetic mix became subject to further drift and selection, potentially increasing the variance between Africa and out-of-Africa populations further.

There has clearly been selection on the Neanderthal genes we inherited. While most have simply been subject to random drift, some areas of the genome have been purged of Neanderthal content, indicating the out-of-Africa variants were substantially better for survival; other Neanderthal genes persist at much higher frequency than chance would predict, showing signs of strong recent selection.

The race-realist story suggests some of these functional Neanderthal genes affected cognition and improved it — an attractive proposal, if you happen to be vain, non-African, and racist. An equally plausible alternative story: Neanderthal genes were bad for cognition, and mixing populations have had to go through an ongoing, possibly still-incomplete recovery process to purge the cognition-related ones. This is the trouble with just-so stories — a creative person can make up a plausible-sounding one for any conclusion.

So what have we actually found? Many Neanderthal genes are substantially enhanced in out-of-Africa humans and show signs of recent strong selection — examples include keratin and skin-pigment genes, immune-system genes (thought to matter for local disease resistance), and lipid-metabolism genes (important for cold-weather metabolism, particularly elevated in Europeans)5. There are also large gene regions sparse in Neanderthal ancestry, where Neanderthal variants have been purged from the modern non-African genome; these regions are consistently enriched for genes highly expressed in the brain and testes, and the surviving Neanderthal alleles there show signatures of purifying selection — usually interpreted as evidence that many Neanderthal variants affecting neurodevelopment and male fertility were deleterious 6.

We have also identified specific Neanderthal genes affecting neurology, linked to small shifts in cognition or mental health — sleep/chronotype, mood, smoking status, possibly pain sensitivity — but for most of these there’s no strong, consistent evidence of positive selection; they look more like standing variation with small effects 7. The exception is a Neanderthal chronotype gene, found in 20% to 55% of out-of-Africa populations (versus 2% for neutral Neanderthal variance) and showing some of the strongest selection signals of any Neanderthal gene, likely tied to seasonal light-duration shifts outside Africa. Its individual-level effect size on sleep pattern, though, is small — far smaller than standing variance between individuals, with differences showing up only in aggregate.

Where does that leave us? The investigation is still young, but so far Neanderthal admixture clearly introduced functionally different genes, some selected for and others against. On cognition, neurology, and behavior specifically, many Neanderthal genes appear to have been bad for survival and selected out over time; the genes we’ve found affecting brain function mostly haven’t shown a strong enough effect to produce observable selection, with the one exception — the sleep-timing gene — having an effect that’s unobservable at the individual level. Most Neanderthal alleles affecting complex traits, including cognition, are weakly deleterious or effectively neutral, with a vey small minority beneficial 8. Finding an individual Neanderthal gene with a large positive effect on cognition that’s been under strong positive selection is, at this point, incredibly doubtful, given how thoroughly these searches have been conducted. What remains possible are cumulative effects of many small, individually weakly-selected polygenic variants — but most genome regions affecting cognition, again, are under heavy purifying selection, meaning even small tweaks are usually catastrophic. And being weakly selected, we have no a priori prediction to make about what direction any theoretical difference might be.

Circling back to race realism: there’s a profound lack of evidence to support the hypothesis that our out-of-Africa environment produced significant selection pressure for improved-cognition genes acting on our Neanderthal genetic heritage. If there’s an effect at all, we should expect it to be undetectable at the individual level, showing no measurable selection signal, and if anything marginally negative rather than positive.

Appendix: additional race-realist quotes

The main pieces (this one and its companion) each keep only a few representative quotes from race-realist writers, to avoid piling on. For readers who want a fuller sense of how the field talks about itself in its own words, here’s a longer collection.

Notes

This is part 1 of a 2 part article on and around “race realism” the racist pseudoscience of Nathan Cofnas and others that surfaces in the Cofnas/Arday story. Click here for part 2: “Is There Such a Thing as Race? Population Genetics, Heritability, and Neanderthal Admixture”

Jason Arday, Cambridge’s youngest Black professor, came under academic investigation over copying the work of others without citation and inaccuracies on his CV and resigned before it concluded. He was reported dead shortly after, in a death widely assumed, though not officially confirmed, to be suicide. Blame for his suicide is being hotly directed in multiple directions: some accuse those who brought the allegations of racially motivated harassment; others fault the institutions and specific colleagues who elevated Arday without scrutinizing his record, leaving him exposed once real scrutiny arrived. Nathan Cofnas, central to exposing him, has since been precautionarily suspended by Ghent University over claims his conduct was racially targeted. His defenders say he was applying standards Arday’s work didn’t meet, and that DEI pressure was why no one had applied them sooner. His detractors call him a racist.

Cofnas has been a postdoctoral researcher on what advocates call “race realism” or “hereditarian” ideas of racial hierarchy. As Cofnas describes, it is the view that “racial categories reflect biological realities rather than arbitrary social constructs” and that “genetic differences between races play a role in producing observed group differences in cognitive ability and other traits.”1 More specifically, race realists would argue that whites and Asians are more genetically predisposed to developing higher cognitive abilities than Black Africans and their descendants. Applied to policy, this is used as part of arguments against DEI initiatives in academia, on the reasoning that Black people will always be at a disadvantage academically and that forcing representative numbers of Black academics will inevitably mean underperforming hires and unfair discrimination against more capable white candidates2.

Cofnas’ suspension comes only four years after the suspension of fellow race realist Michael A. Woodley of Menie from Vrije Universiteit Brussel, after his writings were cited in the manifesto of a racist mass murderer in the US in 2022.

A short list of the main problems with the race realist research program are

• Race categories do not line up with the clustering of functional genetic traits that have been subject to strong selection. This is the core of their argument and is documentedly false.

• The authors claim evidence for genetic race based differences in cognition where there is none. Reports and data sets they cite do not demonstrate what they claim.

• The authors rely on speculative story telling for persuasion (“just-so” stories) rather than data, when stories can be made up to support any arbitrary thesis.

• None of the authors work in or have backgrounds in genetics, neurology, or any disciplines centered in the rigorous and quantitative study of human evolution.

• The authors ignore patient and repeated critique debunking their arguments and resort to publishing in their own journals and reaching to the popular press for attention.

• Among the authors are claims of academic bias in not supporting their work, ignoring methodological feedback about why they are not taken seriously in the fields specializing on the subjects.

• Taking assertions based on unsound methods and then making strong policy arguments about how to treat individual people in line with traditional white supremacist arguments is hard to see as anything other than scientistic cover for racism.

I had heard of writers saying these things in academic backwaters. However modern algorithm based social media is in some ways the great equalizer, and things that had been in the backwaters can suddenly be flushed into the limelight. This is the revival of long since discredited theories, but it seems that internet dynamics now imply that checking this is not a one time task but an ongoing management issue.

As I wrote to address this list of issues, it became a bit too big for one blog posts, so I split it into two. Here is the first. I’ve written it in a way that hopefully also gets into interesting facts about the study of human evolution, so time spent reading is a bit more useful than simply reading an attacking on a small sketchy group of writers and academics.

The field is fringe

A 2023 quantitative study by sociologist Bernard Koch (UCLA) found that hereditarian race-and-IQ researchers remain a small, active, tightly collaborating community within academia — diverse in gender, age, race, and geography — but their papers are cited at much lower rates than comparable psychology work, even while receiving disproportionately high public engagement via Google searches, Reddit, and social media. One attempt to legitimize race realism produced a bibliography counting over 350 journal articles and books since 2012 claiming that racial classifications are biologically meaningful and that group differences in test scores, education, income, or crime partly originate from race-based genetic differences.3 A follow-up analysis of this bibliography, however, found that only 60 of the papers actually qualified as race realist, and that most of the cited papers didn't actually make the stated claims, or referenced them only to refute them.4 Of the 60, 22 appeared in one niche journal.

Of the 21 most active “race realist” scholars identified in this bibliography, not a single one works in genetics, neuroscience, or a research program focused on human evolution. Fields include Management, Psychology, Economics, and an oddly high number in Education.

Race realism has been thoroughly and forcefully critiqued by academics studying genetics and human evolution, and race realists’ persistent pushing of arguments that have been debunked on methodological and evidential grounds gives rise to the accusation that race realism is simply racism with an artificial “scientistic”5 veneer.

As my own PhD focused on human evolution, this question sits directly in my bunk house. As both a data journalist and evolutionary anthropologist, I felt obliged to try to organize the critique in a way that is a bit more widely understandable.

Despite being largely debunked, two interrelated problems make communicating the debunking of race realism difficult.

• Brandolini’s Bullshit Asymmetry Principle: “The amount of energy needed to refute bullshit is an order of magnitude larger than is needed to produce it.” Some of the refutations, including on simple sounding foundational arguments, actually take some time to explain.

• Attacking a straw man. Seemingly out of fear of yielding any points, many researchers responding to race realism attack simplified, obviously flawed “straw man” versions of it. Some race realists do use these arguments, but many are more sophisticated. This causes confusion: people who hear the more sophisticated version wonder why critics obsess over clinal distributions of gene frequencies vs. hard racial boundaries, when no reasonable person really believes in hard boundaries. Compounding this, refutations often use technical language (like “clinal”6) that most people don’t understand — and the result is sometimes not “accepting the wise, hard-to-understand professional,” but leaning toward the race realist who can calmly point out how they’re being misrepresented.

It’s useful to confront the more flawed versions of race realism, but I think it’s more effective to leave those for footnotes and focus on the foundational flaws of the best, “steel man” version. I’ll first distinguish the steel man and straw man versions, then look at the methodological and theoretical problems with the steel man. See the Public Policy section below for a sense of how race realists themselves frame policy implications. It’s not just academic curiosity, but has a racist agenda recycled from debunked social Darwinism

Before that, I want to list some things that are true within race realism that may make people uncomfortable to say explicitly, followed by a list of caveats.

Some true things within race realism (If this list makes you upset, please have patience and read the second list before writing it off)

• People can mostly be divided into racial categories commonly used in government statistics. While there are boundary and in-between cases, the overwhelming majority of people can be replicably categorized by race by most people7.

• These racial categories mostly map onto historically located populations that had some degree of geographic and reproductive separation and therefore some degree of different evolutionary history. There are identified statistical differences in gene frequency among these race groups8.

• … and some of this maps onto physical/physiological differences. Race based statistical (on average) differences in “below the neck” genetic evolution are documented9.

• There are statistical racial differences in cognition and behavior10.

• Not all of these observed cognition and behavior differences have been shown to be environmental/developmental/cultural effects. Some may theoretically be explained by genetic differences11.

• There are genes identified to have effects on cognition and behavior. We have data suggesting between-race statistical differences in some genes which have been shown to have effects on cognition and behavior.12

• There are race based differences in genetics which have been the subject of evolution.13

Now for a list that will make a different set of people angry.

Some caveats

• Race is a social construct, a concept mapped onto visual phenotypic variation. Those race categories do not align consistently with clustering of genetic variance. That clustering differs for different traits, often clashing with “race” boundaries.14 Different cultures, moreover, have different race categories.

• Most of the variance in human genetics is within racial groups, not between them… the between continental group variance is generally estimated around 5% to 10%. In other words, most ( over 90%) of the variance in functional genes can be seen similarly within all race groups.15

• Empirical evidence consistently shows that environmental and cultural effects explain the overwhelming bulk of the difference in cognition and behavior metrics.16

• The remaining, small observed difference is unknown in origin — there is no reason to assume it's genetic, and we know there are remaining environmental/cultural effects impossible to control for with known methods. That residual is not an estimate of the effect of racial genetic distribution. At best, it's an upper bound on it17

• While there may, theoretically, be statistical cognitive differences between racial groups caused by genetics, we can't say which direction those differences run based on evidence we have or can have with known methods. Bluntly: we don't have the data to say whether white or Black children are genetically more inclined to develop into geniuses — it could go either way18

• All we can say is that whatever the effect is, it is miniscule compared to environmental effects and has no place in policy discussion.

Race Realism: definition 1 (The straw man version)

Humanity divides into a handful of fixed, biologically discrete races, each with its own essential and largely immutable package of traits — including intelligence and behavior — such that a person’s race reliably predicts their cognitive ability and behavioral disposition. Racial categories are sharp-edged natural kinds, like species, with clear boundaries and little meaningful overlap between groups.

This is obviously wrong on the surface, but effectively no one positioning themselves as an academic actually advocates this. Attacking this looks like a straw man argument and will plasuibly backfire.

Race Realism: definition 2 (The steel man version)

Human genetic variation is structured, not random — race/ancestry correlates with geography and demonstrates genetic clustering. These race groupings have shown real predictive utility in contexts like forensics and biomedicine. Sharp racial boundaries or essentialist claims about fixed traits are objectively unfounded, but this does not mean there is not gene or trait clustering. Clustering of gene frequencies may create differences in attribute averages, even if there is substantial overlap between race groups. Measured average differences in cognitive test scores and some behavioral outcomes between racial/ethnic groups are real, persistent across studies and decades, and only partially explained by socioeconomic or environmental factors. A substantial portion of the residual gap likely reflects genetic differences between groups, given that 1)cognitive ability is highly heritable within populations, 2) group ancestry correlates with genetic clustering, and 3) cross-race adoption studies reveal remaining differences between races in cognition after environment is controlled. “A significant proportion of difference between Black and Whites in cognitive ability is caused by genetics”

This version avoids many of the obvious pitfalls of the straw man version. It still has major problems … they just take longer to explain.

The Core Methodological Problem

A central empirical argument for race realism is that studies controlling for environment — parental income, education, even parental race — still show lower IQ scores19 for Black children. The most-cited of these is Weinberg, Scarr, and Waldman's 1992 Minnesota Transracial Adoption Study20, which tracked the IQ of teens adopted as infants into white families (see graph above for main study results). Its headline finding was that for infant adoptees most of the racial IQ gap disappeared a with adoption into a white family. A battery of other population studies find the same pattern: controlling for socioeconomic and family-environment variables reduces the interracial IQ gap to negligible levels, but rarely erases it entirely, leaving a residual unexplained effect.21

Race realists like Cofnas take this residual as evidence of race-associated genetic variance. This is where the argument breaks down — and the same break shows up again when race realists turn to evolutionary explanations for the residual.

The problem, in both cases, is the same: absence of an explanation is not evidence for any particular explanation. An adoption study can only tell us about the factors it actually measured — family environment, in this case. It says nothing about other unmeasured factors known to matter enormously, including social acceptance and rejection, race-based expectations among teachers and peers, and pre-natal and early childhood effects.22 A design that can't control for those factors can't rule them out, which means it can't support a genetic explanation for the residual either. (One data point pointing the other way: a 1961 German study of mixed-race children born to Black and white American GIs and raised by white German mothers — outside the American racial context entirely — found no IQ difference by race at all.23 )

Across the various studies with environmental controls, the remaining unexplained effect sits somewhere between 0 and 0.3 standard deviations.24 That translates into massive, nearly complete overlap in the range of IQ, and there’s no reason to assume this small remaining difference is genetic rather than one of the many environmental factors these designs simply can’t reach, like society-wide racial bias.

Race realists also reach for an evolutionary story to explain how a genetic difference could have arisen in the first place: as human populations spread into colder climates, the demands of forward planning and complex tasks would have selected for different cognitive traits. This is what evolutionary biologists call a “just-so story”25: a plausible-sounding narrative invented after the fact, with no way to test it against the many equally plausible stories one could tell instead. This specific just-so story about relative selective pressures relies on broad, speculative and unnamed assumptions about the specific cognitive requirements for human survival in diverse African environments (vs in Europe or Asia). Even people specializing in research on pre-agricultural humanity are still discovering things about life at the time, with early hunter gatherer societies looking very little like today’s immediate return hunter gatherers living at the global margins. One could argue, with exactly the same amount of evidence, that “surviving and navigating hyper-selective ecological or economic pressures in densely populated Africa selected for high cognitive resilience and problem-solving, yielding a genetically superior cognitive distribution compared to European populations”. This is the problem with just-so stories. You can make up one for any assertion.

In short: whatever is left over once you control for environment is a “residual”, not a genetic signal. It’s just as consistent with an unmeasured environmental disadvantage as with a genetic one — speculative and ungrounded evolutionary stories are not evidence.

Public Policy and the Racism Question

Cofnas and other hereditarian “race realist” writers argue that mainstream institutions suppress engagement with hereditarian hypotheses out of political discomfort rather than scientific rebuttal, treating the topic as taboo in ways that violate norms of open inquiry. They will at times argue that race realism is a purely descriptive (non-normative) project, that they are not racist, but simply research (and expose) race-based discrepancies. However, race realists operate in close proximity to race-based policy analysis/recommendation and frequently move directly into it.

On the topic of immigration, race realists like Richard Lynn, Michael Woodley of Menie, Heiner Rindermann, and Edward Dutton argue on genetic grounds that immigration from non-white areas will inevitably lead to a drop in innate intelligence in western populations26, an argument that dovetails with the white supremacist “great replacement” theory. Charles Murray and Richard Lynn argue against welfare on genetic grounds arguing like Rindermann, Gottfredson, Rushton, and Jensen that unequal outcomes are a natural outcome of genetic difference between races and should be accepted27. Lynn (recently deceased) went further and argued for eugenics-based policies, a Nazi era policy that has been seeing a resurgence from the far right fringes. And, of course, the discussion moves into DEI policies as Cofnas Linda Gottfredson and others contend DEI frameworks assume group outcome disparities must stem from bias, foreclosing alternative explanations including genetic ones, and that diversity hiring targets rest on an unproven assumption of equal underlying trait distributions across groups28. In academia, they further argue that aggressively pursuing DEI goals places Black academics in positions they aren’t capable of performing, at the expense of white academics who would otherwise be favored under a meritocracy.

In a 2024 blog post, Cofnas wrote that under a truly meritocratic system, “Black professors [at Harvard] would approach 0 per cent,” and that Black people would “disappear from almost all high-profile positions outside of sports and entertainment.” He attributes this to average group differences in test performance rather than selection bias, objecting to DEI’s tendency to treat any outcome gap as automatic evidence of discrimination. The post, along with his broader writing, led to the termination of his Cambridge research affiliation. Other race realists have put the underlying claim even more starkly still — as J. Philippe Rushton and Arthur Jensen wrote, “The racial hierarchy in cognitive ability and social behavior is consistent, global, and biologically based.”

Circling back to Arday, Cofnas’ repeated promotion of this ideological position makes it hard not to perceive his public attacks on Arday’s record as based on racial bias, even as they may have been factually valid and reacting to racial bias in enforcement of academic standards at Cambridge. This specific case brings up complicated issues. To put the issue bluntly, is it OK or not to do the right thing for wrong reasons? As academics we should feel not just free to bring up professional misconduct, but we should feel obliged to do so when we see it. When there are institutional failures, it also is reasonable to go outside of institutional channels to address those failures. If Cofnas personally advocates racist beliefs, does that negate his right to pursue legitimate academic critique just because the target of those critiques is Black? If a professional academic engages in self harm or commits suicide based on legitimate academic critique, does that make the critic responsible? Is the critic responsible for the harms of a social media storm fed by the criticism, even if the criticism is legitimate and part of the profession? There is a complex debate to have with conflicting reasonable positions. Whatever decision Ghent University makes in its investigation of Cofnas, there will be heated disagreement about the decision, if it is basing its decision solely on the Arday case.

Taking a step back to race realism, though, objectively investigating human genetic variation related to cognition and behavior is not intrinsically racist, and neither is using “race” as a category to study human variation, with the caveat that one understands and foregrounds the well established limits of the artificial category and the problems of assuming “races = functional genetic clusters”. It starts to become racist when motivated reasoning steers results toward racist conclusions — when speculation gets asserted as fact, when fundamental critiques of causal reasoning from specialists are ignored and advocates retreat into self-organized journals to defend against critique, and when evidence is blatantly cherry-picked.

Taking heavily contested or outright debunked statistical conclusions involving insignificant effects and building heavily impactful normative policy demands on top of them, as with Linda Gottfredson’s call against egalitarian assumptions underlying affirmative action and DEI29, speaks to pseudoscientific posturing and ideological motivation, not “scientific honesty”. The racial categories used in hereditarian research don’t correspond to reliable coherent genetic clusters for functional traits. Building a genetic hypothesis for cognition on a socially-constructed, genetically imprecise category, propped up by just-so stories, is scientifically unsound — regardless of the policy recommendations drawn from it, but particularly heinous when those recommendations happen to be the same promoted by blatant racists.

The mainstream study of race and human evolution has dark chapters in its history, where it was used as a scientistic ritual to validate vile race-based government policy and social behavior. Those ideas were overturned a long time ago. As academics specializing in human evolution, we have a special responsibility to be rigorous and humble in our claims, given that history — and empirical claims about group cognitive inferiority have historically been used to justify discriminatory policy, disinvestment, and social exclusion. It’s a sad thing to see such methodological sloppiness used again for the same purpose. Even if these people are not coming from our disciplines, they borrow the language of our fields, so we have some responsibility to set the record straight. Sadly, this seems an ongoing responsibility of something to manage and not something we can just address once and move on.

Brushing aside methodological critique from one’s peers and pivoting to accusations of ideological bias in grant funding speaks to motivated reasoning more than a defense of free inquiry. The existing extensive body of research on the effects of Neanderthal genes on functional traits in modern humans shows that the research questions themselves are not taboo (see follow up blog). Circling back to the DEI argument in academia … without making any general claims for or against DEI policies or specific implementations, argument against DEI goals based on the kind of inferred cognitive differences race realists claim is racist arguments, given the data, collective analysis, and repeated critique already done.

With Ghent and the Arday/Cofnas decision, the decision is difficult and will inevitably generate conflict. A separate issue, though, is whether Ghent University should fire him over his racist research and find some way to also fire the permanent professor who hired him, Bouke de Vries, who initiated a far right racist research program at Ghent after being hired on an ERC grant30.

• Ideological violence is minuscule in Germany when compared to violence overall, with 0.5% of killings being ideologically driven.

• Right wing dominates in all categories of ideological crime

• Islam aligned matches Right for ideological killing, but is actually 4 times greater than right on a “per capita” basis1 in ideological killing

• Left wing matches right for non-bodily harm violence, is lower but still present in all other categories

• 2/3 of anti-LGBTQ violence is right aligned, 1/3 is Islam aligned. On a per capita basis, however, Islam aligned dominates, with 2.5 times the per capita rate of anti-LGBTQ violence as right-aligned.

• Anti-LGBTQ violence is a small fraction of hate violence in Germany, overwhelmingly dominated by right wing anti-foreigner, racist, xenophobic violence.

Short read (8 minutes)

(Addendum on method details and data over time at the end)

Last weekend, a young Lebanese-German Islamist attacked Berlin’s Christopher Street Day LGBTQ pride festival, killing one woman and injuring 29 others, several critically. He was known to police as he had previously tried to reach Syria to join IS, had been arrested on terrorism related charges, but was released with a suspended sentence. This incident reignited conversations in Germany about Muslim extremist violence, immigration, policing, and treatment of offenders and suspects. It is in the context of a substantially declining perception of public safety in Germany over the last 10 years.2

I write this to make the statistics on the subject as transparent and comprehensible as possible. I’ve decided to break the topic into two posts. This post will put Muslim extremist violence and crime in the perspective of ideological crime and violence more generally in Germany, comparing to right wing and left wing aligned motives. It echoes a similar blog I posted last year following the Charlie Kirk killing, helping clarify the relative scale of left wing vs right wing vs Islamist violence in the US. The next blog will be more of a deep dive into the composition of the very diverse Muslim and Muslim-immigrant background community in Germany and the place of extremists and radicalization within the community, as well as what is being done about it. This will be followed by a similar articles on right wing and left wing Germany and the pathways to radicalization in these spheres. As the statistics show below, very different impacts come from left and right radicalization, and these differences are important.

How bad is the problem of ideological violence in Germany?

Before diving into the question of Islamist or Islamist-adjacent violence, I think it is important to look at ideologically motivated violence generally in Germany in the context of overall violence. “Violence” is a sometimes arbitrary term. Different institutions define and count it differently. The most unambiguous form, however, is murder. The first table here compares ideologically motivated murder and manslaughter to overall murder and manslaughter in Germany 2017 – 2025.

If you find the chart hard to read, this is the point… ideological killing is minuscule compared to overall rates of killing in Germany. In the chart, you have to squint to see the small sliver of ideologically motivated killing each year, even for peak years. To be specific, ideological killing represents about 0.5% of killing overall in Germany, echoing similar figures on political and hate driven murders in the US (0.3% of the total). The same is similarly true for other forms of crime… politically motivated violence and crime is a a tiny fraction of violence and crime generally. It stands out because it more consistently generates news articles and is usually done with an intention to get public attention. This causes us to have a distorted perception of how large of an issue it is. For perspective, a small, 1% reduction in the murder rate in Germany would save about twice the lives of eliminating all ideologically motivated killings. If your goal is simply to reduce killing, there are numerous policy initiatives that would be guaranteed to have a dramatically larger effect on reducing killings than any new policy that was ideology specific.

Still, for the specific people targeted, those specifically the victims, ideological crimes are impactful and sometimes tragic. They are crimes like any other that must concern us, and arguably there is something especially noxious about violence meant to coerce and terrorize the public.

Within the general category of ideologically motivated crimes, what are the biggest sources of crime and violence?

This can be answered via data from the Bundeskriminalamt (BKA) — Germany’s Federal Criminal Police Office. The following chart displays different ideologically motivated crime types: killing, violent crime with bodily harm, general violent crime, and all crime including non-violent. It breaks this down by “right extremist”, “left extremist”, and “Muslim extremist”.

An explanation of chart categories:

Killing (Mord, Totschlag und Tötung auf Verlangen) and all crime are self explanatory. For the “Killing” numbers, these are both attempted and completed by total number of victims. For German state accounting, violent crime includes that involving bodily harm (Körperverletzungen), as well as a broader category of violent crime that also includes arson, rioting, dangerous transport disruption, and resisting law enforcement, but does not include non-arson-based property damage or other infrastructure attacks (Gewalttaten). The BKA lists this data for 5 categories: right, left, religious (Islamist), foreign ideology-based, and unassigned. I leave out the unassigned data. This category is a wide mix that includes Reichsbürger/Selbstverwalter (”Reich citizens”/sovereign citizens) as well as Querdenken (anti-covid restrictions, mainly), but also a wide array of other motivations.3

Foreign Ideology based is also a broad category. Much does not fit into right/left/Islamist categorization, but some more naturally does: that related to Israel’s wars on Gaza and other neighbors. Based on BKA data, I break out the fraction that is Israel/Gaza related (Nahost), and distribute this between left and Islam aligned categories.4 To be clear, the German government differentiates between foreign political motivation and explicit religious motivation. As groups like Hamas, Hezbollah, the Iranian government are specifically Islamic, it feels meaningful to group them with clearly Islamic motivated crimes, so i do so here. However, there are also valid reasons to separate them, depending on the question being asked. I flag this here as something that could be debated.

While left and right aligned likely seem sensible, choosing a term for Islam adjacent crime is complicated. The more extreme crimes in Germany, particularly murder, are almost always Salafist jihadi Islamist. They would not be Shia. Shia related terrorism is mostly happening in other countries. However, there are violent crimes that are not necessarily Salafist, and there are are Shia non-violent crimes in Germany, particularly Iranian espionage or Hezbollah fundraising related, that are Shia. while likely all Muslim killings are Islamist, using the term “Islamist” becomes problematic when talking about ideological crimes more generally. I choose the term “Islam aligned” as a more general term.5

Circling back now to the chart, one can see clearly a few points

• Different ideologies contribute to ideological crime in very different patterns.

• In all categories, there is more right wing contribution than from other ideologies.

• The left are on par with the right in violent crimes (Gewalltatten).

• Islam aligned are on par with right for ideological killing.

• The left, while generally contributing less, does contribute to all categories of crime. Its contribution to violent crime is on par with right wing contribution, though less for bodily harm and far less for killing (though still non-zero).

• Islam contributes much less proportionally to non-lethal crime than to lethal. Islam aligned contributes nearly as much as right wing aligned to ideological killing.

How about per capita? How much do these different communities contribute to ideological crime and violence per person in these communities?

This next chart takes the same data makes this per capita comparison, normalized to the overall German population.

In this chart we see the relative risk of someone in these communities is of committing a specific ideological crime compared to overall German population.6 see extended section for important nuances on per capita constructions.

Things that stand out

• Ideological criminality on sub lethal crimes is now comparable between Muslim and left.

• The widest variance on per capita ideological crime by ideology is in the category of lethal crimes.

  • There is 3.5 times the per capita risk of lethal violence from the right aligned compared to left aligned actors.

  • There is more than 4 times the per capita risk of lethal violence from Islam aligned as right aligned actors.

Those last points are the kind of thing that get people’s attention most. Doubling back, it is important to remember that ideologically motivated crime is quite small compared to other crimes. The overwhelming majority of people in all of these categories would not commit murder or even commit a non-violent ideological crime.

That said that final observation is a distilled version of the statistic that concerns people who advocate for stronger filtering of Muslim immigration, for stronger integration requirements, and for greater monitoring of radicalization channels.

Many people would object even to trying to do this analysis out of principle, or would rightfully point to methodological issues in defining source population (the denominator). However, this is exactly the kind of analysis that people do when considering “How dangerous they are”, whomever “they” is. It is also the kind of information that needs to be considered when steering resources effectively toward monitoring, prevention, and de-radicalization.

There is an idea that the left avoids physical harm. The data show that there is truth to this, though it is also certainly not completely true. If one breaks out violent crimes that do not cause bodily harm from those that do. the left by far dominates this sub-category (see additional chart in footnotes)7.

The fact that Islam aligned actors have such a jump in relative use of lethal violence is something that calls to be addressed. At least two opposing mechanisms may be driving this. The first is a drive toward lethal violence among Salafist jihadis that would drive an increasing lethality of action based in notions of sacred religious duty. The second would be the effect of existing strict immigration/deportation policies and social/economic precarity based in minority status. These factors would be assumed to suppress ideological crimes below a high threshold of radicalism (where one no longer caries about personal consequences). It is a very different thing for a right wing or left wing German activist to break the law than for someone who faces deportation and potential immigration impacts on their family and community for breaking the law. Whatever the cause, the higher amount of lethal ideological crimes stands out quite strongly in the data.

LGBTQ hate crime specifically

The CSD attack in Berlin has brought vocal calls for increased protection of LGBTQ events. At the same time, the LGBTQ scene leans strongly left and there have been equally strong calls to not politicize the event, to guard against those using it to motivate broad policing of Muslim communities or immigration restrictions through distorted representations of Islam aligned anti-LGBTQ violence. These voices assert that the overwhelming majority of anti-LGBTQ violence comes from the German right, not from Islam aligned extremists.

How significant is anti-LGBTQ violence and who is committing it in Germany?

The German BMI keeps statistics on violent hate crime by target group.8 To get a sense of how levels of anti-LGBTQ violence sit compared to other forms of hate violence, the chart below visually displays incident numbers by ideology, 2017-2025.9

As you can see the overwhelming majority of violent hate crimes were in the interrelated categories of anti-foreigner, xenophobic, and racist, and are overwhelmingly right wing. The next categories down are antisemitic and anti-Muslim, also both heavily right wing dominated. Only below these 5 do you get to the anti-LGBTQ categories of Gender-related and Sexual Orientation related. 3 broadly Xenophobic categories account for 8 times as many violent hate crimes in Germany as anti-LGBT violent crimes. As all hate crimes, anti-LGBTQ violence needs to be addressed, but race/nationality/xenophobia based hate crimes are a dramatically larger problem which appears to get proportionally less national media attention.10

Almost all ideologically labeled hate crime in Germany is right wing. On the chart, there is some small amount of easily visible foreign ideology and Islam related violence. In this context, the variance in motivation within ”foreign ideology” is important, as it itself varies from target to target, though some, like “antisemitic” are more clearly Nahost and Islam related. There is some negligible amount of left aligned violence, but it only shows up visually in this chart in the “Social Status” related hate category, related to anti-capitalist actions, where even that is minuscule compared to other categories.

The next chart looks more closely at the combined LGBTQ categories.

The LGBTQ categories tell a different story than the overall accumulated hate crimes. here, right aligned ideology does still dominate, but not as profoundly. Islam aligned contributes 30% to anti-LGBTQ crimes. There is some truth to “both sides”. Right aligned actors are clearly the main threat to the LGBTQ community, but Islam aligned area a substantial source of violence. This becomes even more stark when you take the per capita view. While Islam aligned actors account for only half as many total violent anti-LGBTQ acts as conservative/right wing actors, given the community sizes, Islam aligned actors are on average 2.5 times higher a risk of anti-LGBTQ violence as right aligned actors. Again, there is something true on both sides of the discussion: right anti-LGBTQ hate crime dominance overall and much stronger per capita risk from the Muslim community. And also to repeat, this is not to make a general statement about all right aligned actors or all Muslims, but a statement about the relative “density” of risk coming from these communities. For both right and islam aligned, one can get more specific to find sub communities where the risk is concentrated more strongly, where one should be looking to intervene in order to reduce violence. At the same time, as most violent hate crimes are committed by those who are not beforehand known to be extremists, one has to look more broadly than “known extremists” to monitor and intervene effectively.

The policy view

All of these charts accurately display government data. People from different rhetorical positions focus on different views. Voices on the Left focus on the fact that more ideological crime overall is from the Right. Voices on the right focus on the substantially higher per capita Muslim risk of killing people out of ideological reasons than either right or left. Both of these assertions are true. Of course, different actors will also have blind spots or distortions, even within their chosen view. Right wing actors tend to not acknowledge how much bodily violence comes from the right, how much ideological crime generally. From the left, there is a denial or minimization of the existence of left wing physical violence and a deflecting away from talking about Islamist violence to talking about right wing violence.

I think it is useful to look at all the data to see where greatest risk is and greatest opportunities for positive intervention. It is also important to keep in mind how ideological crime is designed to hijack our attention away from other issues that might be more impactful, even if less attention-grabbing, and how some targets are able to get media attention more than others who are sometimes even more impacted.

After the Berlin attack, there was a flurry of party statements triggering further party statements. Conversations have been re-opened about sources of ideological violence, surveillance of potential radicalization channels and how wide or narrow that net should be cast, de-radicalization initiatives, police powers and detention, investing in support of liberal voices in Muslim communities, support of cultural autonomy vs integration, and filters on immigration.

In general, the party positions are as follows…

CDU: Calls for tighter screening, expanded pre-trial detention for assessed security risks, and stricter handling of known threat individuals (Gefährder); after this attack specifically criticized Die Linke for omitting the Islamist motive. Sees monitoring/tracking of radicalization pathways as a precondition for any deradicalization work to be effective, not a substitute for it.

SPD / Greens: Responded to the attack with general "defense of open society" language without assigning ideological motive; publicly pushed back on CDU for "politicizing" the tragedy. Broadly support funding for deradicalization and civil-society/liberal voices within Muslim communities over expanded surveillance powers.

Die Linke: Initial statement emphasized protection from right-wing violence and did not name the attacker's Islamist background; after CDU criticism, argued (via its left flank) that Islamist violence should itself be understood as a form of right-wing violence and accused others of “racializing” the incident to promote a right wing agenda. Most resistant among the left parties to expanded anti-terror monitoring or detention powers, emphasizing social support for Muslim communities.

BSW: Broke from the rest of the left bloc by naming the Islamist motive directly and calling for tougher action against known threats (echoing CDU/AfD's "Gefährder" framing), while simultaneously invoking LGBTQ+ solidarity language.

AfD: Frames the attack as proof its immigration and security warnings were correct; pushes for reduced naturalization and a harder line against "Gefährder," alongside longstanding calls to curb Islam's public/institutional presence (mosque restrictions, no corporate status for Islamic associations). Officially rejects blanket/collective deportation of residents with legal status as well as undifferentiated treatment of the heterogenous Muslim world.

…

There are about 6.7 million Muslims or people with Muslim immigration backgrounds in Germany. In a follow up post, I’ll dive into the details of “Muslims” in Germany: immigration history, variance in religiosity, and the spectrums of their political and religious views, as well as the place in these communities of radical political activism and violent extremists, mostly Salafists. On the one hand, we have about 6% of people with Muslim immigration backgrounds describing themselves as atheist and 25% describing themselves as not or not very religious. On the other hand we have a recent survey of teenagers in Niedersachsen that found that 1/3 of Muslim youth held the view that violence against those who criticize Islam or Mohamed is justified, a view that has no place in a liberal democratic society. It is a very diverse community, and there is a lot to disentangle.

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Addendum on methods and time series data (3 minutes read)

Below is the data on ideological crime and violence presented year by year, 2017-2025.

Total Ideological Crime and Violence

Per Capita Ideological Crime and Violence

The specific date range (2017-2025) is because of a change in German government accounting for 2017, where religious ideological crime (almost entirely Islamist) began to be tallied separately from foreign ideology crime.

Civil society organizations report much higher numbers of ideologically motivated violent incidents than are captured by government statistics on ideologically motivated violent crimes. There are many unpredictable drivers of this difference. Some are not actually violent crimes. Some are unreported crimes. Some are unfounded reports to NGOs who do not assess the legitimacy of individual reports but just report and aggregate them. Within this, there are certainly some, like the unreported incidents, that we would ideally want to include, but we have no way to separate them from the fabrications or misrepresented reports. Official police reports give us the most stable, systematic, and reliable numbers, although even these can be critiqued. For this reason, I don’t include the civil society estimates, but just note that they exist and are substantially larger, in some case by a factor of 2.

The complexities of accounting for “Islam aligned” and how to fold what fraction of “foreign ideology aligned” into “Islam aligned” are detailed in the footnotes from the “short read” section.

There is a further complexity in the per capita numbers … What is the population you are looking at? Each of these categories is diverse, and most of the people in them do not commit crimes, let alone murder. There is an impulse to say that one should not lump them together, and there is a lot of policy-essential truth to that. For most policy initiatives, we should not lump all Muslims together, and neither should we lump all left aligned or right aligned together. This is a rough starting place for a conversation that should get more specific, with that specificity depending on the exact question. We want to have the category big enough that it has a natural conceptual boundary and also contains the crimes in question. Secular 3rd generation Germans of Turkish descent are a very different category than religious Shia, again totally different from Salafist jihadi or political Islamist. Similarly, those who vote for an anti-immigration party (CDU, BSW, AfD) are a different category from those who are known members of a neo-Nazi organization.

If we want to make a meaningful between-group comparison, we should also have a similar granularity. It would be meaningless to talk about how dangerous neo-Nazis are compared to Muslims. Muslims on average are obviously less dangerous, but we have already done a more fine grain filtering of “right aligned” for dangerousness to get to “neo-Nazi” where more ideological violence would be concentrated. For this comparison, i use a similarly course-grained measures for all categories. Once one talks specific policy, then one should use more fine grained analysis based on the policy context. For example, if one wanted to discuss de-radicalization programs that address Salafism, then one is already talking about the Sunni community and not Shia. Even representatives of AfD, known to be more Islam-critical, emphasize that one needs more fine grained discussion that does not lump all Muslims together.

When we have information that helps us steer policy, we should use it.

This brings up the possible point that statistics should only look at those who are members of known extremist groups to avoid encouraging various kinds of broad group hate or discrimination. However, this gets to the second structural problem: the majority of those convicted of even the most violent offenses ARE NOT previously known to be members of an extremist group. We don’t have the data we need. We can’t meaningfully use right wing killings to talk about killings per member of a neo-Nazi group, since the great majority of those guilty of such killings are not known members of such groups. The same for Islamist or Left extremist. The category has to be broad enough to actually contain the perpetrators of the crime. This also gets to the idea that what we want to know is how dangerous on average a member of a specific community is, given that we don’t know which ones are dangerous.

The course-grained view is a meaningful starting place.

—

The tables in this extended section present a lot of data which can be a bit overwhelming at first. Two big things do leap out, though, and seeing them helps to organize the charts, both involving recent increases in ideological crime.

• There is a very large spike in Islam-aligned ideological crime and violence with the Israeli war on Gaza following Oct 7. This is obvious across categories.

• The last 3 years have also seen a steady growth in right and left ideological crimes and violence.

Officials attribute the intensification of the Islamist radicalization environment to October 7/Gaza, but individual attacks vary in how directly Gaza-linked they are. Some perpetrators fit a general jihadist/IS framework rather than being specifically Gaza-triggered. The emotional intensity of Gaza coverage is cited as an amplifying factor for radicalization broadly, including among minors radicalized online. Baden-Württemberg's 2024 Verfassungsschutz report specifically notes that graphic images from the Israel-Palestine conflict feed generalized extremist hatred of "the West" within the scene, and flags that security services are increasingly confronting minors who are radicalizing and planning serious violence after being indoctrinated by online influencers and radical preachers. Gaza functions more as an accelerant on an existing online-radicalization pipeline than a standalone cause.

For the right, the explanation likely lies largely in the German election cycle with 2024 being a major election year. Synthesizing with this have been documented increases in Russian produced disinformation being fed into, used, and amplified by Rechtsextremismus, Reichsbürger/Selbstverwalter, and "Verfassungsschutzrelevante Delegitimierung des Staates" scenes.

For the left, it has been a mix of all of these influences… The Israel/Gaza war, German election dynamics, and Russian disinformation (particularly for the new populist BSW party, which explicitly advocates for closer German ties with Russia).

The fact that there have been such substantial increases in ideological violent crime across ideologies is cause for concern and something that needs to be addressed and monitored lest it escalate.

Continued rise in Ukraine’s CPI score through 2025 reflects international political scholars’ and business leaders’ optimism for Ukraine’s future … exposure of corruption schemes reflects proper functioning of anti-corruption institutions

The last months have been very dynamic around corruption and anti-corruption fights in Ukraine. Ukraine’s National Anti-Corruption Bureau (NABU) has exposed corruption schemes potentially reaching into the billions of dollars involving the energy, military, and financial sectors. The Energoatam energy sector graft scheme has received the widest international attention. Timur Mindich, Zelensky’s long time business partner in the Kvartal 95 entertainment and media company, was exposed as central to the corruption scandal and fled the country for Israel. Multiple arrests followed within Zelensky’s close circle, including of his closest advisor, Andriy Yermak. An artificially war-extended parliament, long overdue for fresh elections and woefully lacking in public trust, has attempted again to rush through legislation that would undercut anti-corruption efforts. However, civil society continues to effectively resist such legislation, and the exposure of these various scandals is evidence of the effective functioning of these independent anti-corruption institutions, a fact that is reflected in various international anti-corruption metrics.

This article is a follow up to a review from summer 2025 and is meant as a comprehensive catch-up on the complex web of exposed corruption schemes, investigations, court cases, and civil society engagement.

Note: There are a lot of Ukrainian names which may be confusing for an international audience. At the end of the article, there is an extensive “cast of characters” for the story.

— 6 minute read—

July 2025 saw the Zelensky government attempt to dismantle the independence of anti-corruption organizations and put them under the control of the Prosecutor General and thus the president’s office. This move, which observers believe was an attempt to shield members of the President’s inner circle from the emerging Midas investigation, met with fierce domestic and international backlash, with protests across 17 cities in Ukraine. Dubbed “the Cardboard Revolution” for the ubiquity of cardboard signs, the war-time street demonstrations were the largest since the 2013/2014 Maidan protests that ousted pro-Russian president Yanukovych. the protests demanded the restoration of independence for the National Anti-Corruption Bureau (NABU, which investigates state corruption) and the Specialized Anti-Corruption Prosecutor's Office (SAPO, which prosecutes). Combined with pressure from the EU, the protests forced an equally fast reversal from Zelensky and his shrinking Servants of the People Party1, forcing a full restoration of independence of these institutions.

As i covered here last summer, this reassured international partners that Ukraine was maintaining its path toward democracy and real institutional reform and that civil society was vigilantly holding the government in line even with the suspension of elections during wartime.2 The issue is of high concern internationally both for rule of law and to understand that aide is being spent effectively in the fight against Russia. Money intended for the front line and civilian infrastructure repair being siphoned off by corrupt actors is a valid concern for donor nations.

Since then, NABU has been very active. There has been widespread disruption of circles close to Zelensky as high level corruption is exposed and more and more of Zelensky’s inner circle either are arrested or have fled the country.

While there is a relatively constant flow of arrests and charges on lower level corruption charges reported in NABU’s Telegram channel, there have been several intertwined state-level scandals exposed under NABU’s Midas investigation.

Energoatom energy scandal

While Ukraine has been dealing with Russian destruction of energy infrastructure, an exceptionally harsh winter, and widespread depravation from lack of heating. Zelensky’s closest long term business partner, Timur Mindich, along with a large team of other associates, orchestrated a $100 million + extortion scheme from companies working on repairing energy infrastructure. All companies who wanted to receive lucrative re-construction contracts via the Ukrainian nuclear energy operator Energoatom were required to give 10% to 15% of the value of all energy contracts back to Mindich and accomplices as cash kick-backs. This led in winter 2025 to many people being arrested and charged. Mindich himself was tipped off and fled the country for Israel, where he is now living in Herzliya. Israel does not have an extradition treaty with Ukraine.

4 individuals were arrested for the back office work of laundering the money. 3 of them had their collective $1.2 million bail paid for by a furniture manufacturer, Vanguard, which had no digital footprint or record of furniture manufacture, which was only just established and only had on the book assets of $24. The bail was arranged by Deputy Head of the Presidential Office Oleh Tatarov. Tatarov had previously been arrested and charged on corruption charges, but had the prosecution of his case procedurally buried and closed after it was taken over by the President’s Office. Tatarov’s case is held up as a prime example of exactly why there needs to be independent investigation, prosecution, and adjudication. He is now a person of interest for the now independent NABU in the Energoatom scandal.

Fire Point defense scandal

Established in mid-2022, Fire Point specializes in the production of long-range strike drones and cruise missiles, including the FP-1 deep-strike system and the FP-5 Flamingo missile. Fire Point has rapidly grown to be Ukraine’s largest and most funded defense contractor. While the company has been praised for its contribution to Ukraine's ability to conduct precision strikes deep inside Russian territory, it has
simultaneously become the focal point of a major procurement and
influence-peddling scandal.

Midas wiretap recordings of Mindich in conversation with former Defense Minister Rustem Umerov revealed the two discussing Fire Point, suggesting circumvention of independent competitive bidding processes to in a way that indicated that Minidich was a secret financial beneficiary of Fire Point and that Umerov himself had vested interests. The conversations capture Mindich acting as a "de facto controller" or shadow beneficiary of Fire Point, negotiating directly with top state officials on the company's behalf. Mindich was recorded complaining to Umerov about underfunding for Fire Point. During their discussions of $7 billion in defense expenditures related to Fire Point and the possible sale of 33% interest in Fire Point to private investors, Umerov allegedly asked Mindich whether certain procurement or policy decisions "would suit us" or be in "our interest". Critics and investigators argue this phrasing suggests an inappropriate and potentially corrupt level of coordination between the Defense Ministry and a private contractor.

They also discussed trying to approve the purchase of subpar bullet proof vests from an Israeli company connected to Mindich (eventually rejected by the military).

According testimony on 13 May 2026 by Denys Shtilerman, the co-founder and lead designer of Fire Point, Mindich offered $1billion in cash to buy out 50% of Fire Point. The transaction ultimately collapsed because Fire Point's legal team could not verify the source of Mindich's capital.. Shtilerman’s testimony was backed up by NABU acquired evidence of the movement of black money for the purpose of the purchase. The public revelation of this $1 billion offer significantly escalated the scandal. It provided investigators with a baseline valuation of how much "shadow cash" Mindich's tier of the corruption network was attempting to legalize by embedding themselves in the wartime defense-industrial base… far more than the current amount officially identified in the Energoatom scandal. Since then, Shtilerman has gone to London and is in dialogue with lawyers about whether or not he is returning to Ukraine.

The Ukrainian Defense Ministry's anti-corruption watchdog called for suspending Umerov from government based on credible evidence of "abuse of office and possible disclosure of state secrets." They also raised concerns that Fire Point’s dominant position effectively “monopolizes” the market for deep-strike systems, potentially hindering the development of more cost-effective alternatives from other domestic manufacturers. There are reports of overpriced drones, exaggerated production rates of missiles, and performance below specifications. Based on this, they have called for the partial nationalization of Fire Point to assure transparency and proper, effective spending of the defense budget.

Umerov is now identified by NABU as a “person of interest” in the investigation and has been removed as Defense Minister. Zelensky then transferred Umerov to the National Security and Defense Council to act as negotiator with the US.

Sense Bank scandal

In 2023, Sense Bank (then Alfa Bank) was nationalized in a corruption scandal involving Russian oligarch connections. A Board of Directors was established, appointed by the government in an independent and merit-based process.

However, leaked recordings capture a conversation from May 2025 between Oleksandr Tsukerman (a key figure in the Energoatom scheme, in charge of money laundering) and Vasyl Vesely, identified as the "overseer" of Sense Bank on behalf of the Presidential Office.¹⁴ In the recording, Vesely lists six specific individuals he wants appointed to the bank’s nine-member supervisory board to ensure a "reliable majority" for his network.

A few months later, The cabinet of Mnisters of Ukraine appoints exactly these 6 individuals to the Board. This revelation led to at least one resignation from the board. Moreover, the revelation that a state-owned bank, intended to symbolize the "de-oligarchization" of the economy, was subject to backroom appointments has caused significant damage to Ukraine’s reputation for corporate governance reform.

Dynasty real estate scandal

Recently information was leaked strongly indicating that a luxury land development involving Zelensky’s inner circle is part of the Energoatom money laundering pipeline. In 2021, land was purchased in a village outside of Kyiv for a “cooperative land development”. In reality, the land was for the building of 4 multi-million dollar riverside mansions for Timur Mindich, Andriy Yermak (Zelensky’s right hand man and central adviser), Oleksiy Chernyshov (former CEO of the state energy company, Naftogaz, and former Minister of Communities and Territories Development), and one “Vova” whom NABU has not publicly identified. The lands, valued at at least $6 million, were publicly owned and were acquired by the Dynasty project questionably at dramatically submarket rates The development itself was budgeted at about $12 million with funds being funneled to it for laundering from the Energoatom extortions.

This month, both Yermak and Chernyshov have been arrested and charged in relation to the Energoatom scandal. “Vova” is a short form for Volodymyr. while sources for Ukrainska Pravda identify Vova as Zelensky, NABU has said it is not investigating the president. NABU emphasized that there is no direct paper trail or financial evidence linking President Zelensky himself to the illicit funds funneled from Energoatom. Under the Ukrainian Constitution, a sitting president hass immunity from criminal prosecution, placing him outside the jurisdiction of NABU and SAPO during his term.

Civil Code “reform” and Bill 15150

Last week protests erupted again across Ukraine following the passing of Bill 15150 through the 1st round of voting in the Verkhovna Rada (the Ukrainian parliament). Spearheaded by Parliamentary Speaker Ruslan Stefanchuk (Servants of the People, Zelensky’s party), this draft was introduced as an alternative to an earlier attempted update Ukraine’s marriage laws (Bill 14394), heavily criticized for failing to follow EU norms, including recognition of gay marriage.

A massive 830 page document meant to “modernize” family law, it caused outrage from civil society activists for numerous and diverse problems. Like with the legislative attempt to dismantle the independence of NABU and SAPO, it was passed lighting fast with no meaningful discussion or opportunity for public evaluation via a what is reported to be an illegal circumvention of proper oversight rules

Bill 15150 failed to update marriage laws to recognize gay marriage and forbade the recognition of gay marriage or civil unions from abroad. It would establish state ability to strip rights from people deemed “mentally ill”… a Soviet era oppression and corruption mechanism used to neutralize dissidents. These provisions sharply contrast with EU law.

The bill also pervasively used the dangerously vague term “boni mores” (“good morals”) to legitimize arbitrary discretionary power for Zelensky appointed judges who claim defense of “good morals” as the basis of their decision. Specifically it would make illegal whistleblower reports from the military for undermining the reputation of the state. This is particularly troubling in a post-Soviet country as such wide ranging judicial discretion was traditionally been a vector for corruption in the USSR.

Most suspiciously, in a non-sequitur buried in the bill, it updated property laws to declare “Register Irrefutability”, make it almost impossible to reverse illegal acquisition of public land. It would make it impossible to reverse land title changes.

The bill mandates that once a property title is entered into the state registry, no court can reverse it — meaning corrupt land grabs become permanent after a brief waiting period. It also would explicitly legalize any occupation (theft) of public lands if held privately for 10 years. All one has to do is occupy the land, avoid prosecution for a few years, and then draw out court cases for a few more years and the land is yours. This is echoes the procedures used to neutralize Tatarov’s court case. The bill was largely drafted by Tatarov and parliamentary speaker Ruslan Stefanchuk and would effectively legalize the Dynasty real estate land acquisition after the fact as well as legitimize any past corrupt land dealings.

It passed first vote with the help of Zelensky’s pro-Russian and pro-Oligarch coaloition parners, but against the resistance of pro EU opposition parties European Solidarity and Holos. Still facing protests and inevitable resistance from the EU for running counter to EU norms of good governance and human rights, it would need to pass a 2nd vote and then would go to Zelensky for approval.

—

Yermak and Chernyshev were both arrested this month and face charges in relation to the Energoatom scandal. Also arrested from Zelensky’s circles have been Ihor Kolomoisky (his oligarch campaign backer) and German Halushenko (Former Energy and Justice Minister). Current suspects or “persons of interest” from Zelensky’s circle include Rustem Umerov, Oleh Tatarov, Ivan Bakanov, Serhiy Shefir, Serhiy Trofimov, and Kyrylo Tymoshenko.

International Metrics of Corruption

The exposure of these scandals is simultaneously evidence of high level corruption and of the functioning of NABU and SAPO in fighting that corruption. As such, they have been met with cautious optimism internationally about Ukraine’s democratic future and rule of law. Two graphs show Ukraine’s progress over the years in primary metrics for corruption, through 2025: Transparency International’s Corruption Perception Index (CPI, top of article) and the World Bank’s Control of Corruption Index (CCI, below). The CPI heavily relies on expert assessments and business surveys, where the CCI integrates a broader array of data sources—including citizen surveys and non-governmental watchdogs. These are updates of similar graphs from the 2025 article.

On both metrics, Ukraine has over the last many years been steadily improving. For 2025, Ukraine has continued the upward trend on the CPI, while it leveled out on the CCI. This, however, has been in the context of a general worsening in terms of corruption metrics for many other reference countries, including the US, EU countries, and, of course, Russia. Many people in the US would likely be jealous of Ukraine’s independent anti-corruption institutions, given the lack of them in the US and current widespread perceptions of crisis around corruption of institutions in the US.

Already by 5 years ago, Ukraine was performing better than the lower end of EU countries like Romania and Hungary on low-level (as opposed to state-level) corruption metrics, in large part due to reforms instituting centralized electronic administration which have only expanded since then. (see blog)

Public Opinion

In January, the Kyiv International Institute of Sociology conducted a survey of public perception of the current government. It can be taken as a current upper bound on public trust in government, given this most recent round of arrests and the conflict over the new Civil Code. While public trust in the military remains strong and Ukraine has maintained net trust in Zelensky, already by January, public trust in parliament, the Verkhovna Roda, was catastrophically low. The Verkhovna Rada has remained far past its mandate because of war-time suspension of elections. A vote now would almost certainly lead to a very different composition of parliament. The coalition of Zelensky’s Servants party and the pro-Russian and pro-oligarch parties seems to have earned massive distrust among Ukrainians, with much speculation that when new elections happen, there will likely be a new coalition of military figures like Zaluzhnyi and activist technocrats like those from Holos, coming in with relatively higher levels of trust.

The KIIS survey results are echoed qualitatively by other surveys from the Razumkov Center, Kyiv Security Forum, PolitArena, and Ipsos, though with somewhat lower Zelensky trust registering in other surveys.

A lot is unfolding quickly right now in Ukraine around exposure and prosecution of corruption. NABU and SAPO are doing remarkable work and people anticipate a further cascade of arrests and testimonies. The dismantling of the autocratic structures around the president’s office through the resignation and arrest of Andriy Yermak indicates that the state’s anti-corruption architecture has established independence and functionality up to the highest levels of government. Anti-corruption advocates, such as Transparency International Ukraine, have noted that without civil society commitment the “victory for the people,” that came with the Cardboard revolution protests, the investigation into the Energoatom and Yermak cases would likely have been disabled by the president’s office. The revelations that are happening now would have been hard to imagine in the past, so in many ways, these arrests should be seen in a positive light, shocking as they are. While many actors (ie, Russia) will frame these scandals as evidence of the untrustworthiness of governance in Ukraine, these exposures are, in fact, evidence of government correction.

Civil society activists are watching to assure that the trial of top figures like Yermak, Chernyshov, and Halushenko proceeds transparently, government/industry increases in transparency, and that parliament drafts and passes legislation transparently in the interests of the people and proper governance. Further government attempts at interference in corruption prosecution would most likely be met with widespread protests, as would passing of Bill 15150 in anything like its present form.

Caution and civil society engagement are essential, but there is reason for optimism.

One thing I often tell people is that the risk of dying violently is greater in Oakland California than it is in Kyiv right now, and being in a city further west like Lviv is even safer. That is not hyperbole. Partly people overestimate the actual life risk during war time. Sometimes people also underestimate violence in many US cities. As I am often in conversations with people from N America and Europe, i made a simple graph comparing risk of violent death for 2025 in Oakland, California to Kyiv, Ukraine, and to Paris, France as a stand-in for Europe.

The graphs compares the risk for civilian deaths both on average and as faced by the economic median citizen. The latter controls for the fact that there is substantial variation in how much of the violence is either inter-gang violence or acquaintance violence in specific poor hot spots. Despite 2025 being the safest year for Oakland in the past 60 years, the city is about 3 times more violent than Kyiv including Russian attacks.1 Even when one subtracts out inter-gang violence and intra-community, acquaintance violence in poor hot spots, Oakland was still slightly more dangerous than Kyiv in Oakland’s safest year in 6 decades. With the same gang/hotspot-intracommunity violence subtracted out, Paris has about 1/7th the risk of violent death as either Kyiv or Oakland: 5 people per million killed in 2025 compared to 35 for Oakland and Kyiv.

The point of this comparison is not to say that one should or should not feel concerned, on one city or another, but instead to show the discrepancies in how we perceive risks of violence and also to make some sense for outsiders about how there can simultaneously be a crisis of war and still a vibrant city life continuing.

This is, of course, Kyiv. The experience in Kharkiv, closer to the front, is different, with a significantly higher risk. In the west, a city like Lviv will be again different, with a substantially lower risk. In the countryside, away from Russian missiles and drones, it will be different again.

It should be kept in mind that this does not include military deaths. It’s just intended to give one window on the civilian experience. The current relative safety of people in Kyiv and other cities and the ability to keep going with life depends on the risks taken by men and women serving as defenders on Ukraine’s front lines with Putin’s army, and everyone has some family or friends in the military. A more holistic analysis of the risks of death associated with life in Kyiv would include those experienced by the military on the front lines, on which life in Kyiv depends. Indeed all of us in the EU have been depending on their sacrifices, keeping the primary line of Russian invasion far from EU territories.

It should also be kept in mind that simplified statistics aside, violence is experienced differently, and while someone in Oakland is more likely to die from weapons-based violence than in Kyiv, the daily awareness of and consciously dealing with risk is different as everyone on a regular basis interfaces with alerts coming on their cell phones about drones potentially approaching the city.2 This causes a different kind of stress.

For myself, understanding the objective risk is part of why i feel relaxed visiting Ukraine. If you are thinking about visiting (which i highly recommend and which Ukrainians appreciate), I hope this informal comparison is useful and puts things in perspective.

‘continuing to hope for war’s end and for eventual justice.

Sources

Kyiv City State Administration. (2025). Аналіз демографічної ситуації та міграційних процесів у столиці на основі даних мобільних операторів та обліку ВПО [Analysis of the demographic situation and migration processes in the capital based on mobile operator data and IDP registration]. Department of Social Policy, KMDA.

National Police of Ukraine. (2025). Про стан протидії злочинності та результати оперативно-службової діяльності Головного управління Національної поліції у м. Києві за 2021–2025 роки

United Nations Office on Drugs and Crime. (2023). Global Study on Homicide 2023: Trends, Contexts, and Drivers (Sales No. E.24.IV.1). United Nations. [Includes historical nationwide baseline homicide data for Ukraine from 2015–2021].

Office of the United Nations High Commissioner for Human Rights. (2025). Report on the Protection of Civilians in Armed Conflict: Civilians Casualties in Ukraine, Annual Overview 2025. United Nations.

Klitschko, V. V. (2023–2025). Operational Briefings on Municipal Infrastructure and Essential Good Consumption Metrics in the Capital under Martial Law. Kyiv City State Administration Press Service.

Civilian Casualties Remain Alarmingly High as Short and Long-Range Weapons Devastate Lives Across Ukraine, UN Human Rights Monitors Say, https://ukraine.un.org/en/301284-civilian-casualties-remain-alarmingly-high-short-and-long-range-weapons-devastate-lives

2025 deadliest year for civilians in Ukraine since 2022, UN human rights monitors find, https://ukraine.ohchr.org/en/2025-deadliest-year-for-civilians-in-Ukraine-since-2022-UN-human-rights-monitors-find

Oakland homicides fall to 25-year low – how did it stem the violence? https://www.theguardian.com/us-news/2026/mar/16/oakland-homicides-gun-violence

Data USA, Oakland, CA https://datausa.io/profile/geo/oakland-ca/

Ukraine Crime Rate & Statistics https://www.macrotrends.net/global-metrics/countries/ukr/ukraine/crime-rate-statistics

Insecurity and crime: the first figures for 2025 https://www.vie-publique.fr/en-bref/301862-insecurite-et-delinquance-les-premiers-chiffres-pour-2025

2

Ukraine uses an official, countrywide phone-based air alert system powered by the "Air Alarm" (Повітряна тривога) app. . Integrated directly with the regional civil defense system, it sends push notifications of aerial threats (missiles, drones, artillery) by region.
https://visitukraine.today/blog/143/air-alert-app-that-notifies-about-danger-in-certain-region-in-your-smartphone

On 8 April, a 2 week ceasefire was agreed between the US and Iran, and on 16 April with Lebanon, but the ceasefires as of today remain unstable. So far, well over 12,000 have been confirmed killed (overwhelmingly in Iran and Lebanon), with certainly far more yet to be identified and confirmed. Violence has on some level spread to at least 20 countries. While these direct casualties are concentrated in Iran, secondary deaths are likely far higher in Sudan where internal conflicts amplified by external actors have generated famine conditions and where the US/Israel Iran war has caused the suspension of flow of aid.

Data for this mortality tracker is gathered from reliably fact-checking media counts from military actions (primarily Al Jazeera, AP, BBC, but including others) and directly from local health ministries and NGOs with track records of accuracy and legitimate identification and confirmation processes (HRANA, Hengaw, Gaza Health Ministry, Lebanon Health Ministry, etc). The tracker also includes violence from terror attacks and violent protests outside the Middle East reported by reliably fact-checking news agencies.

In short, every few days, i am doing the following

• looking at official health ministry updates from Gaza, Lebanon, Israel as well as the same from NGOs doing counts like HRANA (Iran), SOHR (Syria), Magen David Adom (Israel)

• doing AI assisted news searches for reported casualties in the previous days (particularly for West Bank) that are not identified in these tallies. (Gemini and Perplexity)

• checking with Al Jazeera’s casualty tracker to see if i missed something and have any undercounts. Al Jazeera is reporting lower numbers than those reported by HRANA, for example, so i take HRANA’s numbers, but sometimes my AI search misses some reports that Al Jazeera gets, so I incorporate those.

This should not be seen as a comprehensive identification of all mortality or a central estimate, but a lower bound of mortality.

Human Rights Activist News Agency (HRANA), a primary source for BBC reporting and others for casualties in Iran, is the source for Iranian numbers. HRANA is known for being conservative in their estimates with high standards for verified casualties, with documentation, eye witness, and cross-verification. While the first iteration of this tracker included a fraction of unconfirmed deaths would be converted to confirmed, the current iteration of the tracker only counts HRANA confirmed deaths. It is certainly an underestimate of actual total mortality. There were early estimates that the HRANA confirmed numbers were about a 60% undercount, but a precise estimate of the undercount is not currently available. I have in my last updates also included Iranian casualties identified by Norwegian NGO Hengaw. As they have a a different presence than HRANA (especially Kurdish focused), they capture a different set of casualties in their lists. As i don’t have access to their lists by name/ID and only their military and non-combatant totals, i use the higher of the two for each category. IN this moment, HRANA has identified somewhat more non-combatants and Hengaw has identified dramatically more combatants killed. It’s quite likely that these lists have non-overlap on both sides, so certainly both counts would be higher if the lists were merged.

At this point (20 April) the Civilian to Combatant Ratio among deaths (CCR) identified this way is about 0.3, where combatant status could be verified. In about 25% of cases in the HRANA list, combatant status could not be established. This is dramatically different, over an order of magnitude less civilians killed per combatant compared to estimates from the Israel/Gaza war.

Notes:

• The US/Israel bombing of Iran comes less than 2 months after widespread protests in Iran, associated violence, and violent crackdown. HRANA reports over 7000 confirmed killed and another 12000 reported killed but not yet confirmed. Included in the confirmed killed are over 200 security personnel. Iran Insight claims access to Iranian (IRGC) internal reports that estimate over 35,000 killed in protest suppression in a period of a few weeks.

• The highest mortality from the war may actually be in Sudan, not from direct violence linked to the US/Israel Iran War but from the combined impacts of disruption of the flow of food aid and medical aide, the dramatically worsening famine there reported by the IPC, and the reduced flow of precision weaponry from Iran causing the violence to be less targeted. Some sources claim an additional 600 people are dying every day in Sudan, beyond those already dying because of violence and humanitarian crisis there. While nothing like accurate data is available, if this is even partially true, this would imply that the US/Israel/Iran War war-driven mortality is substantially larger in Sudan than in Iran itself.

I’m writing this as we are entering year 5 of Russia’s full-scale invasion of Ukraine.

Part of Russia’s guiding myth for the invasion is that Ukraine and Russia are actually the same people and have been so since time immemorial. This is a myth.

It is in some ways irrelevant to political legitimacy. Ukraine is a separate independent nation. The Ukrainian people have experienced genocide at the hands of Russians. The will of the people is to not be part of Russia. Russia is violently invading, violating national sovereignty, as well as committing a plethora of war crimes in the process.

Still, the myth is part of their propaganda, both internal and external and it is useful to deconstruct it. For those who don’t know the history of the region, it also helps to make sense of why Ukrainians often refer to Russia as “Muscovy”. This isn’t just an insult but foregrounds Russia’s history in a way that dismantles this Imperial myth which has been used to legitimize the murder of millions of Ukrainians.

Russia is not “Russia”. That would imply that it is the (Kyiv) Rus. It is Muscovy.

Russian language is not “Russian”. Muscovy is a Slavic-based hybrid language of Slavic, Finno-Ugric, and Turkic languages.

These myths of Russia go back centuries. They were politically manufactured and those political motivations still exist and are core to the “Russian myth”.

—

There once was a nation called Kyiv Rus, centered on the Old East Slavic speaking city of Kyiv. The Kyiv Rus was for centuries a powerful hub of European commerce, connecting the Baltics to the Middle East via Greece. It had a unique hybrid government system balancing central powers with local democratic institutions. There is some academic controversy over the origin of Kyiv Rus centering on the balance of Viking vs Slavic influences. Strong Viking influence and trade through the area is clear, and the early Rus rulers were of Viking origin (as attested by independent travel writings of the era and by Scandanavian descent DNA in ruling lineages). However, it is also clear that the society and culture were of local Slavic origin and the Vikings who did settle among the Slavs rapidly adopted local culture and language, much as the Normans in France. The modern consensus is that the assimilation of Varangian Vikings into Slavic society was at least partially cooperative and not purely an act of violent conquest. Kyiv Rus lasted from the 9th to the 13th century, ending with the Mongol invasion and the burning of Kyiv.

There is no continuity between modern Russia and Kyiv Rus. While there was a small village called Moscow on the far east frontier of Kyiv Rus, in a forested area sparsely populated by a mix of Finno-Ugric and Slavic people, it was annihilated by the Mongols. The Mongols rebuilt Moscow, in the 13^th and 14^th century, establishing by fiat the “Duchy of Muscovy” as a vassal to collect taxes in the Mongol Yoke. It grew as a Mongol vassal state from a mixed population of Slavic and Finno-Ugric tribes. The Muscovy eventually broke free from the Mongols under Ivan the Great to become an independent imperial power. In government and administrative culture, Muscovy, being shaped by the Mongols, was more hierarchical, centralized, and authoritarian in comparison to the more democratic historical cultures of Ukraine and Belarus, influenced more by the Vikings and later Lithuanians and Poles, as well as by extensive periods of effective and actual independence.

Linguistically, Ukrainian, Russian, and Belarussian are all descended from Old East Slavic. Ukrainian and Belarussian are slight developments of Old East Slavic and are mutually intelligible with each other and Old East Slavic. The Muscovy language is a hybrid language mixing Old East Slavic with Church Slavonic (imported with the church from the Balkans), Finno-Ugric, and Turkic influences, the latter from their Mongol rulers. It is not mutually intelligible with Ukrainian or Belarussian.

The myth of a united Russian culture and people was created by the Muscovy ruler Ivan the Great in the 15^th century, who expanded his title to “Grand Duke of Muscovy, Sovereign of all Rus” to try to legitimize his authority over the territories of the former Kyiv Rus, mostly then under the control of the Lithuanians and Poles. His heir, Ivan the Terrible, crowned himself the “Tsar of all Russia”, dropping the title “Grand Duke of Muscovy”. However, for the next 150 years, western powers continued to use the name “Muscovy”, including in treaties and official correspondence, denying the claims to the former Kyiv Rus. The myth was definitively elaborated by a desperate Ukrainian scholar, Baranovych, in the 1600s in a letter written to the religious leaders in Moscow, arguing that “we are all one people”. He did this to save his position of economic and social power during the Muscovy annexation of Kyiv and Chernihiv, formerly under Polish rule. The arguments in his letter formed the rhetorical base of the myth of Russian unity. Peter the Great took the last huge step of this Muscovy myth-making process in 1721 changed his title to “Emperor of Russia” after the defeat of Sweden and successfully demanded all foreign governments drop the Muscovy name and use the name “Russia” when referring to his government. This is the point where it went from an illegitimate claim of territory to a gaslighting fiction that “Muscovy” is Rus.

The next several centuries, first under the Russian empire and then under the Soviet Union (The Russian empire under new management) were a period of Russian attempts at suppressing and denying the existence and legitimacy of Ukrainian culture. Ukrainian language was framed as “Russian that had been contaminated by Polish”, even though ironically it was the Russian language which was the hybrid language. Ukrainian speakers were oppressed, arrested, and murdered in the millions.

Inside of Russia, people mostly do not know this history, do not understand how historically shallow the concept of “Russia” is, and are indoctrinated in the “Russia” myth. In Western education, the rich and important history of this region is mostly left out of all but specialist history books, a legacy perhaps of the Cold War.

In Ukraine (and amongst some anti-government Belarussians), this history is very alive with all families having stories of surviving Russian oppression and of family members who didn’t.

Perhaps someday we can look forward to a democratic revolution in Muscovy, with a voluntary name change for language and place back to “Muscovy”, to symbolically drop imperial ambitions. Until then, we see daily in news this name “Russia” which essentially imbeds assertion of imperial ambition over Ukraine, and within Russia the range of public debate is not about whether or not to pursue empire, but (on a good day) between whether to dominate Ukraine by violence (Putin) or economic and political coercion (Navalny, RIP).

It’s also good to remember that Ukrainians are not the only ones who want to be free from Muscovy rule.

Below are a few useful resources…

• Animated Map of Europe over the centuries, including Ukraine
  Note the emergence of Muscovy out of the Golden Horde and its’ expansion west.

• 23 lectures on the history and making of modern Ukraine by Yale history professor, Dr. Timothy Snyder

• A short history of Muscovy absorption of Ukrainian intellectual capital during the Russian empire period and the parallel attempts at erasure of Ukrainian identity by Mariana Matveychuk.

• An historical timeline of Ukraine in regional context

— The short read (4 minutes with charts). Longer detailed read below —

Israeli messaging portrays their army, the IDF, as “the most moral army” in the world. However, in Gaza, Israeli soldiers a horrific number of innocent civilians to get to combatants, mostly women and children. This disregard for civilian casualties is on par with the severity of the war crimes of the civilian massacres in Srebrenica in the 1990s, with the difference that the Gaza attacks are over 15 times larger in scale. Over 125,000 estimated Gazans have been killed by Israeli soldiers, including over 40,000 children.1 The ICC has issued arrest warrants for Prime Minister Netanyahu and former defense Minister Gallant, accusing both of war crimes and crimes against humanity. A case against Israel has been filed in the International Court of Justice for violation of the 1948 Genocide Convention. Even Israeli activists and NGOs are now publicly calling it genocide.

In a previous post, I estimated the scale in terms of loss of life in Gaza. The death toll in Gaza is dramatically more than the widely cited strict counts of identified dead kept by the Gaza Health Ministry, now at over 70,000 dead, including 20,000 children. This is both because of bodies yet to be recovered and from indirect deaths caused by starvation and avoidable disease, mostly affecting children and the elderly.

For this post, i investigated the targeting of those killed, specifically looking at the Civilian to Combatant Ratio (CCR).2 I was able to improve on previous estimates using the Gaza Health Ministry counts and excess combat age deaths among men by looking at the bias toward attacking men revealed by looking at deaths just outside of combatant age. (I was able to further improve on my previous estimates by using data on male bias among non-combatant deaths from previous conflicts and IDF upper and lower bound estimates on total combatants killed to better characterize combatant/non-combatant identity on the GHM list and to extrapolate better off of the list. See technical account below for details)

The revealed CCR of the dead confirmed by the GHM is worse than otherwise recently estimated. The chart here shows the result of this analysis, with predicted combatants by age within the Gaza Health Ministry counts of positively identified dead for 31 July 2025.

While no deaths from war are good, and no civilian casualties are good, it is generally accepted that a Civilian to Combatant Ratio of 1.0 is a clear indication of either disregard for or explicit targeting of civilians. Israel’s 2014 attacks on Gaza had a CCR of 1, already evidence of disregard for innocent lives. The current CCR, however, shows a radical increase in callousness toward innocent, uninvolved Palestinian Arab lives in the current conflict. A recent documentary, Breaking Ranks, shared interviews with Israeli soldiers reporting widespread encouragement for arbitrary shooting of unarmed Gazans, widespread expressed desire within the Israeli Defense Force to kill all Arabs and completely depopulate Gaza, this message of genocide being explicitly promoted by Jewish rabbis in Israel encouraging killing of civilians, and commanding officers ordering subordinates to commit war crimes. The behaviors and sentiments reported by these soldiers is reflected in the overall battlefield statistics.

The following chart of CCR for select historical and current conflicts puts the Israeli killing of civilians in perspective.3 The CCR gives an intuitive sense of the relationship to care vs disregard for civilians, being a direct expression of civilian costs per targeted combatant. However, because the CCR can be so huge for the worst conflicts, like Rwanda in 1994 or the Darfur conflict of 20 years ago, leave these off the chart.

Looking at Israel’s position on the list speaks for itself.

While Israel’s conduct in Gaza is not like Rwanda, it is still among the worst cases in modern warfare, like the massacres in Srebrenica in the 1990s or the unfolding crisis in Sudan. While previous rounds of attacks on Gaza were already well above the CCR threshold of 1.0, the current CCR estimate with areas accessible the Gaza Health Ministry shows a dramatic shift in disregard for civilian life. Even estimates of the CCR including Israeli controlled areas and tunnel fighting are well above previous conflicts.

The data support the reports of increasing genocidal intent in the IDF military.

International reporting is more and more turning against Israel’s conduct in the war in Gaza. While we are reaching an international consensus on the horrific nature of Israel’s conduct and there have been many attempts to quantify it, the mainstream depictions of how bad it is are still often underestimates.

There is currently an official “ceasefire” in Gaza. In reality, Israel continues to attack Gaza, albeit at reduced levels, having killed over 400 people in the last several weeks, as opposed to thousands.4 This ceasefire-in-name is also highly unstable, with Israel continuing to heavily restrict food aid and blocking moves toward an international peace-keeping force in Gaza.

The Civilian to Combatant Ratio is one lens on the horrors of war and how nations conduct themselves in war. Israel’s exceptionally high CCR in Gaza, as well as the established overall scale of the attack, underpin how important it is that the international community presses for a complete cessation of the violence and for the creating of institutions that would allow for a sustained peace and for a rebuilding of Gaza society.

— The Longer Read, 15 minutes with charts and footnotes —

In the rest of this post, I’ll go through the numbers, for those who want to get more deeply into the justification of the analysis and findings.

In the following sections i will …

• Review some of the previous work estimating Civilian Combatant Ratio (CCR) from Israeli strikes on Gaza

• Derive the Gaza CCR Method 1: Estimate the CCR using official Israeli figures of militants killed in Gaza in comparison to GHM death counts

• Derive the Gaza CCR Method 2: Derive a lower bound for the anti-male bias in Israeli attacks, independent of combatant status, and use this to derive an estimate of the CCR, adjusted for unidentified casualties from aerial bombardments

• Adjust the resulting CCR estimate for indirect mortality (war induced starvation and disease)

(Updating the previous post of this analysis, I add observations showing the large number of combatants which must be missing from the GHM list as well as give a more refined analysis of the GHM list using the observed male bias in previous Israeli attacks on Gaza affecting non-combatants.)

Previous estimates of the CDP/CCR

Most of the analyses of the the CCR for Gaza rely on counts of identified dead done by the Gaza health Ministry.

The Gaza Health Ministry keeps a running list of confirmed deaths5 because of the chaos of war, the list is imperfect, but to alleviate this problem, the GHM regularly cleans the list for errors and updates methods of documentation when hose methods prove less reliable. The result is what is considered the most reliable real-time tally of confirmed deaths. While Israel publicly criticizes the list, behind closed doors, it is relied on as accurate by Israeli intelligence. The GHM frequently publishes tallies of the total count of the list, and these tallies are used by press as a measure of total casualties. It is important to recognize, though that this is not an estimate of total mortality, but only a list of confirmed deaths. Total mortality has been established to be around 65% larger than this list, at least while reported in the midst of the more intense pre-”ceasefire” conflict. Occasionally, the GHM publishes the list itself. The last time they did this was with list 11 in July 2025.

The reliability of the GHM death counts has been hashed out repeatedly in countless articles. It is reliable, and despite Israel’s continuous public objections of bias, in private, they admit to their reliability and use them in their own intelligence work.6 See the footnote below for an overview of the analysis establishing the reliability of these numbers.7 It is important to keep in mind that they are NOT an estimate of the number killed, but a lower bound of the number killed based on a count of confirmed and identified dead. Subsequent studies have established that they are an undercount by about 41% to 45%.

The table below lists some of the estimates that have been done of the civilian vs combatant death toll in Gaza. They can be divided according to whether they are attempting to give a principled lower bound of the CCR or an estimate of the actual CCR.

Because the GHM counts do not list the status of the individual as combatant/non-combatant, the typical way to generate an absolute upper bound estimate of combatant casualties (and thus a lower bound on the CCR) is to look at the excess deaths for men of fighting age. The overwhelming majority of those serving in Hamas’s Al Aqsa brigade (Hamas’s military) are 18-35 years old, with dwindling numbers within combat ranks through early 40s. Conservative estimates for such an upper bound then take “potential combatant age” as 15-60. The typical way to determine excess deaths is to subtract female deaths from male deaths within this age.8 This is the method used in the articles by Spagat and Silverman (2023), Gaffney (2024), and Cockerill (2024). Gaffney and Cockerill both get the same estimate: CCR=4.9 Spagat and Silverman get almost double: CCR = 8. This is reasonable and doesn’t represent a conflict in the numbers. First, they are lower bounds, not actual estimates. Second, Spagat and Silverman perform their estimate only a month into the war, when it was dominated by airstrikes which have a far higher CCR in a dense urban environment like Gaza. This is illustrated by the actual estimate of Geitner less than one month into the war: CCR = 16. Unlike the previously mentioned reports, Geitner performed an actual estimate, not a lower bound. It was, however, done in the early weeks of the war which was entirely due to air strikes. Finally, one of the most recent estimates was from Israeli journalist Yuval Abraham in August 2025: CCR = 5.10 This is an actual estimate rather than a lower bound, facilitated by the leak of internal Israeli intelligence on combatants known or assumed to be killed, which Abraham then compared to the Gaza Health Ministry counts of known dead. While this gives a higher CCR than the previous lower bound estimates, it is also inadvertently a dramatic underestimate, as it compares combine known and probable combatant dead to known total people dead, an apples to oranges comparison. I correct this in the next section

Estimating the CCR Method 1: Israeli identified combatants

Yuval Abraham in August published an estimate of the CDP using leaked internal intelligence assessments of the combatants known to be killed vs probably killed as of May 2025, comparing these to the list of confirmed dead. the Israeli list is based on a reliable list of Hamas and Islamic Jihad members. This was an important analysis, but it makes an apples to oranges comparison that significantly underestimates the CDP.

The Israeli list of combatants known to be killed would include, at the least all combatants in the GHM list, as this public list includes names and Israeli government issued ID. Anyone on the unconfirmed list would not be on the GHM death list, as the Israeli military uses the GHM list as part of confirming deaths. An apples to apples comparison would compare the confirmed combatants killed list to the confirmed dead list OR the total confirmed and unconfirmed combatants killed to the total estimated killed, not the GHM count, which is a substantial underestimate of those killed.
The first of these calculations is simple with the numbers already in Abrahams analysis.
The second is facilitated by the results of two surveys done in Gaza in early 2025 which independently assessed the amount that the GHM list underestimates the overall violent death toll: 41% and 45% respectively.11 I take the more conservative of these two and assume that the GHM count is 41% less than the actual total. The table below gives the results.

By comparing unconfirmed combatants to confirmed total, Abrahams significantly underestimates the CDP, which results in 80% underestimate of the number of civilians killed per combatant. Looking specifically at the estimate among the unconfirmed, one gets a remarkably high CCR. This, however, is in line with the Geitner analysis. This makes sense as most of the unconfirmed deaths are due to the inability to recover bodies due to the infrastructure destruction of airstrikes.

Note: The Israeli leaked lists are of “identified” dead. In this way they are a bit like the GHM list in that they do not include unidentified dead and are a lower bound of combatant dead, not an estimate. They include known unknowns, but do not include unknown unknowns.

Estimating the CCR Method 2: Derive lower bound using observed sex bias in Israeli killing, independent of combatant status

The following graph shows the GHM mortality count by age and sex as of 31 July 2025. In this graph, male deaths are coded as clearly non-combatant if they are below 15 or above 59 years old. A number of male deaths in the 15-59 range equal to the number of female deaths slightly adjusted for Gaza age structure are also added to the definitely non combatant category. The result is a visual illustration of how estimates are frequently done of an upper bound of number of combatants killed.12

It is worth noting that the spike in combat age men means that there is some preference for targeting (at least potential) combatants. However, the excessive deaths outside of combat range and of women demonstrates actually avoiding civilian deaths is generally a low priority in the current Israeli military, as has been attested now by numerous Israeli combatants emerging from the conflict.

If one were to interpret this graph as an estimate of actual combatants killed, what immediately stands out is a sudden, dramatic reduction in the killing of men once they reach combatant age and a jump back up again once they reach 60. This would be absurd, as we know that the opposite bias is in play, an emphasis on killing men between 18 and 40. This method, thus, exaggerates the number of possible combatants. we can, however, partially correct this through estimating the rate of targeting men, independent of combatant status.

The following graph shows the relative intensity of direct deaths from Israeli strikes, male vs female as of 31 July 2025 from the GHM counts. This is the ratio of male vs female deaths divided by the ratio of the sexes in the population, by year of age. Given the correction for sex-based age structure, sex-blind targeting should generate a ratio varying closely around 1.

As you can see the male:female mortality ratio is relatively even until about 10 years old, at which point it climbs rapidly until age 18 where it plateaus, starting to descend gradually again at 43 years old. Given that Hamas recruitment is heavily geared toward 18-25 year olds and participation in combat drops off rapidly after 35 years of age, the fact that there is still a strong imbalance toward killing men well past the age of 50 demonstrates a targeting of men independent of combatant status.13

Some of this “male targeting” is structural. While IDF will frequently bomb people in homes, a significant fraction of the IDF killings are in public spaces. In Gaza, men are significantly more likely to be out in public than women. Partially, this has to do with the structuring of Gaza society around women’s work vs men’s work, with women being disproportionally at home, caring for children and elders and taking care of the home. Palestinian society is also very protective of women, and so men are more likely to be out and about where they are more at risk, particularly subject to Israeli violence in the proximity of aid distribution centers. Independent of intentionally targeting men, the IDF effectively targets men just for this structural difference in where people are, based on sex. In addition, there is explicit targeting of men, and specifically military-age men, regardless of whether they are combatants or civilians. As IDF captain Yotam Vilk put it, despite rhetoric in military training about making sure of “means, intent, and ability” to cause harm before firing on an enemy, in Gaza there was no talk of strict protocols of engagement, but targeting for numerous arbitrary reasons “… a suspicion of walking where it’s not allowed. A man aged between 20 and 40.”14 Of course, where it “is allowed” to be is constantly changing and poorly communicated, with even Israeli soldiers not knowing or deciding arbitrarily in the moment where some “red line” is.15

Whether the male bias in targeting is intentional or structural is important for policy concerns, but not essential for the purpose of estimating the bias. We expect this bias to be most strong in the 18-40 age range and to drop off gradually after that. Excess male deaths outside of the primary combatant age would be almost entirely due to this combatant-blind male bias in targeting. If we pick an age range clearly outside of the dominant combatant age, we can take the sex-bias in deaths within this range as a lower bound measure of the non-combatant male targeting bias.16 Because this bias will be significantly stronger within the 18-40 range (as attested by Israeli soldiers themselves), this would be a lower bound of the combatant-blind male bias in targeting.

For this analysis, i choose an age range and then take the anti-male bias at the top of this age range as the combatant-blind anti-male bias in targeting. For the lower end, I’ll use 15 years old. While certainly , there would be a substantially decreasing number of soldiers below 18, an appropriate combatant-blind male bias should correct the death count by age to generate very low but non-zero numbers of combatant deaths below 18. The fractions of combatants killed in previous Gaza conflicts dwindles rapidly over 45 years old, being negligible over 55. See the graph from B’tselem data in the footnotes for illustration. Aiming to avoid overestimate of number of combatants under the upper cutoff while negligibly missing those over the cutoff, I use an upper cutoff of 55 years old.

To test this method, I use the age and sex distribution of Gazan deaths available from B’tselem from before the 7 October 2023 war in a list of total casualties identified by combatant or non-combatant status.17 On the one hand the combatant casualty age distribution from the past can act as a proxy for the expected age distribution of combatant casualties in the current conflict. On the other hand, this data gives us an opportunity to see how the method of identifying male bias works as a way of estimating a lower bound of the CCR in a dataset where the CCR is known.

Starting from the historical B’tselem data, the data allows us to calculate the actual male bias in non-combatant deaths by age, rather than estimate it as a fixed bias. In my first analysis of GHM List 11, I used a cutoff of 55 years as the point at which to estimate male bias, reasoning that there would be too few combatants in this range to significantly effect the spot estimate of bias at this age. This gave a bias of 2 (males twice as likely as females to die). I then took this as a lower bound male non-combatant bias within combatant age range. Looking at the chart of actual male bias from historical data quickly shows two things. First, the historical male bias in deaths was much higher at 55… 3.4 instead of 2. Second, if the same strategy was used on the historical data, this would successfully be a lower bound, but would still dramatically underestimate the male non-combatant bias within combatant range, which went up past 10 times as likely. (see graph above)

This data set allows us to compare two methods for generating a lower bound of non-combatant numbers in a sex and age identified casualty list

• Simple model: Assume as many men are non-combatants among dead as there are women within combatant age range (15-65) among the dead

• Flat 55y Male bias model: Apply the male:female mortality ratio at 55 years old as a lower bound expected male bias among non-combatants within combatant age range

The results are shown in the chart above.

The actual CCR from the data is 1.5:1. The simple model gives a CCR of 0.45:1… a 70% underestimate of the actual CCR from historical data.

The 55y Male Bias model is a significantly improved lower bound: CCR = 0.9:1. However, this is still a 40% underestimate of the known CCR. It slightly underestimates older combatants, but to a much greater degree overestimates younger combatants. If instead, I used 37 years old as the estimation point for male bias, this would give almost exactly the correct CCR for the historical data. However, there are too many differences in the data distribution to apply this unproblematically to the current GHM casualty list for 2023-2025. While there is clearly a strong male bias in the non-combatant deaths in the current GHM data set, it is also quite clear that the male bias is lower than in the historical data set and has a much shallower drop off after a later peak. There can be several explanations for this. The most likely and influential explanation is a heavier impact from more indiscriminate air strikes. Even when there is some intentional targeting, the widespread collateral damage will be very high in civilian numbers and will be much more age and sex neutral in their effects. More airstrikes, while increasing the CCR will also tend to decrease overall male bias in mortality and flatten the age distribution of bias. For this reason, I would stay with the 55 year cutoff but be very mindful of it as a lower bound and not an estimate.

Returning to the estimate of the CCR for 2023 -2025 from the GHM list

The more simple gender parity model assumes that since there are equal numbers of men and women at a given age, there should be equal numbers of non-combatant men killed as women, if attacks were random. Of course, they are not random and there is a bias toward killing men, but before even getting to this point, the population of non-combatant men is actually lower than of women, especially through combatant age range. One correction that can be done would be to remove the expected combatants from the population for deriving the sex ratios at each age. This we can do by estimating the number of combatants and using the age distribution from the B’tselem data on historical combatant casualties in Gaza (see chart above) and applying this to the total number of estimated combatants. It has been estimated by US intelligence that in mid 2023, there were approximately 27500 Hamas militants and another 4000 PIJ militants in Gaza. Likewise, US intelligence had estimated approximately 12500 more recruits by January 2025. Extrapolating this to the time of GHM List 11 gives 18000 new recruits. The B’tselem combatant age distribution is already concentrated in the late teens and early 20s, but reports suggest exceptionally heavy recruitment among 18-23 year olds, so for rough approximation, we can assume that half are in the 18-23 range and the other half follow the existing age distribution of combatant casualties.
The following graph gives the actual vs adjusted male:female ratio in the population by age.

Applying this correction, finding the male bias at 55 years old in casualties (2.0 times as many men as women killed at 55 years old), and applying this expectation through combatant age range gives a CCR of 0.49

This is well above the CCR = 1 threshold usually indicating reprehensible disregard for civilian casualties.

This CCR of 4.9 is a lower bound, not an estimate, however. The historical data indicates that this would give a 40% underestimate of he CCR. If this method generates the same underestimate now as in the historical data, this would give us an estimated CCR of about 8. Given the differences in the conflict intensity, this should be taken with a lot of uncertainty, however.

To get an upper bound, we could use the male bias by age distribution from the B’tselem historical non-combatant casualty list instead of using a flat male bias.

These observed historical male biases are dramatically larger, particularly within combatant age range, where they range from 1.6 at age 65, through 3.9 at 55, up to 10.6 at age 19.18 Assuming this age-wise distribution of male bias in non-combatant casualties generates dramatically fewer predicted combatants and a resulting CCR of 94.0. This we can take as an upper bound on the civilian to combatant ratio of mortality on the GHM list. Almost all of the deaths on the current GHM list would be predicted as civilians from following the same male bias as observed in Israeli attacks in previous conflicts in Gaza.19

As a reality check, we can compare this to the CCR lower bound derived from the IDF list, which is 6.5. This is between the central estimate and lower bound derived from the male bias model.

Accounting for anti-male targeting bias tremendously reduces the estimated number of combatants killed. For the central estimate, based on a combatant age range of 15-55, accounting for this bias results in over a 50% reduction of the number of estimated combatants killed among the confirmed dead: 8500 instead of 18050. The totals of dead were taken from 31 July 2025. Two months before this, as reported in Abrahams 2025, the IDF estimated that there were 7330 combatants among the known dead. If we add 10% to this for it being 10% longer of warfare, it would give us about 8000. The estimate accounting for anti-male bias aligns then with this, given that this is an estimated upper bound of the number of combatants among the confirmed dead.

See the “Non-Combatants and Combatants in Gaza on GHM List 11” graph at the beginning of the post to see the resulting estimate of combatants and non-combatants by age on the GHM list, using this method.

However, there are two further corrections we need to make to the estimate

1. correcting for the heavier civilian mortality observed in airstrikes, which account for the overwhelming majority of the unconfirmed (but survey) established dead.

2. correcting for indirect deaths from avoidable disease and starvation, hitting almost exclusively non-combatant children and elders.

The uncounted dead from violence

In the previous blog, I used survey based estimates of known but uncounted dead to estimate the amount of uncounted dead. These studies suggested the total known dead to be 66% more than those confirmed in the GHM list. I then reasoned that these were primarily from air strikes and that this is why they were not counted, being still buried. Previous studies have shown that in Gaza, airstrikes have generated a CCR of 18-20. This is certainly part of this group of dead, but the missing spike in the 18-23 range of dead indicates that there is also a sizable population of dead combatants not on the GHM list, and this would drive the CCR down considerably. Both sides have their motivations to hide data they have on the numbers of dead combatants, but this does not mean that there is a flaw in the GHM confirmation process. The missing combatant dead are explainable by the GHM simply not having access to the bodies to confirm death. One such group would be those who have died in tunnel warfare, which estimates put between 1000 and 3000 dead… a significant amount of combatants, but no source is claiming it as the majority. Another group would be those who have died in open street battle in areas of Gaza currently controlled by the IDF. This would include both very high numbers of combatants and also a particularly male skewed ratio of non-combatants, particularly in combatant age range, given the IDF’s behavior in previous ground conflicts. It is also quite possible that these would not have been identified as dead in the surveys, but just as missing. These would not necessarily have been included in the estimated GHM undercount amount.

This all makes it difficult to nail down a reliable estimate of the number of unconfirmed dead as well as the CCR of these dead.

To start to try, we can assume that the survey established undercount is a lower bound on excess violent dead. We can also assume that the increases in the GHM list since the ceasefire have been mostly from identifying those already dead… approximately 45 per day since 7 October 2025. Therefore, I take the GHM count at the ceasefire date and apply the survey established undercount rate to get a lower bound on the number people killed through violence. To get an upper bound on the number of combatants in this list, I use the same CCR as on the GHM list, since this has not been pulling from either tunnel deaths or areas controlled by Israel. The question is, how many combatants to add to this that are only Israeli known?

The best we can do is to use claims from Israel to give an upper bound on additional combatants. We have two sources we can use for this.

For an upper bound on total combatants killed, we can use the public claims of Israel on number of combatants killed. In the January 2025, the IDF publicly claimed 17,000 to 20,000 combatants dead.20 Subtracting out the 1609 killed outside of Gaza gives approximately 15,400 to 18,400 combatants killed in Gaza.

For a lower bound, we can use the IDF leak from May 2025, a summary of a list of known combatant deaths (7330) and combatants suspected dead (1570).21

If we take “suspected” to mean “80% likely”, this gives us a total estimate of 8600 dead combatants in May 2025. Extrapolated to the ceasefire date gives an estimated 11,000,but this should really be seen as a lower bound estimate, given that it is from an “identified” list. Add to this the consideration that at least 2000 combatants should be beyond the GHM list and survey established known dead, with 1000 being a usual claimed lower estimate of deaths in tunnel fighting and another 1000 being a very low estimate of those dead in open street battles in Israeli controlled areas.

Combining these numbers and limitations gives a rough estimated overall CCR among all violent deaths of 4.0, with a lower bound of 3.0 and an upper bound of 12.0. While a very high CCR is possible with substantially exaggerated Israeli combatant casualty numbers, it is more likely the case that high non-GHM-confirmed combatant casualties bring the overall CCR down to around 4.0… still higher than Israeli public claims, but lower that some counter estimates that don’t account for these casualties.

Worth noting is that the range between upper and lower bounds of the CCR is substantially reduced including the unconfirmed dead, because while we are unsure of the number of combatant deaths on the GHM list, we are sure of some minimum, either on or off the list.

Indirect deaths from starvation and avoidable disease

Finally, we can add the indirect deaths. As explained in the previous post, some early publications without grounding data from Gaza published dramatic overestimates of indirect mortality from starvation and avoidable disease. These have been corrected with household surveys in Gaza which establish that these were about 12 for every 100 violently killed as of January 2025. 22

This is dramatically less than the 4 to 1 estimates given elsewhere, but still substantial. To estimate the impacts through the ceasefire, I assumed for a lower bound that the rate of indirect deaths continued at the same pace through 2025, for an upper bound assume it was double and used the middle for a middle estimate. This then gives an additional 15,000 to 30,000 dead, entirely non-combatant and mostly children and elderly. This brings the estimated CCR up to 4.6 (lower bound 3.7, upper bound 15.4).

From the perspective of methods, this analysis demonstrates how the usual-method simple lower bound calculation can be a dramatic underestimate. Once the fog of war has lifted, we may be able to better assess this model.

From the perspective of policy and human rights concerns, we sometimes can not wait for the fog of war to lift and need to make decisions based on the information available. This analysis reveals how the disregard for civilian lives by the Israeli military in the urban environments accessible by the Gaza Health Ministry is on par with the war crimes of Srebrenica.

As previous generations’ world wars led to the development of international norms of engagement that limit the horrors of war, the analysis and charts above demonstrate how far outside of these norms Israel has moved. Israel is not alone in this, but it is a glaring example of disregard for innocent lives while pursuing military objectives

13

The following is a graph of combatants killed in Gaza in the years before the October 7 attack.

Note: this blog post documents an initial analysis and methods. The next blog post updates this analysis with improved data. The short version is that the Gaza Health Ministry is almost certainly not listing many combatant dead, as they do not have access to areas where the most intense fighting. Combining GHM counts and IDF claims of combatant dead currently gives bout 80% civilians among dead, rather then this initial estimate of 90%, which characterizes those areas accessible to the GHM and subject more to aerial bombardment.

I leave this post for reference. See updated post for improved estimates accounting for more precise estimates of male bias in non-combatant deaths from previous conflicts and estimates of missing combatant numbers from Gaza Health Ministry counts caused by inaccessibility of Israel controlled areas.

— The short read (4 minutes with charts). Longer detailed read below —

Israeli messaging relentlessly portrays their army, the IDF, as “the most moral army” in the world. However, in Gaza, Israeli soldiers have killed nine innocent civilians for every combatant, mostly women and children. This horrific disregard for civilian casualties is on par with the severity of the war crimes of the civilian massacres in Srebrenica in the 1990s, with the difference that the Gaza attacks are over 15 times larger in scale. Over 130,000 estimated Gazans have been killed by Israeli soldiers, including over 40,000 children1. The ICC has issued arrest warrants for Prime Minister Netanyahu and former defense Minister Gallant, accusing both of war crimes and crimes against humanity. A case against Israel has been filed in the International Court of Justice for violation of the 1948 Genocide Convention. Even Israeli activists and NGOs are now publicly calling it genocide.

In a previous post, I estimated the scale in terms of loss of life in Gaza. The death toll in Gaza is dramatically more than the widely cited strict counts of identified dead kept by the Gaza Health Ministry, now at over 69,000 dead, including 20,000 children. This is both because of bodies yet to be recovered from airstrikes and from indirect deaths caused by starvation and avoidable disease, mostly affecting children and the elderly. (see footnotes for link)

For this post, i investigated the targeting of those killed, specifically looking at the Civilian to Combatant Ratio (CCR) and the Civilian Death Percentage (CDP), two different ways of formulating and reporting the same statistic2. I was able to improve on previous estimates using the Gaza Health Ministry Counts and excess combat age deaths among men by looking at the bias toward attacking men revealed by looking at deaths just outside of combatant age. The revealed CCR is dramatically worse than otherwise recently estimated. This is backed up by Israel’s own estimates of number of combatants killed. The calculations and data are explained in more detail in the longer methods section below, as well as a review of previous estimates. The chart here shows the result of this analysis, with predicted combatants by age within the Gaza Health Ministry counts of positively identified dead for 31 July 2025.

While no deaths from war are good, and no civilian casualties are good, it is generally accepted that a Civilian to Combatant Ratio of 1.0 is a clear indication of either disregard for or explicit targeting of civilians. Israel’s 2014 attacks on Gaza had a CCR of 1, already evidence of disregard for innocent lives. The current CCR of 9, however, shows a radical increase in callousness toward innocent, uninvolved Palestinian Arab lives in the current conflict. A recent documentary, Breaking Ranks, shared interviews with Israeli soldiers reporting widespread encouragement for arbitrary shooting of unarmed Gazans, widespread expressed desire within the Israeli Defense Force to kill all Arabs and completely depopulate Gaza, this message of genocide being explicitly promoted by Jewish rabbis in Israel encouraging killing of civilians, and commanding officers ordering subordinates to commit war crimes. The behaviors and sentiments reported by these soldiers is reflected in the overall battlefield statistics.

The following two charts of CCR and CDP for select historical and current conflicts puts the Israeli killing of civilians in perspective3. The CCR gives a better intuitive sense of the relationship to care vs disregard for civilians, being a direct expression of civilian costs per targeted combatant. However, because the CCR can be so huge for the worst conflicts, like Rwanda in 1994 or the Darfur conflict of 20 years ago, I also share the data in CDP format.

Looking at Israel’s position in these charts speaks for itself.

While Israel’s conduct in Gaza is not like Rwanda, it is still among the worst cases in modern warfare, like the massacres in Srebrenica in the 1990s or the unfolding crisis in Sudan. While previous rounds of attacks on Gaza were already well above the CCR threshold of 1.0, the current CCR estimated at 9.3 shows a dramtic shift in disregard for civilian life. The civilian to combatant ratio for Israel in the current Gaza conflict is in fact almost twice that of the Islamist groups that fought in the Iraq War.

There is, of course, targeting of Palestinian military in Gaza and some preference shown sometimes to avoid civilian casualties. It is not the same as the unrestrained genocidal attacks in Rwanda or in Darfur 20 years ago. However, the data show at the least how dramatically low of a priority it is overall, avoiding civilian casualties, and support the reports of increasing widespread explicit genocidal intent in the IDF military.

International reporting is more and more turning against Israel’s conduct in the war in Gaza. While we are reaching an international consensus on the horrific nature of Israel’s conduct and there have been many attempts to quantify it, the mainstream depictions of how bad it is are still often underestimates.

There is currently an official “ceasefire” in Gaza. In reality, Israel continues to attack Gaza, albeit at reduced levels, having killed over 240 people in the last several weeks, as opposed to thousands4. This ceasefire-in-name is also highly unstable, with Israel continuing to heavily restrict food aid and blocking moves toward an international peace-keeping force in Gaza.

The Civilian to Combatant Ratio is one lens on the horrors of war and how nations conduct themselves in war. Israel’s exceptionally high CCR in Gaza, as well as the established overall scale of the attack, underpin how important it is that the international community presses for a complete cessation of the violence and for the creating of institutions that would allow for a sustained peace and for a rebuilding of Gaza society.

— The Longer Read, 12 to 15 minutes with charts and footnotes —

In the rest of this post, I’ll go through the numbers, for those who want to get more deeply into the justification of the analysis and findings.

In the following sections i will …

• Review some of the previous work estimating Civilian Combatant Ratio (CCR) from Israeli strikes on Gaza

• Derive the Gaza CCR Method 1: Estimate the CCR using official Israeli figures of militants killed in Gaza in comparison to GHM death counts

• Derive the Gaza CCR Method 2: Derive a lower bound for the anti-male bias in Israeli attacks, independent of combatant status, and use this to derive an estimate of the CCR, adjusted for unidentified casualties from aerial bombardments

• Adjust the resulting CCR estimate for indirect mortality (war induced starvation and disease)

Previous estimates of the CDP/CCR

Most of the analyses of the the CCR for Gaza rely on counts of identified dead done by the Gaza health Ministry. The reliability of the GHM death counts has been hashed out repeatedly in countless articles. It is reliable, and despite Israel’s continuous public objections of bias, in private, they admit to their reliability and use them in their own intelligence work5. See the footnote below for an overview of the analysis establishing the reliability of these numbers.6 It is important to keep in mind that they are NOT an estimate of the number killed, but a lower bound of the number killed based on a count of confirmed and identified dead. Subsequent studies have established that they are an undercount by about 41% to 45%.

The table below lists some of the estimates that have been done of the civilian vs combatant death toll in Gaza. They can be divided according to whether they are attempting to give a principled lower bound of the CCR or an estimate of the actual CCR.

Because the GHM counts do not list the status of the individual as combatant/non-combatant, the typical way to generate an absolute upper bound estimate of combatant casualties (and thus a lower bound on the CCR) is to look at the excess deaths for men of fighting age. The overwhelming majority of those serving in Hamas’s Al Aqsa brigade (Hamas’s military) are 18-35 years old, with dwindling numbers within combat ranks through early 40s. Conservative estimates for such an upper bound then take “potential combatant age” as 15-60. The typical way to determine excess deaths is to subtract female deaths from male deaths within this age.7 This is the method used in the articles by Spagat and Silverman (2023), Gaffney (2024), and Cockerill (2024). Gaffney and Cockerill both get the same estimate: CCR=48. Spagat and Silverman get almost double: CCR = 8. This is reasonable and doesn’t represent a conflict in the numbers. First, they are lower bounds, not actual estimates. Second, Spagat and Silverman perform their estimate only a month into the war, when it was dominated by airstrikes which have a far higher CCR in a dense urban environment like Gaza. This is illustrated by the actual estimate of Geitner less than one month into the war: CCR = 16. Unlike the previously mentioned reports, Geitner performed an actual estimate, not a lower bound. It was, however, done in the early weeks of the war which was entirely due to air strikes. Finally, one of the most recent estimates was from Israeli journalist Yuval Abraham in August 2025: CCR = 59. This is an actual estimate rather than a lower bound, facilitated by the leak of internal Israeli intelligence on combatants known or assumed to be killed, which Abraham then compared to the Gaza Health Ministry counts of known dead. While this gives a higher CCR than the previous lower bound estimates, it is also inadvertently a dramatic underestimate, as it compares combine known and probable combatant dead to known total people dead, an apples to oranges comparison. I correct this in the next section

Estimating the CCR Method 1: Israeli identified combatants

Yuval Abraham in August published an estimate of the CDP using leaked internal intelligence assessments of the combatants known to be killed vs probably killed as of May 2025, comparing these to the list of confirmed dead. the Israeli list is based on a reliable list of Hamas and Islamic Jihad members. This was an important analysis, but it makes an apples to oranges comparison that significantly underestimates the CDP.

The Israeli list of combatants known to be killed would include, at the least all combatants in the GHM list, as this public list includes names and Israeli government issued ID. Anyone on the unconfirmed list would not be on the GHM death list, as the Israeli military uses the GHM list as part of confirming deaths. An apples to apples comparison would compare the confirmed combatants killed list to the confirmed dead list OR the total confirmed and unconfirmed combatants killed to the total estimated killed, not the GHM count, which is a substantial underestimate of those killed.
The first of these calculations is simple with the numbers already in Abrahams analysis.
The second is facilitated by the results of two surveys done in Gaza in early 2025 which independently assessed the amount that the GHM list underestimates the overall violent death toll: 41% and 45% respectively.10 I take the more conservative of these two and assume that the GHM count is 41% less than the actual total. The table below gives the results.

By comparing unconfirmed combatants to confirmed total, Abrahams significantly underestimates the CDP, which results in 80% underestimate of the number of civilians killed per combatant. Looking specifically at the estimate among the unconfirmed, one gets a remarkably high CCR. This, however, is in line with the Geitner analysis. This makes sense as most of the unconfirmed deaths are due to the inability to recover bodies due to the infrastructure destruction of airstrikes.

Estimating the CCR Method 2: Derive lower bound using observed sex bias in Israeli killing, independent of combatant status

The following graph shows the GHM mortality count by age and sex as of 31 July 2025. In this graph, male deaths are coded as clearly non-combatant if they are below 15 or above 59 years old. A number of male deaths in the 15-59 range equal to the number of female deaths slightly adjusted for Gaza age structure are also added to the definitely non combatant category. The result is a visual illustration of how estimates are frequently done of an upper bound of number of combatants killed.11

It is worth noting that the spike in combat age men means that there is some preference for targeting (at least potential) combatants. However, the excessive deaths outside of combat range and of women demonstrates actually avoiding civilian deaths is generally al low priority in the current Israeli military, as has been attested now by numerous Israeli combatants emerging from the conflict.

If one were to interpret this graph as an estimate of actual combatants killed, what immediately stands out is a sudden, dramatic reduction in the killing of men once they reach combatant age and a jump back up again once they reach 60. This would be absurd, as we know that the opposite bias is in play, an emphasis on killing men between 18 and 40. This method, thus, exaggerates the number of possible combatants. we can, however, partially correct this through estimating the rate of targeting men, independent of combatant status.

The following graph shows the relative intensity of direct deaths from Israeli strikes, male vs female as of 31 July 2025 from the GHM counts. This is the ratio of male vs female deaths divided by the ratio of the sexes in the population, by year of age. Given the correction for sex-based age structure, sex-blind targeting should generate a ratio varying closely around 1.

As you can see the male:female mortality ratio is relatively even until about 10 years old, at which point it climbs rapidly until age 18 where it plateaus, starting to descend gradually again at 43 years old. Given that Hamas recruitment is heavily geared toward 18-25 year olds and participation in combat drops off rapidly after 35 years of age, the fact that there is still a strong imbalance toward killing men well past the age of 50 demonstrates a targeting of men independent of combatant status.12

Some of this “male targeting” is structural. While IDF will frequently bomb people in homes, a significant fraction of the IDF killings are in public spaces. In Gaza, men are significantly more likely to be out in public than women. Partially, this has to do with the structuring of Gaza society around women’s work vs men’s work, with women being disproportionally at home, caring for children and elders and taking care of the home. Palestinian society is also very protective of women, and so men are more likely to be out and about where they are more at risk, particularly subject to Israeli violence in the proximity of aid distribution centers. Independent of intentionally targeting men, the IDF effectively targets men just for this structural difference in where people are, based on sex. In addition, there is explicit targeting of men, and specifically military-age men, regardless of whether they are combatants or civilians. As IDF captain Yotam Vilk put it, despite rhetoric in military training about making sure of “means, intent, and ability” to cause harm before firing on an enemy, in Gaza there was no talk of strict protocols of engagement, but targeting for numerous arbitrary reasons “… a suspicion of walking where it’s not allowed. A man aged between 20 and 40.”13 Of course, where it “is allowed” to be is constantly changing and poorly communicated, with even Israeli soldiers not knowing or deciding arbitrarily in the moment where some “red line” is.14

Whether the male bias in targeting is intentional or structural is important for policy concerns, but not essential for the purpose of estimating the bias. We expect this bias to be most strong in the 18-40 age range and to drop off gradually after that. Excess male deaths outside of the primary combatant age would be almost entirely due to this combatant-blind male bias in targeting. If we pick an age range clearly outside of the dominant combatant age, we can take the sex-bias in deaths within this range as a lower bound measure of the non-combatant male targeting bias15. Because this bias will be significantly stronger within the 18-40 range (as attested by Israeli soldiers themselves), this would be a lower bound of the combatant-blind male bias in targeting.

For this analysis, i will choose an age range and then take the anti-male bias at the top of this age range as the combatant-blind anti-male bias in targeting. For the lower end, I’ll use 15 years old. While certainly , there would be a substantially decreasing number of soldiers below 18, an appropriate combatant-blind male bias should correct the death count by age to generate very low but non-zero numbers of combatant deaths below 18. The fractions of combatants killed in previous Gaza conflicts dwindles rapidly over 45 years old, being negligible over 55. See the graph from B’tselem data in the footnotes for illustration. Aiming to avoid overestimate of number of combatants under the upper cutoff while negligibly missing those over the cutoff, I use an upper cutoff of 55 years old, but also give 5 years above and below to see the sensitivity to this assumption. I then compare the results against Israel intelligence numbers of combatants killed and against documented age structure of dead combatants in previous fighting, specifically looking at predicted percent of mortality over 45 vs that observed in previous conflicts. The Table below gives the CDP/CCR estimates as well as total combatant estimates among known dead.

As you can see from the first columns in the table, accounting for anti-male targeting bias tremendously reduces the estimated number of combatants killed. For the central estimate, based on a combatant age range of 15-55, accounting for this bias results in over a 50% reduction of the number of estimated combatants killed among the confirmed dead: 8500 instead of 18050. The totals of dead were taken from 31 July 2025. Two months before this, as reported in Abrahams 2025, the IDF estimated that there were 7330 combatants among the known dead. If we add 10% to this for it being 10% longer of warfare, it would give us about 8000. The estimate accounting for anti-male bias aligns then with this, given that this is an estimated upper bound of the number of combatants among the confirmed dead.

Another check we can do, again, is to look at the fraction of combatants predicted to be in the 45+ age range. Historical counts of combat deaths in Gaza from B’tselem (see footnotes) put this number at 3.4% in the 2000-2023 time frame. The calculations here, for the central estimate, give 3.6% in the above 45 age category, an effectively indistinguishable match.16

The following modified version of the graph above shows how this method corrects the typical approach which does not account for observed anti-male bias in targeting. As you can see the shape of the predicted non-combatants follows a much more sensible pattern through the potential combatant ages.

However, there are two further corrections we need to make to the estimate

1. correcting for the heavier civilian mortality observed in airstrikes, which account for the overwhelming majority of the unconfirmed (but survey) established dead.

2. correcting for indirect deaths from avoidable disease and starvation, hitting almost exclusively non-combatant children and elders.

Both of these corrections are displayed in the last columns of the table above.

We can assume that the unconfirmed dead have a CDP of 93% in line with that implied by Abrahams data and just under that observed in the Geitner data. Surveys done in early 2025 demonstrated an undercount by the GHM of 41% and 45% respectively. Taking the lower of the two and assuming that the GHM count is a 41% undercount, the CDP of 85.9 is corrected to 88.6 for the central estimate. The CCR moves from 6.1 to 7.8.

Based again on survey data from early 2025, it was established that, beyond violent deaths, there was an additional 12% who died indirectly from the violence, from starvation and avoidable disease. The survey was done, however, before the switch to Israeli/US controlled distribution of food and before the drastic escalation in crisis of starvation in Gaza. Typically, this indirect mortality amplifier would increase over time as the delayed effects of infrastructure damage unfold, but in this case infrastructure damage was actually still increasing. I make a conservative estimate of a 50% increase in the amplifier effect of indirect causes of death from war, giving an 18% amplifier, with this additional mortality having a CDP of 100, effecting almost entirely young children and elderly. This adjusts the central estimate of the CDP to 90.3 and the CCR to 9.3.

See my previous blog for more details on the basis of both of these corrections.

From the perspective of methods, these 3 analysis based on these 3 corrections demonstrates how the usual-method simple lower bound calculation can be a dramatic underestimate.

From the perspective of policy and human rights, it reveals how the disregard for civilian lives by the Israeli military is on par with the war crimes of Srebrenica.

As previous generations’ world wars led to the development of international norms of engagement that limit the horrors of war, the analysis and charts above demonstrate how far outside of these norms Israel has moved. Israel is not alone in this, but it is a glaring and extreme example of disregard for innocent lives while pursuing military objectives

12

The following is a graph of combatants killed in Gaza in the years before the October 7 attack.

20-25 years ago, there was some limited use of child soldiers in Palestine, but owing to internal reforms, this practice was much more restricted in the years after 2013, as can be seen in the mortality figures. You can see that there is a rapid drop off in combatants after age 25, and that there are only negligible numbers over 45. Specifically, about 3% of overall combatant casualties are over the age of 45 in the 2014-2023 period, even less if considering the period from 2000.

Edit update: This version (24 Nov 2025) is an update based on more recent figures from the Gaza Health Ministry and incorporating findings of increasing rates of documentation of deaths in the GHM numbers over time as more bodies are recovered.

Barring Israel’s few closest allies, the international consensus is that Israel is in the process of committing genocide in Gaza. In a separate post, I will look into the nature and history of genocide, why Israel’s actions are seen as genocidal by the international community, and why debate initiated by Israel about whether it is specifically genocide only serves to diffuse the horror of mass killing. In this post, however, I look more narrowly at the number deaths. Estimating deaths in the fog of war is difficult and emotionally numbing, but is also essential.

Numbers of deaths being reported internationally, citing counts by the Gaza Health Ministry of identified violently killed, are horrific. However, as horrific as these reported numbers are, they are significantly below the actual number of people killed by the Israeli assaults. When we add together people who have been directly killed through guns and bombs with estimates of second order deaths from preventable and treatable diseases or starvation, we get in the neighborhood of 132,000 people as an estimate of how many have died. Because of lack of access to data, this is inevitably a rough estimate. The derivation is detailed below.

The Gaza Health Ministry’s (GHM) count of confirmed violent deaths on 19 November 2025 was 695131. These numbers are often reported by news agencies as if they were the death toll, but these are only counts of clearly identified, violent death. They do not capture the total violent deaths, as they don’t capture all those buried under rubble or those who were killed along with anyone else who would have reported them dead, as when entire families are killed or when hospitals are bombed and those who handle the counts are killed. They also do not count second order deaths at all. While people are now widely aware that there is starvation happening in Gaza, the scale of people suffering, people dying is hard to fathom and very difficult to count. Few realize that these second order deaths are in historically similar circumstances significantly larger in scale than the first order deaths from bullets and bombs. It is rarely made clear in international reporting on the death toll in Gaza that the GHM’s counts are simply a lower bound on death on some of the deaths.

Israeli authorities continuously argue that the GHM numbers can not be trusted because of their relationship to Hamas, but international experts and even Israeli intelligence services2 have long confirmed that the GHM counts in all previous conflicts have been shown to be undercounts and that Israel’s more extreme undercounts are even farther from the truth3. Further, because of the chaos and intensity of this specific ongoing assault, the mechanisms for keeping these counts are hindered, resulting now in an even greater undercount of direct violent deaths. Two independent investigations, using different methods, have shown that the GHM currently underestimates the direct deaths from Israeli assaults by 41% to 45%. A Lancet report in February 2025 found a 41% underestimate4. A Nature paper followed in June 2025, finding that the rate of undercount was by then 45%5.

Taking the lower of these two, applied to current GHM statistics gives a figure of approximately 112,000 direct deaths. Given, however, the worsening situation for keeping statistics, it is most likely that the undercount has gotten worse, not better.

And of course, it is important to remember that the majority of these deaths are women and children and the overwhelming majority of those killed are innocent civilians. If one assumes that bystander adult men and women are killed in equal numbers, then from the excess male deaths, one gets about 1 in 5 of those killed directly by bombs and guns being combatants6, one of the worst ratios of civilian to combatant deaths of any 21st century conflict.

However, as horrifying as 112,000 dead sounds, this is only direct violent deaths: people shot, people caught in explosions, people buried in destroyed buildings. We know that more have died from preventable or treatable diseases and from malnutrition. Hospitals in Gaza have been annihilated, and as everyone knows food is only let in under Israeli control in inadequate amounts in inhuman operations where war crimes7 are regularly documented against civilians trying to feed their families.

In any conflict, there is going to be a “multiplier effect”8, where the disruptions of war lead to further deaths from disease and starvation even after the bombs and bullets have paused. Very roughly speaking, we see that destruction of infrastructure would scale up with direct, violent deaths, and that indirect deaths increase with destruction of infrastructure. The horrors of this particular war in this way are what tipped the balance of international sentiment against Israel and led in the last months to more unified accusations of genocide. Such second-order deaths are unarguably the fault of Israel as the one in control of all the factors causing them, as the one who has so regularly targeted hospitals for destruction, obliterated housing, displaced the population, and prevented aide from reaching people.

How many are these “indirect” deaths?

The amount of second order deaths varies wildly from one conflict to another, anywhere from negligible second order deaths in more urban settings with high interconnectivity with other nations to a multiplier of 8 to 9 second order deaths to every first order death from violence in more isolated, rural contexts9.

The administrative structures that might have been able to keep these counts in Gaza have been severely undermined by Israel’s 2 years of assault, but we repeatedly hear witness accounts of widespread starvation. At the same time, it is a conflict with high international visibility and some attempt to get food into the area.

An earlier report attempted to use analogy to the conflict in Darfur and use a multiplier of 4 to 1, analogous to that documented in the wider time period of that conflict10. However, Professor Mike Spagat, who has conducted extensive research on the conflict thinks that this analogy is unreasonable, given the international attention on Gaza and the effects of this on available aid and attempts to deliver food and resources into Gaza11. Professor Spagat’s research team then conducted a survey at the beginning of 2025 of 2000 households in Gaza to estimate the violent deaths and excess deaths from disease and starvation. While their survey results supported the finding of significant undercounts of violent deaths by the GHM, they came to much more conservative findings for the excess deaths from disease/starvation… specifically they estimated about 8540 excess non-violent deaths (non-violent deaths beyond those expected). This translates to about 0.11 indirect deaths for every direct violent death, inline with a more internationally connected urban conflict12. This is qualitatively inline with the results of an independent Medicin sans Frontier of deaths among its staff and their families, done in March 202513.

The situation has, of course, deteriorated since then. That was 14 months into the conflict, and we are now entering year 2. The situation got dramatically worse with the change to Israeli/US food distribution, with a doubling of the number of people being exposed to famine conditions.14 While there was most likely more than a doubling of the rate of indirect deaths with a doubling of famine exposure and prolonged famine exposure, we can take a doubling of the rate of indirect death as a conservative estimate. This would translate to approximately 20,740 excess nonviolent deaths from disease and starvation or 19 more indirect deaths for every 100 violent deaths.

Taking the current GHM statistics, taking into account the undercount rate for violent deaths, and accounting for indirect deaths gives 130,000+ dead so far in Gaza from Israel’s assault. This represents 6% of Gaza’s 2023 population, dead. These numbers are still possibly an undercount, given the conservative assumptions used to derive them.

People are still dying.

These deaths are preventable.

We do not know for certain how bad it is in Gaza. Until Israel allows regular unrestricted international access to Gaza, we will not know for sure how catastrophic the death toll is. However our inability to make a precise estimate must not stand in the way of making some estimate and acting on this information, especially when those responsible for the genocide are in control of access to the data.

Further the work that is being done there is evidence that one can do meaningful evidence gathering even in the context of heated violent conflict.

I thought it would be good to do a follow up on the different data sets as there is being some discussion now on how much right wing motivations dominate. As the data sets have different filters and capture different aspects of ideological violence, it’s good to understand what they each contribute to the overall picture. the three data sets that i used were

• FBI data on hate crime based homicides2

• Anti-Defamation League (ADL) counts of politically motivated homicides3

• Nowrasteh’s counts of politically motivated homicides as published by the Cato Institute4

In the combined data set, 73% of all ideologically driven homicides were right wing driven in 2016-2025. 11% were left wing driven and 16% were Islamist driven. (See the original blog). These numbers are sensitive to the exact time period chosen. Most clearly, if one includes the last 25 years, the Islamist attack of 9/11 swamps the data and makes Islamist drivers responsible for over 80% of ideologically driven homicides

I’ve decided to create separate charts of the 3 data sets. While hate crimes are not intrinsically attached to ideology, spot checking cases indicated that they overwhelmingly are, whether it be white supremacy, Christian nationalist religious extremism, or occasionally leftist identity politics. The main difference between hate crime and politically motivated homicide is of premeditation and explicit intent to change political or social dynamics. Conceptually there is a blur of categories, but usually, they seem to be prosecuted and counted separately.

First, here is the FBI data.

Note, for FBI hate crime counts, there were not any clear Islamism based hate crimes in this period5. I categorized anti-white, anti-male, and anti-heterosexual hate crimes as left wing, checked through anti-Jewish case by case, and counted all others as right wing. While anti-Jewish rhetoric can be found on the left, on the right, and within Islamist rhetoric, that I could see, 13 anti-Jewish murders in this time period were right wing (white supremacist, neo-Nazi, or Black Hebrew Israelite) and 5, from one incident, were left wing.

For this time period, the shares of right wing / left wing /Islamist drivers for FBI documented hate crime are 87% / 13% / 0%.

Here is this data broken out by bias type…

Of course, there is something problematic about simplifying this to a right/left spectrum, and whether this is appropriate or not really depends on the question you are exploring. For example, if one is looking generally at authoritarianism and racial supremacy, then you would lump together both white supremacists and Black Hebrew Israelites. However, if one were to look more narrowly at white Christian nationalism, then obviously one would not include the Black Hebrew Israelites. Of course, if one is looking at authoritarianism, then arguably one should be including Communists and Islamicists, or arguably anyone attempting to impose their views through violence, which would then make all politically motivated homicides in this “authoritarianism” category. As one gets to Libertarian killers and the Sovereign Citizen movement, some are more right wing and others more left wing. It’s not simple, and fringe ideologies tend to be overrepresented in these murder cases.
That said, most of the cases here are from traditionally understood right and left groups. I lump together on the right diverse groups like neo-Nazis, white supremacists, Christian Nationalists, Incels, and anti-abortion activists and lump together on the left such ideologies as queer identity politics, antifa, non-religious black supremacist, anti-racism activist, Nation of Islam. One could (and should) divide this up in different ways depending on the question, particularly if one were looking for sub-community-specific intervention possibilities, so further investigation and re-analysis is encouraged in this case. I would be interested to hear of other proposed ways to organize this data.

There were only 3 out of 170 homicides with multiple biases. While I did not access and find these specific cases, the most commonly referred to combinations in hate crimes generally were race/national origin (Asian or Middle Eastern) and sexual orientation/gender expression, both right wing motivations.

—

Now, here is the ADL data on politically motivated homicides in the same time.

This gives us a different picture, particularly as it includes the Pulse nightclub Islamist attack in 20166. Here, the shares by ideology as drivers are 76% / 5% / 19% for right wing / left wing / Islamist.

—

Finally, i have a slightly modified version of Nowrasteh’s counts7. I checked specifically through Nowrasteh’s left wing counts and concur with all these case identifications. The modifications i made were not consistently in one direction, and so do not generate a huge qualitative difference, but see the footnote for details.

As you can see, Nowrasteh catches more left wing violence than identified by the ADL, especially in the 2021-2024 period, as well as more cases of Islamist homicide. While the ADL data is only available through 2024, Nowrasteh’s counts to September 2025, catching in 2025 the January 1 New Orleans Islamist attack as well as several more left wing attacks. This, for the 2016 - 2025 period gives 45% / 17% / 38% for right/left/Islamist drivers. This is a very different picture.

Again, i would stand behind Nowrasteh’s assessment for left wing cases in this time period, and i think this represents a bias in the ADL data. Again, if you follow the links, you can decide for yourself. However, the ADL data also has higher numbers for right wing driven homicides. My take is that these are all legitimate cases. For this reason, i chose to take the higher of the two (ADL and Nowrasteh data sets) for a given category and year and then add these to the FBI hate crime counts, which i assumed to be non-overlapping8. This may not be entirely accurate of an assumption, but a spot check indicated that overlap would be small if present. This gives the final count i shared as the top chart in my last post…

4

https://www.cato.org/blog/politically-motivated-violence-rare-united-states
also see Nowrasteh’s substack blog, which links to a list of the actual cases comprising the data.

Laissez-Faire, Laissez-Passer w/ Alex Nowrasteh & David Bier

Politically Motivated Violence is Rare in the United States

Charlie Kirk, the co-founder of Turning Point USA, was assassinated while speaking at an event in Utah yesterday. The police have not arrested a suspect, but the assassin was likely motivated by political disagreements with Kirk. Politically motivated murder is unacceptable, inherently bad like all murder, and doubly so because of how socially corrosive…

Read more

a year ago · 95 likes · 49 comments · Alex Nowrasteh

Short read … 2 minutes (longer continuation below … 4 minutes)

The recent killing of Charlie Kirk has reignited debate about politically motivated violence. Unfortunately, much of the conversation has focused on premature, arguably obsessive, speculation about the suspect’s politics rather than the broader patterns of ideological violence in the United States. A more useful discussion centers on sources of ideologically driven violence more generally and what can be done about them.

Alex Nowrasteh of the Cato Institute has argued that ideological violence in the US remains rare, with about half of recent cases driven by the right and the rest split between left-wing and Islamist motivations1. By contrast, a 2021 Washington Post article2, drawing on ADL data3, claimed that right-wing violence overwhelmingly dominates. In this post, I combine Nowrasteh’s carefully coded, publicly accessible dataset with newer ADL counts and FBI tallies of hate-crime homicides4. Nowrasteh’s count corrects the ADL undercounts of left-wing and Islamist cases and including FBI hate crimes gets us more inclusively to what we are at the root concerned about… how polarization and extreme views drive violence. This broader view shows that while ideological killings are indeed rare, right-wing violence is more dominant than Nowrasteh suggested. This is illustrated here in the top chart.

Key findings:
• The clear majority of ideology-based homicides are right-wing motivated.
• Left-wing and Islamist violence, while smaller in scale, remain recurring issues.
• Right-wing homicides have declined in recent years but still far outnumber others.

Nowrasteh breaks down ideological homicides as 54% right-wing, 23% left-wing, 21% Islamist in the past 5 years. Expanding the data set leads instead to 82%, 10%, and 7% respectively. The conclusion—that right-wing violence dominates—remains unchanged, but the imbalance is more severe when hate-crime homicides and ADL data are included.

These numbers should be seen as approximations and lower bounds, given the difficulty of defining and categorizing ideological violence. Still, the patterns are clear. From a policy perspective, right-wing extremism deserves to be the primary focus of counter-terrorism and de-radicalization efforts. At the same time, left-wing and Islamist violence, though smaller in scale, should not be ignored. Effective prevention requires dismantling the drivers of extremist violence across the spectrum, with particular attention to the effects of far-right rhetoric.

--- continued ---

Why focus on homicide? Ideological violence takes many forms, but homicide is the most extreme and the most consistently tracked5. Even so, definitions remain contested. Should all hate crimes count as political violence? What about attackers with some degree of mental illness, who may latch onto ideology as a justification? Some cases blur the line between ideology-driven and pathology-driven violence, but from a prevention standpoint, both must be understood.

Defining “right” and “left” is also slippery. In practice, movements and motivations overlap. White supremacists, incels, and sovereign citizens are generally coded as far-right, but their rhetoric sometimes blends with other currents. Similarly, is a Chinese nationalist attacking Taiwanese Americans on the right or the left? Extremist categories often blur—just as in the 1980s, when French far left Communists, disillusioned by scandal, defected en masse to the far-right National Rally, or in Germany where the founding of the AfD has drawn more from left wing Die Linke than from centrist conservatives.

Definitions vary and some specific cases may blur categories. This is suggested to be especially so in the “darker corners” of the internet.

Also, it is worth noting that these numbers do not look at biases in police use of deadly force. This has been an ongoing issue in the US6 and is certainly in some cases worth including in far right violence. However, the scale of the problem is slippery to nail down due to conflation with a variety of factors, and determining whether such are hate or ideologically motivated is even more difficult, so it is not included.

Looking at the Cato Institute and ADL annual data sets.

These ambiguities likely explain why ADL and Cato data diverge. The ADL data shows almost no left-wing or Islamist homicides between 2020 and 2024. The Cato study, by contrast, finds significant left-wing and Islamist cases. Both datasets are incomplete, but Nowrasteh makes his case list publicly available, allowing scrutiny (see his substack blog for a link). In going through his case list, i did make a few different calls about ideology, but the corrections did not have major changes on the overall results7. My synthesis takes the higher of the two counts in each year as a baseline for each ideological category.

The result strongly reinforces the conclusion that right-wing violence dominates, but shows, as with Nowrasteh, that ADL’s underestimates of left-wing and Islamist political killings are still significant, even excluding 2016’s Pulse nightclub killings. See chart here.

Between 2020–2025, right-wing, left-wing, and Islamist motivations account for about 75%, 13%, and 12% of politically motivated homicides in this expanded count. The predominance of right wing violence among politically motivated homicides aligns with the data on assassinations. There have only been 5 assassinations of politicians in the US in the last 25 years, including federal, state, and local level… all 5 have been Democrats8.

Percentages by ideology depend considerably on timeframe analyzed, however. Widening the window to the last 10 years, Islamist violence rises in its share due to the Pulse nightclub attack (64% / 11% / 25%). If we expand to the last 25 years, then 9/11 swamps everything else and the make-up of politically motivated murder is about 88% Islamist. As left wing attacks tend to generate fewer deaths and Islamist attacks more, focus on incident counts vs death tolls tell different stories. Deaths are ultimately the most pressing measure, however.

Including Hate Crime Homicide

While different dynamics drive politically motivated crimes vs hate crimes mainly to do with premeditation and explicit ideological drive, I think it is important to include the hate crime based homicides as hate of specific groups is usually closely linked to ideology. Including FBI hate-crime homicide data (2020–2024) gives us a closer-to-complete picture of ideologically driven homicide. Coding bias types as right or left wing9, and combining them with politically motivated homicide data, shows that right-wing killings account for an even larger share than Nowrasteh’s 2025 report suggests: 82%, 7%, and 10% respectively for right, left and Islamist drivers. Hate crime motivations for murder are even more heavily skewed toward right wing motivations than are politically motivated homicides. See the top chart again for details.

To see all three of these data sets examined separately, see my follow up blog, linked here.

Scale

Political violence is rare compared to the overall US homicide rate, but its impact is outsized. That is its intention: to terrorize beyond the act itself and to change political and social dynamics as it undermines democracy and political dialogue across identity lines. It also attracts disproportionate media coverage, making it seem more common than it is. While it has outsized impacts it is still useful to put in in the context of all homicides, for scale. As can be seen in the chart here, in the context of all US homicides, ideological killings are almost invisible, just barely visible on the left of the graph in the peak years of political violence.

Whether this should be framed as political violence being rare or the US being a particularly violent place is perhaps a matter of perspective. For a wealthy nation, the US is a relatively dangerous place. For perspective I also include a map of nations coded by annual per capita homicide rate.

Where to go with this information?

Political violence should be seen as part of this larger problem with violence in the US. Gun control may play some role, but deeper factors are at play and are likely far more important. Addressing political violence requires in part tackling the broader drivers of homicide in America. Canada, with higher per capita gun ownership and significantly lower homicide rates, demonstrates that a myopic focus on gun control would miss the mark10.

I think we all at least intuitively have a sense that political violence in the US is linked to the same forces driving polarization. We need to look more deeply, however, into the specific paths that lead people in different identity groups and ideologies to find their way to violence. There are many theories including internet rabbit holes, social isolation, and positive feedback loops driving more and more extreme positions in identity groups of many kinds, as well as the US’s perceived escalating issues with mental illness. We need to understand these pathways more if we are to propose meaningful interventions beyond speech limitations and more general sensible calls to cool down political rhetoric. We should be looking at the specific predictors of political and hate driven violence and see if the kinds of interventions we should propose are general across ideologies or if there are different drivers specific to different ideologies. Scott Atran’s Talking to the Enemy (2010) is an excellent starting point, a deep dive into extensive empirical research on support for or engagement in extremist violence. An update on this research in the current world of social media mediated belief acquisition would be useful.

Also, even as we need to remember that ideologically driven violence comes from different ideologies and we need to address all of it, we can’t forget that the overwhelming majority of ideologically driven murder is coming from the right. Even if the last few years have seen steady declines in right wing murders, it still has been the dominant source of politically motivated homicides. Any suggestion otherwise is not grounded in reality.

1

https://www.cato.org/blog/politically-motivated-violence-rare-united-states

Also on Nowrasteh’s substack blog…

Laissez-Faire, Laissez-Passer w/ Alex Nowrasteh & David Bier

Politically Motivated Violence is Rare in the United States

Charlie Kirk, the co-founder of Turning Point USA, was assassinated while speaking at an event in Utah yesterday. The police have not arrested a suspect, but the assassin was likely motivated by political disagreements with Kirk. Politically motivated murder is unacceptable, inherently bad like all murder, and doubly so because of how socially corrosive…

Read more

a year ago · 94 likes · 44 comments · Alex Nowrasteh

Photo by Sasha Gulich

The short read (about 6 minutes)

“Anti-corruption reform” to many people sounds like a boring, technocratic topic, the concern of accountants. However, rule of law is the foundation of good governance and economy, and corruption reform is the stuff of revolutions. To business people wanting to work with people in a country, understanding the efficacy of anti-corruption institutions is essential to understanding the economic landscape. In the context of Russia’s full-scale invasion of Ukraine, understanding the restraints or lack thereof on corruption is central to trust in aide going to help the people of Ukraine and to effective military defense, rather than to the entrenchment of power and wealth asymmetries through illicit diversion. Ukraine is currently emerging out of a crisis of democracy, centered on anti-corruption reform.

On July 24 2025, the Ukrainian parliament (Rada) passed bill 12414. At the last second, illegally slipped into this bill were provisions pushed by Zelensky and his allies eliminating the independence of the National Anti-Corruption Bureau of Ukraine (NABU) and the Specialized Anti-Corruption Prosecutor's Office (SAPO), giving Zelensky (via the Chief Prosecutor, under his direction) the ability to direct investigations, see all evidence in investigations, and shut down or transfer corruption investigations1. These changes made Zelensky and his allies effectively immune to corruption investigations. Within the 24 hours between the bill’s introduction and Zelensky’s rushed signing of the bill. The largest protests Ukraine has seen since the 2013 Euromaidan protests swept across Ukraine to 17 cities demanding that the bill be reversed2. EU representatives added their voice and muscle and threatened to withhold 10s of billions of euros in Ukraine aid unless the bill was reversed and Zelensky followed through on the other anti-corruption reforms Ukraine had committed to as part of EU alignment negotiations. Parliament held an emergency session and repealed these aspects of the bill, and one member of Zelensky’s party resigned, citing deception around the contents of the bill and intense pressure from Zelensky to pass the bill. People power for now saved Ukraine from backsliding on democratic reforms, with a bit of help from abroad.

Ukraine is unarguably rife with corruption, infecting much of its government, and it is dominated by the illegitimate wealth of oligarchs. The impressive volume of convictions and current investigations and prosecutions for corruption are, however, also a sign of substantial progress being made in reigning it in. This progress is thanks to the strength of civic engagement and public protest shown in the Euromaidan protests that drove the creation of NABU and SAPO and last month’s anti-corruption protests, and also thanks to international help and pressure. Before this is over, a lot more Ukrainian politicians will be in jail, including from Zelensky’s circles. This is a good thing.

I present here three graphs placing corruption in Ukraine in context with comparison to the range within the EU and to Russia. The picture they give both justifies historical images of corruption in the former Soviet states and demonstrates simultaneously the positive effects that effective anti-corruption institutions have, even in the face of resistance from the corrupt, and the need for continued efforts and reinforcement of these institutions. The graphs are of the following metrics…

1. Transparency International’s Corruption Perception Index (CPI): 1997-2024

2. The World Bank’s Control of Corruption Index (CC): 1996-2023

3. Results from Transparency International’s Global Corruption Barometer survey question on paying bribes: 2013 and 2021

Corruption is a varied phenomena, ranging from high-level corruption, like diversion of funds or kickbacks for government contracts at inflated values, to petty corruption like bribe paying for government services or a passing grade in university. The CPI and the CC, the two lead international measure of state corruption, are, with differing details, based on surveys of business executives doing business in the countries being evaluated as well as of country experts. The World Bank’s CC has more of an emphasis on existence and functioning of anti-corruption institutions. While both balance measures of high-level and petty corruption, the CC has relatively higher weight on petty corruption. These and other methodological differences can cause the results between the two to differ.

Looking at results over time of both the CPI and CC we see something similar, that Ukraine and Russia both came out of the dissolution of the Soviet Union quite low on expert evaluated corruption, but both have made progress. The mechanisms and motivation of that progress were of course different. Ukraine’s progress came largely from bottom up civic society pressure, with politically independent anti-corruption institutions being primary demands of the Euromaidan protests. In Russia, the mechanism was more top-down directive aiming at state efficiency. In both cases progress was facilitated by digitization and transparency initiatives. Also clear from both is that Ukraine is currently evaluated as having made more progress. While the CC does not express the same perception of significant backsliding in Russia as the CPI, it also did not evaluate Russia as having made as much progress to be backsliding on. The CPI, which has measures available through 2024, suggested a backsliding beginning in Ukraine last year. This is in line with the frequent reporting on such within Ukraine and is exemplified by July’s bill 12414. While on both measures Ukraine is still below the currently worst performing EU states (Romania, Bulgaria, Hungary), it has made significant progress and is already at or past the performance of these same states in previous years. This suggests that if Ukraine follows through on its EU commitments to reform, Ukraine should soon be within the current range of EU states on anti-corruption measures.

The Global Corruption Barometer country surveys are done only sporadically and so do not have the same kind of temporal resolution. However, they have the advantage of being a more direct measure of experienced, as opposed to perceived, corruption. The third graph presents answers to one survey question: “Have you paid a bribe in the last year?” This is an objective measure specifically of petty corruption.

In 2013, petty corruption was pervasive in both Ukraine and Russia, but by 2021, both countries had made significant progress3. Ukraine is actually performing better than the 3 worst performing EU countries. This data was, of course, taken before the full-scale invasion, so it does not reflect the trajectories taken by these countries since then, so it needs to be taken with a grain of salt for understanding the current situation.

All 3 of these metrics present a similar picture for Ukraine. 15 to 20 years ago, the image of Ukraine as a corrupt post-Soviet state accurately reflected the experiences of experts and regular citizens. However, the stories of Ukrainian progress due to institutional reform and civil society participation also are echoed in expert opinions and in people’s daily lives.

Zelensky’s attempt at capturing anti-corruption institutions exemplifies why many still perceive significant large scale corruption threats in Ukraine. However, all metrics of corruption point to significant progress having been made in Ukraine and the rapid and spectacular civil society mobilizing against the neutering of these institutions is reflective of how corruption in Ukraine is perceptibly resisted by the power of civil society. Zelensky has world attention and acts for now as the face of Ukraine during wartime, but Zelensky is not Ukraine. Ukrainians have shown that Ukrainians are Ukraine. The combination of this public and civil society pressure, external EU reciprocal pressure, and the functioning of newly empowered and bolstered anti-corruption institutions like NABU, SAPO, ARMA and the Economic Security Bureau following conditionality of EU aid bodes well for the continued decrease corruption in Ukraine, despite the fog of war.

For more details and references, see the long read continuation, below.

More details …

Ukraine, being an ex-Soviet state, has inherited massive problems with both large scale and petty corruption. At the same time, Ukrainians have shown the will to confront corruption through youth driven public protest and civil society pressure.

While it only appeared as a minor news item in most major Western media4, Draft Law No. 12414 and the massive and rapid protests against it were experienced in Ukraine as a crisis of democracy. The subject of anti-corruption investigation and prosecution may seem technocratic minutiae, but in reality it is fundamental to the rule of law and core to the program of Ukrainian EU alignment and movement toward joining the EU. For international partners looking to support the war effort, it is also of fundamental importance to the understanding of the effectiveness of aid.

The bill was passed in a rush immediately before intended summer parliamentary recess, involving deception of MPs and intense pressure on MPs to approve the bill without discussion.

Opposition MPs, particularly from anti-oligarch party Holos, along with anti-corruption NGOs mobilized civil society resistance, amplified by extraordinarily rapid youth protests, which escalated even more rapidly that the Euromaidan protests had. While the war had set a tacit agreement in society to suspend conflict around governance, this tacit agreement was founded on a shared understanding in Ukrainian society of a commitment to a path forward toward a democratic state free from corruption in alignment with the EU. Zelensky and his circle broke that agreement and their hubris was met with immediate and fierce resistance. EU partners meanwhile had for many months been quietly pressuring Zelensky against his democratic backsliding. After the passing of this bill against vocal resistance, EU concern transformed into explicit public objections to authoritarian moves from Zelensky and his advisers, Andrii Yermak5 and Oleh Tatarov6. The EU threatened withholding 20 billion euros in aid and froze EU integration talks, conditional on the reinstatement of independence of NABU and SAPO as well as progress on other missed deadlines for anti-corruption reform7 . Following widespread protests and international criticism, Zelensky introduced a new bill to reverse the changes, and parliament in an emergency meeting came back from recess and passed the new bill on July 31.

Civil society organizations and primarily youth-driven public protest in Ukraine demonstrated an ability to check moves toward autocracy. Zelensky emerges legally still president, but with perceived legitimacy and public trust significantly damaged8.

Institutional reform is important for Ukrainians. International partners look for assurance that the massive flows of resources into Ukraine are being used as intended rather than diverted to make internal power imbalances worse. Especially EU negotiators looking to help Ukrainian institutions align to EU norms rule of law is central. While much Ukrainian historical corruption is linked to Russian control, Ukraine has its own indigenous problems with corruption, which serves as a propaganda point for those who want an end to Ukrainian war aid. Understanding corruption and anti-corruption progress and strategies is vital.

Corruption Metrics

Corruption is a notoriously difficult subject to quantify. Thieves aren’t interested in advertising their thievery to statisticians. Exposed evidence of corruption may indicate higher levels of corruption or it may indicate a better functioning of anti-corruption institutions. Lack of evidence of corruption may be a sign of a well-functioning government or a sign of one good at covering its tracks. Also, corruption is a diverse phenomena. In one country, it may be more severe but regular, predictable in scale and context. In another country, it may be lower in scale, but more pervasive and unpredictable. These have different impacts on people’s lives and on doing business. Also, cross-culturally, people will have different moral codes implying different local definitions of “corruption”. For example, nepotism and the doing of special favors for relatives and close friends is a classic example of corruption, and yet, there are cultures where this is not just acceptable, but actually expected of one… a person in such a context would be considered morally suspect if they did not give preferential treatment to friends and relatives9.

Still evidence exists and people talk about their experiences. This is why some of the best regarded metrics of corruption rely heavily on the perceptions of international business people trying to conduct business in a country as well as on the opinions of country experts accumulating years of qualitative knowledge of a country.

The most well-known metric of corruption is Transparency International’s Corruption Perception Index (CPI). The CPI is an annual ranking that measures the perceived levels of public sector corruption in countries and territories around the world. Based on surveys and assessments from various organizations, relying primarily on reports from the business executives and country experts, the index scores countries on a scale from 0 (highly corrupt) to 100 (very clean)10. It aims to capture in an aggregate scale such diverse phenomena as bribery, diversion of public funds, abuse of public office (for example sale of state assets at submarket rates for kickbacks), nepotism, and state capture.

The CPI chart here looks at CPI for the EU (high, low, and average) plus Russia and Ukraine. The CPI numbers indicate that both Russia and Ukraine emerged from the USSR highly corrupt, but that both were making significant strides in reigning in corruption since around 2009. In Ukraine, this was accelerated in 2014 with the Euromaidan protests and the subsequent overthrow of the pro-Russian Yanukovich government and creation of independent anti-corruption investigation and prosecution institutions (NABU and SAPO). In Russia, corruption has been perceived to increase to Soviet levels since the full-scale invasion, where Ukraine has continued its anti-corruption progress despite the war until stalling and starting to backslide in 2024.

The World Bank's Control of Corruption Indicator (CC) is a metric that similarly measures perceptions of the extent to which public power is exercised for private gain, on a scale of -2.5 to +2.5. The zero point is the global average; the CC is a relative rather than absolute measure. Where the CPI more strongly emphasizes high-level corruption, the CC strikes more of a balance between lower-level corruption (like petty bribes) an higher-level corruption (like "state capture", where elites legally or functionally take control of state institutions for personal gain), and emphasizes the effectiveness of a country's anti-corruption framework. The data for the CC indicator is a composite of information from a wide variety of sources, including surveys of businesses, citizens, and experts, as well as assessments from various international organizations, think tanks, and non-governmental organizations.

For the graph here, covering 1996 -2023, the discontinuities in the EU numbers in the early 2000s were from the expansion of the EU to include relatively more corrupt governments like Romania, Bulgaria, and Hungary. Overall the CC shows a steady trajectory of improved rule of law in Ukraine and, to a much lesser extent, Russia. By 2023, Ukraine was nearing the global average, while Russia was still heavily lagging behind. Both have still been outside the perceived corruption range of the EU states. While the CC scores do not suggest the backsliding in Russia over the last years in the way the CPI does, it characterizes Russia as consistently worse than Ukraine since the metric was started. The progress in Ukraine against high-level government corruption has come from more systematic, digitized, transparent, accessible accounting of public resources and funds and from the founding of well-funded, independent anti-corruption institutions able to investigate even high-level government officials.

Both of the CPI and CC foreground high-level corruption … the kind seen in business and in the moving of large amounts of resources for the benefit of small groups of the powerful. Transparency International’s Global Corruption Barometer (GCB), however, is a society-wide survey done more sporadically which aims to capture low-level corruption… the kind of things regular people encounter in their daily lives from lower level officials. One element of the survey asks “Have you had to pay a bribe in the last year?” The chart here gives some comparative results.

While this presents less resolution over time than the other metrics, it does a better job at focusing exclusively on this low-level petty corruption and doesn’t conflate it with higher level corruption. It is also less subjective as a measure. The trajectory does present a different relative picture, with both Ukraine and Russia hovering at or even above Romania at the bottom end of the EU. Also, while both Ukraine and Russia have drastically improved on this measure, the improvements in Ukraine have been greater: from 2013 to 2021 a 56% reduction in bribery in Ukraine compared to a 40% reduction in Russia. The drivers of reform are quite different in the two contexts, with anti-corruption reform being driven in Ukraine from below by civil society pressure and protest and in Russia by efficiency focused top-down control. In both cases, it has come from digitization efforts reducing contact between government officials and people (thus reducing opportunities for petty corruption) and from new anti-corruption institutions.

All of these different metrics show similar things. First, yes, Ukraine has had a history of corruption. Second, yes, corruption is still a very real, significant problem in Ukraine. Third, however, quite significant improvements have been made. Already Ukraine is at or above historical levels of corruption for the lower end of the EU in terms of high-level corruption. In terms of petty corruption Ukraine by 2021 was already performing better than the lower end of the EU spectrum (Bulgaria, Romania, and Hungary). Given the progress that is being made in investigating and prosecuting corruption and the inevitable consequences of institutional reforms being made, it is likely that these trajectories will continue for the next years despite the war, putting Ukraine within the range of EU performance on all anti-corruption metrics. Bill 12414 could have been a significant blow to this progress, but the strength of civil society and public engagement were able to counter autocratic tendencies in the Zelensky government.

This progress, of course, is being made by rooting out existing corruption and holding people accountable for misuse of state power and resources. Given the pervasiveness of corruption, this will inevitably be a “bumpy ride”. In a system where corruption is rampant, getting to a position of power is difficult without engaging in corruption. Parties in Ukraine should not be seen in a binary of corrupt vs not corrupt but existing on a spectrum of corruption intensity and spectrum of commitment or opposition to anti-corruption institutional reform and accountability, with variance within party members as well as between parties.

A 4^th metric of corruption: convictions and current prosecutions and investigations

While it is hard to assess levels of corruption within parties, one can look at numbers of who is currently under investigation or currently or recently being prosecuted for corruption as an informal metric of corruption. Zelensky’s party, Servants of the People, was founded in 2019 with backing from oligarch Kolomoisky, who is now in jail for embezzlement charges. Several Servants MPs have already been convicted for corruption. On the other side, several Servants MPs have resigned over feelings of being manipulated by the party and not wanting to be associated with power grabs and corruption that has happened within the party11.

As one informal metric of corruption, 15 of the 31 MPs currently under investigation by NABU are from Servants12. Also, the High Anti-Corruption Court (HACC), a specialized, independent judicial body in Ukraine created to hear and rule on corruption cases against top government officials and civil servants, keeps a public post of its highest profile convictions13. Its independence reduces the possibility for high ranking officials to manipulate the trials. As another corruption metric, 19 of 68 top HACC convictions are Zelensky linked, being Servants MPs or appointed to office by Zelensky. Caution should be taken in what exactly is read into these cases. It is clear that there are high levels of corruption in Zelensky’s party and circles. There is, however, also a spread of corruption cases through many of the parties.

Of course, there are also accusations of Zelensky’s government making false claims of corruption or of working with the Russians as a way to silence opponents, particularly against Poroshenko’s party, European Solidarity, Zelensky’s primary opposition in government. With Zelensky’s recent attacks on NABU and pushing of investigations against NABU prosecutors based on flimsy of non-sequitor evidence, one needs to take extra caution in counting corruption investigation as evidence of corruption. This is why anti-corruption investigations need to be independent and depoliticized, so that investigations can be trusted, those in government can be held to account, and those in opposition are shielded from false accusations.

Corruption and anti-corruption reforms in Ukraine

When Ukraine achieved independence with the catastrophic collapse of the Soviet Union, it inherited a history of institutional corruption magnified by massive private capture of state-owned resources by people who went on to form the very small, very powerful multi-billionaire “oligarch” class. Zelensky’s party, Servants of the People was formed and came rapidly to power in 2019 through the backing of one of these oligarchs, Kolomoisky, now in jail for embezzling billions.

This is a precipitous moment for the Ukrainian government around rule-of-law, democracy, and corruption. There are at least two competing rhetorics. The one story is that Ukraine is a corrupt ex-Soviet state, no different substantially from Russia. In this story, aid to Ukraine would be just enabling corrupting oligarchs and undermining democracy within Ukraine by increasing wealth inequality. In the other story, Ukraine is more akin to an EU country and has been shaking off the Soviet legacy of top-down control and corruption. In this story, the Euromaidan protests are pointed to as the evidence of popular power and civil society’s control of government and ability to contain corruption and the reforms of 2022 as evidence of progress against the old Soviet corruption.

There is a vital question here about how international wartime aid helps or undermines democracy in Ukraine, both now and in Ukraine’s post-war future. If the bleeding off of aid for private gain leads to further entrenchment of power imbalances, it could sabotage Ukraine’s democratic future. And, of course, the country’s defense vitally needs the support. In the week after Zelensky sought to take control of NABU and was forced to reverse course, NABU announced that it had been working to uncover a ring of traitors in Ukraine diverting funds from drone defense for personal gain. The perpetrators include an MP from Zelensky’s own party, Oleksii Kuznetsov14. This is exactly why transparent and effective institutions for uncovering corruption are necessary.

As part of negotiations for alignment with the EU for future EU membership and as prerequisite for the receiving of billions of euros in EU aid (Ukraine Facility Aid), the Zelensky government agreed to 16 institutional reforms to combat corruption and promote government efficiency15. The government had missed several deadlines for the completion of these commitments. By the 25 July EU deadline, 4 commitments still remained: 1) decentralization reform coordinating local and federal authorities, 2) independent, transparent, and merit based selection of judges for the High Anti-Corruption Court (HACC) judges, 3) Asset Recovery and Management Agency (ARMA) reform to facilitate tracking and seizing of illegal money and asset flows, and 4) anti-corruption institutional reform reinforcing the independence of NABU and SAPA and establishing an Economic Security Bureau. The reinstatement of independence of NABU and SAPA on 31 July and the long-delayed appointment of Oleksandr Tsyvinsky as head of the Economic Security Bureau have now satisfied the anti-corruption institutional reform requirement, and a bill belatedly passed and signed by Zelensky on 27 July reforming ARMA has now satisfied the ARMA reform requirement. The other two requirements have legislation currently moving through the Verkhovna Rada. This all looks very positive for institutional reform in Ukraine and for the unfreezing of suspended EU aid in the next quarter.

Ukraine has a lot of work to do. These reforms do not instantly “fix” the problem of corruption in Ukraine, but set up a firmer base for this work. There is still a lot of corruption to weed out and many more politicians will be arrested and jailed before Ukraine makes it to median EU standards on transparency, accountability, and rule of law. Many of these politicians will certainly be coming from Zelensky’s circles.

Zelensky has shown himself to have autocratic impulses, enmeshed in a network rife with corruption. He has purged his government of independent minded voices with the firing of popular and unarguably successful military chief-of-staff Valery Zaluzhny16 and the recent reshuffling of the cabinet to repopulate it with loyalists17, has shown his willingness to sabotage anti-corruption institutions for political gain, and is surrounded by a tight circle including people whose corruption investigations and prosecutions he has already sabotaged18. The attempt to take over NABU and SAPO itself came after other sabotage attempts including a flood of spurious investigations with non-sequitor evidence and claims of “Russian influence” in NABU and SAPO, widely considered to be a transparent smear attack to discredit investigations of those close to Zelensky19. Zelensky’s highly advertised actions against corruption have been criticized as cynical moves to take uncooperative corrupt politicians and replace them with more loyal corrupt politicians20. However, Zelensky has facilitated significant prosecutions of many corrupt politicians, including within his own party. His time in office has seen the arrest and conviction of his own oligarch backer, Kolomoisky. Even amidst unsupported allegations of Russian influence, his government has rooted out an enormous amount of real Russian influence from Ukrainian institutions, including from the SBU (Ukrainian secret service, descendent of the KGB)21.

However, Ukraine is not Zelensky, and this is the big message that can be taken from the volatile last few weeks in Ukrainian politics. Zelensky happened to be in power when Russia initiated the full scale invasion and he rose to the occasion to be the international face of resistance, but before this, he was middle of the road in terms of relationship to Russia and anti-corruption reform22. Zelensky tried to seize state power and undermine anti-corruption investigation free from political influence, but the rapid public mobilization against it showed that, for now at least, power does to some extent rest in the people. Ukraine has seen a sequence of major public mobilizations to reject Russian control and reign in corruption: the student-led 1990 Revolution on Granite23, the 2004 Orange Revolution, the 2013 Euromaidan protests, and now what some called the “Cardboard Revolution” of 2025. Recent history shows that “people power“ is real in Ukraine. It is not the Ukrainian government that stopped the Russian full scale invasion from taking Ukraine. It was Ukrainian civil society and horizontal volunteer mobilization. Repulsion of Russian forces came from the massive wave of volunteers who rose to the call to defend country, the horizontally organized networks of assistance, and the will of those volunteers to see that resources spent on defense were not diverted for personal gain but end up contributing to saving lives.

Ukraine also has help in the form of EU pressure.

The war continues, and elections have been suspended because of the difficulties of having an election during wartime. However, Zelensky and his Servants Party have already been having greater difficulty governing. Nominally, they are the majority, but only nominally. Several Servants MPs have already been removed from office. 15 are currently under investigation by NABU. Add onto this that there are many Servants MPS who simply don’t show up to parliamentary work and that there are another several who have quit the party. The latest MP to leave is Dmytro Kostiuk who has left the party in protest over what he felt was deception from the party about Bill 12414 and aggressive pressure to pass it without examination24. The result of this decreasing number of voting MPs is that Servants has had to work in coalition with other parties to actually pass legislation and is on the verge of losing even a technical majority in the Rada. This fragility of Zelensky’s vote margin in the Rada is perhaps a good thing for balance of power. While he remains with the legal authority of president, his power and perceived legitimacy are for now damaged.

As reform institutions start to take hold in Ukraine, it is likely that corruption will be more curtailed, past acts of corruption will continue to be investigated and prosecuted, and democracy will be better protected. The institutional reforms will have their effects whether individuals in power want it or not. This is the benefit of well crafted institutions. While Zelensky and the Servants of the People party have shown mixed impulses around corruption and rule of law, the combination of new, empowered and transparent institutions, continued EU pressure, and public engagement are likely to continue the documented trend of improvements to governance and accountability. As the World Bank CC measure and Transparency International’s CPI are measures of expert opinion in wide-ranging contexts of on-the-ground engagement with the Ukrainian government, we’ll hopefully have some measure of their success or failure in upcoming metrics.

The Trump administration has claimed to be targeting “the worst of the worst”, but the data speak the opposite. Under Joe Biden the rate of criminality among those arrested by ICE was about 70%, representing targeted ICE arrests. The targeting of criminals in ICE raids has decayed dramatically under Trump, with those with criminal records now down to 38% of those detained in ICE raids. This percentage again is expected to continue to decline based on the pattern of criminality of those moving through detention and based on expansion of ICE raids. Moreover, the characterization of undocumented immigrants as criminals is dramatically off-base, with rates of observed criminality among undocumented immigrants about half that of US citizens (see below).

These two points and associated graphs about the explosion of ICE deportations and the decay in targeting of criminals are the bigger takeaways for the quick readers. For a more full picture, read on and check out the next 7 graphs and 2 maps…

US news is flooded with reports covering immigration arrests and deportations: reports of mass raids and arrests, specific incidents of people integrated in their communities for decades being arrested and deported, US military and relatives without papers being arrested and sent to countries they have never been to before, and waves of protests around the US and heavy handed military response. At the same time, there are reports of deportations overall not being significantly larger in scale than previous administrations and of Trump’s constant demands to increase the scale of deportations to meet his campaign promises. The budget for ICE is now being expanded with a multi-billion dollar budget increase, making ICE's new $27 billion annual budget larger than the combined budgets of the FBI, ATF, DEA, U.S. Marshals Service, and the Bureau of Prisons.

In the following, I want to start to put these numbers in context. In order, I’ll do the following

• First, I’ll put these interior deportations in the greater context of all US deportations, including border deportations, to make sense of the mixed messaging around deportation numbers. I’ll also summarize findings on rates of attempts at illegal border crossing over time.

• Then I’ll put these numbers in the context of the overall population of undocumented immigrants integrated into US communities, and these undocumented immigrants in the context of the overall US population.

• Finally, I’ll begin to look at who these people are who are being deported… How long they have been in the US, what fraction are employed and in what industries, and the fractions deported with criminal records.

The overall purpose of this substack blog is to try to take abstract feeling numbers and try to frame them in ways that can be easier to understand for scale and context. Sometimes, I will use absolute numbers to show changes over time for shorter time periods, but other times, I’ll frame the results in terms of percentages, to make scale a bit easier to intuit.

US undocumented immigrants, immigration enforcement, and reform are big, complicated topics, and to make matters worse, the data keeping and administrative language is confusing and easy to misunderstand[ii]. For that reason, I’m going to keep this post relatively surface level and get into some other details like economic impacts of undocumented labor and ways ICE raids are being executed and targeted in future posts. This post is a bit long, but I give the metaphor of close up photos of an elephant to explain. If one were to see a single close-up photo of an elephant, one wouldn’t have any idea of what one is looking at… you need a certain minimum number of photos before you see “elephant”. I’m hoping that in 9 charts, 2 maps, and a few pages of text to be able to see in a basic away the elephant of undocumented immigrants and deportations in the US.

Dynamics of Arrest, Detention, Deportation, Release

An alternative title for this section could be “Why it is so hard to quantitatively communicate deportation dynamics over time.” Different agencies are involved in arrests, following different procedures and different protocols for sharing data, some detainees are transferred from one agency to another, and terminology changes over time. Also, given the average 3 months of detention before case resolution, the statistics on removals for a given month do not reflect the arrests made that month, which is relevant as enforcement policies have been changing rapidly.

Here is a very brief rundown of how the majority of immigration enforcement actions proceed. There are two government agencies responsible for deportations, both under the Department of Homeland Security (DHS): Customs and Border Protection (CBP) and Immigration and Customs Enforcement (ICE). Generally, CBP operates at the borders and ICE operates within the US, though sometimes CBP is involved deeper inside the US as well. CBP itself has two sections Office of Field Operations (OFO) and Border Patrol (BP). OFO handles actions at ports of entry and Border Patrol patrols the border between ports and regions near the border.

The US government has not officially used the term “deportation” for several decades. The umbrella term used by DHS is “repatriations”, divided into returns and removals. Removals involve official proceedings to deport someone and tend to have significant consequences for future admission to the US. Returns are when someone is allowed to “volunteer” to withdraw their request for entry, has less consequences for future entry, and is something that happens at the border. There was also another category, “expulsions”, a special kind of immediate removal mandated as a COVID response. An “encounter” is when the CBP decides that someone is inadmissible by normal standards. This may lead to return, immediate removal, transfer to ICE detention for judicial hearing, or release into the US for asylum or parole hearings (usually when they think these hearings have a reasonable basis to allow them to stay). ICE conducts raids inside the country. These ICE encounters lead to arrest and detention for hearings, joining with those sent to ICE by CBP from the border. These hearings may lead to removal or release back into the US or in criminal cases to jail for criminal hearings. Thus expedited removals are all from the CBP encounters in border region, while removals from ICE detention may be from CBP border arrests or from interior ICE arrests. One has to be careful about which numbers are being reported in a given set of government statistics: arrests, detentions, removals, returns and from interior arrest, from the border, or mixed. And just for one more layer of confusion, sometimes annual numbers are reported in calendar years (Jan- Dec) and sometimes in financial years (Oct – Sep).

We want to know how many and who are being turned away at the border and among those arrested and removed, who were arrested at the border and who were arrested in interior raids. This is complicated to quantify. Detentions times are long: 3 months on average. The case resolution statistics of a given month correspond to arrests made in previous months. In some cases, even 6 months into Trump’s administration, hearings are still based on arrests under Biden. Also, records are not released at the same time for forced returns at the border as for ICE detentions, so current border returns need to be estimated.[iii]

The next charts give the total deportations in the US broken down by removals (interior vs border where data is available), returns, and Title 42 (COVID regulation) expulsions[iv]. They cover different time periods partially to highlight different things and partially because of different kind of data available over different periods.

Several things stand out in looking at these charts. As you can see from the first chart through FY24, total deportations peaked under Bill Clinton driven by voluntary returns, while removals peaked under Barack Obama. It also shows the next peak, driven by Covid era Title 42 expulsions under Joe Biden. As you can see from the second graph through June 2025, overall monthly deportations under Trump are not particularly large. This is the ground for reports about Trump not meeting promises about deportations. They are still a third to a half of the rate under Biden with the COVID expulsions and are insignificant compared to rates under Obama and Clinton. Even as interior ICE arrests continue to grow, the September projections are still only 2/3 of the Trump-stated goal of 3000 deportations per day. This is entirely because most deportations historically have happened at the border, with Clinton relying on informal low-consequence voluntary returns and Obama relying on more consequential formal border removals. Increasing interior removals has a relatively insignificant effect on reducing overall deportation numbers, compared to a similar proportional change in border deportations.

Border and interior removals are often lumped together, including in government statistics. However, they are quite different in both process and social and economic impacts within the US. A big reason that previous administrations have focused on border returns and removals over ICE raids is that the cost per deportation is drastically lower. It’s easier to examine someone’s papers at the port of entry or to patrol a border than it is to search out people in the US.[v] A bigger reason for many, however, is that ICE raids involve taking someone already integrated into US communities and industry and is more socially and economically disruptive than preventing someone from beginning the process of entering the US labor force and community.

If we give special attention to interior ICE arrests, then we are seeing now a rapid escalation into uniquely disruptive levels of interior arrests and deportations. Deportations flowing from ICE raids is up quite dramatically, even as detentions coming from CBP arrests is down. In FY 2024, Biden’s administration averaged about 7000 interior removals per month. In his 2025 term Trump has averaged about 16000 per month… well over double the amount of removals following ICE raids. This aligns with widespread reporting on ICE raids, administration stated goals, and the widespread protest and mobilized resistance happening to ICE raids across the US.

Attempted illegal border crossings and reduced border deportations

We can say how many are people intercepted trying to cross illegally into the US, but we don’t know exactly how many successfully evade border control. However, the CBP, keenly interested in this issue, have used data-based models to estimate the monthly success rate for border control evasion. Using these model-based estimates, in relationship to border encounter rates we can estimate total illegal border crossing attempts. The pair of charts here replicate and extend the analysis of USAFacts[vi], itself based on DHS data and efficacy estimates over time.

The first chart shows how the Obama administration migrant crackdown (2008 – 2016) saw a drastic reduction in attempted border crossings. However, the Covid pandemic saw these numbers jump back up again as attempted migration from economically hit Central and northern South America exploded. The second chart, focusing on monthly border encounters in recent months, shows how attempts at illegal immigration have been in a decline since 2023. So, while attempted illegal border crossings are indeed down 92% since their peak in 2023, most of the decline happened during the Biden administration. The peak under Biden coincides with the COVID crisis, but analysis by the Cato institute suggests that the peak and subsequent decline had mostly to do with the labor hungry job market in the US in relation to rising unemployment in source countries and COVID related border rules (Title 42) which both blocked legal avenues for immigration and incentivized repeat attempts at illegal border crossing.[vii] The decline coincided with decreased demand for labor in the US and the rescinding of Title 42 restrictions and their perverse incentives.
Trump’s harsh rhetoric about border security surely had some effect in reducing crossing attempts, but this was in the context of an already 75% reduced rate of border attempts under Biden by the end of 2024.

Given that efficacy of border security measures was already quite high, at about 80% capture rate, this drastic reduction in border attempts inevitably meant a decline in border arrests. This has made it impossible for the Trump administration to meet its promises of mass deportations via increased focus on the border. The perceived problem of mass illegal border attempts had already mostly dissolved by the time Trump came into office owing to changing economics and Biden border policies, leaving only the dramatically more costly and disruptive route of interior ICE raids and arrests to meet these campaign promises, a path that is provoking protests and proving to be increasingly unpopular[viii].

The population of undocumented immigrants in the US: Deportations Context

Tracking the population of undocumented immigrants in the US is complicated by the pressures the undocumented experience to maintain a low profile and remain unseen. Undocumented immigrants try not to advertise their entering the US, but also their leaving the US to return home. Estimates of the population of undocumented immigrants from previous years when projected to 2024 give between 13 and 14 million people[ix]. While most estimates vary within +- 10%, working off of a foundation of US census reports, they do roughly agree on the pattern of increase and decrease over time. The chart here of population over time takes estimates from Pew through 2022, extrapolated through 2025 based on recent developments.

From the 1990s until about 2006, there was a steady rise in the population of undocumented immigrants in the US from about 3,5 million to about 12 million. It subsequently slowly fell until it started to surge massively in 2019.[x] As reported In Migration Policy Institute analysis, the numbers have steadily increased by about 3 million leading to an estimate of 13 to 14 million undocumented immigrants in the US[xi]. However, Trump’s recent executive actions cancelling the TPS program for Colombians, Venezuelans, Haitians, and Nicaraguans, and the parole program for Venezuelans has with simple executive actions instantly increased the population of undocumented immigrants in the US by 850,000 to possibly nearly 15 million people. In comparison, again, ICE interior deportations will by September be at about 30,000 people per month being arrested and deported from the US: a rate of about 6% per year of undocumented immigrants integrated into US communities being arrested and deported.

Who are the people being arrested and deported?

The Trump administration claims that arrests are focused on dangerous individuals with criminal records. However, we are inundated with a continuous flow of new reports to the contrary[xii]: a gay hairdresser arrested and deported to El Salvadoran detention claimed to be a Venezuelan gang member, an elderly Iranian woman living in the US for almost 50 years fully integrated in her community arrested for deportation, an elderly US army veteran forced to repatriate to South Korea after serving the US in combat and living in the US for half a century, family members of US military being deported to countries they are supposedly “from” but to which they have never been, people who came to the US as infants now deported as adults to countries whose language they don’t even speak. Beyond the individual reports and claims, what do the statistics show? A recent AP news article reports that the facts about who is being deported do not match the Trump administration claims about criminality of deportees.[xiii]

According to recent estimates by Penn State’s Jennifer Van Hook, extrapolated to 2025 with adjustments for Trump’s TPS and parole cancellations, about 55% of currently undocumented immigrants have been in the US for over 10 years, 40% have been in the US for over 20 years.[xiv] This corresponds with PEW research findings which show less than ¼ of undocumented immigrants in the US less than 5 years.[xv] States like Texas and California which have been major targets for ICE raids actually have even larger proportions of their undocumented population in the US for more than 5 years. These are individuals and families integrated into communities, working and paying taxes, including paying into and subsidizing programs like social security which as undocumented immigrants they can not access, complicating the conversation about financial implications of undocumented immigrants in the US.

Undocumented immigrants are, of course spread unevenly around the US, being attracted to both labor demand and communities of co-nationals for support networks. The map here, based on PEW data, gives the 2022 estimated distribution of undocumented immigrants by state in terms of percent of state population (see map notes for comments on 2025 numbers). The percent of the local workforce is going to be higher than this, as the population of undocumented immigrants in the US is highly skewed toward working age people motivated to be in the US primarily for work. According to the same PEW data, 78% of undocumented immigrants are working age, 95% of them employed.

The next chart looks at the industry with the highest numbers of undocumented workers by state. While agricultural work has been the focus of much attention and historical activism in the US with regards undocumented labor, and there are many states in which agriculture has the highest percentage of undocumented among its workers, there is no state in which the majority of undocumented workers are actually in agriculture. Nationally, construction has the highest number of undocumented workers.

The conversation on the economics of this are complicated. Some argue that this suppresses wages for US citizens looking for work while taking government resources. Others argue that these are jobs that US citizens do not want and do not apply for, that industries would collapse without inexpensive labor, and that undocumented immigrants pay more in taxes than they receive in government services owing to their undocumented status. This will be the subject of a future blog.

The last several administrations have spoken of focusing on deporting undocumented immigrants with criminal records. This was a theme of the Biden administration and has been an obsession of Trump administration communications on deportations. Without going into the complexities of international relations, US involvement in current states of affairs throughout Latin America, and ethics and policy alternatives for dealing with criminal migrants, it is worth looking at the facts about who is being deported.

The first thing to note is that studies on the subject consistently show that, opposite to the claims of the Trump administration, immigrants are significantly less likely to commit crimes than the general population, and this extends to undocumented immigrants.[xvi] This is intuitive. Simply from a self-centered perspective, undocumented immigrants come to the US for work and are exceptionally motivated to steer clear of trouble with the law. Texas, in addition to being a primary destination for undocumented laborers, is the one state that tracks arrest records by immigration status. A 2020 PNAS study analyzing these records showed that immigrants, including undocumented, were, per capita, 50% as likely to be arrested for violent crimes as citizens, 40% as likely for drug-related offences, and 25% as likely for theft and property related offences. Cato Institute estimates align with this, finding that in 2023, US citizens, aged 18-54, had an incarceration rate of 1.2%, while undocumented immigrants of the same age range in the US had an incarceration rate of 0.6%. Legal immigrants had an even lower incarceration rate of 0.3%. While of course, there are criminals in any population, the Trump characterization of the undocumented population in the US as particularly criminal is wildly inaccurate and opposite to the facts.

Beyond mischaracterizing the population of undocumented immigrants as criminal is the question of how well the Trump administration is targeting criminals among undocumented immigrants. Trump claims to be focusing on the “worst of the worst”. The data reported by ICE says otherwise, with only 37% of those currently in detention from ICE arrests having a criminal record, as opposed to 70% under Biden. However, this difference is a drastic underestimate of how much worse the targeting of criminals is getting under Trump. As detentions are on average incarcerated for 3 months before case resolution, the percent criminality of those currently detained lags those currently being arrested by about a month and a half. Given the very low criminality of the immigrant population and the increasing ICE detention rate, we can only expect the rate of criminality among the arrested to plummet even further as ICE raids become even more indiscriminate.

The chart here shows the rate of criminality among those in ICE detention over time, in aggregate and subdivided into those arrested by ICE in interior raids and those arrested by CBP at the border and sent to ICE detention. If you look at the rate of criminality in the overall ICE detention population, this has gone up slightly in the last months. However, this is entirely due to the dramatic shift in the detained population from being dominated by very low criminality CBP transfers from border arrests to being dominated by those from somewhat more targeted ICE arrests[xvii]. The Trump administration has maxed the amount it can arrest in border encounters. The only way to increase arrests is through ICE raids. This small increase in criminality is not a sign of targeting of criminals, but a shift to interior raids, and as is very obvious from this data, interior raids are becoming less discriminate.

Future posts

This post has been a bit long, but the topic is big and complicated, even to give a basic overview.

While this post certainly does not reflect well on the accuracy of Trump administration claims, the purpose of the blog is to create a neutral and non-partisan reflection of the data in terms that concern us for policy, voting, and civil participation. Feel free to share graphics and/or download and use data and analysis. Citations and links appreciated.

In future posts, I hope to expand on several aspects of the issue of undocumented immigration, including economic impacts, relationship to the job market, more details on criminality, and putting all of these in the context of various policy proposals. There also are supposed to be some significant updates to the data coming out in the fall, at which point, I intend to come back and redo and expand some of these charts with data as opposed to estimates and projections. I say that there is supposed to be data coming, because while this is mandated by congress, the Trump administration has been undermining all government agencies’ abilities to keep and share data through defunding programs mandated with keeping data and explicit orders to remove data from government websites and centralize control over data[xviii]. Specifically with regards ICE and DHS data, there have been disturbing reports of gibberish numbers in some of the most recent reports[xix]. While hopefully this specific problem will be fixed in data updates in the next month, this is likely going to be a front of fighting over policy and power in the US, access to accurate information needed to understand what our government is doing and the state of our world.

[i] The Department of Homeland Security (DHS) currently has statistics published on removals divided into removals from interior arrests vs border arrest removals, plus returns and expulsions, published through FY 2024 … meaning until September 2023. To get estimates on these figures through June 2025 and to project through September 2025, one has to use the more up to date monthly numbers on arrests, detentions, and removals published by ICE. However, these give the numbers of book-ins to ICE detention by arresting agency (ICE for interior arrests and CBP for border arrests), current numbers in detention, averages amounts of time in detention, and total removals. From these numbers, based on previous years’ correlations, one can extrapolate from total removals to the subset of removals from interior arrests for FY 2025 to-date figures. Estimates of returns and total border removals are assuming that the ratio of CBP transfers to ICE to overall CBP removals and returns is the same as in 2024. There is not an obvious incentive for this ratio to change, given the CBP ability to do expedited returns. If anything, it is a moderate underestimate of expedited removals and returns. However, given the drastic drop in overall documented border encounters any error here would have small effect on the overall numbers of estimated and projected deportations. Given that the average time in detention is currently about 45 days, one can reasonably assume a 3 month lag in resolution of detention cases and consequent removals and then use this to project future numbers reasonably well for the next 3 months purely based on the numbers known now to be in ICE custody by arresting agency. The figures here are the results of those extrapolations and projections.

[ii] A dictionary of terms and agencies relevant to immigration and enforcement in the US

Deportations: There is no legal term “deportation” in US government procedures. What we think of as “deportation” (turning someone away at the border or taking someone from inside the US and sending them to another country) is broken down in a few ways. I’ll use the word “deportation” to include both removals and returns (see below).

ICE (Immigration and Customs Enforcement): the government agency primarily responsible for enforcing immigration law inside the US. About 20,000 people work for ICE.

CBP (Customs and Border Protection): The government agency responsible for enforcing immigration law at the US borders, ports, and airports. This includes the Border Patrol. There are currently about 60,000 people employed by CBP.

Both ICE and CBP are part of the Department of Homeland Security (DHS). All 3 of these agencies share statistics in different ways. ICE maintains more detailed and more promptly updated statistics. CBP is slower and breaks down numbers differently, and of course DHS, sharing aggregate statistics is slower still in reporting.

Removals: these are people who are deported from the US or turned away at the border based on a court procedure or legal finding. This results in a “black mark” on your record and will usually prevent you from legal re-entry into the US for some years. Removals may be border removals (from arrest at the border) or interior removals (from arrest via ICE inside the US). At the border, they may be “expedited” removals happening immediately, or they may involve arrest and transfer to ICE detention for hearing and subsequent release or removal.

Returns: these are people turned away at the border without legal proceedings where someone is allowed to volunteer to leave or not enter the US. This does not result in a black mark on your record. These are then broken down into “Administrative” and “Enforcement” returns.

Returns are almost always CBP, happening at the border.

When someone is arrested by ICE, if they are in the end deported, they would be a removal.

CBP and ICE keep separate records. Some ICE detentions/removals are from ICE arrest and others from CBP transfer. This makes accounting and tracking interior vs border removals tricky.

Criminality: A rising concern with ICE removals in the last decades has been the disruption caused by untargeted deportations which remove contributing members of US communities, disrupting lives, communities, businesses, and economy. This has led to initiatives to give legal pathways for citizenship to those who have been in the US longer (like Obama’s DACA) as well as a shifting of deportation focus towards those with criminal records under Biden, which has been an increasingly expressed emphasis for the Trump administration. ICE does maintain statistics on those with criminal record, with pending charges and those with only immigration or minor offenses. However, “Criminal conviction” is a very broad category, containing the obvious like homicide, assault, etc, but also some non-violent crimes, like multiple intercepted attempts at illegal immigration or traffic violations. As reported in a recent CNN investigation, looking at those listed in ICE deportation statistics as having “criminal conviction” in the period October 2024 – May 2025, 75% of those convictions have been for either traffic or immigration related offences, and only about 25% have involved more serious aggravated felonies.

Unauthorized immigrants: This is a technical term for people who are not conventionally authorized to enter and stay in the US. However, it is confusing because included in this category are a number of different kinds of individuals who are authorized to be in the US. The three “authorized unauthorized” categories are TPS (864k people), Parole (1.3M people), and DACA (535k people), totaling at the end of the Biden administration about 2.7 million people. This compared to an estimated population of over 13 million undocumented immigrants … the “unauthorized unauthorized”. DACA applicable individuals are adults brought to the US as children who have via Obama legislation temporary authorization to stay in the US with avenues to getting normal legal status . TPS is Temporary Protected Status, given to people from specific countries deemed unsafe to return to because of war, political situation, or natural disaster. There is also “Parole”, separate from TPS but similarly purposed. Trump has recently ended the Parole programs for Columbians, Nicaraguans, Haitians, and Venezuelans and the Parole program for Venezuelans, by decree, adding 850,000 people to the population of undocumented immigrants in the US, as they are now suddenly ordered to leave the country after years of building their lives in the US.

Border Encounter: CBP keeps statistics of “border encounters”, which are encounters at or near the border by CBP which involve a decision of “inadmissible”. So, confusingly, of all encounters at the border, only some fraction are called “border encounters”. More confusing still, a large fraction of inadmissibles are actually legally permitted to enter the US and stay. This would include refugees, asylum seekers, and those eligible for Parole or TPS. So neither is “border encounters” simply a count of people encountered by CBP nor are “inadmissibiles” people not legally admittible to the US.

English language is arguably different from US government terminology.

[iii] The estimate is from known current CBP border encounter rates and past rates of border returns based on encounters. Likely this rate is increased, but it is also legally constrained by rules on asylum, parole, etc, so a simple extrapolation from recent rates will not be too far off. Also, given the drastic drop in CBP encounter rates, the underestimate will not hugely impact overall statistics.

[iv] The CBP removal and return numbers for the more recent months here are estimates, not reported counts. The monthly data that are published for FY 2025 are the CBP encounter rate, the ICE detention rate, ICE removal rate, and ICE detention rate by arresting agency (ICE or CBP), which means interior vs border arrest. From previous years, we have the same statistics plus CBP direct removal and return rate. I make two assumptions to fill out the data for this chart. First, I assume that ICE removals by agency are in the same proportion as detentions by agency. I also assume that the ratio of CBP encounters transferred to ICE to all CBP returns and removals is constant. The full suite of data for FY 2025 will be available in 2026, at which point, this analysis can be revisited, to see how well these assumptions worked for estimating CBP removals and returns in FY 2025 .

[v]With a $10 billion annual budget, ICE has in the last years averaged about 40,000 removals. In a simplistic view, this translates to about $250,000 spent per person removed from the US, without accounting for other costs to the US like economic disruption from lost labor.

[vi] https://usafacts.org/articles/what-can-the-data-tell-us-about-unauthorized-immigration/

https://usafacts.org/answers/how-many-migrant-encounters-are-there-along-the-us-mexico-border/country/united-states/
https://ohss.dhs.gov/khsm/cbp-encounters
https://www.cbp.gov/newsroom/stats/nationwide-encounters

[vii] https://www.cato.org/blog/biden-didnt-cause-border-crisis-part-1-summary

[viii]

Strength In Numbers

Americans don't want mass deportations | Weekly roundup for July 20, 2025

Dear readers…

Read more

a year ago · 103 likes · 14 comments · G. Elliott Morris

[ix] 11 Million in 2022: https://www.pewresearch.org/short-reads/2024/07/22/what-we-know-about-unauthorized-immigrants-living-in-the-us
11.7 Million in 2023: https://cmsny.org/us-undocumented-population-increased-in-july-2023-warren-090624/
13.7 Million in 2023: https://theconversation.com/who-are-immigrants-to-the-us-where-do-they-come-from-and-where-do-they-live-247430

[x] https://pewrsr.ch/3MNDkkW

[xi] https://www.migrationpolicy.org/news/unauthorized-immigrant-population-mid-2023

[xii] An assortment of stories …
• Veteran's daughter, 48-year resident, detained by ICE. www.newsweek.com/alma-bowman-veteran-daughter-detained-ice-immigration-2077893

• Father of Marines violently arrested by ICE. www.npr.org/2025/06/27/nx-s1-5442653/father-of-u-s-marines-violently-arrested-by-ice

• Afghan ally detained, facing fast-tracked deportation by ICE. www.military.com/daily-news/2025/06/30/afghan-ally-who-was-detained-ice-now-facing-fast-tracked-deportation.html

• ICE detains Marine veteran's wife, a breastfeeding mother. www.cbsnews.com/news/ice-detains-marine-corps-veteran-wife/

• U.S. military veterans detained by ICE under Trump. www.theguardian.com/us-news/2025/jun/28/us-military-veterans-detained-trump

• Iranian woman, 47 years in US, detained by ICE. www.theguardian.com/us-news/2025/jun/27/ice-detains-woman-iran-new-orleans

• Iranians arrested after decades in US deportation drive. www.latimes.com/world-nation/story/2025-06-29/after-decades-in-the-us-iranians-arrested-in-trumps-deportation-drive

• Iranian woman suffers panic attack during husband's ICE arrest. www.nbcnews.com/news/us-news/iranian-woman-suffers-severe-panic-attack-ice-agents-arrest-husband-rcna215119

• Honduras mother sues over ICE arrest at court. www.theguardian.com/us-news/2025/jun/27/honduras-mother-ice-arrest-lawsuit

• Aurora woman's husband sent for deportation by ICE. www.cbsnews.com/colorado/news/aurora-woman-visits-husband-ice-detention-facility-sent-for-deportation/

• Colorado: Immigrants without criminal records detained by ICE. www.cpr.org/2025/04/07/undocumented-immigrants-without-criminal-records-detained-by-ice/

• Purple Heart veteran pressured to self deport by ICE after nearly 50 years in US

https://www.npr.org/2025/06/24/g-s1-74036/trump-ice-self-deportation-army-veteran-hawaii

[xiii] https://apnews.com/article/fact-check-trump-immigration-crime-ice-criminal-dangerous-violent-99557d9d68642004193a9f4b7668162e

[xiv] https://theconversation.com/who-are-immigrants-to-the-us-where-do-they-come-from-and-where-do-they-live-247430

[xv] https://www.pewresearch.org/short-reads/2024/07/22/what-we-know-about-unauthorized-immigrants-living-in-the-us/

[xvi] https://www.migrationpolicy.org/content/immigrants-and-crime
https://www.cato.org/policy-analysis/illegal-immigrant-incarceration-rates-2010-2023

[xvii] For those who remember some of the more interesting bits from statistics classes, this is a classic Simpson’s paradox. Where distortion in overall statistics comes from sampling at different rates from different subpopulations. In this case, arrests are “sampling” from border and interior arrests population, but sampling different ratios from sub-population at each time causes the reverse trend in the aggregate of the trends in the subpopulations.

[xviii] https://theconversation.com/vanishing-data-in-the-u-s-undermines-good-public-policy-with-global-implications-258230

https://www.eff.org/deeplinks/2025/06/dangers-consolidating-all-government-information

[xix] https://tracreports.org/whatsnew/email.250718.html

Hi. I’m glad you found your way here to my new writing project, Untangling Threads.

This project comes out of a long standing frustration I have as a regular news reader, wanting news in grounded statistical perspective, instead of out of context numbers or isolated stories. I want to understand and feel the scale and scope of events, distinguish the merely attention grabbing from the fundamentally important. I do this work myself and want to share it here.

This project is meant to be politically neutral, evidence-based, meant to hopefully challenge the very human tendency most of us share to withdraw into mutually reinforcing, polarized echo chambers of similar views and reactions.

As a research scientist, I have a training to think about numbers, particularly in relation to people, that most complicated of subjects. For many things that concern us, there is a lot of data out there, but often not in the most accessible of places or not coordinated with other data sets to facilitate comparison. For other things we are concerned with, the data is not kept, but we can arrive at reasonable estimates and ranges of possibilities based on the data that is there. I often do this myself, just so I can understand. Sometimes others find it useful, so I’m sharing it here.

Please do subscribe. It’s free.

My intention for now is to post once every month or so, with perhaps longer intervals if I am working on a trickier project. Eventually, as I see what format serves best, I’ll increase the frequency of posts. What seems to be working now is to have a short read with data summary followed by a longer continuation on the topic.

As the intention is to find the statistics that are actually relevant for decision making, this also brings up the question of values and how we juggle them, individually and collectively. Values discussion will sometimes have to be part of finding the right statistics.

I’m an American living in Europe. A main focus will be on US and global politics, but I have always travelled and have connections around the world, so I will sometimes touch on local/regional issues of other parts of the world.

For the 2016, 2020, and 2024 presidential elections, I was running poll average-based computer simulations of the US presidential elections. I’ll continue this here as well as have links to others doing competent simulation work with different assumptions.

I hope you find it interesting and useful!

Again, please do subscribe if you like the idea of the project. The subscription is free and will get the occasional posts sent to your email. There is also a paid subscription. For now, all the content is free, so this is just if you would like to support the project and help me accelerate the writing. As the project grows, I’ll introduce more for the paid subscription, like more in-depth articles, special foci on more details of the statistics, but the free subscription will always be there and will be the main focus of the project.

In these times, stay present, stay grounded, and may you have good fortune in participating in making the world we live in.

Dr. Karl Frost
July 2025