In this post you've written that you're "not trying to attack me" and "no-one is saying the majority of poor people are criminals." In the post I was replying to, you wrote, and I'm quoting again, "The majority of poor people are not criminals despite the assertions made in these posts."
I do not claim that the majority of poor people are criminals. I never have said anything of the sort, despite what you wrote in that post when you attacked me with that false claim.
Now you're saying that the majority of criminals are poor, but you're attributing that as "being due primarily to their criminal nature and other behaviors associated with that criminality." That's incredibly offensive.
We disproportionately incarcerate Black Americans because they're more likely to be born into extreme poverty due to generation after generation of economic and social oppression. They are NOT any more likely to have a "criminal nature and other behaviors associated with that criminality" than white Americans, yet Black Americans are five times more likely to be incarcerated than whites. Tell me again how that's just due to their "criminal nature." 
You claim in this post that the relationship between poverty and crime is because of "their criminal nature." You claimed in your previous post that these poor folks have "criminal inclinations tending to move them down the socioeconomic ladder" and that all they have to do to climb out of poverty is to "work harder." Can you not see how that smacks of racism, classism, and rightwing extremism? That's something you'd expect to hear if you'd accidentally tuned into Rush Limbaugh in the early 90s. That isn't something you'd expect to read on a progressive, Democratic forum in 2022!
I'm really not sure what to say to you. We've known for decades that as wealth disparity and income inequality get worse, so do crime rates.
A new survey by Gallup, a polling organization, appears to go some way to verifying Becker’s theory. It asked 148,000 people in 142 countries about their perceptions of crime and how safe they feel across four measures: whether they trust the local police; whether they feel safe walking home alone; if they have had property or money stolen; and whether they have been assaulted over the past year. Testing the correlation between these questions and the amount of income inequality (as measured by the Gini coefficient) in any given country shows a strong and positive relationship (see chart above).
Whether people feel safe walking home alone or not shows the strongest relationship with inequality. In Venezuela, for example, four-fifths of respondents said they do not feel safe walking home alone—kidnappings and extortion are a common occurrence in the country. Its income distribution is the 19th-most unequal in the study. In contrast, fully 95% of people in Norway said they feel safe walking home alone. Sure enough, it is 12th most equal country of the 142.
FACTORS THAT PREDICT VIOLENT CRIME
Firearm ownership is a statistically significant predictor of homicide rate (t=4.43), but the effect of each firearm is vanishingly small (and that of each homicide disproportionately large) at the margin. For example, the model suggests that a population of 1.2 million must reduce its firearm ownership rate by 1 per 100 (i.e. destroy up to 12,000 firearms) to avoid one homicide per year. Alternatively, each additional homicide per year would prompt the purchase of up to 12,000 additional firearms.
Racial diversity. This analysis was colorblind. I used publicly available data from the Kaiser Family Foundation for the racial composition of each state (White, Black, Hispanic, Asian, Native, and of two or more races). The diversity index represents the probability of a random pairing of individuals being of different racial groups. The analysis found that more-diverse populations have higher rates of homicide (t=4.75) and robbery (t=3.41). This statistical finding might seem disturbing, but the magnitude of the effect is rather small: If we were to make our hypothetical population of 1.2 million of any single race, the model predicts that we would avoid only three homicides per year.
GDP per capita. The analysis found an inverse relationship between GDP and both homicide (t=-5.86) and robbery (t=-5.29). In other words, as a population gets wealthier, homicides and robberies both decrease. The model implies that our hypothetical population of 1.2 million could avoid one homicide per year by increasing GDP by $1,700 per person.
Income inequality. The analysis found an interaction between the Gini coefficient and the GDP per capita that was a strong predictor of both homicide (t=6.80) and robbery (t=7.06). In other words, the wealthier the population and the bigger the gap between the highest and lowest income earners, the more homicides and robberies. The model suggests that our hypothetical population of 1.2 million, assuming the current US GDP per capita of $57,466 and Gini coefficient of 0.41, would avoid 60 homicides per year if it had Canada’s Gini coefficient of 0.34 while holding all other variables constant.
Overall, our results support the theory that as income inequality increases, so does property crime, and that the spatial context plays important role in the relationship. Policies aimed at reducing localized income inequality may help to reduce the incentive for property crime. If households have similar income, then there should be no reason for people to steal from their neighbors. However, we must caution readers on the robustness of these results to other cities. Block groups can vary dramatically in size across large and small cities which may impact the results.
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