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How Poverty is The Main Cause of Crime

Categories: Criminal Behavior

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Published: Dec 16, 2021

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brooklyn eagle. (2019,October 15). New York's most desperate caught up in 'crimes of poverty'. Retrieved from https://brooklyneagle.com/articles/2019/10/15/new-yorks-most-desperate-caught-up-in-crimes-of-poverty/
Ginni Correa. (2020,june 18). Addiction center. Retrieved from https://www.addictioncenter.com/addiction/low-income-americans/
OSAC. (6/18/2019). Venezuela 2019 Crime & Safety Report. Retrieved from https://www.osac.gov/Country/Venezuela/Content/Detail/Report/b0933dac-4154-4dc2-89c1-160ca3b2c4c2
Tom Mack. (3 JAN 2020). Leicestershire live . Retrieved from https://www.leicestermercury.co.uk/news/leicester-news/man-stealing-metal-feed-family-3699993

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‘Poverty Is The Parent Of Revolution And Crime’ – Aristotle

September 15, 2020
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Poor countries are disproportionately affected by domestic war and other forms of conflict. The World Bank acknowledges that “on average, a country that experiences major violence has a poverty rate significantly higher than a country that had no violence.” The ten poorest countries, as reported by the World Bank in 2019, have a GDP per capita of less than USD 600. The list includes war-ridden countries such as Sudan, Afghanistan, the Central African Republic, and the Democratic Republic of Congo. Of course, there are outliers – Malawi and Sierra Leone, for example, ranked 59th and 46th, respectively, on the Global Peace Index. The index weighs factors including a country’s level of violent crime, political terror, conflicts fought, and percentage of displaced people. Of the top ten richest countries represented on the Global Peace Index, all but one ranked in the top 27th. Four ranked within the top ten, with Iceland placing 1st. This stark contrast paints a harsh picture of the relationship between wealth and peace.

The first of the United Nations Sustainable Development Goals is to “end poverty in all its forms everywhere.” The World Bank reported a decrease in global poverty of approximately 26% between 1990 and 2015. These years saw nearly 1.1 billion people leave extreme poverty (began earning more than USD 1.90 a day). Increased productivity and the expansion of the middle class are largely responsible for this reduction in global poverty.

However, COVID-19 raises serious concerns that this progress may be reversed. It is likely that those who can least afford to weather it will feel the global recession the most. The United Nations University World Institute for Development Economics Research predicts that the pandemic is likely to increase global poverty by 500 million people – 8% of the global population. Further, the World Bank estimates that between 40-60 million people may fall into the category of extreme poverty.

Not enough is being done to eradicate poverty and create a more equitable distribution of resources. According to the United Nations, nearly one in every ten employed workers lived in extreme poverty in 2018. Approximately 20,000 people die every day from malnutrition when the earth has enough resources to feed the world one and a half times over. In 2017, Oxfam, a charitable organization, revealed that the “world’s richest 1% get 82% of the [world’s] wealth.” Oxfam also claimed that the 22 wealthiest men on earth have more wealth than all the women in Africa – of which there are over 500 million.

The UN Millennium Project found that poor countries are “more likely to have weak governments.” Weak government institutions positively reinforce poverty because state institutions rely on public funding to maintain public goods, such as education, trade regulations, the justice system, police forces, and healthcare. When state institutions are insufficiently funded, the goods they can provide to the public are limited. In 2000, the World Health Organization ranked 191 countries’ health system performance. In that same year, the ten countries with the lowest GDP per capita all ranked lower than 143 rd place. Myanmar and Sierra Leonne ranked 190 th and 191 st , respectively. Without the resources to enable taxpayers to prosper, public institutions further diminish their source of income, trapping poor countries in a vicious cycle.

“Poor and hungry societies are much more likely than high-income societies to fall into conflict over scarce vital resources, such as watering holes and arable land,” says the UN Millennium Project. In 1997, the Democratic Republic of Congo (DRC) hurdled into a civil war. In that same year, DRC also experienced its lowest GDP per capita (USD 140) since it gained independence in 1960. Militia groups and the country’s military fought for control over the country’s east, and although the war technically ended in 2004, internal conflict continues. Many militia groups extract the county’s natural resources, particularly coltan, which is plentiful in DRC. Tantalum, which is extracted from coltan, is a common – and, therefore, highly sought-after – component in electrical devices like mobile phones. Guerillas have exploited these resources to fund weapon purchases. In 2012, the United States implemented the Dodd-Frank Act to ban companies from using “conflict materials” like tantalum. However, this had the unintended consequence of increasing unemployment in the industry. As their poverty worsened, many of the newly unemployed workers were driven to join militia groups instead.

Poverty is not the only cause of war. A country’s socio-political environment, its history, or its geography may all be factors. Wars also often have a religious or ethnic component. Whether poverty is a cause of war or merely a symptom, it reinforces the likelihood of internal conflict.

The cost of poverty is too high. Poverty is intolerable on its own. Paired with an increased likelihood of war, it is beyond unjustifiable. It is relatively easier for developed countries to look inward and prioritize inequalities at home. However, it is a global responsibility to eliminate all forms of poverty, everywhere. To eradicate poverty, world leaders must first let go of their nationalistic ideologies. Barack Obama once said, “As the wealthiest nation on Earth, I believe the United States has a moral obligation to lead the fight against hunger and malnutrition, and to partner with others.” This “we are all in this together” philosophy will be essential if the world has any hope of achieving the United Nation’s target of eliminating extreme poverty by 2030.

Changing the world’s wealth distribution will rely heavily on rich countries. The developed world holds an inequitable amount of wealth. Free from political or religious motives, privileged countries should work collaboratively to raise the standard of living in the poorest countries. This will mean less money to spend on domestic issues. However, in perspective, it makes sense for the world to focus its resources on those who need them most. This is not to say developed countries should not spend public money at home – they should. It is to say that where first world countries can go without, they should, to help their neighbors. The biggest barrier to this approach is the peoples’ capacity for compassion. Compassion will dictate the sacrifices people are willing to endure. In turn, this will influence the countries’ political will to substantially help those in need.

Billionaires are a first world problem, but they affect us all. A large proportion of the world’s wealth is heavily concentrated on a small percentage of the upper class. According to an analysis by Forbes, the top 10 richest men hold more wealth than many countries, the likes of Saudi Arabia, Switzerland, Turkey, and Belgium included. One person should not legally be allowed to have more money than they can spend, while another does not have enough to eat. Ending the era of billionaires would drastically narrow the income inequality gap.

Beyond all else, people must believe they can lift the world out of poverty. The world has already achieved more than it thought possible. International newspaper The Economist reported in 2017 that “someone escapes extreme poverty every 1.2 seconds.” In 2015, 1.1 billion fewer people were living in extreme poverty than there were in 1950. That should be evidence enough that change is possible.

Eradicating global poverty is not insurmountable. It will be hard. But it is necessary.

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Poverty and Crime

How it works

Poverty isn’t the ‘mother of crime.’ However it is one noteworthy benefactor. Crime exists, since individuals need something they don’t have, and are not willing to comply with the law(s) on the books to get it. What poverty does is, it decreases the things needy individuals have accessible to them, along these lines offering undeniably more things for needy individuals to want—and substantially more inspiration to them to carry out a crime to get it.

Along these lines, more needy individuals perpetrate crimes than individuals who aren’t.

Be that as it may, this is definitely not a causal relationship, it is a parallel one. Similar variables that reason poverty are the elements that expansion crime rates among poor populaces. That is the reason just tossing cash at poor networks doesn’t help. Until the point that we address the elements that reason the poverty itself, we can’t address the existing together crime rates.

Poverty isn’t the ‘mother’ of crime. It is just home to it. Poverty isn’t having a job, fear for the future and living for one day at a time. Poverty is feebleness, absence of opportunity.

Individuals in evident poverty come up short on the fundamental things of being a living person. This makes them frantic. Urgent individuals will effectively get what they require. It is science. I have a straightforward investigation that may encourage you (or anybody with a similar inquiry) to get it.

Think about a food that you truly don’t like. I am not discussing the food that disturbs or sickens you, however one that you just truly don’t like and that you could never eat. Presently, go three or four days without eating anything. Toward the finish of that period, get a portion of that food that you don’t like. How would you respond to it now?

It’s obvious, while a few criminals are greedy and unenergetic, and keeping in mind that a few people appear to be conceived ‘terrible’, numerous individuals in poverty have no real way to get what they require as people outside of taking it. Along these lines, they swing to crime.

Not all time Poverty is mother of all crimes and in some cases the rich people also commit crime due to their wealth as they think that their wealth can hide their all crimes. So we can say that crimes are not only associated with poverty as it also have involvement of wealthy and rich people.

However there may be a little of positive connection among poverty and crimes. suppose that if you are a poor or an employee with salary , salary that even cannot fulfill your basic needs ,what you will do, you could get baffled or pressured; this pressure would explode any time. Suppose you have children and they are hungry and you don’t have cash, what might you do? You might steal cash to feed them.

According to relative-deprivation theory, individuals commit crimes to send a signal to the State that the system they are forced to live in is inherently biased against them and their socio-economic standing in the society (Chester, 1976; Hughes and Carter, 1981; Stack, 1984). In this manner, even people with business and real salary gaining opportunity will be slanted towards carrying out crime in light of hardship of essential needs and general imbalance in the public arena. In any case, not all exploration around there passes on that financial variables and crime are positively related. Certain theories hypothesize a negative connection between total or relative poverty and property crime with the under lying clarification that crime dies down with boundless perpetual poverty by bringing returns down to crime.

Cohen, Kluegel and Land (1981: 511) state that “income inequality results in housing, employment and activity patterns by upper income individuals that lower criminal opportunity”. Returns to crime and opportunity theories contend that, controlling for the general income distribution, a reduction in absolute poverty is associated with a proportional growth in all income groups including that of the potential victims and offender.

Poverty can’t prompt crime or originate from the demonstration of being stupid. No one will want to born poor. It is a power a lot more prominent than what we can imagine that has the ability to compose or change predetermination. Good and bad is given. It is educated. Not all underprivileged take, murder, explicitly assault or perpetrate crime. A perfect example is Oprah Winfrey. She was naturally introduced to poverty in rustic Mississipi. She didn’t assault or carry out a crime; rather she ended up a standout amongst the most practiced ladies on the planet. Helping other people who used to resemble her. JK Rowling, a creator of the smash hit, Harry Potter was once on welfare installments. She too did not perpetrate any criminal demonstrations in spite of being poor. Here and there being with next to nothing make oneself more propelled to run, to escape from poverty.

As hard and as quick as one can run, in some cases there is no chance to get exit. In all nations and countries there will be a considerable measure of crime and natives breaking a lot of laws. However , this may just occur in times of distress. A period where one can never have enough pay to pay for the necessities throughout everyday life. Along these lines, it is right to take from others with the end goal to get by, to ensure family. Thus poverty is equipped for crime. It is just human nature to do as such. By and by not all poverty prompts crime. Particularly in nations, for example, Australia. Australia being one of the simple not many that give welfare installments to the individuals who don’t acquire enough to help themselves or their family. It is up to the low salary workers to utilize and ensure that cash admirably. A simple way out. A getaway course from poverty, the mother of crime.

rich individuals likewise do enormous crimes however are not been rebuffed on account of their cash influence. What’s more, the poor ones victimize or take to feed their family as the rich one don’t offer occupations to them and subsequently it makes them to be a criminal itself. Numerous individuals who are rich have been picked up that cash by crimes like burglary and so forth. The needy individuals additionally get rebuffed on their crimes and will in general take in an exercise from it however the rich ones utilize their cash to pay the attorneys and to decrease or complete their discipline.

The best killers, ruffians and exchanging weapons were truly well off some were previous specialists hijackers seize for no particular reason it might unravel some crime, however greed is dependably around the corner. Mostly are those who were abused by their folks or family members and take out their rage on others. Insights demonstrate that 90% of the time their friends, folks or somebody near them have had a criminal record.

A popular thing to say, may be it is true or not, is that, People who rob, rape, and murder do so because they lack a functioning conscience and moral self-control. It is not poverty that causes violent crime, but poor character.2 Different sorts of Poverty, Physical Poverty does not cause crime. Generally, Immigrants are dependably the poorest, however in truth the absolute most joyful to be here.

Poverty of the Spirit is the causal factor. Absence of guardians, awful guardians, absence of direction in life prompts Poverty of the soul, which leads to crime. Physical poverty can contribute, however isn’t a main driver. Being poor has never been the catalyst for somebody to act criminally. There are numerous crimes propelled by sheer insatiability and which are frequently carried out by individuals who are as of now well-to-do or even inside and out rich. Additionally, there are yet different classifications of crimes which are propelled by various things inside and out. Sex crimes, for example, might be the consequence of impulses caused by clinical conditions like ‘paraphilias’, or by different sorts of psychological instability.

Many top achievers worldwide come from a underprivileged background. The enormous corruption in our country is not committed by the poor, but by people with jobs, food, houses and cars. Wealthy and sometimes very wealthy individuals and even companies commit crime. Who said needy individuals can’t be greedy? Some destitute individuals are far more greedy than the rich. It’s simply the ethical conviction and control that has the effect. Thinking that the reduction of poverty will solve our crime problem is pie in the sky. The main thing that will change is the criminal will currently have a full stomach when he goes out to murder, assault and take. Crime is digging in for the long haul except if our administration actualizes more serious ramifications for offenders and responsibility. Poverty does not cause crime. Crime causes poverty. If poverty is the mother of crime, lack of good sense is its father.

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Relationship Between Poverty and Crime

The psychology and root causes of global crime are not yet fully explored and are at the centre of a heated debate. Some people believe that poverty incites and is to blame for the majority of casual crimes, others argue that the problem lies within deeper institutional and class inequalities. This paper will consider both sides of the argument and make case for inequality rather than poverty being the prime reason for people committing crimes.

First of all, there is a reasonable amount of evidence linking poverty with crime, however, stating that it is the only cause of crime would be far too trivial. Webster and Kingston (2014) suggest that although poverty may be considered a necessary circumstance for committing a crime, the people that commit them are often driven by other reasons. For instance, research has shown that people committing crimes often feel powerless and frustrated being “exploited and experiencing violence and abuse” (Webster and Kingston, 2014, p. 5). Ultimately, the authors argue that poverty, defined as mere lack of material needs, in most cases cannot be the only cause for committing a crime. In turn, it is often accompanied by psychological problems and poor mental well-being which are larger consequences of social inequality.

Furthermore, apart from the specific psychological background of each committed crime, there are more common social problems caused by inequality that drive people to become criminals. In particular, the literature review on crime prevention done by WWF (2020) gives an example of countries with high poverty that do not necessarily have higher crime rates and vice versa. Moreover, the paper confirms that factors causing crime include “breakdown in social norms and values… unemployment, incomplete education, a break down in family structures, limited opportunities and exclusion from the formal economy” (WWF, 2020, p. 3). Therefore, the report confirms the ideas of Webster and Kingston by linking the causes of crime to a variety of factors associated with poverty and inequality alike.

Finally, though the aforementioned authors have proven that poverty cannot be a single root cause for committing crimes, it is still one of the major factors influencing people’s decisions. Papaioannou (2017) proposes that poverty may indeed be regarded as a decisive motive for the crime. The author argues that people constantly living on the line of poverty are, on average, more likely to commit a crime in cases when they feel that their well-being, income and lifestyle are put in jeopardy. Papaioannou elaborates on this idea by providing an example of Asian ex-British colonies where a “standard deviation decrease in annual rice production increases property crime by 21.2 percent” (2017, p. 25). However, the provided case study is too narrow to extrapolate its results to the entire world as well as the conclusions of the author do not really contradict the ideas presented in the previous paragraphs.

In conclusion, though it is hard to deny that poverty is a major reason for people committing crimes, it is far from being the only root cause. In fact, the prime motivators for committing a crime can be classified into two distinct categories: psychological and social. The former is often associated with feelings of helplessness and frustration caused as well as potential trauma of abuse caused by the living conditions of the lower class. The latter encompasses a variety of factors such as low levels of employment and lack of education which are, similar to psychological factors, are often caused by social inequalities rather than poverty itself.

Papaioannou, K. J. (2017). “Hunger makes a thief of any man”: Poverty and crime in British colonial Asia . European Review of Economic History, Volume 21, Issue 1, 2017, Pages 1–28.

Webster, C. S. and Kingston, S. (2014). Anti-poverty strategies for the UK: Poverty and crime review. Joseph Rowntree Foundation .

WWF. (2020). Literature review: Crime prevention and high-value poaching . USAID.

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The Oxford Handbook of the Social Science of Poverty

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27 Poverty and Crime

Patrick Sharkey, Associate Professor of Sociology, New York University.

Max Besbris, PhD Student in Sociology, New York University.

Michael Friedson, Postdoctoral Fellow, New York University.

Published: 05 April 2017
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This article examines theory and evidence on the association between poverty and crime at both the individual and community levels. It begins with a review of the literature on individual- or family-level poverty and crime, followed by a discussion at the level of the neighborhood or community. The research under consideration focuses on criminal activity and violent behavior, using self-reports or official records of violent offenses (homicide, assault, rape), property crime (burglary, theft, vandalism), and in some cases delinquency or victimization. The article concludes by highlighting three shifts of thinking about the relationship between poverty and crime, including a shift away from a focus on individual motivations and toward a focus on situations that make crime more or less likely.

The relationship between poverty and crime is complex. There is substantial evidence indicating that poverty is associated with criminal activity, but it is less clear that this relationship is causal or that higher levels of poverty in a neighborhood, a city, or a nation necessarily translate into higher levels of crime. Perhaps the most powerful illustration of this empirical reality comes from the simple observation made by Lawrence Cohen and Marcus Felson several decades ago in introducing their “routine activities theory” of crime. During the 1960s, when poverty and racial inequality were declining in American cities, the crime rate was rising ( Cohen and Felson 1979 ). The experience during the economic downturn from 2008–2012 provides a more recent example. Despite the rise in poverty and sustained unemployment over these years, crime has not risen in any remarkable way. The implication is that in order to understand the relationship between poverty and crime it is necessary to move beyond the assumption that more poor people translates directly into more crime.

One of the major shifts in criminological thinking, spurred in large part by Cohen and Felson’s ideas, is an expansion of focus beyond the characteristics or the motivations of potential offenders and toward a broader view of what makes an incident of crime more or less likely. This entails a shift from a focus on who is likely to commit a crime toward a focus on when, where, and why a crime is likely to occur ( Birkbeck and LaFree 1993 ; Katz 1988 ; Wikström and Loeber 2000 ). The basic insight of routine activities theory is that the likelihood of a crime occurring depends on the presence of a motivated offender, a vulnerable victim, and the absence of a capable guardian ( Sampson and Wikström 2008 ). Whereas traditional approaches to understanding crime focus primarily on the first element of this equation, the offender, these approaches ignore the two other moving parts: the vulnerable victim, and the presence or absence of capable guardians ( Wikström et al. 2012 ).

The “situational” perspective on crime has important implications for understanding the complexities of the relationship between poverty and crime. It forces one to consider how poverty affects the motivations of offenders, how poverty affects the vulnerability and attractiveness of potential targets, and how poverty affects the presence of capable guardians. We will consider each of these issues throughout the chapter. The research that we review reinforces the point that crime cannot be understood primarily in terms of individual characteristics, incentives, or resources. It has to be understood in terms of situations, attachments, networks, and contexts. This insight is central to a wide range of research in the field, and it frames our approach to considering the relationship between poverty and crime.

In this chapter we review theory and evidence on the relationship between poverty and crime at the level of the individual and at the level of the community. We make no attempt to be comprehensive, but instead we focus on major patterns of findings in the literature and important theoretical and empirical advances and developments. The research that we review considers criminal activity and violent behavior, using self-reports or official records of violent offenses (homicide, assault, rape), property crime (burglary, theft, vandalism), and in some cases delinquency or victimization. This approach, which reflects the dominant focus of research in criminology and sociology, places less emphasis on (or ignores completely) other types of less visible, underreported or understudied criminal activity or deviant behavior, including crime or abuse committed by police or elected officials, domestic violence, crimes committed in prison, and many types of financial or “white-collar” crime. It is important to acknowledge that the disproportionate focus on what might be thought of as “street crime” is likely to lead to biased conclusions about the overall strength of the relationship between poverty and crime. This bias arises due to the dearth of research on crime occurring outside of low-income communities (most notably white-collar crime) and because of the use of official records to measure criminal activity. Official reports of arrests reflect some combination of criminal activity, enforcement, and reporting. These are potential sources of bias that are present in much of the criminological literature and thus are present in this review as well.

The chapter proceeds with a review of the literature on individual- or family-level poverty and crime. Although the literature demonstrates a consistent association between poverty and crime, there are multiple interpretations of this association that have been put forth in the literature. Poverty may lead directly to some types of criminal activity. However, the link between poverty and crime also may be spurious, or it may be mediated by other processes related to labor force attachment, family structure, or connections to institutions like the military or the labor market. We then move to the level of the neighborhood or community. Again, the literature shows a consistent positive association between community-level poverty and crime, although the functional form of this relationship is less settled. A prominent strand of research has argued that community-level social processes play a central role in mediating the association between poverty and crime, generating resurgent interest in the importance of social cohesion, informal social control, and other dimensions of community organization that help explain the link between poverty and crime.

Our review of the literature concludes by highlighting three shifts of thinking about the relationship between poverty and crime: (1) a shift away from the idea that criminal activity is located within the individual, and toward a perspective that locates the potential for criminal activity within networks of potential offenders, victims, and guardians; (2) a shift away from a focus on individual motivations and toward a focus on situations that make crime more or less likely; and (3) a shift away from a focus on aggregated deprivation as an explanation for concentrations of crime and toward a consideration of community social processes that make crime more or less likely.

Individual Poverty and Crime

Evidence for a positive association between individual or family poverty and criminal offending is generally strong. A review of 273 studies assessing the association between different dimensions of social and economic status (SES) and offending concludes that there is consistent evidence from multiple national settings that individuals with low income, occupational status, and education have higher rates of criminal offending ( Ellis and McDonald 2001 ). However, evidence based on self-reported data on delinquent behavior is less consistent ( Tittle and Meier 1990 ; Wright et al. 1999 ). A recent study based on comparable surveys conducted in Greece, Russia, and Ukraine showed no consistent association between social and economic status and various self-reported measures of delinquent or criminal behavior ( Antonaccio et al. 2010 ).

Given this conflicting evidence, it is important to clarify that the claims made in this section are based primarily on research that examines poverty or economic resources and that considers criminal offending as an outcome. Evidence for an association between economic resources and crime is more consistent across settings and is generally quite strong, particularly in the United States ( Bjerk 2007 ). As a whole, however, the studies reviewed do not appear to provide strong evidence that these relationships are causal, nor is the overall association between poverty and crime particularly surprising—this association is consistent with virtually all individual- and family-level theories of criminal behavior. Poverty is associated with self-control and cognitive skills ( Hirschi and Gottfredson 2001 ), with family structure and joblessness ( Matsueda and Heimer 1987 ; Sampson 1987 ), with children’s peer networks ( Haynie 2001 ; Haynie, Silver, and Teasdale 2006 ), and with the type of neighborhoods in which families reside and the types of schools that children attend ( Deming 2011 ; Wilson 1987 ). The association between individual poverty and criminal offending may reflect some combination of all of these pathways of influence.

Alternatively, poverty may have direct effects on crime if the inability to secure steady or sufficient financial resources leads individuals to turn to illicit activity to generate income or if relative poverty in the midst of a wealthy society generates psychological strain ( Merton 1938 ). The “economic model of crime,” put forth formally by economist Gary Becker (1974) and elaborated and refined by an array of criminologists ( Clarke and Felson 1993 ; Cornish and Clarke 1986 ; Piliavin et al. 1986 ), suggests that crime can be explained as the product of a rational decision-making process in which potential offenders weigh the benefits and probable costs/risks of committing a crime or otherwise becoming involved in criminal activities ( Becker 1974 ). Much of the research assessing the economic model of crime has focused on deterrence, or the question of whether raising the costs of criminal behavior reduces crime. However, the theory also has direct implications for the study of poverty and crime, as it suggests that individuals lacking economic resources should have greater incentives to commit crime. Despite the abundance of evidence for an association between economic resources and criminal offending, there is little convincing research demonstrating a direct causal effect. For instance, the experimental programs that are most frequently cited for evidence on the effect of income on various social outcomes—such as the income maintenance experiments of the 1970s or the state-level welfare reform experiments of the 1990s—did not assess impacts on crime ( Blank 2002 ; Munnell 1987 ).

There are, however, a small number of studies that provide persuasive, if not definitive, evidence supporting a direct causal relationship between individual economic resources and crime. One example is a recent study that exploits differences in cities’ public assistance payment schedules in order to assess whether crime rises at periods of the month when public assistance benefits are likely to be depleted. As predicted by the economic model, crimes that lead to economic gain tend to rise as the time since public assistance payments grows, while other types of crime not involving economic gain do not increase ( Foley 2011 ). Another example comes from a set of experimental studies in which returning offenders from Georgia and Texas were randomly assigned to receive different levels of unemployment benefits immediately upon leaving prison, while members of the control group received job-placement counseling but no cash benefits ( Berk, Lenihan, and Rossi 1980 ; see also Rossi, Berk, and Lenihan 1980 ). Although the results are generalizable only to returning offenders, they show that modest supplements of income reduce subsequent recidivism.

A larger base of evidence suggests that unemployment (and underemployment or low wages) is causally related to criminal offending, with a stronger relationship between unemployment and property crime as compared with violent crime ( Chiricos 1987 ; Fagan and Freeman 1999 ; Grogger 1998 ; Levitt 2001 ; Raphael and Winter-Ebmer 2001 ). This finding from the quantitative literature finds support in ethnographic studies arguing that the absence of stable employment and income are important factors leading to participation in informal and illicit profit-seeking activity, ranging from drug distribution and burglary to participating in informal or underground economic markets ( Bourgois 1995 ; Venkatesh 2006 ; Wright and Decker 1994 ).

The evidence linking unemployment with criminal behavior can be interpreted in multiple ways. Economists studying this relationship tend to view criminal activity as a substitute for employment in the formal labor market. From this perspective, individuals who cannot find work or whose wages are low, relative to opportunities in the informal or illicit labor market, are likely to choose criminal activity as an alternative (or supplemental) source of income ( Fagan and Freeman 1999 ; Grogger 1998 ). Criminological and sociological perspectives acknowledge the importance of income as a mechanism underlying the relationship between joblessness and crime but view employment as one of many social bonds that connect individuals to other individuals and to institutions in ways that reduce the likelihood that they will become involved in criminal activity. In their life-course model of deviance and desistance, Robert Sampson and John Laub describe the set of attachments that individuals form at different stages in the life course, including college attendance, military service, and entrance into marriage ( Sampson and Laub 1993 , 1996; Laub and Sampson 2003 ). The formation and maintenance of individuals’ bonds to romantic partners and family, to employers and institutions, and the informal social controls that arise from these social bonds do not only reduce the probability of criminal activity but also help explain patterns of desistance over time. Marriage and employment, for example, can alter the offending trajectory of individuals by serving as turning points from the past to the present, by increasing supervision and responsibilities, and by transforming roles and identities ( Laub and Sampson 2003 ).

The implication is that the relationship between poverty and crime may not be direct and causal; it is plausible that this relationship may be indirect or even spurious. Unemployment is only one characteristic that may confound the relationship between poverty and crime, but there are many others. Growing up in a single-parent household or in a community dominated by single-parent households is strongly related to criminal activity and also is associated with poverty ( Sampson 1987 ; Sampson and Wilson 1995 ). Association with delinquent peers is another potential confounder, as are cognitive skills, work ethic, and exposure to environmental toxins like lead or environmental stressors like violence ( Anderson 1999 ; Matsueda 1982 , 1988 ; Nevin 2007 ; Reyes 2007 ; Stretesky and Lynch 2001 ).

This discussion leaves us with three possible models of the relationship between individual or family poverty and crime. The first model posits that this relationship is direct and causal. In this model, which is reflected in the economic model of crime, poverty and the inability to secure stable and well-paid employment in the formal labor market provide greater incentives for individuals to commit crimes in order to generate income and associated benefits. The second model posits that the relationship between poverty and crime is mediated by other processes, such as the formation of social attachments to romantic partners, jobs, or institutions like the military. The third model posits that the association between poverty and crime is spurious and is the result of bias due to confounding factors. This model would suggest that poverty is linked with crime because it is associated with other criminogenic characteristics of the family or the individual.

The evidence available provides the strongest support for the first two models. Poverty is likely to be linked to crime both because the poor have greater incentives to commit crime and because poverty affects individuals’ environments, their relationships, their developmental trajectories, and their opportunities as they move through different stages of the life course. The strongest evidence in support of this conclusion comes from the literature on unemployment, wages, and crime. However, there is very little convincing evidence that focuses purely on the direct effect of poverty on crime. We consider this to be an important gap in the literature.

Community Poverty and Crime

Despite the myriad ways that individual poverty may be linked with individual criminal activity, the aggregation of individuals who have greater incentives or propensity to commit crime does not necessarily lead to more crime in the aggregate. One simplistic illustration of why this is the case emerges when we return to the situational framework of offenders, victims, and guardians with which we began the chapter. Focusing in particular on the presence of attractive and vulnerable victims, one might conclude that in areas where poverty is concentrated there are likely to be fewer attractive victims vulnerable to potential offenders ( Hannon 2002 ). If one were to consider only the second dimension of the crime equation, one might arrive at the hypothesis that in periods where poverty is rising or in places where poverty is concentrated there should be fewer crimes committed. Just as with theories that focus only on the prevalence of motivated offenders, this hypothesis is simplistic and incomplete.

Theories that focus exclusively on the number of potential offenders or the number of potential victims within a community are equally deficient because they do not consider the ecological context in which criminal activity takes place. Moving beyond the individual-level analysis of crime requires a consideration of social organization within the community; the enforcement of common norms of behavior by community residents, leaders, and police; the structure and strength of social networks within a community; and the relationships between residents, local organizations, and institutions within and outside the community ( Sampson 2012 ; Sampson and Wikström 2008 ).

An illustrative example comes from the Moving to Opportunity (MTO) program, a social experiment that randomly offered vouchers to public housing residents in five cities that allowed them to move to low-poverty neighborhoods. The most common reason that families gave for volunteering for the program was that they wanted their children to be able to avoid the risks from crime, violence, and drugs in their origin neighborhoods ( Kling, Liebman, and Katz 2007 ). However, when data on criminal activity were analyzed years later, the results showed that youth in families who had moved to neighborhoods with lower poverty were no less likely to report having been victimized or “jumped,” seeing someone shot or stabbed, or taking part in violent activities themselves ( Kling, Liebman, and Katz 2007 ; Kling, Ludwig, and Katz 2005 ). A complicated set of findings emerged, with very different patterns for girls and boys. Whereas girls reported feeling more safe in their new communities, boys in families that moved to low-poverty neighborhoods were less likely to have been arrested for violent crimes but more likely to be arrested for property crimes, to have a friend who used drugs, and to engage in risky behaviors themselves ( Clampet-Lundquist et al. 2006 ; see also Sharkey and Sampson 2010 ).

The results from Moving to Opportunity reveal the complex ways in which individuals and aspects of their social environments interact to make crime more or less likely. Boys in families that moved to new environments may have changed their behavior with new opportunities for property crime available to them, but they may also have been subject to greater scrutiny from their new neighbors and from law enforcement. Girls in the same families were likely to be seen in a different light by neighbors and police, leading to different behavioral and social responses ( Clampet-Lundquist et al. 2006 ). The interaction between the characteristics of youths themselves; the types of potential targets that existed in their new communities; and the level of supervision, suspicion, and policing in the new communities created an unexpected pattern of behavior within the new environment.

This example highlights the complexity of community-level models of poverty and crime. In an attempt to synthesize some of the core ideas that have been put forth in the criminological literature on community-level crime, we focus on three stylized facts that guide our discussion of the community-level relationship between poverty and crime. First, crime is clustered in space to a remarkable degree ( Sampson 2012 ). This empirical observation has been made repeatedly by scholars in different settings and in different times, but the study of space and crime has been refined considerably in recent years. Crime is not only spatially clustered at the level of the neighborhood or community, but it is concentrated in a smaller number of “hot spots” within communities ( Block and Block 1995 ; Sherman 1995 ; Sherman, Gartin, and Buerger 1989 ). The spatial dimension of crime leads to questions about the underlying mechanisms that might explain why certain spaces or areas appear to be criminogenic. Over the past few decades, the concentration of poverty has emerged as a primary explanatory mechanism.

This leads to our second stylized fact: the level of poverty in a community is strongly associated with the level of crime in the community ( Patterson 1991 ; Krivo and Peterson 1996 ). This relationship is found not only in the United States but also in nations such as the Netherlands and Sweden, where the levels of concentrated poverty and violent crime are substantially lower (e.g., Sampson and Wikström 2008 ; Weijters, Scheepers, and Gerris 2009 ). Despite the robustness of this relationship across contexts, there is conflicting evidence on the functional form of this relationship. One of the central arguments in William Julius Wilson’s classic book The Truly Disadvantaged (1987) was that urban poverty in the United States transformed in the post–civil rights period, and the new type of concentrated neighborhood poverty that emerged during this period led to the intensification of an array of social problems including a sharp rise in violent crime. Sampson and Wilson (1995) argue that areas of concentrated poverty provide a niche where role models for youth are absent and residents are less fervent in enforcing common norms of behavior, leading to elevated levels of crime. This argument has served as a primary explanation for the rise and concentration of urban crime from the 1960s through the 1990s, but it has been challenged recently by research investigating the form of the relationship between neighborhood poverty and crime. Analyzing data on neighborhood crime from 25 cities in 2000, Hipp and Yates (2011) find no evidence that crime rises sharply in extreme-poverty neighborhoods. All types of crime rise with the level of poverty in the neighborhood, but for most types this relationship levels off as the neighborhood poverty rate reaches 30 percent or higher. As the most rigorous study conducted to date on the form of the relationship between neighborhood poverty and crime, we believe the findings from this article should provoke further theoretical and empirical investigations into this important issue.

Discussions about the functional form of the relationship between community poverty and crime lead directly into a broader discussion about the mechanisms underlying this relationship. An extensive ethnographic literature demonstrates how the threat of violence can come to structure interpersonal interactions and outlooks in areas of extreme poverty, creating the need for individuals to take strategic steps to avoid violence ( Anderson 1999 ; Harding 2010 ). The emergence of patterned responses to community-level poverty and violence involving the adoption of unique frames and repertoires of action becomes visible in this research, with consequences that can affect individual behavior and reinforce the atmosphere of threat, further weakening informal social controls and trust within the community ( Anderson 1999 ; Small, Harding, and Lamont 2010 ).

The importance of community trust, social cohesion, and informal social controls has been theorized and analyzed in a resurgent literature on community social processes and crime and violence. The third stylized fact about communities and crime is that social processes at the level of the community appear to play a central role in mediating the association between poverty and crime. In their classic work on the organization of communities and rates of juvenile delinquency, Shaw and McKay (1942) argued that community organization is lower and crime and delinquency are higher in neighborhoods with low social and economic status, high levels of ethnic heterogeneity, and high levels of residential mobility. In the last few decades these ideas have served as the basis for a resurgent interest in the role that structural characteristics of communities play in facilitating informal social controls, in strengthening or weakening community organization, and in increasing or reducing crime ( Sampson and Groves 1989 ; Sampson, Raudenbush, and Earls 1997 ).

The research of Robert Sampson and several colleagues lies at the heart of this resurgence. With the concept of collective efficacy, Sampson builds on the ideas of Shaw and McKay but puts forth a more refined theory of the role of community-level social processes in influencing patterns of crime and violence. In addition to the three dimensions of communities on which Shaw and McKay focused, this research analyzes the importance of family structure and rates of family disruption as central factors influencing the capacity of a community to supervise and monitor teenage peer groups and to establish intergenerational lines of communication, social cohesion, and informal social controls. The “social process turn” ( Sampson, Morenoff, and Gannon-Rowley 2002 ) in research on neighborhoods and crime leads to a new understanding of the link between neighborhood poverty and crime. According to this perspective, neighborhood poverty is associated with criminal activity not because of the aggregation of motivated offenders, but rather because of community-level dynamics that create an environment in which informal social controls over activity in public space are weakened. Community-level poverty is linked with family structure, residential mobility, the density of housing, labor force detachment, physical disorder, legal cynicism, civic and political participation, and community organization, all of which are associated with crime ( Hagan and Peterson 1995 ; Krivo and Peterson 1996 ; Sampson 2012 ; Sampson and Lauritsen 1994 ).

Comparative research is beginning to assess whether the focus on community social processes is applicable in different national settings. Some research using the same methods developed to study collective efficacy in Chicago suggests that the basic relationships are similar in very different places. For instance, in comparable studies conducted in Chicago and Stockholm, neighborhood collective efficacy was found to have a remarkably similar, inverse association with violent crime ( Sampson and Wikström 2008 ). However, such similarities do not suggest that models of poverty, collective efficacy, and community violence can be blindly transferred across national contexts. In a study of Belo Horizonte, Brazil, Villarreal and Silva (2006) find that neighborhood social cohesion is not predictive of crime rates. In a context in which national policies have led to a retrenchment of public sector employment and the welfare state ( Portes and Hoffman 2003 ), the authors argue that informal networks of reciprocity and exchange are central to community sustainability but also have led to a proliferation of informal labor market activity and have emerged at a time of rising crime and violence. This study provides an example of how the relationships among neighborhood social cohesion, neighborhood poverty, and crime may vary across different local or national contexts.

Summary of the Evidence and Three Shifts of Thinking

The evidence we have reviewed suggests a set of core findings that characterize the relationship between poverty and crime at the level of the individual and the community. First, poverty is strongly associated with crime at both levels of analysis. In the rational choice, or economic model, of crime, the individual-level relationship between poverty and criminal behavior is assumed to be direct and causal, but most theoretical models do not make this assumption. We have uncovered very little empirical research that provides convincing evidence for a direct causal relationship between individual poverty and criminal activity. Some suggestive research is consistent with a causal relationship, but most research does not assess it directly. Instead, most theoretical and empirical evidence suggest that poverty is linked with criminal behavior through individual characteristics and conditions associated with poverty, such as joblessness, family structure, peer networks, psychological strain, or exposure to intensely violent environments.

At the level of the community, there is again a strong relationship between poverty and aggregated rates of crime. However, the most prominent theoretical and empirical work on the topic suggests that this relationship is mediated by community-level social processes that facilitate social cohesion and trust and that act to limit criminal activity in the community. Poverty is thus viewed as one of several characteristics of communities that lead to the breakdown of community organization, in turn leading to higher rates of crime.

These findings lead us to identify three interrelated shifts of thinking that are central to understanding the relationship between poverty and crime. These shifts of thinking reflect the insights of criminologists that have been developed over the past several decades, but they may be less familiar to poverty researchers. The first is a shift away from thinking of the potential for crime as lying within the individual offender and instead thinking of the potential for crime as lying within networks of potential offenders, victims, and guardians situated within a diverse group of contexts and settings ( Papachristos 2011 ; Wikström et al. 2012 ). The field of criminology has a long history of locating the source of criminal activity within the individual. Without denying the importance of individual characteristics in affecting the propensity for criminal activity, and without denying the agency of individual offenders, we argue that the traditionally dominant focus on the offender has stifled progress in understanding variation in crime across places and over time.

This point reflects a second shift of thinking, which involves moving from a focus on individual motivations to a focus on situations ( Wikström et al. 2012 ). Applied to the study of poverty and crime, this shift moves away from the idea that crime is driven primarily by economic calculations. While economic benefit is one important motivation for potential offenders, even the rational choice paradigm has been extended to consider other types of noneconomic rewards arising from criminal offending (e.g., Cornish and Clarke 1986 ). The situational approach to crime, by contrast, expands beyond the motivations of individuals to consider the interactions of offenders, victims, and guardians. The role of poverty as a predictor of criminal offending is much more complex in the situational approach to crime. Poverty may produce more motivated offenders, fewer potential victims (for at least some types of crime), and less effective community guardians. Considered together, one would still expect an association between poverty and crime, but the mechanisms underlying this association are more complex than the economic model suggests.

The third shift of thinking moves beyond a focus on aggregated deprivation as an explanation for concentrations of crime and toward a consideration of community social processes ( Sampson, Morenoff, and Gannon-Rowley 2002 ). Concentrated poverty is one of several characteristics of communities, along with others such as high levels of residential mobility, that tend to disrupt processes of informal social control and social cohesion within communities, or collective efficacy ( Sampson, Raudenbush, and Earls 1997 ). The breakdown of collective efficacy provides the context for the emergence of crime and violence within the community, as informal controls over public space are less effective and violations of collective norms of behavior become common.

These three shifts of thinking reflect the findings from a complex theoretical and empirical literature on the relationship between poverty and crime. In the most simplistic model of this relationship, individual poverty causes individuals to commit more crime and the aggregation of poor individuals in a community, a city, or a nation translates directly into more crime. The experience of the United States over the last 50 years demonstrates that this model is not adequate. When poverty is high, crime does not necessarily rise with it. In critiquing the direct, linear, causal model of poverty and crime, we acknowledge that we do not have an equally simple model to replace it. Instead, we argue that the relationship is complex, that it is driven by a number of different mediating mechanisms, and that these mechanisms vary depending on the level of analysis (e.g., individuals, neighborhoods, nations, etc.). While this may not be a particularly satisfying conclusion, the evidence available suggests that it is the most realistic.

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Essay on Poverty Causes Crime

Students are often asked to write an essay on Poverty Causes Crime in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Poverty Causes Crime

Understanding poverty and crime.

Poverty is when people do not have enough money for things like food and a home. Some believe that not having money can lead to crime. When people are poor, they might steal to survive. They might feel they have no choice but to break the law to feed their families.

The Link Between Need and Theft

Imagine you’re really hungry, but you have no money. What would you do? Some might steal food. This is a simple example of how needing something badly can push someone to commit a crime, like theft.

Desperation Leads to Bad Choices

A person who can’t find a job and is desperate might feel forced to sell illegal things or join gangs. They might think this is the only way to make money. This shows how not having options can lead to crime.

Crime as a Last Resort

Sometimes, people who are poor see crime as the only way out. They might not see any other way to change their lives. So, they take risks to try to escape poverty, which can include doing illegal things.

Breaking the Cycle

To stop this problem, everyone needs to help. If poor people get support, like education and jobs, they might not turn to crime. Helping them can make our communities safer for everyone.

250 Words Essay on Poverty Causes Crime

The link between poverty and crime.

Many people wonder if not having enough money can lead to crime. The answer is not simple, but often, when people are very poor, they might break the law to get what they need or want.

Struggle for Basic Needs

When families do not have enough money, they struggle to buy food, pay for a house, or get medicine. This can make someone feel desperate. In desperation, stealing or other illegal ways to get money can seem like the only choice.

Lack of Education and Jobs

Schools in poor areas might not be very good, making it hard for kids to learn and get good jobs later. Without a good job, it’s tough to earn money legally. This can lead some to choose crime as a way to make money.

Feeling Left Out

People who are poor often feel left out because they can’t have what others do. This feeling can turn into anger or jealousy, which might lead them to take things that aren’t theirs.

Not everyone who is poor will commit a crime. But when there’s no other way to survive, some might see crime as the last option. It’s like when you’re playing a game and you’re losing badly, you might break the rules just to keep playing.

In conclusion, being poor can sometimes lead to crime, but it’s not the only reason people do bad things. It’s important to help those in need and make sure everyone has a fair chance at a good life to prevent crime.

500 Words Essay on Poverty Causes Crime

Many people wonder if being poor can lead someone to act wrongly. It is a topic that has been talked about for a long time. This essay will explain why some believe that not having enough money can make people commit crimes.

Struggling to Survive

When people do not have enough money, they might find it hard to buy food, find a place to live, or get clothes. This situation is called poverty. Imagine you are very hungry and you have no money; you might think about taking food from a store without paying. This is an example of how poverty can push someone to steal, which is a crime. People who are very poor often feel they have no choice but to break the law to survive.

Education and Opportunities

Schools in richer areas usually have more resources, like books and computers, than schools in poorer areas. Children from poor families might not learn as much or might even stop going to school. Without a good education, it is hard to get a good job. This makes it tough for people to earn enough money legally. So, some might turn to crime as a way to make money because they feel they have no other options.

When people are poor, they might feel left out of society. They see others with nice things and feel they can never have those things. This can make them angry or jealous. Sometimes, to get what others have, they might decide to take things that are not theirs. This is not right, but it shows how feeling left out can lead to crime.

Drugs and Crime

In places where there is a lot of poverty, there might also be more drugs. Drugs can cause big problems. People who use drugs may need a lot of money to buy them, and this can lead to stealing or other crimes. Also, people who sell drugs are committing a crime, and they might do this because they think they can’t make money any other way.

What Can Be Done?

To stop crime, we need to help people get out of poverty. This means making sure everyone has enough food, a safe place to live, and clothes to wear. It also means giving children the chance to go to good schools and learn. When people have what they need and feel they are part of society, they are less likely to commit crimes.

In conclusion, not having enough money can lead to crime because people are trying to survive, they might not have good education or job opportunities, they feel left out, or they get involved with drugs. To solve this problem, it is important to help people live better lives. By understanding the reasons behind crime, we can work to make our communities safer for everyone.

That’s it! I hope the essay helped you.

If you’re looking for more, here are essays on other interesting topics:

Essay on Poverty Begins In The Mind
Essay on Poverty And Homelessness
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Happy studying!

Why do inequality and deprivation produce high crime and low trust?

Benoît De Courson 1 , 2 &
Daniel Nettle 2

Scientific Reports volume 11 , Article number: 1937 ( 2021 ) Cite this article

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Human behaviour

Humans sometimes cooperate to mutual advantage, and sometimes exploit one another. In industrialised societies, the prevalence of exploitation, in the form of crime, is related to the distribution of economic resources: more unequal societies tend to have higher crime, as well as lower social trust. We created a model of cooperation and exploitation to explore why this should be. Distinctively, our model features a desperation threshold, a level of resources below which it is extremely damaging to fall. Agents do not belong to fixed types, but condition their behaviour on their current resource level and the behaviour in the population around them. We show that the optimal action for individuals who are close to the desperation threshold is to exploit others. This remains true even in the presence of severe and probable punishment for exploitation, since successful exploitation is the quickest route out of desperation, whereas being punished does not make already desperate states much worse. Simulated populations with a sufficiently unequal distribution of resources rapidly evolve an equilibrium of low trust and zero cooperation: desperate individuals try to exploit, and non-desperate individuals avoid interaction altogether. Making the distribution of resources more equal or increasing social mobility is generally effective in producing a high cooperation, high trust equilibrium; increasing punishment severity is not.

Inequality and cooperation in social networks

Segregating socioeconomic classes leads to an unequal redistribution of wealth

People prefer coordinated punishment in cooperative interactions

Introduction.

Humans are often described as an unusually cooperative or ‘ultrasocial’ species 1 . The truth is more complex: humans from the same society can cooperate for mutual benefit; or they can simply co-exist; or they can actively exploit one another, as in, for example, crime. A theory of human sociality should ideally predict what mix of these alternatives will emerge under which circumstances. Comparing across industrialised societies, higher inequality—greater dispersion in the distribution of economic resources across individuals—is associated with higher crime and lower social trust 2 , 3 , 4 , 5 , 6 , 7 . These associations appear empirically robust, and meet epidemiological criteria for being considered causal 8 . However, the nature of the causal mechanisms is still debated. The effects of inequality are macroscopic phenomena, seen most clearly by comparing aggregates such as countries or states. It is their micro-foundations in individual psychology and behaviour that still require clarification.

There are, broadly, two classes of explanation for how inequality, a population-level phenomenon, could influence individual-level outcomes like crime or trust. The first class of explanation is compositional: in more unequal societies, the least fortunate individuals are absolutely worse off than in more equal societies of the same average wealth, exactly because the dispersion either side of the average is greater. Some individuals are also better off too, at the other end of the distribution, but if there is any non-linearity in the function relating individual resources to outcomes—if for example the poor becoming absolutely poorer has a larger effect on their propensity to offend than the rich becoming absolutely richer has on theirs—this can still change outcome prevalence in the population 9 , 10 , 11 . In line with compositional explanations, across US counties, the association between inequality and rate of property crime is fully mediated by the prevalence of poverty, which is higher in more unequal counties 2 . Moreover, changes in rates over time track changes in the economic prospects of people at the bottom end of the socioeconomic distribution 12 , 13 . The second class of explanation is psychosocial: individuals perceive the magnitude of social differentials in the society around them, and this affects their state of mind, increasing competitiveness, anxiety and self-serving individualism 8 , 14 . In this paper, we develop an explanatory model for why greater inequality should produce higher crime and lower social trust. Our model provides a bridge between compositional and psychosocial explanations. Its explanation for the inequality-crime association is compositional: individuals offend when their own absolute level of resources is desperately low, and the effect of increasing inequality is to make such desperation more prevalent. On the other hand, the model’s explanation for the inequality-trust association is more psychosocial: all individuals in high-inequality populations end up trusting less, regardless of their personal resource levels.

To provide a micro-foundation in individual behaviour for the macro-level effects of inequality on crime, we must start from explanations for why individuals commit crimes. Economic 15 , 16 and behavioural-ecological 17 approaches see offending as a strategic response to specific patterns of incentive. Economic models predict that offending should be more attractive when the payoffs from legitimate activity are low. This principle successfully explains variation in offending behaviour both within and between societies 12 , 16 . It can also explain the relationship between crime levels and inequality, in compositional manner, because unequal societies produce poorer legitimate opportunities for people at the lower end of the socioeconomic spectrum 2 . However, these models are generally taken to predict that making punishments for crime more severe should reduce the prevalence of offending, because harsher punishment should reduce the expected utility associated with the criminal option. Empirical evidence, though, does not clearly support the hypothesis that increasing punishment severity reduces offending 18 , 19 . There is more evidence for a deterrent effect of increased probability of punishment, though even this effect may be modest 18 , 19 .

Becker 15 pointed out that the puzzle of the weak deterrent effect of punishment severity would be solved if offenders were risk-preferring. The decision to offend is risky in that it has either a large positive payoff (if not caught) or a large negative one (if caught and punished). An individual who prefers risk might thus choose to offend even if the expected utility of offending is negative due to a possible severe punishment. Thus, the question becomes: why would some people—those who commit crime—prefer risk, when people are usually averse to it? To address this question, our model incorporates features of classic risk-sensitive foraging theory from behavioural ecology 20 (for a review in the context of human behaviour, see Ref. 21 ). Risk-sensitive foraging models incorporate a desperation threshold: a level of resources below which it is disastrous to fall, in the foraging case because of starvation. The models show that individuals in sufficient imminent danger of falling below this threshold ought to become risk-preferring. If a risky option is successful, it will allow them to leap back over the threshold; and if not, their prospects will be no more dire than they were anyway. Our model is novel in explicitly incorporating a desperation threshold into decisions about whether to cooperate (analogous in our model to participating in legitimate economic activity) or exploit others (analogous to committing an acquisitive crime).

The desperation threshold is the major theoretical innovation of our model. We justify its inclusion on multiple grounds. First, the ultimate currency in our model is fitness, a quantity with a natural biological interpretation that must necessarily be zero if the individual lacks the minimal resources to subsist and function socially. Thus, it is reasonable that expected fitness should be related to resource levels, but not linearly: there should be a point where, as resources deplete, expected fitness rapidly declines to zero. Our threshold assumption produces exactly this type of function (see Supplementary Sect. 2.1 , Supplementary Fig. S1 ). Second, in experimental games where gaining a payoff is subject to a threshold, people do switch to risk-proneness when in danger of falling below the threshold, as risk-sensitive foraging theory predicts 22 . Although this does not show that such thresholds are widespread or important in real life, it does show that people intuitively understand their implications when they are faced with them, and respond accordingly. Third, there are ethnographic descriptions of ‘disaster levels’, ‘crisis levels’, or ‘edges’ that affect the risk attitudes of people facing poverty 23 , 24 . For example, writing on Southeast Asia, Scott 23 describes the spectre of a “subsistence crisis level—perhaps a ‘danger zone’ rather than a ‘level’ would be more accurate…a threshold below which the qualitative deterioration in subsistence, security and status is massive and painful” (p. 17), as an ever-present factor in people’s decisions. Thus, including a desperation threshold is a simple but potentially powerful innovation into models of cooperation and exploitation, with potential to generate new insights.

In our model, agents repeatedly decide between three actions: foraging alone, foraging cooperatively, or exploiting a cooperative group. Foraging cooperatively is analogous to legitimate economic activity, and exploitation is analgous to acquisitive crime. Agents have variable levels of resources, and their behaviour is state-dependent. That is, rather than having a fixed strategy of always cooperating or always exploiting, each agent, at each interaction, selects a behaviour based on their current level of resources, the behaviour of others in the surrounding population, and background parameters such as the probability and severity of punishment, and the likelihood of resources improving through other means. Agents seek to maximize fitness. We assume that fitness is positively related to resource levels, but that there is a threshold, a critically low level of resources below which there is an immediate fitness penalty for falling. Our investigation of the model has two stages. We first compute the optimal action policy an individual should follow; that is, the optimal action to select for every possible combination of the situational variables. Second, we simulate populations of individuals all following the optimal action policies, to predict population-level outcomes for different initial resource distributions.

To explain the model in more detail, at each time point t in an indefinitely long sequence of time steps (where one time step is one economic interaction), agents have a current level of resources s. They can take one of three actions. Foraging alone costs x units of resources and is also guaranteed to return x. Thus, foraging alone is sufficient to maintain the agent but creates no increase in resources. It is also safe from exploitation, as we conceptualise it as involving minimal interaction with others. Alternatively, agents can team up with n-1 others to cooperate . As long as no other group member exploits, cooperation is mutually beneficial, costing x units but producing a payoff of $\alpha x \left( {\alpha > 1} \right)$ to each group member. Finally, agents can exploit : join a cooperating group and try to selfishly divert the resources produced therein. If this exploitation is successful, they obtain a large reward β, but if they fail, they receive a punishment π. The probability of being punished is γ. The punishment is not administered by peers: we assume that there is a central punitive institution in place, and both the size and probability of punishment are exogenous. In our default case, the expected payoff for exploitation is zero (i.e. $\left( {1 - \gamma } \right)\beta = \gamma \pi$ ), making exploitation no better than foraging alone on average, and worse than cooperating. However, the reward for a successful exploitation, β, is the largest payoff available to the agent in any single time step.

At every time step, each agent’s resource level is updated according to the outcomes of their action. In addition, resource levels change by a disturbance term controlled by a parameter r , such that the mean and variance of population resources are unchanged, but the temporal autocorrelation of agents’ resource levels is only 1 − r . If r is high, individuals whose current resources are low can expect they will be higher in the future and vice versa, because of regression to the mean. If $r = 0$ , resources will never change other than by the agent’s actions. We consider r a measure of social mobility due to causes other than choice of actions.

In the first stage, we use stochastic dynamic programming 25 , 26 to compute the optimal action policy. Fitness is a positive linear function of expected resource level s in the future. However, in computing the fitness payoffs of each action, we also penalize, by a fixed amount, any action that leaves the agent below a desperation threshold in the next time step (arbitrarily, we set this threshold at s = 0). The optimal action policy identifies which one of the three actions is favoured for every possible combination of the factors that impinge on the agent. These include both their own current resource state s , and features of their social world, such as the severity of punishment π, the probability of punishment γ, and the level of social mobility r. A critical variable that enters into the computation of the optimal action is the probability that any cooperating group in the population will contain someone who exploits. We denote this probability p . We can think of 1 − p as an index of the trustworthiness of the surrounding population. Computing the optimal policy effectively allows us to ask: under what circumstances should an individual forage alone, cooperate, or exploit?

In the second stage, we simulate populations of agents all following the optimal policies computed in the first stage. We can vary the starting distributions of resources (their mean and dispersion), as well as other parameters such as social mobility and the probability and severity of punishment. During the simulation stage, each agent forms an estimate of 1 − p , the trustworthiness of others, through observing the behaviour of a randomly-selected subset of other individuals. We refer to these estimates as the agents’ social trust, since social trust is defined as the generalized expectation that others will behave well 27 . Social trust updates at the end of each time step. Agents’ social trust values are unbiased estimates of the current trustworthiness of the surrounding population, but they are not precise, because they are based on only a finite sample of other population members. The simulation stage, allows us to ask: what are the predicted temporal dynamics of behaviour, and of social trust, in populations with different starting distributions of resources, different levels of social mobility, and different punishments for exploitation?

Each of the three actions is optimal in a different region of the space formed by current resources s and the trustworthiness of others 1 − p (Fig. 1 a). Below a critical value of s , agents should always exploit, regardless of trustworthiness . In the default case, this critical value is in the vicinity of the desperation threshold, though it can be lower or higher depending on the value of other parameters. With our default values, exploitation will not, on average, make the agent’s resource state any better in subsequent time steps. However, there is a large advantage to getting above the threshold in the next time step, and there is a region of the resource continuum where exploitation is the only action that can achieve this in one go (intuitively, it is the quickest way to ‘get one’s head above water’). Where s is above the critical value, cooperation is optimal as long as the trustworthiness of the surrounding population is sufficiently high. However, if trustworthiness is too low, the likelihood of getting exploited makes cooperation worse than foraging alone. The shape of the frontier between cooperation and foraging alone is complex when resources are close to the desperation threshold. This is because cooperation and foraging alone also differ in riskiness; foraging alone is risk-free, but cooperation carries a risk of being exploited that depends on trustworthiness. Just above the exploitation zone, there is a small region where cooperation is favoured even at low trustworthiness, since one successful cooperation would be enough to hurdle back over the threshold, but foraging alone would not. Just above this is a zone where foraging alone is favoured even at high trustworthiness; here the agent will be above the threshold in the next time period unless they are a victim of exploitation, which makes them averse to taking the risk of cooperating.

Optimal actions as a function of the individual’s current resources s and the trustworthiness of the surrounding population, 1 − p . ( A ) All parameters at their default values. This includes: α = 1.2, r = 0.1, π = 10, and γ = 1/3 (see Table 1 for a full list). ( B ) Effect of altering the efficiency of cooperation α to be either lower (1.05) or higher (1.30) than ( A ). Other parameter values are as for ( A ). ( C ) Effects of varying social mobility, to be either high (r = 0.8), or complete (r = 1.0; i.e. resource levels in this time period have no continuity at all into the next). Other parameter values are as for ( A ). ( D ) Effect of increasing the severity of punishment for exploiters to π = 15 and π = 20. Other parameter values are as for ( A ). ( E ). Effects of altering the probability of punishment for exploiters to γ = 2/3 and γ = 9/10. Other parameter values are as for ( A ).

We explored the sensitivity of the optimal policy to changes in parameter values. Increasing the profitability of cooperation (α) decreases the level of trustworthiness that is required for cooperation to be worthwhile (Fig. 1 B; analytically, the cooperation/foraging alone frontier for $s \gg 0$ is at $\left( {1 - p} \right) = 1/\alpha$ ; see Supplementary Sect. 2.2 ). A very high level of social mobility r moves the critical value for exploitation far to the left (i.e. individuals have to be in an even more dire state before they start to exploit; Fig. 1 C). This is because with high social mobility, badly-off individuals can expect that their level of resources will regress towards the mean over time anyway, lessening the need for risky action when faced with a small immediate shortfall.

The optimality of exploitation below the critical level of resources is generally insensitive to increasing the severity of punishment, π (Fig. 1 D), even where the expected value of exploitation is thereby rendered negative. This is because a desperate agent will be below the threshold in the next time step anyway if they forage alone, cooperate, or receive a punishment of any size. They are so badly off that it is relatively unimportant how much worse things get, but important to take any small chance of ‘jumping over’ the threshold. The exploitation boundary is slightly more sensitive to the probability of punishment, γ, though even this sensitivity is modest (Fig. 1 E). When γ is very high, it is optimal for agents very close to the boundary of desperation to take a gamble on cooperating, even where trustworthiness is rather low. Although this is risky, it offers a better chance of getting back above the threshold than exploitation that is almost bound to fail. Nonetheless, it is striking that even where exploitation is almost bound to fail and attracts a heavy penalty, it is still the best option for an individual whose current resource level is desperately low.

We also explored the effect of setting either the probability γ or the severity π of punishment so low that the expected payoff from exploitation is positive. This produces a pattern where exploitation is optimal if an agent’s resources are either desperately low, or comfortably high (see Supplementary Fig. S2 ). Only in the middle—currently above the threshold, but not by far enough that a punishment would not pull them down below it–should agents cooperate or forage alone.

We simulated populations of N = 500 individuals each following the optimal policy, with the distribution of initial resources s drawn from a distribution with mean μ and standard deviation σ. Populations fall into one of two absorbing equilibria. In the first, the poverty trap (Fig. 2 A), there is no cooperation after the first few time periods. Instead, there is a balance of attempted exploitation and foraging alone, with the proportions of these determined by the initial resource distribution and the values of π and γ. The way this equilibrium develops is as follows: there is a sufficiently high frequency of exploitation in the first round (about 10% of the population or more is required) that subsequent social trust estimates are mostly very low. With trust low, those with the higher resource levels switch to foraging alone, whilst those whose resources are desperately low continue to try to exploit. Since foraging alone produces no surplus, the population mean resources never increases, and both exploiters and lone foragers are stuck where they were.

The two equilibria in simulated populations. ( A ) The poverty trap. There is sufficient exploitation in the first time step ( A1 ) that social trust is low ( A2 ). Consequently, potential cooperators switch to lone foraging, resources never increase ( A3 ), and a subgroup of the population is left below the threshold seeking to exploit. Simulation initialised with μ = 5.5, σ = 4 and all other parameters at their default values. ( B ) The virtuous circle. Exploitation is sufficiently rare from the outset ( B1 ) that trust is high ( B2 ) and individuals switch from lone foraging to cooperation. This drives an increase in resources, eventually lifting almost all individuals above the threshold. Simulation initialised with μ = 5.5, σ = 3 and all other parameters at their default values.

In the second equilibrium, the virtuous circle (Fig. 2 B), the frequency of exploitation is lower at the outset. Individuals whose resources are high form high assessments of social trust, and hence choose cooperation over foraging alone. Since cooperation creates a surplus, the mean level of resources in the population increases. This benefits the few exploiters, both through the upward drift of social mobility, and because they sometimes exploit successfully. This resolves the problem of exploitation, since in so doing they move above the critical value to the point where it is no longer in their interests to exploit, and since they are in such a high-trust population, they then start to cooperate. Thus, over time, trust becomes universally high, resources grow, and cooperation becomes almost universal.

Each of the two equilibria has a basin of attraction in the space of initial population characteristics. The poverty trap is reached if the fraction of individuals whose resource levels fall below the level that triggers exploitation is sufficiently large at any point. With the desperation threshold at s = 0, his fraction is affected by both the mean resources μ, and inequality σ. For a given μ, increasing σ (i.e. greater inequality) makes it more likely that the poverty trap will result, because, by broadening the resource distribution, the tail that protrudes into the desperation zone is necessarily made larger.

The boundaries of the basin of attraction of the poverty trap are also affected by severity of punishment, probability of punishment, and the level of social mobility (Fig. 3 ). If the severity of punishment π is close to zero, there is no disincentive to exploit, and the poverty trap always results. As long as a minimum size of punishment is met, further increases in punishment severity have no benefit in preventing the poverty trap (Fig. 3 A). Indeed, there are circumstances where more severe punishment can make things worse. When the population has a degree of initial inequality that puts it close to the boundary between the two equilibria, very severe punishment (π = 20 or π = 25) pushes it into the poverty trap. This is because any individual that once tries exploitation because they are close to threshold (and is unsuccessful) is pushed so far down in resources by the punishment that they must then continue to exploit forever. Increasing the probability of punishment γ does not have this negative effect (Fig. 3 B). Instead, a very high probability of punishment can forestall the poverty trap at levels of inequality where it would otherwise occur, because it causes some of the worst-off individuals to try cooperating instead, as shown in Fig. 1 E. Finally, very high levels of social mobility r can rescue populations from the poverty trap even at high levels of inequality (Fig. 3 C). This is because of its dramatic effect on the critical value at which individuals start to exploit, as shown in Fig. 1 C.

Equilibrium population states by starting parameters. ( A ) Varying the initial inequality in resources σ and the severity of punishment π, whilst holding constant the probability of punishment γ at 1/3 and social mobility r at 0.1. ( B ) Varying the initial inequality in resources σ and the probability of punishment γ whilst holding the severity of punishment constant at π = 10 and social mobility r at 0.1. ( C ) Varying the initial inequality in resources σ and the level of social mobility r whilst holding constant the probability of punishment γ at 1/3 and the severity of punishment π at 10.

Though the equilibria are self-perpetuating without exogenous forces, the system is highly responsive to shocks. For example, exogenously changing the level of inequality in the population (via imposing a reduction in σ after 16 time steps) produces a phase transition from the poverty trap to the virtuous circle (Supplementary Fig. S3 ). This change is not instantaneous. First, a few individuals cross the threshold and change from exploitation to foraging alone; this produces a consequent change in social trust; which then leads to a mass switch to cooperation, and growth in mean wealth.

Results so far are all based on cooperation occurring in groups of size n = 5. Reducing n enlarges the basin of attraction of the virtuous circle (Supplementary Sect. 2.5 , Supplementary Fig. S4 ). This is because, for any given population prevalence of exploitation, there is more likely to be at least one exploiter in a group of five than a group of three. Reducing the interaction group size changes the trustworthiness boundary between the region where it is optimal to cooperate and the region where it is better to forage alone. Thus, there are parameter values in our model where populations would succumb to the poverty trap by attempting to mount large cooperation groups, but avoid it by restricting cooperation groups to a smaller size.

In our model, exploiting others can be an individual’s optimal strategy under certain circumstances, namely when their resource levels are very low, and cannot be expected to spontaneously improve. We extend previous models by showing that it can be optimal to exploit even when the punishment for doing so and being caught is large enough to make the expected utility of exploitation negative. Two conditions combine to make this the case. First, exploitation produces a large variance in payoffs: it is costly to exploit and be caught, but there is a chance of securing a large positive payoff. Second, there is a threshold of desperation below which it is extremely costly to fall. It is precisely when at risk of falling below this threshold that exploitation becomes worthwhile: if it succeeds, one hurdles the threshold, and if it fails, one is scarcely worse off than one would have been anyway. In effect, due to the threshold, there is a point where agents have little left to lose, and this makes them risk-preferring. Thus, our model results connect classic economic models of crime 15 , 16 to risk-sensitive foraging theory from behavioural ecology 20 . In the process, it provides a simple answer to the question that has puzzled a number of authors 18 , 19 : why aren’t increases in the severity of punishments as deterrent as simple expected utility considerations imply they ought to be? Our model suggests that, beyond a minimum required level of punishment, not only might increasing severity be ineffective at reducing exploitation. It could under some circumstances make exploitation worse, by pushing punishees into such a low resource state that they have no reasonable option but to continue exploiting. Our findings also have implications for the literature on the evolution of cooperation. This has shown that punishment can be an effective mechanism for stabilising cooperation 28 , 29 , but have not considered that the deterrent effects of punishment may be different for different individuals, due to variation in their states. Our findings could be relevant to understanding why some level of exploitation persists in practice even when punishment is deterrent overall.

Within criminology, our prediction of risky exploitative behaviour when in danger of falling below a threshold of desperation is reminiscent of Merton’s strain theory of deviance 30 , 31 . Under this theory, deviance results when individuals have a goal (remaining constantly above the threshold of participation in society), but the available legitimate means are insufficient to get them there (neither foraging alone nor cooperation has a large enough one-time payoff). They thus turn to risky alternatives, despite the drawbacks of these (see also Ref. 32 for similar arguments). This explanation is not reducible to desperation making individuals discount the future more steeply, which is often invoked as an explanation for criminality 33 . Agents in our model do not face choices between smaller-sooner and larger-later rewards; the payoff for exploitation is immediate, whether successful or unsuccessful. Also note the philosophical differences between our approach and ‘self-control’ styles of explanation 34 . Those approaches see offending as deficient decision-making: it would be in people’s interests not to offend, but some can’t manage it (see Ref. 35 for a critical review). Like economic 15 , 16 and behavioural-ecological 17 theories of crime more generally, ours assumes instead that there are certain situations or states where offending is the best of a bad set of available options.

As well as a large class of circumstances where only individuals in a poor resource state will choose to exploit, we also identify some—where the expected payoff for exploitation is positive—where individuals with both very low and very high resources exploit, whilst those in the middle avoid doing so. Such cases have been anticipated in theories of human risk-sensitivity 21 . These distinguish risk-preference through need (e.g. to get back above the threshold immediately) from risk-preference through ability (e.g. to absorb a punishment with no ill effects), predicting that both can occur under some circumstances 32 . This dual form of risk-taking is best analogised to a situation where punishments take the form of fines: those who are desperate have to run the risk of incurring them, even though they can ill afford it; whilst those who are extremely well off can simply afford to pay them if caught. When we simulate populations of agents all following the optimal strategies identified by the model, population-level characteristics (inequality of resources, level of social mobility) affect the prevalence of exploitation and the level of trust. Specifically, holding constant the average level of resources, greater inequality makes frequent exploitation and low trust a more likely outcome. Thus, we capture the widely-observed associations between inequality, trust and crime levels that were our starting point 2 , 3 , 4 , 5 , 6 . Note that our explanation for the inequality-crime nexus is basically compositional rather than psychosocial. Decisions to offend are based primarily on agents’ own levels of resources; these are just more likely to be desperately low in more unequal populations. Turning these simulation findings into empirical predictions, we would expect the association between inequality and crime rates to be driven by more unequal societies producing worse prospects for people at the bottom end of the resources distribution, who would be the ones who turn to property crime. Inequality effects at the aggregate level should be largely mediated by individual-level poverty. There is evidence compatible with these claims for property crime 2 , 12 , 13 . This is the type of crime most similar to our modelled situation. Non-acquisitive crimes of violence, though related to inequality, do not appear so strongly mediated by individual-level poverty, and may thus require different but related explanations 2 , 36 .

However, the other major result of our population simulations—that more unequal populations are more likely to produce low trust—is not compositional. In our unequal simulated populations, every agent has low trust, not just the ones at the bottom of the resource distribution. This is compatible with empirical evidence: the association between inequality and social trust survives controlling for individual poverty 6 . Thus, our model generates a genuinely ecological effect of inequality on social relationships that fits the available evidence and links it to the psychosocial tradition of explanation 37 . Indeed, the model suggests a reason why psychosocial effects should arise. For agents above the threshold, the optimal decision between cooperation and foraging alone depends on inferences about whether anyone else in the population will exploit. To know that, you have to attend to the behaviour of everyone else, not just your own state. Thus, the model naturally generates a reason for agents to be sensitive to the distribution of others’ states in the population (or at the very least their behaviour), and to condition their social engagement with others on it.

In as much as our model provides a compositional explanation for the inequality-crime relationship, it might seem to imply that high levels of inequality would not lead to high crime as long as the mean wealth of the population was sufficiently high. This is because, with high mean wealth, even those in the bottom tail of the distribution would have sufficient levels of resources to be above the threshold of desperation. However, this implication would only follow if the location of the desperation threshold is considered exogenous and fixed. If, instead, the location of the desperation threshold moves upwards with mean wealth of the population, then more inequality will always produce more acquisitive crime, regardless of the mean level of population wealth. Assuming that the threshold moves in this way is a reasonable move: definitions of poverty for developed countries are expressed in terms of the resources required to live a life seen as acceptable or normal within that society, not an absolute dollar value (see Ref. 36 , pp. 64–6). Moreover, there is clear evidence that people compare themselves to relevant others in assessing the adequacy of their resources 38 . Thus, we would expect inequality to remain important for crime regardless of overall economic growth.

In addition to the results concerning inequality, we found that social mobility should, other things being equal, reduce the prevalence of exploitation, although social mobility has to be very high for the effect to be substantial. The pattern can again be interpreted as consistent with Merton’s strain theory of deviance 31 : very high levels of social mobility provide legitimate routes for those whose state is poor to improve it, thus reducing the zone where deviance is required. Economists have noted that those places within the USA with higher levels of intergenerational social mobility also have lower crime rates 39 , 40 . Their account of the causality in this association is the reverse of ours: the presence of crime, particularly violent crime, inhibits upward mobility 39 . However, it is possible that social mobility and crime are mutually causative.

Like any model, ours simplifies social situations to very bare elements. Interaction groups are drawn randomly at every time step from the whole population. Thus, there are no ongoing personal relationships, no reputation, no social networks, no kinship, no segregation or assortment of sub-groups. The model best captures social groups with frequent new interactions between strangers, which is appropriate since the phenomena under investigated are documented for commercial and industrial societies. A problem in mapping our findings onto empirical reality is that our population simulations generate two discrete equilibria: zero trust, economic stagnation and zero cooperation, or almost perfect trust, unlimited economic growth and zero exploitation. Although we show that the distribution of resources determines which equilibrium is reached, our model as presented here does generate the continuous relationships between inequality, crime, and trust (or indeed inequality and economic growth 41 ) that have been observed in reality. Even the most unequal real society features some social cooperation, and even the most equal features some property crime; the effects of inequality are graded. We make two points to try to bridge the disconnect between the black and white world of the simulations and the shades of grey seen in reality. First, our model does predict a continuous relationship between the level of inequality and the maximum size of cooperating groups. A highly unequal population, containing many individuals with an incentive to exploit, might only be able to sustain collective actions at the level of a few individuals, whereas a more equal population where almost no-one has an incentive to exploit could sustain far larger ones. Second, we appeal to all the richness of real social processes that our model excludes. In unequal countries, although social trust is relatively low, people can draw more heavily on their established social networks and reputational information; more homogenous sub-groups can segregate themselves; people can use defensive security measures, to keep cooperative relationships ongoing and protected; and so forth. Investment in these kinds of measures may vary proportionately with inequality and trust, thus maintaining outcomes intermediate between the stark equilibria of our simulations. Our key findings also depend entirely on accepting the notion that there is a threshold of desperation, a substantial non-linearity in the value of having resources. As we outlined in the Introduction, we believe there are good grounds for exploring the implications of such an assumption. However, that is very different from claiming that the widespread existence of such thresholds has been demonstrated. We hope our findings might generate empirical investigation into both the objective reality and psychological appraisal of such thresholds for people in poverty.

Limitations and simplifications duly noted, our model does have some clear implications. Large population-scale reductions in crime and exploitation should not be expected to follow from increasing the severity of punishments, and these could conceivably be counterproductive. Addressing basic distributional issues that leave large numbers of people in desperate circumstances and without legitimate means to improve them will have a much greater effect. Natural-experimental evidence supports this. The Eastern Cherokee, a Native American group with a high rate of poverty, distributed casino royalties through an unconditional income scheme. Rates of minor offending amongst young people in recipient households decline markedly, with no changes to the judicial regime 42 . Improving the distribution of resources would also be expected to increase social trust, and with it, the quality of human relationships; and this, for everyone, not just those in desperate circumstances.

The model was written in Python and implemented via a Jupyter notebook. For a fuller description of the model, see Supplementary Sect. 1 and Supplementary Table S1 .

Computing optimal policies

We used a stochastic dynamic programming algorithm 25 , 26 . Agents choose among a set of possible actions, defined by (probabilistic) consequences for the agent’s level of resources s . We seek, for every possible value of s and of p the agent might face, and given the values of other parameters, the action that maximises expected fitness. Maximization is achieved through backward induction: we begin with a ‘last time step’ ( T ) where terminal fitness is defined, as an increasing linear function of resource level s . Then in the period T − 1 we compute for each combination of state variables and action the expected fitness at T , and thus choose for the optimal action for every combination of states. This allows us define expected fitness for every value of the state variables at T − 1, repeat the maximization for time step T − 2, and so on iteratively. The desperation threshold is implemented as a fixed fitness penalty ω that is applied whenever the individual’s resources are below the threshold level s = 0. As the calculation moves backwards away from T , the resulting mapping of state variables to optimal actions converges to a long term optimal policy.

Actions and payoffs

Agents choose among three actions:

Cooperate The agent invests x units of resource and is rewarded α · x with probability 1 − p ( p is the probability of cooperation being exploited, and 1 − p is therefore the trustworthiness of the surrounding population), and 0 with probability p . The net payoff is therefore x · ( α − 1) if there is no exploitation and − x if there is. We assume that α > 1 (by default α = 1 . 2), which means that cooperation is more efficient than foraging alone. For the computation of optimal policies, we treat p as an exogenous variable. In the population simulations, it becomes endogenous.

Exploit An agent joins a cooperating group, but does not invest x, and instead tries to steal their partners’ investments, leading to a reward of β if the exploitation succeeds and a cost π if it fails. The probability of exploitation failing (i.e. being punished) is γ .

Forage alone The agent forages alone, investing x units of resource, receiving x in return, and suffering no risk of exploitation.

Payoffs are also affected by a random perturbation, so the above-mentioned payoffs are just the expected values. A simple form such as the addition of $\varepsilon \sim N\left( {0, \sigma^{2} } \right)$ would be unsuitable when used in population simulations. As the variance of independent random variables is additive, it would lead to an ever increasing dispersion of resource levels in the population. To avoid this issue, we adopted a perturbation in the form of a first-order autoregressive process that does not change either the mean or the variance of resources in the population 43 :

Here, µ is the current mean resources in the population and σ 2 the population variance. The term $\left( {1 - r} \right) \in \left[ {0, 1} \right]$ represents the desired correlation between an agent’s current and subsequent resources, which leads to us describing r as the ‘social mobility’ of the population. The perturbation can be seen as a ‘shuffle’. Each agent’s resource level is attracted to µ with a strength depending on r , but this regression to the mean is exactly offset at the population level by the variance added by the perturbation, so that the overall distribution of resources is roughly unchanged. If r = 1, current resources are not informative about future resources.

The dynamic programming equation

Let I be the set of actions ( cooperate , exploit and alone ), which we shorten as I = { C,H,A }. For i ∈ I , we denote as $\phi_{t}^{i} \left( {s, .} \right)$ the probability density of resources in in time step t if, in time step t − 1, the resource level is s and the chosen action i . The expressions of these functions were obtained through the law of total probability, conditioning on the possible outcomes of the actions (e.g. success or failure of exploitation and cooperation), and with the Gaussian density of the random variable.

We can now write the dynamic programming equation, which gives the backward recurrence relation to compute the payoff values (and the decisions) at the period t from the ones at the period t + 1.

Here, $E_{i}$ is the conditional expectation if action i is played. The optimal action for the time step t is ${\text{argmax}}_{i \in I} E_{i} (f_{t} )$ . The resource variable s was bounded in the interval [− 50, 50], and discretized with 1001 steps of size 0 . 1.

For any given set of parameters (summarised in Table 1 ), we can therefore compute the optimal decision rule. Note that we can distinguish two types of parameters:

‘Structural parameters’, i.e. those defining the ‘rules’ of the game (the payoffs for the actions and the level of social mobility r , for example). In the subsequent simulation phase, these parameters will be fixed for any run of the simulations.

‘Input parameters’, such as p and s . In the simulation phase, these will evolve endogenously.

Optimal policies rapidly stabilize as the computation moves away from T . We report optimal actions at t = 1 as the globally optimal actions.

Population simulations

We begin each simulation by initializing a population of N = 500 individuals, whose resource levels are randomly drawn from a Gaussian distribution with a given mean µ and variance σ 2 . At each time step, interaction groups of n = 5 individuals are formed at random, and re-formed at each time step to avoid effects of assortment. There is no spatial structure in the populations. Each individual always follows the optimal policy for its resources s and its estimate of p (see below). Varying N has no effect as long as N > n and 500 is simply chosen for computational convenience.

To deal with the case where several members of the same interaction group choose to exploit, we choose one at random that exploits, and the others are deemed to forage alone (in effect, there is nothing left for them to take). Also, when there is no cooperator in the group, all exploiters are deemed to forage alone.

Rather than providing each individual with perfect knowledge of the trustworthiness of the rest of the population 1 − p , we allow individuals to form an estimate (their social trust ) from their experience. Social trust is derived in the following way. Each agent observes the decision of a sample of K individuals in the population, counts the number k of exploiters and infers an (unbiased) estimate of the prevalence of exploiters in the population: $k^{\prime} = \frac{k}{K}N$ (rounded). The size of the sample can be varied to alter the precision with which agents can estimate trustworthiness. Unless otherwise stated we used K = 50. Since p is the probability that there will be at least one exploiter in an interaction group, it is one minus the probability that there will be zero exploiters. Each agent computes this from their k’ by combinatorics.

An intentional consequence of social trust being estimated through sampling is that there is some population heterogeneity in social trust, and therefore in decisions about which action to take, even for agents with the same resources s . Note also that agents infer trustworthiness not from observing the particular individuals in their current interaction group, but rather, from a cross-section of the entire population. Thus, the estimate is genuinely social trust (the perception that people in society generally do or do not behave well).

Code availability

The Jupyter notebook for running the model is available at: https://github.com/regicid/Deprivation-antisociality . This repository also contains R code and datafiles used to make the figures in the paper.

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Acknowledgements

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No AdG 666669, COMSTAR). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors thank Melissa Bateson, Juliette Dronne, Ulysse Klatzmann, Daniel Krupp, Kate Pickett, and Rebecca Saxe for their input.

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The project was conceived jointly by B.C. and D.N. The model was developed and programmed by B.C. with feedback from D.N. B.C. wrote the first draft of the paper. The two authors revised and refined it collaboratively.

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Urban Relationship Between Poverty and Crime Essay

The relationship between poverty and crime has been a subject of debate. Many people argue that people get involved in crimes because of poverty. They get involved in crimes in order to get money to meet their needs in life. Others believe that crimes are also committed by people who are not poor in the name of gratifying other desires not related to poverty. It can not be fully denied that poverty leads to crime but not all cases of crime are associated with poverty.

A criminal act culminates from moral disposition that drives somebody to take by force that which does not belong to him. Most forms of crime like rape, assault, fraud are meant to gratify a desire by taking another person’s possession by force. There is high correlation between poverty and crime but all forms of crime are not committed by the poor. Criminal acts are moral issues and may have nothing to do with poverty (Short 18).

In America for instance, crime is widespread among all ethnic groups both living in rural and those in cities, the ssrich and the poor. This has increased the government’s effort to investigate the causes of crime and device mechanisms to mitigate or eliminate such cases. The focus is mainly to control the factors that cause crime. The following paragraphs discuss various ways in which poverty may lead to crime.

The areas with high poverty level in the US urban areas have the highest cases of crime but this is inadequate to justify that poverty is the cause of crime. However, there are many ways that poverty contributes to crime. Firstly, the poor are believed to have more cases of mental illness compared to the rich and this causes them to live under stress. When the level of stress increases, people try to relieve it through indulging in violent acts, thefts, robbery among other forms of crime.

According to Short (18), poverty denies the poor chances of getting quality education and jobs especially the youths causing them to spend most of their time in places where they mingle with gangs. This is mostly due to lack of opportunity costs of crime thus engaging them in criminal acts. In this manner, more and more poor youths get involved in criminal acts like robbery, rapes, and violent acts among others.

The impoverished people lack legitimate means of accessing some material goods or living a good life. They lack good jobs that would enable them access these material goods and crime becomes their only option (Short 26). They engage in robbery in order to get money to buy what they desire in life. In this manner, poverty accelerates crime

Most factors that cause crime are associated with poverty. For instance, where there is high rate of unemployment, people lives in adverse poverty which cause depression that exposes them to criminal acts. The cases of crime therefore increases or are directly related to unemployment. High rate of unemployment creates social classes where the employed continue to get rich and the unemployed gets poorer. The cases of crime also continue to increase as the unemployed looks for means of earning a living.

The number of minorities living in an area may increase the cases of crime due to high level of poverty. Most of the minorities like in US inhabits the impoverished urban areas and are discriminated against wages and job opportunities. They get low wages and fewer job opportunities thus making them poorer. This causes most of them to engage in criminal acts thus increasing the rate of crimes in the country.

The population density in an area also determines the poverty level and the rate of crime. It has been proven that highly populated places have many poor people; young, single parents and others.

Despite the above convincing points relating poverty to crime, the issue remains controversial. There are countries with high level of poverty but the rate of crime is very low. The relationship between poverty and crime may not hold in such countries (Short 29). The criminal justice scholars have observed a positive correlation between poverty and crime. They claim that 53% of people who end up in jail are the poor with below $10,000 annual income. They argue that the poor find the benefit of crime surpasses the consequences and they end up committing the crime.

Poverty and crime are separable but on very rare cases. In most countries, developed and less developed, there is positive relationship between poverty and crime. This does not conclude that the poor are criminals. There are many countries with people living in adverse poverty yet they have very low rates of crime. Eliminating or minimizing poverty may reduce the cases of crime but it is not the only solution. The biggest responsibility of minimizing crime lies with the prisons where criminals are confined. They should try to reform the criminal as well as finding out from them the causes of crime. This would help attack crime from the root cause.

Works cited

Short, James. Poverty, Ethnicity, and Violent Crime. Boulder, CO: West view Press, 1997. Print.

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IvyPanda . 2021. "Urban Relationship Between Poverty and Crime." December 21, 2021. https://ivypanda.com/essays/urban-relationship-between-poverty-and-crime/.

1. IvyPanda . "Urban Relationship Between Poverty and Crime." December 21, 2021. https://ivypanda.com/essays/urban-relationship-between-poverty-and-crime/.

Bibliography

IvyPanda . "Urban Relationship Between Poverty and Crime." December 21, 2021. https://ivypanda.com/essays/urban-relationship-between-poverty-and-crime/.

Crime Causes Poverty

Poverty is as a result of crime. While it is not a topic that is usually argued, it can be stated true. One example would be that when a thief enters into a house of an average paid man and steals their belongings, the man goes deeper into poverty. This essay discusses how crime impedes economic growth, the impact of criminals that target the most vulnerable of people, and how apartheid plays a role in shaping people’s lives.

Crime can impede economic growth in South Africa, and this has several effects on the economy. For example, unemployment is still a massive problem in South Africa (Ranchhod, 2019) as the rate sits at 27,1% (Stats SA, 2019), which is immense compared to other countries (Ventura, 2018). One other effect is loss of investment. Investors may not want to spend money in a country where it has one of the highest crime rates in the world (BusinessTech, 2017). Therefore, as investment decreases, economy growth decreases.

Criminals do target the most vulnerable of people, as crime has an effect on poverty in many ways. For example, when someone steals money from another person who already has financial issues, that person gets forced into poverty. Another example is the poor management of South Africa’s government, and many locals can agree to that (BusinessTech, 2015). As more of the government money is spent on personal items for the higher-ranking officers, the less money is spent on the poor.

Apartheid had a massive impact on all South Africans’ lives. While it has ended in 1994, a number of inequalities that remained needed significant changes in the country’s policies (Shaw, 1995:217-218). One example is the BBBEE policy where previously disadvantaged groups become empowered to enhance the economy. Firms have stated that crime has an impact on economic development (Ikejiaku, 2009:455-446). For example, when protests occur due to a lack of basic services in certain areas, the country can lose direct investment.

Poverty is as a result of crime, whether people know it or not. The points discussed in the essay clearly proved that it was the case. Based on how crime had and still has massive repercussions on the people of South Africa, a large amount of effort is needed for the country to truly transform into the “Rainbow Nation” Nelson Mandela envisioned.

Bibliography

BusinessTech. 2015. 9 ways government has failed South Africa. 25 November. [Online]. Available: https://businesstech.co.za/news/government/105087/9-ways-government-has-failed-south-africa/ [Accessed 15 April 2019]. BusinessTech. 2017. South Africa ranks among the most dangerous countries in the world – and it’s costing us. 21 September. [Online]. Available: https://businesstech.co.za/news/lifestyle/200044/south-africa-ranks-among-the-most-dangerous-countries-in-the-world-and-its-costing-us/ [Accessed 15 April 2019] Ikejiaku, B. 2009. ‘Crime’, poverty, political corruption and conflict in apartheid and post apartheid South Africa: The implications on economic development. African Journal of Political Science and International Relations, 3(10): 451-459. Ranchhod, V. 2019. Why is South Africa’s unemployment rate so high? [Online]. Available: https://www.groundup.org.za/article/why-south-africas-unemployment-rate-so-high/ [Accessed 1 April 2019]. Shaw, M. 1995. Urban conflict, crime and policing in South African cities. Africa Insight, 25(4): 216-220. Sicetsha, A. 2018. Crime stats South Africa 2018: the murder rate increased by 6.9%. [Online]. Available: https://www.thesouthafrican.com/crime-stats-south-africa-murder-rate-2018/ [Accessed 1 April 2019]. Stats SA. 2019. Unemployment drops in fourth quarter of 2018. [Online]. Available: http://www.statssa.gov.za/?p=11897 [Accessed 1 April 2019]. Sulla, V. & Zikhali, P. 2018. Overcoming poverty and inequality in South Africa: An assessment of drivers, constraints and opportunities. Washington, D.C.: World Bank Group Ventura, L. 2018. Unemployment rates around the world 2018. [Online]. Available: https://www.gfmag.com/global-data/economic-data/worlds-unemployment-ratescom [Accessed 1 April 2019].

The Demonization of Rural America

B y the time I was seven or eight years old, I was keenly aware of my father’s drug use. He didn’t snort pills in front of me yet—he saved that for my teen years—but he talked about pills freely and I knew he took them. He was meaner than usual when he couldn’t get his pills, and I learned to recognize the signs of withdrawal long before I ever heard that term. Any hope for stability in our lives probably vanished before I could walk. And by the time I became an adult, everyone in my nuclear family—and plenty of my extended family members—was struggling to cope with the impacts of violence, incarceration, and addiction.

I grew up in Appalachian Eastern Kentucky, where systemic poverty has been a challenge for many decades. We always joked that Kentucky was 20 years behind the rest of the country but as a kid, I didn’t understand what we really faced: underfunded schools, inadequate transportation systems, poor healthcare, unreliable utilities. Prescription pain pills flooded into our region and did nothing to cure our collective pain, but instead exacerbated the personal and social struggles that the region is often associated with.

I was born in 1979, so most of this unraveling and destruction took place during the 1980s and 1990s . But it was sometime in the early 2000s when I read about the opioid epidemic online for the first time. At first, I was shocked to learn prescription pills had become a mainstream problem. But next, I was angry. By this time, pain pill manufacturers had changed their formulas so pills could no longer be crushed and snorted or injected; right away, heroin became widely available, which shocked me. When I was little, heroin was a city drug, scary and distant. Someone must have known that opiate-addicted hillbillies were a ripe market for a replacement opiate, just as someone had first found a way to saturate the Appalachian region with highly addictive pills without drawing attention to their crime.

But why wasn’t it talked about until now? Why wasn’t it an epidemic when it was ravaging my family for the last 20 years? Why wasn’t it newsworthy when my father chose pain pills over feeding his family, or when the same thing happened to families all around me?

I already knew the answer to those questions, though. Eastern Kentucky had been a throwaway place for a long time. Through a wide range of experiences, I learned at a young age that we were poor white trash. The stereotypes about us were, and continue to be, disdainful and dismissive, mixed with a potent disgust for good measure. Our accents are signs of ignorance and stupidity; we’re presumed to be shoeless and perpetually pregnant, sometimes—repulsively—even as a result of incest. Lawless and toothless, who would decry a manmade epidemic that wiped out thousands of hillbillies and their worthless children?

Read More: Kentucky Floods Destroyed Homes That Had Been Safe for Generations. Nobody’s Sure What to Do Next

Americans have discarded and scapegoated various socioeconomic groups throughout our history—this is not a new phenomenon. Unlike many biases that we have reckoned with, though, the vitriolic view of Appalachia—and to some extent, other areas of rural America—stems from an entrenched classism that remains unchallenged in our collective moral consciousness.

The most popular Mexican restaurant in our small town of Berea, Kentucky, has several machines where you can buy gumballs, small toys, and even temporary tattoos. When they were little, my kids always begged for a quarter or two so they could buy something after we ate there. But there was one novelty that made me cringe each time and I forbade my children from spending quarters on it: the hillbilly teeth , which are “the first line of fake teeth purposefully designed to look trashy, hillbilly-like, and downright gross.”

The teeth didn’t offend my sensibilities as a young mother; they publicized my shame. I grew up in a holler and the well my father dug for our house never functioned quite right. My parents often had to pump creek water into the well so we would have water pressure and I knew we weren’t supposed to drink it. But we still mixed it into Kool-Aid and coffee, cooked with it, and brushed our teeth with it. Most of the time, we drank milk or pop.

My brother and I both had visible black cavities on our baby teeth and I looked forward to the day they would fall out. But when my permanent teeth grew in, they were spaced too far apart on top and crowded against each other on the bottom; my gums bled at humiliating moments. Somehow, I always knew my teeth were a sign of the particular kind of poverty I came from.

Why didn’t my parents get us clean drinking water and ensure we had proper dental care? The first reason for these oversights was my father’s drug addiction; the second was his relentless abuse of my mother, my brother, and me. Visits to the dentist, fixing the well, braces for my permanent teeth—those concerns fade into the background for both the drug-addicted and traumatized minds.

When I moved away from my hometown, I found a way to hide my accent at college and work, as so many Appalachians do. But I couldn’t hide my teeth or fix them until I was well into adulthood. The hillbilly teeth at the Mexican restaurant served as a cruel reminder that it’s socially acceptable to mock the socioeconomic class I was born into; our problems are a joke.

Another popular, insidious sentiment loomed large in the 2016 election, and I suspect it was infused into early conversations about our opioid problem: “They deserve what they get.”

The 2024 book, White Rural Rage , highlights the problematic conversations around Appalachia in interesting ways. Early in the book, the authors claim that rural America poses “a quadruple threat to democracy” and they begin their critique with Mingo County, West Virginia. The authors decry the fact that this county’s majority vote went to Trump in both 2016 and 2020, but fail to acknowledge an important fact in Appalachian voting and indeed, in voting among many vulnerable populations: less than half of the registered voters cast a ballot in either election.

Even though this book doesn’t claim to focus on Appalachia, Mary Jo Murphy at The Washington Post suggests early in her review of it that “Someone write a new elegy for the bilious hillbilly, because these authors went for his jugular.” She addresses rural Americans from that point after. “Hillbillies” are historically associated with Appalachia, but the poor, white inhabitants of this handful of states don’t represent rural America as a whole. They’re used as an easy target—a convenient stand-in for the diverse population that actually comprises rural America—because they’re considered to be poor, ignorant, white trash that no one will defend.

There will be no social backlash against overt and covert claims that rural Americans deserve everything they get. Poor whites remain a safe target for political commentary and cheap humor alike.

Classism is not just a problem when someone writes a book about it. And it’s not just a problem when people take to social media to blame election results on some of our most disenfranchised citizens. Classism distracts us from solving our collective problems because it keeps us from asking the right questions. Classism tells us to blame rural whites for our country’s ills—just like other populations have been blamed in the past—demonizing our neighbors instead of the dysfunctional systems and perhaps even individuals who hold incredible power over our political and financial wellbeing.

Whether they are poor or not, white or not, rural Americans grapple with the same issues as everyone else: poverty, violence, addiction, and social decay are obviously not unique to rural areas. But this population faces those problems with fewer resources than their urban and suburban neighbors. Just as there is no excuse for bigotry, we cannot justify blaming our country’s challenges on a disempowered socioeconomic group. Placing blame fuels divide. We need to do some collective soul-searching to understand our biases and find a way to move past them.

Finding solutions is the harder work and the right work. That work requires that everyone has a voice and a seat at the table—especially the people who have historically been excluded. If we can find the courage to set aside classist prejudice, we might discover that there are no throwaway places and more importantly, no throwaway people. Not even hillbillies like me.

Photographer Stacy Kranitz has been documenting life in Appalachia for over 13 years to challenge stereotypes and provide an honest look at a complex region .

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‘Critical gaps’ in understanding climate change fuel tropical disease spread

A girl, holding a child, walks to a mobile health clinic in a flood-affected village in Pakistan. Water-logging has led to an increase in spread of malaria in the region. (file)

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A comprehensive review by the UN health agency has revealed critical gaps in understanding the full impact of climate change on malaria, dengue, trachoma and other tropical diseases.

The World Health Organization ( WHO ) study, conducted in partnership with Reaching the Last Mile (RLM), a global health initiative to eliminate neglected tropical diseases (NTDs), found that rising temperatures and changing weather patterns are altering the spread of vector-borne diseases , posing significant health risks.

As the geographical spread of disease vectors like mosquitoes expand, the risk of introducing or reintroducing these diseases to new areas increases. This shift is likely to have the most severe impact on communities already disproportionately affected.

The study analysed peer-reviewed papers from January 2010 to October 2023, crunching data on national disease burdens, healthcare access and climate vulnerability scores.

The majority of data sets used focused on malaria, dengue, and chikungunya, while other NTDs were significantly underrepresented.

Lack of evidence

Only 34 per cent of studies reviewed (174 studies) addressed mitigation and a mere five per cent (24 studies) looked at adaptation, underscoring the dire lack of evidence available to help malaria and NTDs.

Ibrahima Socé Fall, Director of the Global NTD Programme at WHO, emphasized the need for more comprehensive, collaborative and standardized modelling to predict and mitigate effects of climate change on health.

“ This important and timely review reveals alarming trends and is a call to urgent action . Malaria transmission is likely to shift both polewards and to higher altitude, while the mosquito vector responsible for transmission of dengue and chikungunya is predicted to continue to expand its range,” she said.

“If we are to protect and build upon the hard-won victories of the past two decades, the time to mobilize is now.”

Neglected tropical diseases

Neglected tropical diseases (NTDs) are a diverse group of conditions caused by a variety of pathogens, including viruses, bacteria, parasites, fungi and toxins.

These include Chagas disease, dengue, chikungunya, leprosy, rabies, soil-transmitted helminthiases, snakebite, trachoma and yaws. It is estimated that they affect more than one billion people, according to WHO.

Neglected Tropical Diseases
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Nathan Wade ripped by ex over ‘deceitfulness’ for trying to reduce alimony 3 days after getting $53K paycheck: ‘No plausible explanation’

S candal-scarred former Trump prosecutor Nathan Wade was slammed by his estranged wife for receiving over $53,000 in paychecks just days before pleading poverty in their Georgia divorce case.

Joycelyn Wade made the accusations against ex-Nathan just a few days before he made a shocking appearance at a primary election victory party Tuesday for his one-time flame Fulton County District Attorney Fani Willis — a relationship that cost Wade his job.

Wade resigned as special prosecutor in the Georgia election fraud case against Donald Trump on March 15, after a judge ruled Willis could only remain on the case if he stepped down after the duo admitted to being in a romantic relationship while working on the case.

Less than a month later, Wade asked for his alimony and child support payments to Joycelyn to be reduced, claiming that he’d lost his $250 hourly income with the DA’s office.

But Joycelyn’s lawyers Thursday filed court papers claiming Wade received two paychecks totaling $53,000 from Fulton County for his work on the Trump case — just three days after filing the emergency motion to reduce his support payments.

“There appears to be no plausible explanation other than [Wade’s] deceitfulness to justify his pursuit of emergency relief from this court on April 8, 2024, alleging essentially insolvency when he had received $53,000 only three days prior,” wrote Andrea Hastings, a lawyer for Joyceyln.

Joycelyn said the $53,000 checks from Fulton County were for work he did in October and November 2023 — nearly half a year before he resigned on March 15.

“Thus, it is reasonable to infer that there are further outstanding payments due to [Wade] that likely have not been disbursed as of the current date,” Hastings argued.

Hastings called Wade’s motion “baseless” and said he should have to pay for his Joycelyn’s legal fees incurred fighting it.

Joycelyn also previously blasted Wade’s motion to reduce his support payments, arguing he had nearly $500,000 in the bank.

The Wade’s had agreed upon Nathan’s support payments to Joycelyn in a temporary settlement they reached in January . But in early April, Joycelyn claimed Nathan skipped out on $4,400 in child support and money for Joycelyn’s urgent medical procedures.

Wade’s tryst with Willis came into the national spotlight in January after Trump co-defendant Mike Roman sought to have Willis booted from the case and the charges dropped against him based on alleged misconduct that arose from the affair.

The bombshell allegations derailed the criminal case and prompted hearings where Wade and Willis both testified in the case, claiming they became romantic after Wade began working for Willis.

Roman claimed Willis appointed her boyfriend to the lucrative position and then reaped the benefits of his salary, treating her to exotic vacations.

The pair separately testified they roughly split the cost of their getaways, with Wade fronting the cost on his credit card and Willis paying him back her portion in cash.

They couple said they then split in the summer of 2023.

Wade was “all smiles” as he attended Willis’ victory soiree after beating out attorney and author Christian Wise Smith to keep hold of her DA post, according to a post on X by Tamar Hallerman, a reporter with the Atlanta Journal-Constitution.

However, Willis isn’t in the clear just yet.

The 52-year-old prosecutor — first elected to office in 2020 — faces multiple probes by the state and federal legislators over her alleged misuse of taxpayer dollars and her fling with Wade.

Lawyers for Nathan and Joycelyn Wade didn’t immediately return requests for comment on Wednesday.

Nathan Wade ripped by ex over ‘deceitfulness’ for trying to reduce alimony 3 days after getting $53K paycheck: ‘No plausible explanation’

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Audiobooks for Long-Haul Listening

Some books sprint; others take the scenic route. The heady, highly absorbing titles here earn their marathon run times.

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The illustration shows a man sitting in a chair with headphones on and a beard that stretches to his toes, listening while a fly buzzes nearby.

By Alexander Nazaryan

Alexander Nazaryan writes about politics, culture and science.

Hear me out: Summer, with its hikes and bikes trips, is the perfect season for long audiobooks. I mean, the sound of birds is nice and all. Just not for three hours.

Conventional wisdom suggests you should settle for a beach read — or beach listen, in this case. And believe me, I love a fun, sexy mystery like Emma Rosenblum’s “Bad Summer People .” But I save those for winter, when the shores of Fire Island (where Rosenblum’s novel is set) seem impossibly distant.

Use summer for more ambitious projects. I’ve found long audiobooks to be perfect companions for those 10 weeks or so when the kids go off to camp and the pace of life generally slows.

Below, a few of my favorite supersized listens.

THE DYING GRASS, by William T. Vollmann

Vollmann is not known for accessibility ( his first novel was about insects and electricity), but “The Dying Grass” is a remarkably readable account of the 1877 Nez Perce War, made even more so by Henry Strozier’s sensitive narration. As Brig. Gen. Oliver Otis Howard hounds his Native adversaries across Montana and Idaho, the story soars above the awesome landscape, then peers into the hearts of people below. Believe me, time will fly.

Also try: “The Anatomy of Melancholy,” by Robert Burton; “War and Peace,” by Leo Tolstoy

ON HIS OWN TERMS: A Life of Nelson Rockefeller, by Richard Norton Smith

Here is a storied American family in its third generation, with the Rockefeller brothers taking on newfound civic responsibilities. Nelson was the most ambitious of them and maybe the most tragic, his bid for the presidency undone by a divorce . Paul Michael (“The Da Vinci Code”) narrates with stately confidence.

“Ducks, Newburyport,” by Lucy Ellmann

A woman in Ohio thinks about life. About illness, marriage and Laura Ingalls Wilder. She frets about the pies she bakes for a living. Also, there’s a mountain lion. Written as a single sentence stretching more than 1,000 pages, this remarkable 2019 novel thrums with life, a quality highlighted by Stephanie Ellyne’s energetic narration.

Also try: “1Q84,” by Haruki Murakami; “Hitler,” by Ian Kershaw

THE PASSAGE OF POWER, by Robert Caro

The fourth volume of Caro’s encyclopedic biography of L.B.J. begins with the gruff Texan becoming vice president to John F. Kennedy, an odd man out in an administration of Ivy Leaguers. But then comes a shattering Dallas afternoon. Our most esteemed historian , Caro thrillingly tells the story of how Johnson prods Congress and transforms a grieving nation with his civil rights and Great Society legislation.

GRAVITY’S RAINBOW, by Thomas Pynchon

George Guidall is one of the great audiobook narrators , and his rendition of Pynchon’s masterpiece quickly makes clear why as he captures Tyrone Slothrop’s madcap journey across Europe, which involves orgies and Nazis, a Malcolm X set piece and a good deal about ballistics. I can’t imagine a harder book to narrate — or anyone who could do the job as well as Guidall.

THE DAVID FOSTER WALLACE READER

The immensity of Wallace’s achievement can be daunting, but the “Reader” is a perfect distillation of his fiction and nonfiction alike. While most selections are performed by professionals, there are cameos from the Emmy winner Bobby Cannavale; Wallace’s mother, Sally; and Wallace himself, who died in 2008 .

Also try: “The Covenant of Water,” by Abraham Verghese; “Daniel Deronda,” by George Eliot; “And the Band Played On,” by Randy Shilts

A BRIEF HISTORY OF SEVEN KILLINGS, by Marlon James

It is only appropriate that a panoply of narrators (seven in all) take on this kaleidoscopic novel, which is nominally about the 1976 assassination attempt on Bob Marley but is in reality the story of Jamaica. “Brief History” was James’s breakout novel, winning the Man Booker Prize in 2015 . The narration matches the intensity of the prose; it’s as close as you can get to cinema without a screen.

THE SECRET HISTORY, by Donna Tartt

One of the smartest mysteries in the modern American canon, set at a bucolic New Hampshire college. Tartt herself narrates; though she may be a Mississippi native, her voice is neither Deep South nor New England. Like the novel itself, it is entirely her own.

WOLF HALL, by Hilary Mantel

Yes, you may need to consult the printed novel to keep track of the characters, but the effort is well worth it as Mantel pulls you ever deeper into 16th-century England and the life of her indefatigable protagonist, Thomas Cromwell. The narrator, Simon Slater, a noted British actor and composer, only enhances that journey.

RANDOM FAMILY, by Adrian Nicole LeBlanc

LeBlanc spent more than a decade as a virtual member of a South Bronx family as it struggled with drugs and crime, early pregnancy and poverty. Though the tone of Roxana Ortega’s narration is not always entirely in sync with the text, LeBlanc’s reportage is sensitive but not preachy, an unvarnished portrait of New York’s most neglected borough.

Also try: “Watergate,” by Garrett M. Graff; “Demon Copperhead,” by Barbara Kingsolver; “Lenin’s Tomb,” by David Remnick

Explore More in Books

Want to know about the best books to read and the latest news start here..

An assault led to Chanel Miller’s best seller, “Know My Name,” but she had wanted to write children’s books since the second grade. She’s done that now with “Magnolia Wu Unfolds It All.”

When Reese Witherspoon is making selections for her book club , she wants books by women, with women at the center of the action who save themselves.

The Nobel Prize-winning author Alice Munro, who died on May 14 , specialized in exacting short stories that were novelistic in scope , spanning decades with intimacy and precision.

“The Light Eaters,” a new book by Zoë Schlanger, looks at how plants sense the world and the agency they have in their own lives.

Each week, top authors and critics join the Book Review’s podcast to talk about the latest news in the literary world. Listen here .

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Introduction: "Poverty is the mother of all crimes", Marcus Aurelia (121-180AD). Background: It has been a global issue that people are facing poverty, a state where people are facing financial issues and lack of daily essential needs. Thesis statement: I do agree that poverty is the main cause of crime. This essay analyzes how poverty affects crime rates.
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Poverty of the Spirit is the causal factor. Absence of guardians, awful guardians, absence of direction in life prompts Poverty of the soul, which leads to crime. Physical poverty can contribute, however isn't a main driver. Being poor has never been the catalyst for somebody to act criminally.
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Crime is a complicated issue, and other variables like education, healthcare, and housing have to be taken into consideration. The results indicate that there is a relationship between certain types of crime and poverty, and that income inequality is significant to all types of crime. JEL Classification: I32, A13.
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Papaioannou (2017) proposes that poverty may indeed be regarded as a decisive motive for the crime. The author argues that people constantly living on the line of poverty are, on average, more likely to commit a crime in cases when they feel that their well-being, income and lifestyle are put in jeopardy. Papaioannou elaborates on this idea by ...
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Nathan Wade slammed by ex for trying to reduce alimony three days ...
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Poverty and Crime

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Relationship Between Poverty and Crime

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Individual Poverty and Crime

Community Poverty and Crime

Summary of the Evidence and Three Shifts of Thinking

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The Link Between Need and Theft

Desperation Leads to Bad Choices

Crime as a Last Resort

Breaking the Cycle

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Struggle for Basic Needs

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Feeling Left Out

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Struggling to Survive

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Why do inequality and deprivation produce high crime and low trust?

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