Violent Crime and Risk Aversion

A Look at the paper by Ryan et al. on the impact of violent crime on risk aversion with evidence from the Mexican Drug War

By Ken Varghese

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Background

With the election of President Calderon in 2006, Mexico experienced a brief decrease in crime followed by a much more significant rise in violent crime. President Calderon’s efforts in the new War on Drugs removed the heads of the prominent Mexican cartels, however, this only led to more chaos. Groups fractured into smaller cells to fill in the vacancies left by the original cartels. The number of cartels in Mexico rose from six in 2006 to sixteen in 2011 and violence spread over the country as a result. The nature of crime in Mexico also changed as gangs sought more ways to find funds, including methods that impacted the citizenry more directly, such as extortion, kidnapping and auto theft. Citizens easily became targets for more brutal crime such as executions if they refused to cooperate.

In their paper “Impact of Violent Crime on Risk Aversion: Evidence from the Mexican Drug War,” Ryan Brown, Verónica Montalva, Duncan Thomas and Andrea Velásquez, analyze data from this period to gain insight into the ways exposure to violent local crime can effect risk preferences in individuals. The authors believe that, given the increasing brutality of crimes in Mexico and the heightened visibility of violent crime due to media coverage, the population living in affected areas would have been significantly psychologically impacted. They state that this may have a number of possible outcomes such as changing an individual’s perception of how risky their environment and future are or leading individuals to make risk averse decisions as a result of fear of victimization.

The Data

The authors used the Mexican Family Life Survey (MxFLS) to obtain data about local populations. The baseline survey collected data from a period in 2002 and included 8,440 households and 35,600 individuals in 16 states throughout the country. The first follow up survey (MxFLS2) in 2005 and 2006 occurred during a period of relatively stable levels of violent crime, and the second follow-up survey (MxFLS3) in 2009 and 2010 occurred after a major rise in violent crime. Using these three surveys allowed the authors to examine the impacts of different levels of violence on the same individuals over an extended period of time. A trait of the MxFLS that makes this analysis possible, is its low levels of attrition (loss of participants), with 89% of the original respondents being contacted again for the first follow up and 87% for the second follow up. The surveys allowed for the gauging of risk preferences by having respondents decide between sure outcomes and gambles that resulted in either an attractive or unattractive outcome. The differences between the expected values of the gambles and the values of the sure options were used to rank the level of risk aversion. This individual-level data from the MxFLS is combined with monthly municipality-specific homicide data from the National Institute of Statistics and Geography (INEGI) in order to measure how risk preferences vary as the level of local violent crime changes over time. The monthly homicide rate (the primary data used from INEGI) for the period studied can be seen in figure 1 below.

Figure 1 blog

Brown et al. 2017

Analysis and Results

The authors’ primary calculations suggest that an increase of 1 homicide per 10,000 people resulted in a 5% increase in risk aversion in MxFLS3 (compared to the average per capita expenditure from MxFLS2). Table 1 shows how the overall risk aversion distribution changed from MxFLS2 to MxFLS3. Surprisingly, the authors also found that the risk attitudes of households in the lowest quartile of per capita expenditure are not effected by homicide rate like those in other quartiles. This could be because of a variety of reasons, such as the possibility that the change in violence was greater in high income neighborhoods than in low income neighborhoods. The authors also state that it is possible (if fear as a result of violence is what drives the risk preference change) that the worst off individuals may already be past the point where increased local violence no longer significantly impacts their risk preferences. It is also possible that the used measure is noisier for these individuals.

table 1 blog

Brown et al. 2017

It also noteworthy that the influence of violence crimes significantly reduced the earnings of self-employed men and the labor market participation of self-employed women. This finding led the authors to explore whether the primary fear impacting risk preference was financial in nature. In looking at the data, they found that the risk attitudes of the self-employed are not more strongly impacted by local violence than other participants. However, further analysis revealed that risk aversion was more than double the size among participants in the population who were fearful of victimization during the escalation of violence. This provides evidence suggesting that local violent crime is impacting risk attitudes through the fear of victimization rather than increased financial hardship.

How Does This Relate to Development Economics?

As the authors state, there is evidence that increased risk aversion is negatively correlated with involvement in riskier but more profitable investment decisions, occupational choices and migration. This suggests that increased levels of risk aversion as a result of exposure to violent crime can negatively impact household wealth accumulation and can hurt a country’s economic development in the long-term. As such, level of exposure to violent crime and statistics such as local homicide rates should be seen as closely tied to inequality and economic growth. The impact on household wealth accumulation and on willingness to take riskier but more profitable financial decisions can deepen inequality and diminish growth in the long term. Similarly, the authors also mention that the violent crimes impacted self-owned businesses negatively. This is somewhat relevant to the field of microfinance, as some programs find effectiveness primarily in enabling individuals to have more choice by allowing them the option of self-employment. If self-employment becomes less profitable as a result of violent crime, these forms of microfinance will be less successful in affected communities.

References

Brown, Ryan, Verónica Montalva, Duncan Thomas and Andrea Velásquezv (2017). Impact of Violent Crime on Risk Aversion: Evidence from the Mexican Drug War, NBER Working Paper No. 23181 (2017): retrieved from http://nber.org/papers/w23181

Schaffner, Julie (2014). Development Economics: Theory, Empirical Research, and Policy Analysis. Hoboken, NJ: Wiley.

 

3 thoughts on “Violent Crime and Risk Aversion”

  1. Another reason that the poorest people had the lowest increase in risk aversion is that they already have so little to lose. For those with higher incomes, their risk aversion means that losing money impacts them more than gaining money. Another possibility is that it isn’t just the total number of homicides but the number in the area. What I mean is perhaps the richest people don’t see the homicides, but knowing that one more is occurring near them really freaks them out. If there is more violence in poor neighborhoods, they may not react as much to one more homicide because they have become so used to the violence.

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  2. This is a very interesting look on how wealth in countries can be “sticky at the ends”. Meaning the people that are placed in poorer situations tend to be stuck in those situations and the people at the top will never have to experience any sort of crime or adversity. This is also where programs such as MFIs and savings account opportunities allow people to overcome these situations.

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  3. The behavior of individuals in areas with high crime rates is a very interesting topic and directly relates to the concepts talked about in class. I would be interested, though, in seeing how this increased risk aversion affects borrowers decisions to invest in safe over risky projects. We would expect that given a concave utility function, expected utility is lower for risky projects and more credit is allocated to safer projects. This increase in crime, however, may just decrease overall investment. It would be interesting to see empirical evidence pointing either way.

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