Abstract
We use data for Medellín, Colombia, to assess the effect of the homicide rate, individual’s perception of security in their neighborhood of residence, and of the effect of their having been victimized, on life satisfaction. We find a negative effect of the homicide rate on life satisfaction for the subsample of individuals living in their current houses for at least 10 years or more, who had moved to that place at some point in the past. We also find a positive and robust effect of the perception of security in the households’ neighborhood for the whole sample and for different subsamples considered. Having been a victim of an offence is also robustly negatively related to life satisfaction, in particular, in the cases where the offense was robbery.
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Notes
- 1.
See Information System for Security and Coexistence (2009).
- 2.
For these three features, they present a complete literature review of studies that analyzed the impact of this features on life satisfaction.
- 3.
Diener et al. (1999) present a review for physiologist modern and past theories of subjective well-being.
- 4.
Geis and Ross (1998), Ross et al. (2000), Cutrona et al. (2000), among others obtain similar results. Scarbourough et al. (2010) studied the relationship between individual characteristic, neighborhood context, and fear of crime and find that relationship between demographic characteristics and fear of crime is conditions by neighborhood factors.
- 5.
This article mentioned a large literature that emphasizes social disorders in urban disadvantaged neighborhoods, illicit drug use, drug purchasing, and other criminal activities.
- 6.
There are many articles that have tried to link life satisfaction with different themes. An example of those are the following: Helburn (1982), who analyzed the link between geography and quality of life; studies that analyzed unemployment and quality of life, Winkelmann and Winkelmann (1998), Frey and Stutzer (1999), and Blanchflower and Oswald (2003); relationship between absolute and relative income and quality of life, Clark and Oswald (1996), McBride (2000), Easterlin (2001), Deaton (2008), and Ferrer-i-Carbonell (2005); Di Tella et al. (2001, 2003) analyzed the impact of macroeconomics indicators on life satisfaction; Alesina et al. (2004) analyzed the relationship between inequality and quality of life.
- 7.
The Gallup World Poll is a survey covering more than 130 countries in all regions of the world which allows for comparison of patterns of victimization and safety perceptions. The poll asks if in the last 12 months interviewed individuals were victims of property crime or crime against the person (assaulted or mugged).
- 8.
They also presented interesting statistics about the patterns of victimization across groups, like “males are more often victimized than females,” age is negatively correlated with victimization, etc. For more, see DiTella et al. (2008).
- 9.
Those are 1993, 1994, 1996, 1998, 2000, 2002, and 2004.
- 10.
See Map 6.4. There are a total of 242 census sectors (these are spatial units employed by Dane when surveying households) and 249 neighborhoods (these are the spatial administrative units in which the Municipality of Medellin splits the city) in Medellín. In the case of Medellin, these spatial units are very similar.
- 11.
Patterns of crime by country in Latin America can be found in Soares and Naritomi (2010).
- 12.
Bullinton (1992) reports the huge share of cocaine and marijuana entering the US in the 1980s through Bahamas and Miami, and the role of Colombian drug dealers in sending it, as it is also described by Riley (1996). See also Gamarra (2003) who argues that most Colombian migrants to the US since the late 1970s and until the mid 1990s were linked to the growth of the international narcotics trade. See also Thoumi (1995) and Gugliotta and Leen (1989) on this. The relation between drug dealers, guerrillas, and paramilitary groups is described in Villamarin (1996).
- 13.
- 14.
Giraldo et al. (2010) present a complete characterization of the violent crime in Medellin.
- 15.
We present only statistics for murderers captured in the act. Later we are going to use that variable as a proxy for different types of criminal activity.
- 16.
For example, 75% of the victims murdered in Medellín in the first semester of 2009 were murdered in their neighborhood of residence, which is linked to the hiring of killers or to fight for territory control among gangs that belong to the organized crime. See Information System for Security and Coexistence (2009).
- 17.
See Appendix 1 for more details on the way these variables were constructed.
- 18.
Urban areas in Colombia are split into six socioeconomic strata in which, the first one has the lowest QoL levels. The strata are used by authorities to target social spending like that in the supply of public services (water, electricity), housing, health insurance for the poor, etc.
- 19.
Interviewers asked interviewed individuals to specify which one of the 25 crime categories of the survey they were victim of.
- 20.
- 21.
- 22.
In our empirical estimations, we will find the standard U-shaped relationship between life satisfaction and age.
- 23.
Powdthavee (2005) also includes in his estimation an interaction variable between his crime rate and whether the individual had been a victim of crime, finding a positive coefficient on that interaction variable.
- 24.
Gaviria et al. (2010a) had as well found a negative capitalization of the homicide rate on property values in the case of Bogotá.
- 25.
- 26.
We also got estimates of all regressions, splitting the sample with people living in their current neighborhood 9, 8, 7, 6, and 5 years ago, and we found similar results to the ones reported.
- 27.
The result was obtained for the subsample of previous movers by estimating the model with an interaction of the homicide rate and the victimization variable. The coefficient on the interaction variable is negative and significant, while the victimization variable loses its significance. The homicide remains negative and significant.
- 28.
This result was found for the sample of previous movers with the specification in column (ii) in the tables, although other specification led to similar results.
- 29.
This result was found, again, for the sample of previous movers with the specification in column (ii) in the tables, although other specification led to similar results.
- 30.
The research design is based on comparisons of three groups to which households were randomly assigned in the moving to opportunity social experiment, operated in five cities of the USA: Baltimore, Boston, Chicago, Los Angeles, and New York.
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Acknowledgments
We thank the Judicial Police Sectional of the National Police Department, (SIJIN), and the Administrative Department of Municipal Planning of Medellín, for providing the data; and Jorge Eliécer Giraldo for assistance. We also thank comments received by two anonymous referees. The opinions expressed here are those of the authors and not of the Banco de la República de Colombia nor of its Board.
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Appendices
Appendix 1
We use a bivariate kernel density estimator to construct the variables used in our estimations (homicide rate, distance to crime, and arrest rates), and the maps. We use two variables: the distance, in meters, from the centroid of each block to the place where the homicide was committed, and numbers of months elapsed between the date of each homicide and the date the survey was carried out. Given random r-vectors X 1, X 2,…,X n , the multivariate kernel density estimator is defined as
where H is a \( (r\times r)\) nonsingular matrix that generalizes the window width and K is a multivariate function with mean 0 and integrates to 1. We tried with Bartlett Epanechnikov kernel since it is the one with the minimal asymptotic integral squared error, and Gaussian kernel. We use rule-of-thumb method and likelihood cross-validation to the window width.
Appendix 2
Variable | Description |
|---|---|
Homicide rate | Homicide rate per 10,000 inhabitants, by block of each individual interviewed by ECV 2008 (constructed with Kernel procedure) |
Capture rate/homicide rate | Homicide rate per 10,000 inhabitants divided by capture rate per 10,000 inhabitants of the block where the individual live (constructed with Kernel procedure) |
Distance to crime | Average distance between the centroid of each block where individual interviewed by ECV 2008 live and the place where homicides occurred (Estimated using the Kernel) |
Years living in this place | ECV 2008 asked how many year the people have been living in the place where they are actually living |
Safe neighborhood | We constructed a dummy variables that is 1 if individuals answered to have “very safe” and “safe” feeling perceptions of the neighborhood or district where you live |
Victim of robbery, burglary, personal | We constructed a dummy variable that is 1 if individuals interviewed or other member of their household were victims of robbery, burglary, personal. |
Victim of other offenses | We constructed a dummy variable that is 1 if individuals interviewed or other member of their household were victims of other offenses |
Household income | The sum of the income of the members of the household. |
Number of persons in household | Number of persons living currently in the same home |
Age | Age of the interviewed individual |
Age 2 | Age of the interviewed individual squared |
Male | Dummy variable if the interviewed individual is male |
Household head with primary | Dummy variable equal to 1 if educational level of the household head is at least primary studies |
Household head with secondary | Dummy variable equal to 1 if educational level of the household head is at least secondary studies |
Hold head with technique education | Dummy variable equal to 1 if educational level of the household head is at least technique studies |
Household head single | Dummy variable equal to 1 if the household head is single |
Household head married | Dummy variable equal to 1 if the household head is married |
Household head separated | Dummy variable equal to 1 if the household head is separated |
Household head lives with partner | Dummy variable equal to 1 if the household head lives with partner |
Educational attainment | Educational attainment |
House with fixed phone line | Dummy variable equal to 1 if the house has fixed phone line |
House with electricity | Dummy variable equal to 1 if the house has electricity |
House with aqueduct | Dummy variable equal to 1 if the house has aqueduct |
House with sewerage | Dummy variable equal to 1 if the house has sewerage |
Number of rooms in household | Number of rooms in household |
Socioeconomic stratum 2 | Dummy variable equal to 1 if socioeconomic stratum where individual live is equal to 2 |
Socioeconomic stratum 3 | Dummy variable equal to 1 if socioeconomic stratum where individual live is equal to 3 |
Socioeconomic stratum 4 | Dummy variable equal to 1 if socioeconomic stratum where individual live is equal to 4 |
Socioeconomic stratum 5 or 6 | Dummy variable equal to 1 if socioeconomic stratum where individual live is equal to 5 or 6 |
Homeownership, house totally paid | Dummy variable equal to 1 if the house is own and totally paid |
Homeownership, house partially paid | Dummy variable equal to 1 if the house is own and partially paid |
Tenant | Tenant |
House with gas for cooking | Dummy variable equal to 1 if the house has gas for cooking |
House with natural gas | Dummy variable equal to 1 if the house has gas natural gas |
House with internet | Dummy variable equal to 1 if the house has internet |
House with cable TV | Dummy variable equal to 1 if the house has cable TV |
Employed | Dummy variable if individual is employed |
Unemployed | Dummy variable if individual is unemployed |
Household occupation rate | Number of people with employment divided by the number of persons in household |
Household unemployment rate | Number of people unemployment divided by the number of persons in household |
Enrolled in private health insurance | Dummy variable equal to 1 if interviewed is enrolled in private health insurance |
Enrolled in public health insurance | Dummy variable equal to 1 if interviewed is enrolled in public health insurance |
Enrolled in pension fund | Dummy variable equal to 1 if interviewed is enrolled in pension fund |
Good health | Dummy variable equal to 1 if the interviewed answer to has “very good” health or “good” health |
Constant | Constant |
FE past neighborhood | Fixed effect of past neighborhood |
FE current neighborhood | Fixed effect of current neighborhood |
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Medina, C., Tamayo, J.A. (2012). An Assessment of How Urban Crime and Victimization Affects Life Satisfaction. In: Webb, D., Wills-Herrera, E. (eds) Subjective Well-Being and Security. Social Indicators Research Series, vol 46. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2278-1_6
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