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Putting a Price Tag on Security: Subjective Well-Being and Willingness-to-Pay for Crime Reduction in Europe

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Abstract

Using information on life satisfaction and crime from the European Social Survey, we apply the life satisfaction approach (LSA) to determine the relationship between subjective well-being (SWB), income, victimization experience, fear of crime and various regional crime rates across European regions. We show that fear of crime and criminal victimization significantly reduce life satisfaction across Europe. Building upon these results, we quantify the monetary value of improvements in public safety and its valuation in terms of individual well-being. The loss in satisfaction for victimized individuals corresponds to 24,174€. Increasing an average individual’s perception within his neighborhood from unsafe to safe yields a benefit equivalent to 14,923€. Our results regarding crime and SWB in Europe largely resemble previous results for different countries and other criminal contexts, whereby using the LSA as a valuation method for public good provision yields similar results as stated preference methods and considerably higher estimates than revealed preference methods.

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Notes

  1. If e.g. criminals are arrested or crime prevention programs diminish the number of potential criminals, it is not possible to exclude others from the benefits of the reduced risk of victimization (Ehrlich 1996; Head and Shoup 1969). If safety measures cannot be provided to one person without simultaneously providing them to others, the latter can free-ride, i.e. they can consume the provided safety without paying (Hummel 1990). For this reason, public safety or national defense is usually used as the textbook example for public goods and is considered one of the state’s primary functions (Frey et al. 2009; Head and Shoup 1969; Samuelson 1955; Hummel 1990).

  2. Revealed preferences methods have been used to determine the implicit price of public safety in the real estate property market (see e.g. Thaler 1978; Blomquist et al. 1988; Lynch and Rasmussen 2001; Gibbons 2004). Cohen et al. (2004) applied the CVM to the issue of public safety, asking households how much they would be willing to pay to reduce specific crimes—ranging from burglary to murder—by 10% in their communities. Other examples are Ludwig and Cook (2001), who estimate the stated preference for a reduction in gun violence in the US, as well as Atkinson et al. (2005), who investigate respondents’ WTP for different violent crimes in the United Kingdom.

  3. To date, a limited number of studies have applied the LSA to evaluate different phenomena related to crime and safety (cp. Powdthavee 2005; Moore 2006; Frey et al. 2009; Cohen 2008; Kuroki 2013; Cheng and Smyth 2015), while a number of studies investigate the effect of crime on different subjective well-being measures without valuing the estimated effect in monetary terms (see e.g. Michalos and Zumbo 2000; Sulemana 2015; Stickley et al. 2015).

  4. It can be argued that individuals are compensated for the differences in the exposure to different levels of crime on private markets. Assuming equilibrated private markets and rational agents with accurate risk perception, price differentials in private markets fully compensate individuals for the expected utility loss due to the exposure to crime. Even if these strict assumptions are not fulfilled, people might still be partially compensated in private markets, which would have an offsetting effect on life satisfaction. The LSA as measured in this study thus merely captures the residual effect of crime that people are not already compensated for in private markets (see e.g. Van Praag and Baarsma 2005; Luechinger and Raschky 2009; Frey et al. 2009).

  5. The countries included in the dataset are Albania, Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Kosovo, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Romania, Russian Federation, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, Ukraine and the United Kingdom.

  6. The distribution of reported life satisfaction levels across Europe and an illustration of the average reported life satisfaction per region for 2012 are documented in the “Appendix” (Table 5; Fig. 1, respectively). Since the life satisfaction variable is an ordinal categorical variable, the mean is calculated assuming that the distance between each response category is equal, which is a standard procedure in happiness economics (see e.g. Easterlin 1995; Diener and Seligman 2004; Diener et al. 2013).

  7. The mean income \(\bar{x}\) for the open-ended category equals \(x_{i} \left( {\frac{v}{v - 1}} \right)\), where \(x_{i}\) is the lower bound of the upper income interval and \(v\) is a parameter obtained by estimating the regression model \(\log n = \log A + v\log x\) for the top four income categories, where \(n\) is the number of individuals with incomes over a certain amount \(x\), which in this case are equal to the four lower bounds for the same four categories (Parker and Fenwick 1983).

  8. NUTS refers to the “Nomenclature of Territorial Units for Statistics”; it is the regional classification used by Eurostat. The regions usually correspond to administrative divisions within the country and are intended to be of comparable population size at the same level. The standards for establishing regions are 3–7 million people for NUTS 1, 800,000–3 million for NUTS 2 and 150,000–800,000 for NUTS 3. A comprehensive overview of the NUTS—including the current and former NUTS codes—can be found at: http://ec.europa.eu/eurostat/web/nuts/history.

  9. For the discussion of inter-country comparison of crime rates by Eurostat, see: http://ec.europa.eu/eurostat/cache/metadata/de/crim_esms.htm.

  10. Figure 2 in the “Appendix” provides a choropleth map for the share of victimized respondents per region in 2012.

  11. Since the estimates for these variables do not hold particular interest, they will not be individually listed in the following tables, with the exemption of household income and size, which are necessary to calculate the monetary equivalent of changes in the crime variables.

  12. This leaves us with ca. 11,000 observations for the homicide rate on each, country, NUTS 1 and 3 level and ca. 20,000 observations on NUTS 2 level.

  13. See e.g. Glaeser and Sacerdote (1996), Gibbons (2004), Bannister and Fyfe (2001). The pattern of higher crime rates in agglomerations can also be found in the ESS. The share of respondents who report victimization living in suburbs and villages is 22.83 and 14.09%, respectively.

  14. However, there remains a residual risk of incorrectly identifying a respondent as being the actual victim as the victimization question refers to the last five years, whereas the question on household size refers to the moment of the interview.

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Authors and Affiliations

  1. Faculty of Economic Sciences, Chair of Economic Policy and SME Research, University of Goettingen, Platz der Goettinger Sieben 3, 37073, Göttingen, Germany

    Mattheus Brenig & Till Proeger

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  1. Mattheus Brenig
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Correspondence to Till Proeger.

Appendix

Appendix

See Figs. 1, 2 and Tables 5, 6, 7.

Fig. 1
figure 1

Average life satisfaction per region in 2012. Average reported life satisfaction per region in 2012. The life satisfaction question is: ‘All things considered, how satisfied are you with your life as a whole nowadays?’ Answers are given on an eleven-point numeric scale comprising integers running from zero to ten

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Fig. 2
figure 2

Share of victimized respondents per region in 2012. Share of respondents per region reporting victimization in 2012. The corresponding question is: ‘Have you or a member of your household been the victim of a burglary or assault in the last 5 years?’

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Table 5 Overall life satisfaction
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Table 6 Absolute numbers and percentage of respondents who chose the respective answers to the question: ‘How safe do you—or would you—feel walking alone in this area after dark?’
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Table 7 The effect of crime on life satisfaction using ordered probit
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Brenig, M., Proeger, T. Putting a Price Tag on Security: Subjective Well-Being and Willingness-to-Pay for Crime Reduction in Europe. J Happiness Stud 19, 145–166 (2018). https://doi.org/10.1007/s10902-016-9814-1

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