The price of becoming an adult

21 february 2022

The price of becoming an adult

Applied econometricians increasingly make use of registry data – data on the whole population of a country, typically gathered by the national bureau of statistics (Statistics Netherlands, better known as CBS in the case of the Netherlands). In this column I will discuss an example of my research (based on Bindler et al., 2021) using Dutch registry data. Thanks to the large sample sizes and the amount of detail in the data – we know the individual's exact date of birth - we can apply a Regression Discontinuity Design, the details of which are explained below.

.. we take a broader perspective and study the age-victimisation profile for juveniles to better understand the causes of victimisation.

Most crimes are not victimless. In the US, about 4% of respondents of the National Crime Victimisation Survey report being crime victims in just the last six months; similar or larger rates are seen worldwide and over time (Bindler et al., 2020). There is increasing evidence that the individual and societal costs associated with victimisation are large: victimisation harms (mental) health and labour market outcomes for adults (e.g., Bindler and Ketel, forthcoming; Ornstein, 2017; Cornaglia et al., 2014; Currie at al., 2018) and educational outcomes for juveniles (e.g., Monteiro and Rocha, 2017; Foureaux-Koppensteiner and Menezes, forthcoming; Chang and Padilla-Romo, 2020). Yet, despite these severe consequences, just a handful of papers study the determinants of victimisation (e.g., Vollaard and van Ours, 2011 and Card and Dahl, 2011). These papers mostly focus on adults and highlight the role of precautionary behaviour, being in the same environments as potential offenders, and most recently, the role of alcohol (Chalfin et al., forthcoming).

Identifying a causal effect between aging and changing behaviours, such as drinking, and victimisation is challenging

In Bindler et al. (2021), we take a broader perspective and study the age-victimisation profile for juveniles to better understand the causes of victimisation, just as studies of the age-crime profile have informed our understanding of the causes of crime. Using unique register data of all reported victimisations in the Netherlands between 2005 and 2018 linked to national population registers including day of birth, we observe sharp and discontinuous increases in victimisation risk as individuals turn 16 and 18, and only at these birthdays (see Figure 1). We hypothesize that increased victimisation might be due to rights granted at these ages: the right to drive a moped (16), purchase alcohol and tobacco and enter bars and clubs (originally at age 16, increased to age 18 in 2014), enter marijuana-selling coffeeshops (18) and drive a car (18). These rights might change behaviour or activities – e.g., where you are, when you are there, and who you are exposed to – in ways that increase victimisation risk (see, e.g., Ahammer et al., 2021 and DeSimone, 2010). In Bindler et al. (2021), we disentangle the role of the many rights granted at these ages using detailed offense and location data, cross-cohort variation in the minimum legal drinking age driven by the reform that increased the minimum purchasing age, and survey data of alcohol/drug consumption and mobility behaviours.

Figure 1. Age-Victimization Profiles in the Netherlands (any crime):

NOTE - The figure plots the number of victimisations (of any offense) per week (to and from birthdays) over ages 13-22 for the birth cohorts 1990-1999. Blue lines represent males, red lines females. The grey shaded areas mark the parts of the age-profile that are based on a balanced sample. SOURCE - Results are based on calculations by the authors using microdata from Statistics Netherlands.

Design

Identifying a causal effect between aging and changing behaviours, such as drinking, and victimisation is challenging. First, many factors change over the life-cycle that affect the type of activities in which individuals engage as well as their risk preferences and peer group. In addition to this potential for omitted variable bias, we must also contend with potential simultaneity bias – namely that while behaviour can affect victimisation risk, individuals may also adjust their behaviour after victimisation. We leverage exogenous shocks to youths’ rights at key birthdays as well as reforms to minimum legal drinking ages to overcome these challenges and identify how changes in one’s behaviours and routines affect the risk of becoming a crime victim.

We model we estimate is presented in equation (1), where the dependent variable is the percentage share of individuals from date-of-birth cohort c who are victimized of offense category o in week t. The analysis is always conducted separately by gender g (where g=1 for females, = 0 for males).

Our running variable t is measured relative to the birthday of interest; that is, t is normalized to zero in the calendar week of date-of-birth cohort c’s 16^th or 18^th birthday, and t leading up to and following the birthday is negative and positive, respectively. Our baseline specification allows for a split-linear trend in relative time: f(t)=γ₁* t + γ₂* t * D{t≥0}_c. The coefficient of interest, β₁, is the parameter associated with a dummy indicating that t is the week of or after the birthday. We allow for the possibility of temporary effects of birthday celebrations and/or events on behavior by including controls (X_c^bday) for whether week t is the birthday week and whether the birthday falls onto a weekend day. Finally, we include month of birth, δ_mob, and year of birth, δ_yob, fixed effects to control for seasonality and trends in crime over time as well as for national reforms and trends that differentially affect one cohort versus another. We demonstrate that the results are not sensitive to: the choice of bandwidth (52, 39, 13 weeks), clustered standard errors, functional form (simple linear trend versus split-linear trend), and a non-parametric approach using local linear regression with a triangular kernel (Hahn et al., 2001) with both a 26-week bandwidth as well as an optimal bandwidth estimator as in Calonico, Cattaneo and Titiunik (2014).

Three points are important for the interpretation of the parameter of interest β₁. First, as all individuals become eligible for the respective rights at ages 16 or 18, the design is sharp. But, as not all individuals take up these rights (at all or to the same extent), 1captures the intention-to-treat effect, i.e., the effect of simply being eligible for these rights. Second, we estimate the effect of the entire ‘bundle’ of rights granted at each age threshold. Thus, β₁ captures the reduced form effect of turning 16 or 18. Third, the RD design identifies the local average treatment effect parameter (LATE) in the neighborhood surrounding the age cutoffs at which the rights are gained. We later explore whether our findings generalize (i) to the entire population or whether they are driven by specific complier groups and (ii) to ages beyond the cutoff.

There are three issues that are relevant to causal identification in regression discontinuity (RD) designs. First, RD designs assume that there is no manipulation in the running variable (date of birth), yet there is little reason to believe that this would be systematically misreported in the Dutch register data. Second, using age as the running variable in an RD design implies that treatment is ‘inevitable’ (Lee and Lemieux, 2010). In contrast to, for example, criminal behavior (with the prospect of harsher punishment at the age of criminal majority) or saving decisions, it is intuitively hard to imagine that youth intertemporally manipulate their own victimization. Perhaps the most fundamental identifying assumption is that no unobservables change discontinuously at the age 16 and 18 cutoffs. This assumption would be violated if reporting behavior changes at these birthday thresholds even if (true) victimization rates do not change. However, we do not find any empirical evidence that reporting rates conditional on victimization change around birthdays.

Main findings

When we formally estimate the effect reaching the age 16 and 18 birthday thresholds using a regression discontinuity design, we find that:

The chance of victimisation increases significantly by about 13% for both males and females at 16, and about 9% (15%) for males (females) at 18.
These results are not driven by changes in reporting behaviour or by vehicle-related offences, which allows us to rule out a mechanical effect of getting a driver’s license on victimisation (e.g., one cannot have a car stolen if they do not own a car).

These estimates reflect the effect of getting access to a ‘bundle’ of rights. Also, not everyone will make use of all the rights to the same extent. The detailed offence and location information in the Dutch data can provide us with more insights. In terms of the location of the victimisation, we see that victimisation risk while ‘out’ increases relative to at home. This is consistent with multiple channels that change how much an individual goes ‘out’, including moped/driving licenses and age thresholds to purchase alcohol and enter bars, clubs, or coffeeshops.

the relevant policy questions are perhaps related to the optimal timing of granting the rights and whether steps should be taken to off-set the risks associated with them.

The 2014 minimum legal drinking age (MLDA) reform allows us to separate the role of alcohol-related rights from mobility-related rights. Before the reform, individuals could purchase ‘weak’ alcohol and tobacco at age 16 and ‘hard’ alcohol at 18. The reform increased the purchasing age to 18. Accordingly, bars and clubs also raised their entry age from 16 to 18. When we apply our regression discontinuity framework split by whether individuals were allowed to purchase weak alcohol at age 16 or 18, we find that victimisation risk increases at age 16 for the first group but not for the latter (see Figure 2). At the age 18 cut-off, victimisation risk increases in both groups. One hypothesis of policy makers is that it might be better to spread rights over different age cut-offs, so individuals can learn how to safely exercise these rights in a ‘softer’ environment: e.g., soft alcohol versus hard alcohol and mopeds versus cars. Taken at face value, our results do not support this hypothesis. For females, the increase at age 18 is the same for both pre- and post-reform cohorts, meaning that individuals who get all alcohol-related rights only at age 18 do not `compensate’ for not getting access to weak alcohol at age 16. For males, the increase at 18 is slightly larger for post-reform individuals, but the overall increase in victimisation risk still seems larger for the group that could already purchase weak alcohol at age 16. Put more simply, the age-specific gap in victimisation risk between the pre- and post-reform cohorts in Figure 2 is much larger after the 18th birthday than before the 16th birthday. Of course, a caveat to the policy implications of this analysis is that we cannot say anything about whether the costs and long-term consequences of being victimized earlier (at 16) are greater than later (at 18).

Figure 2. Discontinuities in the Victimisation Risk: By MLDA Cohorts:

NOTE - The figures show the average victimisation rates (in percent) per week around the key birthdays (15-19) for any offense for females (left) and males (right). Black markers represent cohorts born between 1990 and 1995 that were allowed to purchase weak alcohol. tobacco and enter bars/clubs at age 16. Grey markers those born between 1998 and 1999, that only got these rights at age 18. The black/grey lines represent simple linear fits. SOURCE - Results are based on calculations by the authors using microdata from Statistics Netherlands.

The MLDA analysis suggests that access to weak and hard alcohol, tobacco, and bars/clubs might be the most important channel driving the increases in victimisation risk. We complement these reduced form findings with descriptive survey evidence that suggests that individuals do indeed change their alcohol and going out behaviours at the relevant age thresholds.

The detailed Dutch register data also allow us to evaluate whether these rights have different effects for sub-samples with different baseline risks of victimisation: single versus dual parent households, low versus higher income households, low versus high crime neighbourhoods, and urban versus rural municipalities (with and without coffee shops). Reaching ages 16 and 18 significantly (and comparably) increase victimisation for almost every subsample. This implies that access to these rights does not exacerbate (nor mitigate) the pre-existing inequalities in victimisation risk and that the rights are taken up universally, and not only by a specific subgroup.

This, can be understood as one component of the price of becoming an adult.

Finally, given that drinking and going out are social activities that individuals typically do not engage in alone, we also assess whether there are observable peer effects associated with these rights. We do not find evidence of systematic spill-overs for the peer groups observed in our analysis who themselves are not eligible for these rights yet.

Potential Policy Implications

Taking away these rights completely is obviously not up for debate. Rather, the relevant policy questions are perhaps related to the optimal timing of granting the rights and whether steps should be taken to off-set the risks associated with them. With respect to the latter, one possibility is an increased effort to provide information and education regarding the various risks. Chalfin et al. (forthcoming) also highlight the potential for an information-related policy intervention, which “have low marginal costs and, as such, are easier to scale”. With regards to the timing, is there an optimal age at which to grant youths rights? Should these rights be given all at the same time or spread over different age cut-offs? Our results do not support the idea that spreading out the rights across ages reduce their overall impact on victimisation risk. We emphasize, however, that this is only a partial answer to the question of optimal ages for granting such rights. We shed light on the effects on victimisation – one potential cost– at ages of 16 and 18. There are other benefits (e.g., the utility of alcohol consumption) and costs (e.g., drunk driving related) associated with these rights. This paper cannot speak to these other costs and benefits, but rather provides insight into the differential effects of granting these rights on one important and understudied piece of the puzzle – victimisation risk. This, indeed, can be understood as one component of the price of becoming an adult.

References

Ahammer, Alexander, Bauernschuster, Stefan, Halla, Martin and Hannah Lachenmaier (2021) “Minimum legal drinking age and the social gradient in binge drinking,” VoxEU.org, 27 March.
Bindler, Anna, and Nadine Ketel (forthcoming) “Scaring or Scarring? Labour Market Effects of Criminal Victimization,” Journal of Labor Economics.
Bindler, Anna, Randi Hjalmarsson, and Nadine Ketel (2020) “Costs of Victimization”. In: Klaus Zimmermann (eds) Handbook of Labor, Human Resources and Population Economics. Springer, Cham.
Bindler, Anna, Randi Hjalmarsson, Ketel, Nadine and Andreea Mitrut (2021) “Discontinuities in the age-victimization profile and the determinants of victimization,” IZA DP No. 14917.
Calonico, Sebastian, Matias D Cattaneo, and Rocio Titiunik (2014) “Robust nonparametric confidence intervals for regression-discontinuity designs,” Econometrica, 82(6): 2295-2336.s
Card, David, and Gordon B. Dahl (2011) “Family Violence and Football: The Effect of Unexpected Emotional Cues on Violent Behavior,” Quarterly Journal of Economics, 126(1): 103–143.
Chalfin, Aaron, Benjamin Hansen, and Rachel Ryley (forthcoming) “The Minimum Legal Drinking Age and Victimization,” Journal of Human Resources.
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