Parallel society: Myth or reality? A question for policy makers

ABSTRACT

The Danish government has produced a public document to announce that deviations from societal values were tolerated no more. They denounced the presence of “alternative values” areas (called “parallel societies”), located mostly where low or no income residents live in social/public housing (officially called “ghetto areas” or ghettoområder). Through the means of agent-based computational simulation, this paper uses a nested “model in a model” approach to study the issue by introducing a policy-simulation-policy loop. The paper is a first attempt to explore whether programmatic policy indications have some actual reflection on the formation of these “alternative values” areas. Results suggest ways in which a sustainable governmental policy on ghetto areas shall not start from assuming divergence where there is none, showing the value of the technique developed for this paper.

KEYWORDS:

• Agent-based modelling
• model in a model
• policy-simulation-policy
• segregation
• parallel societies

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. We are reporting information from the Danish government here. As it will become clear in the following pages, we do not know if parallel societies – “societies within societies” – are possible, whether they are located in a so-called “ghetto”, and/or whether they necessarily constitute a threat.

2. The PARSO model included in the materials includes code that has not been used to produce the results below but that is functional to further the exploration of alternative values areas.

3. Notice that the values for search area and assessment area are four, but the first interval is not 3 but 2, i.e. it goes from 1 to 3, then 6, and 9.

4. This distribution roughly replicates how a variable education is usually coded in a survey design, with primary school degree = 1, secondary = 2, up to max of 5.

5. There are corrections made here for consistency so that, for example, those residents with democracy attitudes in the extremes will be either extremely tolerant or intolerant.

6. This is defined by the number of weeks (“ticks” in the system) in which an agent has not been able to settle.

7. This is $\times 2$ again, if one considers the split with enter/exit, hence reaching 552,960 experiments.

8. A one way ANOVA shows that the number of residents in these areas do not depend on the size of residential areas available: F-statistic $=0.875[df=2,389],$ $p=0.418$.

9. A t-test on these two aspects shows that residents of these “alternative” areas have higher income when residential areas are “on”: $t=3.5219,df=260.06,$ $p=0.0005059$, $mea{n}_{rz=true}=3.040351,$ $mea{n}_{rz=false}$ $=2.239367$. A similar phenomenon is found for income: $t=6.8343,df=225.27,$ $p=7.621e-11$, $mea{n}_{rz=true}=3.276631,mea{n}_{rz=false}=2.084857$.

10. Two t-tests on number of residents and income in “alternative” areas confirm these findings for the effect of social housing for number of residents: $t=0.72065,df=360.98,p=0.4716$, $mea{n}_{sh=true}$ $=2.667526,mea{n}_{sh=false}=2.511616$; and similar results are for income: $t=-0.53373,df=389.96,$ $p=0.5938$, $mea{n}_{sh=true}=2.558935,mea{n}_{sh=false}$ $=2.649616$.

11. It is worth noting that the most crowded area is still $\approx 4\mathrm{\%}$ of the total number of residents in the system. The average number of residents in one of these areas is 2.589, amounting at 0.065% and 0.032% of the total number of residents in the system when they are, respectively, 400 and 800.

12. These numbers remain very low and below five residents on average.