We observed the same pattern for schooling. Table 2.3 shows respondents from elite school backgrounds naming contacts from elite school backgrounds. By the same token, respondents from non‐elite school backgrounds named contacts from non‐elite school backgrounds. Again, diverse ties (i.e. between elites and non‐elites) occurred much less often.
Table 2.1 Network diversity scores based on IQV measures.
| Network diversity | Mean | Median |
|---|---|---|
| Gender IQV (male vs. female) | 0.66 | 0.75 |
| Age IQV (using 6 age categories)a | 0.58 | 0.67 |
| Nationality IQV (Singaporean vs. non‐Singaporean) | 0.37 | 0.36 |
| Race IQV (Chinese, Malay, Indian, Others) | 0.32 | 0.37 |
| Race IQV (Majority – ‘C’ vs. Minority – ‘MIO’) | 0.41 | 0.44 |
| Educational IQV (graduate vs. non‐graduate) | 0.41 | 0.40 |
| Educational IQV (low, middle, high)b | 0.46 | 0.56 |
| Elite IQV (attended an elite school vs. not) | 0.24 | 0 |
| Housing IQV (public vs. private) | 0.37 | 0 |
| Housing IQV (using 4 housing categories)c | 0.54 | 0.64 |
| Tie strength IQV (strong tie vs. weak tie) | 0.48 | 0.60 |
| Spatial IQV (nearby vs. further) | 0.63 | 0.75 |
| Religion IQV (using 8 categories)d | 0.41 | 0.47 |
a Age diversity: Below 30/30 to 39/40 to 49/50 to 59/60 to 69/70 and above.
b Educational diversity using three categories: Low = Primary and below, Secondary, ITE, Pre‐U; Middle = Polytechnic, Professional qualification; High = University degree and above.
c Housing diversity: HDB 1‐ to 3‐room/HDB 4‐room/HDB 5‐room, HDB maisonette/private or condominium apartment or landed property or shophouse.
d Religious diversity: Buddhism/Christianity/Hinduism/Islam/Taoism/Sikhism/Other/No religion.
Table 2.2 Combinations of housing dyads.
| Housing combinations | ||
|---|---|---|
| Ego lives in… | Alter lives in… | Number of ties from ego to alter |
| Public housing | Public housing | 4.3 |
| Private housing | Private housing | 3.1 |
| Private housing | Public housing | 2.6 |
| Public housing | Private housing | 0.8 |
Table 2.3 Combinations of schooling status dyads.
| Schooling combinations | ||
|---|---|---|
| Ego has attended… | Alter has attended… | Number of ties from ego to alter |
| Non‐elite | Non‐elite | 3.9 |
| Elite | Elite | 2.7 |
| Elite | Non‐elite | 2.1 |
| Non‐elite | Elite | 0.4 |
Class Polarization
Class polarization is a growing fact globally. World events, whether Brexit (Alabrese et al. 2019) or the American Presidency (Hochschild 2016) or the Hong Kong protests (Stevenson and Wu 2019), all point to societies facing the pressures of class divisions, especially between elites and the masses. Technological advancement and globalization have exacerbated these class divisions (Jackson 2019). In the case of Brexit, one observer considered ‘the divide between winners and losers of globalization a key driver of the vote’ (Hobolt 2016, p. 1259).
Although there are certainly other causal factors, the widening gap between classes has been linked to the rise of smart technologies. Artificial intelligence (AI) and machine learning (ML) will likely have different effects on different groups. Smart technology will require highly‐skilled workers, paving the way, in turn, for more new discoveries and innovations. At the same time, the automation of jobs will adversely affect middle class groups. There will continue to be a demand for manual labour, but it is likely to be characterized by lower wages, part‐time work, and precarious work models.
The hollowing out of the middle is producing a polarized social structure differentiating the ‘best’ from the ‘rest’ (Jackson 2019). As Manuel Castells comments, ‘Elites are cosmopolitan, people are local’ (cited in Huntington 2004). The various groups conduct their lives separately, going about their routines in different social milieus, eating at different restaurants, living in different neighbourhoods, attending different schools and belonging to different religious congregations. The result is that their networks are closed to each other, and their circles never quite meet (Scott and Leonhardt 2005; Murray 2012; Pew Research Centre, Philip Schwadel 2018).
In Singapore, the high diversity scores for race and religion are no accident. Rather, they are the result of 50 years of nation building based on a multicultural model of racial and religious tolerance. State policies, such as the equal recognition accorded to all racial/ethnic groups,