Party Vote Demographics: Part III

In this series of blog posts, I’m looking at electorate demographics from the 2013 census, and the relationship with votes for political parties in the 2014 election. This is an important disclaimer, so I will repeat it again: correlation does not imply causation. This post will look at religion, marriage, and some other claims.

“Christians vote Conservative and United Future”
In electorates where there are more people who declare a religious affiliation with …
… Christianity (all denominations) (Anglican, Baptist, Catholic, Christian nfd, Latter-day Saints, Methodist, Pentecostal, Presbyterian, Congregational, Reformed, and Other Christian), fewer people voted for the Greens (r=-0.568)
… Māori Christianity (all denominations) (Rātana, Ringatū, Other Māori Christian), a lot more people voted for InternetMANA (r=+0.829) and the Māori Party (r=+0.844)
… Baptism, more people voted Conservative (r=+0.469) and ACT (r=+0.337)
… Anglicanism, slightly more people voted Conservative (r=+0.150), and fewer people voted for Labour (r=-0.486)
… Catholicism, Latter-Day Saints, Methodists, Pentecostals, and Other Christians, more people voted for Labour (r≈+0.5)
… Judaism, more people voted for ACT (r=+0.426), slightly more people voted for the Civilian Party (r=+0.248), United Future (r=+0.221), and National (r=+0.197), and fewer people voted for NZ First (r=-0.466)
… Buddhism, a lot more people voted for ACT (r=+0.819), and fewer people voted for NZ First (r=-0.597) and ALCP (r=-0.657)
… Hinduism, more people voted for Labour (r=+0.565) and ACT (r=+0.448), and fewer people voted for ALCP (r=-0.574)
… Islam, more people voted for Labour (r=+0.603) and ACT (r=+0.446), and fewer people voted for ALCP (r=-0.587) and NZ First (r=-0.495)
… Spiritualism and New Age religions, slightly more people voted for the Greens (r=+0.146)
… no religion, fewer people voted for Labour (r=-0.313)

Discussion: As the number of non-religious people in New Zealand grows (from 29.6% in 2001 to 41.9% in 2013), the role of religion in politics should be gradually eroding away. However, we do have parties with strong ties to religious groups, such as the Conservative Party (apparently popular in electorates with more Baptists and Anglicans). There appears to be no relationship between United Future and number of religious people in a particular electorate, which is surprising given the party’s roots in the Christian-based Future New Zealand party.

There are probably some overlaps between religion and ethnicity; for example, electorates with more Māori Christianity more strongly supported the Māori Party and InternetMANA, which shouldn’t be too surprising. Religions more common among immigrants, including Buddhism, Hinduism, and Islam, followed similar patterns to correlations for recent immigrants. A few interesting observations can be made of the religious preferences of Labour and Green voting electorates, but I’m inclined to dismiss those correlations as coincidence mainly because I can’t think of any reason why those relationships should exist.

“[Insert political side here] are more likely to be married”
In electorates where more people are…
… married (not separated), more people voted for the Conservatives (r=+0.771), National (r=+0.760), and United Future (r=+0.331), and fewer people voted for InternetMANA (r=-0.505), the Māori Party (r=-0.455), and NZ First (r=-0.236)
… divorced, more people voted for NZ First (r=+0.637), the Conservatives (r=+0.273), and the Māori Party (r=+0.110)
… not (have never been) married or in a civil union, slightly more people voted for the Greens (r=+0.175), fewer people voted for NZ First (r=-0.236), the Conservatives (r=-0.752), and National (r=-0.760)

Discussion: There are claims on both sides; some people think right-wingers are more likely to be in marriages because of the religious connections, others think that left-wingers are more likely to be in marriages because of the family connections, and so on. The correlations suggest that electorates with more married people are more likely to vote right-wing, and that Māori Party and InternetMANA supporting electorates have fewer married people. Electorates with a lot of divorcees voted more for NZ First, and all the single ladies people electorates were marginally in more support of the Greens and very strongly not in support of the Conservatives or National. So maybe there could be some truth in the statement that single people are not right-wingers, until the day that they sign a bit of paper with a significant other so that the state recognises them as legally married, when they suddenly get hit by a wave of neoliberalism and realise that Colin Craig had it right all along. Maybe.

“Smart/Educated (not synonymous) people won’t vote for the Civilian Party”
In electorates where there are more people...
… with Bachelors degrees, more people voted for the Civilian Party (r≈+0.38)
… with Honours degrees, more people voted for the Civilian Party (r≈+0.52)
… with Masters degrees, more people voted for the Civilian Party (r≈+0.42)
… with Doctorate degrees, more people voted for the Civilian Party (r≈+0.52)
… with tertiary education degrees, more people voted for the Greens (r≈+0.7), ACT (r≈+0.5), and United Future (r≈+0.37), and fewer people voted for ALCP (r≈-0.4) and NZ First (r≈-0.57)

Discussion: The argument was that more educated people take politics more seriously and wouldn’t dare to waste their vote on a joke party. The evidence reasonably clearly contradicts this, perhaps suggesting that satire might be relatively high-brow or that more educated people may be more disenfranchised with the political system and don’t care about their vote as much. Electorates with more educated people also voted more for the Greens and ACT, and less for ALCP and NZ First, which could be explained by a larger number of highly educated people living in urban/city areas, and thus capturing similar voting patterns as those discovered based on ethnicity and immigration. I note that education is not necessarily a good proxy for “smart”.

“People who use the internet will vote for the InternetMANA Party”
In electorates where there are more households with…
… access to the internet, there were no relationships with party vote
… access to a fax machine, slightly fewer people voted for InternetMANA (r=-0.160)
… access to a fax machine, a lot more people voted for the Conservatives (r=+0.523) and the National Party (r=+0.602)

Discussion: Okay so maybe no one really said this seriously, but it was fun to check anyway. Keith Ng found this surprising, since he “assume[d] people with fax machines were steampunks, therefore more likely to vote Internet[MANA]”. The reasonably strong relationship between fax machines and Conservative/National was a little surprising, but then again, maybe only old rich people can afford to maintain their fax machines. All that oiling and lubricating required to get those things going. Nothing beats the feeling of slightly warm slightly waxed paper telling you how much more money you’ve made. Toasty.

One important demographic that you might be wondering about is gender. There were no correlations found between number of votes for a political party in an electorate and the number of males/females in an electorate. This is largely due to the fact that we’re dealing at this rather coarse granularity, and all of the electorates have a roughly 49/51 male/female split, meaning that there are few differences between electorates.

You might also wonder why Labour, and to a lesser degree National, do not feature in these statistics a lot. The correlations are good for picking out odd relationships that would not appear if all characteristics were randomly (normally) distributed, which are typically relationships that only exist for a few categories of a demographic. Since Labour and National have relatively broad appeal, they largely attract people from all demographic groups, enough that it makes it statistically difficult to extract a relationship.

That’s it for demographic voting correlations. There were a lot more that could have been discussed, but a lot were quite specific (for example, 60-64 year old females who speak Tagalog) and probably not all that useful to discuss at such a broad level. Interesting? Entirely useless? How you interpret the statistics is ultimately up to you.

Okay, if you wanted some more, there's an appendix with some other pointless(ly fun) correlations.

But wait, there's more! Now you can see the data yourself, and pick your choice of political party and demographic variable - I made a visualisation, available here: andrewtzchendata.pythonanywhere.com (Note that it is a work in progress, so if it is completely broken when you try to look at it, try again in a few minutes).

Party Vote Demographics: Appendix II - Other pointless(ly fun) correlations

As part of my investigations into electorate demographics and party voting behaviour, I came across some interesting (yet largely irrelevant) variables in the census data and checked for correlations there:

In electorates where more households…
… owned three or more motor vehicles, slightly more people voted for ALCP (r=+0.107)
… owned two motor vehicles, marginally more people voted for the Conservatives (r=+0.017), fewer people voted for Labour (r=-0.316), and slightly fewer people voted for National (r=-0.128)
… owned one motor vehicle, slightly more people voted for the Greens (r=+0.206)
… owned no motor vehicle, marginally more people voted for the Greens (r=+0.061)

In electorates where more people…
… were regular smokers, fewer people voted ACT (r=-0.462)
… were ex-smokers, more people voted for NZ First (r=+0.574), the Maori Party (r=+0.317), DSC (r=+0.310) and Conservative (r=+0.161), and fewer people voted Labour (r=-0.418)
… had never smoked regularly before, more people voted ACT (r=+0.556), Greens (r=+0.354), and the Civilian Party (r=+0.309), slightly more voted for United Future (r=+0.197) and National (r=+0.109), and fewer people voted for the Maori Party (r=-0.195), ALCP (r=-0.406), and NZ First (r=-0.438)

In electorates where more people voted for…
… ACT, more people spoke Hindi (r=+0.331), Northern Chinese (r=+0.920), Yue (r=+0.858), Sinitic not further defined (r=+0.893), Tagalog (r=+0.561), Afrikaans (r=+0.492), and Korean (r=+0.637)
… ALCP, more people spoke English (r=+0.670), and fewer people spoke Samoan (r=-0.344), Hindi (r=-0.526), Northern Chinese (r=-0.540), French (r=-0.283), Yue (r=-0.528), Sinitic not further defined (r=-0.570), German (r=-0.262), Tongan (r=-0.363), Tagalog (r=-0.414), Afrikaans (r=-0.338), Spanish (r=-0.315), and Korean (r=-0.435)
… the Conservatives, more people spoke Afrikaans (r=+0.446) (here’s looking at you Upper Harbour and East Coast Bays), and fewer people spoke None (e.g. too young to talk) (r=-0.604)
… the Greens, more people spoke French (r=+0.851), German (r=+0.827), and Spanish (r=+0.729)
… InternetMANA, more people spoke Maori (r=+0.688)
… Labour, more people spoke Samoan (r=+0.776), Hindi (r=+0.625), and Tongan (r=+0.660)
… the Maori Party, more people spoke Maori (r=+0.795)
… National, more people spoke German (r=+0.349), Afrikaans (r=+0.396), and Korean (r=+0.223)
… NZ First, fewer people spoke Northern Chinese (r=-0.512), Yue (r=-0.477), and Sinitic not further defined (r=-0.526)
… the Civilian Party, more people spoke French (r=+0.467), German (r=+0.461), and Spanish (r=+0.394)
… United Future, more people spoke French (r=+0.411), German (r=+0.536), Tagalog (r=+0.327), and Spanish (r=+0.266)

Party Vote Demographics: Part II

In the last post, I explained how we’re using electorate census data and electorate voting data to find statistical relationships between electorate demographics and party vote. There are plenty of limitations associated with using this data in this way, so all statistics should be interpreted with caution. Just in case you’d forgotten, I’ll say it again: correlation does not imply causation. This post will look at ethnicity, families, and immigration.

“Māori just vote for the Māori Party and Mana”
In electorates where there are more Māori…
… of any age, a lot more people voted for the Māori Party (r≈+0.76) and InternetMANA (r≈+0.68)
… aged 15-44 years old, less people voted for the Conservatives (r≈-0.50)

Discussion: It should be noted that any statistics involving Māori ethnicity are heavily skewed by the inclusion of the Māori electorates in the analysis. The Māori Party received 10-20 times more votes in Māori electorates than General electorates (InternetMANA’s variation was a little less).

“Polynesian families vote Labour and Asians vote right-wing”
In electorates where there are more…
… Pacific Peoples, a lot more people voted for Labour (r=+0.708), and fewer people voted for ALCP (r=-0.251)
… Asians, a lot more people voted for ACT (r=+0.773), slightly fewer people voted for InternetMANA (r=-0.167) and Māori (r=-0.253), and fewer people voted for NZ First (r=-0.546) and ALCP (r=-0.656)

Discussion: Labour attracted much higher party votes in the Pacific Ms – Mana, Mangere, Manukau, Manurewa, and Maungakiekie. ACT attracted more votes in electorates with more Asians - New Lynn, Mt Roskill, Botany, and Pakuranga (and of course Epsom). These areas (which also happen to all be suburban areas in Central and East Auckland) gave NZ First fewer votes, plausibly on the back of the perception within the Asian community that NZ First is a xenophobic party, and also gave ALCP fewer votes, plausibly due to the stronger anti-drug stances held by most south-east Asians. I should note that individuals can choose more than one ethnicity in the census, so they may be counted more than once in the ethnicity statistics but are only responsible for one (or no) vote in the election.

“The bigger the family, the more likely they’ll vote Labour (Working for Families, etc.)”
In electorates where there are more females with…
… no children, more people voted for the Greens (r=+0.539) and the Civilian Party (r=+0.301), slightly more people voted ACT (r=+0.185), and fewer people voted NZ First (r=-0.568)
… one child, slightly more people voted ALCP (r=+0.268), ACT (r=+0.141)
… two children, more people voted for the Conservatives (r=+0.528) and National (r=+0.437), slightly more people voted for United Future (r=+0.153), and fewer people voted Labour (r=-0.538)
… three children, a lot more people voted for NZ First (r=+0.711), and more people voted for the Conservatives (r=+0.386), DSC (r=+0.353), National (r=+0.197), and the Māori Party (r=+0.178)
… four children, a lot more people voted for NZ First (r=+0.616), and fewer people voted National (r=-0.168)
… five children, more people voted for NZ First (r=+0.403), and fewer people voted for the Greens (r=-0.520)
… six or more children, slightly more people voted Labour (r=+0.277)
… an objection to answering about how many children they have, more people voted for NZ First (r=+0.303)

Discussion: It should be noted that these statistics relate to the “number of children born alive” by females, which isn’t necessarily a direct match for family size. For example, “no children” includes single people and young people, who are less likely to have been pregnant. Therefore it seems more reasonable for there to be a correlation between electorates with more people with no children and party vote for the Greens and Civilian, not because the supporters of those parties are opposed to having children, but simply because those parties may be more popular with young people.

I think in general what these statistics show is that the number of children is probably a very poor indicator of party vote. It could possibly be argued that electorates with women with more children vote more left-wing than right-wing, but I think that would be a pretty tenuous argument based on these statistics. I’m not sure why people would object to Statistics NZ knowing anonymously how many children you’ve had (55,199 individuals), but electorates with more of those people also voted more for NZ First, which perhaps suggests that Winston supporters tend not to trust the government with information about them. Maybe.

“Immigrants vote for [insert various statements here]”
In the general electorates where there are more people…
… born in Asia, greatly more people voted for ACT (r=+0.854), and fewer people voted for NZ First (r=-0.620) and ALCP (r=-0.692)
… born in Middle East and Africa, more people voted for ACT (r=+0.679), slightly more people voted for National (r=+0.160), and fewer people voted for ALCP (r=-0.466)
… born in Australia, more people voted for the Greens (r=+0.496) and National (r=+0.443), slightly more people voted for United Future (r=+0.225), the Conservatives (r=+0.142), and the Civilian Party (r=+0.168), and fewer people voted for Labour (r=-0.539)
… born in the Pacific Islands, greatly more people voted for Labour (r=+0.753), and fewer people voted for ALCP (r=-0.436)
… born in North America, greatly more people voted for the Greens (r=+0.840), more people voted for the Civilian Party (r=+0.368) and United Future (r=+0.330), and slightly more people voted for National (r=+0.143)
… arrived to NZ within the last 2 years, more people voted for ACT (r=+0.482), and fewer people voted for ALCP (r=-0.575) and NZ First (r=-0.638)
… arrived to NZ within the last 3-9 years, more people voted for ACT (r≈+0.63), and fewer people voted for the Maori Party (r≈-0.22), NZ First (r≈-0.61), and ALCP (r≈-0.72)
… arrived to NZ within the last 10-19 years, a lot more people voted for ACT (r=+0.834), and fewer people voted for the Maori Party (r=-0.269), NZ First (r=-0.589), and ALCP (r=-0.721)
… born overseas, more people voted for ACT (r≈+0.7), slightly fewer people voted for the Maori Party (r≈-0.2), and a lot fewer people voted for NZ First (r≈-0.6) and ALCP (r≈-0.7)

Discussion: The census reports data for people who were born overseas only for the General Electorates, which we can use as a proxy for immigration. Firstly, electorates with more Asians, Middle East and Africans voted more for ACT, and really didn’t like NZ First and ALCP. (Electorates with more) Australians voted more for the Greens and National and a lot less for Labour (perhaps a contagion effect from the Labor party across the ditch). As covered previously, Pacific Islanders voted more for Labour (and less for ALCP), and (electorates with more) North Americans really liked the Greens! Electorates with recent immigrants (and also less recent immigrants) liked ACT, plausibly due to the more recent influx of Asian, Middle Eastern, and African immigrants, who also heavily disliked NZ First and ALCP. That trend holds for all immigrants in general as well.

Coming up – even MORE demographics (the final dataset I used had over 1,800 variables)!

Party Vote Demographics: Part I

I was thinking about what the “average voter” for each political party looks like, and my marketing research training kicked in. Pull a bunch of demographic data from the census, try to match it to party vote data from the last election, and hey presto, we should be able to build some reasonably interesting profiles.

It turns out that it’s a bit harder than it seems, and as a result there are a lot of caveats. We can’t match individual demographics to individual votes, because if that data was available it would be a reasonably significant breach of privacy. So we have to decide what level of granularity is sufficient for our analysis – after digging through meshblocks and area units, eventually I settled on electorate demographic comparisons; in electorates where there are more people of x category, were there more or less votes for y party?

Note that I am being careful with my wording there (and I will do my best to be careful with my wording throughout these posts, but will inevitably slip up somewhere). Most importantly, these are correlations, which does not imply causation. This is important, so I will say it again – correlation does not imply causation. If you read this post and make unconditional declarative causative statements I will be upset at you. For many of the correlations I found, it is entirely plausible for it to be a coincidence or for there to be some other factor that explains the relationship. It is tempting to say that certain groups of people are more or less likely to vote for a particular party, but we must remind ourselves that these statistics do not imply causation; these statistics cannot prove that the fact that an individual belongs to a particular demographic group causes them to vote for a particular party. I should also note that all discussions in these posts about why the correlations exist are largely guesses/opinions and not scientific.

Just so that I don’t bore the general audience too much, other statisticsy things that I did to try and make things robust and fair are explained in an appendix post for those who are interested.

For each of the correlations, I’ll include the correlation coefficient, or r statistic. This is a measure of how strong the relationship between the two variables is, ranging between -1 and 1. If r is negative, then as one variable increases the other decreases, and if the number is positive then as one variable increases the other also increases. r=0 would indicate exactly no relationship. The larger the magnitude of the number, the stronger the relationship. For example, r=-0.25 would be a weakly negative relationship, r=-0.8 would be a very strongly negative relationship, r=+0.4 would be a reasonably strong positive relationship, and so on.

To give us some direction, I figured maybe what we should do is explore some commonly held stereotypes about the political parties, and see if they were reflected in the demographics and voting statistics. Let’s start with income and age.

“Richer people vote for ACT, the Conservatives, and National, poorer people vote for Labour”
In electorates where there are more people (aged 15 and over)…
… with zero income, more people voted for ACT (r=+0.358), fewer people voted for Democrats for Social Credit (DSC) (r=-0.472)
… in the $10,001-$35,000 income bracket, more people voted for NZ First (r≈+0.6), and slightly more people voted for DSC (r≈+0.25)
… in the $15,001-$25,000 income bracket, slightly more people voted for the Conservatives (r≈+0.23)
… in the $25,001-$30,000 income bracket, fewer people voted for ACT (r=-0.575)
… in the $25,001-$40,000 income bracket, more people voted for Ban1080 (r≈+0.23)
… earning $50,001 or more (per year), slightly more people voted for National (r≈+0.25)
… earning $50,001 or more (per year), more people voted for United Future (r≈+0.4)
… earning $60,001 or more (per year), more people voted for the Greens (r≈+0.5)
… earning $70,001 or more (per year), more people voted for ACT (r≈+0.38)

Discussion: The statistics would suggest that electorates with richer people do vote for ACT and National, but also vote for United Future and interestingly the Greens! The Green relationship in particular is possibly explained by the support for the Greens in central urban areas, especially in Wellington, that also happen to be areas with higher income individuals. Electorates with more low income individuals did not vote more for Labour. Surprisingly, electorates with more people with zero income also had more people vote for ACT, which could possibly be explained by those electorates having more stay-at-home housewives or young students with no income, dependent on the high(er) income of the main breadwinner in the household.

“Old people vote for NZ First”
In electorates where there are more people…
… aged 15-39 years old, fewer people voted for NZ First (r≈-0.4)
… aged 50-79 years old, significantly more people voted for NZ First (r≈+0.7)
… aged 50 years and older, more people voted for the Conservatives (r increasing from +0.209 to +0.65)
… aged 50-84 years old, more people voted for DSC (r≈+0.47)
… aged 60 years and older, fewer people voted for InternetMANA (r decreasing from -0.164 to -0.406)
… aged 45-54 years old, more people voted for the Māori Party (r≈+0.25)
… aged 60 years and older, fewer people voted for the Māori Party (r decreasing from -0.124 to -0.417)
… aged 55 years and older, more people voted for National (r increasing from +0.252 to +0.515)

Discussion: The stereotype largely holds up – electorates with more young people voted less for NZ First, and electorates with more old people voted more for NZ First (Bay of Plenty, Tauranga, Coromandel, and Whangarei). Other “old-friendly” parties included the Conservatives, DSC, and National, while InternetMANA and the Māori Party were less popular in electorates with more people aged 60 years and older.

“Young people are more left-wing”
In electorates where there are more people…
… aged 20-29 years old, slightly more people voted for the Greens (r≈+0.23) and the Civilian Party (r≈+0.16)

Discussion: The Greens do well at attracting the youth vote on the back of long-term sustainability policies, and they had a lot of party votes in student-heavy electorates like Christchurch Central, Dunedin North, Auckland Central, Rongotai, and Wellington Central. Younger people probably also take politics less seriously (or alternatively are more disenfranchised with the system), hence the Civilian Party. It’s a little odd that there is no statistical relationship between young people and Labour though.

The interesting thing (at least to me) about these statistics is how they reveal people’s biases. The statistics are hard cold truth, but how we choose to interpret the statistics is another matter. Whether we allow ourselves to question our biases or just selectively reinforce them is something I find fascinating. Coming up – more voter demographics!

Party Vote Demographics: Appendix I - Extra Statistics Chat

The census dataset that I am using is http://www.stats.govt.nz/Census/2013-census/data-tables/electorate-tables.aspx (shout out to NZ Herald Data Editor Harkanwal Singh), which conveniently provides numbers at the electorate level. I can also recommend the data files produced from that dataset by Jonathan Marshall which is available on Github which is much nicer to work with. The vote numbers come from electionresults.org.nz, pulled and processed with some code kindly provided by Chuan-Zheng Lee.

After rejecting some of the less interesting variables provided in the census data (mostly about employment), I was left with only 1815 variables to check. Yes, that’s still a lot of variables. When I initially set the analysis to return correlations that were statistically significant at the 5% level of significance, it returned about 12,000 correlations. After talking to Chuan-Zheng I realised that I was dumb and forgot that I was actually working with the entire population, where “statistically significant” no longer makes sense because we’re not working with samples. So I got rid of statistical significance in terms of individual correlations entirely.

A straight bivariate correlation analysis would return a lot of misleading correlations, because in general, if there are more people in an electorate, there are also more people voting, and more people in any demographic category. To counter this I followed some internet advice and used an equation from Steiger (1980) to determine if there was a statistically significant difference between the two correlations:
- r₁₂ the number of people in the electorate vs the number of votes for a particular party
- r₁₃ the number of votes for a particular party vs the number of people in a particular demographic group
To help ensure that the claims made were strong and unlikely to be explained by the variation in electorate populations, I set the analysis to only return correlations where the difference between r₁₂ and r₁₃ was statistically significant at the 0.1% level.

Additionally, any correlations that had an r between -0.1 and 0.1 were removed and analysed separately, as they are so close to 0 that the relationship is likely that there is no relationship between the two variables (which may be statistically significant but not all that interesting for most of what we’re looking at here).

I should probably note somewhere (and here is as good a place as any) that the sample size in most cases was 71 (all the general electorates + Maori electorates), except for the immigrant data which was not available for the Maori electorates (and thus the sample size was reduced to 64).

Where I’ve used r≈ instead of r=, it’s because I’ve actually combined a couple of correlations for ease of communication. For example, “people earning $70,001 or more” is actually “people earning $70,001-$100,000, people earning $100,000-$150,000, and people earning $150,001 or more”, but I didn’t want to manually group that data because hey, I got hungry and needed time to make dinner. It’s an approximation of the strength of relationship at least, and I guess is intended to be more directional than accurate magnitudinally (magnitude-wise? in terms of magnitude?).

Everything was done in Python (without the use of NumPy or SciPy because as it turns out I would rather spend a few hours torturing myself trying to figure out how to implement the algorithms from scratch than spend a few minutes installing some commonly used modules). In retrospect I should have just pulled out R. Fun (questionable) fact: the number of R User Group meetings per month worldwide is (on average) increasing at a rate of 0.6 meetings per month since November 2008.

A Chen by any other name

This post originally appeared on The Co-Op, a blog of young(ish) writers of varying ideological and political perspectives.

On Saturday, the New Zealand Herald published an “special investigation” originally titled Who’s buying our houses?. In this article, data for almost 4,000 house sales in Auckland over the course of three months (Feb-Apr 2015) from one real estate firm is analysed in conjunction with Census 2013 data. It is claimed by the Labour Party that “ethnic Chinese” account for 9% of the Auckland population, but accounted for 39.5% of house transactions during that period. The kicker? The ethnicity of the individuals involved in the house transactions was based on surnames. A complicated sounding Bayesian analysis (which essentially means using probability and past data to predict future data) is used to justify how ethnicity is derived from surnames. In the process, we compare samples of 1.4 million and 4,000 and pretend that everything is okay. The claim is that “Chinese names make up about eight out of the 20 most common ones among Auckland residents but fill 19 of the top 20 places for house buyers.” Ignoring that a couple of the last names (Kim, Singh, Saur) aren't even Chinese, the insinuation is that Chinese people are buying more houses than they should, assuming that everyone should have an equal ability to buy a home.

Keith Ng rips apart the statistics here (tldnr there are many poor assumptions being made to arrive at this conclusion), and Rob Salmond, who claims to have done the quantitative analysis for the Labour Party, defends his work here (tldnr statistics isn’t perfect and this analysis is good enough).

The people who went through the data and reported it are quick to point out the caveats. House buyers sometimes buy via intermediaries such as trusts or lawyers. The data comes from only one agency that may be a biased source of information. The data is for a short period of time where seasonal effects may dominate. Surnames cannot prove whether a buyer is a foreigner or a local.

The people who did the analysis now seem to acknowledge that the data is poor. We feed garbage into the analysis, and we cannot expect anything other than garbage to come out. On the one level, the analysis and assertions made about race and surnames are deeply offensive and frankly unnecessary. On another level, this is simply poor statistics. The analysts should have said “look, we can’t make solid claims from this data, we’re going to get attacked on this”. Instead, they doubled down and said “we did our best with the data”. The conclusions drawn are meant to feed into a national policy debate and be relied upon – working with bad data is only going to lead to bad outcomes for everyone involved. With the many people involved, from the Labour Party to external statisticians to the NZ Herald, someone, somewhere along the line, should have said “we can’t publish this, it’s just not good enough.” That comes before we even get to "we can't publish this, it's offensive".

Knowing all of this, the Labour Party continued anyway and pushed housing spokesperson Phil Twyford forward. He told the NZ Herald: "It's staggering evidence that strongly suggests there's a significant offshore Chinese presence in the Auckland real estate market.” He told The Nation on TV3: “Nearly 40% of houses sold in that period went to people of Chinese descent.” He said on Twitter: “Just look at the numbers. Chinese NZers 9% Akl popn. People of Chinese descent bought 39.5% of houses sold by major Akl real estate firm. This is foreign money.” The message from Twyford is clear – foreigners, specifically Chinese, are responsible for driving up house prices in Auckland. How do we know? Because we looked at their last names.

Maybe the end conclusion is accurate and there is a problem that we need to deal with. But how we got there, and who this targets, is hugely problematic. According to the NZ Herald article, my last name, Chen, is the 6th most common last name in Auckland, while it is the 4th most common last name of people buying homes. The assertion is that the Chinese are buying more property than they should, driving up house prices and creating a property crisis. This implies that people with my last name are a problem.

I’ve written on being a 2nd generation Asian New Zealander before, and how being stuck between two cultures makes it difficult for us to “belong”. To be told that because of my last name, something I did not choose, that I am a problem for “honest hardworking Kiwis”, is crushing. My last name does not singularly define me. Using my last name to determine my “ethnicity” is both inaccurate and offensive. My last name should not indicate whether or not I am more or less likely to buy property in this country. I have roots in Taiwan, but I was born in New Zealand; just because my face looks Chinese and my last name sounds Chinese should not disqualify me from being able to live my life here.

This sort of thing does affect our lives. It feeds into how we are perceived as Asian New Zealanders (and to be clear that’s all Asian New Zealanders because racists tend to not bother to ascertain whether you’re Chinese or not before forming a view about you). We are going to increasingly be criticised and challenged just for trying to live our lives, because someone thought it would be a good idea to use surnames as a determinant of ethnicity. The NZ Herald article even admits that only 40% of people in Auckland with the last name Lee are Chinese. The entire analysis is based on shoddy assumptions (even if the analysis of it is good), but the statistics and conclusions drawn will make people feel more secure in their prejudices and make them feel more justified when they say "yeah, those Chinese are buying too many homes".

Most importantly, it makes me feel like I do not belong. It makes me feel like a drain on society, that I am somehow contributing to a problem when I have done my best for a country that I love. No matter how hard I work, I will always carry my last name with me, and if that is going to cause analysts and political parties to think that I contribute to a housing crisis, then there is little I can do. I can only throw my hands in the air at the futility of it all.

I am incredibly grateful for the opportunity to be educated, find employment, and live in this country. I have seen that our society has become more progressive and accepting as I’ve grown up. I want to be a fully contributing and participating member of this society. I don’t want to be part of the problem, I want to help. Phil Twyford is a good person who genuinely wants to make New Zealand better, and has said that he doesn’t want to offend local Chinese New Zealanders. I know that you didn’t intend to hurt people like me, but unfortunately we’re hurt all the same. To Phil and the rest of the Labour Party: if we have a housing problem, let’s talk about it, but let’s not make this a race/ethnicity problem too. This is not how we fix the problem, this is not how we get to a better New Zealand. My last name may be Chinese, but my identity is Kiwi.

What makes a symbol?

This post originally appeared on The Co-Op, a blog of young(ish) writers of varying ideological and political perspectives.

Symbols are visual metaphors – we see something, and it evokes a set of pre-conceived knowledge. We see ☮ and we think peace, anti-war, pacifist protests of the 70s and 80s, and all the free love that went along with that. It’s a symbol that was used by British nuclear disarmament activists made from the semaphore signals for the letters N and D, later used by anti-war campaigners in the US, anti-apartheid activists in South Africa, crossing national and cultural boundaries around the world. A simple symbol conveys a lot of information – after all, a picture is worth a thousand words. 

A new flag is just another type of symbol – a visual image that should resoundly say “New Zealand” in a way that transcends language and geographic barriers. It should be something that we are proud of, something that when we carry overseas is immediately recognisable. When someone sees the flag, they should think “that’s a good country”. It should evoke some positive emotion, it should bring forth enjoyable memories, it should create some intangible sense that the country that the flag represents is of value. A new flag is as much a marketing exercise as it is about national identity.

In some ways it’s not too important what the actual symbol is, as long as it can be identified as uniquely New Zealand. That’s a good argument for why we should change the flag in the first place – the similarity to Australia’s flag does leave room for confusion, and from a branding perspective that leaves us in a dangerous place. Even with the status quo, New Zealanders do not proudly stand beside their flag – whether it’s at sporting events or when we’re selling tourism, the white fern on a black background features more commonly than our actual flag. For large swathes of the world and even large chunks of New Zealand, the existing flag evokes nothing. That’s something we need to fix.

Of course, symbols are not always independent or mutually exclusive. A flag is a place for multiple symbols to melt together, fighting for space on a limited canvas. The current flag perhaps uses symbols that do not exemplify New Zealand well, from the imperialistic vestige of the Union Jack to the naval waypoint of the Southern Cross (that we can’t even see half the time because of all the clouds). Most New Zealanders cannot relate to these symbols, and most foreigners cannot relate these symbols back to New Zealand. We’re long overdue for a rebrand.

For all the opponents who argue “why now?” the answer is that now is as good a time as any. We can postpone this indefinitely, but the longer we leave it, the longer our international image will be inhibited. We are lucky that we live in a country that doesn’t have the patriotic brainwashing of many others, and that we can even bring up the idea of changing the flag without being completely ostracised from society. The longer we leave the flag, the longer its legacy will last and the harder it will be to change. We have far better symbols than the existing flag, symbols that shout “NEW ZEALAND” to casual passers-by, symbols that New Zealanders are proud to show off to the world. Why should we continue to settle for a flag that poorly reflects who we are?

For all the opponents who argue that this process is costing too much, there’s no answer that will make them happy. Spend too little on the process and get accused of being undemocratic, spend too much and get accused of wasting public funds. How much is the right amount to spend? If we’re going through this process, it is far better to spend too much and get the flag right, than to spend too little and come up with a poor replacement. Let me be clear - we’re not going to be able to make everyone happy. That’s an unfortunate byproduct of politics and democracy, and sometimes compromises are not possible. There are indeed other important issues that need to be addressed, from poverty to housing to education. But our national identity is also similarly crucial, and getting this wrong has long lasting implications.

I don’t know what a new flag should look like. What I do know is that we have uniquely New Zealand symbols, from the Kiwi to the Silver Fern, that tell the world that New Zealanders aren’t far behind. When our young people go on their overseas experiences, I want a flag that makes the locals welcome the visitors with warm hugs. When our business people attend conferences and expos, I want a flag that makes people open up their wallets and trust that their money will be in good hands. When our team marches into the opening ceremony of the Olympic games, I want a flag that makes the crowd cheer louder than they cheer for any other country. We deserve a uniquely New Zealand flag to join our bag of symbols, a symbol that we can be proud of.