Hi all, I am a month slow on noting this as I haven’t been reading blogs over the past couple of months – so just pointing out now that Paul Walker is back blogging over at Anti-Dismal again. I’d suggest heading off and reading this recent post.
Truly, the link between factors such as agglomeration, scale, productivity, and dispersion of income is a pretty danged important issue – and one that keeps being looked past when discussing inequality trends IMO.
Via Owen Williams on Twitter came this gem:
Life in New Zealand
pic.twitter.com/Fnt30ZCKR0
— Owen Williams (@ow) July 22, 2014
This is true, shipping is a pretty big deal. However, Aaron Schiff pointed out another common cost of being in NZ:
@ow @tanya hey, at least this item is available in your country …
— Aaron Schiff (@aschiff) July 22, 2014
This is of course the curse of distance – both from the “production” of goods and from large centres of “consumption” (where the fixed cost of transporting can be spread over more customers). The OECD has discussed this cost before, and NZ’s Productivity Commission also mentions it when discussing why productivity in New Zealand is relatively low.
Nice to see Amazon giving us some concrete examples we can use to discuss the phenomenon though – well nice until you want to buy anything 
First let me cover off the two reasons you have probably clicked on this post:
)I was reading twitter, as you do, when the following tweet popped up:
Believe most people would agree that "politics" is broken and incorporating objectivity into policy via data is a worthy goal. Not Morozov.
— Renee DiResta (@noUpside) July 21, 2014
Objectivity in policy making, more data, rant about politics – how could I disagree! I am an economist, I’m cynical about political parties, I attempt data analysis, and strongly support attempts at objectivity – surely our fine tweeter was talking to my soul.
And yes, data and descriptive analysis to create “knowledge” is undeniably important to the concept of informing policy making.
But I think alarm bells appear whenever politics is termed broken and objectivity is touted as a “solution”. Especially when the critique involved appears to be pointing at someone who tends to say that we can’t just look at ways of breaking down institutions without understanding their purpose – and the ways they actually aid in coordination and welfare. Note: I don’t know if he said something silly today or some such, I just looked on google search and wikipedia – just as a pointer
Here is a good one, straight from twitter:
Without a theory the facts are silent. – Friedrich Hayek
— Dr. Tom Stevens (@DrTomStevens) July 21, 2014
Everywhere we turn nowadays people are talking about Ginis. And sadly, they are not misspelling Genie, they are talking about Gini coefficients.
The reason for this interest in Gini coefficient stems from the fact they are used to measure “inequality” in an income distribution – with books such as the Spirit Level made hay discussing the relationship between Gini coefficients and other social outcomes.
Now I’ve spent a bunch of time talking about the claims (eg for the Spirit Level directly I wrote this and this), but I’ve never written anything directly about the Gini coefficient. There is a good reason for this, while I understand it is a measure of dispersion in a distribution I still had to (and still need to) learn things about the measure and other measures.
However, let me discuss what the Gini coefficient is – or at least one of a multitude of different ways we can view a Gini coefficient.
I have been trying to figure out the latest census figures, especially for sources of population growth. Does anyone know what I am doing wrong?
I calculated the change in population using the post enumeration survey. I counted up the births and deaths from the vital statistics from Infoshare. The difference should be international net migration. In the 2001-2006 period there was a difference, but a small one.
In the latest census, the gap is really quite large. Implied net migration is around 500 people per year, compared to the data that is normally used for international migration (7,500 per year).
Does anyone know why the difference is suddenly so large? Or what I am doing wrong?
Also, sorry I have been missing in action from the blog. Work has been crazy and I was writing this book to, um, agitate people.
