The app is currently illegal in New Zealand

Just because the law hasn’t kept up with technological innovation doesn’t make Uber a bad thing. Now the real fear mongering comes when he talks about Uber’s “surge pricing”

The metering system isn’t authorised and they don’t want to change it because

they want to be able to bring in surge pricing. For those people who complain about the cost of taxi fares, you ain’t seen nothing yet.It’s our job to educate the drivers and the public that

this is sugar-coated poison.

What is surge pricing? I’ll let Uber themselves explain it:

Without a surge pricing mechanism, there is no way to clear the market. Fixed or capped pricing, and you have the taxi problem on NYE—no taxis available with people waiting hours to get a ride or left to stagger home through the streets on a long night out.By *raising* the price you *increase* the number of cars on the road and maximize the number of safe convenient rides. Nobody is required to take an Uber, but having a reliable option is what we’re shooting for

So yes, surge pricing means Uber is really expensive when there is a large demand for car rides. You might pay more $ than you would for normal taxi at the same time, but this ignores the non financial cost you might incur waiting for a normal taxi. So we incur the opportunity cost today when we are trying to find a cab on Saturday night. Surge pricing gives you the option to pay a premium to avoid that. In total it may not be more “expensive” depending on how you value your time. Is that a bad thing? You still have the option of catching a normal cab….I can think of a few nights where surge pricing would have come in mighty handy.

**Update**: by pure coincidence Jesse Mulligan just tweeted the following from LA…turns out Uber can be cheaper than taxis too, no wonder the taxi industry is scared!

]]>Taxi LAX to Venice $49. @uber Venice to LAX $13.

— Jesse Mulligan (@JesseMulligan) July 30, 2014

]]>It’s nearly impossible to overstate the value that economists ascribe to cleverness. (Like most obsessions, this one is not altogether healthy.)

However, there is no fun in leaving it there. There is one part of the statement I want to be pedantic about:

Wage inflation is subdued, reflecting recent low inflation outcomes, increased labour force participation, and strong net immigration.

There are two parts I want to discuss here:

- Increased labour force participation: The Bank is essentially saying that wage inflation is subdued, relative to what we would expect given the increase in employment, due to the fact that labour force participation rose. They are right, totally and completely – labour demand shifted right, and the supply curve was such that most of the change came in quantity not price, neat! However, this can give a misleading impression of the future if we don’t read it carefully – let us not forget that labour force participation rates are at a record high at the moment. As a result, the “capacity” in the economy is more limited – and future lifts in labour demand are likely to lead to nominal wage pressures (note this isn’t the same as higher real wages per se – but more like an increase in inflation expectations) than lifts in employment. This is indeed what the Bank was hinting at with the statement prior “Inflation remains moderate, but strong growth in output has been absorbing spare capacity. This is expected to add to non-tradables inflation.”
- Strong net migration: Hold on a second. We keep being told that strong net migration is pushing up inflationary pressures. Now we are being told that net migration reduced inflationary pressures (note that “wage inflation”, again not real wage growth, is a lot closer to real inflation, and real inflation expectations, than a point in times annual increase in the CPI). Higher population growth does indeed increase “demand” and “supply” so the relevance to monetary policy itself is indeterminate.

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.

]]>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

]]>- The question mark is on purpose – even though it sounds like a statement. In the end, these are issues of balance rather than black and white rights and wrongs. Then again, maybe I’m biased as I see myself as a technocrat individual
- Technocracy is an actual term for a nation governed by technocrats – I didn’t know this when I wrote it (although I did guess )

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

Policy conclusions are not, and cannot be, objective. Descriptive analysis can get as close as we can manage, but we need to apply value judgments (as well as theory which may not give a unique solution in the first place faced with the data) to get a policy conclusion. The value judgments show up when we define a “problem” – and if we aren’t clear with them we can make some awful value judgments.

This is why economics is the way it is, and why value is treated the way it is. This is why the use of economic language of, for example, “tackling obesity” is often misplaced, and why the value judgments being used in many of these types of crusades are uncomfortable when we actually consider them.

Thinking that objective, data driven, analysis can give us our policy solutions alone is a dogmatic position that doesn’t hold up to scrutiny – it is just as bad as those who carefully think through the current institutional structure of groups but then refuse to look at data or dynamic relationships in said groups. Or when someone spends a long time discussing why something fits in as a “problem”, but refuses to consult real world data when making the “solution”. Again all these sides are dogmatic – and I see all of us inadvertently slip into one of these camps at times, it is a natural part of trying to understand such huge issues as a single individual!

Real policy making involves problem definition, based on a multi-dimension ethical view (Kolm, Sen) – the problem definition isn’t something we just pluck out of the air to solve, but in itself is a whole area of analysis! Given that, we can determine what to measure, how to interpret the data, the appropriate theory, and do a “transparent” analysis that provides information – no analysis can be truly objective, but we get as close as we can when all the value judgments (assumptions) involved are clear.

Given that, the two processes of definition and analysis aid each other and it becomes important to do a third step – persuade the general public that you are providing them with knowledge, so they can decide whether this is a decision they agree with. We are after all, a democracy.

I would note here that persuasion isn’t about hiding value judgments that make our arguments untenable to people so they vote for them – it is about giving them a weighted view of the value judgments so that they can decide whether they agree or not. If the public doesn’t view something as a “problem” when we do, it is disappointing for the researcher – but again, we are not dictators and shouldn’t be.

]]>]]>Without a theory the facts are silent. – Friedrich Hayek

— Dr. Tom Stevens (@DrTomStevens) July 21, 2014

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.

Forget for a second that the variable of interest is income – and let us just think that there is some data we are trying to discuss. Many of you will be used to the idea of the mean of our data and the variance of our data – and the relationship this has to the “population” values.

We can think of these measures more generally in terms of moment generating functions – where these moments in some sense describe the distribution in terms of the mean, variance, skew, and kurtosis. This is very useful stuff, and can give us a solid understanding of what sort of data we have in front of us.

However, when we want summary indicators we know we have a bit of an issue with the data set we are looking at – it is right skewed. In other words, there is a very long right tail for our income distribution, and (if the data is unimodal – which it isn’t ) we will have a situation where mode<median<mean. In this case, the central tendency we are interested in discussing for the distribution may not be appropriately described by the mean – hence why it is so common to discuss median income when talking about income distributions.

Cool. However, given this it is still very common for us to turn around and think in terms of “variance” when discussing the statistical dispersion of a series – even when we know we have a situation where the data has a right tail and we discuss the median as our summary statistic for centrality.

Although the variance is commonly defined as the squared difference between an expected random variable from the distribution and its mean, the variance can be rewritten to be independent of the mean – as a result, it is common to keep using the variance to describe statistical dispersion. However, there are a couple of reasons we may not want to use the variance when discussing income inequality as a type of statistical dispersion.

- The most common complaint is that we are interested in “relative” inequality as it is “objective” (this term is in inverted commas on purpose – read it as saying the value judgments are clear, making analysis easier). As a result, we want our measure to be normalised (dimensionless or scale invariant). In this case, it would be common to use the coefficient of variation.
- The variance uses a different “distance function” than other measures of dispersion – in some cases, the implied distance function used by other measures of dispersion is more appropriate.

An alternative in this case is to look at the expected absolute difference between two realisations of a random variable from the given distribution. This is the Gini mean difference.

Both the variance and the GMD can be written in terms of weighted average of the difference between adjacent observations. However, it turns out the distance function that is used by the variance puts greater weight on extreme observations than the Gini mean difference does (where the Gini coefficient is a normalised version of the GMD) – similar to the concern we had about using the mean instead of the median.

This alone doesn’t tell us we should use the GMD instead of the variance – but in the same way we may sometimes prefer the median to the mean to give us a summary measure of something about the distribution, there are times where the GMD (and the Gini coefficient) gives us a more useful summary measure of dispersion than the variance.

For those interested in more details about why we’d use the GMD instead of the variance in some economic applications, and for those who actually want to look through the working, I suggest having a peek at the Chapter I linked at the start of this post.

**Yawn**

The key point I’m trying to get across here is that Gini measures are not mystical values that tell us what is fair or just – they are a certain measure of statistical dispersion, one that bears a relationship to measures such as the variance. We can only interpret what these mean if we can understand the mechanism behind them – namely, why is income distributed in this way. This is the most important step, and yet often seems to be the one people are most happy to just ad hocly throw together

In other words, the income distribution is an outcome from some process which involves individual choice and policy. We need to understand “how” this happened in order to describe what the counterfactual position would be if we changed policy. This gives us our description of policy. It is only then by applying value judgments that we can say one outcome is better to another. Merely running around after a measure of statistical dispersion misses the point that there may well be trade-offs between some outcomes and the inequality measure, and that some inequality associated with this measure may be “good”.

]]>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.

]]>There are few songs that illustrate the give an example of status good behaviour than “Mercedes Benz”

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