Sex, Drugs and Economics

Saturday, 30 June 2018

What the Blitz can tell us about the cost of land use restrictions

Land use regulations are frequently cited as impediments to urban development, economic growth or employment growth (and they are a frequent topic on Eric Crampton's Offsetting Behaviour blog - see here for his latest post on the topic). The problem with land use regulations is that it can be very difficult to work out what would have happened if the regulations were not in place. Comparing areas with and areas without land use restrictions isn't helpful, since land use restrictions are not random, and may be affected by urban development, economic growth or employment growth - exactly the things you want to test the effect of land use restrictions on!

So, in order to test the impact of land use restrictions, ultimately you need some natural experiment where land use change was more permissive in some neighbourhoods than others, and where that assignment to permissive land use change was applied randomly. In a new working paper, Gerard Dericks (Oxford) and Hans Koster (Vrije Universiteit Amsterdam) use the World War II bombing of London ('the Blitz') as a tool that provides the necessary randomisation (they also have a non-technical summary of their paper here). The buildings that were hit by bombs during the Blitz were effectively random, and after controlling for the distance to the Thames and areas that were specifically targeted by Germans during the Blitz, Dericks and Koster show that the density of bombs was also effectively random. If you doubt this, consider that Battersea Power Station (probably the most important target in London) suffered only one very minor hit, and no bridge over the Thames was struck in the whole of the Blitz. If bombing was non-random, then these high-profile targets would have suffered much worse.

How does this relate to land use regulations? Buildings and neighbourhoods that suffered more bombing were more able to be rebuilt with less restrictive land use. That means that areas with higher density of bomb strikes were randomly assigned to less restrictive land use, and so Deriks and Koster use that to test the effect on land rents and employment density. They find:

...a strong and negative net effect of density frictions on office rents: a one standard deviation increase in bomb density leads to an increase in rents of about 8.5%... Back-of-the-envelope calculations highlight the importance of density frictions: if the Blitz bombings had not taken place, employment density would be about 50% lower and the resulting loss in agglomeration economies would lower total revenues from office space in Greater London by about $4.5 billion per year, equivalent to 1.2% of Greater London's GDP or 39% of its average annual growth rate.

Areas with greater density of bomb strikes, and hence less restrictive land regulation, have taller buildings, higher land rents, and greater employment density. None of this is terribly surprising, but the size of the effects are very large.

These results should be of broader interest, especially in other cities where land use restrictions appear to be holding back development, like Auckland. If you think that Auckland and London are too dissimilar in restrictive land use, consider this small example: Auckland protects view shafts to volcanic cones; London protects view shafts to St Paul's Cathedral.

Some land use restrictions are good and necessary. However, they don't come without cost, and this cost in terms of lost employment and output needs to be taken into consideration.

Thursday, 28 June 2018

The new fuel tax will be regressive, provided you understand what regressive means

I really despair over the economic literacy failures of our government and media. The latest example, reported in the New Zealand Herald this morning:

By late 2020, new fuel taxes will mean Aucklanders are paying an average $5.77 more a week for petrol, according to figures to be released by Government ministers today.

And in a startling revelation, the ministers claim that the wealthier a household is, the more it is likely to pay for petrol. They say the wealthiest 10 per cent of households will pay $7.71 per week more for petrol. Those with the lowest incomes will pay $3.64 a week more.

That's all good so far. Higher income households spend more on most goods (what economists term normal goods) including fuel, and so it makes sense that they would end up paying more of the fuel taxes. It's what comes next that's problematic:

This is a complete reversal of the most common complaint about fuel taxes, which is that they are "regressive". That means, the critics say, they affect poor people more than wealthy people.

Finance Minister Grant Robertson will join Transport Minister Phil Twyford and Associate Transport Minister Julie-Anne Genter this afternoon in Auckland to reveal the details of the new excise levies on fuel...

The MoT figures break the population into 10 segments, or deciles, from poorest (decile 1) to wealthiest (decile 10). They show that the wealthier a household is, the more money it is likely to spend on fuel.

In the first year, the average increase for Aucklanders, who will pay both taxes, is $3.80 per week. Decile 1 Aucklanders will pay on average $2.40, for decile 5 the average will be $3.75 and for decile 10 it will be $5.08.

"It's simply not true that fuel taxes cost low-income families more," Twyford said. "The figures show that the lowest-income families will be paying only a half or even a third as much as those on the highest incomes."

It's hard to tell here if Phil Twyford is being economically illiterate, or deliberately misleading. While it is true that, according to his figures, higher income households will pay more of the tax, that doesn't mean that the tax is not regressive. A regressive tax is one where lower income people pay a higher proportion of their income on the tax than higher income people.

So, you need to compare the tax paid with income to determine if the tax is regressive or not. It isn't enough to simply look at the tax paid by each group, and conclude that the tax is not regressive because higher income people pay more. This will be true of every excise tax on a normal good.

The latest data from the Household Economic Survey I could easily find using my slow hotel internet connection was this data for June 2016 (side note: trying to search for data on the new Statistics NZ website may actually be more difficult than for the old site - that's quite an accomplishment!). It doesn't give average incomes for each decile, but it does tell us the ranges. It also isn't limited to Auckland, but I don't think that will make much difference.

Decile 1 (the lowest income households) goes up to an annual income of $23,800. At that income level, the tax paid ($3.80 per week) would be 0.8% of their annual income (and would be a higher percentage for households with income below $23,800). For decile 10 (the highest income households), the minimum income is $180,200. At that income level, the tax paid ($5.08 per week) would be 0.1% of their annual income (and would be a lower percentage for households with income above $180,200).

Clearly, lower income households will be paying a higher proportion of their income on the tax than higher income households. The fuel tax is a regressive tax. Which just leaves the question: Is Phil Twyford being economically illiterate here, or wilfully misleading us in the hopes we wouldn't notice?

Wednesday, 27 June 2018

Why study economics? Uber data scientist edition...

There is a common misconception that the eventual job title that economics students are studying towards is 'economist', in the same way that engineering students become engineers, or accounting students become accountants. But actually, the vast majority of economics graduates don't get jobs with the title of economist. In my experience, the most common job title is some flavour of 'analyst' (market analyst, business analyst, financial analyst, risk analyst, etc.). However, a growing job title for economics graduates is 'data scientist', as for example in this new advertisement for jobs at Uber. The job description is interesting, and demonstrates a wide range of skills and attributes that economics graduates typically obtain:

Depending on your background and interests, you could focus your work in one of two areas:

Economics: Conduct research to understand our business and driver-partners in the context of the economies in which Uber operates. For example: We know that the flexible work model is very valuable to Uber drivers (see Chen et al. Opens a New Window. , Angrist et al. Opens a New Window. ) and that dynamic pricing is vital in protecting the health and efficiency of the dispatch market (see Castillo et al., “Surge Pricing Solves the Wild Goose Chase”); however, it’s likely that consistency (e.g., of pricing or earnings) also carries some value for riders and drivers. What values should we put on these opposing virtues?

Cities and Urban Mobility: Study Uber's impact on riders and cities around the world with a special focus on different facets of urban mobility. For example: What is the relationship between on-demand transportation and existing public transport systems. Do they complement or compete with each other? Or, does this relationship change depending on external factors? What could these external factors be and how do they change rider behavior?

Somewhat surprisingly, these jobs only require a "bachelor’s degree in Economics, Statistics, Math, Engineering, Computer Science, Transportation Planning, or another quantitative field", rather than a PhD (which has more often been the case for tech jobs for economists). However, the one or more years of quantitative or data science experience that is required suggests that picking up a job as a research assistant while studying, and doing some quality research at honours or Masters level is a pre-requisite.

In any case, this demonstrates that some of the coolest jobs for economics graduates are not as 'economists'.

Tim Harford on opportunity cost

One of the first concepts I cover in my ECONS101 and ECONS102 classes is opportunity cost. It is also one of the most misunderstood concepts in economics. The inability to recognise that every choice comes with an associated cost (economists are fond of the phrase, "there is no free lunch") plagues public policy and business decision-making. And yet, the idea that when you choose something you are giving up something else that you could have chosen instead, should be intuitively obvious to anyone who has ever made a decision.

Tim Harford recently covered opportunity costs, in his usual easy-to-read style:

The principle of an opportunity cost does not at first glance seem hard to understand. If you spend half an hour noodling around on Twitter, when you would otherwise have been reading a book, the lost book-reading time is the opportunity cost of the tweeting. If you decide to buy a fancy belt for £100 instead of a cheaper one for £20, the opportunity cost is the £80 shirt you could otherwise have bought. Everything has a cost: whatever you were going to do instead, but couldn’t.

We should weigh opportunity costs with some care, mentally balancing any expenditure of time or money against what we might do or buy instead. However, observation suggests that this is not how we really behave. Ponder the agonised indecision of a customer in a stereo shop, unable to decide between a $1,000 Pioneer and a $700 Sony. The salesman asks, “Would you rather have the Pioneer, or the Sony and $300 worth of CDs?”, and the indecision evaporates. The Sony it is.

And Harford also explains why understanding opportunity costs is consequential:

Drawing our attention to opportunity costs, no matter how obvious, may change our decisions. The notorious falsehood on the campaign bus used by Vote Leave during the 2016 referendum campaign was well-crafted in this respect: not only could the UK save money by leaving the EU, we were told, but that money could then be spent on the National Health Service.

One could certainly debate the premise — indeed, the referendum campaign sometimes seemed to debate little else — but the conclusion was rock solid: if you have more money to spend, you can indeed spend more money on the NHS. (Just another way in which that bus was a display of marketing genius.)

We would make better decisions if we reminded ourselves about opportunity costs more often and more explicitly. Nowhere is this more true than in the case of time. Many of us have to deal with frequent claims on our time — “Can we meet for coffee so that I can pick your brains?” — and find it hard to say no. Explicitly considering the opportunity cost can help: if I meet for coffee I’ll have to work an hour later, and that means I won’t be able to read my son a story before bedtime.

Notice that in the latter example, the opportunity cost cannot easily be measured in monetary terms (how much is reading your son a story before bedtime worth?). However, in terms of impact on our satisfaction or happiness (what economists term 'utility'), we can make a comparison between these different options. You might also want to consider the costs you are imposing on others (whether monetary or otherwise) - economists refer to this as having social preferences (altruism is one example of social preferences). If your decisions affect others (which many decisions do), then others may face opportunity costs from your choices.

The next time you are making a decision, whether small or large, consider what is being given up to get the option you choose. It might not be measured in monetary terms, but there will always be a cost. You'll then make better decisions, or at least decisions that leave you happier overall.