Last week was the ‘potato vacation’ (kartoffel-ferien) at Aarhus University, where my wife and I have been ensconced since September. I must say that, compared to the Americans, the Danes work noticeably shorter hours. First, they have much longer vacations. In addition to the potato week in the Fall (so named because in the old days children were expected to help with the potato harvest), there is also a Spring week off.
Then, the Danes take a week off at Christmas and at Easter and, of course, a long vacation in summer. Over the years I learned not to expect any replies from my European colleagues to e-mails sent in July and August.
When they are not on vacation, the Danes don’t seem to work particularly long hours either. Our apartment is on campus, so I can easily track inflows and outflows. The parking lot fills up between 8 and 9 in the morning, just as in the US, but the exodus here begins earlier, right at 4 pm. After 5 pm the campus is completely deserted.
And the wildlife comes out. Really!
A campus hare (picture taken from our apartment balcony)
In the city, on the other hand, it is not uncommon to see young couples with one or two children in strollers enjoying the Fall weather on weekday afternoons (we’ve been lucky with uncommonly sunny weather over most of September and October). I wonder, does this relaxed way of life help to explain why Denmark enjoys one of the highest fertility rates in Europe?
I do not mean these observation to be negative in any sense (actually, it’s kind of refreshing to see people enjoying leisure and not working themselves to death). They are offered, rather, in the spirit of scientific inquiry. The question is, if the Danes, Norwegians, and other Nordics work so little, how come they enjoy such a high standard of life?
An indirect answer to this question was seemingly provided in one of the talks at the Oslo workshop a week ago. The speaker, talking about Norwegians in this instance, claimed that, while Norwegians are internally not very competitive, they compete fiercely on the international markets. And that competition ensures high labor productivity in Norway. Furthermore, high labor productivity somehow spills from economic sectors exposed to international competition into protected sectors, e.g. banking services (I did not get the mechanism of this, however).
Sounds very good, actually. I particularly liked the implied logic of multilevel selection: Norwegians cooperate together to better compete against non-Norwegians.
The speaker then compared Norwegian and American approaches to banking. Apparently, the Norwegians have done away with paper checks many years ago, because they are very inefficient (they have to be processed by hand). Instead they switched to electronic transactions.
Again, sounds very good. However, the reality is otherwise. At least, I can report on my experiences with the Danish banking, although it is not beyond the realm of possible that the Norwegian banking industry indeed works much better.
We’ve been here six weeks, and I only just now managed to pay the rent for our apartment. And it was quite a saga. First, soon after we arrived on September 1, I took the bill and went to the nearest Danske Bank branch to pay it. But the nice Dane there explained to me that they only deal with investments, they can’t deal with such mundane things as paying bills. That actually turned to be incorrect; people here speak English so well that when use words differently it can trip you up. After I went to two other banks, I finally found out that they simply do not deal with cash, or checks, or bills. There was only one place, central branch of Danske Bank (DB) in downtown Aarhus, that could help me. So I went there, and learned that they (1) couldn’t take my credit card, (2) they couldn’t take my debit card, (3) they couldn’t handle a transfer from a US bank, but (4) they would accept cash. However, (5) they could only accept cash amount of 7,000 kroner or less, and the bill was for more than that. In other words, the only way I could pay this bill was by getting a Danish bank account.
To be continued: Part II
Disclaimer: This blog should not be construed as a critique of the Danish people and Denmark. That would be quite ungrateful, because I am here as a result of an invitation by my Danish colleagues. Furthermore, my wife and I are greatly enjoying our sojourn here. It’s also a great opportunity to observe another society from the inside. These remarks should be taken in the spirit of scientific inquiry.
Your account of your experience with the banking system actually seems to fit the general Norwegian model you discuss i.e. it’s probably very convenient for Norwegians but not so for foreigners. Incidentally, I have a similar experience quite frequently when dealing with institutions in Japan!
One reason that norwegians are better of with less work is that in the USA, the productivity of the people has been seconded by the elites via the FIRE economy. Here is a nice presentation on inequality in the USA
As you in your work are showing immigration is a major cause of elite manipulation. It is also a type of bait and switch whilst in the media emphasising racial equality between white and black, the elites have alterred the balance of power and wealth strongly to themselves. Globalisation has been also part of that same story. In the 1960’s the USA was essentially a homogenous country with gini coefficients almost equal to those in communist countries….and effectively more equal once you include the effect of blat (corruption). Now the USA is one of the most unequal countries in the world with very low rates of cross class movement and an entrenched aristocracies
John, thanks very much for the video. As a highly numerate person (sorry about sounding so arrogant, but facts are facts) I knew all this. But this video, I think, makes a very effective point to people who are not as steeped in numbers.
“In the 1960′s the USA was essentially a homogenous country with gini coefficients almost equal to those in communist countries”
I wouldn’t go that far. Actually I’m not sure there even is that much data for gini coefficients before 1990s for … most countries, never mind the communist ones. Calculating the gini requires much more quality detailed data than just calculating average income and in order to be comparable across countries it has to be constructed in the same way (same thing of course is technically true for averages but the gini is a lot more sensitive to measurement error).
The main data set of this nature that I’m aware of is “Measuring Income Inequality” by Deiniger and Squire, 1996. That does have some sporadic numbers for the time period you’re talking about, and a more consistent series for US (and UK). This data is a bit old but it’s still the only comprehensive one going back that far and the estimates have not been substantially revised as far as I know.
In this data set, for the 1960’s the US gini was in the mid 30’s. Britain was in mid 20’s. Hungary in 1962 was about 26. Yugoslavia 31 in 1963. Bulgaria in 1963 22.5. Czechoslovakia in 1958 27.2 and 22.6 in 1965. Earliest number for Poland is 1976 at 25.8. Earliest for Soviet Union is 1980 at 24.56.
Interestingly, some of the highest inequality in developed world was in … France (in the 40’s). Japan (upper 30’s) was pretty high too if you consider the Japan of 1960 “developed”. And even the Scandinavian countries were pretty high – comparable to US (Sweden) or slightly above it (Norway)
Of course what matters is what happened since then. In Western Europe, including Scandinavia that gini fell, at least until recently. in US, since the mid 1970s, it rose. The “communist countries” pretty much stayed the same until the early 1990s. (There’s also an issue with decomposability of the gini index which tends to bias larger countries, like US, towards larger ginis but that doesn’t affect the general time trends).
Now, if you replaced “USA” with “UK” in your statement, you’d be right.
That’s a nice video btw.
Doing just fine. Nordic banks are profitable and it’s all that matters for banks.
STOCKHOLM | Tue Oct 22, 2013 6:35am EDT
(Reuters) – Growth in core lending activities at Swedbank (SWEDa.ST) eased worries about profitability at Nordic banks in the third quarter, sending shares across the sector higher on Tuesday.
Nordic banks have weathered the crisis of the last five years well. Growth is outpacing the rest of Europe, low rates have kept credit losses muted
It seems that there is confusion between two very different measures of economic inequality: inequality of wealth distribution and inequality of income distribution.
For income inequality we have (Gini index, World Bank data):
Denmark (24) Sweden (25) Norway (25.8) US (45)
However for wealth distribution we have (Gini index, World Bank data):
Norway (63.3) Sweden (74.2) US (80.1) Denmark (80.8)
Accordingly, Denmark has more inequality in the wealth distribution than US has! At the same time, Denmark demonstrates one of the most equal income distributions. Perhaps this huge gap is “the paradox of Denmark”.
More precisely, the question is how it was possible to make super-elite pay huge taxes on the comfortable existence of a “middle class.”
source for gini data was “economics for a developing world” michael todaro. The figures were about .32 for both USA and UK with about .26 for poland and czechoslovakia.
Different sources do give substantially different GINI coefficients for the USA .
Most likely it is not different sources of data, but it is a different measure of income inequality. There are at least four different measures: (Gini1) After-tax income inequality, (Gini2) pre-tax income inequality, (Gini3) income inequality with illegal immigrants, (Gini4) income inequality with illegal immigrants and offshore funds.
There is a strict inequality for these measures:
Gini1< Gini2< Gini3< Gini4
That leads to much confusion.
This is good.
General public would be served well with a presentation showing (with time graphs preferably) the differences between the lot.
Does any such thing exist?
Folks, thanks very much for this great discussion. Let me just say that I hate the Gini index. It’s completely unintuitive, even for a highly numerate person :). So let me say this. I grew up in the Soviet Union. I don’t idealize it. But it was much, much more egalitarian than even the USA of the 1960s, which was much, much more egalitarian than the US of today.
And that concerns the distribution of incomes. Distribution of wealth is a completely different story. BTW, how do you even calculate the Gini index, if a substantial proportion of population has negative wealth??
Whether we like it or not, we can not avoid the topic of inequality and measures of inequality. Gini coefficient is not the worst choice, and it can be quite intuitive, if you keep in mind several empirical regularities. Values of the coefficient between 0 and 0.5 describe unimodal (bell-shaped) distribution of income, while values greater than 0.5 indicate a monotonic (pyramid-like) distribution.
In other words, if GiniGini>0.5 there is only an “intermediate” (between the rich and the poor) segment of the population.
So, this Gini coefficient is directly related to the key political question of the modern US (according to Fukuyama), the question about fate of the “middle class”.
The irony of the current situation is that if you refer to the Gini1(after-tax income inequality) everything is all right with the “middle class, but if you take into account the Gini4 (income inequality with illegal immigrants and offshore funds) the “middle class” is already gone.
Such is the Schrödinger’s cat situation:)
I agree that we don’t want to avoid the issue of inequality – a lot of my blogs are about it. What I argue is that the Gini coefficient is a very bad measure, and that there are much better ones. What’s wrong with calculating how much of the total pie (in terms of either income or wealth) goes to the top 1 percent, top 0.1 percent, etc? That’s much more intuitive.
But even better is to show the whole distribution, as the video, which John pointed us to, does.
Technically, the two representations of data (“distribution of money among the people” and “the distribution of people on the monetary scale”) are equivalent. If we have some data we can present them in one form or another with no loss of information. However, the second view allows us to see the “class structure” of society, such as the presence or absence of the “middle class”. (Gini0.5)
While the first vision, loses sight on the class structure. It’s not very good, because we are primarily interested in major “class structure changes” (transition from a uni-modal to monotonic distribution) in the society, rather than a change of inequality itself.
My major point is that it is much better to see the whole distribution, rather than attempt to summarize it with a single number. Second, Gini is very bad as a single number. Note that when you say how much wealth belongs to the top 10%, 1%, 0.1%, and 0.01% you have summarized the distribution with 4 numbers, which provides more information than a single number.
Additionally, in some rare cases the distribution of incomes or wealth is bimodal, which is even more extreme than a long right tail. Perhaps I’ll write a blog tonight on this.
I’m extremely uncomfortable with Gini in that it appears to be attacking only part of the problem. Applying a mathematical function to a partial measurement doesn’t seem all that useful to me unless it’s only used in the short term in a single place (thus showing change over time with everything else being equal).
For one thing, I don’t see how it deals with the aggregation of power. I don’t doubt that a member of the Soviet nomenklatura was given significant non-income wealth in terms of privilege and housing for example.
Another simple case is that of military hierarchy. A US O-10 might make less money per year than a skilled engineer, but can have thousands of people and a tremendous set of abilities and facilities at their beck and call.
It is obvious that the full distribution is more (or not less) informative than a few numbers describing it. However, the correct choice of those “several numbers” (or measures) may be crucial for determining the dynamics of inequality. Perhaps, it is also clear that there is no perfect for all purposes choice.
For example, using your 4 numbers is very difficult to see the changes in class structure of society.
I think it would be productive to formulate a few basic (intuitively obvious) axioms, such that any measure of social inequality should satisfy.
Looking forward to your future blog on this topic.
BTW, here is another video, this time on Britain: