I want to see whether they’re describing or blaming
I want to see whether they’re describing or blaming
It’s a standard part of the historical tale that working hours went up from feudal times to early Industrial Revolution. Working hours have been gently declining since.
Thus we get stories about medieval peasants getting 70 days holiday a year. Err, no, animal owning peasants do not take 70 days off each year. Those that do rapidly become non-animal owning peasants.
They had 70 holy days a year, something rather different.
The problem with the calculations is that they take account only of the market hours worked. For example, Greg Clark did a monograph about hours worked by peasants. But what he was counting was only the work done on the demesne, the Lord’s Land, in lieu of rent. He didn’t include the work the peasant did on his own land.
My contention would be that total working hours went down in the IR, even as paid market working hours went up.
This would explain one of the basic conundrums about the time. Why in buggery did people put up with it? Under the standard storyline living standards didn’t change for 50 odd years or so. Yet working hours went up. That’s really a decline in living standards therefore. But if total hours, including household production, went down while incomes stayed static then that’s an increase in living standards. Thus we have an incentive for people to put up with the Dark Satanic Mills.
I could write the story now. But it would take a couple of years to really build the proof by constructing proper time use studies. And thus the PhD I would do if I ever do get to retire….
Would love to see the full copy of this:
Human population reduction is not a quick fix for environmental problems
Hmm, it’s actually very boring even though it is edited by Paul Ehrlich.
Came across something over the weekend. Can\’t recall where.
But the writer said that Voltaire\’s view of industry was along the lines that the machines would take all the jobs and this would lead to tyranny.
Anyone know their Voltaire well enough to be able to track that down? For it sounds very like the complaints being made now that the robots are going to take all our jobs and this will lead to tyranny.
The problem with being an autodidact (posh word for untaught) in a subject is that you\’ve no idea at all whether some idea that pops up is original or not. Simply not enough information about what others have been doing.
So, I hope like hell that someone has already thought about this and possibly even given us a measure of how important it is. It\’s obviously true: but it might also be trivial. And if it isn\’t then as I say, I do hope that someone has measureed this and thus I can be told how important it is.
OK, put together a few simple things.
1) Per capita GDP growth pretty much comes from productivity growth.
2) We know from Baumol that increasing prductivity in services is much more difficult than in manufacturing (or agriculture).
3) Services have been growing as a portion of the economy, manufacturing falling.
4) Trend growth rates are now lower than they were 30-50 years ago.
It seems pretty obvious to me that 4) is in part caused by 1-3.
So, has anyone actually tried to measure this? Luis? Chris?
We know that people have generally been getting taller (erm, OK, the average height of those alive has been rising, not that individuals necessarily grow) over the past century or so.
I\’ve been looking around, but cannot find, a time series that tells me the average American height over that time.
I\’ve a basic working assumption which is that something changed around 1980. Ish.
Very roughly indeed, the idea that being stunted by lack of childhood nutrition was something that stopped being a (widespread) problem in the US around or just after WWII. Mebbe a little later. Could even have been the War on Poverty etc of the 60s.
Given human lifespans this doesn\’t start to feed through (sorry) into a change in the average heights of the population until the 80s and obviously it\’s a continuing process still now.
It would also probably be best to have the data broken out by race: as average heights between races are very different and, say, Hispanic immigration might have kept the population average down but not the white/black or Hispanic.
So, does anyone know of such a time series? Average heights in the US by age/race post war?
The purpose is of course terribly flawed. There\’s one statistic out there which changes significantly starting in the 1980s. What I\’m trying to ponder is whether height explains that change…..
I\’m getting confused about a number which is being bandied about.
For example, the wage share of GDP as at page 6 of this.
Which seems remarkably different from the labour share of income here.
And what sparks my interest is this definition of calculating up (or dividing out if you prefer) GDP via the income approach.
And what particulalry sparks my interest is that, well, I\’m not entirely sure that those wage share of GDP figures are telling us quite what the TUC (and others) say it is telling us.
So0, what I\’d be interested in is someone who actually knows this stuff to aid me in stumbling through it.
Now, what I think is true is that labour compensation is one of those bits that go to make up GDP. And the difference between labour compensation and labour wages (which is I think what the TUC is using) is in part the national insurance taxes paid by employers.
compensation of employees: the total remuneration, in cash or in kind, payable by an employer to an employee in return for work done by the latter during the accounting period; the compensation of employees is broken down into: wages and salaries (in cash and in kind); employers\’ social contributions (employers\’ actual social contributions and employers\’ imputed social contributions);
So, a change in the employers\’ national insurance paid is going to lead to a change in the labour wages share of GDP.
Am I right so far?
If I am, then we\’ve had, since 1975 (just the historical date I could find) a rise in employers\’ NI from 8.5% of wages to 13.8% of wages. I think, but am not certain, that the amount at which you start paying has also fallen in real terms, meaning that more wages are now caught in the net. And we\’ve also had the abolition of the cap on employers\’ NI which seems to have taken place in 1985.
Now, this does not mean that we suddenly move 5% of GDP from labour wage share to some other part of the accounting. Because there is that NI free part of wages. Which will reduce the effect: just as the uncapping will increase it.
But, if it is true that the wage share of GDP does not include employers\’ NI, then increases in employers\’ NI are going to reduce the wage share of GDP.
And there\’s a second part too. Mixed income, essentially the incomes of the self-employed, are not included in wage share of GDP either.
mixed income: this is the remuneration for the work carried out by the owner (or by members of his/her family) of an unincorporated enterprise; this is referred to as \’mixed income\’ since it cannot be distinguished from the entrepreneurial profit of the owner;
So, a rise in self-employment will lead to a reduction in the wage share of income as well.
So, and now to the question. The important one that is.
We\’ve most definitely had a change in the wage share of GDP. Yup, we have. From 58% or so in 1955, to 65% or so in 1975, to 53 % or so today.
How much of this is simply due to being an artefact of how the statistic is constructed? How much of it is due to increases in employers\’ national insurance and how much due to an increase in self-employment?
I don\’t actually know that there has been an increase in self-employment but I rather fancy there has.
So, is there anyone out there who can guide me through this? Perhaps it\’s already been done, neatly wrapped up in a nice paper? If not, shouldn\’t it be?
For wouldn\’t it be interesting if the changes in the wage share of GDP were just statistical artefacts resulting from changes in taxation and employment classification, rather than some dastardly plot to increase the returns to either capital or the rich bastards?
I pondered as to whether there was a connection between high marginal tax rates and the substitution of market for household working hours as a result.
It seems that there is a weak connection, but only a weak one.
So does anyone who knows how to do this empirical economics stuff want to pitch in and see if a better handle on the idea is possible?
So, you\’re a bright lot.
Point me at stories and sources for why we subsidise fir and spruce plantations in the UK.
Yes, I know quite a bit, but would love to see what you can point me at.
So, we\’ve a theory. That higher marginal tax rates will lead to people withdrawing their labour from the market.
We expect this to be greatest among women with children. For obvious reasons: the higher the marginal tax rate the more attractive looking after your own children will seem as opposed to working, suffering the tax bite, then paying someone else to do it.
So, to measure this, should we look at female labour force participation rates?
No doubt that (theoretically) high taxes could discourage effort but is this statement empirically relevant? Below is a chart of marginal tax rates (as estimated by the OECD) and the female employment to population ratio for the age range (25-54) for 2010. I have chosen that particular employment to population ratio because it matches the statement in the quote above (the chart looks similar if we look at a different age range or male participation rates).
Well, as he says, doesn\’t really seem to tell us very much.
So, here\’s the task for any budding economist who is looking for a paper to write. Instead of looking at labour force participation, which can be skewed by all sorts of things, cultural influences of course, part time working and so on, let\’s look at what it is that we really want to look at.
Which is we want to know, are these women substituting household production for market production in the face of these high marginal tax rates?
That is the original contention, of course. Not that women (or people in general, as above we just expect women to be more sensitive to this effect) work more or less in total in the face of taxes. But that they substitute away from the taxed activity to untaxed. That untaxed activity could be leisure, of course, or it could be household production.
Now, yes, we do have this information, for the EU at least it is here. How do people spend their time, in personal time, household work, market work and leisure? Split by country and sex (at least, there might be deeper with age group, family structure, not sure).
Marginal tax rates aren\’t that hard to find. So, a paper comparing *both* household and market working hours as against marginal tax rates.
Some of this work has been done in the LIS project (Smeeding is a name to conjure with I think) and I\’ve certainly seen one paper which shows that the average German woman is working more hours in total than the average USian woman. Despite the latter doing many more market working hours.
The result I would expect to see is that the higher the marginal tax rate the more subsitution there is away from market production to household production. And I\’d even expect to see, at times, longer total working hours for the \”same\” living standard at high marginal income tax rates. For market work is subject to the division and specialisation of labour, household production not so much.
To do this one needs to be able to play with Excel and graphs and charts and statistical tests on variance and SD and chi squared and so on. None of which I know how to do so, anyone looking for a paper to write?
That was before the police found the body of a man thought to be one of Japan’s oldest, at 111 years, mummified in his bed, dead for more than three decades. His daughter, now 81, hid his death to continue collecting his monthly pension payments, the police said.
Alarmed, local governments began sending teams to check on other elderly residents. What they found so far has been anything but encouraging.
A woman thought to be Tokyo’s oldest, who would be 113, was last seen in the 1980s. Another woman, who would be the oldest in the world at 125, is also missing, and probably has been for a long time. When city officials tried to visit her at her registered address, they discovered that the site had been turned into a city park, in 1981.
To date, the authorities have been unable to find more than 281 Japanese who had been listed in records as 100 years old or older. Facing a growing public outcry, the country’s health minister, Akira Nagatsuma, said officials would meet with every person listed as 110 or older to verify that they are alive; Tokyo officials made the same promise for the 3,000 or so residents listed as 100 and up.
The effect might be large enough (but probably isn\’t) to change life expectancy figures for the country as a whole.
However, there\’s something else here as well: World Bank, WHO etc figures on life spans are created from the figures that governments themselves provide. So if governmetns are mismeasuring life spans, the international figures are also incorrect.
Which brings me to the subject of Cuba. They really might have the life spans they say they do: but given that they\’re based upon figures that the Cuban Government itself provides, can we be sure about that?
And is there any way to check?
I often have little thoughts (yes, thank you at the back there, very little indeed) and then realise that I\’ve not got the technical skills to even being researching whether they\’re true or not. I might know roughly where the basic information can be found for example, but not know how to put it together.
One that\’s buzzing around at the moment.
Currently we hear a lot that it\’s the UK\’s \”over reliance\” upon finance rather than manufacturing as in, say, Germany, that\’s causing all our problems.
However, I think I recall that Germany\’s recession has been deeper than ours.
So, wouldn\’t a simple little paper like the following be interesting?
Work out the share of each OECD (just to keep things simple) economy that is a) manufacturing and b) finance.
Look at the decline in GDP and the length of time of negative growth.
Compare and contrast the four sets of data.
I have a feeling that countries which had a larger portion of manufacturing had bigger declines in GDP.
Now that would be interesting, wouldn\’t it?