Category: Data Source
Sweden, Medicare, and what really matters
Tino writes:
Medicare was introduced 1965 in the US. Public health coverage for the elderly existed by 1950 in Sweden, but full universal coverage dates to 1955 in Sweden (a public health insurance was founded in 1891, and public municipal public health existed for even longer).
In 1950, before Medicare, and before Universal coverage in Sweden the difference was +2.6 at birth and +0.3 at 65. In 2001-2005 the difference between the Sweden and US was +2.7 at birth and +0.3 years at 65. Identical!
First, regarding the life expectancy at birth we can note that 50 years of different health policy, labor mark policy, welfare state coverage seems to have had zero effect on total outcome.
And:
Last note: around 1900, before the expansion of the welfare state, the estimated life expectancy at birth was 54.0 years in Sweden and 47.3 years in the US, a difference of 5.3 years, twice the current gap.
If you scroll through Tino's blog, you will find various critiques of The Spirit Level. On the health care point, I would stress that Hansonian results also can be used to argue for the extreme exercise of monopsony power, so don't think the policy implications of this are so simple.
Commodity Prices
Following up on my post, Revisiting Simon-Ehrlich, Mark Perry graphs the Dow-Jones AIG monthly index of 19 commodities, inflation adjusted, 1934-2010.
Latino immigrants and crime
The connection between Latino immigration and criminal behavior is much overstated. Here is an excellent article, full of good information. Excerpt:
The overall age-adjusted national imprisonment rates are shown in Chart 1. Hispanic incarceration rates are now between 13 and 31 percent above the white average, depending upon which age range we choose for normalization purposes.
And this:
Another important point to emphasize is the wide disparity in white incarceration rates throughout the country, even when adjusted relative to the number of whites in high-crime age ranges. For example, age-adjusted imprisonment rates for whites in large Southern states such as Florida, Texas, and Georgia may be 200 percent or even 300 percent higher than those for whites in large Northeastern or Midwestern states such as New York, New Jersey, or Illinois, as shown in Chart 5. Although it is impossible to disentangle completely how much of this gap may be due to higher criminality and how much due to harsher judicial systems, it seems likely that both play important roles. So even if the age-adjusted Hispanic incarceration rate is somewhat above the white rate–perhaps 15 percent higher on average–it still falls close to the center of the overall white distribution.
Don't forget this:
Nearly all of the most heavily Latino cities have low or even extremely low crime rates, and virtually none have rates much above the national average. Eighty percent Latino El Paso has the lowest homicide and robbery rates of any major city in the continental United States. This is not what we would expect to find if Hispanics had crime rates far higher than whites. Individual cities may certainly have anomalously low crime rates for a variety of reasons, but the overall trend of crime rates compared to ethnicity seems unmistakable.
And this:
…if we restrict our analysis to major cities of half a million people or more and compare the average crime rates for the five most heavily Hispanic cities–Albuquerque, Dallas, Los Angeles, San Antonio, and El Paso–to the those of the five whitest–Oklahoma City, Columbus, Indianapolis, Seattle, and Portland. This time, the more Hispanic cities are the ones with the lower crime rates–10 percent below the white cities in homicide and 15 percent lower in violent crime. A particularly remarkable result is that gigantic Los Angeles–50 percent Hispanic and frequently perceived as a dangerous urban hellhole–has violent crime rates close to those of Portland, Oregon, the whitest major city in the nation at 74 percent.
And finally:
Los Angeles today ranks as America’s least white European large city. Half of the population is Hispanic, and many of these are impoverished illegal immigrants and their families. Yet all crime rates have been falling steadily over the last two decades, with homicide dropping a further 18 percent just last year. As Chart 14 illustrates, most major crime categories are now back down to where they were in the early 1960s, when the population really did look very much like the actors appearing in “Dragnet” and “Leave It to Beaver.” And indeed, violent crime is now roughly the same as for Portland, Oregon, America’s whitest major city.
There is a lot more which I did not pass along, so read the whole thing. I thank The Browser and Ezra Klein for the pointers.
Revisiting Simon-Ehrlich
Paul Kedrosky revisits the famous Simon-Ehrlich bet:
Without getting into it too deeply, here are some things worth knowing. Given
the above graph of the five commodities’ prices in inflation-adjusted terms, it
will surprise no-one that the bet’s payoff was highly dependent on its start
date. Simon famously offered to bet comers on any timeline longer than a year,
and on any commodity, but the bet itself was over a decade, from 1980-1990. If
you started the bet any year during the 1980s Simon won eight of the ten decadal
start years. During the 1990s things changed, however, with Simon the decadal
winners in four start years and Ehrlich winning six – 60% of the time. And if we
extend the bet into the current decade, taking Simon at his word that he was
happy to bet on any period from a year on up (we don’t have enough data to do a
full 21st century decade), then Ehrlich won every start-year bet in the 2000s.
He looks like he’ll be a perfect Simon/Ehrlich ten-for-ten.
So, what does all this mean? A few things. First, and most importantly, it
means Simon was right but fairly lucky. There is nothing wrong with being lucky,
of course, but compulsive Simon/Ehrlich-citers need to be reminded that it is no
law of nature (let alone of rickety old economics) that commodity prices
(inflation-adjusted or otherwise) trend inexorably downward, even over a
decade.
If the conclusion is that prices go up as well as down, even over a 10 year period, then there isn't much to complain about in Paul's analysis. But I think he misses the key point. The bet was never fundamentally about prices, the bet was about scarcity, living standards and whether we were running out of natural resources–remember that at the time Ehrlich was predicting hundreds of millions would die of starvation and even that England would not exist in the year 2000! Prices were just a convenient but imperfect way to mark the bet to market.
The reason prices have risen in the 1990s is not that things are getting worse but that things are getting better–especially in China and India where things have been getting much better. As China and India have become richer demand has increased tremendously in these countries putting upward pressure on prices. In other words, prices have risen because the value of resources has risen. That's quite different–indeed the opposite–of what Ehrlich was predicting.
To see this concretely take a good which is really fixed in supply, Picasso paintings. Now consider two worlds – in one world the price of a typical Picasso is $50,000; in the other, it's $5 million. Which world would you guess has a higher standard of living?
Stata Data Repository
Even when every dataset is nicely formatted and documented it can be time consuming to merge two or more datasets when, for example, they use different identifiers for countries.
Giulia Catini, Ugo Panizza and Carol Saade have started a Macro Data 4 Stata repository which collects and creates common identifiers for the Penn World Tables, Barro and Lee's Educational data, the World Bank's Development Indicators and about 20 other datasets commonly used in macroeconomics. Any dataset in the repository can be merged with any other with just a couple of standard commands.
Check it out and please do add your own data!
Joel Mokyr on living standards during the Industrial Revolution
One of the notable arithmetical truths about the period of the Industrial Revolution is that it is quite possible (if not certain) that biological living standards in both urban and rural areas rose and yet average living standards declined. This can happen if urban living conditions are significantly worse than rural ones, and the proportion of people living in cities is rising because of migration from the countryside to the towns. It seems likely that the biological measures of living standards were especially sensitive to urbanization. While urban areas may have offered some positive amenities (such as entertainment and more choice in shopping), healthy living conditions were surely not among them.
That is from Mokyr's new and notable The Enlightened Economy: An Economic History of Britain 1700-1850. The obvious question of course is why so many people moved into cities. Did "new goods" make the urban living standard higher than some measures might suggest? Was it to avoid boredom? To avoid "rural idiocy" and invest in future IQ externalities for children?
Here is my previous post on the book.
What are the most borrowed books from UK libraries?
Circa 2009, three out of the top four are by James Patterson. Eventually Ian Rankin and Ruth Rendell make the list. Dan Brown I believe too many people have bought or already read. None of the Booker Final Six from the previous year make the list.
Catherine Cookson used to dominate these metrics but she has been swamped by American popular authors and is down to number ten for the decade. Number one for the noughties is in fact Jacqueline Wilson. That's an odd status to hold: "worth reading, just not worth buying."
A broader point is that non-fiction does very poorly on the "most borrowed" list. I'll offer up the hypothesis that low-brow fiction is what most people actually want to read, whereas many people will buy but not read non-fiction, for purposes of affiliation with the author or the concepts associated with the book.
Overall borrowers are more conservative than buyers, in the literal sense of wanting to borrow the same authors over and over again, yet in different titles.
Hat tip goes to the always-excellent Literary Saloon.
Henry Aaron writes to me
James Kwak's calculation of the value of tax exclusion is incomplete. He leaves out the exclusion from the payroll tax, worth 15.3 percent to the person in his example and to most people, and 2.9 percent (at the margin) for the rest who earn more than the OASDI taxable maximum. The correct math is that the gross wage is 1 + .0765 = 1.0765 to allow for the employer's payroll tax cost. The take home pay that could be used, after both payroll and income tax for someone in the 15 percent bracket is 1 – 0.0765 – 0.15 = 0.7735. That means that the tax wedge is equivalent to a subsidy of 1- [.7735/1.0765] =.7185. That is a 28.15 percent subsidy.
For filers in the 28 percent bracket, which is easy to reach for a couple each of whom earns, say, $75,000, the subsidy is a bit over 40 percent.
The fact I would like to know about the stimulus
I break the stimulus into three parts: the tax cuts and transfer increases, the aid to state and local governments, and the traditional spending programs. Here I'm talking about only the third part. (If you are wondering, I regard the first part as mostly ineffective and the second part as mostly effective.)
Of the workers employed by this third part of the Obama stimulus, what percentage of them already had jobs? What percentage moved from unemployed to employed? More hypothetically, what percentage had jobs but would have lost them, thus effectively counting as a move from "unemployed" to "employed" status?
I'm not talking about the maybe-hard-to-estimate effects from boosting aggregate demand, I'm talking about the "mere counting" aspect of the problem. "We hired him, he didn't have a job before. Now he has a job." What percentage of the hired people fall into that category?
I've read plenty on these studies, but they don't seem "net" to me.
Does anyone know where this information is available?
Census Miscounts
Wow, Justin Wolfers reports on a new NBER paper (ungated) by Trent Alexander, Michael Davern and Betsey Stevenson, that finds big errors in Census data, especially for citizens 65 years and older.
What’s the source of the problem? The Census Bureau purposely messes with the microdata a little, to protect the identity of each individual. For instance, if they recode a 37-year-old expat Aussie living in Philadelphia as a 36-year-old, then it’s harder for you to look me up in the microdata, which protects my privacy. In order to make sure the data still give accurate estimates, it is important that they also recode a 36-year-old with similar characteristics as being 37. This gives you the gist of some of their “disclosure avoidance procedures.” While it may all sound a bit odd, if these procedures are done properly, the data will yield accurate estimates, while also protecting my identity. So far, so good.
But the problem arose because of a programming error in how the Census Bureau ran these procedures. The right response is obvious: fix the programs, and publish corrected data. Unfortunately, the Census Bureau has refused to correct the data.
The problem also runs a bit deeper. If the mistake were just the one shown in the above graph, it would be easy to simply re-scale the estimates so that there are no longer too many, say, 85-year-old men – just weight them down a bit. But it turns out that the same coding error also messes up the correlation between age and employment, or age and marital status (and, the authors suspect, possibly other correlations as well). When you break several correlations like this, there’s no easy statistical fix.
Worse still, the researchers find that related problems afflict the microdata released for other major data sources. All told, they’ve found similar errors in:
- The 2000 Decennial Census.
- The American Community Survey, which is the annual “mini-census” (errors exist in 2003-2006, but not 2001-02, or 2007-08).
- The Current Population Survey, which generates our main labor force statistics (errors exist for 2004-2009).
These microdata have been used in literally thousands of studies and countless policy discussions.
The world’s 25 dirtiest cities
Here is the article, here is the top of the list:
1, Baku, Azerbaijan
2. Dhaka, Bangladesh
3. Antananarivo, Madagascar
4. Port-au-Prince (pre-quake? I believe they are now uncontested #1 or will be soon.)
5. Mexico City
Most of the rest are in Africa. If I did the ranking, Mexico City would do much better than number five, since air pollution isn't as bad as the lack of sanitation in cities such as Conakry (a mere #19). And why does Bangui (CAR) get such an idyllic photo? Nor does Google offer up any nasty photos of the place.
Hat tip goes to the essential Rachel Strohm, Twitter feed here.
Who are the friendliest people on earth?
Chug points me to this latest survey, and here is the list:
1. Bahrain
2. Canada
3. Australia
4. Thailand
5. Malaysia
6. South Africa
7. Hong Kong
8. Singapore
9. Spain
10. United States
That means friendly to expats, not friendly to each other. You’ll notice that English-speaking or English-fluent countries are overrepresented, plus Thailand (ahem).
Here is a critique of the survey and mostly I concur with the criticisms (sorry Omar). More generally, unless it is a woman seeking marriage, I view “friendliness to expats” as a social strategy, often intended for internal consumption, not necessarily insincere but not reflecting true temperament either. It’s not driven by actual friendliness. By the way, how did Spain ever make it to number nine?
Are the Japanese the most or the least friendly people on earth? “Helpful” isn’t the same as “friendly.” In what country are you most likely to make real friends? Marry a native? Aren’t those two variables inversely related?
“Friendly” is one of the words most likely to arouse my deconstructive suspicions.
Who are the biggest donors to Haiti?
Here is an interesting visual, which expresses pledged support to Haiti in per capita terms. #1 is Canada, by a large margin, followed by some of the Nordic countries.
Per capita the U.S. doesn't do so well (NB: I don't think remittances are counted), with less than half of what Guyana supplies. We're also behind Estonia, Switzerland, and United Arab Emirates, among other countries. The visual is measuring earthquake aid pledged, not all foreign aid.
In absolute terms here is another visual; U.S. is #1. I don't think this is using the same metric as above.
Here's another interesting visual. Relative to per capita gdp, Ghana is the single most significant pledger of aid to Haiti.
For the pointer I thank Rahul Nabar.
The incomes and professions of Haitian-Americans
The blog post is here. It's a proposal for "diaspora bonds" (I fear that excess corruption is a problem). I was more interested in this bit:
…nearly one-third of Haitian immigrants in the US belong to households that earned more than $60,000 in 2009. In comparison, less than 15% of the immigrants from Mexico, Dominican Republic and El Salvador in the US had that level of household income. A quarter of Haitian immigrants, especially women, are reportedly in the relatively higher paying health care and education sectors; only a small number of them are in the construction sector.
Hat tip goes to Whirled Citizen.
I was surprised this number was such a big one
Organized labor lost 10% of its members in the private sector last year, the largest decline in more than 25 years.
I meant to blog this the day I read it but somehow I forgot to; here is one source. I haven't seen it receive much discussion in the econ blogosphere. For one thing, it's a sign that the union wage premium isn't so stable.