Category: Data Source
Marijuana sentences to ponder: new data from the Colorado experiment
“This study finds total marijuana demand to be much larger than previously estimated,” Colorado’s study concluded.
And this, which I think suggests the laws in other states are binding for many consumers:
Colorado concluded that visitors account for 44 percent of recreational marijuana retail sales in the Denver area. In the mountains and other vacation spots, visitors to Colorado account for 90 percent of recreational dispensary traffic.
And this, which sounds tautologous, but is not:
“Heavy users consume marijuana much more often, and more intensely, than other consumers,” the study concluded.
Overall heavy users seem to account for about seventy percent of total demand. Here is some detail:
Colorado’s market numbers bore out survey estimates that most marijuana is consumed by heavy daily users. For example, survey authors estimated that a third of all Colorado’s pot consumers use the drug less than once a month. But that group accounts for just 0.3 percent of the total market, analysts concluded.
The full story is here, the study itself is here. For the pointer I thank C., who I believe is not part of that seventy percent of market demand.
Visible Prices
(VP) is a digital humanities project, currently in development, for a collection of prices drawn from literary and historical sources in 18th and 19th century England. Users will be able to search for information relating to a specific good or service, or a specific amount of money. For example, a query for 3 shillings in 1789 reveals that in London, that amount would purchase a bushel of wheat, a quarto of translations from Diderot, or a day’s services of a crippled or deformed child as a companion to an adult beggar. My intent is for the database to make use of the influx of printed texts onto the web in facsimile format, in databases like Google Books, the Hathi Trust Digital Library, Eighteenth Century Collections Online, the British Newspapers Collection, and the London Times Online Archive, to name only a few. Though entry privileges are currently restricted, the goal is to eventually make it possible for registered users to enter data in the process of individual research or classroom activities; and thus to make it possible for researchers specializing in other time periods and regions to extend the scope of the database.
The pointer is from Pam Regis.
How big a deal is replication failure?
From Jason Mitchell at Harvard:
Recent hand-wringing over failed replications in social psychology is largely pointless, because unsuccessful experiments have no meaningful scientific value.
Because experiments can be undermined by a vast number of practical mistakes, the likeliest explanation for any failed replication will always be that the replicator bungled something along the way. Unless direct replications are conducted by flawless experimenters, nothing interesting can be learned from them.
Three standard rejoinders to this critique are considered and rejected. Despite claims to the contrary, failed replications do not provide meaningful information if they closely follow original methodology; they do not necessarily identify effects that may be too small or flimsy to be worth studying; and they cannot contribute to a cumulative understanding of scientific phenomena.
Replication efforts appear to reflect strong prior expectations that published findings are not reliable, and as such, do not constitute scientific output.
The field of social psychology can be improved, but not by the publication of negative findings. Experimenters should be encouraged to restrict their “degrees of freedom,” for example, by specifying designs in advance.
Whether they mean to or not, authors and editors of failed replications are publicly impugning the scientific integrity of their colleagues. Targets of failed replications are justifiably upset, particularly given the inadequate basis for replicators’ extraordinary claims.
The full piece is here, I don’t quite buy it but a useful counter-tonic to a lot of current rhetoric. I found this in my Twitter feed, but I forget whom to thank, sorry!
Addendum: An MR reader sends along this related argument.
Same sex parents and adopted children
The largest-ever study of same-sex parents found their children turn out healthier and happier than the general population.
A new study of 315 same-sex parents and 500 children in Australia found that, after correcting for socioeconomic factors, their children fared well on several measures, including asthma, dental care, behavioral issues, learning, sleep, and speech.
Do note this:
Perceived stigmas were associated with worse scores for physical activity, mental health, family cohesion, and emotional outcomes. The stigmas, however, were not prevalent enough to negatively tilt the children’s outcomes in a comparison to outcomes across the general population.
There is more here, from German Lopez, the study itself is here.
Cambodia fact of the day
93% of that country is satisfied with the degree of freedom in that country, ranking it #3 in the world (New Zealand is #1 by that standard).
There is more here. U.S. is #36.
Happy Fourth of July!
A comparison of programming languages in economics
There is a new NBER working paper with that title, by S. Borağan Aruoba and Jesus Fernandez-Villaverde. Here is the abstract:
We solve the stochastic neoclassical growth model, the workhorse of modern macroeconomics, using C++11, Fortran 2008, Java, Julia, Python, Matlab, Mathematica, and R. We implement the same algorithm, value function iteration with grid search, in each of the languages. We report the execution times of the codes in a Mac and in a Windows computer and comment on the strength and weakness of each language.
Here are their results:
1. C++ and Fortran are still considerably faster than any other alternative, although one needs to be careful with the choice of compiler.
2. C++ compilers have advanced enough that, contrary to the situation in the 1990s and some folk wisdom, C++ code runs slightly faster (5-7 percent) than Fortran code.
3. Julia, with its just-in-time compiler, delivers outstanding per formance. Execution speed is only between 2.64 and 2.70 times the execution speed of the best C++ compiler.
4. Baseline Python was slow. Using the Pypy implementation, it runs around 44 times slower than in C++. Using the default CPython interpreter, the code runs between 155 and 269 times slower than in C++.
5. However, a relatively small rewriting of the code and the use of Numba (a just-in-time compiler for Python that uses decorators) dramatically improves Python ’s performance: the decorated code runs only between 1.57 and 1.62 times slower than the best C++ executable.
6.Matlab is between 9 to 11 times slower than the best C++ executable. When combined with Mex files, though, the difference is only 1.24 to 1.64 times.
7. R runs between 500 to 700 times slower than C++ . If the code is compiled, the code is between 240 to 340 times slower.
8. Mathematica can deliver excellent speed, about four times slower than C++, but only after a considerable rewriting of the code to take advantage of the peculiarities of the language. The baseline version our algorithm in Mathematica is much slower, even after taking advantage of Mathematica compilation.
There are ungated copies and some discussion here.
Disruption Big Time
In an excellent post on the Lepore-Christensen fracas, John Hagel draws on Deloitte’s Shift Index to provide some data on disruption. Disruption has increased by a variety of metrics.
One of the metrics in our Shift Index looks at what economists call topple rate – the rate at which leaders fall out of their leadership position. In this case, we focused on the rate at which public US companies in the top quartile of return on assets performance fall out of this leadership position.Between 1965 and 2012, the topple rate increased by 40%.
OK, but the skeptic might reply that this is only about financial performance. Another more significant measure of fall from leadership position is provided by my old colleague and mentor, Dick Foster, who looked at the average lifespan of companies on the S&P 500. In 1937, at the height of the Great Depression and certainly a time of great turmoil, a company on the S&P 500 had an average lifespan of 75 years. By 2011, that lifespan had dropped to 18 years – a decline in lifespan of almost 75%. At the same time that humans are significantly increasing their lifespan, large companies have been heading rapidly in the opposite direction.
Another measure of disruption is executive turnover which has increased.
Some of Deloitte’s work also speaks to the implicit idea in Piketty that capital accumulation is easy. Once someone has capital, Piketty argues, that capital just grows and grows at r>g. Not so, and less so today than ever before. According to Deloitte the return on capital is decreasing and the volatility is increasing. Here’s the return on assets by top and bottom quartile. Even in the top quartile, r is decreasing but it’s easier than ever before to pick wrong and lose your shirt in the bottom quartile.
Lots more of interest in Hagel’s post and in Deloitte’s work.
Very good sentences
If savings for cross-sectional out-of-pocket nursing home expense risk were held in the form of vehicles, it is large enough to account for the entire stock of transportation equipment in the United States.
That prize goes to Karen A. Kopecky and Tatyana Koreshkova. That paper was just published in American Economic Journal: Macroeconomics as well.
African-American fact of the day (there is a great stagnation)
As sociologist Patrick Sharkey shows in his book Stuck in Place, 62 percent of black adults born between 1955 and 1970 lived in neighborhoods that were at least 20 percent poor, a fact that’s true of their children as well. An astounding 66 percent of blacks born between 1985 and 2000 live in neighborhoods as poor or poorer as those of their parents.
That is from Jamelle Bouie, there is more here, mostly about neighborhood effects.
Arrived in my pile
Morten Jerven, Poor Numbers: How We Are Misled by African Development Statistics and What To Do About It.
This seems to be the place to start on this topic.
Peter de Keyzer, Growth Makes You Happy: An Optimist’s View of Progress and the Free Market.
Steven D. Gjerstad and Vernon L. Smith, Rethinking Housing Bubbles: The Role of Household and Bank Balance Sheets in Modeling Economic Cycles. They present a bank balance sheet account of the Great Recession, with a good deal of background coming from the experimental economics direction.
In 100 Years, Leading Economists Predict the Future, edited by Ignacio Palacios-Huerta.
Robert Howse, Leo Strauss: Man of Peace.
Where did the productivity slowdown come from?
John Fernald has a new NBER paper on this question. Here is the abstract:
U.S. labor and total-factor productivity growth slowed prior to the Great Recession. The timing rules out explanations that focus on disruptions during or since the recession, and industry and state data rule out “bubble economy” stories related to housing or finance. The slowdown is located in industries that produce information technology (IT) or that use IT intensively, consistent with a return to normal productivity growth after nearly a decade of exceptional IT-fueled gains. A calibrated growth model suggests trend productivity growth has returned close to its 1973-1995 pace. Slower underlying productivity growth implies less economic slack than recently estimated by the Congressional Budget Office. As of 2013, about ¾ of the shortfall of actual output from (overly optimistic) pre-recession trends reflects a reduction in the level of potential.
The ungated version is here (pdf).
Words that men are most likely to recognize over women
- codec (88, 48)
- solenoid (87, 54)
- golem (89, 56)
- mach (93, 63)
- humvee (88, 58)
- claymore (87, 589
- scimitar (86, 58)
- kevlar (93, 65)
- paladin (93, 66)
- bolshevism (85, 60)
- biped (86, 61)
- dreadnought (90, 66)
That is from Christina Sterbenz. Here are the words women are most likely to recognize over men:
- taffeta (48, 87)
- tresses (61, 93)
- bottlebrush (58, 89)
- flouncy (55, 86)
- mascarpone (60, 90)
- decoupage (56, 86)
- progesterone (63, 92)
- wisteria (61, 89)
- taupe (66, 93)
- flouncing (67, 94)
- peony (70, 96)
- bodice (71, 96)
…The male words tend to center on transportation, weapons, and science, while the female words mostly relate to fashion, art, and flowers.
The article is here, hat tip Yana.
Prophets of the Marginal Revolution (a recurring series)
“Los Angeles on cusp of becoming ‘major’ walkable city, study says.”
Despite its long love affair with the car, Los Angeles is on the cusp of becoming a “major” walkable urban area. And doing so could do wonders for its real estate market, at least in spots.
That’s the gist of a new report released Tuesday by SmartGrowth America and George Washington University, which measured the number of walkable urban neighborhoods in 30 big metro areas and looked at the potential to develop more.
The original MR post was here, and for the pointer I thank…Alex.
The “average is over” economic recovery proceeds
Average hourly earnings for private-sector American workers rose about 49 cents an hour over the last year, to $24.38 in May. But that wasn’t enough to cover inflation over the year, so in “real” or inflation adjusted terms, hourly worker pay fell 0.1 percent over the last 12 months. Weekly pay shows the same story, also falling 0.1 percent in the year ended in May.
Neil Irwin offers more here. Many people I know thought my earlier prediction of “falling or stagnant wages during the U.S. recovery” was an absurd prognostication, but so far it seems to be on the mark. Just wait for The Great Reset.
Is there a lot more insider trading than most people think?
I’ve long thought so, here are some new results supporting that view:
… a groundbreaking new study finally puts what we’ve instinctively thought into hard numbers — and the truth is worse than we imagined.
A quarter of all public company deals may involve some kind of insider trading, according to the study by two professors at the Stern School of Business at New York University and one professor from McGill University. The study, perhaps the most detailed and exhaustive of its kind, examined hundreds of transactions from 1996 through the end of 2012.
The professors examined stock option movements — when an investor buys an option to acquire a stock in the future at a set price — as a way of determining whether unusual activity took place in the 30 days before a deal’s announcement.
The results are persuasive and disturbing, suggesting that law enforcement is woefully behind — or perhaps is so overwhelmed that it simply looks for the most egregious examples of insider trading, or for prominent targets who can attract headlines.
The professors are so confident in their findings of pervasive insider trading that they determined statistically that the odds of the trading “arising out of chance” were “about three in a trillion.” (It’s easier, in other words, to hit the lottery.)
There is more here, via Ray Fisman.