Including the Ocean Floor, the Feds Own Much More Land than You Think
The Importance of Economic Calculation
In “Economic Calculation in the Socialist Commonwealth,” Ludwig von Mises challenged the socialists to explain how economic calculation could be performed in a socialist economy absent prices. Mises concluded that economic calculation in a socialist economy is impossible, therefore socialism is impossible.Mises wasn’t saying that you couldn’t have a socialist society, he was saying that it’s not an economy, in the sense that decision makers are economizing regarding their decisions about the allocation of resources.
Socialism, at the time, was defined as a system where the state owns the means of production. The state owns the natural resources and the capital, such as the factories necessary for use in the production process. Given this state ownership, the resources are not being traded in any market and since there are no markets for the resources there are also no market prices for the resources.
In an economy where resources are privately owned, the exchange of those resources would provide us with market prices. And those prices provide us with a sound basis for assigning resources to their most productive uses.
This rational calculation is impossible in a socialist economy.
Mises concluded, “Thus in the socialist commonwealth every economic change becomes an undertaking whose success can be neither appraised in advance nor later retrospectively determined. There is only groping in the dark. Socialism is the abolition of rational economy.” (p. 23)
Land Socialism in the United States
My interest in this topic was inspired by Yuri Maltsev. A few years ago, Dr. Maltsev gave a talk at Ferris State University focusing on the evils of Soviet socialism (a portion of the presentation can be seen here). Nobody disagreed with Yuri’s point that the Soviet economy was socialized, however, some took heavy issue with Yuri’s claim that the US economy was also socialized to a large degree.This led me to consider the degree of land socialism in this country. This is a critical issue. Starting with the available land and labor, the structure of production is determined by the available technology and the capital that we derive from the available land and labor. Government control of the natural resources gives the government tremendous control of the whole economy, distorts prices, and diminishes the efficacy of our economic calculation.
Is US land ownership heavily socialized? Let’s begin answering this question by considering the states with the largest percentages of government-owned lands.
Nevada has the largest percentage, 84.9 percent, of federally owned lands, but 30 percent of Alaska is state owned, so Alaska has the largest percentage of government-owned lands. As you can see, due to the Louisiana Purchase and other factors, much of the federal land ownership is in the Western states. I included New York on the list because New York has the highest percentage of state-owned land.
Admittedly there is some false precision in these numbers as the states and the federal government have some difficulty in accurately providing statistics on their land ownership.
Next, consider the largest federal agencies ranked by land ownership.
Texas, with 171 million acres, is the second largest state. We see here that the BLM and the Forest Service are both larger than Texas. And the fourth largest agency, the National Park Service, is larger than all but four states, Alaska, Texas, California, and Montana, slightly larger than New Mexico’s 77.6 million acres.
Note that these numbers do not include the Bureau of Indian Affairs. The federal government claims that the 55 million acres of BIA lands are Indian lands not federal lands. I don’t know if the Indian tribes agree with this assessment. The Indian lands, if they were a state would be the 11th largest state, almost equal in size to Utah’s 54.3 million acres.
The Department of Defense administers 14.4 million acres of land, according to a 2014 Congressional report. (By the way, a 2012 Congressional report with the same title as this report claimed that the Department of Defense administers 19 million acres of land. There is no explanation for the missing 4.6 million acres in the 2014 report.)
The federal government, according to this report, “owns and manages roughly 640 million acres of land” and there is an estimated 200 million acres owned by the various state governments. Therefore, 37.1 percent of US dry land is owned by some government.
A map of the government land ownership will help us put things in perspective.
Note that this map shows only the federal holdings and the Indian lands and omits the 200 million acres of state lands. Still, it provides us with an illustration of the degree of land socialism in this country. The federal government owns most of the land roughly from the Continental Divide west to the Pacific Ocean.
Government-owned Lands in the Oceans
What about the submerged lands? The federal government also claims ownership over what they call the submerged lands of the US. These claims began with 1799 legislation regarding the “customs waters,” allowing the boarding of foreign flag vessels within 12 nautical miles of the coast. Over time, these claims have expanded and in 1945, Harry Truman declared US government jurisdiction and control over the continental shelf. During the next decades, governments of the world claimed increasingly larger amounts of the ocean beds. Problems occurred, however, if two governments disagreed over these claims. This became a United Nations issue in the 1970s, and in 1982, at the United Nations Convention of the Law of the Sea, the countries of the world came to an agreement regarding their Exclusive Economic Zones (EEZ), whereby each country owned the sea and the sea beds out to 200 nautical miles offshore.Due to Congressional resistance of United Nations treaties, Congress did not ratify this agreement. But Ronald Reagan, in 1983 simply proclaimed sovereign rights over the US Exclusive Economic Zone. He ratified the agreement by presidential mandate.
According to a Department of the Interior report, there are 3.9 billion acres in the US EEZ. Reagan’s proclamation was the biggest land grab in US government history.
Consider this map of the US that includes the US EEZ. The various colors highlight the regions of the EEZ.
The federal government owns the sea beds out to 200 nautical miles off of the Atlantic and Pacific coasts and much of the Gulf of Mexico. Due to the Alaska Peninsula and the Aleutian Islands, there is a tremendous amount of EEZ lands off of the Alaskan coast. And the US government claims ownership over immense amounts of the Pacific Ocean, much of which is due to the military use of small islands during World War II.
For instance, the Johnston Atoll in the Pacific was used as an airstrip of about one square mile during WWII. Since this tiny island is now a federal holding it allows the government to claim ownership over 166,000 square miles of ocean sea bed, which is approximately the size of California.
We can now consider the total amount of US government lands.
One point to make here is that there is over 70 percent more submerged lands in the US than the total amount of dry land in this country. That is, the federal government owns more submerged land than the total amount of land in the 50 states.
Thus, 76.9 percent of total land in the United States is government owned. There is no doubt that regarding this essential resource, land, our economy is heavily socialized.
Back to the Calculation Problem
The government ownership of lands leads to several economic problems. Government officials can use their control of the natural resources to reward politically favored industries and punish their political enemies. Second, government restrictions on the use of resources on government lands limits economic growth. Third, and this is Mises’s point regarding socialism, land socialism will create economic calculation problems.The first calculation issue here is the government’s decisions regarding the use of this land and the resources on and under the land. Since the government owns the land, we see no prices for these resources. The government has no way to economize on these resources in the sense that government officials, even if they wanted to, could not efficiently allocate these resources. They make these allocation decisions based on political considerations, so they end up allowing the private sector to access the wrong resources, wrong in the sense that if we were allowed to have private ownership of these resources, we would choose to use the resources much differently, and more efficiently.
The second issue is that the economic calculation of private businesses is distorted by government control of the resources, because government ownership distorts the prices of the resources. If the prices were based on the private ownership of land and resources, for instance, we would see a different array of energy prices. The prices we see for resources do not accurately reflect the underlying realities of resource availability. Even though we are engaged in calculation when we allocate these resources, we are not economizing on the resources in the sense that Mises described.
Due to the high degree of government land ownership, the US government distorts economic calculation in the exact manner that Mises explained and predicted in 1920.
https://mises.org/library/including-ocean-floor-feds-own-much-more-land-you-think
Your Data Footprint Is Affecting Your Life In Ways You Can't Even Imagine
Job decisions, college admissions, health care decisions: All are now being fundamentally altered by your big data, and you might not even know.
Imagine you’re moving apartments and shopping for new furniture at a couple of stores. You see a couch you like, but you’re not sure, so you leave thinking maybe you’ll return another day. But that couch doesn’t take well to rejection. It gets up, leaves the store, and starts following you around as you shop elsewhere and even after you go home having purchased a different couch. Then you start getting offers in the mail for new mattresses.
This is basically people’s experience on the Internet today—where innocently clicking on a link results in ad targeting that’s hard to shake and our purchases quickly reveal more information than we intend, such as the infamous example of Target knowing a woman is pregnant before she’s told her family—and before she's purchased any baby products.
From the credit card offers we receive to recommendations we see on Netflix and posts we see on Facebook, ads and marketing are the obvious example of our personal data being aggregated and analyzed to make predictions about us. National security—facilitated by massive and sometimes illegal data collection by the government—is clearly another. And if you work at a large information-based business, you’ve no doubt heard the terms "big data" and "bottom line" in the same sentence before.
But these (mostly) benign examples that we encounter every day hide the truth about what large-scale government and corporate data collection means and where it's being used. Predictions about you (and millions of other strangers) are starting to deeply shape your life. Your career, your love life, major decisions about your health and well-being, and even if you end up in jail, are now being governed in no small part by the digital bread crumbs you've left behind—many of which you don't even know you've dropped in the first place.
In one study in Los Angeles, police officers deployed to certain neighborhoods by predictive policing software prevented 4.3 crimes per week, compared to 2 crimes per week when assigned to patrol a specific area by human crime analysts. Surely, a reduction in crime is a good thing. But community activists in places such as Bellingham, Washington, have grave doubts. They worry that outsiders can’t examine how the algorithms work, since the software is usually proprietary, and so citizens have no way of knowing what data the government is using to target them. They also worry that predictive policing is just exacerbating existing patterns of racial profiling. If the underlying crime data being used is the result of years of over-policing minority communities for minor offenses, then the predictions based on this biased data could create a feedback loop and lead to yet more over-policing.
At a smaller and more limited scale is the even more sensitive area of child protection services. Though the data isn’t really as "big" as in other examples, a few agencies are carefully exploring using statistical models to make decisions in several areas, such as which children in the system are most in danger of violence, which children are most in need of a trauma screening, and which are at risk of entering the criminal justice system.
In Hillsborough County, Florida, where a series of child homicides occurred, a private provider selected to manage the county’s child welfare system in 2012 came in and analyzed the data. Cases with the highest probability of serious injury or death had a few factors in common, they found: a child under the age of three, a "paramour" in the home, a substance abuse or domestic violence history, and a parent previously in the foster care system. They identified nine practices to use in these cases and hired a software provider to create a dashboard that allowed real-time feedback and dashboards. Their success has led to the program being implemented statewide.
But while they don't have the magic formula for creating love, dating sites are still shaping the romantic lives of the growing portion of the population that use them. Consider that Tinder has an internal rating of how desirable you are. If you're getting a lot of swipes, you won't be shown as frequently to give other people a chance. Another app, Coffee Meets Bagel, guides users to people of their own race or ethnicity, even if they say "no preference" on their profile. Partly, they do this because of what their data reveal: even when users say they have no preference, in private, people gravitate to others like them. That may be true in general, but for any given user, it may nudge them to live more segregated lives than they would otherwise want to, without them knowing at all.
Personal finance is another new area for algorithmic, data-driven predictions, with a number of new "robo-advsior" apps. "We’re getting used to computers actually being pretty credible in terms of the recommendations they make," says Vasant Dhar, a professor at NYU’s Stern School of Business and its Center for Data Science. "It’s not that much of a stretch, where [a computer] actually says, here’s what I suggest with your portfolio."
Even major life decisions like college admissions and hiring are being affected. You might think that a college is considering you on your merits, and while that's mostly true, it's not entirely. Pressured to improve their rankings, colleges are very interested in increasing their graduation rates and the percentage of admitted students who enroll. They have now have developed statistical programs to pick students who will do well on these measures. These programs may take into account obvious factors like grades, but also surprising factors like their sex, race, and behavior on social media accounts. If your demographic factors or social media presence happen to doom you, you may find it harder to get into school—and not know why.
And what about getting a job? Consider a startup called Gild, which has built a database of tens of millions of professionals that contains data purchased from third-party providers plus "anything and everything that’s publicly available," according to CEO Sheeroy Desai. Its system identifies candidates who fit a job opening and analyzes factors that might predict their success. The company says it currently has about 10,000 recruiting and hiring managers using the platform, from employers such as Facebook, HBO, and TD Bank.
Desai says Gild’s speciality is "unifying information across very different sources." Its big data-based recommendations consider factors including job history, language, and behavior on social media sites, and public work samples such as a programmer's open-source code contributions. By analyzing the job movements of millions of people, it rates candidates not only based on their expertise but also how in-demand they might be based on the current job market. It also tries to answer questions like when a given person is most susceptible to a new job offer, how a person’s career track predicts where they’ll be in 10 years, and the likelihood a person will be a good fit at a company.
"The reason the job market is so inefficient is that we have humans making decisions," says Desai. Humans, he says, often have more nuanced judgment than a computer, but that judgment is clouded by lots of little biases that people are blind to. "At the end of the day, companies are still going to make decisions based on humans. We want to make more unbiased recommendations on who you should be interviewing."
On the plus side, recruiters have lauded it for helping them find candidates they might not otherwise have considered, like someone who didn’t go to college. A downside? Candidates trying to negotiate a higher salary against this kind of smart system might find a harder time of it. In either case, job candidates, Desai says, are sometimes shocked at how much interviewers know about them ahead of time.
Russell’s concerns are applicable to many areas where big data touches our lives. What happens when a computer says you’re likely to commit a crime before you do it, and, worse, what if the data underlying that prediction is wrong and you can’t do anything about it? What happens when a dating program is slowly pushing us to a more segregated society because it shows us the people it thinks we want to see? Or when personalized medicine can save lives, but because it is based mainly around genomes sequenced from white people of European descent, it's only saving some lives?
And while it’s true that analytics can already make smarter guesses than humans in many situations, people are more than their data. A world where people struggle to rise above what is expected of them—say a college won’t admit them because they don’t seem like someone with a good chance of graduating—is a sad world. "There’s this danger we lose our identity as people and we become categories," says Dhar.
The hope is with so much continuous data, researchers can for the first time start to disentangle the complex, seemingly unanswerable questions that have plagued our society, from what is causing the obesity epidemic to how to disrupt the poverty to prison cycle. "There’s so many pressing problems that we struggle with in this society, and we are so bad at data-driven policy," says Paul Glimcher, director of the project and a professor of neural science, economics, and psychology at NYU.
For example, how do people decide what to eat? In these decisions, there’s complex interactions between biology, behavior, and environment that have always made this question hard to study comprehensively. But if the Kavli Human Project combines geo-located food shopping and consumption data with health biomarkers with financial details and other data, obesity experts say this will be a "first-of-its-kind bio-behavioral, economic, and cultural atlas of diet quality and health for New York City" that can help them make breakthroughs.
Part of its potential is that it could bring the benefits of big data to those who are currently left out. "I think it’s really not been the case that [big data] has broadly impacted everyone. I think it’s impacted the people who write for The New York Times and Fast Company, and people who read The New York Times and Fast Company," Glimcher says.
Glimcher says he’s disappointed at the ways that big data tools have been used so far. "It’s just terrible," he says. "Sometimes big data is treated as if it’s an organism. And the question is how will this organism interact with us. And we really honestly hate that. We like the idea as scientists, as activists—we are big data. We are designing big data. And the challenge is to design big data that has those positive impacts, not to wait and see."
This is basically people’s experience on the Internet today—where innocently clicking on a link results in ad targeting that’s hard to shake and our purchases quickly reveal more information than we intend, such as the infamous example of Target knowing a woman is pregnant before she’s told her family—and before she's purchased any baby products.
From the credit card offers we receive to recommendations we see on Netflix and posts we see on Facebook, ads and marketing are the obvious example of our personal data being aggregated and analyzed to make predictions about us. National security—facilitated by massive and sometimes illegal data collection by the government—is clearly another. And if you work at a large information-based business, you’ve no doubt heard the terms "big data" and "bottom line" in the same sentence before.
But these (mostly) benign examples that we encounter every day hide the truth about what large-scale government and corporate data collection means and where it's being used. Predictions about you (and millions of other strangers) are starting to deeply shape your life. Your career, your love life, major decisions about your health and well-being, and even if you end up in jail, are now being governed in no small part by the digital bread crumbs you've left behind—many of which you don't even know you've dropped in the first place.
Predictive analytics could save your life—or ruin it
Cities have long seen the potential in big data to improve the government and the lives of citizens, and this is now being put into action in ways where governments touch citizens' lives in very sensitive areas. New York City’s Department of Homelessness Services is mining apartment eviction filings, to see if they can understand who is at risk of becoming homeless and intervene early. And police departments all over the country have adopted predictive policing software that guides where officers should deploy, and at what time, leading to reduced crime in some cities.In one study in Los Angeles, police officers deployed to certain neighborhoods by predictive policing software prevented 4.3 crimes per week, compared to 2 crimes per week when assigned to patrol a specific area by human crime analysts. Surely, a reduction in crime is a good thing. But community activists in places such as Bellingham, Washington, have grave doubts. They worry that outsiders can’t examine how the algorithms work, since the software is usually proprietary, and so citizens have no way of knowing what data the government is using to target them. They also worry that predictive policing is just exacerbating existing patterns of racial profiling. If the underlying crime data being used is the result of years of over-policing minority communities for minor offenses, then the predictions based on this biased data could create a feedback loop and lead to yet more over-policing.
At a smaller and more limited scale is the even more sensitive area of child protection services. Though the data isn’t really as "big" as in other examples, a few agencies are carefully exploring using statistical models to make decisions in several areas, such as which children in the system are most in danger of violence, which children are most in need of a trauma screening, and which are at risk of entering the criminal justice system.
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Recommendations get personal
Dating apps get popular when they are actually connecting people, so it's obvious that their systems usually try to show you matches based on some formula that accounts for the person you say you prefer, what your swipes and clicks reveal, and how others behave. Apps surely increase the number of strangers you can meet, but in the quest for love, research shows that all the work of their matching algorithms are mostly meaningless. You still need to work hard to find the right person, because a formula can’t account for all the uncertainty and individuality about what finding a lasting relationship requires.But while they don't have the magic formula for creating love, dating sites are still shaping the romantic lives of the growing portion of the population that use them. Consider that Tinder has an internal rating of how desirable you are. If you're getting a lot of swipes, you won't be shown as frequently to give other people a chance. Another app, Coffee Meets Bagel, guides users to people of their own race or ethnicity, even if they say "no preference" on their profile. Partly, they do this because of what their data reveal: even when users say they have no preference, in private, people gravitate to others like them. That may be true in general, but for any given user, it may nudge them to live more segregated lives than they would otherwise want to, without them knowing at all.
Money, Health, And School
The emerging and heavily funded field of precision medicine revolves around the fact that doctors can start to personalize diagnosis and treatment based on how others—whether similar to you in their DNA, demographics, disease pattern, or life habits—respond to care. In the future, the goal is that health care will be highly personalized, and improved outcomes and lower costs will result. This is at an early stage, but already, responding to financial incentives in Obamacare, hospitals are using data mining to predict which patients are more likely to be readmitted within 90 days. People at a high risk to return are likely to receive more attentive follow-up care. At one hospital, for example, they are assigned a post discharge coordinator, where someone at a lower risk might not get the same treatment.Personal finance is another new area for algorithmic, data-driven predictions, with a number of new "robo-advsior" apps. "We’re getting used to computers actually being pretty credible in terms of the recommendations they make," says Vasant Dhar, a professor at NYU’s Stern School of Business and its Center for Data Science. "It’s not that much of a stretch, where [a computer] actually says, here’s what I suggest with your portfolio."
Even major life decisions like college admissions and hiring are being affected. You might think that a college is considering you on your merits, and while that's mostly true, it's not entirely. Pressured to improve their rankings, colleges are very interested in increasing their graduation rates and the percentage of admitted students who enroll. They have now have developed statistical programs to pick students who will do well on these measures. These programs may take into account obvious factors like grades, but also surprising factors like their sex, race, and behavior on social media accounts. If your demographic factors or social media presence happen to doom you, you may find it harder to get into school—and not know why.
And what about getting a job? Consider a startup called Gild, which has built a database of tens of millions of professionals that contains data purchased from third-party providers plus "anything and everything that’s publicly available," according to CEO Sheeroy Desai. Its system identifies candidates who fit a job opening and analyzes factors that might predict their success. The company says it currently has about 10,000 recruiting and hiring managers using the platform, from employers such as Facebook, HBO, and TD Bank.
"The reason the job market is so inefficient is that we have humans making decisions," says Desai. Humans, he says, often have more nuanced judgment than a computer, but that judgment is clouded by lots of little biases that people are blind to. "At the end of the day, companies are still going to make decisions based on humans. We want to make more unbiased recommendations on who you should be interviewing."
On the plus side, recruiters have lauded it for helping them find candidates they might not otherwise have considered, like someone who didn’t go to college. A downside? Candidates trying to negotiate a higher salary against this kind of smart system might find a harder time of it. In either case, job candidates, Desai says, are sometimes shocked at how much interviewers know about them ahead of time.
A Dystopian Future?
"I think the opportunity is a rich one. At the same time, the ethical considerations need to be guiding us," says Jesse Russell, chief program officer at the National Council on Crime and Delinquency, who has followed the use of predictive analytics in child protective services. Officials, he says, are treading carefully before using data to make decisions about individuals, especially when the consequences of being wrong—such as taking a child out of his or her home unnecessarily—are huge. And while caseworker decision-making can be flawed or biased, so can the programs that humans design. When you rely too much on data—if the data is flawed or incomplete, as could be the case in predictive policing—you risk further validating bad decisions or existing biases.Russell’s concerns are applicable to many areas where big data touches our lives. What happens when a computer says you’re likely to commit a crime before you do it, and, worse, what if the data underlying that prediction is wrong and you can’t do anything about it? What happens when a dating program is slowly pushing us to a more segregated society because it shows us the people it thinks we want to see? Or when personalized medicine can save lives, but because it is based mainly around genomes sequenced from white people of European descent, it's only saving some lives?
And while it’s true that analytics can already make smarter guesses than humans in many situations, people are more than their data. A world where people struggle to rise above what is expected of them—say a college won’t admit them because they don’t seem like someone with a good chance of graduating—is a sad world. "There’s this danger we lose our identity as people and we become categories," says Dhar.
Big data could solve really fundamental questions of human existence
On the other hand, big data does have the potential to vastly expand our understanding of who we are and why we do what we do. A decade ago, serious scientists would have laughed someone out of the room who proposed a study of "the human condition." It is a topic so broad and lacking in measurability. But perhaps the most important manifestation of big data in people’s lives could come from the ability for scientists to study huge, unwieldy questions they couldn't before.
A massive scientific undertaking to study the human condition is set to launch in January of 2017. The Kavli Human Project, funded by the Kavli Foundation, plans to recruit 10,000 New Yorkers from all walks of life to be measured for 10 years. And by measured, they mean everything: all financial transactions, tax returns, GPS coordinates, genomes, chemical exposure, IQ, bluetooth sensors around the house, who subjects text and call—and that’s just the beginning. In all, the large team of academics expect to collect about a billion data points per person per year at an unprecedented low cost for each data point compared to other large research surveys.
For example, how do people decide what to eat? In these decisions, there’s complex interactions between biology, behavior, and environment that have always made this question hard to study comprehensively. But if the Kavli Human Project combines geo-located food shopping and consumption data with health biomarkers with financial details and other data, obesity experts say this will be a "first-of-its-kind bio-behavioral, economic, and cultural atlas of diet quality and health for New York City" that can help them make breakthroughs.
Part of its potential is that it could bring the benefits of big data to those who are currently left out. "I think it’s really not been the case that [big data] has broadly impacted everyone. I think it’s impacted the people who write for The New York Times and Fast Company, and people who read The New York Times and Fast Company," Glimcher says.
Glimcher says he’s disappointed at the ways that big data tools have been used so far. "It’s just terrible," he says. "Sometimes big data is treated as if it’s an organism. And the question is how will this organism interact with us. And we really honestly hate that. We like the idea as scientists, as activists—we are big data. We are designing big data. And the challenge is to design big data that has those positive impacts, not to wait and see."
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