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4 Ways Employers Respond to Minimum Wage Laws (Besides Laying Off Workers) – Article by John Phelan

4 Ways Employers Respond to Minimum Wage Laws (Besides Laying Off Workers) – Article by John Phelan

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John Phelan
September 25, 2019
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Most of you will be familiar with a supply and demand graph. This shows a demand curve, which graphs the relationship between the price of something and the quantity demanded of that something, as well as a supply curve, which graphs the relationship between the price of something and the quantity supplied of that something. It is probably the most basic—and useful—model in economics.Whether the something in question is a good or a service, shoes or labor, the basic supply and demand model predicts that, ceteris paribus, an increase/fall in the price of something will lead to a fall/increase in the quantity demanded of that something—this is Econ 101.

In the context of minimum wage laws, this model predicts that setting a minimum wage above the equilibrium level or raising it will lead to a lower quantity of labor demanded. Often, people think this means fewer workers employed. So, when minimum wage hikes aren’t followed by increases in unemployment, people cite this as evidence that minimum wage hikes don’t reduce employment.

But a model is an abstraction from reality. In that messy reality, there are a number of things employers can do in response to a minimum wage hike that don’t involve laying off employees.

Remember, the simple supply and demand model says that increasing the price of labor leads to a lower quantity of labor demanded. But an employer doesn’t need to cut workers to achieve that. They can cut their hours instead.

Research from Seattle illustrates this. In 2014, the city council there passed an ordinance that raised the minimum wage in stages from $9.47 to $15.45 for large employers in 2018 and $16 in 2019. In 2017, research from the University of Washington examining the effects of the increases from $9.47 to as much as $11 in 2015 and to as much as $13 in 2016, found:

…the second wage increase to $13 reduced hours worked in low-wage jobs by around 9 percent, while hourly wages in such jobs increased by around 3 percent. Consequently, total payroll fell for such jobs, implying that the minimum wage ordinance lowered low-wage employees’ earnings by an average of $125 per month in 2016. [This was later revised to $74]

As the model predicts, the price of labor increased, and the quantity of labor demanded fell.

A follow-up paper looked at the impact on workers who were employed at the time of the wage hike, splitting them into experienced and inexperienced workers. It found that, on average, experienced workers earned $84 a month more, but about a quarter of their increase in pay came from taking additional work outside Seattle to make up for lost hours. Inexperienced workers, on the other hand, got no real earnings boost—they just worked fewer hours. Again, as the model predicts, the price of labor increased and the quantity of labor demanded fell. Instead of more money, they got more free time.

An employer could try to raise worker productivity to match the new minimum wage. One way to do this is simply to work their employees harder.

One paper by Hyejin Ku of University College London looks at the response of effort from piece-rate workers who hand-harvest tomatoes in the field to the increase in Florida’s minimum wage from $6.79 to $7.21 on January 1, 2009. It found that worker productivity (i.e., output per hour) in the bottom 40th percentile of the worker fixed effects distribution increases by about 3 percent relative to that in the higher percentiles. The author concludes:

These findings suggest that while an exogenously higher minimum wage implies a higher labor cost for the firm, the rising cost can be partly offset by the increased effort and productivity of below minimum wage workers.

Another recent study by economists Decio Coviello, Erika Deserranno, and Nicola Persico looks at the impact of a minimum wage hike on output per hour among salespeople from a large US retailer. “We find that a $1 increase in the minimum wage (1.5 standard deviations) causes individual productivity (sales per hour) to increase by 4.5%,” they note.

Importantly, tomato harvesting and sales are labor-intensive work. Any increase in output per hour can be assumed to come from increased physical effort.

Supporters of higher minimum wages talk almost exclusively about wages. But this is only one part of a worker’s total remuneration. The cost of an employee to the employer is not just the wage but total remuneration, including benefits such as health insurance. If legislation increases the wage, the employer can keep overall remuneration the same by reducing other elements.

A new paper from economists Jeffrey Clemens, Lisa B. Kahn, and Jonathan Meer finds that this is what happens in practice. The authors “explore the theoretical and empirical relationship between the minimum wage and fringe benefits, with a focus on employer-sponsored health insurance.” They find:

[There is] robust evidence that state-level minimum wage changes decreased the likelihood that individuals report having employer-sponsored health insurance. Effects are largest among workers in very low-paying occupations, for whom coverage declines offset 9 percent of the wage gains associated with minimum wage hikes. We find evidence that both insurance coverage and wage effects exhibit spillovers into occupations moderately higher up the wage distribution. For these groups, reductions in coverage offset a more substantial share of the wage gains we estimate.

Simply put, as the minimum wage rises, other elements of worker compensation fall.

If a business that plans to add 10 jobs over a year cancels these plans on the passage of a minimum wage hike, those 10 jobs have been destroyed without ever showing up in the data.

Economists from Washington University in St. Louis use wage data on one million hourly wage employees from over 300 firms spread across 23 two-digit NAICS industries to estimate the effect of six state minimum wage changes on employment. They find “…that firms are more likely to reduce hiring rather than increase turnover, reduce hours, or close locations in order to rebalance their workforce.”

As we look at responses over time, we also see the possibility that employers can substitute capital inputs for labor inputs.

Economists Grace Lordan and David Neumark analyze how changes to the minimum wage from 1980 to 2015 affected low-skill jobs in various sectors of the US economy, focusing particularly on “automatable jobs – jobs in which employers may find it easier to substitute machines for people,” such as packing boxes or operating a sewing machine. They find that across all industries they measured, raising the minimum wage by $1 equates to a decline in “automatable” jobs of 0.43 percent, with manufacturing even harder hit.

They conclude that

groups often ignored in the minimum wage literature are in fact quite vulnerable to employment changes and job loss because of automation following a minimum wage increase.

Minimum wage hikes are bad public policy. Economics, like all social sciences, has difficulty testing its models against data, but even where we can, the evidence bears this out.

John Phelan is an economist at the Center of the American Experiment and fellow of The Cobden Centre.

This article was originally published by the Foundation for Economic Education (FEE).

5 of the Worst Economic Predictions in History – Article by Luis Pablo de la Horra

5 of the Worst Economic Predictions in History – Article by Luis Pablo de la Horra

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Luis Pablo de la Horra
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Uncertainty makes human beings uncomfortable. Not knowing what’s going to happen in the future creates a sense of unrest in many people. That’s why we sometimes draw on predictions made by leading experts in their respective fields to make decisions in our daily lives. Unfortunately, history has shown that experts aren’t often much better than the average person when it comes to forecasting the future. And economists aren’t an exception. Here are five economic predictions that never came true.

1. Irving Fisher Predicting a Stock-Market Boom—Right Before the Crash of 1929

Irving Fisher was one of the great economists of the first half of twentieth century. His contributions to economic science are varied: the relationship between inflation and interest rates, the use of price indexes or the restatement of the quantity theory of money are some of them. Yet he is sometimes remembered by an unfortunate statement he made in the days prior to the Crash of 1929. Fisher said that “stock prices have reached what looks like a permanently high plateau (…) I expect to see the stock market a good deal higher within a few months.” A few days later, the stock market crashed with devastating consequences.  After all, even geniuses aren’t exempt from making mistakes.

2. Paul Ehrlich on the Looming ‘Population Bomb’

In 1968, biologist Paul Ehrlich published a book where he argued that hundreds of millions of people would starve to death in the following decades as a result of overpopulation. He went as far as far as to say that “the battle to feed all of humanity is over (…) nothing can prevent a substantial increase in the world death rate.” Of course, Ehrlich’s predictions never came true. Since the publication of the book, the death rate has moved from 12.44 permille in 1968 to 7.65 permille in 2016, and undernourishment has declined dramatically even though the population has doubled since 1950. Seldom in history has someone been so wrong about the future of humankind.

3. The 1990s Great Depression that Never Happened

Economist Ravi Batra reached the number one on The New York Time Best Seller List in 1987 thanks to his book The Great Depression of 1990. From the title, one can easily infer what was the main thesis of the book, namely: An economic crisis is imminent, and it will be a tough one. Fortunately, his prediction failed to come true. In fact, the 1990s was a period of relative stability and strong economic growth, with the US stock market growing at an 18 percent annualized rate. Not so bad for an economic depression, right?

4. Alan Greenspan on Interest Rates

In September 2007, former Fed Chairman Alan Greenspan released a memoir called The Age of Turbulence: Adventures in a New World. In the book, he claimed that the economy was heading towards two-digit interest rates due to expected inflationary pressures. According to Greenspan, the Fed would be compelled to drastically raise its target interest rate to fulfill the 2-percent inflation mandate. One year later, the Fed Funds rate was at historical lows, reaching the zero-lower bound shortly after.

5. Peter Schiff and the End of the World

Financial commentator Peter Schiff became famous in the aftermath of the 2007-2008 Financial Crisis for having foreseen the housing crash back in 2006 (even a broken clock is right twice a day). Since then, he has been predicting economic catastrophes every other day, with very limited success. There are many examples of failed predictions from which to draw upon. For instance, in a 2010 video (see below), Schiff foretold that Quantitative Easing (the unconventional monetary policy undertaken by the Fed between 2008 and 2014) would result in hyperinflation and the eventual destruction of the Dollar. Unfortunately for Schiff, the average inflation rate per year since the onset of QE has been 1.68%, slightly below the 2% target of the Fed.

 

Luis Pablo is a PhD Candidate in Economics at the University of Valladolid. He has been published by several media outlets, including The American Conservative, CapX and the Foundation for Economic Education, among others.

This article was originally published on Intellectual Takeout.

Review of Philip Tetlock’s “Superforecasting” – Article by Adam Alonzi

Review of Philip Tetlock’s “Superforecasting” – Article by Adam Alonzi

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The New Renaissance Hat
Adam Alonzi
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Alexander Consulting the Oracle of Apollo, Louis Jean Francois Lagrenée. 1789, Oil on Canvas.

“All who drink of this treatment recover in a short time, except those whom it does not help, who all die. It is obvious, therefore, that it fails only in incurable cases.”

-Galen

Before the advent of evidence-based medicine, most physicians took an attitude like Galen’s toward their prescriptions. If their remedies did not work, surely the fault was with their patient. For centuries scores of revered doctors did not consider putting bloodletting or trepanation to the test. Randomized trials to evaluate the efficacy of a treatment were not common practice. Doctors like Archie Cochrane, who fought to make them part of standard protocol, were met with fierce resistance. Philip Tetlock, author of Superforecasting: The Art and Science of Prediction (2015), contends that the state of forecasting in the 21st century is strikingly similar to medicine in the 19th. Initiatives like the Good Judgement Project (GJP), a website that allows anyone to make predictions about world events, have shown that even a discipline that is largely at the mercy of chance can be put on a scientific footing.

More than once the author reminds us that the key to success in this endeavor is not what you think or what you know, but how you think. For Tetlock pundits like Thomas Friedman are the “exasperatingly evasive” Galens of the modern era. In the footnotes he lets the reader know he chose Friedman as target strictly because of his prominence. There are many like him. Tetlock’s academic work comparing random selections with those of professionals led media outlets to publish, and a portion of their readers to conclude, that expert opinion is no more accurate than a dart-throwing chimpanzee. What the undiscerning did not consider, however, is not all of the experts who participated failed to do better than chance.

Daniel Kahneman hypothesized that “attentive readers of the New York Times…may be only slightly worse” than these experts corporations and governments so handsomely recompense. This turned out to be a conservative guess. The participants in the Good Judgement Project outperformed all control groups, including one composed of professional intelligence analysts with access to classified information. This hodgepodge of retired bird watchers, unemployed programmers, and news junkies did 30% better than the “pros.” More importantly, at least to readers who want to gain a useful skillset as well as general knowledge, the managers of the GJP have identified qualities and ways of thinking that separate “superforecasters” from the rest of us. Fortunately they are qualities we can all cultivate.

While the merits of his macroeconomic theories can be debated, John Maynard Keynes was an extremely successful investor during one of the bleakest periods in international finance. This was no doubt due in part to his willingness to make allowance for new information and his grasp of probability. Participants in the GJP display open-mindedness, an ability and willingness to repeatedly update their forecasts, a talent to neither under- nor over-react to new information by putting it into a broader context,  and a predilection for mathematical thinking (though those interviewed admitted they rarely used an explicit equation to calculate their answer). The figures they give also tend to be more precise than their less successful peers. This “granularity” may seem ridiculous at first. I must confess that when I first saw estimates on the GJP of 34% or 59%, I would chuckle a bit. How, I asked myself, is a single percentage point meaningful? Aren’t we just dealing with rough approximations? Apparently not.

Tetlock reminds us that the GJP does not deal with nebulous questions like “Who will be president in 2027?” or “Will a level 9 earthquake hit California two years from now?” However, there are questions that are not, in the absence of unforeseeable Black Swan events, completely inscrutable. Who will win the Mongolian presidency? Will Uruguay sign a trade agreement with Laos in the next six months? These are parts of highly complex systems, but they can be broken down into tractable subproblems.

Using numbers instead of words like “possibly”, “probably”, “unlikely”, etc., seems unnatural. It gives us wiggle room and plausible deniability. They also cannot be put on any sort of record to keep score of how well we’re doing. Still, to some it may seem silly, pedantic, or presumptuous. If Joint Chiefs of Staff had given the exact figure they had in mind (3 to 1) instead of the “fair chance” given to Kennedy, the Bay of Pigs debacle may have never transpired. Because they represent ranges of values instead of single numbers, words can be retroactively stretched or shrunk to make blunders seem a little less avoidable. This is good for advisors looking to cover their hides by hedging their bets, but not so great for everyone else.

If American intelligence agencies had presented the formidable but vincible figure of 70% instead of a “slam dunk” to Congress, a disastrous invasion and costly occupation would have been prevented. At this point it is hard not to see the invasion as anything as a mistake, but even amidst these emotions we must be wary of hindsight. Still, a 70% chance of being right means there is a 30% chance of being wrong. It is hardly a “slam dunk.” No one would feel completely if an oncologist told them they are 70% sure the growth is not malignant. There are enormous consequences to sloppy communications. However, those with vested interests are more than content with this approach if it agrees with them, even if it ends up harming them.

When Nate Silver put the odds of the 2008 election in Obama’s favor, he was panned by Republicans as a pawn of the liberal media. He was quickly reviled by Democrats when he foresaw a Republican takeover of the Senate. It is hard to be a wizard when the king, his court, and all the merry peasants sweeping the stables would not know a confirmation bias from their right foot. To make matters worse, confidence is widely equated with capability. This seems to be doubly true of groups of people, particularly when they are choosing a leader. A mutual-fund manager who tells his clients they will see great returns on a company is viewed as stronger than a Poindexter prattling on about Bayesian inference and risk management.

The GJP’s approach has not spread far — yet. At this time most pundits, consultants, and self-proclaimed sages do not explicitly quantify their success rates, but this does not stop corporations, NGOs, and institutions at all levels of government from paying handsomely for the wisdom of untested soothsayers. Perhaps they have a few diplomas, but most cannot provide compelling evidence for expertise in haruspicy (sans the sheep’s liver). Given the criticality of accurate analyses to saving time and money, it would seem as though a demand for methods to improve and assess the quality of foresight would arise. Yet for the most part individuals and institutions continue to happily grope in the dark, unaware of the necessity for feedback when they misstep — afraid of having their predictions scrutinized or having to take the pains to scrutinize their predictions.

David Ferrucci is wary of the “guru model” to settling disputes. No doubt you’ve witnessed or participated in this kind of whimpering fracas: one person presents a Krugman op-ed to debunk a Niall Ferguson polemic, which is then countered with a Tommy Friedman book, which was recently excoriated  by the newest leader of the latest intellectual cult to come out of the Ivy League. In the end both sides leave frustrated. Krugman’s blunders regarding the economic prospects of the Internet, deflation, the “imminent” collapse of the euro (said repeatedly between 2010 and 2012) are legendary. Similarly, Ferguson, who strongly petitioned the Federal Reserve to reconsider quantitative easing, lest the United States suffer Weimar-like inflation, has not yet been vindicated. He and his colleagues responded in the same way as other embarrassed prophets: be patient, it has not happened, but it will! In his defense, more than one clever person has criticized the way governments calculate their inflation rates…

Paul Ehrlich, a darling of environmentalist movement, has screeched about the detonation of a “population bomb” for decades. Civilization was set to collapse between 15 and 30 years from 1970. During the interim 100 to 200 million would annually starve to death, by the year 2000 no crude oil would be left, the prices of raw materials would skyrocket, and the planet would be in the midst of a perpetual famine. Tetlock does not mention Ehrlich, but he is, particularly given his persisting influence on Greens, as or more deserving of a place in this hall of fame as anyone else. Larry Kudlow continued to assure the American people that the Bush tax breaks were producing massive economic growth. This continued well into 2008, when he repeatedly told journalists that America was not in a recession and the Bush boom was “alive and well.” For his stupendous commitment to his contention in the face of overwhelming evidence to the contrary, he was nearly awarded a seat in the Trump cabinet.

This is not to say a mistake should become the journalistic equivalent of a scarlet letter. Kudlow’s slavish adherence to his axioms is not unique. Ehrlich’s blindness to technological advances is not uncommon, even in an era dominated by technology. By failing to set a timeline or give detailed causal accounts, many believe they have predicted every crash since they learned how to say the word. This is likely because they begin each day with the same mantra: “the market will crash.”  Yet through an automatically executed routine of psychological somersaults, they do not see they were right only once and wrong dozens, hundreds, or thousands of times. This kind of person is much more deserving of scorn than a poker player who boasts about his victories, because he is (likely) also aware of how often he loses. At least he’s not fooling himself. The severity of Ehrlich’s misfires is a reminder of what happens when someone looks too far ahead while assuming all things will remain the same. Ceteris paribus exists only in laboratories and textbooks.

Axioms are fates accepted by different people as truth, but the belief in Fate (in the form of retroactive narrative construction) is a nearly ubiquitous stumbling block to clear thinking. We may be far removed from Sophocles, but the unconscious human drive to create sensible narratives is not peculiar to fifth-century B.C. Athens. A questionnaire given to students at Northwestern showed that most believed things had turned out for the best even if they had gotten into their first pick. From an outsider’s perspective this is probably not true. In our cocoons we like to think we are in the right place either through the hand of fate or through our own choices. Atheists are not immune to this Panglossian habit. Our brains are wired for stories, but the stories we tell ourselves about ourselves seldom come out without distortions. We can gain a better outside view, which allows us to see situations from perspectives other than our own, but only through regular practice with feedback. This is one of the reasons groups are valuable.

Francis Galton asked 787 villagers to guess the weight of an ox hanging in the market square. The average of their guesses (1,197 lbs) turned out to be remarkably close to its actual weight (1,198 lbs). Scott Page has said “diversity trumps ability.” This is a tad bold, since legions of very different imbeciles will never produce anything of value, but there is undoubtedly a benefit to having a group with more than one point of view. This was tested by the GJP. Teams performed better than lone wolves by a significant margin (23% to be exact). Partially as a result of encouraging one another and building a culture of excellence, and partially from the power of collective intelligence.

“No battle plan survives contact with the enemy.”

-Helmuth von Moltke

“Everyone has a plan ’till they get punched in the mouth.”

-Mike Tyson

When Archie Cochrane was told he had cancer by his surgeon, he prepared for death. Type 1 thinking grabbed hold of him and did not doubt the diagnosis. A pathologist later told him the surgeon was wrong. The best of us, under pressure, fall back on habitual modes of thinking. This is another reason why groups are useful (assuming all their members do not also panic). Organizations like the GJP and the Millennium Project are showing how well collective intelligence systems can perform. Helmuth von Moltke and Mike Tyson aside, a better motto, substantiated by a growing body of evidence, comes from Dwight  Eisenhower: “plans are useless, but planning is indispensable.”

Adam Alonzi is a writer, biotechnologist, documentary maker, futurist, inventor, programmer, and author of the novels A Plank in Reason and Praying for Death: A Zombie Apocalypse. He is an analyst for the Millennium Project, the Head Media Director for BioViva Sciences, and Editor-in-Chief of Radical Science News. Listen to his podcasts here. Read his blog here.

The World’s Poorest People Are Getting Richer Faster Than Anyone Else – Article by Alexander Hammond

The World’s Poorest People Are Getting Richer Faster Than Anyone Else – Article by Alexander Hammond

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The New Renaissance Hat
Alexander Hammond
October 29, 2017
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Last Tuesday marked the 25th anniversary of the United Nations’ International Day for the Eradication of Poverty. The date intentionally coincides with the 30th anniversary of the Call to Action, which saw the French anti-poverty campaigner Father Joseph Wresinski ask the international community, in front of 100,000 Parisians, to “strive to eradicate extreme poverty”.

To mark the occasion, Antonio Guterres, the United Nations Secretary-General, was featured in a short video assessing the current state of world poverty. Despite noting such issues as unemployment, inequality, and conflict continuing in some regions, Guterres correctly observed that since 1990 the world has made “remarkable progress in eradicating poverty.”

While it is valuable to acknowledge that problems remain, it is important to reflect on just how far we’ve come.

Alleviating Poverty Fast

The speed of poverty alleviation in the last 25 years has been historically unprecedented. Not only is the proportion of people in poverty at a record low, but, in spite of adding 2 billion to the planet’s population, the overall number of people living in extreme poverty has fallen, too.

As Johan Norberg writes in his book Progress, “If you had to choose a society to live in but did not know what your social or economic position would be, you would probably choose the society with the lowest proportion (not the lowest numbers) of poor, because this is the best judgement of the life of an average citizen.” Well, in 1820, 94 percent of the world’s population lived in extreme poverty (less than $1.90 per day adjusted for purchasing power). In 1990 this figure was 34.8 percent, and in 2015, just 9.6 percent.

In the last quarter century, more than 1.25 billion people escaped extreme poverty – that equates to over 138,000 people (i.e., 38,000 more than the Parisian crowd that greeted Father Wresinski in 1987) being lifted out of poverty every day. If it takes you five minutes to read this article, another 480 people will have escaped the shackles of extreme of poverty by the time you finish. Progress is awesome. In 1820, only 60 million people didn’t live in extreme poverty. In 2015, 6.6 billion did not.

Now let’s consider those people who are still trapped in extreme poverty. The Oxford University scholar Max Roser’s website, Our World in Data, used World Bank databases to estimate that in 2013, there were 746 million people living in extreme poverty. Of these people, slightly more than 380 million resided in Africa, with Nigeria being home to largest number (86 million). Meanwhile, 327 million of those in extreme poverty lived in Asia, with India having the largest proportion by far (218 million). China had 25 million. The remaining 35 million lived in South America (19 million), North America (13 million), Oceania (2.5 million) and Europe (0.7 million.)

Put differently, of those who live in extreme poverty, over 40 percent resided in just two nations: India and Nigeria.

The Poorest of the Poor

Since its economic liberalization reforms in 1991, India’s average income has increased by 7.5 percent per year. That means that average income has more than tripled over the last quarter century. As wealth increased, the poverty rate in India declined by almost 24 percent. But most significantly, for the Dalits – the poorest and lowest caste in Indian society – the poverty rate during this period declined even faster, by 31 percent. That means that in the nation that has by far the largest number of people in extreme poverty, it is the people at the very bottom of the social strata who are getting richer faster.

A similar trend can be seen in Nigeria. Since the new millennium, gross domestic income per capita has increased by over 800 percent, from $270 to over $2,450. There is much work to be done, but this level of progress shows that even in the poorest countries, the speed of economic growth is encouraging.

In order to help the poorest, consider the impact free-market capitalism has had in the last 200 years in alleviating extreme poverty. The Industrial Revolution turned the once-impoverished western countries into abundant societies. The new age of globalization, which started around 1980, saw the developing world enter the global economy and resulted in the largest escape from poverty ever recorded. That is something that the late Father Wresinski would have been eager to celebrate.

Alexander C. R. Hammond is the Research Assistant for HumanProgress.org, a project of the Cato Institute’s Center for Global Liberty and Prosperity. He writes about economic freedom, globalization, and human well-being.

This article was published by The Foundation for Economic Education and may be freely distributed, subject to a Creative Commons Attribution 4.0 International License, which requires that credit be given to the author. Read the original article.

Why Robots Won’t Cause Mass Unemployment – Article by Jonathan Newman

Why Robots Won’t Cause Mass Unemployment – Article by Jonathan Newman

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The New Renaissance Hat
Jonathan Newman
August 5, 2017
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I made a small note in a previous article about how we shouldn’t worry about technology that displaces human workers:

The lamenters don’t seem to understand that increased productivity in one industry frees up resources and laborers for other industries, and, since increased productivity means increased real wages, demand for goods and services will increase as well. They seem to have a nonsensical apocalyptic view of a fully automated future with piles and piles of valuable goods everywhere, but nobody can enjoy them because nobody has a job. I invite the worriers to check out simple supply and demand analysis and Say’s Law.

Say’s Law of markets is a particularly potent antidote to worries about automation, displaced workers, and the so-called “economic singularity.” Jean-Baptiste Say explained how over-production is never a problem for a market economy. This is because all acts of production result in the producer having an increased ability to purchase other goods. In other words, supplying goods on the market allows you to demand goods on the market.

Say’s Law, Rightly Understood

J.B. Say’s Law is often inappropriately summarized as “supply creates its own demand,” a product of Keynes having “badly vulgarized and distorted the law.”

Professor Bylund has recently set the record straight regarding the various summaries and interpretations of Say’s Law.

Bylund lists the proper definitions:

Say’s Law:

  • Production precedes consumption.
  • Demand is constituted by supply.
  • One’s demand for products in the market is limited by one’s supply.
  • Production is undertaken to facilitate consumption.
  • Your supply to satisfy the wants of others makes up your demand for for others’ production.
  • There can be no general over-production (glut) in the market.

NOT Say’s Law:

  • Production creates its own demand.
  • Aggregate supply is (always) equal to aggregate demand.
  • The economy is always at full employment.
  • Production cannot exceed consumption for any good.

Say’s Law should allay the fears of robots taking everybody’s jobs. Producers will only employ more automated (read: capital-intensive) production techniques if such an arrangement is more productive and profitable than a more labor-intensive technique. As revealed by Say’s Law, this means that the more productive producers have an increased ability to purchase more goods on the market. There will never be “piles and piles of valuable goods” laying around with no one to enjoy them.

Will All the Income Slide to the Top?

The robophobic are also worried about income inequality — all the greedy capitalists will take advantage of the increased productivity of the automated techniques and fire all of their employees. Unemployment will rise as we run out of jobs for humans to do, they say.

This fear is unreasonable for three reasons. First of all, how could these greedy capitalists make all their money without a large mass of consumers to purchase their products? If the majority of people are without incomes because of automation, then the majority of people won’t be able to help line the pockets of the greedy capitalists.

Second, there will always be jobs because there will always be scarcity. Human wants are unlimited, diverse, and ever-changing, yet the resources we need to satisfy our desires are limited. The production of any good requires labor and entrepreneurship, so humans will never become unnecessary.

Finally, Say’s Law implies that the profitability of producing all other goods will increase after a technological advancement in the production of one good. Real wages can increase because the greedy robot-using capitalists now have increased demands for all other goods. I hope the following scenario makes this clear.

The Case of the Robot Fairy

This simple scenario shows why the increased productivity of a new, more capital-intensive technique makes everybody better off in the end.

Consider an island of three people: Joe, Mark, and Patrick. The three of them produce coconuts and berries. They prefer a varied diet, but they have their own comparative advantages and preferences over the two goods.

Patrick prefers a stable supply of coconuts and berries every week, and so he worked out a deal with Joe such that Joe would pay him a certain wage in coconuts and berries every week in exchange for Patrick helping Joe gather coconuts. If they have a productive week, Joe gets to keep the extra coconuts and perhaps trade some of the extra coconuts for berries with Mark. If they have a less than productive week, then Patrick still receives his certain wage and Joe has to suffer.

On average, Joe and Patrick produce 50 coconuts/week. In exchange for his labor, Patrick gets 10 coconuts and 5 quarts of berries every week from Joe.

Mark produces the berries on his own. He produces about 30 quarts of berries every week. Joe and Mark usually trade 20 coconuts for 15 quarts of berries. Joe needs some of those berries to pay Patrick, but some are for himself because he also likes to consume berries.

In sum, and for an average week, Joe and Patrick produce 50 coconuts and Mark produces 30 quarts of berries. Joe ends up with 20 coconuts and 10 quarts of berries, Patrick ends up with 10 coconuts and 5 quarts of berries, and Mark ends up with 20 coconuts and 15 quarts of berries.

Production Trade Consumption
Joe 50 Coconuts (C) Give 20C for 15B 20C + 10B
Patrick n/a 10C + 5B (wage)
Mark 30 qts. Berries (B) Give 15B for 20C 20C + 15B

The Robot Fairy Visits

One night, the robot fairy visits the island and endows Joe with a Patrick 9000, a robot that totally displaces Patrick from his job, plus some. With the robot, Joe can now produce 100 coconuts per week without the human Patrick.

What is Patrick to do? Well, he considers two options: (1) Now that the island has plenty of coconuts, he could go work for Mark and pick berries under a similar arrangement he had with Joe; or (2) Patrick could head to the beach and start catching some fish, hoping that Joe and Mark will trade with him.

While these options weren’t Patrick’s top choices before the robot fairy visited, now they are great options precisely because Joe’s productivity has increased. Joe’s increased productivity doesn’t just mean that he is richer in terms of coconuts, but his demands for berries and new goods like fish increase as well (Say’s Law), meaning the profitability of producing all other goods that Joe likes also increases!

Option 1

If Patrick chooses option 1 and goes to work for Mark, then both berry and coconut production totals will increase. Assuming berry production doesn’t increase as much as coconut production, the price of a coconut in terms of berries will decrease (Joe’s marginal utility for coconuts will also be very low), meaning Mark can purchase many more coconuts than before.

Suppose Patrick adds 15 quarts of berries per week to Mark’s production. Joe and Mark could agree to trade 40 coconuts for 20 quarts of berries, so Joe ends up with 60 coconuts and 20 quarts of berries. Mark can pay Patrick up to 19 coconuts and 9 quarts of berries and still be better off compared to before Joe got his Patrick 9000 (though Patrick’s marginal productivity would warrant something like 12 coconuts and 9 quarts of berries or 18 coconuts and 6 quarts of berries or some combination between those — no matter what, everybody is better off).

Production Trade Consumption
Joe 100C Give 40C for 20B 60C + 20B
Patrick 45B n/a 16C + 7B (wage)
Mark Give 20B for 40C 24C + 18B

Option 2

If Mark decides to reject Patrick’s offer to work for him, then Patrick can choose option 2, catching fish. It involves more uncertainty than what Patrick is used to, but he anticipates that the extra food will be worth it.

Suppose that Patrick can produce just 5 fish per week. Joe, who is practically swimming in coconuts pays Patrick 20 coconuts for 1 fish. Mark, who is excited about more diversity in his diet and even prefers fish to his own berries, pays Patrick 10 quarts of berries for 2 fish. Joe and Mark also trade some coconuts and berries.

In the end, Patrick gets 20 coconuts, 10 quarts of berries, and 2 fish per week. Joe gets 50 coconuts, 15 quarts of berries, and 1 fish per week. Mark gets 30 coconuts, 5 quarts of berries, and 2 fish per week. Everybody prefers their new diet.

Production Trade Consumption
Joe 100C Give 50C for 15B + 1F 50C + 15B + 1F
Patrick 5 fish (F) Give 2F for 20C + 10B 20C + 10B + 2F
Mark 30B Give 25B for 30C + 1F 30C + 5B + 2F

Conclusion

The new technology forced Patrick to find a new way to sustain himself. These new jobs were necessarily second-best (at most) to working for Joe in the pre-robot days, or else Patrick would have pursued them earlier. But just because they were suboptimal pre-robot does not mean that they are suboptimal post-robot. The island’s economy was dramatically changed by the robot, such that total production (and therefore consumption) could increase for everybody. Joe’s increased productivity translated into better deals for everybody.

Of course, one extremely unrealistic aspect of this robot fairy story is the robot fairy. Robot fairies do not exist, unfortunately. New technologies must be wrangled into existence by human labor and natural resources, with the help of capital goods, which also must be produced using labor and natural resources. Also, new machines have to be maintained, replaced, refueled, and rejiggered, all of which require human labor. Thus, we have made this scenario difficult for ourselves by assuming away all of the labor that would be required to produce and maintain the Patrick 9000. Even so, we see that the whole economy, including the human Patrick, benefits as a result of the new robot.

This scenario highlights three important points:

(1) Production must precede consumption, even for goods you don’t produce (Say’s Law). For Mark to consume coconuts or fish, he has to supply berries on the market. For Joe to consume berries or fish, he has to supply coconuts on the market. Patrick produced fish so that he could also enjoy coconuts and berries.

(2) Isolation wasn’t an option for Patrick. Because of the Law of Association (a topic not discussed here, but important nonetheless), there is always a way for Patrick to participate in a division of labor and benefit as a result, even after being displaced by the robot.

(3) Jobs will never run out because human wants will never run out. Even if our three island inhabitants had all of the coconuts and berries they could eat before the robot fairy visited, Patrick was able to supply additional want satisfaction with a brand new good, the fish. In the real world, new technologies often pave the way for brand new, totally unrelated goods to emerge and for whole economies to flourish. Hans Rosling famously made the case that the advent of the washing machine allowed women and their families to emerge from poverty:

And what’s the magic with them? My mother explained the magic with this machine the very, very first day. She said, “Now Hans, we have loaded the laundry. The machine will make the work. And now we can go to the library.” Because this is the magic: you load the laundry, and what do you get out of the machine? You get books out of the machines, children’s books. And mother got time to read for me. She loved this. I got the “ABC’s” — this is where I started my career as a professor, when my mother had time to read for me. And she also got books for herself. She managed to study English and learn that as a foreign language. And she read so many novels, so many different novels here. And we really, we really loved this machine.

And what we said, my mother and me, “Thank you industrialization. Thank you steel mill. Thank you power station. And thank you chemical processing industry that gave us time to read books.”

Similarly, the Patrick 9000, a coconut-producing robot, made fish production profitable. Indeed, when we look at the industrial revolution and the computer revolution, we do not just see an increase in the production of existing goods. We see existing goods increasing in quantity and quality; we see brand new consumption goods and totally new industries emerging, providing huge opportunities for employment and future advances in everybody’s standard of living.

Jonathan Newman is Assistant Professor of Economics and Finance at Bryan College. He earned his PhD at Auburn University and is a Mises Institute Fellow. He can be contacted here.

Panel – Artificial Intelligence & Robots: Economy of the Future or End of Free Markets? – Michael Shermer, Edward Hudgins, Zoltan Istvan, Gennady Stolyarov II, Eric Shuss

Panel – Artificial Intelligence & Robots: Economy of the Future or End of Free Markets? – Michael Shermer, Edward Hudgins, Zoltan Istvan, Gennady Stolyarov II, Eric Shuss

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G. Stolyarov II

Michael Shermer

Edward Hudgins

Zoltan Istvan

Eric Shuss

July 28, 2017

Gennady Stolyarov II, Chairman of the U.S. Transhumanist Party, participated in the panel discussion at FreedomFest in Las Vegas on July 21, 2017, entitled “AI & Robots: Economy of the Future or End of Free Markets?” The panelists presented a set of realistic, balanced analyses on the impact of artificial intelligence and automation.

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For this event there was an outstanding speaker lineup, with moderator Michael Shermer, followed by Edward Hudgins, Peter Voss, Zoltan Istvan, Gennady Stolyarov II, and Eric Shuss.

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The general focus of Mr. Stolyarov’s remarks was to dispel AI-oriented doomsaying and convey the likely survival of the capitalist economy for at least the forthcoming several decades – since narrow AI cannot automate away jobs requiring creative human judgment.

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The video was recorded by filmmaker Ford Fischer and is reproduced with his permission.

Visit Ford Fischer’s News2Share channel here.

Visit the U.S. Transhumanist Party website here.

Join the U.S. Transhumanist Party for free by filling out our membership application form here.

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Gennady Stolyarov II Discusses Artificial Intelligence with Ford Fischer

Gennady Stolyarov II Discusses Artificial Intelligence with Ford Fischer

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G. Stolyarov II

July 28, 2017

U.S. Transhumanist Party Chairman Gennady Stolyarov II discusses why artificial intelligence is not a threat to humanity’s existence or to jobs in many professions in the proximate several decades.

This discussion was recorded as part of a larger interview with filmmaker Ford Fischer on July 21, 2017. It was intended to preview and elaborate upon some of Mr. Stolyarov’s remarks at the discussion panel later that same day, entitled “AI & Robots: Economy of the Future or End of Free Markets?”

The video is reproduced on Mr. Stolyarov’s YouTube channel with permission from Ford Fischer.

Visit Ford Fischer’s News2Share channel here.

Visit the U.S. Transhumanist Party website here.

Join the U.S. Transhumanist Party for free by filling out our membership application form here.

Visit the U.S. Transhumanist Party Facebook page here.

Visit the U.S. Transhumanist Party Twitter page here.

AI and the Future of Free Markets: A Nuanced View – Preview of FreedomFest 2017 Panel Comments by G. Stolyarov II

AI and the Future of Free Markets: A Nuanced View – Preview of FreedomFest 2017 Panel Comments by G. Stolyarov II

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G. Stolyarov II

July 18, 2017

Gennady Stolyarov II, Chairman of the United States Transhumanist Party, offers a preview of his forthcoming remarks at the July 21, 2017, FreedomFest panel in Las Vegas, entitled “Artificial Intelligence & Robots: Economy of the Future or End of Free Markets?”

Find more information regarding the FreedomFest panel here.

Visit the U.S. Transhumanist Party website here.

Become a member of the U.S. Transhumanist Party for free by filling out our concise application form.

Bitcoin Is All that Stands between My Family and Starvation – Article by Anonymous Venezuelan

Bitcoin Is All that Stands between My Family and Starvation – Article by Anonymous Venezuelan

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Anonymous Venezuelan
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I am writing this post in response to comments I get from people when I try and explain what Bitcoin is. Uneducated people have told me countless times that bitcoins are only used by criminals. I want to debunk that myth and explain how the real potential for bitcoins is so much bigger than the black market can ever be.

Bitcoin is literally saving my family from hunger and giving them the financial freedom to immigrate in the near future. My parents and sister live in Venezuela. A lot of you might not know exactly what’s happening there so here are the cliff notes.

  1. An incredibly incompetent socialist government took power.
  2. They created strict currency controls that made it impossible for people to buy goods in anything other than their local currency. If you owned a business and needed to import something from overseas you needed the government’s approval to exchange the local currency to US dollars
  3. This made running a business almost impossible. To operate you had to buy US dollars on a black market or bribe a government official to exchange currency.
  4. When oil prices dropped the government quickly ran out of money causing an expected inflation of 1800% in 2017.

For more about what’s going on in Venezuela check our www.reddit.com/r/arepas

Things started to get really bad in Venezuela around 2014. My father owned at the time a successful air conditioning repair business but he knew things were about to take a turn for the worse. We came up with a plan to open a US bank account and convert bolívars (Venezuelan currency) into US dollars so we would be protected from inflation. We quickly ran into logistical problems, physically getting and safely transporting the money out of the country.

Caracas is one of the most violent cities in the world. Carjackings are common and people are killed for their cell phones. The airport police are corrupt and just as likely to rob you, and the money can’t be put in the local bank because you aren’t allowed to have dollars.

I’m 2014 Bitcoin was a new technology so we were very skeptical about it but we didn’t have any other options.

Fast forward to 2017. The economy is Venezuela is dead. My father lost his air conditioning business and people like our neighbors that were middle and upper class a few years ago can’t afford food. Thanks to the rising price of Bitcoin and its relative stability (to the Venezuelan economy), my family is part of a very small fortunate minority that can afford to help feed their community and also potentially immigrate to another country.

Now consider how big the Venezuelan economy is and that other countries like Brazil and Argentina are also experiencing similar problems. If citizens converted only a small amount of their savings into bitcoins this would represent an incredible amount of money.

Bitcoin can give anyone the ability to trade freely and protect themselves financially against corrupt and incompetent governments. In a world of 6 billion people, most of whom have no access or are ineligible for basic banking services, and an increasing number of governments opposing free speech and basic human rights, Bitcoin might not be the perfect hero we want but it’s what we need.

So in summary, Bitcoin is used by criminals the same way cash is used by criminals. If you take one step back you’ll realize that the possible legitimate uses for Bitcoin are far greater than the black market can ever be.

Reprinted from Reddit and the Foundation for Economic Education.

The author of this essay requested to remain anonymous.

The Evidence Weighs in Favor of Immigration – Article by Luis Pablo de la Horra

The Evidence Weighs in Favor of Immigration – Article by Luis Pablo de la Horra

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The New Renaissance HatLuis Pablo de la Horra
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In a previous article, I analyzed the economics of immigration from a theoretical perspective. I concluded that economic theory clearly supports immigration-friendly policies since they benefit all parties involved. In this article, I will examine the empirical evidence on the effects of immigration on host countries and immigrants themselves.

Effects on Employment, Wages, and Public Finances

High immigration rates are often associated with rises in unemployment. The logic behind this (flawed) reasoning is straightforward: if an economy can only absorb a fixed number of jobs and the labor force increases, the unemployment rate will inevitably rise. What’s wrong about this statement? Simple: the economy is not a zero-sum game.

In other words, the number of jobs available increases as the economy grows. After World War II, the US labor force increased dramatically due to immigration and the massive entry of women into the labor market. It moved from 60 million in 1950 to around 150 million workers in 2007. And yet, the unemployment rate in 2007 was as low as 4.6 percent, near full employment.

In a survey paper on the economic effects of immigration, published in 2011, Sari Pekkala Kerr and William R. Kerr concluded that the long-term impact of immigration on employment is negligible. In their own words,

The large majority of studies suggest that immigration does not exert significant effects on native labor market outcomes. Even large, sudden inflows of immigrants were not found to reduce native wages or employment significantly.

As suggested by the research conducted by Giovanni Peri, professor of Economics at UC Davis, immigration has positive effects on productivity since it expands the productive capacity of the economy, which in turn results in higher wages in the long run. Nonetheless, there are certain disagreements on how immigration affects native, low-skilled workers (mainly high school dropouts).

Different studies point at a wage decline between 0 (no effects at all) and 7 percent for this segment of population. Even when assuming the worst-case scenario of a 7 percent decline (which does not consider the investment in capital undertaken by companies to compensate for a decline in the capital-labor ratio), low-skilled immigration has net positive economic effects for host societies, allowing native workers to perform more productive jobs and increasing the specialization of the economy.

One of the most popular arguments against immigration is the issue of welfare benefits. Immigrants are believed to pose a burden on the host economy. Their net fiscal impact (defined as taxes paid by immigrants minus public services and benefits received) is thought to be overwhelmingly negative when compared with the fiscal impact of natives. Yet the evidence does not support this idea. As pointed out by Kerr and Kerr,

It is very clear that the net social impact of an immigrant over his or her lifetime depends substantially and in predictable ways on the immigrants’ age at arrival, education, reason for migration, and similar […] The estimated net fiscal impact of migrants also varies substantially across studies, but the overall magnitudes relative to the GDP remain modest […] The more credible analyses typically find small fiscal effects.

Therefore, there are no good reasons to impose tough restrictions on labor mobility in the name of fiscal sustainability.

The Place Premium: How to Reduce Poverty by Lowering Immigration Barriers

Wage differentials among countries can be explained by drawing on the concept of Place Premium, that is, the increase in earnings that a worker automatically experiences when moving to a high-productivity country. This increase is due to several factors: differences in capital stock, infrastructure, proximity to other high-productivity workers, etc.

The Place Premium of potential immigrants moving to the US has been estimated for a few countries. A Haitian worker that were to relocate to the US would see her PP-adjusted earnings automatically rise by 700% when compared to the same worker in Haiti performing an equivalent job (or a job that requires the same skills and education). Similarly, a worker from Guatemala or Nicaragua would more than triple her earnings, while a Filipino would increase her purchasing power by 3.5 times. In other words, relaxing barriers and letting more immigrants into higher-productivity countries seems to be one of the most effective ways to improve the life of millions of people worldwide.

All in all, the economic benefits of immigration seem obvious for both host countries and immigrants. The data shows that restrictive immigration policies have adverse effects on host economies and prevent would-be immigrants from increasing their income by migrating to higher-productivity countries. Thus, the path to take is clear: we should gradually reduce immigration barriers so that more and more people can take advantage of the benefits of capitalism.

Luis Pablo de la Horra is a Spanish finance graduate from Vlerick Business School.

This article was published by The Foundation for Economic Education and may be freely distributed, subject to a Creative Commons Attribution 4.0 International License, which requires that credit be given to the author.