When I first started studying economics in the early 1970s, one fact quickly stood out. Everyone was an ‘economist.’ That is, everyone seemed to have opinions on all parts of the economy, every law, and every turn of events. I would then and since get disheartened reading stories from those who purport to expound on economics when in reality, they repeat only the commentary made in the press or by politicians whose comments are meant to divide and not inform. Most had part of ‘it’ right, but few seemed to get the big picture. Or if they declared the big picture, they were woefully short of evidence.
As one example, let me assure you that no economist ever introduced the “Trickle Down Economic Theory.” It is a political unicorn; it doesn’t exist and never has. Its roots are with the partisan. It is not a scientific theory. Those who use it show their ignorance or bias. There is so much of this going around (as they say).
Economics is very easy to understand at the level of principle. I love teaching it and watching the ah-ha moments happen as another principle finds its way from the text to the applied. Economics is a broad and deep study, and it takes time to incorporate all of the principles into a cogent and consistent body of knowledge. It is a scientific discipline, the queen of the social sciences, and the most analytical of business knowledge.
What disheartens me the most is the bad economists out there (using Frederic Bastiat’s simple definition of what is a good economist and what is a bad economist. A “bad economist” is one who cannot “see the unseen,” that is, they are not wise or knowledgeable enough to analyze a problem. This bad economist is simply ignorant. On the other hand are the economists who are “bad” and who are not ignorant but willfully misleading because they are driven by ideology or, most often, their paycheck is signed by someone who wants a particular point of view. Years ago, I referred to these as “agency economists.”
Between a good and a bad economist this constitutes the whole difference — the one takes account of the visible effect; the other takes account both of the effects which are seen, and also of those which it is necessary to foresee. Now this difference is enormous, for it almost always happens that when the immediate consequence is favourable, the ultimate consequences are fatal, and the converse. Hence it follows that the bad economist pursues a small present good, which will be followed by a great evil to come, while the true economist pursues a great good to come, — at the risk of a small present evil.
Why would people buy into what the agency economists are selling? Perhaps the message resonates with a fear or a preconceived notion that is confirmed. So you believe what you hear, and before long, the “bad economist” experts have you believing that inequality in wages is always bad, tax cuts for the rich are always bad, capitalism itself has failed, and so many other pieces of nonsense.
I don’t care how you vote, but I do care about the principles on which you make your decision. If you understand the principles of economics and want to vote opposite of me, then, by all means, do so with no ill will from me. If, on the other hand, you vote based solely on what “bad economists,” biased journalists, and biased politicians (a redundant saying) have said to you, each with their own persuasive point of view, and you do so without seeking to understand how what they tell you has to do with well established economic principles then I am saddened beyond belief.
N.B. My Undergraduate economics adviser referred to economics as a layering process, each course adds a new layer, and each has a greater chance of sticking. He liked pointing out this story. Dean Rusk, Secretary of State in the 1960s, was asked why he was hiring Ph.D. economists (good ones, history shows) into positions that did not require an economist. He replied that by the time an economist gets a Ph.D., they have been trained and vested in marginal analysis (a scientific manner of problem-solving), and every problem of state was a study of marginal changes and their impact. In other words, Dean Rusk would not have hired anyone who believed in trickle-down theory, nor a bad economist.
A student today told me that air pollution was up. The fact is, that is not even close to the truth. Typically students (and many people) lean toward the pessimistic. It is little wonder with the constant blaring of bad news and fear-mongering from those whose agenda is attracting eyeballs or votes (or both). The truth, however, is a stubborn thing, but it is not always front and center.
From 1970, aggregate emissions from 6 common pollutants are down 78%. CO2 alone is down 9%.
At the same time, we consumed 43% more energy, had a 62% growth in population, almost a 200% increase in miles traveled by gas-powered vehicles, and nearly a 300% growth in GDP. At the same time, our standard of living (measured by real GDP per capita) rose 244%. (source US BEA)
Most would conclude, I would observe, that with population and vehicle miles and GDP rising, of course, air quality has to suffer. But that is not the case. Why are people so pessimistic? The evidence everywhere is that the world improves.
I am not trying to simplify or dismiss real problems, but I am pointing out that the US is one of the world’s best examples of clean air. As countries get rich, they can spend more on cleaning their environment.
Ourworldindata.com says, “Death rates from air pollution are highest in low-to-middle income countries, with more than 100-fold differences in rates across the world.” Air quality is a normal good. As incomes rise and residents can move beyond mere survival demands, it becomes something they will demand. (https://ourworldindata.org/air-pollution).
The following graph shows worldwide death rates due to air pollution on the vertical axis. As countries become rich, they can afford to demand clean air. In the first graph below, countries defined as low-income are shown. The trend is downward for indoor air pollution, but the death rate due to all air pollution stands at 189 per 100,000 residents.
In the second graph, a similar trend is shown for countries the world bank classifies as rich, showing death rates from air pollution is falling. In 2019 the death rate from all air pollution in these high-income countries is 15 per 100,000 residents or less than 8 percent of the low-income countries. In other words, low-income countries, as of 2019, have shown a great reduction in air pollution deaths over time but have a death rate of almost 13 times the high-income countries.
When a country becomes richer, air quality gets better.
Originally published on November 21, 2019, on LinkedIn, updated lightly October 29, 2022
My blog tagline is economists put the science into data science. Part of the reason I make this claim is many applied econometricians (sadly not all) place a high value on causality and causal inference. Further, those same economists will follow an ethic of working with data that is close to the 2002 guidance of Peter Kennedy and myself.
Judea Pearl discusses “The Seven Tools of Causal Inference with Reflections on Machine Learning” (cacm.acm.org/magazines/2019/3/234929), a Contributed Article in the March 2019 CACM.
This is a great article with three messages.
The first message is to point out the ladder of causation.
As shown in the figure, the lowest rung is an association, a correlation. He writes it as given X, what then is my probability of seeing Y?
The second rung is intervention. If I do X, will Y appear?
The third is counterfactual in that if X did not occur, would Y not occur?
In his second message, he discusses an inference engine, of which he says AI people and I think economists should be very familiar. After all, economists are all about causation, being able to explain why something occurs, but admittedly not always at the best intellectual level. Nevertheless, the need to seek casualty is definitely in the economist’s DNA. I always say the question “Why?” is an occupational hazard or obsession for economists.
People who know me understand that I am a huge admirer, indeed a disciple of the late Peter Kennedy (Guide to Econometrics, chapter on Applied Econometrics, 2008). Kennedy in 2002 set out the 10 rules of applied econometrics in his article “Sinning in the Basement: What are the rules.” I think they imply practices of ethical data use and are of wider application than with Kennedy’s intended audience. I wrote about Ethical Rules in Applied Econometrics and Data Science here.
Kennedy’s first rule is to use economic theory and common sense when articulating a problem and reasoning a solution. Pearl in his Book of Why explains that one cannot advance beyond rung one without other outside information. I think Kennedy would wholeheartedly agree. I want to acknowledge Marc Bellemare for his insightful conversation on the combination of Kennedy and Pearl in the same discussion of rules in applied econometrics. Perhaps I will write about that later.
Pearl’s third message is to give his seven (7) rules or tools for Causal Inference. They are
Encoding causal assumptions: Transparency and testability.
Do-calculus and the control of confounding.
The algorithmization of counterfactuals.
Mediation analysis and the assessment of direct and indirect effects.
Adaptability, external validity, and sample selection bias.
Originally posted on LinkedIn on March 7, 2021, Lightly updated on October 29, 2022.
Subtitle: Please, OHIO, do not pass the raise the wage act as a constitutional amendment.
Do you know how many workers are paid the minimum wage? How big is the problem?
In 2021 it was 1.091 million workers or 1.4 percent of the total wage and salary workers in the US (and less than 0.8 percent of all workers paid wage or salaried).
For nine years, I taught survey methods in a course then called Computer Skills for Economic Analysis. It featured lots of data work and programming leading to economic analysis. (It has since been remastered and renamed econometrics I required as core in the College of Business). One task was to have students update and administer a survey to at least 30 people, asking but not requiring them to try to survey a full age range of people (not just their same-age friends). What resulted was about 4,700 observations over the near-decade. It gave good practice in collecting and merging data and then analyzing questions.
Students and people are unrealistic and pessimistic
One thing that stood out was when we asked what was the unemployment and inflation rate; the answers were amazingly overstated. These were numbers that most people had no idea about, but when asked, they always tended to forecast worse than the actual rates and not by a few percentage points either. Pessimism seemed to reign, and students and respondents always leaned heavily toward the worst case.
The same is the case about whether the minimum wage should be raised, specifically how many people are affected by the minimum wage directly, that is, how many are paid at or below the minimum wage? I always found that even my class of economists overstated this number as well, and again not by a few percentage points.
Students saw being paid at or below minimum wage as a larger problem than it is. They saw the number of persons affected by minimum wage as a relatively large portion of the economy. And they didn’t correctly see the minimum wage as primarily being among the young, inexperienced, and uneducated.
Why they are so pessimistic is an important question not addressed here, but many in the media and political world do benefit from that pessimism.
In 2021, after the COVID recession, fewer workers are paid at or below minimum wage, and they represent an even lower percentage of the total hourly workers than in 2020. (1.091 million and 1.4 percent). By the way, of the 1.091 million workers, only 181,000 were paid at the minimum wage, and 910,000 were paid below due to exceptions and carveouts in the law.
What else can we learn from the BLS report?
Of the 1.091 million workers paid hourly at or below the minimum wage
44.3 percent are 24 years or younger. (Table 1)
52.0 percent are part-time workers (Table 1)
52.8 percent are in the Southern states (Table 2)
73.7 percent are in Service industries (Table 4)
14.9 percent have less than a high school diploma (Table 6)
34.4 percent have a high school diploma and no college (Table 6)
27.2 have some college and no degree (Table 6)
8.8 percent have an Associate degree (Table 6)
12.3 percent have a Bachelor’s degree (Table 6)
65.0 percent are never married (Table 8)
16.4 percent are married, spouse present, and over 25. (Table 8)
In 2021, 76.1 million workers aged 16 and older in the United States were paid at hourly rates, representing 55.8 percent of all wage and salary workers. The percentages shown above are all based on hourly workers.
1.5 percent of hourly workers are paid at or below the minimum wage. This is the same as saying that 0.8 percent of all workers are paid at or below the minimum wage.
The size of the problem is very small.
And in Fall 2022, the Raise the Wage Act is on the Ohio Ballot
This is bad legislation, and even more so by attempting to change the constitution. Ohio voters may want to pass this because they think it is going to do some good, but the good part will be swamped by the bad.
The CBO said the act if passed, would reduce employment by 1.4 million persons, but in this post, you can see that only 1.091 million are currently paid at or below the minimum wage. The disemployment effects would be devastating. The CBO said it would lift 0.9 million out of poverty. But in that post, I show that poverty is already falling.
The worker who faces disemployment is the least productive among all of the low-wage workers. An employer having the potential of hiring a dropout with poor job skills and a student in college will almost always take the ‘better’ hire. At an extreme, the former never gets work and needs it the most, while the latter will, on their own, grow into a better job as they complete their education. So the minimum wage hurts those who advocates suggest it should help the most.
The Raise the Wage Act has been introduced in each US congress since 2017 by Bobby Scott in the House and Bernie Sanders in the Senate. It is back on the table in the 117th Congress. In their analysis, the Congressional Budget Office tells us that the increase of the minimum wage to $15 will raise 900,000 people out of poverty in exchange for a reduction of 1.4 million jobs. Tradeoffs are typical of all government interventions, some people gain, and some people lose. Whether you are in favor of a minimum wage increase comes down to how you weigh these two outcomes.
To quote the CBO report, under the Raise the Wage Act of 2021 by 2025 we will see:
Employment would be reduced by 1.4 million workers, and
The number of people in poverty would be reduced by 0.9 million.
[COVID concerns are discussed below, not all data include the years 2020 and the early months of 2021. Also, the report just cited suggests the deficit will increase by $54 billion, raise prices to all, including the federal government, and would change the distribution of spending. All fascinating aspects I do not talk about in this article.]
The minimum wage decreases employment
Employment effects: Economists have long known that there are negative effects of an increase in a minimum wage when set above the market wage in a labor market. When a minimum wage is effective (above market-level wages), both demanders and suppliers (employers and workers) adapt.
On the demand side, the rise of cost to employers will cause the employer to seek to (1) shift the cost onto consumers in the forms of higher prices (2) shift from dependency on low-wage workers (of lower productivity) into more skilled workers, (3) move away from low productivity labor towards automation and capital, or (4) scale back their production, or to some measure bits of all four possibilities. All four steps result in fewer minimum wage workers being demanded and hired.
On the supply side, those who do not work or who work at lower than minimum wages are more likely to seek a minimum wage job since it is more rewarding. This incentive effect pulls in more workers seeking the now more rewarding minimum wage jobs. Consider a thought experiment: What if there is a student in college who finds that the $7.25 minimum is not enough to compensate for her time away from studying? She essentially goes to college full-time from both a class and a study perspective. When the wage is raised from $7.25 to $15, this becomes more tempting, and she is more likely to enter the labor market and defer other uses of her time, possibly leading to less class or study time or both. She enters the labor market, drawn by the higher reward. An employer now has a richer pool of applicants to choose from now that she and others like her, who have more education and more productivity, are competing for the same jobs that dropouts are competing for. The employer will always hire the most (potentially) productive candidate in the pool of applicants. If she is selected, who is hurt? Answer: the least productive, for example, the dropout with no work experience, who already has a much tougher time getting a job.
What does the literature say about employment effects? Some say that economists have changed their thinking on the employment effects based on studies that show no negative employment impact. One economist remarks thusly and says they are reacting based on the entire literature.
David Newmark and Peter Shirley speak to the disagreement.
What is … puzzling, is the absence of agreement on what the research literature says – that is, how economists even summarize the body of evidence on the employment effects of minimum wages. Summaries range from “it is now well-established that higher minimum wages do not reduce employment,” to “the evidence is very mixed with effects centered on zero so there is no basis for a strong conclusion one way or the other,” to “most evidence points to adverse employment effects.”
David Newmark and Peter Shirley NBER working paper issued in January 2021, and revised in March 2021.
… we assembled the entire set of published studies in this literature and identified the core estimates that support the conclusions from each study, in most cases relying on responses from the researchers who wrote these papers.
And their conclusions?
Our key conclusions are: (i) there is a clear preponderance of negative estimates in the literature; (ii) this evidence is stronger for teens and young adults as well as the less-educated; (iii) the evidence from studies of directly-affected workers points even more strongly to negative employment effects; and (iv) the evidence from studies of low-wage industries is less one-sided.
David Newmark and Peter Shirley NBER working paper issued in January 2021, and revised in March 2021.
The Minimum wage increase will decrease Poverty
Ok, but what about poverty? How much will it decrease and is it effective?
The CBO says 0.9 million workers will be raised from poverty. Isn’t that worth the losses in employment?
Semega, et al. write:
The 2019 real median incomes of family households and nonfamily households increased 7.3 percent and 6.2 percent from their respective 2018 estimates (Figure 1 and Table A-1). This is the fifth consecutive annual increase in median household income for family households, and the second consecutive increase for nonfamily households.
The official poverty rate in 2019 was 10.5 percent, down 1.3 percentage points from 11.8 percent in 2018. This is the fifth consecutive annual decline in poverty. Since 2014, the poverty rate has fallen 4.3 percentage points, from 14.8 percent to 10.5 percent (Figure 7 and Table B-5).
This means before we consider the increase in the minimum wage, that poverty has declined, and median household income has risen in each of the last five years. Incomes are on the rise, and poverty is in decline. The need for the increase in the minimum wage to reduce poverty, while laudable, will only add to the current decline. That is, it is easy to think that poverty is rising and we have to do something, while what we have been doing sees poverty diminishing and income rising.
But 0.9 million workers will be freed from poverty! Most of the minimum wage workers are young, 48 percent of the minimum wage workers are under the age of 25. And poverty amount the youth is already in decline since 2010 as can be seen in Table 11 from the Census report. The market without the minimum wage rise to $15 is already reducing poverty.
The CBO report says that the increase to $15 will decrease 0.9 million from poverty roles. Table 7 puts this in perspective as 0.9 million is about 2.6 % of the total persons in poverty based on the 2019 level. Clearly, the minimum wage is not a significant poverty reduction program for the US. That is, if the argument is that the rise in the minimum wage reduces poverty, the tradeoff is that a two-and-a-half percent reduction is worth the loss of employment and all other disruptions in the marketplace, such as rising prices of goods and services.
COVID ruins everything.
I saw that on a t-shirt, and it certainly offers much truth. Covid also changes the conversation above. Poverty rates may have risen, incomes may have fallen, and unemployment rates are much lower now, but not yet back to the levels of 2019. So COVID ruins this tale in part.
However, the COVID recession is passing; whether this quarter or a couple of quarters from now, it will end, and the recovery that has already begun will return us to the earlier paths or to some slightly adjusted paths. (Update: The NBER Business Cycle Dating Committee has officially declared the COVID recession started in March 2020 and ended in April 2020. )
We know that working from home will not end as workers, and some companies highly prefer this new structural setting. Some goods and services are likely gone not to return because of this structural change, but I imagine that it only hastened the pace of change and not completely bent trends in very opposite directions. Of course, time will tell, and that is what makes this time fascinating from an economy-watch perspective.
Conclusion
I end this with not only my opposition to any increase in the minimum wage but with three things that we tend to do that distort our sense of what action is important in the economy:
We overreact with pessimism. We think things are always worse than they are. Much of this is fostered because bad news gets our attention faster.
Even when the current trend is negative, we are often ignorant of what the long-term trend has been. Poverty may be temporarily turning up, but look at how much better we are now compared to decades ago. Transitory changes are sometimes painful, but ignorance of the size and direction of long-term trends may make us choose ill-advised policy prescriptions today.
Economic principles never change; how we apply them does. Demand curves always slope downward, meaning when prices are higher, we will buy less. When wages are higher, we will employ less (how much less is the real question).
Sometimes my students ask questions that set me to writing. Here is one from Kaden this morning. Are there any safeguards against high prices? Can the government help? I use the price of gasoline as an example.
The market is the safeguard against higher prices. If an item is ‘too highly’ priced, demanders will not purchase it or seek alternatives. I make cutting boards. If my competitors charge a price I think is too high, I can offer my boards at a lower price. It doesn’t matter whether the item is ‘worthless’ or not to some or even to many.
Both phrases in the above paragraph, ‘ too high’ and ‘worthless,’ are normative statements and have no use to us in analytics. But we all think it. Why would I ever pay a price that I believe is too high for a worthless item? I would not.
What if the item was a necessity for me? Then my demand inelasticity would show me not responding to the negative price as much as I might think I would or as much as my complaints suggest. Say 10 gallons of gas per pay period is necessary to keep your job, then you will buy the gas at whatever high price, at least until you can figure out an alternative.
Before I write the next paragraph, you must understand:
There are only three ways prices can come down: (1) market demand decreases, (2) market supply increases, and/or (3) the government enforces a regulatory price cap.
The first two are self-healing. Consumers back off high price items, and firms offer new and substitute products at lower prices to get in on the action. Less demand and more supply always lower prices.
Is there a role of the government to step in and force prices down? In my opinion, never.
The reason is simple. The market is dynamic while price cap regulation is around forever. There is nothing as permanent as a government law or regulation. But let’s do that anyway.
The elected officials think gasoline prices are too high, so they regulate the gasoline market, forbidding the local gas station from charging more than a specific price, say $2 per gallon. When this happens, and the market clearing price is $3.35 per gallon, gas station owners will only get revenue of $2.00 but then cannot buy as much gas from the wholesaler at the higher price. The gas station either has less gas to offer or will go out of business, or may restrict gas to its own favorite customers. (Back in the 1970s, it paid to be friends with the local gas station). Less and favored supply means fewer people can get their needed 10 gallons a pay period.
But lowering the gas price to $2 per gallon encourages consumers to buy more. The market price requires people to evaluate just how much gas they need and are willing and able to pay. The artificially low gas price set by the government encourages consumers to buy much more, and to the extent they can, they will stock up.
The market guarantees that the people who value the gas the most will get it. The government guarantees the low price only to those who participate in some extra rationing scheme, such as standing in line and first-come-first-served.
Valuing is not the same as having an income. Poorer folks can value an item highly. At the market price, there is gas for them. At $2, there may not be, and they aren’t guaranteed to be at the front of the line.
Isn’t $2 gas better than $3.35 gas? Not if the government has enforced regulation on the market. At $2, there is less gas to be had, and it will never be allocated to those who value it the most.
Finally, how did the high price get there in the first place? If it is market-based, the market is self-curing. But what if the high price was because government regulation limited supply, limited how many gas stations there could be? By restricting the supply and likely collecting a nice permit or license fee, there are fewer outlets for consumers to get the gas, and prices would be artificially higher than the market-clearing price.
You can imagine the government granting a patent to a life-saving drug. With only one seller (a monopoly) that exists because of government enforcement, prices are higher than the competitive market would dictate, but the competitive market can’t exist. It is illegal for anyone to make and sell that drug without the permission of the drug owner (and for payment of the requisite fees). I am not saying patents are bad or good, just that there are consequences to action.
Now imagine the government coming in and putting a price ceiling on a drug with a high price propped up by the same government. All of the signaling value of the market price fails.
We have only begun to think of these issues in our class, which is why I love economic analysis.
Inflation hurts us all. Why is it here? Why is inflation that is usually under 2 percent suddenly approaching 10 percent?
We have been here before. I lived through the 1970s until the Volker / Reagan approach broke inflation with large and massive, at that time, back-to-back recessions. Before Volker / Reagan, we heard of the term stagflation, a period of high inflation and declining growth that is now being cast about for our present case. The fix in the 1980s was painful, interest rates rose to 20 percent, and two significant recessions occurred before a quarter of a century of low and no inflation followed. Any fix now will also be painful, but you must know what to fix. Blame is levied on the supply-side disruptions, on consumers, and on the war in Ukraine. All of these contribute, but is there a larger contributor?
It is excess government spending.
What is different this time? It is excess government spending. It is a normal function of government to spend and provide for roads, our military, and all of the services we have come to expect. There is plenty of room to argue whether we should have the government spend more on this and less on that. And democrats want to spend on things that republicans do not and vice versa. That is normal and a result of who wins elections. That is not the problem. What we are seeing now, with the assistance of hindsight (and data), is that government spending in 2020 and 2021 is and was excessive on a historical scale. The excess government spending currently is unprecedented in post-WWII history. It is massive.
In this article, I am only looking at annual amounts of the National Income Product Accounts and looking for historical anomalies. Doing that is eye-opening..
In the last 22 years, government spending was 18 to 23 percent of the GDP in 17 of those years. Government spending is expected to rise during economic downturns and fall during times of plenty and indeed did rise to 24 percent following the recession of 1980-82, and rose again to 25 percent because of the 2008 Great Recession. So normal spending by the government is now around 22 percent, and it rose to 25 percent in two prior large recessions. in nominal terms, GDP is currently 24.9 Trillion dollars. So a three percent rise we saw in previous recessions is about 750 billion dollars.
However, Government spending was 33 percent in 2020 and 31 percent in 2021. That is 11 and 9 percent higher than the 1979 to 2019 stable average of 22 percent that government typically spends. In dollar amounts, today, 9 percent of the GDP is over 2.2 trillion dollars in spending, north of the typical 22 percent level. The 2020 and 2021 levels of Government Spending are historically unprecedented. (I show broader data here)
Inflation is not the fault of the Consumers
There are no excess personal consumption expenditures in the current record, consumers spend about 68 percent of the GDP, which has not appreciably changed in the last two years. It has been this percentage for the last 22 years. Since 1979 and including the current year, personal consumption has never been less than 66 percent and more than 69 percent.
Inflation is not a result of Private Investment
Private investment is critical to building capital for the future, which is critical to growth. Private Investment averages 17 percent of GDP and varies from 13 percent (in the depth of the Great Recession) to 21 percent (in 1979), depending on the business cycle. In 2019 before the pandemic, investment was 18 percent, 17 percent in 2020, and 18 percent in 2021. There is no appreciable variation there to indicate it is somehow a prime mover of inflation (and no theory to back up such an idea either).
Referring to the chart, it is typical that investment is lower during recessions (see 2009), but in the 2020 recession, it was higher than the baseline.
GDP = C + I + G + (X-M)
definition of gross domestic product
It isn’t C, and it isn’t I; it has to be G
In the inflation of the 1970s, there was too much money in the economy, which is why it had to be removed to lead to low inflation. Milton Friedman told us that inflation is always and everywhere a monetary phenomenon. Clearly, inflation is too much money chasing too few goods. Government spending in the excess that we see in 2020 and 2021 led to massive borrowing creating deficits that by far are the highest in history.
In the chart, I show annual consumption, investment, and government spending as a percentage of GDP with adjustments for what is typically the case. Consumption is typically 68 percent of GDP, Investment is 17 percent, and Government spending is 22 percent. When the typical is removed, the exceptions represent excess spending.
What is typical? C = .68*GDP I = .17*GDP G=.22*GDP
Didn’t we have to do all that spending?
This will be debated forever, but I think the arguments for spending in 2020 were more valid because of the 33 percent drop in GDP in the second quarter. However, GDP came roaring back. In 2020 one could argue that the government spending made up for the loss of productive output in the economy, but by 2021 with GDP growing fast and the strongly recovering labor market adding back jobs previously lost to the shutdowns, the need for stimulus spending by the US government was not a strong case and the government spending of 2021 became the primary mover of present inflation. If the federal government continues to spend at rates far above the 22 percent level of GDP they will be pouring fuel on the fire.
The American Rescue Plan, in aggregate, seems too late and added more debt to a recovering economy. Rather than stimulating recovery, it heated an already hot economic engine. When Government Spending exceeds Tax revenue, we have to get the money from somewhere, and evidence is that came from the actions of the Federal Reserve. A FED that already had put an amazingly high amount of money in circulation to recover from the great recession had begun to withdraw it and now has a major crisis on its hands, trying to remove the excess money from the system to calm the inflation. This crisis for the FED does not need more excess Government Spending to further worsen the deepening inflation.
And currently on the table is massive proposed spending through the “Inflation Reduction Act.” The future is far from clear.
No new Government Spending Plans over and above the 22 percent, please!
(Updated with links and more Dec 1, 2020. Updated with SAS Global Forum announcement on Jan. 22, 2021.)
Professors reluctant to venture into these areas do no service to their students for preparation to enter the real world of work.
Today (November 30, 2020) I presented: “Avoiding Pitfalls in Regression Analysis”during the Causal Inference Webinar at the Urban Analytics Institute in the Ted Rogers School of Management, Ryerson University. I was honored to do this at the kind invitation of Murtaza Haider, author of Getting Started with Data Science. Primary participants are his students in Advanced Business Data Analytics in Business. This is an impressive well-crafted course (taught in R) and at the syllabus-level covers many of the topics in this presentation. I met Murtaza some time ago online and have come to regard him as a first-rate Applied Econometrician.
Ethics and moral obligation to our students
Just as Peter Kennedy developed rules for the ethical use of Applied Econometrics, this presentation is the first step to developing a set of rules for avoiding pain in one’s analysis. A warning against Hasty Regression (as defined) is prominent.
(Update 1/22/2021: My paper, “Haste Makes Waste: Don’t Ruin Your Reputation with Hasty Regression,” has been accepted for a prerecorded 20 minute breakout session at SAS Global Forum 2021, May 18-20, 2021. More on this in a separate post later.)
Kennedy said in the original 2002 paper, Sinning in the Basement, “… my opinion is that regardless of teachability, we have a moral obligation to inform students of these rules, and, through suitable assignments, socialize them to incorporate them into the standard operating procedures they follow when doing empirical work.… (I) believe that these rules are far more important than instructors believe and that students at all levels do not accord them the respect they deserve.”– Kennedy, 2002, pp. 571-2” See my contribution to the cause, an essay on Peter Kennedy’s vision in Bill Frank’s book cited below.
While the key phrase in Peter’s quote seems to be the “moral obligation,” the stronger phrase is “regardless of teachability.” Professors reluctant to venture into these areas do no service to their students when they enter the real world of work. As with Kennedy, some of the avoidance of pitfall rules are equally difficult to teach leading faculty away from in-depth coverage.
The Presentation
A previous presentation has the subtitle, “Don’t let common mistakes ruin your regression and your career.” I only dropped that subtitle here for space-saving and not to disavow the importance of these rules in a good career trajectory.
This presentation highlights seven of ten pitfalls that can befall even the technically competent and fully experienced. Many regression users will have learned about regression in courses dedicating a couple of weeks to much of a semester, and could be self-taught or have learned on the job. The focus of many curricula is to perfect estimation techniques and studiously learn about violations of the classical assumptions. Applied work is so much more and one size does not always fit. The pitfalls remind all users to think fully through their data and their analysis. Used properly, regression is one of the most powerful tools in the analyst’s arsenal. Avoiding pitfalls will help the analyst avoid fatal results.
The Pitfalls in Regression Practice?
Failure to understand why you are running the regression.
Failure to be a data skeptic and ignoring the data generating process.
Failure to examine your data before you regress.
Failure to examine your data after you regress.
Failure to understand how to interpret regression results.
Failure to model both theory and data anomalies, and to know the difference.
Failure to be ethical.
Failure to provide proper statistical testing
Failure to properly consider causal calculus
Failure to meet the assumptions of the classical linear model.
How to get this presentation
Faculty, if you would like this presentation delivered to your students or faculty via webinar, please contact me. Participants of the webinar can request a copy of the presentation by emailing me at myers@uakron.edu. Specify the title of the presentation and please give your name and contact information. Let me know what you thought of the presentation as well.
I had hoped to expand friendships with colleagues met and those to be met and those who I have met only online. And, perhaps most of all, I am disappointed that I missed being presented with the 2020 SAS Distinguished Educatoraward.
Honored and humbled
I am still honored and humbled by the 2020 SAS Distinguished Educatoraward and recall the congratulatory call from Lynn with the same original shock and pleasure. Also, I am honored to be invited to speak at the SAS Global Forum. Thanks to all involved, especially the conference chair, Lisa Mendez, who worked so hard to coordinate this gigantic global event. I was pleased to meet her at SCSUG and hear so much of the news about the upcoming event.
My Published Paper
Nevertheless, I want to announce that my paper is now published in the 2020 SAS Global Forum Proceedings. My paper titled Show Me the Money! (thanks to Josh for that part) Preparing Economics Students for Data Science Careers is embedded below and a link to download is on the floating menu bar. The paper is a combination of my journey over my four-decade career and description of our programs and SAS use in the Department of Economics and why economists make great data scientists.
If you take time to read it I would appreciate any feedback you have. We can discuss curriculum or whatever, and I hope to leave this as I retire from UA as a roadmap for faculty that follow.
Updated 4/11/2020: Everyone is interested in how we are doing in Ohio during the COVID19 pandemic. Accordingly, I look at the data from the Ohio Department of Health and assemble it into a report for you. You can read my full report below which includes multiple graphs and tables and can download the pdf. I intend to update the pdf report each day as new data becomes available. Also, you should check back often as the information displayed will change with new data. I will also offer new items as I think of them.
Full disclaimer, I am not an expert in epidemiology nor have I attempted to model the behavior and predict the future. On LinkedIn, I have written about the importance of having a qualified subject matter expert paired with each data modeler. I am nonetheless interested in any suggestions you have. I have added a footnote to each table explaining that the definition of a case changed on April 10 from “confirmed (by a test) cases” to the “confirmed cases plus probable cases” which inflates the data by 47 cases on April 10. This to match definitions by the CDC, but worries me as to the lack of consistency before and after the change date.
First up is Weekly changes in the number of cases, hospitalizations, and deaths. A look at the number of cases shows a considerable decline in the cases. Every data point is an average of the last week of cases. When changes are on way down it suggests that the curve of the total caseload is indeed being bent.
Rates of hospitalizations and deaths are shown in the next graph. This past week Amy Acton said Ohio has tested 50,000 people and our cases are just under 6000, so that means in rough measure that of everyone tested, the large majority of are showing symptoms or clearly in harm’s way, that the positive results are that about 12 percent. That suggests the actual death rate which is 3.9% or all positive cases, maybe as low as (12%) of 3.9% or about 0.4% of all those tested and much less than the death rate out of the population of 11 million. Of course, I do not have individual testing data and this is a bit of hopeful speculation.
I also did a visualization of the hospitalization and death rates by age and sex and posted that to LinkedIn. You can access that here. Similar numbers and heatmaps are in the full report below.
I used SAS® to organize and analyze the data.
Because people are interested in how we are doing in Ohio during the COVID19 pandemic I hope this is of interest to you.
