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Blogging again, maybe. Why Buy America is a Broken Window.

I keep thinking I should return to recording my random thoughts in this space. After all, I pay to maintain the server and keep the domain so I should, right?

I had a thought-provoking phone call yesterday about the unintended consequences of working from home. Actually, it started me thinking I should be recording these thoughts, privately or publicly. More on that call later. 

Buy American

Today I was reading Facebook and a campaign by allamericanclothing.com was encouraging me and others to buy American clothing and create 200,000 jobs. I like American, I like jobs, maybe I should spend more on what their ad was selling.  Actually here was the promise:

“If each American spent $64 on a piece of clothing, it would generate 200,000 new jobs.” – allamericanclothing.com 

That is quite a claim and sadly not true. And it is not true because of the amounts, I have no idea whether $64 from each American will translate to 200,000 new jobs, but let’s say that is correct with the implied 200,000 jobs making American clothing.

Not true

Why is that then not a factually true statement? Because if each American would take $64 and spend on clothing, they must withdraw that $64 from another use, whether consumption or savings. If I increase my clothing budget $64 then other things I would have spent it on will not be made and people will lose their jobs in those areas.  Not because of my measly $64, but from the collective action of “all Americans.” So in round numbers, if 300 million people spend $64, that is $19.2 billion dollars not spent in other places, $19,2 billion dollars not received by suppliers, and some large portion of that $19.2 billion dollars not going to employ lots of workers. 

So spend on American clothing if you want, just know that every action has a cost in what is not purchased and that opportunity cost is that others will not be employed. Yes, your $64 is that powerful. 

Truth in a 1946 book

Henry Hazlitt in his excellent 1946 book, Economics in One Lesson (freely available at the Foundation for Economics Education) repeats the oft-ignored classical tale of the hoodlum that destroys a baker’s window with a brick. Someone says, here is the good thing, the silver lining. at least there is a job created for the glazier. He gets work, money for services, and can go buy stuff, feed his family, and so forth and so on. See that is a good thing for the economy. Sure a window was broken, but fixing it creates a job and puts money into the system. Lots of people believe this story because they want to, it sounds good. Just like increasing my clothing budget for American Made clothes by $64, it sounds good, perhaps my patriotic duty, and it helps create those 200,000 jobs. 

Buth those same people who sing the praise of what the window-fixing money will do for society, forget one thing. Now the baker whose window was broken can’t buy a new suit. The money he was going to use to buy a new suit just went to the glazier. The money to the glazier and then on to whomever he shares it with is a benefit, but not a net benefit. The new window cost a tailor the business of making the baker a new suit. No new suit for the baker, no new business for the tailor, and no sharing of that stream of income to all with whom the tailor would have shared it.

 So next time you see a Buy American ad (which by the way I am not against), realize that there is a benefit that more Americans will be employed making the American good, but others, including Americans, will have less demand for their services making the good you did not make. 

More

The oft-ignored classical tale owes to Frédéric Bastiat (1801-1850) an amazing French economist and journalist. His popular, common sense writings have educated millions, but alas rarely does his wisdom, and the wisdom of writers like Hanry Hazlitt, permeate the bastions of politicians and sad to report of economists as well. I believe in part this is why

Found at https://society6.com/product/joan-robinson_mug?sku=s6-8742430p30a27v199

British Economist Joan Robinson, (1903-1983) said the main reason to study economics is to avoid being deceived by economists. I always hasten to add politicians. 

Bastiat was particularly hard on the politicians as well as the economists who supported policy, that they would often focus on the present good of a policy and ignore the longer-term harm. He referred to those who thought this way as bad economists. Good economists were those who could see what is unseen. The unseen includes the opportunity costs of the policy which may include near-term harm, but certainly ignored was the long-term harm.  This focus on only the good part of the policy and ignoring all other aspects happens today all of the time, but Hazlitt and Bastiat, and Smith (1723-1790) show that it is a constant occurrence. 

Here is a great little video on the Broken Window Fallacy from Art Carden. I do like the learn liberty videos and use many in my classes. 

Used in every class

My final comment in this first in a while entry is: Economics in One Lesson is such an important book that I have used it as the first assignment in my classes, read the lesson in chapter 1, read the broken window fallacy in chapter 2, and then review any additional chapter. I find that the students get an amazing amount of insight about how to think before we buckle down to the economic text. I encourage everyone who has made it this far to read chapter 1 and 2, and they are both very short chapters. You will enjoy it. 

For those who do not click the link and read the first chapter, you will miss that Henry Hazlitt reduces economic analysis down to a single sentence and an amazing sentence at that. Ready, here goes.

“The Art of economics consists in looking not merely at the immediate but at the longer effects of any act or policy; it consists in tracing the consequences of that policy not merely for one group but for all groups.”

Until next time.

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COVID-19 in the State of Ohio, updated daily

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.

weekly changes in cases of covid-19

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.

rates of cases

 

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.

OH_report_COVID19

Download the report here.

Proper citation requested. Steven C. Myers. 2020. Ohio Covid19 report. accessed at https:econdatascience.com/COVID19 on (your access date).

Request for Comments to myers@uakron.edu

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Bubble Chart in SAS SGPLOT like Hans Rosing

Robert Allison blogs as the SAS Graph Guy. He recreates using SAS PROC SGPLOT the famous bubble chart from Hans Rosing of Gapminder Institute. Hans shows that life expectancy and income per person have dramatically changed over the years. Because Hans Rosing is a ot the father of visualizations, Robert produces this graph (shown here) and this very cool animation.

I can’t wait to see  Economic Freedom and income per person soon in one of these graphs. My students are trying to do this right now.  At this point in the term they are acquiring two datasets from Heritage on 168 countries, which contain the index of economic freedom for 2013 and 2018. Then they are cleaning and joining them so they can reproduce the following figure and table in SAS PROC SGPLOT for each year.

 

 

 

 

 

 

 

 

 

 

 

 

I have written about this project in prior terms here. Once they have this data joined and the above figures reproduced then they will move on to the final project for this semester. They will be looking through the 1600 World Development Indicators of the World Bank.  Each team of students will choose 5 and will join that to their data to answer the question:

Does Economic Freedom lead to greater Human Progress?

I may share their results, for now this is some pretty cool graphics from the SAS Graph Guy. 

 

 

 

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Ethics Rules in Applied Econometrics and Data Science

Failing to follow these rules brings about ethical implications if not direct unethical behavior. To knowingly violate the rules is to be, or at least risk being, unethical in your handling of data.

I am honored to have a version of this essay appearing in Bill Frank’s 97 Things about ethics everyone in data science should know. Pick up a copy now. 
Published August 25, 2020

I have taught ethics in Applied Econometrics and Data Analysis for at least the last 20 years. But I rarely have used the word ethics, resorting to phrases such as data skepticism, and other attitudes that suggest acting ethically.

Nothing in the past 20 years has had as much impact on me and my classroom teaching and my ethics of data analysis as Peter Kennedy’s “Sinning in the Basement: What are the Rules? The Ten Commandments of Applied Econometrics.” This essay also appears in his Guide to Econometrics (Kennedy, 2008).

From the moment I read this paper, I was completely transformed and forever a disciple of his. I was fortunate to host him on my campus where he spoke of the misuse of econometrics and failure of research to make it past his editor’s desk at the Journal of Economic Education. One example, a paper is rejected because they did not acknowledge a problem in their analysis, ignored it, and probably hoped the editor would not notice. Being honest and transparent enough to acknowledge a problem of which the authors were aware, but unable to solve is sometimes enough Peter would point out. Hiding one transgression suggests other ethical abuses of data.

I used the word ethical, but Peter did not, preferring the oft used word sin and sinning. But the point is made. When I got my Ph.D. at The Ohio State University in 1980, I had taken nine separate statistics and econometrics courses over the 5 years that I was there. I learned classical estimation and inference from some of the best professors, and yes we had to “sin in the basement” (by pilgrimage to the mainframe computer in those days) because there was scarcely a day’s instruction of how to use the computer much less conduct what Peter would call the moral obligation of applied econometrics 20 years later.

Kennedy says “… 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)” I could not agree more and have tried to follow faithfully these rules and to teach my students and colleagues to do likewise.

Failing to follow these rules brings about ethical implications if not direct unethical behavior. To knowingly violate the rules is to be, or at least risk being, unethical in your handling of data. To unknowingly violate the rules would nevertheless lead to unintended consequences of poor outcomes that could be avoided.

The Rules of Applied Econometrics

Rule 1:

Use common sense and economic reasoning

Rule 2:

Avoid Type III errors

Rule 3:

Know the context

Rule 4:

Inspect the data

Rule 5:

Keep it sensibly simple

Rule 6:

Use the interocular trauma test

Rule 7:

Understand the costs and benefits of data mining.

Rule 8:

Be prepared to compromise

Rule 9:

Do not confuse statistical significance with meaningful magnitude

Rule 10:

Report a sensitive analysis

Failing to well and fully articulate the problem (rule 1) is so critical that to not spend time on the problem and the common sense and economic theoretical solution can lead to serious flaws in the study from the very first step. It might lead to a violation of rule 2 where the right answer to the wrong question is discovered? What if you fail to inspect the data (Rule 4), fail to clean the data and provide for necessary transforms, fail to control for selection bias, then you will have results based on assumptions that are not realistic and produce wrong results that are unduly influenced by the dirty data. The importance of this cannot be over emphasized. Recall that Griliches exclaims that if it weren’t for dirty data, economists wouldn’t have jobs. What if you violate Rule 7 and, knowingly or not, allow the data to lie to you. In the words of Nobel Prize winning economist, Ronald Coase (1995), “If you torture the data long enough, it will confess.” A violation of Rule 9 might lead you to worship R2 or participate in p–hacking. It might cause you to ignore a huge economic implication (large magnitude) only because it has a large p-value. Violations of Rule 10 may be the largest of all. Suppose in the spirit of discussing “sinning” you believe your model is from God (as suggested by Susan Athey) then why would you ever look at alternative specifications or otherwise validate the robustness of your findings. What you find is what you find and alternatives be dammed.

“Many data scientists make bad decisions – with ethical implications – not because they are intentionally trying to do harm, but because they do not have an understanding of how the algorithms they are taking responsibility for actually work. (Jennifer Priestly, August 2019, LinkedIn post).” Likewise many in the field who ignore the Rules for Applied Econometrics risk doing real harm, not out of intentionality, but out of ignorance or neglect. This later lack of motive is just as real and likely more widespread than the intentional harm, but harm occurs in any event.

The American Economic Association adopted ethical, code of conduct guidelines that states in part: “The AEA’s founding purpose of ‘the encouragement of economic research’ requires intellectual and professional integrity. Integrity demands honesty, care, and transparency in conducting and presenting research; disinterested assessment of ideas; acknowledgement of limits of expertise; and disclosure of real and perceived conflicts of interest.”

The AEA statement does not go directly to data ethics, but is suggestive since little economic research and no applied economic research can be conducted without data. The AEA statement is a beginning, but I suggest that those who do applied economic research would do well to hold to the rules for sinning in the basement. This is so important now since going to the basement is no longer the norm and many more analysts should be trying to avoid sinning wherever and whenever they have their hands on their laptop.

References:

American Economic Association (2018) Code of Professional Conduct, Adopted April 20, 2018, accessed at https://www.aeaweb.org/about-aea/code-of-conduct  on October 7, 2019.

Coase, Ronald. (1995) Essays on Economics and Economists, University of Chicago Press,

Kennedy, Peter E. (2002) “Sinning in the Basement: What are the rules? The Ten Commandments of Applied Econometrics.” Applied Econometrics, Blackwell Publishers Ltd. Accessed at https://onlinelibrary.wiley.com/doi/abs/10.1111/1467-6419.00179. 

Kennedy, Peter E. (2008) A Guide to Econometrics, 6th edition, Cambridge, MIT Press.

Gola, joanna. 10 Commandments of Applied Econometrics (series), Bright Data, SAS Blog, first in the series accessed here: https://blogs.sas.com/content/brightdata/2017/03/01/10-commandments-of-applied-econometrics-or-how-not-to-sin-when-working-with-real-data/

Priestley, Jennifer L. (2018) “The Good, The Bad, and the Creepy: Why Data Scientists Need to Understand Ethics.” SAS Global Forum, Dallas. April 28-May 1, 2019.

Further Reading:

Zvi Griliches (1985) “Data and Econometricians–The Uneasy Alliance.” The American Economic Review, Vol. 75, No. 2, Papers and Proceedings of the Ninety Seventh Annual Meeting of the American Economic Association, pp. 196-200 accessed at http://www.jstor.org/stable/1805595 on October 7, 2019.

DeMartino, George and Deirdre N. McCloskey, eds. (2016). The Oxford Handbook of Professional Economic Ethics, Oxford University Press, New York.

Author contact:

Steven C. Myers
Associate Professor of Economics
College of Business Administration
The University of Akron
myers@uakron.edu
https://econdatascience.com
https://www.linkedin.com/in/stevencmyers/

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Remembering Gary Becker

Rarely have I read the first few paragraphs of an article and felt that it captures the essence of the words to follow as well as I just have in Heckman, et al., Gary Becker Remembered. Perhaps this impact is because of the subject matter and my interest forged in the 1970s. Gary Becker made study of economics at The Ohio State University so exciting for me and my student colleagues such as Randy King, Tim Carr and Patricia Shields.  

It was a period where the 2nd edition of Human Capital had just come out, when we had read mimeograph copies of his Woytinsky lecture and struggled with his Theory of the Allocation of Time. Generally, his work and thought permeating all we did and studied. He made so much sense and conveyed so much wisdom.  It was a time we were working for the National Longitudinal Surveys under Herb Parnes, taking micro and labor from him, Belton Fleisher, Don Parsons and Ed Ray. All who made my interest in labor and labor econometrics all the more deep. Additionally, George Rhodes and Jerry Thursby pushed me econometrically and I had all that wonderful access to the early waves of the original NLS cohorts. It was a great time, although I did not necessarily think so at the time being a typical graduate student with little time to consider words like “great time.” It was at least exciting and rewarding, and over the next 40 years fulfilling.  


 

We who were running massive number of wage equations had him ever in our thoughts. We built on his foundation while stretching the model this way and that, but always on his foundation. While I have long ceased running wage regressions, my students in econometrics regularly do as they practice the techniques and at the root of their work is making sure they who are not all labor economic oriented, understand that the foundation on which they learn and build is Beckers, making them read the now basic work that was so vivid in the 70s. Whether they apply techniques from Ronald Oaxaca or Jim Heckman or others, it is Becker with whom they must first contend. He was an “intellectual giant” always the scientist, depending on the evidence from the causal link of theory to data and analysis and on to testable conclusion. Come to think of it, few others can boast of such a tight link between the economic model and the econometric model and testable results over and over that confirms his brilliance.

Here is the first three paragraphs of that remembrance from:
 

 James Heckman, Edward Lazear, Kevin Murphy.  Gary Becker Remembered, Journal of Political Economy. October 2018, Vol. 126, Issue S1, Pages S1-S6 Accessed at https://www.journals.uchicago.edu/doi/full/10.1086/698751 on Jan 1, 2019.

“Gary Becker was an intellectual giant. No one had a greater impact on broadening economics and making its impact felt throughout the social sciences than Becker. Indeed, Milton Friedman once described Gary Becker as the most important social scientist of the second half of the twentieth century.

“For those of us who knew him, he was the most creative thinker we ever encountered. It was his astounding imagination that made many of his early critics think of him as a heretic. They were correct: he was a heretic much like Luther, Copernicus, and Galileo, who transformed their worlds, just as he transformed economics. He brought a rigorous and insightful approach to issues that were viewed as inherently noneconomic. Eventually, he won over the economics profession, detractors and all, who eventually became converts.

“Becker was a scientist in the true sense of the word. He believed that economics was useful only if it explained and helped to improve the world. He practiced what he preached and carefully analyzed all of the social problems he addressed. He was innovative yet rigorous, open to new thought yet disciplined in sticking to the established rules of analysis. Most importantly, he extended the boundaries of economics to much of social science.” 

Read the rest at https://www.journals.uchicago.edu/doi/full/10.1086/698751

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SAS Certificate in Economic Data Analytics

 

SAS Certificate in Economic Data Analytics

The Department of Economics is preparing economists with strong econometrics and programming skills  to prepare them to be data analysts and data scientists, ready to serve and lead business, governmental and not-profit institutions with their data analytic needs. Economists make the best data scientists, and in a team environment are a critical part of the data science team.  This owes to the rigorous analytical training that economists go through in programming, statistics, mathematics, econometrics and forecasting, but more importantly in their education in critical thinking and  problem solving in an environment that is both theoretical way and backed by scientific data evidence. In other words, economists stand out to employers. We have many testimonials that when economists are hired they move up in the organization quickly. The challenge is often getting employers to consider economists in the range of disciplines they hire.  One way we are using is to recognize our students for a particular skill, that of knowing SAS programming. 

The Economic Data Analytics Certificate of Completion is jointly sponsored by the SAS Institute and the Department of Economics  at The University of Akron. 

Spring 2018 SAS Certificate Presentation

The jointly offered certificate is based on student’s successful completion of the economics major core coursework in data analytics along with economic theory at either the undergraduate of graduate level.  Each honoree also successfully complete a major research study using SAS as the primary tool of their data analytic project. These data projects, most often the students senior research project, are a valuable asset to the student to help convince employer of their ability and interest in research. 

Importance of Economic Analysis to Data Science

Economists are unique in their approach to data analytics due to their unique training. They are story tellers, they are able to articulate problems and to take a rigorous approach to the solution of those problems. They understand the importance to understand the question: “why?”

Economists own causality and understand observational data, some are even said to be obsessed with the data generating process. Economists have a a strong linear regression toolkit and are well familiar with the processes that push beyond that level of rigor. Economists understand minimizing and maximizing of objective functions under constraint.