October 2019 - Econ Data Science

October 19, 2019

My time with the MS Analytics Students at LSU

Last week I had the pleasure of presenting two papers at the 2019 South Central SAS Users Group Educational Forum in Baton Rouge on the campus of the E. J. Ourso College of Business at Louisiana State University. My thanks to Joni Shreve and Jimmy DeFoor who chaired this conference and treated this traveler so well. (Especially want to call out the chicken and sausage gumbo). I want to reflect on two things. The students and SAS.

As a LSU Professor, Joni Shreve had an outsized role in not only serving the forum as its academic chair, but in also encouraging her MS Analytics students to attended over the two days, October 17-18, 2019. Many of those students attended one or both of my papers. I met most of them and had long side conversations with a few. To a person I was impressed with their interest in analytics and what this economist from up north had to say about the state of applied analytics. These students each have very solid futures. Of course I encouraged them to add an applied econometrics course to their studies (see here or here or even here).

When I started writing the papers for this conference I was focused on SAS. It is after all a SAS conference. I was happy to contribute what may be new SAS techniques to the participants, but the fuller message was not about SAS techniques, but about the process of problem solving, and turning insights into solutions. It is about telling the story, not of SAS, but of the problem and solution. Firm articulation of the problem and the development of a full on testing strategy are messages that rise above any particular software. I am grateful to participants, students and faculty alike who in conversation after assured me that they got the message.

The student are currently in a practicum where Blue Cross and Blue Shield of LA, Director of IT, Andres Calderon, as an Adjunct Professor at LSU, is directing them in a consultative role helping them solve a real business problem. This is ideal education for analytics students. I want to thank Andres for his kind words about my presentations and the value of them to the wider analytic community. I know our conversations will continue and I will be the better for them, better than that, so will the students.

I was made to feel a part of the LSU MS Analytics program if even for two days and I am grateful to Joni Shreve for letting me have that rewarding opportunity.

And about the picture, my wife has threatened to tell Zippy (UA mascot).

October 18, 2019

SAS code for SCSUG HOW

Get the SAS code and data for the HOW workshop: Do You Know When Your Data Is Lying To You? The HOW of Regression Analysis with Quantitative And Qualitative Variables, October 18.

Get the code for the workshop

October 14, 2019January 12, 2020

Data Scientist Jobs Are Increasing For Economists: Evidence from the AEA

Economists, especially Econometricians, are in hot demand in the field of Data Science. Last March I posted Amazon’s Secret Weapon: Economic Data Sciences which was one of many similar articles on the high demand. It is the entire premise of this blog and my work at university is to highlight this and point economists and our business data analytic students in that direction. Our curriculum is centered on SAS because having the students learning to program at a base level and to learn the power of SAS is a good basis for future job employment (see Data Analytic Jobs in Ohio – May/June 2019).

Because we are looking for a couple of PhD economists for tenure track positions, I thought to wander around in JOE (Job Openings for Economists) and eventually wandered into wondering how many Data Science jobs were directly advertising in the JOE competing with academic positions (including ours).

So to sharpen my SAS SGPLOT skills i collected some data and found that indeed Data Scientists are in increasing demand over time in JOE , bur not as much as exists in the general market of Indeed.com. Clearly in JOE job listings in the August to December timeline are the best time to find a data science job, and August 2019 should grow as more jobs are added leading up to the ASSA meetings in San Diego in January. If you’re there look me up, but I suspect I will be in an interviewing room from dawn to dusk.

Enjoy! Comments welcomed.

What do you think about the SGPLOT?

 5/5

For those wanting to see the SAS code

My apologies, Elementor does not handle txt code so well, or I have not yet figured that out. (Small amount of research shows the lack of a code widgit is a problem with Elementor.)

Code with data and image are available at https://github.com/campnmug/SGPLOT_Jobs

data ds;
input date MMDDYY10. total DStitle NotDStitle;
t=_n_;
Datalines;
2/1/2014 0 0 0
8/1/2014 2 2 0
2/1/2015 0 0 0
8/1/2015 5 2 3
2/1/2016 1 1 0
8/1/2016 11 5 6
2/1/2017 1 1 0
8/1/2017 12 6 6
2/1/2018 2 1 1
8/1/2018 14 11 3
2/1/2019 7 4 3
8/1/2019 12 6 6
;
run;

Title1 bold 'Data Scientist Jobs Are Increasing For Economists: Evidence from the AEA';
Title2 color=CX666666 'Advertisement for Data Scientists in Job Openings for Economists (JOE)';
title3 color=CX666666 "Counts shown are the result of a search of all listings for 'Data Scientist'";
proc sgplot;
vbar date / response = total discreteoffset=-.0 datalabel DATALABELATTRS=(Family=Arial Size=10 Weight=Bold)
legendlabel="Total Data Scientist Jobs" dataskin=gloss;
vbar date / response = DStitle transparency=.25 discreteoffset=+.0 datalabel DATALABELATTRS=(Family=Arial Size=10 Weight=Bold)
legendlabel="Job title is 'Data Scientist' " dataskin=gloss;
yaxis display = none ;
xaxis display = ( nolabel);
inset "To put this in perspective:" " " 
"Most 'Data Scientist' and 'Economist' jobs" 
"are not advertised in JOE" 
"A search for 'Economist' and 'Data Scientist'" 
"on Indeed.com yields 514 jobs on Oct 14, 2019" 
/ position=topleft border
TEXTATTRS=(Color=maroon Family=Arial Size=8 
Style=Italic Weight=Bold);
inset "Aug 2019" "preliminary" 
/ position=topright noborder
TEXTATTRS=(Color=black Family=Arial Size=8 
Style=Italic );

format date worddate12.;
footnote1 Justify=left 'JOE listings are at https://www.aeaweb.org/joe/listings'; 
footnote2 Justify=left 'Only active listings in either the Aug-Jan or Feb-Jul timeline were searched.'; 
footnote3 Justify=left 'Search conducted on Oct 14, 2019, so the last count will grow as new jobs are entered into the system.'; 
footnote4 ' ';
footnote5 Justify=left bold Italic color = CX666666 'Created by Steven C. Myers at EconDataScience.com' ;
run;
run cancel

October 7, 2019August 5, 2020

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, 6^th 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.

Month: October 2019

My time with the MS Analytics Students at LSU

Like this:

SAS code for SCSUG HOW

Like this:

Data Scientist Jobs Are Increasing For Economists: Evidence from the AEA

For those wanting to see the SAS code

Like this:

Ethics Rules in Applied Econometrics and Data Science

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.

References:

Like this: