Tag: economics

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ASSA2020 – Teaching econometric students with SAS(r)

As the new decade begins, I am preparing for my flight to San Diego where my colleague, Sucharita, and I will be interviewing for the Department of Economics as we seek to hire two tenure-track assistant professors for the department to replace the three faculty who are leaving in May. I always enjoy the ASSA (Allied Social Science Association) meetings, but this time I will miss all of the sessions and activities as we have a full interview schedule. As I have reported Data Scientist Jobs Are Increasing For Economists: Evidence from the AEA. We are looking for those who will teach data science to our students.

S285_sas100KIt has been 41 years since I began my academic career. I leave it at the end of this Spring semester and I will miss teaching econometrics and data science to our students. Those who know me understand my passion for SAS(r) in the econometrics curriculum and I am not dissuaded by the presence and importance of R and Python.  Students who learn to program in SAS, learn far more than the analytic power of the worlds leading analytical solution. They learn in one environment how to acquire data, to manipulate and manage that data, to analyze it with powerful procedures and to visualize and report results from that data.

SAS is a great skill for students and their proficiency with SAS prepares them both for careers in SAS and for careers using other languages and systems. I argue from the experience of my students that SAS provides a platform from which those students may easily learn any other language or system that an employer will have. I cannot say the same for R and Python, partly out of ignorance and partly because I have not heard or read that R and Python provide the same firm foundation for future learning of other languages and systems.

Every new Ph.D. economist we interview will be proficient in STATA, few will be proficient in SAS, and many will not list SAS in their skill set.  The willingness of the candidate to learn and teach SAS is critical to our Economics  and Business Data Analytics programs. The University of Akron partners with SAS Global Academic Programs and offers a joint Certificate in Economic Data Analytics to each qualified graduate. Our students are ready to turn data into action using SAS and the unique qualities of critical thinking, problem solving and story telling that is part of all economic curriculums. Economists do put the science into data science. Data Science is far more than predictive analytics. You can make predictive analytics work beautifully in many cases, but there is no substitution for knowing why something works. Economists are masters of explanation and causality, and have the statistical prowess to back it up.

In an earlier blog posting I reviewed the data science textbook I used last semester (A Data Science Book Adoption: Getting Started with Data Science) and in one of the figures I showed that in Ohio while there were over 600 jobs lisiting ‘SAS; there were just fewer than 30 listing ‘STATA.’  Today as I write this there are 521 SAS listings and only 15 STATA listings in Ohio, and nationwide the numbers are 17K SAS jobs to 1.5K STATA jobs. (Indeed.com). I think we are on the right track.

Teaching economics and econometrics with SAS gives students a firm foundation for productive and profitable analytic careers in all data science fields. And our students have done very well in that space.

Wish us luck as we look for two new assistant professors of economics who will contribute to our students’ success. And for those who have read this far, I have been honored as the SAS Distinguished Educator for 2020 and will receive that award at the SAS Global Forum in Washington DC (March 29-April 1). I will also speak on educating economics students for data science careers. You too can attend, register here. Message me at LinkedIn if you are coming, I would love to see you. – Steven C. Myers (Akron)

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Economic Freedom: Solve Problems, Tell Stories

Time and time again we hear employers wanting two qualities out of their data scientists, be able to solve problems and tell stories. How important is economic freedom? Does it lead to greater standards of living? The answer can be shown in tables of results well laid out, but visualizing those results has an even greater impact and better tells the story.

If a “picture is worth a thousand words” then a SAS SGPLOT is worth many pages of tables or results. Can you see the story here?

Economic Freedom is shown to be associated with ever higher standards of living across countries.

The problem is whether countries with higher levels of economic freedom also have higher standards of living. It appears that is true. The association seems undeniable. Is it causal? That is another question that the visual begs. Chicken and Egg reasoning doesn’t seem likely here. It does appeal that the association is one way. For that to be established, we have to answer is economic freedom necessary for higher standards of living. And we have to determine that if the economic freedom had not been accomplished would the standard of living not been as high.

More on that in a future post on the importance of “why.” For now, enjoy the fact that their seems to be a key to make the world better off. Oh, not just from this graph, but from countless successes in countries in the past. My undergraduate analytic students are expanding on this finding to see if their choices from the 1600 World Development Indicators of the World Bank hold up in the same way as GDP per-capita does here in this graph. We/they modify the question to “Do countries that have higher economic freedom also have greater human progress?” I am anxious to see what they find.

The Economic Freedom data comes to us from The Heritage Foundation. Let me know what you think about the visual.

This is a followup to my post on my blog at econdatascience.com “Bubble Chart in SAS SGPLOT like Hans Rosing.”

The SAS PROC SGPLOT code to create the graph is on my GITHUB repository. It makes use of Block command for the banding and selective labeling based on large residuals from a quadratic regression. The quadratic parametric regression and the loess non-parametric regression are to suggest the trend relationship.

Sorry Data not included.

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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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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).

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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. 

 

Updated to final 2019-2020 numbers
Preliminary 2019-2020 numbers
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
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Time Series data will lie to you, or take a random walk in Chicago.

Do you know that data lies? Come talk to me at MWSUG (Midwest SAS Users Group Conference) and I will help you protect yourself against lying data.
MWSUG September 29-October 1, 2019 Chicago http://www.mwsug.org/2019/index.html

One of the papers i am presenting is on time series data. Time series analysis is pretty intense and there is as much art as science in its modeling. My paper is BL-101 “Exploring and characterizing time series data in a non-regression based approach.”

Nobel Prize economist Ronald Coase famously said:  “If you torture the data long enough, it will confess.”  It will confess to anything, just to stop the beating. I think there is a corollary to that, “If you don’t do some interrogation, the data may just tell a lie, perhaps what you want to hear.

Consider the following graph, assembled with no torture at all and not even a short painless interrogation. The graph shows that money supply and the federal debt track each others time path very closely. It tempts you to believe what you see.  Do you believe that when the money supply increases we all have more to spend and this will translate into dept? Do you have an alternate reasoning that explains this movement? If so, this graph confirms your thoughts and you decide to use it to make or demonstrate or prove your point. Good stuff huh?

Sadly you just fell to confirmation bias and because you have failed to investigate the data generating process of the series, you fell for the lying data. You have found correlation, but not causation. in fact, you may have found a random walk.  Don’t cheer yet, that is not a good thing to make your case. 

“But,” you think, “I like that graph and besides the correlation between Money Supply and Debt is really high so it has to mean something! right?“

Sadly, no. 

Mathematically, if the series are random walks then changes in the series are only generated by random error. Which means the correlation between the two variables will be very low. 

A random walk takes the form of

y(t) = y(t-1) + e

which says that the currently observed variable at time t, is equal to the immediate past value plus a random error term. The problem here can be seen by subtracting y(t-1) from each side yielding a new and horrifying equation that says that any growth observed is purely random error, that is

Change in y = y(t) – y(t-1) = e.

Since you cannot write an equation to predict random error, it stands to reason that you cannot predict current or forecast future changes in the variable of interest.

Consider the next graph. The percentage change over the last year in the money supply is graphed against the percentage change over the last year of debt.  See a definite pattern? I do not. 

The correlation between money supply and debt in the first graph is 0.99 where 1.0 would be perfectly one-to-one related. In the second graph the correlation falls to 0.07 meaning there is almost no relationship between them.

The lesson: You should do more investigation, torture is not necessary, but no investigation is never desirable. 

Economists are obsessed in determining the data generating process (DGP)which take a lot of investigation. Economists know traps like random walks and know ways to find the true relationship between money supply and debt, if any. Ignore the DGP and your quick results could be a lie. Torture the data, and again you could find a lie (it just may take a long time of wasteful actions). 

So come take a random walk in Chicago with me at MWSUG. 

After the conference my paper will be available on the conference proceedings. 

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A Data Science Book Adoption: Getting Started with Data Science

buy this book

In my undergraduate business and economic analytics course, I have adopted Murtaza Haider‘s excellent text Getting Started with Data Science. I chose it for a lot of reasons. He is an applied econometrician so he relates to the students and me more than many authors. I truly have a very positive first impression. 

Updated: November 7, 2020

On my campus you can hear economics is not part of data science, they don’t do data science, that is, data science belongs to the department of statistics (no to the engineers, to the computer science department, and on and on like that.)  We have come a long way, but years ago, for example, the university launched a major STEM initiative and the organizers kept the economic department out of it even though we ask to be part of it. Of course, when they did their big role out, without our department, they brought in a famous keynote speaker who was … wait for it … an economist.

My department , just launched a Business Data Analytic economics degree in the College of Business Administration at the University of Akron.  We see tech companies filling up their data science teams with economists, many with PhDs. Our department’s placements have been very robust in the analytic world of work. My concern is seeing undergraduates in economics get a start in this field. and Murtaza Haider offers a nice path. 

Dr. Haider, has a Ph.D. in civil engineering, but his record is in economics, specifically in regional and urban, transportation and real-estate, and he is a columnist for the Financial Post. and I can attest to his applied econometrics knowledge based on his fine book which I explore below.

WHAT IS DATA SCIENCE

Haider has a broad idea of what is data science and follows a well-reasoned path on how to do data science. Like my approach to this class, he is heavy into visualizations through tables and graphics and while I would appreciate more design, he makes an effort to teach the communicative power of those visualizations. Also, like me, he is highly skeptical of the value of learning to appease the academic community at the expense of serving the business (non-academic) community where the jobs are. I really appreciate that part of it.

PROBLEM SOLVING AND STORYTELLING

He starts with storytelling. our department recognizes that what our economists do, what they do to bring value is they know how to solve problems and tell stories. Again this is a great first fit. He then moves to Data in a 24/7 connected world. He spends considerable time on data cleaning and data manipulation. Again I like how he wants students to use real data with all of its uncleanliness to solve problems. Chapter 3 focuses on the deliverables part of the job and again I think he is spot on. 

Then through the remaining chapters he first builds up tables, then graphs, and onto advanced tools and techniques. My course will stop somewhere in the neighborhood of chapter 8.

(Update: Chapter 8 begins with the binary and limited dependent variables, and full disclosure my last course did not begin this chapter, we ended in Chapter 7 on Regression). Perhaps the professor in the next course will consider Getting Started in Data Science for Applied Econometrics II.  (Update: Our breakdown in our Business Data Analytics economics degree is that Econometrics I is heavily coding and application-based, while econometrics II is a more mathematical/ theoretical based course with intensive data applications.  It is a walk before you run approach, building up an understanding of analysis and data manipulation first. )

I use a lot of team-based problem-based learning in my instruction and Haider’s guidance through the text is instructing teams how to think through problems to get one of many possible solutions, not highlighting only one solution. In this way, he reinforces both creativity in problem-solving. I like what I read, I wonder what I will think after students and I go through it this term. (Update: I/we liked the text, but did not follow it page by page.  The time constraint of the large data problem began to dominate and crowd out other things, hence why I did not get to Chapter 8, my proposed end. However, because in course 1 which emphasizes data results over theoretical knowledge, I was well pleased.)

PROBLEM ARTICULATION, DATA CLEANING, AND MODEL SPECIFICATION

Another reason I like the book so much is he cites Peter Kennedy, the now passed, research editor for the Journal of Economic Education. Peter was very influential on me and applied econometricians who really want to dig into the data. Most of my course is built around his work and especially around the three pillars of Applied Econometrics.: (1) the ability to articulate a problem, (2) the need to clean data, and (3) to focus deeply on model specification. He argues that most Ph.D. programs fail to teach the applied, allowing their time to focus on theoretical statistics and propertied of inferential statistics. Empirical work is often extra and conducted, even learned, outside of class. I have never taught like that (OK, maybe my first year out of my Ph.D.), but my last 40 years have been a constant striving to make sure my students are prepared for the real as opposed to the academic world. Peter made all the difference bringing my ideas into sharp focus. I like Haider’s work, Getting Started with Data Science, because it is written like someone who also holds the principles put forth by Peter Kennedy in high regard. 

SOFTWARE AGNOSTIC, BUT TOO MUCH STATA AND NOT ENOUGH SAS

On page 12 he gets much credit for saying he does not choose only one software, but includes “R, SPSS, Stata and SAS.” I get the inclusion of SPSS given it is IBM Press, but there is virtually no market for Stata (or SPSS)  in the state of Ohio or 100 miles around my university’s town of Akron, OH. Also, absent is python, which is in heavy use in the job market.  You can see the number of job listings mentioning each program in the chart below. 

I am highly impressed with Haider’s book for my course, but that does not extend to everything in the book. My biggest peeve is his heavy use of Stata. I would prefer a text that highlights the class language (SAS) more and was more sensitive to the market my students will enter.  

Stata is a language adopted by nearly all professional economists in the academic space and in the journal publication space, however, I think this use is misguided when the book is to be jobs facing and not academic facing. While he shows plenty of R, there is no python and no SAS examples. All data sets are available on his useful website, but since SAS can read STATA data sets that isn’t much of a problem.

Numbers for all of indeed.com listings in August 2019: Python, 70K; R 52K; SAS 26K, SPSS 3,789; Stata 1,868

SAS Academic Specialization

Full disclosure, we are a SAS school as part of the SAS Global Academic Program and offer both a joint SAS certificate to our students as well as offering them a path to full certification. 

(Update: The SAS joint certificate program has been rebranded and upgraded to the SAS Academic Specialization and is still a joint partnership between the college or university and SAS, but now in three tiers of responsibilities and benefits. We are at tier 3 and the highest level. Hit the link for more details.) 

We also teach R as well in our forecasting course and students are exposed to multiple other programs over their career including SQL, Tableau, Excel (for small data handling, optimization, and charting/graphics), and more. 

Buy This Book

Most typical econometric textbooks are in the multiple hundreds of dollars (not kidding) and almost none are suitable to really prepare for a job in data science. This book on Amazon is under $30 and is a great practical guide. Is it everything one needs? Of course not, but at the savings from $30 you can afford many more resources.

More SAS Examples

So it is natural given our thrust as a SAS School, that I would have preferred examples in SAS to assist the students. Nevertheless, I accepted the challenge to have students develop the SAS code to replicate examples in the book. This is a great way to avoid too much grading of assignments. Let them read Haider’s examples, say a problem that he states, and then solves with STATA. He presents both question and answer in STATA and my student’s task is to answer the problem in SAS. They can self check and rework until they come to the right numerical answer, and I am left helping only the truly lost.  

Overall, I love the outline of the book. I think it fits with a student’s first exposure to data science and I will know more at the end of this term. I expect to be pleased. (Update: I was.) 

If you are at all in data science and especially if you have a narrow idea that data science is only Machine Learning or big data, you need to spend time with this book, specifically read the first three chapters and I think you will have your eyes opened and a better appreciation of the field of data science.

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Poverty Progress

Between 1980 and today the world is getting better, humans are making amazing progress. 

GDP per-capita in the US rose from $28,590 to $54,542, almost doubling as measured in 2010 dollars.

Worldwide, extreme poverty fell by over half as measured by the world bank.  (42 percent of the world’s population was in extreme poverty in 1981, but by 2015 only 9.9% of the world was in that state).

The number of wage salary workers that are paid at or below the federal minimum wage in the US fell from 15% to 2%.

The US Official Rate of Poverty rose by 0.5 percentage points.

Wait. What?

The world is improving even if you don’t think so.

Ask your friends about the drop in extreme poverty. I bet most get it wrong. My evidence is from the Misconception Study conducted by Gapminder Foundation. In fact, take their test to see how many misconceptions you have about the world. (It is right on the front page at https://www.gapminder.org/). Out of 12 questions administered to thousands across the world, the average score for every group is less than if the answer had been chosen by random. 

Once misconception is the world is getting worse, when indeed it is really getting much much better. But stories of better do not lead the news, only stories of woe. Further if you got your education in the 70s and 80s as I did you may have many misconceptions simply because you believe data learned correctly then has not changed. 

Why has the official poverty rate not fallen with all this world wide progress?

If world extreme poverty is down, why is the US official poverty rate so flat, nearly the same now as almost 50 years ago? The first problem is world wide poverty is bench marked on an absolute income standard. The OPR in the US is a relative income standard. They measure very different things. 

The second problem is income is the wrong measure for poverty. Using bad measures of important concepts like poverty creates a misconception that the problem is much worse and virtually unsolvable and attracts policy prescriptions to do exactly the wrong thing. 

The World Bank expects Extreme Poverty to essentially vanish by 2030. The US government has made no such forecast by any year in the future. 

 

What is the better measure of US poverty?

Meyer and Sullivan track what it costs to consume at a level not to be in poverty, that is, to create a consumption poverty rate (CPR) that is shown on the last track. The better question is not do the US poor have enough income, but do they have enough consumption? Without getting into what poverty programs are good and bad, the case of food stamps, now SNAP, is such instructive. Take two families with identical income and one of them received one or more consumption based forms of assistance such as SNAP and clearly one is relatively better off. The OPR does no consider any assistance to the people in poverty that they measure. 

But a goal to eradicate poverty needs to be based against an absolute standard with policy clearly targeting families to get them across that standard. We do not want people deprived. It is not about income, its about existence beyond deprivation. 

One of the reasons for the consumption poverty rate (CPR)  is consumption is a better predictor of deprivation than income. (Perhaps two people have the same income, but one cannot afford to put good food on the table, who is worse off?).

You can find their excellent paper here (https://leo.nd.edu/assets/249750/meyer_sullivan_cpr_2016_1_.pdf)

To listen to the news media and the advocacy groups everything is a crisis and a disaster and the world and the US is getting worse. The nice thing about data is it proves the obverse, the world and the US is getting better at a rate begun in about 1980 that is astonishing. But good news does not bleed and therefore will not lead.

So here is what is remarkable: From 1980 to 2015 the consumption poverty rate fell by 9.4 percentage points, while the official poverty rate rose by 0.5 percentage points.

So what make more sense, that the US has a poverty rate of 13.5 (in 2015) that is virtually impossible to lessen or eliminate, or a Poverty rate based on consumption that is 3.5% of the population that we might be able to further reduce.  

I would like to see the end of poverty wouldn’t you? 

Be a Data Skeptic and do your own research

We hear constantly about bias reporting and fake news and you should be motivated to be skeptical about any data you hear reported and motivated to search out the actual facts.

In other cases, such as the FBI’s hate crime data, the data are not reliable without understanding how it is collected. The data is fine, but year to year comparisons are not easily possible because of the data design. (see The importance of data skepticism. Hate crimes did not rise 17% in one year. )

Many data websites do exist to help you find actual facts. 

Some of the best fact based sites are
https://Justfacts.org
https://Gapminder.org
https://Fred.org
https://humanprogress.org.

So the message is be humble, don’t believe everything at face value and learn how and do your own research.

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Data Analytic Jobs in Ohio – May/June 2019

“Economists put the science in data science,” at least that is how the tag line goes on this blog. As we address our new Business Data Analytics degree in the College of Business Administration we need to know if our earlier plans for what is taught technically is still a good idea. Currently we teach SAS, R, and Tableau in Economics and students get SQL and JMP in other business courses. 

Searches for jobs in Ohio and within 100 miles of Arkon Ohio were preformed by the author on Indeed.com to see how many jobs included certain key words. The geographical area “Ohio” is well known and bounded, the area “100 miles of Akron” includes jobe no only in NE Ohio, but includes jobs outside NE Ohio as this definition touches the circle of influence of the Columbus area and the Pittsburg area. There is no way to know whether all jobs in Columbus and Pittsburg are counted or only those to the NE and NW respectively of both cities. 

Software Preference

SQL is the most mentionned software/language by far. After that R, Python, SAS and Tableau ranked in that order. Java and HTML are mostly used in web design and non analytic use. Salesforce was included because of the decision this week to acquire Tableau. 

Two interesting points. (1) Excel was originally included and eliminated from Figure 1, because Excel was mentionned in 19,370 jobs in Ohio and 12,129 for jobs with 100 miles of Akron, OH. (2) SAS and SQL was examined with the result that 60% of SAS jobs in Ohio and 67% of SAS jobs within 100 miles of Akron also included mention of SQL. 

Figure 1: Job including software. The software was included in the description, but not distinguished whether recommended or required. Source: authors calculations.

SAS Presence

There are a good number of SAS mentions which is good for our students since we are a SAS program offering a SAS Certificate in Economic Data Analytics.  As figure 2 shows, SAS is preferred by those employed by business, statistics and economics degree holders and figure 3 shows a preference for SAS in Fortune 500 companies. 

Figure 2 SAS use highest among Business, statistics and economics degree holders employed and surveyed. Source: Butchworks.com.
Figure 3: SAS preferred by Fortune 500 company employees. Source: Butchworks.com.

Skill areas included

Searches were also done by key words, not just on software with the results shown in Figure 4. Shocking to economists is that “econometrics,” the study of applying data analysis to typically economi data has only 29 listings in Ohio. However, every econometric student knows regression and logit and statistical inference and prediction and forecasting and more, and we know most economics students go into data analytics with ease, so what to conclude. The term econometrics is foreign to the job opportunity listings and perhaps it is time for a more relevant and descriptive naming of what is taught in econometrics.  

A typical economics student and especially one who gets our new Business Data Analytics can compete for most of the jobs including each of the keywords shown below making the new degree a very robust and rewarding one.  

Figure 4: Key terms of skills included at Indeed.com. Source: authors calculations.

Wraping up

To complete the analysis of jobs, Figure 5 shows that jobs mentioning “management” is incredibly large. i speculate that this is because job descriptions include not only jobs for managers, but also word use such as “reporting to management” and “data management.” 

Nevertheless, by including the names of departments in our college (except accounting), we get a sense of opportunities for various of our college majors, but a deeper search looking at sub fields such as supply chain, human resources, risk, insurance would have to be done, but the numbers are suggestive. 

Just like Excel as discussed above, the word “data” is mentionned in nearly 20,000 jobs in Ohio and almost 13,000 within 100 miles of Akron. So many jobs now require data savy on the part of employees that any of the colleges degrees offered in teh college of buainess administration at the University of Akron (including accounting) leads to lots of openings  advertising for their data skills.  

And the bottom line

Our new economics degree, Business Data Analytics promisses to produce graduates in high demand.

Figure 6: Mentions of the names of the various departments in the college and a comparison to searches for the word "data" and "Excel." Source: authors calculations.

References

  1. Indeed.com

  2. SAS, R, or Python 2018 Deeper Dive: Examining Preference Predictors

  3. Department of Economics, College of Business Administration, The University of AKron, Akron, OH
  4. Our SAS Certificate in Economic Data Analytics

 

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A Github Economics and Data Science repository

Vikesh Vkkoul, an analyst with an MA in Applied Economics, has a nicely done collection of articles and more of Economics and Data Science at github. He also has a good set of Data Science Resources on his site as well. Check him out.