Applied Economics is a broad field with many applications.
Applied Economics is a broad field of study covering many topics. Recognizing the wide range of applications has led departments of Agricultural Economics across numerous universities to change their degree program names to Applied Economics. In 2008, the American Agricultural Economics Association changed its name to the Agricultural and Applied Economics Association (AAEA).
This trend is noted in research published in the journal Applied Economic Perspectives and Policy:"Increased work in areas such as agribusiness, rural development, and environmental economics is making it more difficult to maintain one umbrella organization or to use the title “agricultural economist” ... the number of departments named" Agricultural Economics” has fallen from 36 in 1956 to 9 in 2007."
This brief podcast from the University of Minnesota's Department of Applied Economics is an example of this trend:
It discusses the breadth of questions and problems applied economists address in their work including obesity and food systems, environmental and water resource economics, development, growth, trade, and technological change; public sector economics, health policy and management, human resources and industrial relations. Applied research in this area is often interdisciplinary including biology, engineering, health and animal sciences, and nutrition as an example.
Why study applied economics? A few inspiring quotes from Southern Illinois University introduction to their programs in Agribusiness Economics:
If you want to prove sustainable resource use saves money and protects the land…
If you understand that the wheat crop here can make a difference for a hungry child across the ocean…
Applied Economics emphasizes quantitative and analytics skills ideal for careers in data science
Many applied economics master's degrees are designed to serve as a very attractive terminal degree for professionals.
To quote, from Johns Hopkins University’s Applied Economics program home page:
“Economic analysis is no longer relegated to academicians and a small number of PhD-trained specialists. Instead, economics has become an increasingly ubiquitous as well as rapidly changing line of inquiry that requires people who are skilled in analyzing and interpreting economic data, and then using it to effect decisions ………Advances in computing and the greater availability of timely data through the Internet have created an arena which demands skilled statistical analysis, guided by economic reasoning and modeling.”
Many applied economics programs are STEM designated programs reflecting the emphasis that applied economics places on quantitative and analytics skills. The University of Pittsburg has designed their STEM designated M.S. in Quantitative Economics specifically with data science roles in mind. Virginia Tech offers an online Master of Ag and Applied Economics, the first I have seen in an Agricultural and Applied Economics department specifically designed to incorporate economics with data science and programming.
The focus on causality differentiates economics from other fields.
Once armed with predictions from machine learning an AI, businesses will start to ask questions about what decisions or factors are moving the needle on revenue or customer satisfaction and engagement or improved efficiencies. Essentially they will want to ask questions related to causality, which requires a completely different paradigm for data analysis.
In a KDnuggets interview, Economist Scott Nicholson (Chief Data Scientist at Accretive Health and formerly at LinkedIn) comments on the differences between economists and data scientists:
"In terms of applied work, economists are primarily concerned with establishing causation. This is key to understanding what influences individual decision-making, how certain economic and public policies impact the world, and tells a much clearer story of the effects of incentives. With this in mind, economists care much less about the accuracy of the predictions from their econometric models than they do about properly estimating the coefficients, which gets them closer to understanding causal effects. At Strata NYC 2011, I summed this up by saying: If you care about prediction, think like a computer scientist, if you care about causality, think like an economist."
As data science thought leader Eugene Dubossarsky puts it in a SuperDataScience podcast:
“the most elite skills…the things that I find in the most elite data scientists are the sorts of things econometricians these days have…bayesian statistics…inferring causality”
Nobel Prize Laureate Joshua Angrist discussed the new opportunities for students graduating with economics and quantitative skills that are available at firms like Amazon because of their interest in causal questions and running experiments:
In another interview Angrist emphasizes opportunities for Economics bachelor's degree holders:
"There's a very strong private sector market for economics undergrad especially economics undergrads who have good training in econometrics...like Amazon and Google and Facebook and Trip Adviser they are looking for people that can do some statistics but a lot of the questions that they are interested in are causal questions. What will be the consequences of changing prices for example or changing marketing strategies and these companies have discovered that the best training for that is undergrad work in economics or econometrics. We really specialize in causality in a way regular data science does not.....someone who trains in data science might learn a lot about machine learning but won't necessarily learn about for example instrumental variables or regression discontinuity methods and those turn out to be very useful for the tech sector."
A post at the Uber Engineering blog explains how they find these skills to be valuable in a business setting:
"One of the most exciting areas we’ve been working on is causal inference, a category of statistical methods that is commonly used in behavioral science research to understand the causes behind the results we see from experiments or observations...causal inference helps us provide a better user experience for customers on the Uber platform. The insights from causal inference can help identify customer pain points, inform product development, and provide a more personalized experience...At a higher level, causal inference provides information that is critical to both improving the user experience and making business decisions through better understanding the impact of key initiatives."
Economics provides a foundation with long lasting value and offers a bright future.
Economics combines mathematically precise theories (like microeconomics) and empirically sound methods (like econometrics) to study people's choices and how they are made compatible. As a social and behavioral science and a quantitative and technical field, learning to think like an economist and applying those skills will never go out of fashion. There are a number of both undergraduate and graduate degree programs in economics and applied economics across the country and I would encourage you to check them out. I've listed a few more examples of applied economics programs below.
***This post is an update to an original post made in September 2010 found here.
Related Posts:
Will There Be a Credibility Revolution in Data Science? https://econometricsense.blogspot.com/2018/03/will-there-be-credibility-revolution-in.html
Why Data Science Needs Economics. http://econometricsense.blogspot.com/2016/10/why-data-science-needs-economics.html
Economists as Data Scientists http://econometricsense.blogspot.com/2012/10/economists-as-data-scientists.html
References:
'What is the Future of Agricultural Economics Departments and the Agricultural and Applied Economics Association?' By Gregory M. Perry. Applied Economic Perspectives and Policy (2010) volume 32, number 1, pp. 117–134.
References:
'What is the Future of Agricultural Economics Departments and the Agricultural and Applied Economics Association?' By Gregory M. Perry. Applied Economic Perspectives and Policy (2010) volume 32, number 1, pp. 117–134.
Additional Graduate Programs in Applied Economics and Related Fields
Western Kentucky University - M.A. in Applied Economics (Also UG and GR options in Agriculture and Food Science
Murray State University - M.S. Agriculture/Agribusiness Economics
Virginia Tech - M.S. Ag and Applied Economics
University of Cincinnati - M.S. Applied Economics
Clemson University - M.S. Applied Economics and Statistics
Montana State University - M.S. Applied Economics
Cornell University - M.S. & M.P.S. in Applied Economics and Management
Oklahoma State University - M.S. Agricultural Economics and MAg in Agribusiness
Texas A&M - M.S. in Agricultural Economics
North Dakota State University - M.S. Agribusiness and Applied Economics
University of Illinois - M.S. Agricultural and Applied Economics
University of Missouri - Agricultural and Applied Economics
Auburn University - M.S. Agricultural Economics and Rural Sociology (various programs)
AAEA - Directory of additional programs at the graduate and undergraduate levels
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