Machine learning in economics is still a new subject. Although machine learning (ML) is slowly gaining interest among economists, still we see a lack of information. What exactly machine learning entails, what makes it different from classical econometrics and, finally, how economists and businesses along with them can make the best use of it. But let’s examine current knowledge and see how machine learning is used in economics. It will lead us to one conclusion – machine learning in economics will keep growing rapidly and its impact on the market will soon become fundamental.
One of the companies, that does research on how Artificial Intelligence and machine learning can influence economics is PWC UK, which is one of the leading consulting companies in the world. In their report, called “The economic impact of artificial intelligence on the UK economy” that was published in June 2017 we can read that “UK GDP will be up to 10.3% higher in 2030 as a result of AI – making it one of the biggest commercial opportunities in today’s fast-changing economy. The impact over the period will come from productivity gains (1.9%) and consumption-side product enhancements and new firm entry stimulating demand (8.4%)”.
So, according to PWC, Artificial Intelligence along with machine learning can contribute considerably to economic growth in three main areas:
- Improvement of productivity
- Product enhancement
- Stimulating new companies
These three areas are essential for the economics and market development in general. You can judge, just by looking at them, that machine learning in economics will have a massive impact on the market’s and society’s development and in fact the pace of that development as well. Machine learning will be a necessity for every new company entering the market.
What is common with machine learning and economics?
Well, the shortest and obvious answer is that machine learning and economics are based on data. We have two approaches: traditional, which is econometrics and innovative, which is machine learning. Both of them have a lot of overlap. Econometrics is basically statistics geared towards answering economic questions. Machine learning in economics has a similar purpose but with the usage of huge amount of data. Also, machine learning in economics is not based on exactly the same models as econometrics.
So we can say that econometrics and machine learning are just two different roads to the same destination. But those roads are quite different. As Paul A. Samuelson and William D. Nordhaus have written in their book Economy – econometrics are allowing economists “to sift through mountains of data to extract simple relationships”. Applied econometrics uses real-world data for assessing economic theories, developing econometric models, analyzing economic history, and forecasting. All of that is done by econometricians with the usage of certain models.
Machine learning in economics – the game-changer
On the other hand, we have machine learning with all its benefits. Machine learning algorithms are capable of analyzing hundreds of millions of bytes in order to find correlations, connections, and even predictions. Some of them are very difficult to spot without machine learning algorithms. As you already know, machine learning applications and algorithms are much faster, more accurate and effective in their work than human scientists. All they need for their job is big data they can base on. Therefore, machine learning in economics reaches a level that is absolutely out of range for standard, traditional econometrics.
But that doesn’t mean econometrics and machine learning exclude themselves! Stanford University in one of the studies predicts “development of new econometric methods based on machine learning designed to solve traditional social science estimation tasks”*. So what that means, we can expect synergy of both disciplines. Machine learning and economics (econometrics to be exact), will take the advantages of another in order to create the most efficient predictive method. So both are needed: econometrics and machine learning in economics.
And with that, we go back to the main topic – how machine learning is used in economics?
Improvement of productivity
According to PWC, machine learning in economics can increase productivity by up to 14.3% by 2030. Machine learning is a catalyst for productivity growth. In the near future, many current jobs and tasks will be performed totally by machine learning and Artificial Intelligence algorithms or with usage of them. Just think about such jobs as factory workers, cleaning crews, cashiers (even now in more and more shops there are self-service checkouts!), guides (audio guides are already on the market), receptionists, tourist information workers and hundreds more. These jobs are considered simple, and such tasks can easily be performed by machine learning and Artificial Intelligence algorithms, apps and devices.
And these professions that will still require human presence will base increasingly on machine learning and Artificial Intelligence. We can predict that one of the key skills of the future worker will be the knowledge of how to co-operate with the Artificial Intelligence algorithms in his or her work. Another job of the not-too-distant-future will be a machine learning specialist and a big data scientist. We will look closer to that subject in one of the following posts.
Demand for the big data scientists and machine learning specialists
Big data and machine learning are demanding for new specialists and scientists. And that demand grows rapidly. For instance, as Indeed.com shows we can observe a 29% increase in demand for data scientists year over year and an almost unimaginable 344% increase since 2013.
We will tackle that subject much more in one of the following articles, now just the essence. If you’re after job with perspectives for the future – go for big data, there will be plenty of work for you in the coming years.
The World Economic Forum’s 2018 Future of Jobs Report surveyed more than 300 of the world’s largest companies and 85% of them said they wanted to expand big data analytics by 2022**. And what about machine learning specialists? As another report shows, in 2018 there were about 3000 people with skills and background in Artificial Intelligence and in the US itself, there was demand for more than 9000 specialists.
So you clearly see the impact of the big data and therefore machine learning in economics. Future markets will be overfilled with it.
Product enhancement
Thanks to the economics machine learning, current, and future products are and will be better and better tailored to the market’s expectations. Why do we say so? Machine learning can help in increasing the quality of products and services, but also in giving more personalized products and varieties of them to the customers. What’s more, new companies entering the market are able to measure customer’s demand for certain products with amazing precision.
Machine learning in economics can analyze tons of data necessary to make the right business decisions regarding introducing a new product to the market or changing existing ones. Even right now every serious company conducts lots of surveys and studies before making even the smallest change to the product. Doesn’t matter if we talk about packaging, taste, size, price or any other factor. Everything just has to be examined as thoroughly as possible. With the development of economics machine learning, that trend will go sky-high. Imagine machine learning systems doing all the surveys and analytics for the big corporations. Everything would be so much quicker and more accurate.
Machine learning algorithms will execute hundreds of surveys, “talk” with thousands of people worldwide and analyze all data available in order to deliver 100% effective product demanded by the market. And all of that at the same time! Currently, it takes a lot of time to gather proper candidates for the survey, execute it and write a summary. And then you need to analyze and combine data from several countries where surveys had taken place. That takes weeks and months to finish the whole process. Machine learning can shorten it to just days or even less.
Based on that, we can predict that the product of the future will meet our expectations and requirements much better than it happens now.
Forecasts and predictions
When it comes to prediction, standard econometric models tend to “over-fit” samples and therefore the outcome might be misleading. Machine learning algorithms are much more accurate and deprived of human opinions and judgments. In traditional econometrics, the more complex the model you are basing on is, the higher is the variance and the lower is the bias. So you might expect forecasting error, sometimes smaller, sometimes larger, but it always is there.
This is the point where machine learning in economics comes in. Machine learning algorithms can minimize forecasting error and do the forecast much faster and with the usage of more data. What’s more, machine learning algorithms can analyze many alternative models at the same time, when in traditional econometrics you can analyze just one model at a time.
How does that help? Economics will be a much more precise discipline of knowledge and companies and other organizations will be more encouraged to use it in their work. Just take the economic predictions. What would you do if you had the ability to predict the financial crisis? Or if you could predict the outcome of the elections? Or if you could find out what technology or service will be needed in two years?
Summary
In general, this is what economics machine learning is about. To help to enhance products and services, improving productivity and predicting the future by giving trustworthy forecasts about economics, market, society, politics or technology. But for a change, these predictions actually CAN be trustworthy.
Current predictions are mostly based on what someone thinks, whether it’s a one-person or a company. It’s not a reliable source. Forecasts of the future will be based on big data. Machine learning algorithms will analyze the tenths of thousands of gigabytes of data in order to find the most probable outcome or trend. It will no longer be based on “reading tea leaves” so we might expect that its accuracy will be considerably higher. And as we mentioned earlier – a synergy of machine learning in economics and econometrics can lead to much more accurate models, combining the ability to analyze huge amounts of data and traditional modeling.
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