Data Science and Economics - it takes two to tango

The #datascience revolution that is underway is because of the 33.5 million times increase in semiconductor capacity over the last fifty years. By fitting more transistors on a chip, speed of processing has accelerated. In what has become known as Moore's Law - the number of transistors on a microchip?doubles every two years.?While you can stream #YouTube shorts or order #Uber on your phone, the Tech companies continue to hoard your data to disentangle the signal from noise. This allows companies to improve their offerings both in terms of the products created as well as what is offered to the customer from the multitudes of products. By personalizing each product’s online presence to match what the customer wants, there are attractive and addictive opportunities available for a consumer to spend their time and money. On the internal side of the equation, a company also needs to decide how many employees to hire, what to pay them, how to automate processes such as marketing and sales support, how much to invest in fixed versus variable capital, and how to spend in the presence of competitors. The revenue earned by a customer’s trust is important but so are the costs associated for the firm. What ties together this part of data science to the actions that a business takes? Consumer theory in economics is about how to spend your scarce time and money to maximize utility. Economics for the producer is about how to maximize profits with the given technology. These are topics covered in neoclassical microeconomics theory. Economics is also about how firms act strategically considering the actions of other firms. This is under the purview of #GameTheory. Firms and consumers are making decisions in a specific macroeconomic environment. Economics enriches our understanding of the macroeconomic environment (financial markets, inflation, unemployment, growth). Courses on #Financialmarkets, #Macroeconomics and #EconomicGrowth are important to understand the forces acting on demand and supply-sides. Economics is also about setting up the best models for firms that allow it to price it’s products and services. Should a firm offer its product as a PAYG like Uber? Offer advertising for its search service like #Google to maximize profits? Or should it offer subscription like #Spotify? Or should it offer both, like #Amazon? This is in the realm of #IndustrialOrganization in economics.

Policy makers, communities and individuals also care about social welfare, such as health, education, and equality. Indeed, #Development economics is about how incremental social welfare can be brought about with the right set of interventions. This is in the realm of understanding causal impacts through statistical models. Finally, economic theory has made strides in a deeper understanding of how markets (e.g., kidney, marriage, retail) should be organized (e.g., through auction and market mechanisms) for maximizing social welfare. These courses dovetail with designing of algorithms, data mining, pattern detection, machine learning and natural language processing and can help businesses make the best decisions in the interests of customers. Finally, #behavioural economics offers clues to companies as to how to make a product more addictive, while at the same time-sharing insights with consumers on exercising greater self-control and fulfil commitments to oneself. This complementary approach of economics combined with data science not only helps create the strategic foundations to answer business relevant questions. It also helps build a scalable framework for optimization and causal inference, which can provide a north star to the company’s management to make customer-centric decisions.

#DataScience on its own is a valuable resource and will continue to be in demand. However, combined with #economics, it becomes an irresistible lever for making measurably impactful decisions for businesses, customers, and society.