Nicola Bianchi
Nicola Bianchi

STRATEGY
Assistant Professor of Strategy

Print Overview

Nicola Bianchi received his PhD in Economics from Stanford University in 2015.  He is currently an Assistant Professor in the Department of Strategy. Professor Bianchi's interests include the economics of education and innovation. 

In education, his current work focuses on the consequences of increased access to higher education. In innovation, his current work examines the effect of compulsory licensing on invention, as well as the effects of technology transfers. 

In most of his research projects, Professor Bianchi exploits historical public policies to address economic questions that, although currently relevant, would be hard to tackle with only modern data.



Areas of Expertise
Data Analysis
Data Analytics
Labor Economics
Economics of Education
Innovation
Intellectual Property
Public Policy

Print Vita
Education
Ph.D., 2015, Economics, Stanford University
M.S., 2008, Economics, Universita Bocconi, Summa Cum Laude
B.A., 2006, Business Administration, Universita Bocconi, Summa Cum Laude

Academic Positions
Assistant Professor, Strategy, Kellogg School of Management, 2015-present

Other Professional Experience
Faculty Associate, Institute for Policy Research, 2015-present

Print Research
Research Interests

Public Economics, Labor Economics, Economics of Education, Economic History, Innovation.



Working Papers
Bianchi, Nicola. 2016. The Indirect Effects of Educational Expansions: Evidence form a Large Enrollment Increase in STEM Majors.
Baten, Joerg, Nicola Bianchi and Petra Moser. 2016. Does Compulsory Licensing Discourage Invention? Evidence From German Patents After WWI.

 
Print Teaching
Teaching Interests

Data Analytics, Business Analytics.


Full-Time / Evening & Weekend MBA
Business Analytics II (DECS-431-0)

This core course is equivalent to the course DECS-440 (MMM Business Analytics).

This sequel to DECS-430 extends the statistical techniques learned in that course to allow for the exploration of relationships between variables, primarily through multivariate regression. In addition to learning basic regression skills, including modeling and estimation, students will deepen their understanding of hypothesis testing and how to make inferences and predictions from data. Students will also learn new principles such as identification and robustness. The course has an intense focus on managerial relevant applications, cases and interpretations.