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First Semester 
Teaching style
Lingua Insegnamento

Informazioni aggiuntive

Course Curriculum CFU Length(h)


The educational objective of this course can for convenience be broken down into two complementary parts.
- In the first phase, the main themes of the ICT (Information and Communication Technology) markets will be introduced. In general terms, ICT means the set of technologies that are used to manage information, understood as any type of thing that can be digitized. The potential of the ICT sector is linked to the fact that it simultaneously provides technologies that are used in the most productive sectors and goods and services to consumers. The economic laws studied in the basic courses of microeconomics apply to ICT markets, but they also have peculiar characteristics: the presence of large economies of scale, externalities due to the network of final users (consumers), switching costs that make it difficult to leave an old platform in favour of another.
- in a second phase, some recent empirical studies will be deepened. This will allow us to understand how the huge masses of data currently made available to the economic analyst can be used to deepen the understanding of some social phenomena and, at the same time, to design adequate interventions.

Consistent with the Master of Science Degree in Data Science, Business Analytics, and Innovation, the expected learning outcomes, based on all Dublin descriptors, are as follows:

A) Knowledge and understanding skills. The student will acquire knowledge of the functioning of ICT markets and will have the ability to understand the incentives of agents operating in those markets.

B) Ability to apply knowledge and understanding. At the end of the course, the student is able to use the economic models learned to analyze public policies in ICT.

C) Autonomy of judgment. At the end of the course, students are able to express their views on the implications of the choices made by economic agents who make decisions in a market like that of ICT. She/he also knows how to evaluate the quality of an economic model, also being able to judge its effectiveness.

D) Communicative Skills. At the end of the course, the student is able to present with clarity and completeness the economic models studied and knows how to discuss the main assumptions and results. The student is also able to explain to third parties the analytical procedures learned during the course, having the ability to highlight the advantages and limitations in front of any audience.

E) Learning Skills. Thanks to the methodological tools and the lessons learned, at the end of the course the student has considerably improved his / her ability to analyze the phenomena of the ICT market and has acquired basic knowledge for the continuation of the course of study.


Knowledge of microeconomics, mathematics, statistics and/or econometrics at a basic level is required.


PART A: The microeconomics of ICT markets
1. Product differentiation and digital markets;
a) The basic Hotelling model,
b) Versioning, bundling, and other strategies.

2. Markets with networks
a) Network Externalities;
b) Standard wars

3. Two-sided platforms and intermediaries;

4. Information and reputation in intermediated product markets.

5. Using Big Data to address Economic and Social Problems

PART B: sing Big Data to address Economic and Social Problems.
This part partially follows the course developed by Raj Chetty (Harvard University) and made available so that it can be adapted and used by other universities. The teaching materials created by Chetty and his team (complete lecture videos and lecture slides) are available at:

5. Determinants of intergenerational mobility:
a) The role of neighbourhoods,
b) The role of early life years.

6. Topics in education economics:
a) Higher Education and Upward Mobility,
b) Teachers and "charter schools".

7) Improving health outcomes:
a) The "Google flu Trends" lesson,
b) Long-Term Impacts of Health Insurance Coverage.

8) Evidence-based Tax Policy:
a) Tax salience
b) Social Insurance and Saving Choices

9) Improving Judicial Decisions.

The second part is a course developed by Raj Chetty (Harvard University) so that it can be adapted and used by other universities. The teaching materials created by Chetty and his team (complete lecture videos and lecture slides) are available and can be used at

Teaching Methods

The teaching is divided into:
- 30 hours of desk lectures, during which exercises to be carried out at home will be distributed, subsequently taken back in the classroom;
- 6 hours of classroom presentations and discussions on previously assigned topics and projects.

Verification of learning

Learning is normally verified through a written work lasting up to 2 hours, containing 3 questions divided into various points to be developed.
The exam text is always designed so as to require the student to highlight his mastery in the algebraic derivation of economic relations, in their graphic representation, in the commentary on the economic phenomena of reference, in the knowledge of the institutional framework of reference.
The exam test score is expressed in 30 points. The exam can only be passed in the face of at least two fully satisfactory answers.

Consistent with the descriptors identified in the training objectives, the following will be evaluated:
1) clarity in expressing the theoretical contents inherent in the analysis of digital markets and their peculiarities. (assessment of knowledge and understanding).
2) the ability to re-elaborate the concepts and to apply them to cases not perfectly corresponding to the examples of the textbook but still dealt with in class or assigned to the individual deepening of the student (assessment of the ability to apply knowledge and understanding).
3) the ability to know how to suggest a specific form of analysis or regulatory intervention, motivating its responses through the theoretical tools addressed and illustrating the logical path followed (assessment of autonomy of judgment).
4) Clarity of display and commentary, the ability to synthesize, mastery of the algebraic derivation of formal relationships and their graphic representation (assessment of communication skills)

The final grade attribution interval goes:
- from 18/30: for a level of elementary knowledge of the subject, that is, when the student can only frame the topic in the economic perspective required, he knows how to set at least the basic elements of the economic models, both in the graphic and in the analytical part, and develops the written work with a barely sufficient command of language;
- at 30/30, with possible honors, if the student will show adequate command of the technical and economic language, he will be able to systematize the acquired knowledge in a logical and coherent way, he will be able to formally set the reference economic models and will be able to support the analysis with a rigorous algebraic and graphic elaboration of the expressed concepts.


Textbook for Part A of the programme:
- Belleflamme, P. and Peitz, M. (2016) Industrial Organization: Markets and Strategies, second edition,Cambridge University Press, Capitoli 5; 10; 11 [solo sezione 11.1]; 20; 21 [solo 21.1.1]; 22 [solo 22.1 e 22.3]

-Reference articles for Part B:

- Athey, S. (2017) Beyond prediction: Using big data for policy problems. Science 355, 483–485;

5) Intergenerational mobility:
- “The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility”, by Raj Chetty, John N. Friedman, Nathaniel Hendren, Maggie R. Jones, Sonya R. Porter, NBER, January 2020.

6) Topics in economics of education:
- “Closing the Gap: The Effect of a Targeted, Tuition-Free Promise on College Choices of High-Achieving, Low-Income Students.” by Susan Dynarski, C.J. Libassi, Katherine Michelmore, and Stephanie Owen. 2018. NBER Working Paper No. 25349
- "The Promise and Peril of Predictive Analytics in Higher Education", by Manuela Ekowo and Iris Palmer. 2016. T. New America Education Policy Program Report.
- “Accountability and Flexibility in Public Schools: Evidence from Boston’s Charters and Pilots.” by Atila Abdulkadiroǧlu, Joshua D. Angrist, Susan M. Dynarski, Thomas J. Kane, and Parag A. Pathak. 2011. Quarterly Journal of Economics 126 (2): 699–748.

7) Improving health outcomes:
- “Childhood Medicaid Coverage and Later Life Health Care Utilization” by Laura R. Wherry, Sarah Miller, Robert Kaestner, and Bruce D. Meyer, The Review of Economics and Statistics, May 2018, 100(2): 287–302
- “Detecting influenza epidemics using search engine query data”, by Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski & Larry Brilliant, NATURE| Vol 457| 19, February 2009: 1012-1015
- “The Parable of Google Flu: Traps in Big Data Analysis”, by David Lazer, Ryan Kennedy, Gary King, Alessandro Vespignani, SCIENCE, Vol 343 14 March 2014: 1203-1206.

8) Tax policy:
- “Salience and Taxation: Theory and Evidence”, by Raj Chetty, Adam Looney, and Kory Kroft, American Economic Review, 2009, 99:4, 1145–1177 [sctions I-IV]
- “Active vs. Passive Decisions and Crowd-out in Retirement Savings Accounts: Evidence from Denmark”, by Raj Chetty, John N. Friedman, Søren Leth-Petersen, Torben Heien Nielsen, Tore Olsen, The Quarterly Journal of Economics (2014), 1141–1219 [da to be studied up to page 1168]

9) Improving judicial decisions
- “Human Decisions and Machine Predictions.”, by Jon Kleinberg, , Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan.
2021. Quarterly Journal of Economics

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