Measuring the impact of e-government

Estonia offers about 1500 e-services in its e-governance ecosystem, producing over 340 million interactions in 2014 alone – almost a million a day. At the same time, Estonian residents digitally identified themselves more than 8 million times and provided more than 6 million digital signatures. The spread of e-governance and its usage is indeed extensive. Yet, surprisingly little is known about the impact of Estonian e-services. This class is designed to focus precisely on the issue of impact by asking:

  1. What is the economic, political and social impact of Estonian e-governance and digital services?
  2. How to accurately measure and model impact in these domains?
  3. How to make use of and analyze large datasets generated by the e-government ecosystem?

This class is an empirical, problem-based class where you will acquire specific skills that are essential to understand and estimate the underlying concepts of impact evaluation in general and public e-services in particular. You will acquire knowledge on theories of technological diffusion and adoption, you will learn how to work with data automatically generated by the Estonian e-governance system and various e-services, and you will apply powerful analytical techniques on actual data provided by the state institutions. The latter include aggregated data from the logs of X-Road, digital ID, internet voting, and various survey data that measure technology usage. Focus on empirical assessment of e-government’s impact, problem solving nature, and use of system generated datasets makes this class unique and first of its kind in Estonia.

Credits:           (6 ECTS)
For whom:      compulsory for students of Democracy and Governance MA program;
open for all MA and BA students.
Schedule:         Spring term 2016, Tuesdays 10.15-11.45, Lossi 36-305
Language:        Course is held in English, project work can be submitted in Estonian.

Instructors and contacts

Kristjan Vassil (kristjan.vassil@ut.ee) Lossi 36-313
Office hours: Tuesdays 9.00-11.30
Mihkel Solvak (mihkel.solvak@ut.ee), Lossi 36-312
Office hours: upon request

Organizational questions should be addressed to Kristel Vits (kristel.vits@ut.ee). Questions on specific lectures should be addressed to the instructor responsible for the given lecture (see Schedule below)

Learning outcomes

This course is an applied problem based class where participants acquire specific and unique skills that help them to carry out relevant analytics or policy-related tasks in future work. As per specific study outcomes the course is aimed at students being able to:

  • Have a good understand of the ecosystem of Estonian e-government, public digital services and its development;
  • Can understand and analyze the relationship between Estonian e-government and problems from her own field of specialization;
  • Can describe and analyze the social, political and economic impact of specific technological solutions;
  • Knows the main theoretical explanations of technology diffusion and impact;
  • Knows which institutional, political and legal prerequisites are needed for successful implementation of select e-services;
  • Can analyze the potential diffusion and impact of specific e-services based on evidence and data analytics;
  • Can construct metrics and use these to evaluate the impact of e-services;
  • Knows what data structures and types are needed for impact analysis;
  • Can analyze datasets generated by scholars from her own and other disciplines;
  • Can find numeric associations, interpret their meaning and present these in a easily understandable matter with practical suggestions for IT developers, policy makers and the general public;
  • Can generalize and apply the Estonian e-government experience to e-service implementation and impact to the experience of other countries.

Grading

  • Participation (20% of the grade) – We require attendance in classes and active participation in discussions.
  • Online workshops (35% of the grade)
    The course contains a total of 8 practical data analysis exercises conducted online. These entail detailed and easy to follow step-by-step instructions on how to conduct an analysis on the given dataset using a specific analytic tool (with data and code provided) together with the annotated output for interpretation. For example, how to conduct a t-test to see if verifying an e-vote (see: https://www.valimised.ee/eng/nutitelefon) increases people’s trust in voting online or how to predict the share of people voting online in the next election using a regression method or S-curve. Don’t be afraid of the some of the buzzwords, the exercise use non-technical language and are designed in a manner understandable and usable also by beginners in data analysis. Using open source software the student can either follow the provided instructions or/and play around with the data. The provided instructions are focused on the analytical technique and can hence also be used as a blueprint of “how to?” for any other analysis in the future where such a technique might be suitable. The exercises end with a short test questionnaire that can be answered based on the exercise.
  • Project work (45% of the grade)
    Project work entails a concise empirical impact analysis of a chosen e-service (max 10 pages, 1.5 spacing, size 12 Times New Roman). The project needs to outline a specific problem based on the issues covered in class or the student’s own field of specialization. We will not limit the aim and scope of the projects, they can as innovative and unconventional as you can imagine, provided they are feasible and evidenced based. Given that we are working with actual data on Estonian e-services and will have multiple guest speakers who are responsible for running many of these services the projects could ideally even lead to proposals to add some new functionality to certain services, all innovative ideas are welcome. In any case, the projects need to discuss how to measure the problem at hand and run a preliminary analysis with the available data. A more detailed instruction on how to do the project work together with possible datasets will be provided in class. The best projects will be selected for further development into a possible master or BA thesis or a peer reviewed publication with the help of an experienced analytics team.

Download the detailed overview of the course