Visualizing Geospatial Data In Stata: Spatial Maps In Stata - Live Online Course
The creation of spatial maps has become a valuable tool for researchers interested in both the spatial data visualization and the identification of geographical patterns in the data. This interactive course therefore, offers an introduction to the mapping techniques currently available in Stata for visualization of geospatial data. The workshop opens with an overview of the peculiar characteristics of geospatial data, the implications of such for the analysis of such data and an overview of the special terminology used in this field, before moving on to discuss the concept of spatial proximity and its relevance to the visualization of spatial data analysis. The remainder of the workshop is dedicated to the principle mapping techniques, using a combination of both official Stata commands and a series of user written commands, currently adopted to visualize geospatial data in Stata. Participants will learn how to import geospatial data shapefiles in Stata and merge them with their existing Stata databases in order to create, compare and customize different mapping techniques. At the end of the course, participants will be able to autonomously implement (with the help of the Stata routine templates specifically developed for the workshop) the appropriate mapping methods, given both the nature of their spatial data and the analysis in hand, within their own research. TARGET AUDIENCE This online course is of particular interest for criminologists, social psychologists, sociologists, economists, epidemiologists and political scientists seeking to acquire the requisite tools required for the exploration and visualisation of spatial data in Stata. PREREQUISITES A knowledge of basic statistics (distributions of variables, position indices, dispersion indices) and the statistical software Stata is recommended. PROGRAM SESSION I: SPATIAL DATA 1. General characteristics of spatial data 2. Types of spatial objects 3. Spatial coordinate systems 4. Maps and shapefiles 5. The transformation of spatial databases SESSION II: SPATIAL PROXIMITY 1. Spatial distance 2. Spatial proximity matrices 3. Spatial lags 4. Spatial autocorrelation SESSION III: VISUAL ANALYSIS OF SPATIAL DATA 1. Visual analytics and data science 2. Thematic maps 3. Dot maps 4. Graduated symbol maps 5. Diagram maps 6. Choropleth Maps 7. Isarithmic Maps 8. Multivariate Maps SUGGESTED READING Anthamatten, P. (2021). How to Make Maps: An Introduction to Theory and Practice of Cartography. Abingdon: Routledge. Lambert, N. & Zanin, C. (2020). Practical Handbook of Thematic Cartography: Principles, Methods, and Applications. Boca Raton, FL: CRC Press. DATE AND LOCATION The next edition of this training course will be offered online on a part-time basis on the 30th of September from 9:00 am to 1:30 pm CET. COURSE LEADERS Professor Maurizio Pisati, University of Milano-Bicocca, Department of Sociology and Social Research, Milan (IT). REGISTRATION FEE AND REGISTRATION FORM Full-time Students*: € 155 Ph.D. Students: € 205 Academic: € 370 Commercial: € 495 *To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year. Our standard policy is to provide all full-time students, be they Undergraduates or Masters students, access to student participation rates. Part-time master and doctoral students who are also currently employed will however, be allocated academic status. Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however applied to companies, Institutions or Universities providing a valid tax registration number. Register Here The number of participants is limited to 8. Places will be allocated on a first come, first serve basis. The course will be officially confirmed, when at least 5 individuals are enrolled. Course fees cover: I) teaching materials – copies of lecture slides, databases and Stata programs specifically developed for the course; ii) a temporary licence of StataNow™ valid for 30 days from the day before the course commences. Individuals interested in attending this course must return their completed registration forms by e-mail to TStat by the 20th of September 2026.
This interactive online course offers an introduction to the mapping techniques currently available in Stata for visualization of geospatial data. The next edition of this training course will be offered online on a part-time basis on the 30th of September.
Econometrics, Statistics and Quantitative Methods (JEL C)
Urban, Rural and Transportation Economics (JEL R)
GTAPinR Course "Running the GTAP Model using R packages HARr and tabloToR" General Information Dates: Open enrollment Learners can enroll in this course at any time. Once payment has been received and access to the learning management system has been granted, learners will have 56 days to complete the course before access ends. Since the course is self-paced, instructor assistance is not available during or after the course. No feedback or grading beyond automated assessments will be provided. Location GTAPinR is delivered fully online. There is no onsite component to this course. Offered by Center for Global Trade Analysis, Purdue University Enrollment Fees $625 Professional (Developed Country) $470 Professional (Developing Country) $315 Student (Developed Country) $160 Student (Developing Country) Why You Should Enroll Enhance your GTAP skills with open-source R tools designed to streamline simulation and analysis! Execute the GTAP Model directly in R - Learn how to run GTAP simulations end-to-end within the R environment using the open-source HARr and tabloToR packages Streamline data handling and model workflows - Gain practical skills in reading and writing GTAP data, solution files, and header arrays directly in R, enabling more efficient experiment setup and result processing Build practical skills for GTAP workflows - Build hands-on skills in GTAP workflows – setting closures, applying shocks, solving the model, and extracting results all in R environment Strengthen policy analysis with advanced visualization - Turn outputs into insight with R visualization workflows, improving interpretation, communication, and presentation of simulation results Flexible, self-paced learning for busy schedules - With open enrollment and a 56-day completion window, the course is designed to fit into busy schedules while providing structured modules, practical exercises and assessments Course Content This course aims to empower GTAP users with practical tools in R statistical software to enhance simulation analysis for policy making through improved processing of economic model outputs. The specific objective of this course is to introduce the R packages "HARr" and "tabloToR" which are open-source programs specifically designed to interface with the GTAP Model, as well as its outputs and inputs. The "HARr" package serves as a bridge between the GTAP Data Bases and the R environment by enabling the reading and writing of the header array (.har) file format adopted in the GEMPACK suite. The "tabloToR" package facilitates GTAP simulations by interpreting the model in GEMPACK-style TABLO (.tab) files and solves it within the R environment after the model inputs are defined. This course covers topics such as basic installation of the R packages, utilizing its features, as well as data visualization emphasizing practical, hands-on experience. This course is geared towards current users of the GTAP Model who are interested in running the model and processing its inputs and outputs within the R environment. Course Structure The course is delivered fully online and includes reading materials, codes, audio-visual lectures, hands-on exercises, and weekly assessments, all designed to enhance learning. As a self-paced online course, participants will work through the weekly plan of study outlined in the learning management system. Since the course is self-paced, instructor assistance is not available during or after the course. No feedback or grading beyond automated assessments will be provided. Learners will have 56 days from the date access to the learning management system was granted, to complete the course before access ends. Content Overview Module 1 - Getting Started with GTAPinR Module 2 - Reading and Writing Databases with HARr Module 3 - Solving the Model with tabloToR Module 4 - Wrap-Up and Evaluation Time Commitment Participants should expect to spend approximately 2-6 hours per module on this course. Prerequisites Participants are expected to know how to run the GTAP Model and be familiar with the inputs used in running the model. Participants are also expected to know how to use R statistical software. Enrollment Individuals interested in enrolling in this course must register on the GTAP website, after which they will be provided details for making payment by bank transfer or credit card. Once payment has been received and access to the learning management system has been granted, learners will have 56 days to complete the course before access ends.
Enroll in the GTAPinR Course to enhance your GTAP skills with practical tools in R statistical software for policy making.
Public Economics & Economic Policy (JEL H)
Economic Development, Innovation and Growth (JEL O)
Macroeconomics and Monetary Economics (JEL E)
Agricultural and Environmental Economics (JEL Q)
2027 Short Course in Dynamic Global Trade Analysis
2027 Short Course in Dynamic Global Trade Analysis "Applied General Equilibrium Analysis using a Dynamic Multi-Region Model" General Information Course Dates (Course phases are not offered independently.) Phase 1 (Online): January 18 - February 28, 2027 Phase 2 (Onsite): March 8-12, 2027 Application Deadline August 30, 2026 Location Purdue University, 403 Mitch Daniels Blvd., West Lafayette, Indiana 47907 USA Offered by Center for Global Trade Analysis, Purdue University Enrollment Fees $5,000 USD Professional (Developed Country) $3,750 USD Professional (Developing Country) $2,500 USD Student (Developed Country) $1,250 USD Student (Developing Country) Why You Should Apply Move Beyond Static Analysis: Model the Global Economy Dynamically! Master dynamic CGE analysis with the GTAP-RD framework - Analyze how the impact of trade and climate policy shocks unfold over time. Develop the skills to design, run, and interpret recursive dynamic simulations using GTAP-RD Models. Address High-Impact Global Policy Challenges - Sharpen your skills to produce policy-relevant analysis. Apply your modeling toolkit to mentored capstone projects focused on real-world impacts of major trade blocks and climate frameworks. Implement real-world baseline scenarios - Learn how to construct business-as-usual baselines using Shared Socioeconomic Pathways (SSPs), projections, and dynamic closures for credible long-run policy analysis. Build proficiency in the dynamic software - Gain extensive hands-on experience with RunDynam through guided labs that translate conceptual model features into operational workflows for scenario setup, policy execution, and result analysis. Learn directly from GTAP experts - Combine flexible online preparation with an immersive onsite residency at Purdue University, collaborating with peers and GTAP experts to deepen understanding and refine your own applications. Course Content The objective of this course is to introduce participants to dynamic economic analysis in an applied general equilibrium setting. This course is designed to provide participants with ample hands-on training with software that has been tailored for undertaking analysis using a dynamic recursive model. Participants will interact with economists working on global trade and environmental issues using a dynamic framework. The end goal of the course is for participants to leave with the capability of conducting and analyzing their own dynamic simulations. Course Structure The GTAP Dynamic Short Course is comprised of an online instruction phase followed by an intensive onsite course delivered by a team of GTAP specialists in data, model construction, and policy analysis. Note that the online and onsite phases of the course are not offered independently. Prerequisites It is expected that applicants have undertaken courses on multi-region general equilibrium analysis (such as the standard GTAP Short Course) or have completed a significant piece of analysis using a dynamic multi-region GE model or another, comparable general equilibrium model. Evidence of the latter (e.g., citations or links to papers, reports, and/or presentations) should be included with application. Application Individuals interested in taking this course must apply on the GTAP website. All applications will be reviewed following the deadline. Those accepted into the course will receive an email including an invitation letter and details for registration and payment. Note that application does not guarantee acceptance.
Join the GTAP Dynamic Short Course at Purdue University for a comprehensive exploration of dynamic economic analysis.
Research & Teaching Assistant (Finance) m/f/d | Part-Time (70%)
What you can look forward to: - A “we, not me” culture – a highly collaborative, team-oriented work atmosphere with open and honest communication - A “can do” spirit – we are looking for people who want to make a difference - Exciting research projects for doctoral studies and intensive exchange with the supervising professors - Close integration of theory and practice Your responsibilities - Conduct quantitative data analysis, support empirical research projects, and manage, clean, and analyze survey and field data using statistical software - Apply artificial intelligence (AI), blockchain, and digital technologies to support innovative research projects, particularly in agricultural and related fields - Collaborate with researchers, prepare research materials, and ensure accurate documentation of research findings - Support the preparation, supervision, and delivery of courses, tutorials, and guidance for bachelor’s and master’s theses - Contribute to scientific and practice-oriented publications, academic collaborations, and the communication of research results Your profile - A strong Master’s degree (good to very good) in Economics, Mathematics, Computer Science, or a closely related quantitative discipline, demonstrating solid analytical, statistical, and methodological expertise - Strong quantitative and analytical skills with experience in empirical research and statistical analysis - Proficiency in statistical software such as MATLAB, R, Stata, or similar programming tools - Experience with randomized controlled trials, field survey data, and research methods in development economics - Highly motivated, responsible, and independent researcher with the ability to work effectively in an academic environment We seek the best candidates. In doing so, we value personality, talent, determination, and character. We expressly emphasize that all individuals—regardless of gender, nationality, ethnic or social background, religion/ideology, disability, age, or sexual orientation—are equally welcome. Applicants with disabilities will be given preferential consideration if equally qualified. For inquiries, please contact Prof. Raša Karapandža, Ph.D. | Rasa.Karapandza@ebs.edu | Telefon: +49 (0) 6723 9168 - 244 Become part of our team and apply online, stating your earliest possible start date and reference number 16584. EBS University of Business & Law | Human Resources | hr@ebs.edu Visit us at: www.ebs.edu/de/stellenangebote
Enrich EBS. We are a private university dedicated to educating tomorrow’s leaders who strive to make a difference in the world -economically, socially, and ecologically. We aim to help shape global transformation through outstanding knowledge transfer and the promotion of strong interpersonal skills. At EBS Business School, EBS Law School, and EBS Executive School, a total of 2,200 students and professional development participants engage in learning and research. With our education philosophy of Challenge-Based Learning, personalized coaching, extracurricular engagement, and a powerful alumni network, EBS holds a unique position in the German higher education landscape. We are part of SRH—a leading provider of education and healthcare services with 17,000 employees. SRH operates private universities, educational centers, schools, and hospitals. We are looking for a research assistant (m/f/d) for the Chair of Finance (Prof. Raša Karapandža Ph.D.) at EBS Business School, starting September 1st, 2026.
als wissenschaftliche/n Referentin/Referenten des Vorstands. Neben der Erledigung unterstützender Aufgaben für den Vorstand des RWI in allen Bereichen des Wissenschaftsmanagements besteht die vielseitige Tätigkeit in der wissenschaftlichen Forschung in einem der Forschungsschwerpunkte des Instituts. Von Bewerbern/Bewerberinnen wird erwartet, dass sie neben einem Prädikatsexamen und ersten Erfahrungen in der wissenschaftlichen Forschung über fundierte Kenntnisse in ökonomischer Theorie und angewandter Ökonometrie sowie über die Fähigkeit zum eigenständigen Arbeiten innerhalb eines Teams verfügen. Darüber hinaus setzen wir Belastbarkeit, Flexibilität, Organisationstalent, ausgezeichnete Ausdrucksformen in Wort und Schrift und sehr gute Fremdsprachenkenntnisse (v.a. Englisch) voraus. Ebenso erwarten wir Kenntnisse im Umgang mit gängiger Software (insbesondere statistisch-ökonometrischen Programmen). Erste Ein-blicke in die Organisationsstrukturen einer Forschungseinrichtung und das Wissenschaftsmanagement sind wünschenswert. Die Stelle ist mit der Möglichkeit zur Promotion verbunden, die im Rahmen eines strukturierten Doktorandenprogramms am RWI aktiv unterstützt wird. Die Stelle ist in Vollzeit zu besetzen und ist zunächst voraussichtlich für die Dauer von zwei Jahren befristet, die Befristungsdauer orientiert sich an der angestrebten Qualifizierung. Eine Verlängerung ist möglich und erwünscht. Die Vergütung erfolgt nach dem Tarifvertrag für den öffentlichen Dienst der Länder (TV-L) mit den Sonderregeln für Beschäftigte an Hochschulen und Forschungseinrichtungen entspre- chend der mit der Stelle einhergehenden Verantwortung in der Entgeltgruppe 13. Die Beachtung der Schwerbehindertenrichtlinien und der Vorschriften des Gesetzes über Teilzeitarbeit ist gewährleistet. Wir fördern die Gleichstellung aller Geschlechter. Bitte richten Sie die üblichen Bewerbungsunterlagen, gerne auch per E-Mail, unter Angabe der Kennziffer 70001-06/26 bis zum 31. August 2026 an RWI – Leibniz-Institut für Wirtschaftsforschung Herr Tilo Schneider Postfach 10 30 54, 45030 Essen, personal@rwi-essen.de Nähere Informationen zum Institut unter www.rwi-essen.de Bitte beachten Sie bei der Übersendung Ihrer Bewerbung per E-Mail, dass bei diesem Übermittlungsweg Ihre Daten unverschlüsselt sind und unter Umständen von Unbefugten zur Kenntnis genommen oder auch verfälscht werden könnten. Gerne können Sie uns Ihre Unterlagen per Post zukommen lassen. Essen, 21. Juli 2026
Das RWI – Leibniz-Institut für Wirtschaftsforschung ist ein führendes Zentrum für wirtschaftswissenschaftliche Forschung und evidenz- basierte Politikberatung in Deutschland und wird überwiegend aus Mitteln des Bundes und der Länder finanziert. Das Institut informiert mit seinen Arbeiten über ökonomische Entwicklungen und deren Ursachen, erleichtert Politik und Unternehmen sachgerechte Entschei-dungen und fördert in der Öffentlichkeit das Verständnis für wirtschaftliche Zusammenhänge. Das Institut sucht zum nächstmöglichen Zeitpunkt eine/n
RWI – Leibniz-Institut für Wirtschaftsforschung
7 Reasons for Choosing the Master in Economics at Iscte Business School 1. Don’t miss the opportunity of being part of one of the most reputed Economics programs in Europe Ranked 33rd in its category, among the Top 200 Best Master Programmes, in Western Europe. 2. Learn from the best methodologies and software Interact regularly with reputed practitioners who will offer their vision and experience in a series of workshops aimed at discussing and analyzing economics topics; You will be challenged to understand complex current and future economic issues that affect everyone’s livelihood; You will build the skills needed to contribute to economic debates and policy choices; You will learn to handle advanced modeling techniques and use computational software which is integrated into regular classes (for example PYTHON, R, STATA); You will have access to a set of comprehensive databases that will support you on your thesis and research. 3. Benefit from a reputed faculty, with an engaging learning style The faculty of Iscte Business School has authored more than 100 books in different management disciplines, many of which are now required reading at other Portuguese universities; There is a close working relationship between students and faculty and students benefit from weekly contact hours, continual email contact and a sustained relationship with the faculty throughout their professional lives; Both semesters in the first year provide solid foundations in the core subjects (microeconomics, macroeconomics and econometrics) and equip you with the tools of economic analysis while allowing the choice of 3 electives in the field of Economics and other related areas, such as Economics of Banking and Financial Markets, Monetary Theory and Policy, Applied Economic Modelling or Computation for Economics and Business. 4. Thrive in a welcoming culture where personal development and creativity are encouraged Participate on the personal development programme (PdP) that enables students to discover their leadership qualities and creativity; The welcome week allows you to engage in networking, team-building and idea-sharing from day one of the course. This spirit is reinforced year-round with a host of other activities; Mentoring program - with their mentors, students have the opportunity to discuss their academic progress and career prospects, and benefit from their recognized professional experience; 5. See your merit recognized and rewarded throughout the program Award for the best MSc in Economics Student (sponsored by Ernst & Young) Award for the best candidates 6. A truly international outlook Study trip to Frankfurt, which encompasses an academic institution and the European Central Bank; Possibility of doing a Double Degree - it allows students to graduate with two diplomas from two Universities (Iscte and a partner university). For the Msc in Economics, the partner universities are Warsaw University in Poland (AMBA accredited) and Southwestern University of Finance and Economics in China (EQUIS accredited) Possibility of doing an exchange / Erasmus program with our partner universities. 7. Get excited with the program’s career prospects Our career services will support you with counselling, access to opportunities, the IBS Career Forum and presentations in class by companies; Professional placements for MSc in Economics graduates are excellent, as this degree gives you access to most job opportunities that arise in economics, finance and management - employment rate of 100% up to 12 months after completion of the Programme; The sectors most represented in the recruit of master's graduates are Audit & Consulting, Banking, Large Distribution & Retail and Telecommunications; Close contact with a large pool of successful alumni now working for high-profile national and international companies. Application Process The Master in Economics is designed for high-caliber graduate students and taught exclusively in English, in an international and multicultural atmosphere, therefore candidates must show evidence of a completed Bachelor degree as well as proficiency in the English language. Applications are exclusively submitted online, on Iscte’s website. Several information will be required for applicants to type in, namely Personal Information, information about Education and also Working Experience. Candidates will have to upload some Documents as well: Diploma/Transcript of Records from the bachelor's degree awarded by a university or institution of higher education; ID card/Passport Photo Proficiency in the English language (optional); The results of GMAT or GRE exam (optional) Candidates still concluding their undergraduate degree must also add: Declaration of Commitment (download available on application form) Transcript of Records with current grading Application Applications are open until August 26, 2026. Videos Student Testimony - https://youtu.be/flSP9PnxD9w See why you should enrol in our MSc Programme! - https://www.youtube.com/watch?v=GRibf9UwFQY Student Testimony -https://www.youtube.com/watch?v=c-33gPgAJy4 Would you like to know more? Check all the information available at our website regarding the MSc in Economics and get in touch with your Program Manager! Catarina Delgado masters.ibs@iscte-iul.pt Tel.: (+351) 210 464 211
The MSc in Economics is a full-time (evening classes), two-year Program, fully taught in English. It is is recommended for students with a good technical background who wish to obtain a rigorous, in-depth education in economic theory and policy at the graduate level, to prepare a successful career as professional economists working for central banks, the banking industry, government institutions, and international organisations or as economic advisors and financial consultants. It also provides rigorous preparation for students who wish to advance to a PhD program.
We have a fantastic opportunity for a Senior Business Data Analyst to join our Group Data and AI Office (GDAIO) Team at Royal London. The Senior Business Data Analyst plays an important role in supporting data-informed decision-making within GDAIO. This position will help shape complex data analysis projects, develop analytical approaches, and provide meaningful insights to support business decisions. The role combines analytical expertise with collaborative project delivery, supporting high standards of data quality, accuracy, and integrity across the organisation. The Senior Business Data Analyst will support value-chain mapping of data, interpret system performance metrics, agile team performance data, and financial data, and help check and challenge GDAIO involvement in key initiatives based on return on investment. This role is important in developing and refining processes that turn complex datasets into actionable business intelligence and support better outcomes in the financial services market. Working closely with Data Engineering teams, Business SMEs, Business Sponsors and Senior Leadership, the Senior Business Data Analyst will analyse complex data ecosystems, translate findings into practical recommendations with the Head of Data Strategy and the Head of Data Architecture, and support project teams to align with organisational objectives. This role combines hands-on analysis with collaborative leadership, particularly during complex analysis or key project definition phases. More about the role: Shape and deliver complex data methodologies and analysis projects, developing analytical approaches that support business priorities Support the optimisation and phasing of data sourcing to the Enterprise Data Model (EDM) Evaluate important business processes and identify data-driven improvement opportunities Develop thoughtful analytical solutions to help address complex business challenges Provide clear insights to cross-functional teams and senior stakeholders based on advanced data analysis Help ensure analytical outputs align with business strategies and regulatory requirements Support data quality assessments, migrations and improvement initiatives Coordinate the delivery of high-impact analytical projects, helping ensure timely completion and effective use of resources Role model Royal London values across the GDAIO and broader organisation Support and coach junior Business Data Analysts, helping them build confidence and access development opportunities such as roundtables and conferences Encourage the use of data-driven decision-making practices across the organisation Contribute to initiatives that improve data literacy and analytical capability across business areas What you will bring to the role: Experience in data analysis and modelling, ideally within financial services or another regulated environment Experience delivering analytical projects with multiple stakeholders or interdependencies, especially migrations Knowledge of Agile ways of working and how they can support analytical delivery Good understanding of data lifecycle management and governance Practical experience with data visualisation tools, SQL, cloud-based data platforms, or statistical software Strong analytical and problem-solving skills, with the ability to turn complex data insights into clear business recommendations Good organisational skills, including the ability to balance priorities and support effective delivery Collaborative stakeholder management skills, with the ability to build effective relationships across different teams and levels of the organisation We know that everyone’s experience looks different. If you’re excited about this role and think you could make a positive impact in our data team at Royal London, we’d love to hear from you -even if you don’t meet every requirement. About Royal London We’re the UK’s largest mutual life, pensions and investment company, offering protection, long-term savings and asset management products and services. Our People Promise to our colleagues is that we will all work somewhere inclusive, responsible, enjoyable and fulfilling. This is underpinned by our Spirit of Royal London values; Empowered, Trustworthy, Collaborate, Achieve. We've always been proud to reward employees by offering great workplace benefits such as 28 days annual leave in addition to bank holidays, an up to 14% employer matching pension scheme and private medical insurance. Inclusion, diversity and belonging We’re an inclusive employer. We celebrate and value different backgrounds, perspectives and experiences across Royal London. We welcome applications from people of all backgrounds and are committed to creating a workplace where everyone can feel included, respected and able to thrive.
Join the Group Data and AI Office (GDAIO) Team at Royal London as a Senior Business Data Analyst and support data-informed decision-making within the organization.
Along with bias, variance is one of the main factors by which economists, statisticians and data scientists judge estimators, helping them decide which ones to use. Thus, understanding both concepts will help new students of economics — as well as statistics and data science — to dive into the weeds of models and make the best judgments possible. Mathematical definition of variance As with bias, the definition of variance can — well — vary, at least when we rely on words. The mathematical definition, below, is more precise: \begin{equation*} Var(x) = \mathbb{E}(X^2) - \mathbb{E}(X)^2 \end{equation*} Let’s break this formula down. “Var” simply stands for variance, and so “Var(x)” means “the variance of x”. Meanwhile, the expectation symbol (the fancy “E”) gives the expected value, or mean, of the quantity inside it — check out our article on mathematical symbols if you’re unfamiliar. This equation states that variance of x is the expected value of “x squared” minus the squared “expected value of x”*. Notice that both terms in this equation are squared somehow. This is very important, for reasons we’ll soon get to. Recall that variance measures how much a random variable deviates from its mean. There are two ways to vary from a mean: being too small or too large. When we want to measure this variation, we want a nice, straightforward number that gives us a good sense of how far the variable’s specific realizations could be from its mean*. *Note that much of this language is, while good for building intuition, very imprecise. Words are not very good at capturing truths about statistics or probability — the invested reader should lean on the mathematical statements first and foremost for these concepts. The most natural way to generate such a number would be to measure multiple realizations of a random variable, then add up all of the deviations from the mean. We could add all of these deviations together to get the number. But, this method has a fatal flaw. “Negative”, or too-small, deviations can cancel out with “positive”, or too-large deviations, when we simply add them together. This is why we square the values. As a quick example, suppose we have a random variable y with mean 0, and we record the following realizations of y from an experiment: 1, -3, 2, 2, -1, and -1. The sum of the deviations is 0, so if we looked at this sum alone, we might think that y doesn’t vary at all. But none of the outcomes were 0 — so obviously, y must have at least some variance! We can easily solve this problem by squaring the values of the deviations, then adding them together and finding the average. So, for example, the variance of this example y is the average of 1 + 9 + 4 + 4 + 1 + 1, which is 20/6 = 10/3. Now, we have a single number that tells us how much realizations of the random variable y will likely vary from its mean**. **Again, language can be imprecise here where math is very clear. This statement is NOT implying that the realizations of y are bounded, only that the realizations of y vary from its mean with some frequency. There could still be extremely large or extremely small deviations from the mean far outside of this implied “range”, just with very low probability. Variance doesn’t tell us any information about what values y can or cannot take on; it only tells us how “spread out” the realizations of y are relative to its mean. This is why it’s so important that we use squared values in the variance formula. Squares are always positive, and so we will always get a non-negative measure of a variance. The positive and negative deviations won’t cancel each other out. Finally, note that it is actually possible for a quantity to have zero variance. All constants — like the number 38 — have zero variance. (It’s a little weird to talk about constants in this way, because constants aren’t random variables, but it’s a very clear illustration all the same.) High variance is (usually) a bad thing One of the main things to know about variance is that, typically, we want as little of it as possible. The more variance we have, the harder it is to tell what that random variable is supposed to be varying around — in other words, what its expected value is. This makes drawing conclusions from datasets difficult. Let’s illustrate this point with the following table. It contains the realizations of two random variables, X1 and X2, both of which are Normally distributed. In other words, we draw a random number from a Normal distribution 6 times for each. Try to use the table to answer the question: what are the expected values of X1 and X2? In other words, what values are these random variables centered around? Give the table a look and see if you can guess. X1 X2 Draw 1 -0.2 14.08 Draw 2 0.38 -4.94 Draw 3 -0.41 -6.19 Draw 4 -2.38 9.58 Draw 5 -0.58 -27.22 Draw 6 -0.87 -13.15 The answer? Both X1 and X2 are centered around 0; in other words, their mean is 0. This might have been fairly easy to guess with X1, which has many values close to 0. But X2 has a much higher variance than X1, which means its realizations are much more spread out, and it’s harder to tell that it’s centered around 0. For the curious, these values were generated from the following distributions: X1 ~ N(0,1) and X2 ~ N(0,15). Variance’s role in the analysis of real data When we gather data to run a regression, hoping to test hypotheses and learn something about the real world, inevitably the variables we collect will have some variance. This isn’t a bad thing — in fact, we need at least some variance in order to conduct any analysis. If variables never changed, we wouldn’t be able to tell how other factors affect them. Still, too much variance is a bad thing. The table above clearly showed one reason why: when values fluctuate wildly, it’s hard to tell what the expected value of a random variable is. Of course, we have computers that can parse data for us, but even a computer cannot magically find the expected value without enough data. Let’s suppose we do want to find the expected values of these random variables, using the data we have. This is an extremely common analysis. If we fed the X2 column into a program and asked it to find the expected value for us, it’d give us -4.64. That’s pretty far off from 0. Meanwhile, if we used a computer to find the expected value of the X1 column, we’d get -0.67. Much closer! The lower variance certainly helped. There is a simple way to improve the accuracy of our analysis, even for the X2 column: collect more data! This is where the Law of Large Numbers and other helpful statistics theorems come in. If we were to generate, say, 10000 random draws from these distributions, we would get estimates for both expected values very close to zero. If you wish to see this for yourself, there are many helpful tutorials online about how to code a Normal distribution and conduct these draws in your statistical software program of choice. Give it a shot! Population vs. sample variance It’s important to mention the difference between the sample variance and the population variance. The sample variance is what we’ve collected in the data. For example, if we were to use the numbers in the table above and find the estimated variance given the numbers for X1, we’d have generated a sample variance. The population variance is the true variance of the process underlying the random variable. In the case of X1, that variance is 1, since we generated the draws using a normal distribution with variance 1. This is an important distinction to know about, since it’s usually obvious from the context which is being discussed, but seldom actually mentioned. Heteroscedasticity vs. homoscedasticity Finally, it’s worth mentioning the concepts of homoscedasticity and heteroscedasticity as they are intimately linked to variance and commonly discussed. When the variance of a random variable is unchanging, it’s called homoscedastic. This is the “default” case, which enables much of our statistical inference to be valid. However, sometimes random variables have a variance that changes. This can be over time or over different values of the underlying random variable. For example, spending by low-income households feature lower variance than spending of high-income households. Larger companies typically have bigger fluctuations in profit than smaller companies do. Heteroscedasticity invalidates our usual methods of drawing conclusions from data unless we notice and correct for it. This is a topic that is often discussed in statistics and econometrics courses. Consider taking some coursework in these subjects to learn more! Good to Know Standard deviation is another key concept in statistics that measures the variance of a random variable. It’s simply the square root of the variance. Since the variance is essentially the deviations from the mean squared, the standard deviation is thus the deviations from the mean put back into the same units that the mean is measured in. For this reason, statisticians, data scientists and economists might use the standard deviation rather than variance for the purposes of extended discussion, since it’s a more intuitive measure. It’s easier to see when a particular value is very unlikely when thinking in terms of standard deviation. Suppose we know that a random variable has a mean of 0 and a variance of 4, and a realization of that variable is — say — 6, it seems that this realization isn’t terribly unlikely. Still, this means we know that the standard deviation is 2, and the value 6 is three times that. Turns out, this is quite unlikely!
Variance is a key concept in statistics and econometrics that influences a great deal of formulae, statistical tests, model setups, estimation strategies, and more. It’s one of the major characteristics of random variables and probability distributions, which are ubiquitous in statistics and economic models. This is also a case of a very accurate name: variance is a quantity that measures how much a random variable can be expected to differ from its expected value (or its mean). In other words, variance measures how much something varies from the value we’d expect it to take on.
Offer Description We are seeking for an experienced accountant to join our team. In this role, you will be reporting to the Chief Administrative Officer of the Consortium, the successful candidate will be responsible for the following main activities: support the implementation of the new accounting software Ad Hoc revolution web; ensure financial accuracy and compliance while tracking the financial data through detailed records and reports; reconciling general ledgers and bank statements; support the preparation of the financial statements for management, and tax reporting; ensuring compliance with local, provincial and national regulations; support the forecasting cash flow; maintaining and updating CERIC budget and bookkeeping records; support the financial reporting of all the funded and internal projects of CERIC-ERIC, according to the funding agency's policies, internal procedures and rules of CERIC. Where to apply E-mail: hr@ceric-eric.eu Requirements Research Field: Economics Education Level: Bachelor Degree or equivalent Skills/Qualifications: University degree in Economics, Accounting or equivalent. Experience in accounting for a minimum period of 8 years. Good knowledge of the accounting software Zucchetti "Ad Hoc" or "Ad hoc revolution web". Good knowledge of the Italian fiscal rules. Good knowledge of English (minimum B2 level), both written and spoken. Excellent knowledge of MS Office Suite. Competences considered a plus: Eager to help others; Friendly, cheerful, positive and self-driven; Strong work ethic; Keen to learn, adapt and apply to new challenges; Able to work independently and as part of a team; Highly organized, comfortable with multi-tasking and with good attention to detail; Excellent interpersonal and communication skills with the ability to communicate with colleagues across divisions and seniority; Knowledge of additional languages of the Consortium is an advantage. Additional Information Benefits: 12 months Fixed – term contract, with the possibility of extension; Possibility to work partially remotely; Salary range from 32.000 € – to 38.000 € annually, will be commensurate with previous experience and qualifications of the candidates. Bonus based on performance. Benefits: 33 days of holidays per year, flexible working time, meal vouchers; Training possibilities. Selection process How to apply: To apply for this job, please send your CV and a motivation letter to hr@ceric-eric.eu, with the following subject: Accounting officer. For more information about the role, please contact: administation@ceric-eri.eu. Deadline for application: July 24, 2026. Additional comments About CERIC: The Consortium was set up with the European Commission’s decision on June 2014, (214/392/EU) and its Statute was published in 26/6/2014 in the EU official Gazette. CERIC provides open access to some of the most advanced analytical facilities in Europe to help science and industry advance in all fields of materials, biomaterials and nanotechnology. Website for additional job details: CERIC Job Details Work Location(s) Company/Institute: CERIC-ERIC Country: Italy State/Province: Trieste City: Basovizza Postal Code: 34149 Street: S.S. 14 – km 163,5 in AREA Science Park
CERIC-ERIC is seeking an experienced accountant for a full-time position with the possibility of remote work.
Postdoctoral Research Fellow or Social Science Research Scholar at Stanford (USA) or Heidelberg University (Germany)
The team led by Pascal Geldsetzer at Stanford University is looking for a talented researcher with experience in econometric/quasi-experimental approaches for causal effect estimation (e.g., regression discontinuity and difference-in-differences) to help us expand our work on the link between shingles vaccination and dementia/ cognition (see: https://www.nature.com/articles/s41591-026-04474-8), as well as the relationship between vaccines, infections, and dementia more generally. Data sources for our work in this area are large-scale electronic health record data, medical claims data, mortality registries, and epidemiological cohort studies. The researcher will be expected to publish in high-impact general science and clinical journals. We are looking for someone to start as soon as possible but there is no specific deadline for the application – we hire on an ongoing basis. We also partner with Heidelberg University for candidates interested in being based in Germany. Remote working options are possible; on-site at Stanford or Heidelberg are of course welcome, too. Required Qualifications: • Doctoral degree with quantitative training (including causal inference) or relevant research experience • Strong coding skills in R, Stata, or other statistical software package. • Good communication skills in English. This is a two-year position with strong possibility of renewal. We also offer attractive opportunities for those interested in a longer-term career in academia that is not on a faculty tenure track. Application Instructions: Please send a CV (no cover letter or letters of reference required) by email to pgeldsetzer@stanford.edu. Please only apply if you would seriously consider this opportunity.
We are looking for a talented researcher with experience in econometric/quasi-experimental approaches for causal effect estimation (e.g., regression discontinuity and difference-in-differences) to help the Geldsetzer lab expand its work on the link between shingles vaccination and dementia (see: https://www.cell.com/cell/fulltext/S0092-8674(25)01256-5; https://www.nature.com/articles/s41586-025-08800-x; https://jamanetwork.com/journals/jama/fullarticle/2833335).
The University The University of Neuchâtel was established in 1838. In 2024, we have been ranked among the top 26 small universities (< 5000 students) worldwide by Times Higher Education and among the top 3 Universities to do a master's by Inomics. We are dedicated to offering our students the best possible learning experience, with proximity between teaching staff and students being a priority. Neuchâtel enjoys a central location in Switzerland and a magnificent environment, near the lake and mountains. Zurich and Geneva can be reached in less than 1.5 hours by direct train connections. Courses and instructors The cohort size is below 20 students, which favors a profound learning experience and gives participants privileged contact with instructors. All courses are in English. They are taught by academic professors and practitioners, including PhD economists with a policy background at the World Trade Organization, the Swiss National Bank, the United Nations Conference on Trade and Development (UNCTAD), the Federal Department of Finance (FDF), and the State Secretariat for Economic Affairs (SECO). Structure of the program The full-time study program includes three semesters of courses (72 ECTS) and a master's thesis written during the third semster (18 ECTS), either on an academic topic (e.g. for students who wish to pursue a PhD) or as part of an internship. The program can be extended to six semesters to accommodate professional activities and family life. In addition, there is an option to set up a personalized study program for candidates planning to spend less than four days a week studying. Specializations Data Science Sustainability To obtain a specialization, students must complete a specific set of courses and a master's thesis on a topic in the field. However, students do not have to commit to a specialization before the end of the study program. The relevant courses are listed in the study plan. Fees Application fee: CHF 100. Tuition fee: CHF 515 per semester for Swiss residents and CHF 790 for non-Swiss residents. Job prospects We strive to place our students in excellent jobs. The quantitative skills acquired during the program are highly relevant on the job market. Our students enjoy many opportunities for internships and jobs. Past students were employed in the financial and real estate sectors, public administration, consulting companies, higher education, and non-governmental and international organizations. Moreover, many students started PhD studies at, for example, the University of Maryland, the Jonkoping International Business School, the University of Bern, and the University of Lausanne. A 2024 survey among our graduates shows that 91% found a job less than three months after graduation. Data from a 2016 Swiss Federal Statistical Office survey reveals that 12 months after graduation, they have an average salary of CHF 75k annually. Academic prizes Henri Grandjean Prize for the best dissertation on an economic or social topic: CHF 500 Interdisciplinary Academic Prize for Sustainable Development: CHF 2'500 Admissions The official starting date is September each year, but the programme can also be started in the spring semester (February). The deadline to apply for the spring semester (February intake) is 30 November, and the deadline to apply for the fall semester (September intake) is 30 April. Applications are reviewed year-round. On average, complete applications receive a response within four weeks. For more information, please consult our admission page. Students with a degree in economics or business administration/management from Switzerland are directly admitted. Other applications are evaluated on a competitive basis. Students with less than 30 ECTS in economics and quantitative methods can be offered a pre-program with tailored bachelor-level courses to complement their academic background. We offer personalized pre-programs for students with bachelor's degrees in political science, international relations, other quantitative fields, and Swiss Universities of Applied Sciences. For more information, please get in touch so that we can evaluate your specific needs. We recommend visa applicants apply at least six months before the beginning of classes.
There are still some slots available for the fall intake 2026. We therefore welcome applications until mid-August 2026. Our students will become expert users of the economics toolkit and apply it to the domain that interests them. We offer rigorous training in modern economic methods, focusing on real-world applications, not technical details. The program teaches economic expertise that is directly relevant in a professional environment. Students learn to use micro- and macroeconomic models for economic analysis and state-of-the-art empirical methods in cutting-edge software (e.g., R, Python, and Stata). In addition, they will be able to communicate evidence-based analyses competently to private and public stakeholders. Listen to what our former program director says about our program and philosophy.
Research Assistant: Foreign and Defense Policy; Demographics and Political Economy
Overview The American Enterprise Institute (AEI) seeks a full-time, in-person research assistant to work with international political economy scholar Dr. Nicholas Eberstadt. The position’s major responsibilities include conducting research on topics in demographics and political economy. Assignments will include data collection and analysis, and administrative duties will include scheduling and organizing events, among other responsibilities. The anticipated start date for this position is as soon as possible. Soon-to-be graduates are welcome to apply. Responsibilities Core Responsibilities Research, write, and edit to support research and policy papers, studies, and reports on demographics, political economy, and economics. Collect and analyze data, provide background research, and write memos. Help plan, organize, and coordinate major public events and private meetings by inviting speakers, drafting public invitations, preparing materials, and coordinating all logistics with AEI’s Events department and media team. Manage long-term scholarly projects by drafting proposals, mapping timelines, and preparing regular progress reports for AEI management. Additional Responsibilities Coordinate various activities and ensure proper communication among AEI departments and teams including Government Relations, Academic Programs, Development, and media. Manage logistics and administrative tasks, including scheduling, tracking expenses, and planning and staffing public-facing events, private meetings, and roundtables. Qualifications A bachelor’s degree in economics, political economy, mathematics, or statistics (required) 0–2 years of previous experience, through either an internship or a paid position Proficiency in Python, R, or Stata (required) Skills in Microsoft PowerPoint and Excel (preferred) Data analysis skills (required) Strong writing and editing skills, excellent organizational skills, attention to detail, and facility with deadlines Benefits AEI offers generous benefits, including 18 days of paid leave (15 days of paid annual leave and 3 personal/floating holidays); 12 days of paid sick leave; paid family leave; medical and dental coverage, in addition to a health savings account; medical and dependent care FSA; a tuition assistance program of up to $5,200; life and disability insurance at no cost to employees; and a 403b retirement plan with a contribution from AEI, among other benefits. For more information, you can see a full summary of our benefits at https://www.aei.org/jobs/benefits/. Compensation is based on a number of factors, including but not limited to: scope and responsibilities of the position; a candidate's full-time, applicable work experience; key skills and qualifications; as well as internal and external market considerations. The salary range for this full-time, exempt position is $50,000 - $54,000. Salary is just one component of AEI’s total compensation package for employees. Options Apply for this job online Email this job to a friend Share on your newsfeed Need help finding the right job? We can recommend jobs specifically for you! Click here to get started. Application FAQs Software Powered by iCIMS
The American Enterprise Institute (AEI) seeks a full-time, in-person research assistant to work with international political economy scholar Dr. Nicholas Eberstadt.
Master Course in Global Development at the Department of Economics - Roma Tre University
Key info Master's degrees at the department of Economics, Roma Tre University Applications are open now Application deadline: September 15th, 2026 Program duration: 24 months Attendance: on-campus How to apply https://www.uniroma3.it/int/education/enrolment/enrolment-with-a-foreign-qualification/ Get in Touch To find out more please visit https://economia.uniroma3.it/en/study/academic-offer/ or send an email to orientamento.economia@uniroma3.it The Department of Economics The Department of Economics was born in 1993 to promote training, research advancement and disseminate culture in the economic, law, mathematical and statistical fields, with a highly multidisciplinary approach. Now it encompasses around 50 lecturers, 15 research fellows, and 30 PhD students from different disciplines: economists, statisticians, jurists, mathematicians, and finance and marketing experts. A vibrant community connected to institutions and high-level academy around the world, with a strong orientation towards research networks and funds attraction. The campus has been designed and equipped to offer high-quality education with advanced digital technologies. The computer lab has two rooms with 85 workstations and a selection of scientific software. The Pierangelo Garegnani library has two large reading rooms, with 250 study stations disposed on two levels. A large collection of books is available for consultation and borrowing. Electronic databases allow accessing a large selection of scientific journals and data collections. The spaces outside the buildings and their terraces are extremely liveable and useful for socializing. The Department of Economics of Roma Tre University offers three Master’s degree programs: 1. Economics (with two curricula in English) 2. Environmental Economics, Labour and Sustainable Development (with one curriculum in English) 3. Economics and Management of Digital Transformation (in Italian) MASTER’S DEGREE IN ENVIRONMENTAL ECONOMICS, LABOUR AND SUSTAINABLE DEVELOPMENT Our Master's Degree in Environmental Economics, Labor, and Sustainable Development is designed to provide an in-depth understanding of the economic, social, and environmental sustainability issues through both theoretical knowledge and empirical analysis tools. Aligned with the United Nations' 2030 Agenda for Sustainable Development, this program emphasizes the possible coexistence of economic growth and environmental protection, inclusive development, and decent work, as well as the relationship between profits and social welfare at both global and local levels. By enrolling in this Master’s program, specifically in the Global Development curriculum, you have the unique opportunity to apply for a DOUBLE DEGREE Program with Gustave Eiffel University in Paris, which offers a master's degree on the same topic. This collaboration expands your educational horizons and enriches your academic experience. The program offers three distinct curricula to cater to diverse interests and goals: Global Development (English) – Examines the broader aspects of global development. Through this innovative course, you will develop a comprehensive understanding of current development issues and acquire the necessary skills to analyse and effectively address them. The program covers a wide range of topics, including sustainable development, development policies and projects, innovation policy, international trade and migration, spatial economics, environmental sustainability, management of development programs, and labour rights. This Master’s program is ideal for those who are interested in studying the causes and effects of disparities in development processes and learning how to influence their trajectories. You will delve into questions such as why hunger persists in many parts of the world, why some development strategies succeed while others fail, and what factors contribute to the prosperity of certain regions. Moreover, you will explore the roles of governance, public policies, economic agents, businesses, and various national and international governmental and non-governmental organizations and institutions in the global development process. Upon completion of this program, our graduates are equipped with the necessary knowledge and skills to pursue diverse career paths. You can apply for highly qualified positions in private and public enterprises and agencies, national and international research centres, international organizations, and NGOs. Alternatively, you may choose to further your studies by enrolling in Ph.D. programs. More information at https://economia.uniroma3.it/wp-content/uploads/sites/5/file_locked/2026/05/Brochure-Global-Development-Economia-dellAmbiente-e-dello-Sviluppo-26-27-con-syllabus-1-2-2-mg.pdf Environment and sustainable management (Italian) – Focuses on sustainable economics and the environment. This curriculum helps you to develop and consolidate the knowledge and skills you need to face some of the most difficult transitions of our time. You will learn by tackling challenging global problems and business cases with outstanding researchers and international professionals. By participating in dynamic and interactive modules, featuring world-renowned experts you will acquire natural resources management capabilities; equip yourself with key knowledge in management, alternative finance, business models and budgeting applied to the circular economy; exchange feedback with key international players coming from the most important organizations and UN agencies. Technological Change, Labor and Welfare (Italian) – Explores the impact of technological changes with special emphasis on the transformations induced in the field of labour and welfare by the green and digital transition. The changes in sustainable development are studied from an interdisciplinary perspective with attention to social and labour issues, the adaptation of social policies and public and private welfare and labour relations, the adaptation of human capital in economic and social transitions, and the dynamics of the labour market and production systems and human resources from the perspective of sustainability and inclusion.
Summary The Department of Economics of Roma Tre University offers a Master’s degree program in Environmental Economics, Labour and Sustainable Development. Within the Master’s program, one curriculum is taught in English: Global Development. There is an opportunity to apply for DOUBLE DEGREE Programs.
Master Course in Economics at the Department of Economics - Roma Tre University
Key info Master's degrees at the department of Economics, Roma Tre University Applications are open now Application deadline: September 15th, 2026 Program duration: 24 months Attendance: on-campus How to apply https://www.uniroma3.it/int/education/enrolment/enrolment-with-a-foreign-qualification/ Get in Touch To find out more please visit https://economia.uniroma3.it/en/study/academic-offer/ or send an email to orientamento.economia@uniroma3.it The Department of Economics The Department of Economics was born in 1993 to promote training, research advancement and disseminate culture in the economic, law, mathematical and statistical fields, with a highly multidisciplinary approach. Now it encompasses around 50 lecturers, 15 research fellows, and 30 PhD students from different disciplines: economists, statisticians, jurists, mathematicians, and finance and marketing experts. A vibrant community connected to institutions and high-level academy around the world, with a strong orientation towards research networks and funds attraction. The campus has been designed and equipped to offer high-quality education with advanced digital technologies. The computer lab has two rooms with 85 workstations and a selection of scientific software. The Pierangelo Garegnani library has two large reading rooms, with 250 study stations disposed on two levels. A large collection of books is available for consultation and borrowing. Electronic databases allow accessing a large selection of scientific journals and data collections. The spaces outside the buildings and their terraces are extremely liveable and useful for socializing. The Department of Economics of Roma Tre University offers three Master’s degree programs: 1. Economics (with two curricula in English) 2. Environmental Economics, Labour and Sustainable Development (with one curriculum in English) 3. Economics and Management of Digital Transformation (in Italian) MASTER’S DEGREE IN ECONOMICS With our Master's Degree in Economics you will learn to understand and discuss critically a broad spectrum of micro- and macro-economic issues and the economic and financial problems that companies, consumers and institutions are facing. Enrolling in this Master, you have the opportunity to apply for DOUBLE DEGREE Programs with the French Universities. Two curricula and one joint-master course: Economic Analysis (English) - By choosing the Economic Analysis curriculum you will acquire solid training and ability to analyse issues and economic problems of both a micro- and macro-economic nature. In addition to solid statistical-mathematical and instrumental foundations for predictive analyses, the curriculum allows you to acquire a thorough knowledge of the main economic theories relating to the behaviour and interaction between economic agents, income distribution and determination of employment levels. A selected number of students can also get the Master in International Macroeconomics and Financialization by enrolling (restricted access) to the Double Degree Program with the Université Sorbonne Paris Nord. Upon the completion of this curriculum you will be able to undertake a career as an economic analyst in Italy and abroad. Some of our students are employed in public institution and agencies (e.g., the European Commission, the Italian Ministry of economic affairs, the Antitrust authority, the National statistics institution); some have entered the private sector, (e.g.in banks and financial intermediaries, consulting companies, industrial companies). Other students have continued their studies in a higher education institution undertaking prestigious Master’s and PhD programs in the EU, UK, and USA. More information at https://economia.uniroma3.it/wp-content/uploads/sites/5/file_locked/2026/06/Economic-Analysis-scienze-economiche-26-27.pdf Structural Change, inequality, and employment (EPOG; English) - The Economic Policies for the Global transition (EPOG+) is an Erasmus Mundus Joint Master’s degree in economics, supported by the European Union. It offers a world-class integrated Master’s programme on the (digital, socioeconomic, ecological) transition processes with a pluralist approach and interdisciplinary perspectives. It involves more than 40 partners and associate partners in Europe and the world. More information available at https://www.epog.eu/ Finanza e dinamiche macroeconomiche (Italian) - This curriculum focuses on the impact of finance on investments, growth and income distribution.
Summary The Department of Economics of Roma Tre University offers Master’s degree programs in Economics. Within the Master’s program, two curricula are taught in English: a) Economic Analysis; b) Structural change, inequality, and employment (EPOG); There is an opportunity to apply for DOUBLE DEGREE Programs.
(Senior) Research Data Analyst, Housing Center
The AEI Housing Center seeks a full-time, in-person (senior) research or data analyst to assist in creating, managing, and analyzing large datasets to derive insights and inform US housing policy. Responsibilities will include creating, cleaning, and analyzing new datasets; conducting statistical analyses; and helping draft research papers and other materials. The weight of these responsibilities may vary depending on whether the position is filled by a (senior) research analyst or (senior) data analyst. A (senior) research analyst will focus on researching and writing, and a (senior) data analyst will focus on analyzing data. This position requires superior technical and quantitative skills, the capacity to develop additional skills, and high proficiency in writing, research, communication, and organization. The ideal candidate will have a demonstrated interest in and knowledge of housing policy. Creativity, attention to detail, and the ability to multitask with minimal supervision are essential. A master’s degree in public policy, data analytics, or other quantitative methods field is preferred. Final title and salary are commensurate with experience. Please outline in your cover letter which title you feel your qualifications most accurately represent. AEI offers a stimulating and harmonious work environment and excellent benefits. Applicants must submit a cover letter demonstrating their interest in housing policy and noting their quantitative and technical experience and skills, a resume, an unofficial academic transcript, and a writing sample with the online application. Finalists will be asked to pass an analytical assessment. Responsibilities Research or Data Analyst Perform statistical analysis and modeling according to detailed specifications to support small studies and fulfill ad hoc requests. Combine processes, techniques, and methodologies from various fields to perform predictive and statistical analysis on large and often disparate datasets. Create data listings, tabulations, graphs, and figures for studies and reports. Integrate data from various sources as required. Assemble financial, workforce, technological, economic, statistical, and other operational datasets to inform comparative and predictive analyses of housing issues. Examine data for trends and anomalies to draw actionable conclusions. Forecast trends and derive key indicators. Senior Research or Data Analyst Using statistical software, perform statistical analysis and modeling to support small to large studies and fulfill ad hoc requests. Develop models, algorithms, and metrics to facilitate data interpretation. Work with various AEI employees to present and operationalize data to achieve accessible insights. Combine processes, techniques, and methodologies from various fields to perform predictive and statistical analysis and modeling on large and often disparate datasets. Create data listings, tabulations, graphs, and figures for studies and reports. Examine data for trends and anomalies to draw actionable conclusions. Forecast trends and derive key indicators. Integrate data from various sources as required. Take action to solve problems and issues that routinely arise in areas of assigned responsibility. Apply deep professional knowledge to independently solve a full range of complex problems. Physical Requirements and Working Conditions at AEI The employee is regularly expected to stand, sit, talk, hear, and use hands and fingers to operate a computer, telephone, and keyboard. This job requires close-up vision for computer work. The employee must be able to lift up to 30 pounds. The employee must be able to work in a confined area. Moderate noise (e.g., from computers, phones, printers, and light traffic) is expected. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential responsibilities associated with this position. Qualifications Required Research or Data Analyst: a master’s degree with zero years of full-time, applicable work experience or a bachelor’s degree and at least two years of full-time, applicable work experience Senior Research/Data Analyst: a master’s degree and at least one year of full-time, applicable work experience or a bachelor’s degree and at least three years of full-time, applicable work experience A degree in economics, finance, real estate, public policy, computer science, or a related discipline Knowledge of Microsoft Excel Fluency in data analysis tools and languages including Stata, R, and Python Strong communication and organizational skills A demonstrated interest in housing and public policy research A strong understanding of statistical methods and data science techniques Strong problem-solving skills Knowledge of data governance and compliance standards The capacity to meticulously document all data processes, methodologies, and analyses An ambitious outlook, comfort with working independently, and readiness to learn new skills as projects demand The ability to manage multiple projects and meet deadlines The ability to work effectively in a fast-paced and team-oriented environment Desired Experience with Structured Query Language (including PostgreSQL, MySQL, DBT, and DuckDB) and data engineer workflows (including extract, transform, load and extract, load, transform) Proficiency with geospatial analysis and visualization, particularly with web-mapping tools, such as Leaflet, Mapbox, Deck.gl, and ArcGIS or QGIS Experience with web development using JavaScript, TypeScript, Node.js, or PHP Knowledge of data visualization tools such as Tableau, Quarto, ggplot2, or Matplotlib Familiarity with version-control software such as Git/GitHub, Docker, and Python Benefits AEI offers generous benefits, including 18 days of paid leave (15 days of paid annual leave and 3 personal/floating holidays); 12 days of paid sick leave; paid family leave; medical and dental coverage, in addition to a health savings account; medical and dependent care FSA; a tuition assistance program of up to $5,200; life and disability insurance at no cost to employees; and a 403b retirement plan with a contribution from AEI, among other benefits. For more information, you can see a full summary of our benefits at https://www.aei.org/jobs/benefits/. Compensation is based on a number of factors, including the position’s scope and responsibilities; a candidate's full-time, applicable work experience; key skills and qualifications; and internal and external market considerations. The salary range for this full-time, exempt position is $65,000–$85,000. Salary is just one component of AEI’s total compensation package for employees.
The Housing Center at the American Enterprise Institute (AEI) is the nation’s leading market-oriented housing research center. It operates at the intersection of data-driven housing knowledge and policy solutions. Analysts will have access the nation’s foremost housing database, conduct cutting-edge research, and participate in creating an unprecedented set of metrics to improve the operation of housing markets, mortgage lending practices, and housing policy. Involving direct work with the center’s codirectors on the full range of the center’s initiatives, this role encompasses data science, business intelligence, and statistical programming. The anticipated start date for this position is as soon as possible.
PhD in Economics - University of Pavia & University of Milan - Admission to the 2026/2027 Academic Year (Cycle 42)
The Doctorate in Economics at the University of Pavia and the University of Milan is a four-year, fully funded Study and Research programme, designed for students aiming for academic or professional careers in economics. No tuition fees Fully funded Extra funding for research visits abroad Access to spaces, software (Stata, Matlab,…), and a massive and fast server (Indaco). Both Departments at the University of Pavia and the University of Milan have been awarded the Department of Excellence recognition by the Italian Ministry for the outstanding quality of their research. Funding & Scholarships available: This year, we are offering 5 fully-funded scholarships: 4 general PhD grants 1 specific grant funded by the Istituto Superiore di Sanità (ISS) on the research topic: "Use of Statistical–Econometric Models for the Analysis of Population Health Data and Registry Data" Key Dates: • Application deadline: June 30, 2026 • Interviews (online): July 17, 2026 • Results announced: shortly after the interviews, by July 21 • Programme starts: October 1, 2026 Discover more about our program here: https://economiaemanagement.dip.unipv.it/it/phd-economics Find all the information to apply here: https://economiaemanagement.dip.unipv.it/it/phd-economics/admission
The Call for the University of Pavia & University of Milan Doctoral Programme in Economics is open. Deadline to apply: June 30, 2026
The position The successful candidates will work closely in all stages of research with Fabrizio Colella, Rafael Lalive, and Josef Zweimüller on various labor economics projects related to discrimination, migration, occupational mobility, and skill requirements. Projects may also involve collaboration with coauthors such as Christian Dustmann (University College London), David Card (UC Berkeley), and Philipp Kircher (Cornell). The position provides an excellent opportunity to acquire hands-on experience performing state-of-the-art research in applied economics. The Research Fellows will be involved in different stages of research, including brainstorming, data manipulation, data analysis, literature review, and manuscript editing, and will receive support for future career development. Previous Fellows have gone on to attend PhD programs at UC Berkeley, the Norwegian School of Economics (NHH), USI Lugano, the University of Lausanne, and the University of Bern. What we offer A diverse array of on-the-job learning opportunities and guidance from faculty mentors within a stimulating and interdisciplinary academic setting. The chance to contribute to research projects alongside a team of young researchers. Exposure to academic life through seminars and reading groups. Office space, university IT infrastructure access, and support for future PhD applications. Salary range (60%–80%): 37,000–48,800 CHF/year (≈ $46,000–$62,500 USD/year) The ideal candidate Has completed, or is in the process of completing, an M.Sc. in Economics or other quantitative fields (including Computer Science, Engineering, Mathematics, Physics, and more). Has a long-term interest in pursuing academic research in economics or data analysis. Has strong intrinsic motivation, a cooperative attitude, and the ability to work independently. Possesses strong computer and analytical skills. Knowledge of programming languages and statistical software such as Stata, R, or Python, and experience with text analysis is a plus. If you are interested, please apply and upload your CV, academic transcripts, and other supporting documents by May 10th using the following link: https://usi.qualtrics.com/jfe/form/SV_41OkXu3dCxp2Y9o Reference Letters are optional. Letter writers will be contacted by us via email and asked to upload the letter to our application portal. Invitations for online interviews will be scheduled on a rolling basis starting May 5th, 2026. Only shortlisted candidates will be contacted. We welcome applications from individuals of all backgrounds who share our commitment to diversity, equity, and inclusion.
The new Labor Market Insights (LMI) center for labor economics research invites applications from highly motivated and talented candidates for three Predoctoral Research Fellows in Labor Economics. The positions are for one year (up to two years on mutual agreement). The starting date is flexible and may be as early as September 2026. Two Fellows will be based at the Institute of Economics (IdEP) at the Università della Svizzera italiana (USI) in Lugano, Switzerland, while one will be based at the Department of Economics of the University of Lausanne (UNIL), Switzerland.
Università della Svizzera italiana (University of Lugano)
PhD student position in Operations Management — KU Leuven
PhD student position in Operations Management — KU Leuven (ref. BAP-2026-224) Laatst aangepast: 02/04/26 The Research Centre for Operations Management, Faculty of Economics and Business, KU Leuven (Belgium) has a vacancy for a PhD student position at the intersection of Operations Research and Supply Chain Management. We are looking for a full-time PhD student to do research on supply chain visibility and network resilience, under the supervision of profs. Jannik Matuschke, Erik Demeulemeester, and Maximiliano Udenio. Project Global supply chains are large, complex networks that are typically opaque even to the firms that operate within them. A firm generally has visibility only to its direct (tier-1) suppliers and, at best, a small fraction of its tier-2 suppliers. However, empirical supply networks routinely span 10 to 15 tiers. This lack of visibility creates significant vulnerability: firms cannot anticipate, prepare for, or respond to disruptions that originate deep in their upstream network. This PhD project aims to develop a formal framework for supply chain visibility and resilience using tools from Operations Research and Operations Management, such as network theory, combinatorial optimization, and econometrics. The project pursues three interrelated aims: first, to characterize how network structure affects the cost and robustness of supply chain visibility; second, to study what actions a focal firm can take within its local sphere of influence to improve its resilience, including the design of its sourcing strategy; and third, to validate the theoretical framework empirically using real-world supply chain network data. Results of the research will be communicated through top-level publications in operations research and operations management journals, and through presentations at international conferences. Although the focus is on research, the candidate can be asked to give guidance to Master’s students, to be involved in teaching, and to supervise and correct exams. Profile You recently obtained your master’s degree (or you are about to graduate in 2026) in Business Engineering, Mathematics, Operations Research, Industrial Engineering, (Business) Economics, or a similar degree with an equivalent academic level. You have a strong interest in combinatorial optimization, network analysis, and/or supply chain management. Excellent results in prior studies are an asset (cum laude — distinction, or higher). Experience with optimization software (such as Gurobi, CPLEX, or similar) and/or programming languages (such as Python, MATLAB, or R) is an asset. You can conduct independent research (demonstrated, e.g., by an excellent Master’s thesis) as well as collaborate well in teams. You have excellent command of spoken and written English and are equipped with good communication skills. You are willing to participate in (international) seminars and conferences. You are willing to enroll in the PhD program of the Faculty of Economics and Business at KU Leuven, which entails 21 ECTS of relevant coursework. You will also give guidance to students, be involved in teaching activities and in the supervision of exams. Offer A fulltime PhD position for 1 year, starting 2026, September 15, renewable up to a maximum of four years upon yearly successful evaluations. The work should lead to a PhD degree. An interesting and diverse job within a dynamic and pleasant working environment, in a group conducting research at the highest international standards. A social, tolerant, and constructive workplace that offers ample opportunities for personal development. A correct and beneficial salary. Numerous opportunities for personal growth, illustrated by e.g., conference participation, “doctoral schools” courses on transferable skills, participation in educational initiatives, and publication opportunities. Applications should be submitted through KU Leuven’s online platform together with: a motivation letter with a statement of skills and research interests; a curriculum vitae with at least 2 references; a writing sample from the master thesis or relevant (individual) coursework; university diploma copies and complete transcripts. Interested? For more information please contact Prof. dr. Maxi Udenio Castro, mail: maxi.udenio@kuleuven.be or Prof. dr. Jannik Matuschke, mail: jannik.matuschke@kuleuven.be. You can apply for this job no later than April 30, 2026 via the online application tool. KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. Tewerkstellingspercentage: Voltijds Locatie: Leuven Solliciteren tot en met: 30/04/2026 23:59 CET Tags: Ingenieurswetenschappen, Wiskunde, Industriële Ingenieurswetenschappen, Economie
The Research Centre for Operations Management at KU Leuven is seeking a PhD student for research on supply chain visibility and network resilience. Website van de eenheid
KU Leuven Faculteit Economie & Bedrijfswetenschappen (FEB) - Operations Management
How AI is Re-shaping Economics Postgraduate Education - An Insider View from a Current PhD Student
Below I present three main reasons why it is becoming increasingly difficult to imagine postgraduate education without the use of AI. Whether you’re a student or a lecturer, knowing how to use AI effectively — or just being aware of how students are already making use of it — will help you succeed. 1) AI offers better searches for niche information Many of the common AI models can be used as a search tool, making them the first truly new innovation enabling Internet searches since the emergence of sites like Yahoo! and Ask.com - and of course google - in the second half of the 1990s. This search feature makes it a great study and research partner, beyond what a typical user can accomplish with a search engine. One of the weaknesses of traditional search engines is that it can be hard to find the specific information you need, especially if you’re searching for something niche, or if the language used for that topic is generic. For example, consider a scenario where your favorite bakery is a small mom-and-pop shop named “Croissant”. Or, for a more economics-relevant example, consider searching for content about the inverse elasticity of substitution in (specifically) the neoclassical growth model. In both cases, it can be extremely difficult to find the right information you need. In the first case it’s hard to craft a good search with such limited language, since “croissant” will return far too many irrelevant results. And in the latter case, the need to be extremely specific (thus using so many words) means that a traditional search will end up getting lots of irrelevant content as well. Even using advanced search functions might be ineffective, or at least time-consuming. But AI has the ability to sidestep a lot of these problems. That’s partly because if an LLM answers with something you weren’t looking for, you can just tell it that, and it will pivot to your intended meaning instead. This makes LLMs ideal tools to begin a search for esoteric information — although the ever-present chance of hallucination must be kept in mind (more on that later!). 2) AI models are great as study tools AI models can go far beyond being search engines — they can explain their output and interact with the reader. This is where one of their core use cases lies: as a study tool, or even a supplementary TA. AI models are frequently used by economics postgraduate students, with some using them on a daily basis to aid studies. AI can parse even the most complicated equations and break down complicated or long-winded paragraphs into more digestible bits. This helps students learn new concepts faster than if they’d spent time puzzling over arcane lecture notes, reading a textbook, finding a helpful YouTube video, or waiting to attend office hours. If someone is puzzled over how to get started with a difficult problem, asking an AI model can help them know where to begin. Still, there are limitations. There’s no shortcut to true understanding, and sometimes AI output isn’t sufficient. And of course, LLMs suffer from the hallucination problem, so AI output must be treated with suspicion and tested rigorously to ensure it’s accurate. In the context of an economics course, though, ensuring output is accurate can be a great way to learn! In my personal experience as a PhD student, fellow economics postgrads have agreed that AI is best used to either help with starting a difficult problem or help understand the steps taken in a sparsely-explained proof, problem, or model setup. Often, research papers, textbooks and lecture notes skip several important steps at a time when deriving or showing a concept, much to the ire of students. AI can help bridge the gap by explaining the omitted steps. This is something that AI models have grown quite adept at — given a specific enough prompt. In this way, AI takes on the role of a miniature TA, available at any time a student may be studying. Further, AI can even be used to generate practice problems and mock exam questions, making them good tools to ensure understanding. But these aren’t the only things AI can be helpful for. They can also help economics students parse long stretches of code… 3) AI is an ideal partner for coding Economics is an increasingly broad and cross-disciplinary field. As such, knowledge of mathematics and social sciences are important, but increasingly, data science competence and coding skills are becoming invaluable for economists. This is where LLMs are particularly useful, especially for postgraduate students who don’t have as much practical experience yet. One of the first things that a professor recommended to me during my economics PhD was to use AI to help me write code. Economists are increasingly required to use statistical software programs to do their jobs and their research, and even limited coding skills go a very long way. Even so, the vast majority of economics courses don’t teach software skills — often, there’s only time for an introductory course into one program, and students end up having to learn the rest on their own. This is a gap in the economists’ education that, for better or worse, degree programs often don’t have enough time to cover. Nobody should rely fully upon AI to code everything for them, and my professor wasn’t telling us to do so. Rather, as coding is an important but secondary skill for an economist, my professor recommended that we use AI to help us learn while doing. And, as he pointed out, using AI in this manner makes managing the hallucination problem quite easy: simply test the code the AI gave you. If it works, great! If not, clearly the AI made a mistake. Of course, this process works best for small pieces of code — asking the AI to change the color of a graph, or create a formula to update a matrix, for example. Asking it to write an entire research paper, or compile code to clean a whole database, is a recipe for disaster (and, as I argued before in an article justifying why I did not use AI for my PhD application, is only robbing yourself of the ability to learn). Conclusion: AI can be great, but remember: it’s only a tool There are, of course, pitfalls to avoid with AI. Relying on its output too much is a mistake, and students who over-use it may fail to learn as much as they thought. This is important because economics exams are typically held in person, without the aid of computational devices (outside of perhaps a small calculator). Thus, when grades are on the line, AI often won’t be available. For this reason alone, it’s imperative that students learn course content and how to think about and solve economics problems without relying on AI. In fact, the advent of AI makes it even more likely that, at least in the short run, economics degree programs will continue to rely on in-person exams to assess students. Yet aspiring economists shouldn’t fear this. Any economist worth their salt will know their field, and won’t need to rely on AI to do their job. Yet AI tools can — as with so many other industries — greatly increase the productivity of an already capable economist. Likewise, economics instructors shouldn’t be afraid of AI tools. Embracing them and training students to use them properly can actually increase engagement and learning. Even if you as a lecturer don’t feel confident teaching how to use AI, it’s imperative that you are aware of AI’s capabilities. This applies when designing any assessments, projects, or homework that isn’t done in person, as students could make use of AI while doing them. How has AI been used in your economics degree program? Have you experienced professors or students recommending it, or actively incorporating it into the syllabus? Share your experiences in the comments below! Header Image Credits: halfpoint, via Canva Pro License / Daniil Komov from Pexels, via Canva Pro License / metamorworks from Getty Images, via Canva Pro License
We all know that AI models are becoming increasingly commonplace, with hundreds of software companies now offering AI-powered tools, and dozens of iterations of ChatGPT and similar LLMs popping up every year. While some bemoan AI tools, they’ve also been lauded as powerful and helpful tools that we ought to be using every day. Regardless of where you stand on the AI-usage spectrum, one thing is certain: AI models are reshaping education, even at the highest levels. Economics is no exception. Even for PhD students like me, AI tools are becoming a feature of student life — and when used smartly, can be a great asset to learning.
PhD in Economics 2026/2027 - Five fully funded 4-year scholarships available
The PhD in Economics is a 4-year programme designed for highly qualified and motivated students who wish to acquire the research and analytical skills of the international scientific community in Economics. The Programme is offered in close cooperation with UniCA-University of Cote d’Azur (Nice, France). Students attend one year and a half of intensive course work, part of which (2-3 months) is hosted at the UniCA. After the training period, students will proceed to supervised research work, yielding an original dissertation to be discussed in a final, public examination. It is expected that students produce articles publishable in international journals. PhD students are exposed to the vivid research environment and participate to various ongoing research activities of the Institute of Economics at Scuola Sant’Anna (seminars, conferences, research projects). The Programme is also part of a large international network of institutions and scholars, supporting exchanges of research students and faculty. Areas of study and research facilities The training covers traditional domains of economic analysis (micro, macro, econometrics) but also offers the unique opportunity to study and get involved in research fields often neglected in standard PhD Courses. In line with the core research interests and expertise of the Institute of Economics at Sant’Anna and associate institutions, Advanced and Field topics encompass: evolutionary perspectives on economic change, firm-industry dynamics, economics of innovation, agent-based computational economics, economic networks and complexity, economic history and history of economic thought, and a broad range of quantitative tools for the analysis of economic dynamics and causal inference. PhD students have access to research facilities (software, hardware, data) available via the Scuola library, the Institute of Economics, and the computational infrastructure of the Department of Excellence L'EMBEDS. Job Opportunities The Programme is primarily intended for students willing to pursue an academic career but also provides professional research skills to work in public and private research institutions. Former PhD candidates now work as postdoctoral research fellows, assistant professors, etc., in well-reputed Italian and international universities (e.g., Paris-Sorbonne, Utrecht University, University of York, New School New York, Cornell University, University of Bielefeld, Sciences Po, University of Strasbourg), research institutions (e.g., UN-ECLAC, ECB, Bank of Italy, STATEC-Luxembourg, OECD-Paris, JRC-Seville, Italian Ministry of Economic and Finance), and in the private sector (e.g., UBS). Contacts Prof. Alessio Moneta (Coordinator) Email: alessio.moneta@santannapisa.it Phone: +39.050.8826.60 Information: info-phdeconomics@santannapisa.it
The Sant'Anna School of Advanced Studies has launched the call for applications for its Ph.D. in Economics (2026/2027 academic year). Five fully funded 4-year scholarships are available. Application deadline: April 15, 2026, at 12:00 pm (CEST)