The Institute for Employment Research (IAB) and the School of Business, Economics and Society of the University of Erlangen-Nuremberg (FAU) offer a joint doctoral programme in labour market research in cooperation with TU Dortmund. GradAB is an interdisciplinary and flexible structured programme. The courses provide high-level training on economic and sociological labour market and social policy research, on methods, and data. Quantitative as well as qualitative approaches are welcome. 7 DOCTORAL SCHOLARSHIPS We aim for equal opportunities and work-life balance! Doctoral students benefit from a professional research and policy-consulting environment in one of the key institutions advising high-ranking social policymakers, IAB’s large network of renowned national and international universities, research and policy institutions. IAB’s unique data resources on employment and social security (administrative and survey data). The scholarship provides financial support of 1,650 €/month plus a monthly research cost allowance of 130 € for participation in scientific conferences and further training. Extra support for research visits is available. In addition, most of our GradAB participants work as junior researchers on a 26 % position in one of IAB’s research departments (employment subject to social insurance contributions/ minimum 1,220 € gross salary /month). We invite applications from outstanding graduates in the fields of economics, sociology, survey statistics or other social sciences who hold a master’s degree and have a strong interest in labour market research. The IAB aims at increasing the share of doctoral students with disabilities. For this reason, applications from persons with disabilities are highly welcome. BEGINNING 1 October 2026 MAXIMUM DURATION up to 4 years APPLICATION Please submit your application in English by 15 March 2026 CONTACT Dr Sandra Huber, GradAB coordinator Email: apply.gradab@iab.de FURTHER INFORMATION www.iab.de/en/gradab
Make your career in academics and policy consulting
The Institute for Employment Research (IAB)
EDS – Two year Master's program in Economics & Data Science - University of Genoa
LEARNING OUTCOMES The first year of the course is mainly devoted to equipe students with background postgraduate knowledge of both economics and quantitative techniques. One the one hand, students will learn some of the workhorse basic models that form the core of modern applied microeconomics and macroeconomics; one the other hand, students will receive a solid background in mathematics for economics, statistical modelling, optimization techniques and programming skills (Paython and R). In the second year students will deepen their understanding of some markets like energy, health and financial ones and they will expand their quantitative toolbox by attending courses in risk management, economic policy evaluation and big data techniques. The economics and management taught courses will complement the theoretical analysis with real world applications of various quantitative techniques (econometrics, agent-based-models, etc.). At the end of the course EDS students will have the ability to understand complex economic and financial phenomena and to analyze them in a data-driven logic. EDS has close links with both public and private firms and organizations. Hence, students are strongly encouraged to apply for internships positions during their second year. A number of Erasmus positions are also available. Writing up a Master Dissertation is a prerequisite for receiving the degree. The Dissertation can also be written as part of an internship position. CAREER PROSPECTS EDS students will be qualified for a range of positions, like: Economic Consultant Data Scientist Economic Analyst in the energy, environment, and health sectors Expert in the field of economic policy evaluation Risk analyst Economic researcher INTERNATIONAL PROFILE Since the Master is an English taught course, it attracts students from all over the world, which in turn contributes to the creation of an international environment among students who will also be able to enjoy the lively atmosphere of Genoa, uniquely positioned on the Mediterranean cost in the North of Italy. ADMISSION REQUIREMENTS While we refer to (https://corsi.unige.it/en/corsi/11267/futuri-studenti-ammissione-iscrizione) for admission requirements, we particularly welcome students with a first cycle (Bachelor level) degree and that have achieved strong quantitative skills in mathematics and statistics; while some previous exposure to economics and management would be ideal is not strictly required. English language Requirement - Minimum B2 level APPLICATION For application procedure, see https://unige.it/en/internazionale/pre-assessment-procedure-master-degrees-taught-english-only-non-eu-students-living FEES From 0 to 3000 euros, depending on income DEADLINES A first intake of students will be admitted by 28th February 2026; a second intake by 15th April 2026. Late admissions can be considered only for EU students.
Economics & Data Science (EDS) is a two-year master’s degree, entirely taught in English, whose aim is to train economists with a very solid background in statistics and data science techniques. Our students will learn how to combine deep theoretical knowledge of certain markets (finance, health, energy) with the command of statistical, econometrics and data science tools to analyze them.
Econometrics, Statistics and Quantitative Methods (JEL C)
Summer School on Modern Topics in Time Series Analysis 2026
5th Klagenfurt-Bielefeld Summer School on Modern Topics in Time Series Analysis Main Facts Time: September 7-11, 2026 Venue: University of Klagenfurt, Austria Capacity: 40 participants Fee: EUR 550 (PhD students); EUR 700 (others) Target Audience The summer school’s target audience are primarily PhD students. However, the school also welcomes young post-doctoral researchers (from universities, research institutes or (central) banks) and professionals with a strong formal background seeking to extend their knowledge base in time series analysis. A good preliminary knowledge of the main work-horses of time series analysis, e.g., estimation and specification of vector autoregressive models, is expected. Important Dates May 15, 2026: Deadline for Application June 1, 2026: Notification of Acceptance June 15, 2026: Deadline for Registration Cancellation Policy Until August 2, 2026: free of charge | up to August 23, 2026: 50% of the fee will be retained | after August 23, 2026: no reimbursement possible. Programme / Schedule The summer school provides a PhD-level introduction to a range of modern topics in time series analysis taught by a selection of top researchers in Europe: Introduction and Overview: Dietmar Bauer (Bielefeld University) and Martin Wagner (University of Klagenfurt) Econometric Modelling of Climate Change: Eric Hillebrand (Aarhus University) Bootstrap: Jens-Peter Kreiß (TU Braunschweig) Structural VAR: Martin Bruns (University of East Anglia) Nonlinear VAR: James Duffy (Oxford University) Bayesian Methods for (Macro and Financial) Time Series Analysis: Gregor Kastner (University of Klagenfurt) Tensor and Network Econometrics: Roberto Casarin (Ca’ Foscari University of Venice) Hidden Markov Models: Roland Langrock (Bielefeld University) Each half-day will be dedicated to one topic in two 90-minute lectures. Presentations will roughly follow a common layout introducing the main theory of the area, potentially providing examples and discussing software available and finally indicating relevant questions at the research frontier. Schedule The summer school will start on Monday, September 7, 2026 at 9:00 (registration at 8:30) and will close on Friday, September 11, 2026 in the early afternoon. Details will be communicated directly to the participants. Application PhD students provide: - A motivation letter including the subject of their PhD thesis and name of their supervisor - An up-to-date CV listing all time series and statistics-related courses taken so far - Proof of enrolment in PhD studies Others: - A motivation letter - An up-to-date CV listing all time series analysis-related experience Please pack all documents into a single PDF file (size limit 5MB). Applications are to be sent to QED [at] aau [dot] at. Practical Information The conference fee covers social events, accommodation and transport. Some impressions from MTSA 2022 and 2024 include positive feedback about the balance between lectures and breaks, networking opportunities, and impactful presentations by instructors. Contact Information University of Klagenfurt Universitätsstraße 65-67 9020 Klagenfurt am Wörthersee, Austria +43 463 2700 4102 www.aau.at
The Summer School on Modern Topics in Time Series Analysis is held at the University of Klagenfurt from September 7-11, 2026. This program targets PhD students and offers many insights from leading researchers on time series analysis. Enjoy the engaging athmosphere, learn and mingle with fellow PhD students. Connect to your peers as well as the experts providing introductions to modern topics in time series analysis.
KEYNOTE SPEAKERS Sanjeev GOYAL University of Cambridge, GB Michael KÖNIG Vrije Universiteit Amsterdam, NL Sudipta SARANGI Virginia Tech, US WORKSHOP AND SUBMISSION INFORMATION Submit a paper by February 28, 2026 here. Register here. Earlybird before March 31, 2026: CHF 80.00. Regular until August 31, 2026: CHF 120.00 International workshop on the economics of SIGNED NETWORKS, including: • analysis of signed networks • formation of signed networks • networks of conflict • firm networks • corporate networks • network resilience • economics of networks in general • network science in general ORGANIZING COMMITTEE Berno BUECHEL Chair of Microeconomics, University of Fribourg, Switzerland, and member of the Swiss Center for Data + Network Sciences Qingchao ZENG, Post Doc, University of Fribourg, Switzerland Project partners: Dominik KAROS, Tim HELLMANN Inquieries about organisation and administration: karin.loetscher@unifr.ch Funded by Swiss National Science Foundation SNF, project Signed Networks: Strategic Network Formation and Centrality For related job openings see https://www.unifr.ch/amabe/en/chair/open-positions.html
International workshop on Signed Networks, Sept 3-4, 2026, in Fribourg, Switzerland. Signed networks capture both positive and negative ties, which is relevant for various applications including friendships at work, interactions on social media, and alliances between countries.
During the recent 2026 ASSA Meeting, the American Economic Association (AEA) recognized outstanding contributors to the economics profession with various awards. Key Award Winners Distinguished Service Award: Elizabeth Asiedu from Howard University was awarded for her significant contributions to economics and mentorship of underrepresented groups, particularly through her work with the Association for the Advancement of African Women Economists. Distinguished Economic Education Award: KimMarie McGoldrick from the University of Richmond, recognized for her impactful scholarship and dedication to teaching and mentoring over three decades. Diversity and Inclusion Initiatives Syracuse University’s Department of Economics for the Departmental Seed Grant aimed at promoting diversity through the RAISE program. Professional Development Grant for URM Faculty to Muhammad Usman Taj from Midlands Technical College for his research on labor market dynamics. Additional Honors Risav Ganguly from Howard University received the Andrew Brimmer Undergraduate Essay Prize. The CSQIEP Award for Outstanding Research Paper in LGBTQ+ Economics was given to Christopher S. Carpenter (Vanderbilt University), Donn Feir (University of Victoria), Krishna Pendakur (Simon Fraser University), and Casey Warman (Dalhousie University) for their paper entitled “Nonbinary and Transgender Identities and Earnings: Evidence from a National Census,” forthcoming in the American Economic Review: Insights. Donna Ginther (University of Kansas) received the Carolyn Shaw Bell Award for her research on gender and race in economics. Mira Frick (Princeton University) received the 2025 Elaine Bennett Research Prize for her work developing theoretical models that capture real-world data and speak to ongoing empirical debates. Janet Yellen was honored with the inaugural Janet L. Yellen Award for Excellence in Public Service extending beyond scholarly research. For more information about the awards, visit the Awards section of the AEA website. Image Credits: Image generated via Canva AI (prompts: award; medal; academic)
The American Economic Association has announced the winners of its awards during the 2026 ASSA Meeting. Key recognitions include the Distinguished Service Award for Elizabeth Asiedu, the Distinguished Economic Education Award for KimMarie McGoldrick, and multiple awards focusing on diversity, gender, and inclusion in economics.
Why Choose IDEA? World-Class Education: Acquire a solid foundation in economics and finance with rigorous analytical training and advanced quantitative methods. Global Opportunities: Participate in the IDEA-MIBF Double Degree Program with Lingnan University in Hong Kong, specializing in international finance and banking. Expert Faculty: Learn from internationally recognized and diverse researchers dedicated to your success. In addition to faculty members, prestigious international scholars regularly visit, teach and advise in our program. Personalized Learning: Tailor your studies with a wide range of specializations to align with your career goals. In addition to standard courses, specialized topics such as Energy and Environmental Economics, Artificial Intelligence, Machine Learning, and Networks are available. Value and Accessibility: Given that the UAB is a public university, the IDEA program offers a top-tier education at a fraction of the cost to the students compared to similar international master’s programs, making high-quality economic training more accessible for talented students worldwide. Vibrant Location: Experience the rich culture and dynamic economic environment of Barcelona. Your Future Starts Here: Top Placements: Our graduates have secured positions in the private sector (e.g. Morgan Stanley, Deloitte) and at academic and international institutions such as OECD, Inter-American Development Bank, and Ph.D. programs MIT and Minnesota. Another natural continuation option available to the top students is to complete the Ph.D. at IDEA. The Ph.D. program forms part of ENTER, an international network of universities allowing for (extended) visits and exchanges. Career Advancement: Whether you aim for a Ph.D. or to enter the professional world, our program equips you with the skills and network to succeed. Funding Opportunities: Partial Fellowships Available: We offer financial support to outstanding applicants to help you focus on your studies. Teaching and Research Assistance Opportunities: Students can develop their skills through teaching and research assistance. Interested? On December 10th 2025 we will provide a 30min info session at 15h Central European Time (CET) which you can attend using this Zoom link Use this opportunity to ask questions and learn about the program directly. If you want to receive a reminder email or other updates, please fill this form . Join Us Today! How to Apply: Prepare Your Documents: Transcripts (with official translations if necessary) Statement of purpose Two letters of recommendation Optional: GRE and English proficiency test scores Submit Your Application: Complete the online application form. Pay the non-refundable €60 application fee. Apply early as admissions are rolling and spaces can fill quickly! Take the leap towards an exciting future in economics. Apply now!
Join the IDEA Master's Program in Economics in Barcelona and become part of an elite community shaping the world's economic landscape. IDEA is a joint project between the Department of Economics at the Universitat Autònoma de Barcelona (UAB), the Institut d'Anàlisi Econòmica (IAE-CSIC), and the Barcelona School of Economics (BSE). The three institutions are characterized by their excellence in research and education.
Econometrics, Statistics and Quantitative Methods
Public Economics & Economic Policy (JEL H)
Economic Development, Innovation and Growth (JEL O)
Macroeconomics and Monetary Economics (JEL E)
Postdoctoral Research Scholar: Accelerating Asia's Decarbonization Through Trade and Investment Policies
The Power Transformation Lab (https://pwrlab.org/) at the University of California San Diego is seeking a highly motivated Postdoctoral Research Scholar to contribute to a two-year project focused on helping Southeast and South Asian countries balance trade and investment with their decarbonization goals. The successful candidate will work with a team of researchers from the UCSD School of Global Policy and Strategy and Griffith University, and with numerous civil society organizations in the region. The researchers will be fully integrated into the UCSD community, which includes a highly interdisciplinary school of public policy scholars, robust campus units researching power and energy systems, and a world-class think tank studying contemporary China. Research Focus The project aims to identify and promote effective trade and investment policies to accelerate decarbonization. The research will involve: Data collection and modeling of trade, Chinese and non-Chinese investments, and localization policies in target geographies, including Indonesia, Vietnam, Thailand, Bangladesh, Pakistan, and India. Collecting and analyzing existing clean energy trade and localization policies in Southeast and South Asia. Exploring the evolution of Chinese investments and alternative investment flows in shifting global value chains (GVCs) and export opportunities. Evaluating and preparing case studies where localization policies have successfully advanced or hindered decarbonization efforts. Proposing policy and investment strategies that balance localization objectives with decarbonization goals. QUALIFICATIONS Basic qualifications (required at time of application) At the time of application candidates must either have a Ph.D. (or equivalent degree) or be ABD (all but dissertation) in economics, political science, public policy, or a related field. Additional qualifications (required at time of start) Applicants are required to hold a Ph.D. or received a Ph.D. degree by the start of the appointment. Preferred qualifications Background in trade policy and energy economics; Prior expertise or knowledge in the regional foci (South Asia, Southeast Asia, China) APPLICATION REQUIREMENTS Document requirements Curriculum Vitae - Your most recently updated C.V. Cover Letter Statement of Research Job Market Paper (Optional) Reference requirements 3-5 required (contact information only) Apply link Help contact: gps-ap@ucsd.edu ABOUT UC SAN DIEGO The University of California is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected status under state or federal law. As a university employee, you will be required to comply with all applicable University policies and/or collective bargaining agreements, as may be amended from time to time. Federal, state, or local government directives may impose additional requirements. As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct, are currently being investigated for misconduct, left a position during an investigation for alleged misconduct, or have filed an appeal with a previous employer. JOB LOCATION San Diego, CA
Contribute to helping Southeast and South Asian countries balance trade and investment with their decarbonization goals
University of California San Diego - Economics Department
Graeme Blackett Appointed New Chair of Scottish Think-Tank "Prosper"
Blackett, a leading economist from Scotland, in a statement coinciding with his appointment, has stressed the need for unity among politicians, business leaders, and civic society to support the tens of thousands of vulnerable individuals experiencing financial distress in the country. Blackett, founder of the Edinburgh-based consultancy BiGGAR Economics, noted that "There has been low productivity and limited growth in the UK and Scottish economies for two decades". He pointed out that many individuals are struggling to meet their basic needs. However, he expressed optimism regarding potential opportunities in energy transition and emerging technologies like AI, urging collective action to navigate these challenges and capitalize on them. "Complexity requires collaboration. We need everyone to work together to identify what needs to be done, and then to do it," he said. Blackett's comments were made in the context of latest figures showing that a staggering 80% of businesses anticipating weak or very weak growth in the Scottish economy over the next year, according to the latest Scottish Business Monitor by Strathclyde University’s Fraser of Allander Institute. Blackett's appointment at Prosper takes on a particular relevance in 2026, which will see the Scottish Parliament elections taking place in May. On this topic, Blackett added that: "Politicians often talk about economic policy in terms of pulling levers. However, the economy has no control room and it is not a machine. Nevertheless, everything that politicians do matters to the economy — all policy has consequences, sometimes intended and sometimes unintended." Founded in 1931 during a time of great economic uncertainty, Prosper is a membership organisation that aims to bring together diverse sectors to devise strategies for economic growth in Scotland. Image Credit: prosper.scot (permission to use kindly granted)
Prominent economist Graeme Blackett has been appointed the new chair of Prosper, the Scottish membership organisation and think-tank. The appointment was announced in a press-release on the Prosper website. He takes over from Joanna Boag-Thomson.
IWH Doctoral Programme in Economics (IWH-DPE) Call for Applications – Fall 2026 Intake
The IWH Doctoral Programme in Economics (IWH-DPE) is a structured programme with a compact course phase during the first 1,5 years and full funding for five years. We were rated among the top 20 PhD programs according to the INOMICS Handbook 2024 and equip our PhD graduates with a rigorous economic training and comprehensive research insights. The IWH provides an excellent environment for students with an interest in economic policy who wish to enter the international academic job market or research-intensive policy institutions as indicated by our placements, for example at universities, central banks, and ministries. PhD candidates join one of the four research departments, are supervised by dedicated supervision teams, work closely with leading senior researchers in their field from day one of the programme, and receive mentoring from experienced peers. Information on the topics that prospective students can specialise in within each of the four departments can be found on our website. We cooperate with the Central-German Doctoral Program in Economics (CGDE); doctoral degrees are awarded by our local and international partner universities, depending on the supervisor(s). We encourage outstanding students with a Master’s degree in economics or related fields, such as mathematics, statistics, business administration, accounting and finance, to apply for admission. The deadline for applications is 15 January 2026 (24:00 CET). Interviews will take place in Halle on 27 and 29 January 2026 and decisions will be communicated shortly after. The following information is required for an application: Personal details Letter of motivation: Explain your goals, career plans, and motives to apply to the IWH-DPE. Discuss your study plans, areas of interest, and which department you prefer. Your statement should be written in English and should not exceed 500 words. Certificates and transcripts: An English or German transcript including courses, grades, and awarded degrees is required, degree certificates alone are insufficient. If you have not yet completed your current degree, attach a provisional transcript. Letters of recommendation: Two recommendations from faculty members that describe your academic ability and potential should be uploaded to our application portal. If the letters of recommendation are not available, they can also be sent directly to LOR-DPE@iwh-halle.de by the recommenders. The following documents may be submitted: GRE: Applicants are encouraged, but not required, to submit their GRE scores. Ask the Educational Testing Service (ETS) to report your test scores using the DI code: 4051. Writing sample: For example, submit a paper written in English during your studies, such as a Bachelor’s or Master´s thesis Any additional information relevant to your application. Further information on the programme is available at our IWH-DPE website. Please apply online for the position 2026-01 via our application site by 2026-01-15 Contact persons: Programme Director: Professor Michael Koetter, Ph.D. (Tel +49 345 7753 727, michael.koetter@iwh-halle.de) Programme Manager: Dr Andrej Drygalla (Tel +49 345 7753 812, andrej.drygalla@iwh-halle.de)
The Halle Institute for Economic Research (IWH) is one of the leading economic research institutes in Germany. Four departments conduct academic research in macroeconomics, financial economics, labour and productivity, and law, regulation, and factor markets.
Halle Institute for Economic Research (IWH) - Member of the Leibniz Association
Abdolnasser Hemmati Reappointed as Governor of Central Bank of Iran
Tehran, IRNA – Abdolnasser Hemmati has reportedly been reappointed as the governor of the Central Bank of Iran (CBI). This announcement follows the acceptance of Mohammadreza Farzin's resignation by President Masoud Pezeshkian earlier today. The 67-year-old economist and politician, Hemmati, previously held the position of CBI governor from 2018 until May 2021 and was also the minister of economy in the current and former Iranian administrations. His career has been marked by significant political engagement, including a run as a reformist candidate during the 2021 presidential elections, in which he came third. His candidacy was rejected in the 2024 presidential election by Iran's Guardian Council, which must approve candidates. Despite his extensive experience, Hemmati's previous role as the economy minister was cut short when he was impeached by parliament on March 2, 2025. His reinstatement as CBI governor signifies a pivotal return as the nation continues to navigate economic challenges. Hemmati’s appointment will be finalized after receiving approval from the Cabinet and a presidential decree, expected to be put forward in the upcoming cabinet meeting on Wednesday. The change at the top of the CBI is made in the context of growing public concerns, including protests, about inflation and the high cost of living. The move highlights the ongoing adjustments within the Iranian economic administration as the country seeks stability amidst these ongoing financial, political and social pressures. Image Credits: Mohammed Rezi Abassi / Mehr News Agency - Free for use under CC 4.0
Abdolnasser Hemmati has officially been reappointed as the governor of the Central Bank of Iran, replacing Mohammadreza Farzin. Hemmati has a prominent background in both economic and political fields, having previously served in various significant roles during different administrations.
Urban, Rural and Transportation Economics (JEL R)
Agricultural and Environmental Economics (JEL Q)
PhD Positions at IESE Business School (Barcelona)
PhD Positions at IESE Business School IESE Business School invites applications for full-time PhD positions for the upcoming intake (September 2026). IESE’s PhD is a five-year, full-time, research-oriented program that prepares students for an academic career at top business schools worldwide. PhD specializations include: Finance; Accounting; Operations, Information & Technology; Organizational Behavior; Strategy, Entrepreneurship; Marketing; and Business Ethics. Specializations: https://www.iese.edu/master-research-management/specializations/ & https://www.iese.edu/phd-in-management/specializations/ IESE’s PhD program is a structured doctoral program based on the traditional U.S model, beginning with rigorous coursework (2 years, awarded with a Master’s Degree) and then transitioning into the PhD phase (2-3 years). During the Master of Research in Management (coursework phase) - Years 1 & 2, students acquire methodological, analytical, and theoretical skills essential for high-quality research, while receiving close guidance from a faculty advisor. Students start specializing in their field of interest through elective coursework. Upon completion, students progress to the PhD phase - Years 3 to 5, which includes: Developing and defending the dissertation proposal Conducting independent research under faculty supervision Presenting research in internal seminars and international conferences Optional funded research stays abroad Participation in funded summer schools and external doctoral courses Language training (e.g., Spanish) is also available. Key Details Funding: Full tuition coverage and competitive stipend (1.500€/month net, 22,680€/year gross) for the entire duration of the program (5 years). Additional funding is also available for international research stays, conferences and workshops. Supervision: You will have a faculty member expert in your area of interest assigned as mentor. Research Areas (8): Strategy, Organizational Behavior, Marketing, Business Ethics, Entrepreneurship, Operations, Finance and Accounting. Program Duration: 4–5 years structured in coursework (2y), research and dissertation (2-3y). Other benefits: Access to extensive data, resources, and research support Professional development workshops, MBA teaching training, and networking opportunities Comprehensive health insurance, cafeteria discount + dedicated working space in our doctoral room at our Barcelona Campus. Candidate Profile We are seeking candidates who: Demonstrate strong analytical, quantitative, and communication skills. Are highly motivated for a research-oriented academic career. Can thrive in an international and collaborative academic environment. How to Apply: Candidates should submit their application through our webpage by January 20, 2026. APPLY HERE. Applicants must present: Academic transcripts from completed Bachelor's degree Official GMAT or GRE scores (mandatory for all candidates) Proof of English proficiency (TOEFL 100 / IELTS 7) unless studies were completed in English 3 letters of recommendation Motivation letter Statement of research interests IESE seeks candidates with strong academic potential, typically ranking in the top 10% of their undergraduate class, and with a clear interest in pursuing an academic research career. Application's process: Deadline Round 1: January 20, strongly recommended. Deadline Round 2: April 20, this round follows a rolling admissions process, meaning applications will be reviewed continuously and candidates admitted as decisions are made. Interview: Conducted online with a faculty member. Interviews are only granted to short-listed candidates. After reviewing all applications faculty will contact you if they are interested in conducting an interview Decisions Announced: By mid-March Upcoming Event: Join the Live Online PhD Open Day with the Academic Director of the Program on December 9, 2025 at 14:00 (Barcelona time). Register: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fapply.iese.edu%2Fphd-open-day-online-9dec%2F&data=05%7C02%7CEAlarcon%40iese.edu%7C7270811de2b349e3e0b808de236f2920%7Ced0cd196c46d43d9813e500e8c413eda%7C0%7C0%7C638987157788076769%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=NSMaFDMUwzwbdb%2FOAYTlYVnR1fiSk4cBCjXyI2374l8%3D&reserved=0">https://apply.iese.edu/phd-open-day-online-9dec/ Career Outcomes IESE PhD graduates have a strong placement record, with more than 90% obtaining academic positions in international universities and business schools. The program provides extensive support for research development and job-market preparation. Academic Placements: Our PhD graduates have started academic careers in leading universities and business schools such as: Emory University, London Business School, Copenhagen Business School, Georgia TECH, KU Leuven, IE University, Erasmus University Rotterdam, Universidad Católica de Chile, Macquarie University Sidney, Utrecht University, Zhejiang University, Católica Lisbon School of Business & Economics, UCLA Anderson School of Management, Frankfurt School of Finance & Management, HEC Montreal, INSEAD, Saïd Business School, among others. Why Join IESE IESE Business School is the graduate business school of the University of Navarra. one of the world’s leading business schools. IESE’s MBA and Executive Education programs are regularly ranked among the top 10 in the world. More information: IESE PhD Faculty: https://www.iese.edu/master-research-management/faculty/ & https://www.iese.edu/phd-in-management/faculty/ Specializations: https://www.iese.edu/master-research-management/specializations/ & https://www.iese.edu/phd-in-management/specializations/ Contact: Email: doctoral@iese.edu Phone: +34 93 253 42 00 Website: www.iese.edu/phd-in-management and https://www.iese.edu/master-research-management/
A doctoral program in Barcelona that is 100% funded and prepares you for a world-class career in academia.
The Intuition Behind Regression Analysis
It’s all about the variance The basic question behind many regression analyses is: how much does x affect y? Or, stated another way: when x changes, how much does y change as a result? This is a great way to think about what a regression is attempting to do. In essence, a regression measures how much of y’s variation results from x varying, ignoring the variation in other regressors (the Frisch-Waugh Theorem illustrates how a regression can isolate the effect of a single variable on y, but that’s beyond the scope of this article). The regression attributes whatever variation in y that x explains to a coefficient (usually denoted by a lower case beta symbol: 𝛽), and whatever variation x can’t explain to a term we call the “error”. Finally, statistics are computed that tell us how unlikely it is that the effect of x on y is due to random chance — if it’s very unlikely, we call it a “statistically significant” result. An example: Let’s consider high-end TV sales as our y variable, and “income of consumers” as the x variable. How should consumer income affect the sales of high-end TVs? Clearly, the wealthier people are, the more likely they are to spend money on a nice TV — or even multiple TVs. Therefore we’d expect a positive relationship between x and y. After gathering TV sales data and running our regression, if more TVs seem to be sold when consumers in the area have higher incomes (or after their incomes increase), we have some good evidence to support our hypothesis. The 𝛽 coefficient on the x variable “consumer income” might be statistically significant, and it can indicate by how much exactly an increase in income is expected to affect TV sales. This might sound straightforward, but much is happening “under the hood”. Before diving into some more complex scenarios, let’s draw a picture so we can visualize what’s happening when a regression is run. The geometry of regression analysis You’ve seen plenty of graphs in math classes before, with y on the vertical axis and x on the horizontal axis. Most regressions cannot easily be depicted in this way, for one simple reason. Most regressions include far more than two variables! When we collect data to run a regression, we end up having a series of y values (a vector), and several series of x values (a matrix). To compare the variation in y and x, we “project” y onto the “space” of x. Figure 1 below depicts this. Figure 1: The Geometry of Regression Analysis In Figure 1, we can see the vector of y data we’ve collected, which appears as the hypotenuse of the triangle. Let’s break this image down to understand all of its pieces, and how they’re represented by the tables of numbers a computer will give us. Suppose that the y vector is a paper airplane you’re launching, and the x “space” contains the forces of wind, gravity, and your own muscle power. You throw your paper airplane and record its flight. Your muscles launch the paper airplane forward, gravity pulls it downward, and the wind blows it around wildly. Similar to this scenario, the regression of y on x measures how much the x variables “pull” y in the direction it’s going, as the y vector “flies” over the space of x. If y is TV sales and x is income, the regression measures how much the force of “income gravity” changes the direction of the “TV sales airplane”’s flight, for example. Consider the space of x in Figure 1, which appears to be a 2-dimensional-looking plane. If x has absolutely zero power to affect y, then the y vector will be perfectly vertical. Why? Because as the y vector takes flight, the x space has no power to change its trajectory. This would be as if you threw a paper airplane straight up into the air and both gravity and the wind turned off at the same time. But if the x space does influence y, then as x works or changes, y’s trajectory is changed too. So, in Figure 1, we can see that x has some power over y. How much power? One way to measure it is by looking at the “base” of the triangle. This measures the exact horizontal distance that y has traveled as it’s moved through the x space. This quantity is called the projection of y onto x; it is the portion of x that explains y’s trajectory. This is also what gives us the model’s predicted values of y. Another way to measure this explanatory power is by looking at the vertical distance between y and the space of x in Figure 1. This distance represents the portion of y’s trajectory that x does not explain at all. This is the “error” term! The best model is typically the one that minimizes this distance, or in other words, minimizes the sum of the error (which we usually measure using squared values, so that negative and positive errors don’t cancel each other out). This picture hides a bit of math that’s going on under the hood. When you run a regression, the computer predicts an estimate of y for every set of x data points you have (the base of the triangle), then computes the resulting error (the vertical line) by subtracting this estimate from the actual y data point (the hypotenuse). In other words, together the error term and the projection constitute y. In order to figure out where y’s position is, we must know both the projection and the error — the explained and unexplained portions of y’s variance. This is easy to see from the formal regression equation Y = X𝛽 + ε. The first term, X𝛽, is the data times the coefficient our regression calculated for it; it creates the prediction of y based solely on the data, which is the base of the triangle. The error term is what’s left over, denoted by ε, which is again the vertical line in the triangle. Finally, we can use the ratio of these projection and error vectors to compute a very important statistic known as R2. This is a number that measures how much variation in y is explained by your regression. It’s one of the first things that students of statistics learn about regression analysis, and while it has problems (data scientists now typically use transformed versions of R2 that account for factors like scale and over-fitting), it’s a great window into what your regression’s version of Figure 1 probably looks like. High values of R2 indicate a good fit, which indicates that the model is useful for inference or prediction (one of which is usually the end goal of the analysis). In search of unknowable truths Figure 1 might make a regression seem very powerful, but unfortunately, a regression does not yield a certain answer. In other words, we can never learn the 100% precise answer to our research question for certain. This might be a surprising statement, but it’s true! This is a very important point to realize about regression analysis. We can admittedly get very, very close to a certain answer, but the actual real-world “true” answer is usually impossible to know for certain. In most of the sciences, including economics and data science, these “true” values are called “population parameters”, and our regression analysis gives us estimates of them. This is due to myriad real-world factors — like the difficulty of gathering fully comprehensive data, a limit to available computing power, and the imprecision of measurements we take. In science, it simply is not possible to have 100.00% certainty about things the majority of the time. This is one of the reasons why having a solid theoretical reasoning behind a regression model is important. The statistical evidence can only point us in the right direction. After the dust settles, it’s up to the data scientist, economist or biologist, etc. to correctly interpret the results and their real-world implication(s). This brings up another fascinating point, that an astute reader might have already wondered by now. What if we’re already confident that we know something about y before running our regression? Consider, for example, a macroeconomic model that attempts to explain inflation. This is something that’s been studied often before, and we’ve generated a lot of knowledge about it already. Can we account for that knowledge when running a new regression using new data? The answer is yes! But, it can seem a little complicated at first… Using our existing knowledge in scientific ways Bayesian regression methods include techniques that allow us to incorporate existing knowledge — or even just a good guess — into a regression analysis. This is done by using probability distributions. Before progressing further, it’s helpful to define what a non-Bayesian method is. We refer to them as “frequentist” methods. Introductory statistics courses (and this whole article so far) discuss frequentist methods and techniques. In essence, these are the “default” setting for regression analysis. Frequentists have a research question, and start to answer it by gathering data. In contrast, the Bayesian statistician doesn’t begin with the data, (s)he begins with a probability distribution that comes from their existing knowledge. What do we know about TV sales before collecting data on income? It’s very likely that more TVs are sold in wealthier areas, and when incomes of consumers increase. This information isn’t very controversial, but if we ignore it before running our regression, we’re essentially saying that we know nothing about TV sales at all before running the regression. This can be inefficient. Why throw away existing information when trying to answer a question? So, the Bayesian statistician takes a probability distribution that they believe is accurate, which we call a “prior” distribution. Then, they collect data and set up a regression as the frequentist did before, and run a regression in mostly the same way. This results in a “posterior” distribution that the data has created. The frequentist relies entirely on this posterior; the Bayesian merges the prior and the posterior together before deciding if there’s evidence for statistical significance or not. This Bayesian practice might seem arbitrary and unscientific, even blasphemous! How can we be allowed to insert our own beliefs into a scientific process? Fortunately, there are a few things keeping Bayesian methods “in check”. First, many frequentist statistics and test methods are secretly Bayesian methods in disguise. In many cases it’s possible to show that by using specific priors, the frequentist and Bayesian methods are the same. We can think of it in this simple way: frequentist methods simply use the “default” priors, while Bayesian methods are akin to an expert going and changing the default settings for a specific purpose. Second, if a data scientist, economist etc. uses a bad prior without sufficient justification, peer reviewers will point it out, and reputable journals might refuse to publish the work. Peer review is a cornerstone of science, after all, and statistical interpretation is not immune to its effects. It’s important to note too that frequentist methods are far from perfect. Consider that the rule of thumb for a statistically significant result is 5% (in words, “this result is 95% likely to be due to a true relationship between x and y, and 5% likely to be due to random chance”)*. This means that there’s a chance that we get the wrong result even though we did nothing wrong during the regression analysis. In some cases, this can even mean that a frequentist regression leads us to conclude something crazy that a Bayesian regression wouldn’t — like that the sun has exploded, when common sense would tell us that it clearly has not. For a beautiful and hilarious illustration of this exact scenario, look at the linked xkcd comic. *This is a reason why the standards for natural sciences, or medical science, are often more strict than for economics or data science. When human lives are at stake, a 5% error rate might be considered unacceptably imprecise. Both frequentist and Bayesian methods help us uncover the true relationships at work in the world around us. At the same time, there are many factors working against us… Setting up a regression can be tricky Regardless of which statistical techniques you employ, a regression really only measures how much y and x vary together, and uses that to estimate the strength of the relationship between x and y. But reality is so complicated that it can disrupt or obfuscate the true link between the variables, or even mislead us into thinking that a relationship exists when it truly doesn’t. Consider, for example, the determinants of high salaries. What factors probably contribute to having a higher salary? Talent, work experience, education, and charisma immediately come to mind. Other factors might include location, the type of industry, workplace politics, labor market factors, and even luck. All of these factors could be x variables that we gather data on before running a regression on y, which in this case is a vector of observed salaries. But there’s no obvious way to measure many of these variables. How do we measure talent or charisma, for instance? There’s no correct answer to this, although there are surely many wrong answers! Even for more obvious factors, how to measure them can still be a tricky thing. Work experience can be approximated by age, for example. But age might not be a good measure of experience if a worker has switched jobs often. We could use the measure “years working in the same industry” instead, but this still isn’t perfect, as the definitions of industries themselves can be arbitrary. If someone worked as a marketer for Apple, and later got a job in the marketing team at PepsiCo, are they working in the same industry? From this quick example, it’s clear that a solid theory should undergird any regression, and clear justifications need to be made for how exactly the practitioner gathers and prepares the data and sets up the regression equation. But there’s much more; we haven’t yet touched the mathematics of this subject. Warning: not everything is as it may seem! Mathematically, there are many ways that reality complicates the relationship between x and y. These include, but are not limited to: cases where x and y affect each other; cases where x affects both y and the error term; cases where x, y or both trend over time; and cases where the error term has patterns or isn’t expected to be zero. These types of issues occur commonly in real-world data, particularly economic data (that is very seldom experimental**). For example, consider using a regression to investigate the effect of an increase in government taxes on GDP growth. **So much so that the economists who popularized randomized controlled trials (RCTs) in economics won the 2019 Nobel Prize for their work, a stark contrast to fields like biology and psychology that use RCTs all the time. It might seem obvious that increasing taxes lowers GDP. However, if the government uses the net-new tax revenue to buy goods or services (for instance, construction projects), the true effect is much less clear. Recall that the aggregate demand equation is GDP = C + I + G + NX, where G is government spending. Higher taxes probably lower GDP, but increase G, since taxes fund government spending. But G increases GDP. So…what is the true effect? Were we to run a regression including GDP and G without accounting for this feedback loop, we would likely reach an incorrect conclusion. The previous example was a case where x and y affect each other. Let’s briefly examine the other cases mentioned above. We can return to our salary example to examine a case where x affects both y and the error term. Consider using “years of education” as an x variable to predict salaries. We’d have to account for the fact that as years of education increase, the variance of earnings increases. That’s because, all else equal, individuals with few years of education tend to have low earnings, while highly-educated workers tend to experience a wider range of earnings. Thus, the error term increases as x increases. When data trends over time — due to seasonality (think sales of ice cream) or simply the passage of time (macroeconomic variables like GDP and population tend to grow over time), it can be hard to disentangle the true effect of x on y, because that effect is hidden among the time trend that affects both variables. Finally, consider a case where the error term has a pattern. This is very often the case with financial data, for instance predicted stock market returns. Financial assets often fluctuate partially based on their previous values. This may be because people assess their future worth based on their current price, and because people have expectations that an asset will continue to perform similarly to how it’s currently performing. Either way, the error term in a regression of financial asset value is likely to vary in a pattern, which will obfuscate the true effect of x on y. Regression is an art and a science Running a good regression can be the cornerstone of an excellent research paper or the key reason for a new business strategy, but they’re far from simple or easy to execute well in many real-world cases. Understanding the issues at play, and figuring out how best to untangle the relationship between y and x, is a bit of an artful science. It requires a thorough understanding of the theory underlying the analysis, and running better regressions is a skill that comes with time. Hopefully, this article has offered a helpful glimpse into the machinery behind this ubiquitous tool in the data scientist’s arsenal, and will aid you the next time you begin to gather data for your own analysis! Image Credit: Generated with Canva AI Image generator (Prompt: Regression)
Regression analysis is a key topic in economics, data science, and computer science, but its roots go further back than these disciplines. Sir Francis Galton is credited with the term, after studying the heights of parents and their children, and noticing that those heights tended to “regress” back towards the mean. That name became associated with this type of analysis, and has stuck, even though a more accurate name might have been something like “variance analysis”. We’ve written previously about what a regression analysis is. If you’re completely new to the idea of a regression, that’s a good place to start. In this article, we’ll look more closely at this tool in the data scientist’s, economist’s and statistician’s toolbox, to understand exactly what’s happening when we run one.
Python and Statistics for Financial Analysis
Instructor: Xuhu Wan Course Details 4 modules Flexibility: 1 week at 10 hours a week, learn at your own pace Intermediate level Taught in English, available in 29 languages (AI Translation) Skills You'll Gain Data Visualization Financial Trading Statistical Analysis Data Import/Export Statistical Inference Regression Analysis Financial Data Financial Modeling Jupyter Model Evaluation Course Overview Python is now becoming the number 1 programming language for data science, especially in the financial industry. This course combines both Python coding and statistical concepts and applies them to analyze financial data, such as stock data. Objectives By the end of the course, you can achieve the following using Python: Import, pre-process, save, and visualize financial data into pandas Dataframe. Manipulate financial data by generating new variables using multiple columns. Apply key statistical concepts into financial contexts. Build and evaluate a trading model using linear regression. Course Structure Module 1: Visualizing and Munging Stock Data - 3 hours to complete Module 2: Random Variables and Distribution - 2 hours to complete Module 3: Sampling and Inference - 3 hours to complete Module 4: Linear Regression Models for Financial Analysis - 4 hours to complete Instructor Xuhu Wan from The Hong Kong University of Science and Technology. Additional Information There are 5 assignments included in this course. Enroll now for free!
Enroll in this course to gain essential skills in Python and statistics for financial analysis.
Hong Kong University of Science and Technology
This Master+ one-year program is fully taught in English. Applicants must provide satisfactory evidence that they have an adequate knowledge and understanding of written and spoken English. A minimum score of 90 on TOEFL IBT or 6.5 on IELTS is highly reccommended if the candidate does not hold an English-taught Bachelor degree. Compulsory Modules ▪ Financial Economics ▪ Bank Risk and Regulation ▪ Bank Structure and Competition ▪ Banking and Credit Risk ▪ Banking and Market Risk ▪ Econometrics of Banking and Finance Optional Modules (2 out of 3) ▪ Portfolio Management ▪ Quantitative Finance ▪ Derivatives Mentorship and Training Experience (required) ▪ “Journal-format” dissertation ▪ Research Methodology ▪ Communication and Exposure Compulsory Research Training Program in Banking and Finance Students will also follow the Intermediate module of the Research Training Program in Banking and Finance delivered by the LAPE research center with the aim of acquiring state-of- the-art skills to conduct empirical research (Training on Writing Research Papers, Handling Datasets and Softwares, Research Seminar Series, etc.) Separate fees may be charged for this program.
The Master+ in Banking and Finance is a unique opportunity to follow a standard Master program combined with a specific training delivered by a well-established Research Center dedicated to banking studies, LAPE, in the Université de Limoges in France. (http://www.unilim.fr/lape/). Teaching staff are active scholars in Banking or Financial Risk practitioners. The program also hosts international visiting professors who are leading experts in their fields.
Soka University Economics Wins Top Award at ISFJ Policy Forum
On December 13 and 14, 2025, at the Japan Policy Student Conference (ISFJ), held at Meiji University, the Nobuyuki Kanazawa Seminar from Soka University's Faculty of Economics secured the esteemed Breakout Prize, the highest accolade in their subcommittee. The award was announced in a press release by Soka University. The ISFJ aims to foster a better society through student-created policy proposals, with participation from 116 teams across 25 universities nationwide. After a thorough review by experts, awards were presented to teams whose work was deemed outstanding. The Kanazawa Seminar, which took part in the Labor and Employment subcommittee, delivered a compelling presentation focused on "Promoting Women's Participation - Considering the Paternity Leave System for Men." Their research highlighted the imbalance in domestic responsibilities that impede women's career advancement. Through empirical analysis and interviews, they examined how the paternity leave system could positively affect men's involvement in childcare and housework. Based on their findings, the team advocated for the promotion of paternity leave for men as a means to foster an environment where women can succeed professionally. Their rigorous research and innovative proposals received high praise from the panel of experts. A member of the winning team remarked, "It was challenging to coordinate discussions around our various commitments, but we succeeded by leveraging each member's strengths. We aspire for our research to enhance women's participation in society." The achievement of the Kanazawa Seminar underscores the proactive role that students can play in addressing critical social issues, affirming their commitment to making significant contributions to policy development.
Soka University's Faculty of Economics won the 'Breakout Prize' at the 2025 ISFJ Policy Forum for their proposal on promoting women's participation in the workforce through enhanced paternity leave for men.
Clinical Assistant Professor in the Robert C. Vackar College of Business and Entrepreneurship/Economics
Faculty Title: Clinical Assistant Professor Job Class Code: 10034 FLSA: Exempt Position Information Posting Number: FRGV1943 Working Title: Clinical Assistant Professor in the Robert C. Vackar College of Business and Entrepreneurship/Economics Number of Vacancies: 1 Location: Rio Grande Valley, Texas Division/Organization: Provost - Academic Affairs Appointment Period for Non-Tenure Position: 3 years Tenure Status: Non Tenure Track FTE: 1 About UTRGV: UTRGV serves the Rio Grande Valley and beyond via an innovative and unique multicultural education dedicated to student access and success. By championing leading research, healthcare, and application of university discoveries, we support sustainable development, community engagement, and well-being. The University of Texas Rio Grande Valley (UTRGV) is committed to building and sustaining a highly qualified and talented faculty to pursue excellence in everything that we do, including teaching, research, and service. Drawing on our unique identity as the leading Hispanic-Serving Institution in a bilingual and bicultural environment, UTRGV will promote student access and success by building from strength in education and creating a vibrant campus experience. By 2027, we will accelerate transformation in the Rio Grande Valley and beyond by driving meaningful advancements in research and creative works, expanding access to high-quality and affordable healthcare, and fostering authentic community engagement and integration. UTRGV is a distributed campus, one university spanning four counties and multiple locations. Our purpose is to be a university for the entire Rio Grande Valley and beyond. Over 91% of the students enrolled at UTRGV identify as Hispanic, making UTRGV one of the top 3 universities with the highest percentage of Hispanic students. For additional information, please visit UTRGV website. The Robert C. Vackar College of Business and Entrepreneurship is AACSB-accredited at the graduate and undergraduate levels and offers a Ph.D. in Business Administration with concentrations in Accounting, Finance, Information Systems, Management, and Marketing. Minimum Qualifications Candidates must have a Ph.D. in Economics or a closely related field prior to the start of the Fall 2026 semester and must provide evidence of teaching effectiveness and research potential. Discipline Specific Required Qualifications Applicants must have earned at least 18 graduate credit hours in the field of Economics. Preferred Qualifications Experience at teaching Economics to a diverse student body is desirable, as is the ability to use technology to support teaching and learning. It is also desirable that the applicant have an active basic, applied, or pedagogical research agenda consistent with the AACSB Scholarly Academic faculty qualification. License or Certification Required? Salary: Commensurate with Qualifications and Experience Desired Start Date 09/01/2026 Posting Detail Information EEO Statement: It is the policy of The University of Texas Rio Grande Valley to promote and ensure equal employment opportunities for all individuals without regard to race, color, national origin, sex, age, religion, disability, sexual orientation, gender identity or expression, genetic information or protected veteran status. In accordance with the requirements of Title VII of the Civil Rights Act of 1964, the Title IX of the Education Amendments of 1972, Section 504 of the Rehabilitation Act of 1973, and the Americans with Disabilities Act of 1990, as amended, our University is committed to comply with all government requirements and ensures non-discrimination in its education programs and activities, including employment. We encourage women, minorities and differently abled persons to apply for employment positions of interest. Open Date: 11/24/2025 Additional Information UTRGV is a distributed institution. As such and as assigned, the position may require presence at multiple locations throughout the Rio Grande Valley. Work is performed primarily in a general office environment. This position is security sensitive and thereby subject to the provisions of the Texas Education Code §51.215. The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length. This position has the option to elect the Optional Retirement Program (ORP) instead of TRS, subject to the position being 40 hours per week and at least 135 days in length. Special Instructions to Applicants Complete all sections on the application. If you are applying for the first time, please complete all biographical information including address, email and phone number. You may update this at any time by selecting to edit your profile in the application. Security sensitive; All UTRGV employees are required to have a criminal background check (CBC). CBCs will be conducted for all candidates invited for an on-campus interview. Please Note: Texas law requires faculty members whose primary language is not English to demonstrate proficiency in English as determined by a satisfactory paper-based test score of 500 (computer-based of 173 or internet-based of 61) on the Test of English as a Foreign Language (TOEFL) or a satisfactory test score of 6.0 on the International English Language Testing System (IELTS). Incomplete applications will not be considered. All positions are subject to budget approval.
We seek applicants for one non-tenure-track position in Economics at the rank of Clinical Assistant Professor. The position is for three years with the possibility of renewal. Candidates must have a Ph.D. in Economics or a closely related field by the beginning of Fall 2026. We seek excellent teachers committed to the success and social mobility of our students. Experience of teaching Economics in higher education in a culturally diverse setting is strongly preferred. We are a Hispanic-serving institution located in South Texas near the US-Mexico border. Even though this is primarily an instructional position, 10% of its workload consists of Research. Hence, modest publication shall be encouraged and expected.
SRRNet’s 24TH th International Conference on Corporate Social Responsability and Sustainability
Conference Details Conference Theme: Corporate Social Responsibility in Times of Global Uncertainty: Building Resilient, Inclusive, and Sustainable Futures Organizations across Europe, Africa, Asia, and beyond are navigating overlapping crises — from climate change and digital disruption to geopolitical instability and social inequalities. CSR is increasingly called upon not just to mitigate harm, but to actively foster resilience, trust, and inclusive growth across diverse contexts. We invite scholars and practitioners worldwide to contribute original research that explores the role of CSR in an era marked by global uncertainty and rapid change. The conference seeks to examine how organizations respond to complex challenges such as climate change, social inequality, digital disruption, and geopolitical instability, while striving to build more resilient, inclusive, and sustainable futures. How can corporations act responsibly in times of crisis? What innovative practices and policies are emerging across diverse regions, and how do they shape both local communities and global agendas? We invite scholars and practitioners worldwide to contribute original research that explores the role of CSR in an era marked by global uncertainty and rapid change. The conference seeks to examine how organizations respond to complex challenges such as climate change, social inequality, digital disruption, and geopolitical instability, while striving to build more resilient, inclusive, and sustainable futures. How can corporations act responsibly in times of crisis? What innovative practices and policies are emerging across diverse regions, and how do they shape both local communities and global agendas? We welcome interdisciplinary contributions that bring together insights from management, economics, political science, sociology, environmental studies, and related fields. Both empirical and conceptual papers, as well as comparative perspectives from Europe, Africa, Asia, and beyond, are encouraged to foster a rich dialogue on the evolving global landscape of CSR. The conference is intended to be interdisciplinary and welcomes contributions from anyone with a perspective on this important topic. Papers are welcome on any topic related to this broad issue, and suggested themes for papers include: Conference Topics CSR and Global Crises – corporate responsibility in contexts of war, pandemics, migration, and geopolitical instability; Bridging Local Communities and Global Agendas: CSR for Inclusive Growth – CSR’s role in addressing inequalities, supporting rural development, and fostering trust in societies with diverse cultural and economic backgrounds; Digital Responsibility Beyond AI – data ethics, cybersecurity, digital inclusion, data ethics, cybersecurity, and responsible technology adoption; Resilient Communities – CSR’s role in fostering social cohesion, rural development, and sustainable livelihoods; Transnational CSR – how multinational corporations adapt CSR across different cultural, regulatory, and economic contexts; Legal and Regulatory Perspectives on CSR – comparative legal frameworks, international standards, enforcement challenges, and governance mechanisms; Entrepreneurial and SME Perspectives – CSR in startups and small-to-medium enterprises, social entrepreneurship, and innovation-driven responsibility; Consumers and CSR – consumer expectations, ethical consumption, trust, and the role of public opinion in shaping corporate responsibility; Circular Economy and Sustainable Business Models – CSR in resource efficiency, waste reduction, green innovation, and regenerative practices; CSR in Global Supply Chains – responsible sourcing, human rights, labor conditions, and the tension between globalization and localization; CSR and Environmental Resilience – corporate strategies for biodiversity, climate adaptation, renewable energy, and disaster preparedness; Regional Perspectives on CSR – lessons and challenges from Europe, Africa, Asia, and Latin America; cross-cultural and comparative studies; CSR and Stakeholder Engagement – the role of NGOs, governments, and communities in co-creating responsible business practices; Measuring CSR Impact – methodologies, metrics, reporting standards, and critical evaluations of ESG frameworks; Education and CSR – embedding responsibility in business education, capacity-building, and leadership development; Energy and sustainability - renewable energy development, efficiency innovations, and responsible corporate practices that reduce carbon footprints and foster resilience; National and extractive resources conservation - strategies for managing and conserving a nation’s natural and extractive resources responsibly, the role of corporate accountability in protecting ecosystems and local communities; Environment and health - the impact of pollution, climate change, and corporate environmental policies on public health, sustainable business practices for healthier, more inclusive societies. Submissions Although preference will be given to full papers, abstracts of 300-500 words will also be considered. All papers and abstracts should be sent by 20th June 2026, by email to davideacrowther@aol.com Each author may co-author multiple papers, but may present no more than two. All submissions must adhere to the APA 7th edition formatting guidelines. Submissions should be original and not previously published or under consideration elsewhere. Final version of the accepted papers will be required by 20th of July 2026. Publications One of the distinguishing features of the conferences is SRRNet’s focus on subsequent publication of papers presented. SRRNet publishes at least one book of selected papers from each conference but also encourages every participant to publish their work. Advice will be given during the conference. Full details concerning other publishing opportunities for the papers presented at the conference will be provided during the conference. The Network currently publishes two fully refereed and indexed official journals in association with Emerald Publishing: Social Responsibility Journal, and Technological Sustainability. The Network also publishes two book series, within which the book(s) from the conference will be published: Science, Technology and Sustainability – published by World Scientific, and Developments in Corporate Governance and Responsibility – published by Emerald. All books published in these series are fully indexed. Committee Prof. Dr. David Crowther - SRRNet President and chair of scientific committe Assoc prof. Valentin TOADER, PhD, chair of the conference Assoc prof. Monica Maria Coroș, PhD, chair of the conference Assoc prof. Oana Adriana Gică, PhD, chair of the conference Dr. Shahla Seifi - SRRNet Conferences Chair. Join us in Cluj-Napoca to share insights, exchange ideas, and shape new perspectives on how CSR can foster resilience, inclusion, and sustainability worldwide! Registration Fees 1. Early bird registration (the latest on July 20th, 2026): Delegate: €360 Student delegate (bachelor, master, doctoral student): €250 Accompanying person: €300 Delegate from low income countries*: €245 Delegate from middle income countries**: €280 2. Regular registration (the latest on August 25th, 2026): Delegate: €550 Student delegate (bachelor, master, doctoral student): €400 Accompanying person: €400 Delegate from low income countries*: €385 Delegate from middle income countries**: €440 * Affiliated with Higher Education Institutions from low-income countries according to OECD’s list ** Affiliated with Higher Education Institutions from lower-middle or upper-middle income countries according to OECD’s list Click for Bank transfer payments Make payments via Credit/debit card here For each paper, at least one author must be registered as a Delegate or as a Student Delegate. For all delegates, the fee includes documentation, coffee breaks, meals, and a social programme during the conference days. For accompanying persons, the fee includes meals and a social programme during the conference days. It does not include participation in the conference.
Call for papers SRRNet’s 24th conference will take place in Cluj-Napoca, the second largest city of Romania, often referred to as the heart of Transylvania. It is one of Romania’s most vibrant academic, cultural, and technological hubs, making it an ideal host for our international conference. It will be organised jointly by SRRNet, the Centre for Research and Entrepreneurship in Tourism and Hospitality (CRETH/CECATO), and the Faculty of Business from Babeș-Bolyai University. With a strong tradition of academic excellence dating back to 1581, the university is recognized for its interdisciplinary research, multicultural character, and commitment to innovation. Hosting more than 45,000 students across a wide range of fields, Babeș-Bolyai University is the largest and one of the most prestigious universities in Romania.
Faculty of Business, Babes-Bolyai University Cluj-Napoca
Circular Economy and Sustainable Business Models
Bridging Local Communities and Global Agendas: CSR for Inclusive Growth
National and extractive resources conservation
Legal and Regulatory Perspectives on CSR
The UK and EU have successfully negotiated terms for the UK to rejoin the Erasmus+ programme in 2027, allowing young people from various backgrounds to access opportunities for study and training abroad. This agreement was announced by the UK government in a press release, and delivers on commitments made during the UK-EU Summit earlier this year. Erasmus+ offers educational and training opportunities not just for students in higher education, but also for apprentices, further education students, sports clubs and adult learners. It is estimated that over 100,000 individuals in the UK could benefit from the programme in its inaugural year. The UK Minister for EU Relations, Nick Thomas-Symonds, emphasised the significance of this initiative, stating that it aims to break down barriers and widen horizons for all young people, ensuring that everyone can access transformative educational experiences. Moreover, the UK has agreed to a reduced contribution for the first year, amounting to approximately £570 million for the 2027/28 academic year. This is 30% lower than the cost envisaged by the Trade and Cooperation agreement from earlier this year. Despite the reduced cost, full participation benefits are guaranteed for UK taxpayers. Earlier this year, the European Commission proposed an expansion of the Erasmus+ scheme as part of its Multiannual Financial Framework (MFF) for 2028-2034, including an intention to increase the budget by around 50%. As such, an extension of the agreement for the UK beyond 2028 would need a new agreement on the UK's level of financial contribution in the context of the increased overall budget. Aside from Erasmus+, discussions on energy market integration and a food and drink trade deal have also progressed, indicating a favourable shift in UK-EU relations that addresses broader public priorities. The UK government has already created a page with more information, including eligibility and timescales at https://www.gov.uk/guidance/the-erasmus-programme Image Credit: AI generated image with Canva. Prompts: UK; EU; Students
The UK government has reached an agreement with the EU for the UK to join the Erasmus+ programme in 2027, expanding educational opportunities for young people. The initiative promises to support over 100,000 participants in its first year, with a focus on inclusivity for disadvantaged groups.
PhD position in Energy Economics - Industrial Economics within the ENERPOL project
Start date: 1 September 2026 Project and funding framework Within the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Action (MSCA), we offer a position for a Doctoral Candidate (DC), starting 1 September 2026 or as soon as possible thereafter. The position is funded for 36 months by the ENERPOL MSCA Doctoral Network. Subject to satisfactory progress, a fourth year of funding will be provided by UCLouvain. ENERPOL is a European research and training network exploring the role of households in the green energy transition. It addresses challenges such as volatile energy prices, inequality, and household risk exposure. ENERPOL provides doctoral candidates with a training opportunity in cutting-edge methods, theory, and transferable skills within an international consortium of leading universities and non-academic partners. ENERPOL training includes: several international summer schools and workshops, cross-national supervision teams dedicated to the project of the PhD, one academic and one industry secondment. This doctoral project hosted at UCLouvain focuses on the theoretical and empirical analysis of the trade-offs between demand-side response and consumer protection, using tools from economics and energy system analysis. The project aims to contribute to policy recommendations on retail market design and tariff structures within the wider EU policy framework. Host institution and research environment The position will be hosted at the Louvain Institute of Data Analysis and Modeling in Economics and Statistics (LIDAM) at UCLouvain. LIDAM brings together researchers in economics, econometrics, statistics, and operations research, and offers an active research environment with regular seminars, workshops, and international collaborations. Your tasks The doctoral candidate will: Complete PhD-level coursework within the doctoral programme at UCLouvain (or equivalent programmes). Conduct independent and collaborative research in energy economics and industrial economics, under the supervision of faculty members at UCLouvain and within the ENERPOL network. Develop original research questions and carry out theoretical and empirical analyses using appropriate economic and econometric methods. Prepare and complete a PhD dissertation, with the objective of producing research suitable for publication in international peer-reviewed journals. Present research findings at international conferences and ENERPOL training events. Participate in international research stays and secondments at academic and non-academic partner institutions. Contribute, to a limited extent, to teaching or dissemination activities related to the PhD project. Attend all ENERPOL training events and mandatory secondments organised within the framework of the ENERPOL doctoral programme. Required Qualifications Applicants should have: A Master’s degree (or equivalent) in Economics or a closely related field, with a strong quantitative orientation. Solid training in microeconomics and applied econometrics. Interest in energy economics, industrial economics, and policy-relevant research. Experience with statistical or numerical software (e.g. Stata, R, Matlab, or similar). Ability to work independently as well as collaboratively in an international research environment. Proficiency in English (written and spoken). The following would be considered an advantage: Prior exposure to energy markets, regulation, or applied policy analysis. Experience with large datasets, administrative data, or advanced econometric methods. MSCA Eligibility Requirements The candidate must not have resided or carried out their main activity (work, studies, etc.) in Belgium for more than 12 months in the 36 months immediately prior to the start date (1 September 2026). The candidate must not hold a doctoral degree at the date of recruitment. Employment Conditions The successful candidate will be recruited as a full-time doctoral researcher under an employment contract at UCLouvain, in accordance with Belgian labour law and the collective bargaining agreement applicable to university scientific staff. Contract duration: 36 months, funded under the Marie Skłodowska-Curie Doctoral Network, with the possibility of a one-year extension funded by UCLouvain, subject to satisfactory progress. Remuneration: The position offers a fixed lump-sum remuneration for the duration of the MSCA funding, consisting of a gross monthly salary of EUR 2,610, supplemented by a monthly mobility allowance of EUR 710, and, where applicable, a monthly family allowance of EUR 660. As the remuneration is fixed, it is not subject to indexation or seniority-based salary increases. Indicatively, for a single researcher without children, this corresponds to a net monthly income of approximately EUR 2,730, depending on the individual tax situation. The employment includes full social security coverage, pension rights, paid annual leave, holiday pay and an end-of-year bonus, and is subject to Belgian income tax and social security contributions. Salary and employment conditions comply with the requirements of the European Commission for Marie Skłodowska-Curie Doctoral Networks (Horizon Europe). Application procedure Applications must be submitted in English as a single PDF file, named: Lastname_Firstname.pdf. Applicants should fill in the form via the link below. The application PDF should contain: Statement of intent (max. 1 page), indicating the position(s) applied for. Curriculum vitae (expected Master’s completion date if applicable) Bachelor’s and Master’s transcripts and degree certificates (if available) One writing sample (e.g. Master’s thesis or research paper) A brief statement detailing periods of residence, work, and study during the 36 months prior to the start date (required for MSCA positions) Formal language certificates (e.g. TOEFL or IELTS), if available Standardized test scores (e.g. GRE), if available Names and contact details of at least two academic referees (letters of recommendation may be requested at a later stage) Applicants should clearly indicate which position(s) they are applying for in their motivation letter. Candidates may indicate more than one position if they wish to be considered across funding schemes. Applications received by 15 February 2026 will receive full consideration. Interviews and offers are expected to take place in March 2026. Applications should be sent via this form. For more information about this position, please contact: Bert Willems, LIDAM, UCLouvain Email: bert.willems@uclouvain.be
Within the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Action (MSCA), we offer a position for a Doctoral Candidate (DC), starting 1 September 2026 or as soon as possible thereafter. The position is funded for 36 months by the ENERPOL MSCA Doctoral Network. Subject to satisfactory progress, a fourth year of funding will be provided by UCLouvain.
UCLouvain - Louvain Institute of Data Analysis and Modeling in economics and statistics (LIDAM)
PhD position in Energy Economics - Public Economics within the ENERPOL project
Start date: 1 September 2026 Project and funding framework Within the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Action (MSCA), we offer a position for a Doctoral Candidate (DC), starting 1 September 2026 or as soon as possible thereafter. The position is funded for 36 months by the ENERPOL MSCA Doctoral Network. Subject to satisfactory progress, a fourth year of funding will be provided by UCLouvain. ENERPOL is a European research and training network exploring the role of households in the green energy transition. It addresses challenges such as volatile energy prices, inequality, and household risk exposure. ENERPOL provides doctoral candidates with a training opportunity in cutting-edge methods, theory, and transferable skills within an international consortium of leading universities and non-academic partners. ENERPOL training includes: several international summer schools and workshops, cross-national supervision teams dedicated to the project of the PhD, one academic and one industry secondment. This doctoral project hosted at UCLouvain focuses on the theoretical analysis of policy interventions addressing energy-related inequality, drawing on public economics and information economics. The analysis may involve calibration and, where relevant, empirical components developed in collaboration with network partners, with the aim of informing policy recommendations on income and energy taxation. Host institution and research environment The position will be hosted at the Louvain Institute of Data Analysis and Modeling in Economics and Statistics (LIDAM) at UCLouvain. LIDAM brings together researchers in economics, econometrics, statistics, and operations research, and offers an active research environment with regular seminars, workshops, and international collaborations. Your tasks The doctoral candidate will: Complete PhD-level coursework within the doctoral programme at UCLouvain (or equivalent programmes). Conduct independent and collaborative research in energy economics and public economics, under the supervision of faculty members at UCLouvain and within the ENERPOL network. Develop original research questions and carry out theoretical and empirical analyses using appropriate economic and econometric methods. Prepare and complete a PhD dissertation, with the objective of producing research suitable for publication in international peer-reviewed journals. Present research findings at international conferences and ENERPOL training events. Participate in international research stays and secondments at academic and non-academic partner institutions. Contribute, to a limited extent, to teaching or dissemination activities related to the PhD project. Attend all ENERPOL training events and mandatory secondments organised within the framework of the ENERPOL doctoral programme. Required Qualifications Applicants should have: A Master’s degree (or equivalent) in Economics or a closely related field, with a strong quantitative orientation. Solid training in microeconomics and applied econometrics. Interest in energy economics, industrial economics, and policy-relevant research. Experience with statistical or numerical software (e.g. Stata, R, Matlab, or similar). Ability to work independently as well as collaboratively in an international research environment. Proficiency in English (written and spoken). The following would be considered an advantage: Prior exposure to energy markets, regulation, or applied policy analysis. Experience with large datasets, administrative data, or advanced econometric methods. MSCA Eligibility Requirements The candidate must not have resided or carried out their main activity (work, studies, etc.) in Belgium for more than 12 months in the 36 months immediately prior to the start date (1 September 2026). The candidate must not hold a doctoral degree at the date of recruitment. Employment Conditions The successful candidate will be recruited as a full-time doctoral researcher under an employment contract at UCLouvain, in accordance with Belgian labour law and the collective bargaining agreement applicable to university scientific staff. Contract duration: 36 months, funded under the Marie Skłodowska-Curie Doctoral Network, with the possibility of a one-year extension funded by UCLouvain, subject to satisfactory progress. Remuneration: The position offers a fixed lump-sum remuneration for the duration of the MSCA funding, consisting of a gross monthly salary of EUR 2,610, supplemented by a monthly mobility allowance of EUR 710, and, where applicable, a monthly family allowance of EUR 660. As the remuneration is fixed, it is not subject to indexation or seniority-based salary increases. Indicatively, for a single researcher without children, this corresponds to a net monthly income of approximately EUR 2,730, depending on the individual tax situation The employment includes full social security coverage, pension rights, paid annual leave, holiday pay and an end-of-year bonus, and is subject to Belgian income tax and social security contributions. Salary and employment conditions comply with the requirements of the European Commission for Marie Skłodowska-Curie Doctoral Networks (Horizon Europe). Application procedure Applications must be submitted in English as a single PDF file, named: Lastname_Firstname.pdf. The application PDF should contain: Statement of Intent (max. 1 page), indicating the position(s) applied for. Curriculum vitae (expected Master’s completion date if applicable) Bachelor’s and Master’s transcripts and degree certificates (if available) One writing sample (e.g. Master’s thesis or research paper) A brief statement detailing periods of residence, work, and study during the 36 months prior to the start date (required for MSCA positions) Formal language certificates (e.g. TOEFL or IELTS), if available Standardized test scores (e.g. GRE), if available Names and contact details of at least two academic referees (letters of recommendation may be requested at a later stage) Applicants should clearly indicate which position(s) they are applying for in their motivation letter. Candidates may indicate more than one position if they wish to be considered across funding schemes. Applications received by 15 February 2026 will receive full consideration. Interviews and offers are expected to take place in March 2026. Applications should be submitted using this form. For more information about this position, please contact: Bert Willems, LIDAM, UCLouvain Email: bert.willems@uclouvain.be