The field of quantitative research has transitioned from classical regression models to research designs emphasizing causal interpretation. The credibility revolution has enhanced reliability in empirical economics by prioritizing research design quality and employing more experimental and quasi-experimental methods. The module introduces a potential outcome model and direct acyclic graphs to analyse the causal effects of policies in natural experiments and randomized control trials. It further discusses in detail recent developments in difference-in-difference and synthetic control methods. Staggered policy implementation and heterogenous impact is discussed. Finally, synthetic difference in difference, combining strengths of both methods is presented. The presentation of these methods is accompanied by practical demonstrations in R. Staggered difference-in-differences is illustrated through examples showcasing the heterogeneous impacts of preferential trade agreements and sanctions on trade flows. Synthetic control is demonstrated by evaluating the impact of Brexit on UK trade with the EU.

Endogeneity-related issues in empirical research continue to receive increasing academic attention, serving as pivotal benchmarks for quality in many academic journals. In this workshop, we'll delve into the complexities of endogeneity bias, exploring its sources such as the omission of variables, errors in variables, and simultaneous causality. Led by Dr. Ghasem Zaefarian, Associate Professor of Marketing at Leeds University Business School, this workshop will provide attendees with an overview of endogeneity bias and its potential sources. From outlining the nuances of the issue to discussing techniques like instrumental variables and instrument-free approaches, participants will gain insights into mitigating endogeneity bias in their research. Whether you're designing a new research project or analyzing data to uncover cause-and-effect relationships, this methodological workshop is designed to give you a head start with the tools needed to navigate endogeneity bias effectively.

An ever-increasing share of information is recorded as digital text. Until recently, however, text analysis relied on meticulous human examination, a method inherently limited in scalability when confronted with the vast corpora now at our disposal. The quantity of individual documents within widely-used databases, such as those containing newspaper articles and tweets from Twitter, has surged into the tens of millions or billions more. This burgeoning abundance of large-scale corpora has sparked a heightened enthusiasm for machine learning techniques for text analysis, a trajectory that is poised to persist and intensify with the continual expansion of textual data reservoirs. This workshop will introduce how to perform text analysis using machine learning tools. The main topics covered include: What Is Text Analysis? Text data preprocessing. An overview of text analysis techniques used in economics. Word (text) similarity analysis with an example of measuring aggregate-level Brexit uncertainty. Topic analysis with an example of measuring topic-level Brexit uncertainty. Sentiment analysis with an example of measuring public sentiment towards Brexit using Twitter data.

The configurational approach refers to an integrative analytical technique, involving mechanisms that simultaneously and jointly considers strategy, organizational and environmental characteristics (Wiklund and Shepherd, 2005). The configurational approach is useful when: (i) The antecedent factors are not clearly discernible due, in parts, to close linkages, mutual dependencies and interconnected processes amongst the factors (Meyer et. al, 1993) and (ii) The same antecedent factors may lead to conflicting outcomes or more confoundingly when equifinality results from a combination of different organizational configurations (Meyer, et. al). Of the several available tools, the set theoretical deduction based Qualitative Comparative Analysis (QCA) method provides several distinctive advantages. First, QCA provides inferences on facts that we don’t know from those that we do know (Thomann and Maggetti, 2020), by establishing external and internal validities and a mode of reasoning (rationale). Second, QCA provides the modus operandi through the reliance on cases (Rihoux, 2013). QCA considers a small (n=20) to intermediate (n<200) set of cases to achieve generalization (external validity) through in-depth search within the cases (internal validation) and an inductive, iterative, and exploratory mode of reasoning (establishing the rationale). Third, because QCA is iterative, it can blend in-depth qualitative induction with quantitative deductions that can be generalized via statistical techniques. Simply put, QCA can help in clustering and analyzing the underlying causal configurations of a set of cases or a set of conditions. In this context, through this workshop, we introduce the fuzzy set qualitative comparative analysis (fsQCA), an analytical technique originally developed by Charles Ragin, of the Department of Sociology, University of California (Irvine). QCA is broadly divided into the traditional or Crisp Set QCA (csQCA) and the more refined fsQCA. Crisp set QCA deals with dichotomous outcomes (e.g. inclusion/exclusion) while fsQCA also includes the various shades in-between much like the interval scales of a survey instrument. So, one may simplify (but not overtly) csQCA as akin to measuring through a nominal scale and then considering what is in and what is out. Likewise, fsQCA can be perceived as measuring using an ordinal scale (e.g. Likert), where one considers the various shades between in and out and then determining the level to be considered. Through this workshop we endeavor to obtain a working exposure to the configurational approach using the fuzzy set qualitative comparative analysis.

The Workshop on ‘How to Develop Articles for Publication in International Business’ (90 mins) will address academic research in international business, leading to publication in top journals. The workshop will address identifying research topics, the role of theory and theory development, creating the research paper, and publishing in journals. Publishable research should aim to develop new knowledge, with a focus on making substantive contributions to the field. Research should be framed within relevant, extant theory, and simultaneously aim to advance existing theory or otherwise develop new theory. The workshop will be taught by Professor Gary Knight, who has substantial experience conducting research and publishing in top journals, in international business.

This workshop will help you to analyse where your strengths and experiences are and where you have gaps in your knowledge of development needs. Participants will each work on a personal development plan which they can take away and use as part of their career development. The session is led by an experienced coach and mentor in HE, and will use tools which have been successful in the past in directing ECRs and more junior academics to think about what sort of academic role is best for them and what institution fits with their values. While the focus will be on a holistic career, participants may want to concentrate on their research trajectory.