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Probably because of their interpretability and transparent nature, synthetic controls have become widely applied in empirical research in economics and the social sciences. This article aims to provide practical guidance to researchers employing synthetic control methods. The article starts with an overview and an introduction to synthetic control estimation. The main sections discuss the advantages of the synthetic control framework as a research design, and describe the settings where synthetic controls provide reliable estimates and those where they may fail. The article closes with a discussion of recent extensions, related methods, and avenues for future research.

This paper discusses empirical approaches macroeconomists use to answer questions like: What does monetary policy do? How large are the effects of fiscal stimulus? What caused the Great Recession? Why do some countries grow faster than others? Identification of causal effects plays two roles in this process. In certain cases, progress can be made using the direct approach of identifying plausibly exogenous variation in a policy and using this variation to assess the effect of the policy. However, external validity concerns limit what can be learned in this way. Carefully identified causal effects estimates can also be used as moments in a structural moment matching exercise. We use the term “identified moments” as a short-hand for “estimates of responses to identified structural shocks,” or what applied microeconomists would call “causal effects.” We argue that such identified moments are often powerful diagnostic tools for distinguishing between important classes of models (and thereby learning about the effects of policy). To illustrate these notions we discuss the growing use of cross-sectional evidence in macroeconomics and consider what the best existing evidence is on the effects of monetary policy.

Microfoundations have become an important theme in recent macromanagement research. However, the international management (IM) field is an exception to this. We document the lack of attention on microfoundations in IM research by focusing on knowledge sharing – a key IM research field – which we investigate by means of a keyword-based literature study of the leading IM and general management journals. We discuss possible reasons why microfoundations have so far met with less resonance in IM research. We point to the training and background of IM scholars as possible reasons. We also highlight the significance that IM scholars place on context and structure in explanation. These may be seen as contrary to a microfoundations perspective, a view that we show is incorrect. We end by identifying several microfoundational issues in IM research, calling for a sustained effort with respect to theory, heuristics, and empirics.

What is the point of ideology critique? Prominent Anglo-American philosophers recently proposed novel arguments for the view that ideology critique is moral critique, and ideologies are flawed insofar as they contribute to injustice or oppression. We criticize that view and make the case for an alternative and more empirically oriented approach, grounded in epistemic rather than moral commitments. We make two related claims: (a) ideology critique can debunk beliefs and practices by uncovering how, empirically, they are produced by self-justifying power and (b) the self-justification of power should be understood as an epistemic rather than moral flaw. Drawing on the recent realist revival in political theory, we argue that this genealogical approach has more radical potential, despite being more parsimonious than morality-based approaches. We demonstrate the relative advantages of our view by discussing the results of empirical studies on the contemporary phenomenon of neopatriarchy in the Middle East and North Africa.

We develop a representation of creative evolution in economics based on the theory of the adjacent possible. We start by introducing an epistemological framework for economic theorizing that copes with unknowability and the unlistability of possibility spaces. From this framework, we discuss the use of knowledge in creatively evolving systems and derive four main results: that local knowledge is itself a mechanism of movement through the adjacent possible; that all action is entrepreneurial action; that causality is ambiguous; and that individuals can agree to disagree. We then apply these results to decision-making, innovation, and the emergence of institutions and commons in creatively evolving systems.

The Validity and Reliability of the scales used in research are essential factors that enable the research to yield beneficial results. For this reason, it is useful to understand how the Reliability and Validity of the scales are measured correctly by researchers. The primary purpose of this study is to provide information on how the researchers test the Validity and Reliability of the scales used in their empirical studies and to provide resources for future research. For this purpose, the concepts of Validity and Reliability are introduced, and detailed explanations have been provided regarding the main methods used in the evaluation of Validity and Reliability with examples taken from the literature. It is considered that this study, which is a review, will attract the attention of researchers.

Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions. Randomizing treatment and intermediate variables is also insufficient. Despite these difficulties, the study of causal mechanisms is too important to abandon. We make three contributions to improve research on causal mechanisms. First, we present a minimum set of assumptions required under standard designs of experimental and observational studies and develop a general algorithm for estimating causal mediation effects. Second, we provide a method for assessing the sensitivity of conclusions to potential violations of a key assumption. Third, we offer alternative research designs for identifying causal mechanisms under weaker assumptions. The proposed approach is illustrated using media framing experiments and incumbency advantage studies.

Climate complexities and global warming have made sustainable development a customary topic in environmental literature. Since then, various diggings have been happening in academia. Amongst them tourism and eco-innovation receives the heap due to its contribution to economic development. The study, thereby, examines the impact of tourism, economic growth and eco-innovation on environmental degradation in China. The secondary data has been extracted from World Development Indicators (WDI) database from 1988 to 2020. The nexus among the variables have been examined using Nonlinear Autoregressive Distributed Lagged (NARDL) model. Findings reveal that international tourism receipts, expenditures and number of tourist arrival, GDP, national income and inflation are positively correlated with environmental degradation, while sustainability-oriented eco-innovation is related negatively in case of China. This study has provided help to the regulators while developing new policies regarding environmental degradation by controlling emissions from economic and tourism development and using sustainability-oriented eco-innovation.

Since Edward Leamer's memorable 1983 paper, “Let's Take the Con out of Econometrics,” empirical microeconomics has experienced a credibility revolution. While Leamer's suggested remedy, sensitivity analysis, has played a role in this, we argue that the primary engine driving improvement has been a focus on the quality of empirical research designs. The advantages of a good research design are perhaps most easily apparent in research using random assignment. We begin with an overview of Leamer's 1983 critique and his proposed remedies. We then turn to the key factors we see contributing to improved empirical work, including the availability of more and better data, along with advances in theoretical econometric understanding, but especially the fact that research design has moved front and center in much of empirical micro. We offer a brief digression into macroeconomics and industrial organization, where progress—by our lights—is less dramatic, although there is work in both fields that we find encouraging. Finally, we discuss the view that the design pendulum has swung too far. Critics of design-driven studies argue that in pursuit of clean and credible research designs, researchers seek good answers instead of good questions. We briefly respond to this concern, which worries us little.

This work nests the Agent-Based macroeconomic perspective into the earlier history of macroeconomics. We discuss how the discipline in the 70’s took a perverse path relying on models grounded on fictitious rational representative agent in order to try to pathetically circumvent aggregation and coordination problems. The Great Recession was a natural experiment for macroeconomics, showing the inadequacy of the predominant theoretical framework grounded on DSGE models. After discussing the pathological fallacies of the DSGE-based approach, we claim that macroeconomics should consider the economy as a complex evolving system, i.e. as an ecology populated by heterogenous agents, whose far-from-equilibrium interactions continuously change the structure of the system. This in turn implies that more is different: macroeconomics cannot be shrink to representative-agent micro, but agents’ complex interactions lead to emergence of new phenomena and hierarchical structure at the macro level. This is what is taken into account by agent-based models, which provide a novel way to model complex economies from the bottom-up, with sound empirically-based microfoundations. We present the foundations of Agent-Based macroeconomics and we discuss how the contributions of this special issue push its frontier forward. Finally, we conclude by discussing the ways ahead for the fully acknowledgement of agent-based models as the standard way of theorizing in macroeconomics.