Creating and destroying jobs across East Asia Pacific: A country-level analysis on wages, exports, finance, regulation and infrastructure

This paper is the first to analyse a much broader range of correlates of job growth simultaneously for each country individually across all 12 East Asian and Pacific countries with stratified randomised enterprise survey data between 2009 and 2012. It acknowledges the strong data limitations and deviates from the standard approach of using pooled, cross-country regressions in analysing enterprise survey data which reduces the usefulness of findings for policymakers in individual countries by neglecting variations across diverse countries with unique business, regulatory and institutional environments. Potential policy responses are derived from the multivariate econometric analyses while highlighting the importance of idiosyncratic country conditions

The limits of overly simplistic theory in textbook economics: the case of child labour

Alexander Krauss outlines why analysing complex phenomena like child labour needs to be done using cross-disciplinary approaches and mixed methods

How nobel-prize breakthroughs in economics emerge and the field's influential empirical methods

What drives groundbreaking research in economics? Nobel-prize-winning work has had an important impact on public policies, but we still do not understand well what drives such breakthroughs. We collect data on all nobel-prize discoveries in economics to address this question. We find that major advances in the field of economics are brought about by methodological innovation: by developing new and improved research methods. We find that developing for example econometrics in 1933, randomised controlled trials in 1948 and new game theory methods in 1950 were essential to opening the new fields of corporate finance, experimental economics and information economics, respectively. We identify the development of new methods as the main mechanism driving new discoveries and research fields. Fostering this general mechanism (generating novel methods) holds the potential to greatly increase the rate at which we make new breakthroughs and fields. We also show that many of the main methods of economics – such as randomised controlled trials, natural experiments, regression discontinuity, instrumental variables and other statistical methods – had been developed and used in other fields like public health, before economists adopted them. This shift towards more powerful empirical methods in the field has important implications on developing new and better methods and adopting them from related fields to make new advances more rapidly

Redefining the scientific method: as the use of sophisticated scientific methods that extend our mind

Scientific, medical, and technological knowledge has transformed our world, but we still poorly understand the nature of scientific methodology. Science textbooks, science dictionaries, and science institutions often state that scientists follow, and should follow, the universal scientific method of testing hypotheses using observation and experimentation. Yet, scientific methodology has not been systematically analyzed using large-scale data and scientific methods themselves as it is viewed as not easily amenable to scientific study. Using data on all major discoveries across science including all Nobel Prize and major non-Nobel Prize discoveries, we can address the question of the extent to which “the scientific method” is actually applied in making science’s groundbreaking research and whether we need to expand this central concept of science. This study reveals that 25% of all discoveries since 1900 did not apply the common scientific method (all three features)—with 6% of discoveries using no observation, 23% using no experimentation, and 17% not testing a hypothesis. Empirical evidence thus challenges the common view of the scientific method. Adhering to it as a guiding principle would constrain us in developing many new scientific ideas and breakthroughs. Instead, assessing all major discoveries, we identify here a general, common feature that the method of science can be reduced to: making all major discoveries has required using sophisticated methods and instruments of science. These include statistical methods, particle accelerators, and X-ray methods. Such methods extend our mind and generally make observing, experimenting, and testing hypotheses in science possible, doing so in new ways and ensure their replicability. This provides a new perspective to the scientific method—embedded in our sophisticated methods and instruments—and suggests that we need to reform and extend the way we view the scientific method and discovery process

Science of science: a multidisciplinary field studying science

Science and knowledge are studied by researchers across many disciplines, examining how they are developed, what their current boundaries are and how we can advance them. By integrating evidence across disparate disciplines, the holistic field of science of science can address these foundational questions. This field illustrates how science is shaped by many interconnected factors: the cognitive processes of scientists, the historical evolution of science, economic incentives, institutional influences, computational approaches, statistical, mathematical and instrumental foundations of scientific inference, scientometric measures, philosophical and ethical dimensions of scientific concepts, among other influences. Achieving a comprehensive overview of a multifaceted field like the science of science requires pulling together evidence from the many sub-fields studying science across the natural and social sciences and humanities. This enables developing an interdisciplinary perspective of scientific practice, a more holistic understanding of scientific processes and outcomes, and more nuanced perspectives to how scientific research is conducted, influenced and evolves. It enables leveraging the strengths of various disciplines to create a holistic view of the foundations of science. Different researchers study science from their own disciplinary perspective and use their own methods, and there is a large divide between quantitative and qualitative researchers as they commonly do not read or cite research using other methodological approaches. A broader, synthesizing paper employing a qualitative approach can however help provide a bridge between disciplines by pulling together aspects of science (economic, scientometric, psychological, philosophical etc.). Such an approach enables identifying, across the range of fields, the powerful role of our scientific methods and instruments in shaping most aspects of our knowledge and science, whereas economic, social and historical influences help shape what knowledge we pursue. A unifying theory is then outlined for science of science – the new-methods-drive-science theory

Climate change, resource depletion and population growth: the elephant in the room

Following the COP22 talks in Marrakesh, Alexander Krauss and Thomas Kastning, argue that politicians are ignoring the solution with the largest potential to mitigate climate change: slowing population growth

Measures of effectiveness in medical research: reporting both absolute and relative measures

Biomedical research, especially pharmaceutical research, has been criticised for engaging in practices that lead to over-estimations of the effectiveness of medical treatments. A central issue concerns the reporting of absolute and relative measures of medical effectiveness. In this paper we critically examine proposals made by Jacob Stegenga to (a) give priority to the reporting of absolute measures over relative measures, and (b) downgrade the measures of effectiveness (effect sizes) of the treatments tested in clinical trials (Stegenga, 2015a). After exposing significant flaws in a central case study used by Stegenga to bolster his first proposal (a), we go on to argue that neither of these proposals is defensible (a or b). We defend the practice, in line with the New England Journal of Medicine, of reporting both absolute and relative measures whenever feasible

Generalization bias in science

Many scientists routinely generalize from study samples to larger populations. It is commonly assumed that this cognitive process of scientific induction is a voluntary inference in which researchers assess the generalizability of their data and then draw conclusions accordingly. We challenge this view and argue for a novel account. The account describes scientific induction as involving by default a generalization bias that operates automatically and frequently leads researchers to unintentionally generalize their findings without sufficient evidence. The result is unwarranted, overgeneralized conclusions. We support this account of scientific induction by integrating a range of disparate findings from across the cognitive sciences that have until now not been connected to research on the nature of scientific induction. The view that scientific induction involves by default a generalization bias calls for a revision of the current thinking about scientific induction and highlights an overlooked cause of the replication crisis in the sciences. Commonly proposed interventions to tackle scientific overgeneralizations that may feed into this crisis need to be supplemented with cognitive debiasing strategies against generalization bias to most effectively improve science

Recursive Definitions of Monadic Functions

Using standard domain-theoretic fixed-points, we present an approach for defining recursive functions that are formulated in monadic style. The method works both in the simple option monad and the state-exception monad of Isabelle/HOL's imperative programming extension, which results in a convenient definition principle for imperative programs, which were previously hard to define. For such monadic functions, the recursion equation can always be derived without preconditions, even if the function is partial. The construction is easy to automate, and convenient induction principles can be derived automatically.Comment: In Proceedings PAR 2010, arXiv:1012.455