135 results found
Joffe M, 2022, Profit rate dynamics in US manufacturing, International Review of Applied Economics, ISSN: 0269-2171
The attributes and dynamics of the profit rate distribution provide indispensable information on how the economy works. Edith Penrose, in The theory of the growth of the firm¸ took agency, managerial capabilities, heterogeneity and open-endedness as characteristic of the economy. Schumpeter had a similar view. Neoclassical theory, in contrast, envisages convergence to a standard rate of return, invoking inter-industry capital flows and diminishing returns as the main mechanism. I analysed the data on US manufacturing, 1987–2015. There was evidence of convergence, attributable to loss of supra-normal profits in two industries. The features of the distribution confirm Penrose’s view. Neoclassical theory fares poorly: the data do not support ‘a standard rate of return’, and no plausible macro shock exists that could have produced the observed dispersion. The symmetry of the observed distribution indicates that neither market power nor intangible assets play major roles in determining the shape of the profit rate distribution; risk, however, is relevant if reformulated. Intersectoral capital flows were weak, and there was no evidence of diminishing returns. Penrose’s conception of heterogeneous managerial capacity refers to a concept of economic power distinct from market power, corresponding to differential ex ante strength; differential profit outcomes represent ex post strength.
Greenland S, Mansournia MA, Joffe M, 2022, To curb research misreporting, replace significance and confidence by compatibility A Preventive Medicine Golden Jubilee article, Preventive Medicine, Vol: 164, Pages: 1-5, ISSN: 0091-7435
It is well known that the statistical analyses in health-science and medical journals are frequently misleading or even wrong. Despite many decades of reform efforts by hundreds of scientists and statisticians, attempts to fix the problem by avoiding obvious error and encouraging good practice have not altered this basic situation. Statistical teaching and reporting remain mired in damaging yet editorially enforced jargon of “significance”, “confidence”, and imbalanced focus on null (no-effect or “nil”) hypotheses, leading to flawed attempts to simplify descriptions of results in ordinary terms.A positive development amidst all this has been the introduction of interval estimates alongside or in place of significance tests and P-values, but intervals have been beset by similar misinterpretations. Attempts to remedy this situation by calling for replacement of traditional statistics with competitors (such as pure-likelihood or Bayesian methods) have had little impact. Thus, rather than ban or replace P-values or confidence intervals, we propose to replace traditional jargon with more accurate and modest ordinary-language labels that describe these statistics as measures of compatibility between data and hypotheses or models, which have long been in use in the statistical modeling literature. Such descriptions emphasize the full range of possibilities compatible with observations. Additionally, a simple transform of the P-value called the surprisal or S-value provides a sense of how much or how little information the data supply against those possibilities. We illustrate these reforms using some examples from a highly charged topic: trials of ivermectin treatment for Covid-19.
Joffe M, 2019, Mechanism in behavioural economics, Journal of Economic Methodology, Vol: 26, Pages: 228-242, ISSN: 1350-178X
Behavioural economics promises to bring economics closer to being evidence based. However, its ability to do this may depend on a methodological issue: whether the findings of behavioural economics are used to modify or extend standard theory, or to contribute towards replacing it where required – respectively the incremental and selective replacement strategies. I focus on the incremental approach, in terms of its implied causal mechanism. Two stages are involved, corresponding to the prediction of standard theory and to a separate component that aligns it with actual observations. In behavioural economics, one possible interpretation of the language of ‘biases’ is such a two-stage approach. More explicitly, Rabin advocates it in the form of PEEMs (Portable Extensions of Existing Models). A more direct, one-stage approach may have some advantages, at least for some research topics.
Joffe M, 2017, Causal theories, models and evidence in economics-some reflections from the natural sciences, Cogent Economics and Finance, Vol: 5, ISSN: 2332-2039
Models have been extensively analysed in economic methodology, notably their degree of ability to provide explanations. This paper takes a complementary, comparative approach, examining theory development in the natural sciences. Examples show how diverse types of evidence combine with causal hypotheses to generate empirically based causal theories—a cumulative process occurring over a long timescale. Models are typically nested within this broader theory. This could be a good model for research in economics, providing a methodology that ensures good correspondence with the target system—especially as economics research is largely empirical, and has effective methods for causal inference. This paper analyses the key features of three successful theories in the natural sciences, and draws out some lessons that may be useful to economists. Some examples of good practice in economics are noted, e.g. involving money and banking, and the growth of the state. On the other hand, the widespread pre-crisis use of dynamic stochastic general equilibrium (DSGE) models that ignored the financial sector raises the question, how to realise what has been omitted? Nesting models in an empirically based causal theory could solve this. Furthermore, some phenomena have clear explanations, but mainstream theory obscures them, as with the Lucas puzzle about the direction of international capital flows. And, the prevailing theories about capitalist growth do not explain the basic evidence on its temporal and spatial distribution. Economics could beneficially learn from the natural sciences.
Joffe M, 2017, The Roots of Growth: Entrepreneurship, Innovation and the Capitalist Firm, Economic Complexity and Evolution, Pages: 257-267
The spectacular growth record of capitalist economies in the past 200 years is frequently attributed to entrepreneurship and/or innovation. This cannot be the whole story, because entrepreneurship has a far more widespread historical and geographical distribution than these high-growth countries, and occurs particularly in rather stagnant societies; innovation contributes to economic growth, but it is unclear why it has become so much more prevalent in capitalist societies, or why it takes a form that is growth-promoting in that context. Thus, entrepreneurship and innovation only contribute to dynamism in a particular institutional context: a real economy that is dominated by capitalist firms, which are able to purchase all their inputs including labour, making it easy to change the technology, workforce, product, location, etc. Because entrepreneurship and innovation have tended to be analysed in capitalist societies, this extra component has been taken for granted. But it is not a natural, ubiquitous feature—it has its specific history, notably the development of “entity shielding” that protects the firm from its shareholders as well as from outsiders, enabling it to accumulate assets, including premises and equipment, as well as less tangible items such as expertise, relationships and reputation. These features of the capitalist firm shape entrepreneurship and innovation, and make them effective. The central imperative to make a profit provides direction for entrepreneurs and innovators, and the potential rewards of success provide an incentive both for their performance and for their choosing these roles. The capitalist firm’s flexibility of inputs gives scope for the inventiveness of entrepreneurs and innovators, and its potentially large market magnifies the success of their efforts.
Joffe M, 2016, Energy Use, Health Implications of, International Encyclopedia of Public Health, Pages: 468-473, ISBN: 9780128036785
The improvement of health in the developed part of the world has historically gone hand-in-hand with increasing energy intensity. However, each further increase in energy use produces ever-smaller health gains. Meanwhile, a large majority of humanity has problems with access to energy - fuel poverty and insecurity being major components of poverty. The type of energy usage also has health impacts, such as through time and/or monetary cost, indoor air pollution, and car use. Global climate change is a major consequence and has serious health effects. Health has an important role to play in policy development, and especially in identifying win-win situations.
Almond D, Edlund L, Joffe M, et al., 2016, An adaptive significance of morning sickness? Trivers-Willard and Hyperemesis Gravidarum, ECONOMICS & HUMAN BIOLOGY, Vol: 21, Pages: 167-171, ISSN: 1570-677X
Joffe M, 2015, Selection Bias Due to Parity-conditioning in Studies of Time Trends in Fertility, EPIDEMIOLOGY, Vol: 26, Pages: E67-E67, ISSN: 1044-3983
Rehfuess EA, Best N, Briggs DJ, et al., 2013, Diagram-based Analysis of Causal Systems (DACS): elucidating inter-relationships between determinants of acute lower respiratory infections among children in sub-Saharan Africa., Emerg Themes Epidemiol, Vol: 10, ISSN: 1742-7622
BACKGROUND: Effective interventions require evidence on how individual causal pathways jointly determine disease. Based on the concept of systems epidemiology, this paper develops Diagram-based Analysis of Causal Systems (DACS) as an approach to analyze complex systems, and applies it by examining the contributions of proximal and distal determinants of childhood acute lower respiratory infections (ALRI) in sub-Saharan Africa. RESULTS: Diagram-based Analysis of Causal Systems combines the use of causal diagrams with multiple routinely available data sources, using a variety of statistical techniques. In a step-by-step process, the causal diagram evolves from conceptual based on a priori knowledge and assumptions, through operational informed by data availability which then undergoes empirical testing, to integrated which synthesizes information from multiple datasets. In our application, we apply different regression techniques to Demographic and Health Survey (DHS) datasets for Benin, Ethiopia, Kenya and Namibia and a pooled World Health Survey (WHS) dataset for sixteen African countries. Explicit strategies are employed to make decisions transparent about the inclusion/omission of arrows, the sign and strength of the relationships and homogeneity/heterogeneity across settings.Findings about the current state of evidence on the complex web of socio-economic, environmental, behavioral and healthcare factors influencing childhood ALRI, based on DHS and WHS data, are summarized in an integrated causal diagram. Notably, solid fuel use is structured by socio-economic factors and increases the risk of childhood ALRI mortality. CONCLUSIONS: Diagram-based Analysis of Causal Systems is a means of organizing the current state of knowledge about a specific area of research, and a framework for integrating statistical analyses across a whole system. This partly a priori approach is explicit about causal assumptions guiding the analysis and about researcher judgment, and wrong
Joffe M, 2013, The Concept of Causation in Biology, ERKENNTNIS, Vol: 78, Pages: 179-197, ISSN: 0165-0106
Joffe M, Holmes J, Jensen TK, et al., 2013, Time Trends in Biological Fertility in Western Europe, AMERICAN JOURNAL OF EPIDEMIOLOGY, Vol: 178, Pages: 722-730, ISSN: 0002-9262
Joffe M, 2013, What Would a Scientific Economics Look Like?, Evidence, Inference and Enquiry, ISBN: 9780197264843
This chapter compares biology with the practices of economics to determine the extent to which mainstream economic theory can be regarded as 'scientific'. This survey of practice in the two disciplines shows that biological theory is derived from description and experimentation. In contrast, the ideal in mainstream economics is to derive theory from axioms. When economic theory is compared with the available evidence, a disjunction is found between the empirical findings and conventional theory. The disjunction is explained by a fundamental mismatch between the evidence and the basic theoretical categories and structure. The central issue is the relationship of evidence to theory: to put it simply, which should come first? The conclusion is that regularities that emerge from a comparative historical perspective, including use of econometric, statistical, and qualitative studies, could provide the type of evidence that could form a secure foundation for theorising in economics.
Joffe M, 2013, What causes creative destruction?, Long Term Economic Development: Demand, Finance, Organization, Policy and Innovation in a Schumpeterian Perspective, Pages: 431-438, ISBN: 9783642351242
Schumpeter’s descriptive metaphor "creative destruction” has inspired a great deal of important research. He was clear that the continual transformation underlying economic growth is an intrinsic feature of the system, but left no clear causal account of the underlying process. His principal narrative concerned the entrepreneur, an "agency” explanation rather than a causal one in the usual sense. However, closer examination reveals that this does not fit with the observed historical pattern of continuing per capita growth, which is specific to the type of capitalist economy that has only existed in the past two centuries. He also introduced a more systemic view, but this is not very well developed in his writings and the causal mechanism is unclear. Connected with the ambiguity in respect of causation, Schumpeter was also unclear about the relative roles of large and small firms in innovation, at times seeing large corporations as the engine of growth, but at other times seeing them as a threat to the dynamism of the entrepreneur. Comparison with the historical record shows that neither view well represents the general process of capitalist transformation.
Methods of diagrammatic modelling have been greatly developed in the past two decades. Outside the context of infectious diseases, systematic use of diagrams in epidemiology has been mainly confined to the analysis of a single link: that between a disease outcome and its proximal determinant(s). Transmitted causes ("causes of causes") tend not to be systematically analysed.The infectious disease epidemiology modelling tradition models the human population in its environment, typically with the exposure-health relationship and the determinants of exposure being considered at individual and group/ecological levels, respectively. Some properties of the resulting systems are quite general, and are seen in unrelated contexts such as biochemical pathways. Confining analysis to a single link misses the opportunity to discover such properties.The structure of a causal diagram is derived from knowledge about how the world works, as well as from statistical evidence. A single diagram can be used to characterise a whole research area, not just a single analysis - although this depends on the degree of consistency of the causal relationships between different populations - and can therefore be used to integrate multiple datasets.Additional advantages of system-wide models include: the use of instrumental variables - now emerging as an important technique in epidemiology in the context of mendelian randomisation, but under-used in the exploitation of "natural experiments"; the explicit use of change models, which have advantages with respect to inferring causation; and in the detection and elucidation of feedback.
Joffe M, 2011, The gap between evidence discovery and actual causal relationships, PREVENTIVE MEDICINE, Vol: 53, Pages: 246-249, ISSN: 0091-7435
Joffe M, 2011, The root cause of economic growth under capitalism, CAMBRIDGE JOURNAL OF ECONOMICS, Vol: 35, Pages: 873-896, ISSN: 0309-166X
de Nazelle A, Nieuwenhuijsen MJ, Anto JM, et al., 2011, Improving health through policies that promote active travel: A review of evidence to support integrated health impact assessment, ENVIRONMENT INTERNATIONAL, Vol: 37, Pages: 766-777, ISSN: 0160-4120
Joffe M, 2011, Brief Note Introducing the Language of Causal Analysis, JOURNAL OF HEALTHCARE ENGINEERING, Vol: 2, Pages: 111-116, ISSN: 2040-2295
Joffe M, 2011, CAUSALITY AND EVIDENCE DISCOVERY IN EPIDEMIOLOGY, Workshop on Explanation, Prediction, and Confirmation in Biology and Medicine, Publisher: SPRINGER, Pages: 153-166
Rehfuess EA, Briggs DJ, Joffe M, et al., 2010, Bayesian modelling of household solid fuel use: Insights towards designing effective interventions to promote fuel switching in Africa, ENVIRONMENTAL RESEARCH, Vol: 110, Pages: 725-732, ISSN: 0013-9351
Mindell J, Biddulph J, Taylor L, et al., 2010, Improving the use of evidence in health impact assessment, BULLETIN OF THE WORLD HEALTH ORGANIZATION, Vol: 88, Pages: 543-550, ISSN: 0042-9686
Joffe M, 2010, What has happened to human fertility?, HUMAN REPRODUCTION, Vol: 25, Pages: 295-307, ISSN: 0268-1161
Joffe M, 2010, The role of strategic health impact assessment in sustainable development, International Journal of Green Economics, Vol: 4, Pages: 1-16, ISSN: 1744-9928
Health is one of the main ways of judging effects on humans and is a component of the three overlapping dimensions of sustainable development: environmental, social and economic. Yet, the assessment of health impacts has been neglected as a way of arguing for the importance of policies to foster sustainable development or of evaluating policies from this viewpoint. Health impact assessment (HIA) is one way of trying to do this and has developed considerably in recent years, but its impact is small compared to the challenges that we face. In addition, time constraints and other limitations mean that the role of good quality evidence is not as prominent as it should be and collaboration between different types of expert cannot be adequately developed. It is proposed that a more thorough process should be introduced, 'Strategic Health Assessment' (SHA), that is not tied to particular capital projects and can therefore overcome these limitations. © 2010 Inderscience Enterprises Ltd.
Joffe M, 2010, Validity of Self-reported Time to Pregnancy, EPIDEMIOLOGY, Vol: 21, Pages: 160-161, ISSN: 1044-3983
Joffe M, 2010, Semen quality analysis and the idea of normal fertility, ASIAN JOURNAL OF ANDROLOGY, Vol: 12, Pages: 79-82, ISSN: 1008-682X
Rehfuess EA, Tzala L, Best N, et al., 2009, Solid fuel use and cooking practices as a major risk factor for ALRI mortality among African children, JOURNAL OF EPIDEMIOLOGY AND COMMUNITY HEALTH, Vol: 63, Pages: 887-892, ISSN: 0143-005X
Joffe M, Key J, Best N, et al., 2009, The role of biological fertility in predicting family size, HUMAN REPRODUCTION, Vol: 24, Pages: 1999-2006, ISSN: 0268-1161
Key J, Best N, Joffe M, et al., 2009, Methodological Issues in Analyzing Time Trends in Biologic Fertility: Protection Bias, AMERICAN JOURNAL OF EPIDEMIOLOGY, Vol: 169, Pages: 285-293, ISSN: 0002-9262
Joffe M, 2008, Energy use, health implications of, International Encyclopedia of Public Health, Pages: 341-347, ISBN: 9780123739605
The improvement of health in the developed part of the world has historically gone hand-in-hand with increasing energy intensity. However, each further increase in energy use produces ever-smaller health gains. Meanwhile, a large majority of humanity has problems with access to energy - fuel poverty and insecurity being major components of poverty. The type of energy usage also has health impacts, such as through time and/or monetary cost, indoor air pollution, and car use. Global climate change is a major consequence and has serious health effects. Health has an important role to play in policy development, and especially in identifying win-win situations. © 2008 Copyright © 2008 Michael Joffe Published by Elsevier Inc. All rights reserved.
Joffe M, 2008, The need for strategic health assessment, EUROPEAN JOURNAL OF PUBLIC HEALTH, Vol: 18, Pages: 439-440, ISSN: 1101-1262
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