Recently we uploaded to the arxiv a paper entitled: Improving the Economic Complexity Index. There, we compared three metrics of the knowledge intensity of an economy, the original metric we published in 2009 (the Economic Complexity Index or ECI), a variation of the metric proposed in 2012, and a variation we called ECI+. It was brought to our attention that the definition of ECI+ was equivalent to the variation of the metric proposed in 2012. We have verified this claim, and found that while the equations are not exactly the same, they are similar enough to be our own oversight. More importantly, we now ask: how many variations of the original ECI work? In this paper we provide a simple unifying framework to explore multiple variations of ECI, including both the original 2009 ECI and the 2012 variation. We found that a large fraction of variations have a similar predictive power, indicating that the chance of finding a variation of ECI that works, after the seminal 2009 measure, are surprisingly high.

In part I of this analysis, the striking similarities of the declining oil production in the North Sea, Indonesia and Mexico were used to model the future maximum possible oil production per annum in all larger countries and regions of the planet from 2015 to 2050. In part II, the oil export and oil consumption patterns, that were established in recent decades, are combined with the consequences of the forecast declines in regional oil production that were developed in part I of this analysis. The results are quantitative predictions of the maximum possible region-by-region oil consumption during the next 20 years. The predictions indicate that several of the larger oil consuming and importing countries and regions will be confronted with the economic consequences of the onset of the world’s final oil supply crisis as early as 2020.

How much knowledge is there in an economy? In recent years, data on the mix of products that countries export has been used to construct measures of economic complexity that estimate the knowledge available in an economy and predict future economic growth. Here we introduce a new metric of economic complexity (ECI+) that measures the total exports of an economy corrected by how difficult it is to export each product. We use data from 1973 to 2013 to compare the ability of ECI+, the Economic Complexity Index (ECI), and Fitness complexity, to predict future economic growth using 5, 10, and 20-year panels in a pooled OLS, a random effects model, and a fixed effects model. We find that ECI+ outperforms ECI and Fitness in its ability to predict economic growth and in the consistency of its estimators across most econometric specifications. We then combine ECI+ with measures of physical capital, human capital, and institutions, to select a robust model of economic growth and test the robustness of ECI+.

Despite theoretical and methodological improvements by national accounts framework revisions, not without disputes and confrontations of views, the growing complexity of economic and social phenomena under globalization circumstances has led to increasing difficulties in the design, monitoring and implementation of specific policies depending on GDP indicator. The paper focuses on the analysis of the GDP relevance and limitations in its interpretation, including a retrospective view. Some inconsistencies as regards the metrics of GDP (illegal activities, unobserved economy, self-consumption in rural households, owner’s imputed rents) are highlighted. Because the GDP does not take into account the impact of important factors of progress (depletion of natural resources, environmental factors, urban concentration and rural depopulation etc.) and does not reflects neither the citizens wellbeing (starting from Easterlin Paradox), efforts to develop new statistical standards in order to complement/substitute GDP

In Part One of this paper we use the harmonic analogy of a musical octave to analyze mathematic ratios of U.S. real GNP. These ratios are generated by bringing together figures for U.S. real GNP over intervals of time – “spreads of years” – as numerator and denominator in a single fraction. Using a range of 7-year to 18-year “spreads,” we find that this approach provides strong evidence that American economic history is composed of four 14-year quarter-cycles within a 56 year circuit in the real GNP of the United States, 1869-2007. These periods correlate closely with analysis by Nickolai Kondratiev and provide a framework for predicting an annual steady state rate of growth for the United States falling between 3.4969% and 3.4995% per year. In Part Two of this paper we provide three postscripts including: (1) correlations / speculations on the political and social consequences of this model, (2) simplification / expansion of the geometries implied and (3) analysis / prediction based upon this approach, as concluded by a brief afterword.

Drawing on recent contributions inferring financial interconnectedness from market data, our paper provides new insights on the evolution of the US financial industry over a long period of time by using several tools coming from network science. Following [1] a Time-Varying Parameter Vector AutoRegressive (TVP-VAR) approach on stock market returns to retrieve unobserved directed links among financial institutions, we reconstruct a fully dynamic network in the sense that connections are let to evolve through time. The financial system analysed consists of a large set of 155 financial institutions that are all the banks, broker-dealers, insurance and real estate companies listed in the Standard & Poor’s 500 index over the period 1993−2014. Looking alternatively at the individual, then sector-, community- and system-wide levels, we show that network science’s tools are able to support well known features of the financial markets such as the dramatic fall of connectivity following Lehman Brothers’ collapse.

Cuando disminuyeron los precios petroleros se apeló a las devaluaciones para obtener fondos con que enjugar déficits y aumentar el gasto público. Esta decisión de carácter político se tomó por primera vez en una reunión en casa del jefe de la fracción parlamentaria de AD, Dr. Carlos Canache Mata, quien había sido comisionado por el entonces candidato de AD a la Presidencia de la República, Dr. Jaime Lusichi, para realizar una reunión e intercambiar ideas en torno a medidas que instrumentaría el nuevo gobierno de AD. Fue en 1983. Fui uno de los asistentes como miembro del equipo asesor del Dr. Lusinchi y su asesor personal, como me lo había solicitado. Éramos 7 personas. Una, muy vinculada a los sectores económicos, recuerdo que comenzó diciendo: “La única forma de salvar la democracia es devaluando el bolívar para recabar fondos y repartirlos a los pobres, que han aumentado a causa del desastre copeyano con el gobierno de Luis Herrera”. Otros hablaron. Casi de último razoné mi oposición argumentando que transitaríamos el camino del Cono Sur, generando inflación por la importación a costos más elevados y por el aumento de la circulación monetaria que implicaría el crear más bolívares con los mismos o menos dólares de las exportaciones petroleras. Así sucedió. Cuando el Dr. Lusinchi tomó posesión de la presidencia en 1984, vinieron los decretos devaluando el bolívar. Ese año, los ingresos públicos por manipulación cambiaria pasaron de 75.000 millones de bolívares, que era el presupuesto aprobado a finales de 1983, a 104.000 millones, esto es, 29.000 millones de bolívares más (38,7%), que no salieron de ninguna medida impositiva a la actividad productiva ni de exportaciones petroleras. Peor aún. Se tomó esa medida al par que la economía sufría efectos recesivos. Disentí públicamente.

A very timely issue for economic agent-based models (ABMs) is their empirical estimation. This paper describes a line of research that could resolve the issue by using machine learning techniques, using multi-layer artificial neural networks (ANNs), or so called Deep Nets. The seminal contribution by Hinton et al. (2006) introduced a fast and efficient training algorithm called Deep Learning, and there have been major breakthroughs in machine learning ever since. Economics has not yet benefited from these developments, and therefore we believe that now is the right time to apply Deep Learning and multi-layered neural networks to agent-based models in economics.

China’s strategies for oil, refining and petrochemical production are very different from those in the West, as analysis of Sinopec’s Annual and 20-F Reports confirms. Clearly no western company would ever dream of spending such large amounts of capital for so little reward. But as a State Owned Enterprise, Sinopec’s original mandate was to be a reliable supplier of raw materials to downstream factories, to maintain employment. More recently, the emphasis has changed to providing direct support to employment, through increased exports of refined products into Asian markets and increased self-sufficiency in petrochemicals”.

Con una fuerte deuda a cuestas, la fuentes de acceso a divisas por el suelo y la espiral de los intereses creciendo sin cesar, nuestros países debieron recurrir al Fondo Monetario Internacional. Éste conformaba la alcabala obligatoria para acceder al refinanciamiento bancario internacional, imponiendo las condiciones para ello. Tales condiciones implicaron, entre otras cosas, el desmantelamiento de las barreras arancelarias. Sin protección, las industrias regionales fueron arrasadas por la competencia extranjera. Fue así que la mayor parte de la región debió volver a concentrarse, como en tiempos coloniales, en los recursos naturales.