A unified framework of trade in value added : physical, monetary, exchange rates, and GHG emissions

Koopman et al. (2014) developed a method to consistently decompose gross exports in value-added terms that accommodate infinite repercussions of international and inter-sector transactions. This provides a better understanding of trade in value added in global value chains than does the conventional gross exports method, which is affected by double-counting problems. However, the new framework is based on monetary input--output (IO) tables and cannot distinguish prices from quantities; thus, it is unable to consider financial adjustments through the exchange market. In this paper, we propose a framework based on a physical IO system, characterized by its linear programming equivalent that can clarify the various complexities relevant to the existing indicators and is proved to be consistent with Koopman's results when the physical decompositions are evaluated in monetary terms. While international monetary tables are typically described in current U.S. dollars, the physical framework can elucidate the impact of price adjustments through the exchange market. An iterative procedure to calculate the exchange rates is proposed, and we also show that the physical framework is also convenient for considering indicators associated with greenhouse gas (GHG) emissions.

Compilation of a regionally-extended inter-country input-output table and its application to global value chain analysis

Studies on the rise of global value chains (GVCs) have attracted a great deal of interest in the recent economics literature. However, due to statistical and methodological challenges, most existing research ignores domestic regional heterogeneity in assessing the impact of joining GVCs. GVCs are supported not only directly by domestic regions that export goods and services to the world market, but also indirectly by other domestic regions that provide parts, components, and intermediate services to final exporting regions. To better understand the nature of a country's position and degree of participation in GVCs, we need to fully examine the role of individual domestic regions. Understanding the domestic components of GVCs is especially important for larger economies such as China, the US, India and Japan, where there may be large variations in economic scale, geography of manufacturing, and development stages at the domestic regional level. This paper proposes a new framework for measuring domestic linkages to global value chains. This framework measures domestic linkages by endogenously embedding a target country's (e.g. China and Japan) domestic interregional input–output tables into the OECD inter-country input–output model. Using this framework, we can more clearly understand how global production is fragmented and extended internationally and domestically.

Exploring the economic value of personal information from firms' financial statements

Currently personal data gathering in online markets is done on a far larger scale and much cheaper and faster than ever before. Within this scenario, a number of highly relevant companies for whom personal data is the key factor of production have emerged. However, up to now, the corresponding economic analysis has been restricted primarily to a qualitative perspective linked to privacy issues. Precisely, this paper seeks to shed light on the quantitative perspective, approximating the value of personal information for those companies that base their business model on this new type of asset. In the absence of any systematic research or methodology on the subject, an ad hoc procedure is developed in this paper. It starts with the examination of the accounts of a number of key players in online markets. This inspection first aims to determine whether the value of personal information databases is somehow reflected in the firms’ books, and second to define performance measures able to capture this value. After discussing the strengths and weaknesses of possible approaches, the method that performs best under several criteria (revenue per data record) is selected. From here, an estimation of the net present value of personal data is derived, as well as a slight digression into regional differences in the economic value of personal information.

Enhanced methionine levels and increased nutritive value of seeds of transgenic lupins (Lupinus angustifolius L.) expressing a sunflower seed albumin gene

With the aim of improving the nutritive value of an important grain legume crop, a chimeric gene specifying seed-specific expression of a sulfur-rich, sunflower seed albumin was stably transformed into narrow-leafed lupin (Lupinus angustifolius L.). Sunflower seed albumin accounted for 5% of extractable seed protein in a line containing a single tandem insertion of the transferred DNA. The transgenic seeds contained less sulfate and more total amino acid sulfur than the nontransgenic parent line. This was associated with a 94% increase in methionine content and a 12% reduction in cysteine content. There was no statistically significant change in other amino acids or in total nitrogen or total sulfur contents of the seeds. In feeding trials with rats, the transgenic seeds gave statistically significant increases in live weight gain, true protein digestibility, biological value, and net protein utilization, compared with wild-type seeds. These findings demonstrate the feasibility of using genetic engineering to improve the nutritive value of grain crops.

Unlocking the Value of Variation in CKD Prevalence

Peer reviewed

Parametric & nonparametric approaches to event studies: an application to a hotel’s market value

The main objective of this paper is twofold: on the one hand, to analyse the impact that the announcement of the opening of a new hotel has on the performance of its chain by carrying out an event study, and on the other hand, to compare the results of two different approaches to this method: a parametric specification based on the autoregressive conditional heteroskedasticity models to estimate the market model, and a nonparametric approach, which implies employing Theil’s nonparametric regression technique, which in turn, leads to the so-called complete nonparametric approach to event studies. The results that the empirical application arrives at are noteworthy as, on average, the reaction to such news releases is highly positive, both approaches reaching the same level of significance. However, a word of caution must be said when one is not only interested in detecting whether the market reacts, but also in obtaining an exhaustive calculation of the abnormal returns to further examine its determining factors.

Pitfalls on computing liquid-liquid phase equilibria using the k-value method

Poster presented in the 11th Mediterranean Congress of Chemical Engineering, Barcelona, October 21-24, 2008.

The curricular value of mathematics in non-mathematics degree

Higher education should provide the acquisition of skills and abilities that allow the student to play a full and active role in society. The educational experience should offer a series of conceptual, procedural and attitudinal contents that encourage “learning to know, learning to do, learning to be and learning to live together”. It is important to consider the curricular value of mathematics in the education of university undergraduates who do not intend to study mathematics but for whom the discipline will serve as an instrumental. This work discusses factors that form part of the debate on the curricular value of mathematics in non-mathematics degrees.

The effect of innovation on hotel market value

The purpose of this article is to analyze the effect of hotel innovations on firm value. Specifically, this study fills a research gap in the previous literature by examining this effect through market value and by distinguishing the potentially different impacts of distinct innovation types: product, process, organization and marketing. This research contributes to consolidating the empirical evidence of hotel innovation and performance by analyzing whether distinct types of innovation lead to different levels of results. The findings show that innovations are perceived to have a positive impact on the future sales of the company: in a four-day period (0,+3), there is an increase in stock exchange returns of 1.53%. In terms of innovation types, process and marketing innovations are found to have a higher positive effect on hotel market value than product and organization innovations; which is explained by potential cost differences among innovations.

SOA-Based Model for Value-Added ITS Services Delivery

Integration is currently a key factor in intelligent transportation systems (ITS), especially because of the ever increasing service demands originating from the ITS industry and ITS users. The current ITS landscape is made up of multiple technologies that are tightly coupled, and its interoperability is extremely low, which limits ITS services generation. Given this fact, novel information technologies (IT) based on the service-oriented architecture (SOA) paradigm have begun to introduce new ways to address this problem. The SOA paradigm allows the construction of loosely coupled distributed systems that can help to integrate the heterogeneous systems that are part of ITS. In this paper, we focus on developing an SOA-based model for integrating information technologies (IT) into ITS to achieve ITS service delivery. To develop our model, the ITS technologies and services involved were identified, catalogued, and decoupled. In doing so, we applied our SOA-based model to integrate all of the ITS technologies and services, ranging from the lowest-level technical components, such as roadside unit as a service (RS S), to the most abstract ITS services that will be offered to ITS users (value-added services). To validate our model, a functionality case study that included all of the components of our model was designed.

A Quantile-Based Probabilistic Mean Value Theorem

For non-negative random variables with finite means we introduce an analogous of the equilibrium residual-lifetime distribution based on the quantile function. This allows us to construct new distributions with support (0, 1), and to obtain a new quantile-based version of the probabilistic generalization of Taylor's theorem. Similarly, for pairs of stochastically ordered random variables we come to a new quantile-based form of the probabilistic mean value theorem. The latter involves a distribution that generalizes the Lorenz curve. We investigate the special case of proportional quantile functions and apply the given results to various models based on classes of distributions and measures of risk theory. Motivated by some stochastic comparisons, we also introduce the “expected reversed proportional shortfall order”, and a new characterization of random lifetimes involving the reversed hazard rate function.

Dynamic D-Vine copula model with applications to Value-at-Risk (VaR)

Regular vine copulas are multivariate dependence models constructed from pair-copulas (bivariate copulas). In this paper, we allow the dependence parameters of the pair-copulas in a D-vine decomposition to be potentially time-varying, following a nonlinear restricted ARMA(1,m) process, in order to obtain a very flexible dependence model for applications to multivariate financial return data. We investigate the dependence among the broad stock market indexes from Germany (DAX), France (CAC 40), Britain (FTSE 100), the United States (S&P 500) and Brazil (IBOVESPA) both in a crisis and in a non-crisis period. We find evidence of stronger dependence among the indexes in bear markets. Surprisingly, though, the dynamic D-vine copula indicates the occurrence of a sharp decrease in dependence between the indexes FTSE and CAC in the beginning of 2011, and also between CAC and DAX during mid-2011 and in the beginning of 2008, suggesting the absence of contagion in these cases. We also evaluate the dynamic D-vine copula with respect to Value-at-Risk (VaR) forecasting accuracy in crisis periods. The dynamic D-vine outperforms the static D-vine in terms of predictive accuracy for our real data sets.

Prognostic Value of Secretoneurin in Critically Ill Patients With Infections.

OBJECTIVES Secretoneurin is produced in neuroendocrine cells, and the myocardium and circulating secretoneurin levels provide incremental prognostic information to established risk indices in cardiovascular disease. As myocardial dysfunction contributes to poor outcome in critically ill patients, we wanted to assess the prognostic value of secretoneurin in two cohorts of critically ill patients with infections. DESIGN Two prospective, observational studies. SETTING Twenty-four and twenty-five ICUs in Finland. PATIENTS A total of 232 patients with severe sepsis (cohort #1) and 94 patients with infections and respiratory failure (cohort #2). INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS We measured secretoneurin levels by radioimmunoassay in samples obtained early after ICU admission and compared secretoneurin with other risk indices. In patients with severe sepsis, admission secretoneurin levels (logarithmically transformed) were associated with hospital mortality (odds ratio, 3.17 [95% CI, 1.12-9.00]; p = 0.030) and shock during the hospitalization (odds ratio, 2.17 [1.06-4.46]; p = 0.034) in analyses that adjusted for other risk factors available on ICU admission. Adding secretoneurin levels to age, which was also associated with hospital mortality in the multivariate model, improved the risk prediction as assessed by the category-free net reclassification index: 0.35 (95% CI, 0.06-0.64) (p = 0.02). In contrast, N-terminal pro-B-type natriuretic peptide levels were not associated with mortality in the multivariate model that included secretoneurin measurements, and N-terminal pro-B-type natriuretic peptide did not improve patient classification on top of age. Secretoneurin levels were also associated with hospital mortality after adjusting for other risk factors and improved patient classification in cohort #2. In both cohorts, the optimal cutoff for secretoneurin levels at ICU admission to predict hospital mortality was ≈ 175 pmol/L, and higher levels were associated with mortality also when adjusting for Simplified Acute Physiology Score II and Sequential Organ Failure Assessment scores. CONCLUSIONS Secretoneurin levels provide incremental information to established risk indices for the prediction of mortality and shock in critically ill patients with severe infections.

Joint development and value capture in Los Angeles : local policy formulation /

"DOT-I-83-23."

Value engineering study of highway shoulder maintenance /

FCP category 7.