A Causal Analysis of Life Expectancy at Birth. Evidence from Spain

Background: From a causal point of view, there exists a set of socioeconomic indicators concerning life expectancy. The objective of this paper is to determine the indicators which exhibit a relation of causality with life expectancy at birth. Methods: Our analysis applies the Granger causality test, more specifically its version by Dumitrescu–Hurlin, starting from the information concerning life expectancy at birth and a set of socioeconomic variables corresponding to 17 Spanish regions, throughout the period 2006–2016. To do this, we used the panel data involving the information provided by the Spanish Ministry of Health, Consumer Affairs and Social Welfare (MHCSW) and the National Institute of Statistics (NIS). Results: Per capita income, and the rate of hospital beds, medical staff and nurses Granger-cause the variable “life expectancy at birth”, according to the Granger causality test applied to panel data (Dumitrescu–Hurlin’s version). Conclusions: Life expectancy at birth has become one of the main indicators able to measure the performance of a country’s health system. This analysis facilitates the identification of those factors which exhibit a unidirectional Granger-causality relationship with life expectancy at birth. Therefore, this paper provides useful information for the management of public health resources from the point of view of the maximization of social benefits

Changes in R&D Expenditure and Productivity Growth: A Causal Analysis

A study was conducted to directly test the presence of causal relationship between changes in research and development (R&D) expenditure and productivity growth. Granger causality tests are performed using annual time series data for the period 1956-1983. Three measures of productivity are used -- National Income, National Income per person employed, and National Income per hour of work in the nonresidential business sector. Results show that changes in R&D expenditure affect the growth rate of the 3 productivity measures with different degrees of intensity. National Income per hour of work shows the highest growth rate, with the peak effect occurring in the 2nd year and maintaining a high growth rate through the 4th year. National Income shows the 2nd highest growth rate. The growth rate of all 3 measures decreases significantly in the 4th year. These results suggest that when projecting economic growth, planners should take R&D investment levels into account

Performance assessment and league tables. Comparing like with like

We formulate performance assessment as a problem of causal analysis and outline an approach based on the missing data principle for its solution. It is particularly relevant in the context of so-called league tables for educational, health-care and other public-service institutions. The proposed solution avoids comparisons of institutions that have substantially different clientele (intake).Caliper matching, causal analysis, multiple imputation, non-ignorable assignment, performance indicators, potential outcomes

Synergy and redundancy in the Granger causal analysis of dynamical networks

We analyze by means of Granger causality the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. Whilst we show that fully conditioned Granger causality is not affected by synergy, the pairwise analysis fails to put in evidence synergetic effects. In cases when the number of samples is low, thus making the fully conditioned approach unfeasible, we show that partially conditioned Granger causality is an effective approach if the set of conditioning variables is properly chosen. We consider here two different strategies (based either on informational content for the candidate driver or on selecting the variables with highest pairwise influences) for partially conditioned Granger causality and show that depending on the data structure either one or the other might be valid. On the other hand, we observe that fully conditioned approaches do not work well in presence of redundancy, thus suggesting the strategy of separating the pairwise links in two subsets: those corresponding to indirect connections of the fully conditioned Granger causality (which should thus be excluded) and links that can be ascribed to redundancy effects and, together with the results from the fully connected approach, provide a better description of the causality pattern in presence of redundancy. We finally apply these methods to two different real datasets. First, analyzing electrophysiological data from an epileptic brain, we show that synergetic effects are dominant just before seizure occurrences. Second, our analysis applied to gene expression time series from HeLa culture shows that the underlying regulatory networks are characterized by both redundancy and synergy

MODELING THE SUPPLY CHAIN USING MULTI-TIERED CAUSAL ANALYSIS

Multi-tiered causal analysis is not really a technique but rather a procedure or process that models the push/pull effects of the supply chain by linking a series of multiple regression models together, based on marketing investment strategies and trade investments to retailers. The conceptual design applies in-depth causal analysis to measure the effects of the marketing mix on consumer demand at retail (pull—-consumption/retail sell-through) and links it, via consumer demand, to shipments from the manufacturer (push) to the retailers. This situation is known as a two-tiered model. In the case of more sophisticated distribution systems, a model of three tiers (or more) can be deployed—-incorporating, for example, wholesalers (that is, consumer to retailer to wholesaler to manufacturing plant).Agribusiness,