Literacy skills seem to fuel literacy enjoyment, rather than vice versa

Children who like to read and write tend to be better at it. This association is typically interpreted as enjoyment impacting engagement in literacy activities, which boosts literacy skills. We fitted direction-of-causation models to partial data of 3,690 Finnish twins aged 12. Literacy skills were rated by the twins’ teachers and literacy enjoyment by the twins themselves. A bivariate twin model showed substantial genetic influences on literacy skills (70%) and literacy enjoyment (35%). In both skills and enjoyment, shared-environmental influences explained about 20% in each. Direction-of-causation modelling showed that skills impacted enjoyment. The influence in the other direction was zero. The genetic influences on skills influenced enjoyment, via the skills--> enjoyment path. This indicates active gene-environment correlation: children with an aptitude for good literacy skills are more likely to seek out literacy activities. To a lesser extent, it was also the shared-environmental influences on children’s skills that propagated to influence children’s literacy enjoyment. Environmental influences that foster children’s literacy skills (e.g., families and schools), also foster children’s love for reading and writing. These findings underline the importance of nurturing children’s literacy skills.

The impact of inpatient bed capacity on length of stay

Objective Large reductions in inpatient length of stay and inpatient bed supply have occurred across health systems in recent years. However, the direction of causation between length of stay and bed supply is often overlooked. This study examines the impact of changes to inpatient bed supply, as a result of recession-induced healthcare expenditure changes, on emergency inpatient length of stay in Ireland between 2010 and 2015. Study design We analyse all public hospital emergency inpatient discharges in Ireland from 2010 to 2015 using the administrative Hospital In-Patient Enquiry dataset. We use changes to inpatient bed supply across hospitals over time to examine the impact of bed supply on length of stay. Linear, negative binomial, and hospital–month-level fixed effects models are estimated. Results U-shaped trends are observed for both average length of stay and inpatient bed supply between 2010 and 2015. A consistently large positive relationship is found between bed supply and length of stay across all regression analyses. Between 2010 and 2012 while length of stay fell by 6.4%, our analyses estimate that approximately 42% (2.7% points) of this reduction was associated with declines in bed supply. Conclusion Changes in emergency inpatient length of stay in Ireland between 2010 and 2015 were closely related to changes in bed supply during those years. The use of length of stay as an efficiency measure should be understood in the contextual basis of other health system changes. Lower length of stay may be indicative of the lack of resources or available bed supply as opposed to reduced demand for care or the shifting of care to other settings.

How Perspectives of a System Change Based on Exposure to Positive or Negative Evidence

The system that shapes a problem can be represented using a map, in which relevant constructs are listed as nodes, and salient interrelationships are provided as directed edges which track the direction of causation. Such representations are particularly useful to address complex problems which are multi-factorial and may involve structures such as loops, in contrast with simple problems which may have a clear root cause and a short chain of causes-and-effects. Although students are often evaluated based on either simple problems or simplified situations (e.g., true/false, multiple choice), they need systems thinking skills to eventually deal with complex, open-ended problems in their professional lives. A starting point is thus to construct a representation of the problem space, such as a causal map, and then to identify and contrast solutions by navigating this map. The initial step of abstracting a system into a map is challenging for students: unlike seasoned experts, they lack a detailed understanding of the application domain, and hence struggle in capturing its key concepts and interrelationships. Case libraries can remedy this disadvantage, as they can transfer the knowledge of experts to novices. However, the content of the cases can impact the perspectives of students. For example, their understanding of a system (as reflected in a map) may differ when they are exposed to case studies depicting successful or failed interventions in a system. Previous studies have abundantly documented that cases can support students, using a variety of metrics such as test scores. In the present study, we examine the ways in which the representation of a system (captured as a causal map) changes as a function of exposure to certain types of evidence. Our experiments across three cohorts at two institutions show that providing students with cases tends to broaden their coverage of the problem space, but the knowledge afforded by the cases is integrated in the students’ maps differently depending on the type of case, as well as the cohort of students.

(Testing the causality between Exports, Oil Rent, and Gross Domestic Product in Kuwait: An empirical study for the period (1980 -2017: اختبار السببية بين الصادرات وإيجار النفط والناتج المحلي الإجمالي في الكويت: دراسة تجريبية للفترة (1980 - 2017)

The study of the relationship between oil prices, exports, and economic growth has captured the interest of economists for decades, especially for oil-exporting countries. This study intends to determine the relationship and the direction of causation among oil rent, exports, and economic growth in the short-run and long-run, and the causation effects among the variables. Time series data collected from both the world bank and International Monetary Fund databases for the period 1980 to 2017. The series tested for stationarity, cointegration, and causation using the unit root, cointegration, and pairwise granger causality tests. The results revealed that there was a long-run association among the variables. On the other hand, causation only exists between export and economic growth in both directions. Eviews10 statistical software used for the analysis.

Performance pay and low-grade stress: An experimental study

BACKGROUND: Although recent economics literature suggests a link between performance-related pay (PRP) and ill health, this finding is contested on the grounds that this link is plagued by endogeneity between the two variables of interest. OBJECTIVE: This study investigates the adverse effects of performance-related pay on stress which is an important determinant of physical health. METHODS: Forty subjects were randomly assigned to two equal groups: either being paid by performance or being paid a flat fee. Both objective (saliva samples to measure cortisol elevation) and subjective (self-reported stress level) measures of stress were obtained before and after participation in the experiment. This experimental methodology purges the effects of self-selection into performance pay and identifies the direction of causation from performance-related pay to stress which is measured by cortisol levels. RESULTS: Those who were paid for their performance experienced higher levels of stress, both in terms of perceived stress and in terms of objectively measured cortisol levels, compared to those who were paid a flat fee for minimum performance. CONCLUSIONS: Performance-related pay induces objectively measurable stress. Self-reported stress levels and the objective stress measure obtained by measuring cortisol move in a similar direction for the PRP and non-PRP groups, but only the cortisol group shows statistically significant differences between the PRP and non-PRP. This also suggests that individuals underestimate the stress caused by performance pay.

Time-frequency linkages of international housing markets and macroeconomic drivers

Purpose The studies on international housing markets have not modeled frequency domain and focused only on the time domain. The purpose of the present research is to fill this gap by using the state-of-the-art econometric technique of wavelets to understand how differences in the horizon of analysis across time impact international housing markets’ relationship with some of the key macroeconomic variables. The purpose is to also analyze the direction of causation in the relationships. Design/methodology/approach The author uses the novel time–frequency analysis of international housing markets’ linkages to the macroeconomic drivers. Unlike conventional approaches that do not distinguish between time and frequency domain, the author uses wavelets to study house prices’ relationship with its drivers in the time–frequency space. The novelty of the approach also allows gaining insights into the debates that deal with the direction of causation between house price changes and macroeconomic variables. Findings Results show that the relationship between house prices and key macroeconomic indicators varies significantly across countries, time, frequencies and the direction of causation. House prices are most related to interest rates at the higher frequencies (short-run) and per capita income growth at the lower frequencies (long-run). The role of industrial production and income growth has switched over time at lower frequencies, particularly, in Finland, France, Sweden and Japan. The stock market’s nexus with the housing market is significant mainly at high to medium frequencies around the recent financial crisis. Research limitations/implications The present research implies that in contrast to the existing approaches that are limited to the only time domain, the frequency considerations are equally, if not more, important. Practical implications Results show that interested researchers and analysts of international housing markets need to account for the both horizon and time under consideration. Because the factors that drive high-frequency movements in housing market are very different from low-frequency movements. Furthermore, these roles vary over time. Social implications The insights from the present study suggest policymakers interested in bringing social change in the housing markets need to account for the time–frequency dynamics found in this study. Originality/value The paper is novel on at least two dimensions. First, to the best of the author’s knowledge, this study is the first to propose the use of a time–frequency approach in modeling international housing market dynamics. Second, unlike present studies, it is the first to uncover the direction of causation between house prices and economic variables for each frequency at every point of time.

Two genetic analyses to elucidate causality between body mass index and personality

Background/Objectives: Many personality traits correlate with BMI, but the existence and direction of causal links between them are unclear. If personality influences BMI, knowing this causal direction could inform weight management strategies. Knowing that BMI instead influences personality would contribute to a better understanding of the mechanisms of personality development and the possible psychological effects of weight change. We tested the existence and direction of causal links between BMI and personality.Subjects/Methods: We employed two genetically informed methods. In Mendelian randomization, allele scores were calculated to summarize genetic propensity for the personality traits Neuroticism, Worry, and Depressive Affect and used to predict BMI in an independent sample (N=3 541). Similarly, an allele score for BMI was used to predict eating-specific and domain-general phenotypic personality scores (PPSs; aggregate scores of personality traits weighted by BMI). In a Direction of Causation analysis, twin data from five countries (N=5 424) were used to assess the fit of four alternative models: PPSs influencing BMI, BMI influencing PPSs, reciprocal causation, and no causation.Results: In Mendelian randomization, the allele score for BMI predicted domain-general (β=0.05; 95% CI 0.02, 0.08; P=.003) and eating-specific PPS (β=0.06; 95% CI 0.03, 0.09; P<.001). The allele score for Worry also predicted BMI (β=-0.05; 95% CI -0.08, -0.02; P<.001), while those for Neuroticism and Depressive Affect did not (P≥.459). In Direction of Causation, BMI similarly predicted domain-general (β=0.21; 95% CI 0.18, 0.24; P<.001) and eating-specific personality traits (β=0.19; 95% CI 0.16, 0.22; P<.001), suggesting causality from BMI to personality traits. In exploratory analyses, links between BMI and domain-general personality traits appeared reciprocal for higher-weight individuals (BMI>~25).Conclusions: Although both genetic analyses suggested an influence of BMI on personality traits, it is not yet known if weight management interventions could influence personality. Personality traits may influence BMI in turn, but effects in this direction appeared weaker.

Network explanations and explanatory directionality

Network explanations raise foundational questions about the nature of scientific explanation. The challenge discussed in this article comes from the fact that network explanations are often thought to be non-causal, i.e. they do not describe the dynamical or mechanistic interactions responsible for some behaviour, instead they appeal to topological properties of network models describing the system. These non-causal features are often thought to be valuable precisely because they do not invoke mechanistic or dynamical interactions and provide insights that are not available through causal explanations. Here, I address a central difficulty facing attempts to move away from causal models of explanation; namely, how to recover the directionality of explanation. Within causal models, the directionality of explanation is identified with the direction of causation. This solution is no longer available once we move to non-causal accounts of explanation. I will suggest a solution to this problem that emphasizes the role of conditions of application. In doing so, I will challenge the idea that sui generis mathematical dependencies are the key to understand non-causal explanations. The upshot is a conceptual account of explanation that accommodates the possibility of non-causal network explanations. It also provides guidance for how to evaluate such explanations. This article is part of the theme issue ‘Unifying the essential concepts of biological networks: biological insights and philosophical foundations’.