Nonparametric approach to rank global petroleum business opportunities

Crude oil continues to be one of the significant energy sources. Several countries do not have enough indigenous oil and gas resources. These countries resort to overseas business of Exploration and Production (E&P) of oil to secure a stable supply. Profitability, risk and growth guide overseas investment decisions. Selection of overseas investment opportunities are critical for a firm because of uncertainty in identifying and quantifying the attendant geological, commercial, social and political risks as well as return on investment. To secure overseas oil acreage, business entities intend to invest in overseas E&P destination having reasonable petroleum reserve, favorable contract terms (fiscal terms), well-developed infrastructure, sound legal system, minimum country risk (CR) (economic, social and political) and facilitate relative ease to do business in that country. The countries have varied mix of these parameters, and it leads to growing concern to screen and rank overseas investment opportunities. Methodologies to rank global opportunities should take into consideration the risk factors such as petroleum potential, infrastructure, geo-political scenario, contract terms, etc. We coin the term for the numerical rank as Globalization Index (GI), which is a function of the factors considered to affect the decision of a business entity in screening the global destinations for venturing in to E&P business of crude oil. This paper is an attempt to model these factors by invoking Alternating Conditional Expectation methodology to find GI.     

Key considerations for choosing a statistical method to deal with incomplete treatment adherence in pragmatic trials

Pragmatic trials offer practical means of obtaining real-world evidence to help improve decision-making in comparative effectiveness settings. Unfortunately, incomplete adherence is a common problem in pragmatic trials. The commonly used methods in randomized control trials often cannot handle the added complexity imposed by incomplete adherence, resulting in biased estimates. Several naive methods and advanced causal inference methods (e.g., inverse probability weighting and instrumental variable-based approaches) have been used in the literature to deal with incomplete adherence. Practitioners and applied researchers are often confused about which method to consider under a given setting. This current work is aimed to review commonly used statistical methods to deal with non-adherence along with their key assumptions, advantages, and limitations, with a particular focus on pragmatic trials. We have listed the applicable settings for these methods and provided a summary of available software. All methods were applied to two hypothetical datasets to demonstrate how these methods perform in a given scenario, along with the R codes. The key considerations include the type of intervention strategy (point treatment settings, where treatment is administered only once versus sustained treatment settings, where treatment has to be continued over time) and availability of data (e.g., the extent of measured or unmeasured covariates that are associated with adherence, dependent confounding impacted by past treatment, and potential violation of assumptions). This study will guide practitioners and applied researchers to use the appropriate statistical method to address incomplete adherence in pragmatic trial settings for both the point and sustained treatment strategies.     

Correction to: Assessing recall bias and measurement error in high-frequency social data collection for human-environment research

Population and Environment - The original version of this article unfortunately contained a mistake. The name of “Md. Ehsanul Haque Tamal” is now corrected in the author group of this...     

The nexus between extreme weather events,sexual violence,and early marriage: a study of vulnerable populations in Bangladesh

Population and Environment - This study aims to explore whether a relationship exists between extreme weather events, sexual violence, and early marriage. We selected two districts in Bangladesh...     

Assessing recall bias and measurement error in high-frequency social data collection for human-environment research

Population and Environment - A major impediment to understanding human-environment interactions is that data on social systems are not collected in a way that is easily comparable to natural...     

Enrolling girls without learning: Evidence from public schools in Afghanistan

While more girls are now attending school in Afghanistan than prior to 2010, there is a lack of evidence on how school attendance is helping their cognitive development. We use data from a large sample of all‐girls state‐funded school students to estimate for the first time the “learning profile” for Afghanistan. Students enrolled in grades 4 to 9 were assessed using the Early Grade Reading Assessment (EGRA) and the Early Grade Mathematics Assessment (EGMA) test items. We find that higher grade progression (particularly in grades 6–9) leads to almost no gain in numeracy (addition and subtraction) skills that should have been achieved in early grades. Similar results follow from the analysis of student performance in the EGRA test, particularly in oral reading fluency (correct words read per minute) and oral reading comprehension (number of correct answers out of 12). These findings warn that simply enrolling girls in school without improving the relationship between grade completion and learning is unlikely to transform the lives of women in Afghanistan.     

Comparing the transpirational and shading effects of two contrasting urban tree species

Urban Ecosystems - Urban trees are getting increasing attention as a tool to mitigate urban heat island effects. A more functional and quantitative view of transpirational and shading effect,...     

Beliefs About Others’ Perceptions—Gender Typicality: Scale Development and Relationships to Gender Nonconformity,Sexual Orientation,and Well-Being

The purpose of this study was to develop a measure of “other-perceived” gender typicality and test its relationship with gender nonconformity, sexual orientation, and psychological distress. Data on these variables were collected from 632 participants (142 heterosexual men, 77 gay men, 50 bisexual men, 174 heterosexual women, 85 lesbian women, and 104 bisexual women). We designed a Beliefs About Others’ Perceptions—Gender Typicality (BOP-GT) scale on which participants reported how masculine or feminine they thought other people perceived their gendered behavior, interests, appearance, and activities. Factor analysis identified five factors: appearance, emotional response, sporting interests, occupational interests, and interpersonal style. Recalled childhood gender nonconformity (CGN) was significantly associated with each BOP-GT subscale. Heterosexual men rated that other people perceived them to be more masculine scoring on the BOP-GT compared to heterosexual women. Gay men and lesbian women showed cross-sexed patterns on the BOP-GT total scale, appearance, emotional response, and sporting interests scores. Bisexual men and women were generally no different from the other male and female groups, respectively. Bisexual women had greater distress scores, and the BOP-GT emotional response subscale was associated with somewhat greater distress scores. The BOP-GT measure may have use in future research on understanding gendered self-concepts.     

Measuring the determinants of relative economic performance of rural areas

This paper examines the determinants of economic performance of 149 English rural Local Authority Districts (LADs). A Three Stage Least Square (3SLS) estimation procedure was employed to jointly determine the influence of a wide range of indicators representing economic, human, cultural and environmental capital, as well as less tangible or ‘soft’ factors on three distinct components of economic performance: productivity, employment and labor market participation. The results reveal that a range of facets of economic and human capital, including the three key drivers of productivity (skills, investment and enterprise), spatial factors (peripherality and accessibility), and other key factors (economic structure, government infrastructure, road infrastructure, and occupational health), are significant determinants of economic performance in rural areas. This study is of value since it proposes a method for modelling the determinants of economic performance which is transferable to other environments in the UK and beyond. In addition, it proposes a set of benchmarks of economic performance using readily available data, and highlights some implications for rural policy and several avenues for future research.     

Stochastic differential equation systems for an SIS epidemic model with vaccination and immigration

Two Itô stochastic differential equation (SDE) systems are constructed for a Susceptible-Infected-Susceptible epidemic model with temporary vaccination. A constant number of new members enter the population and total size of the population is variable. Some conditions for disease extinction in the stochastic models are established and compared with conditions in deterministic one. It is shown that the two stochastic models are equivalent in the sense that their solutions come from same distribution. In addition, the SDE models are simulated and the equivalence of the two stochastic models is confirmed by numerical examples. The probability distribution for extinction is also obtained numerically, provided there exists a probability for disease persistence whereas the expected duration of epidemic is acquired when extinction occurs with probability 1.