Objective: We define prediction bias as the systematic error arising from an incorrect prediction of the number of positive COVID cases x-weeks hence when presented with y-weeks of prior, actual data on the same. Our objective is to investigate the importance of an exponential-growth prediction bias (EGPB) in understanding why the COVID-19 outbreak has exploded. To that end, our goal is to document EGPB in the comprehension of disease data, study how it evolves as the epidemic progresses, and connect it with compliance of personal safety guidelines such as the use of face coverings and social distancing. We also investigate whether a behavioral nudge, cost less to implement, can significantly reduce EGPB. Rationale: The scientific basis for our inquiry is the received wisdom that infectious disease spread, especially in the initial stages, follows an exponential function meaning few positive cases can explode into a widespread pandemic if the disease is sufficiently transmittable. If people suffer from EGPB, they will likely make incorrect judgments about their infection risk, which in turn, may lead to reduced compliance of safety protocols. Method: To collect data on prediction bias, we ran an incentivized, experiment on a global, online platform with participation from people in forty-three countries, each at different stages of progression of COVID-19. We also constructed several indices of compliance by surveying participants about their frequency of hand-washing and use of sanitizers and masks; their willingness to pay for masks; their view about the social appropriateness of others’ behavior; and their like/dislike of government responses. The prediction data was used to construct several measures of EGPB. Our experimental design permits us to identify the root of under-prediction as EGPB arising from the general tendency to underestimate the speed at which exponential processes unfold. Results: Respondents make predictions about the path of the disease using a model that is substantially less convex than the actual data generating process. This creates significant EGPB, which, in turn, is significantly and negatively associated with non-compliance with safety measures. The bias is significantly higher for respondents from countries at a later stage relative to those at an early stage of disease progression. A simple behavioral nudge that shows prior data in terms of raw numbers, as opposed to a graph, causally reduces EGPB. Conclusion: Behavioral biases concerning the comprehension of disease data are quantitatively important, and act as severe impediments to effective policy action against the spread of COVID-19. Clear communication of future infection risk via raw numbers could increase the accuracy of risk perception, in turn, facilitating compliance with suggested protective behaviors. © 2020 Elsevier Ltd

Understanding the water metabolism of developing country cities is challenging because of insufficient knowledge of how social, infrastructural and hydrological dimensions are coupled. Using Bengaluru City in India, we demonstrate how fine-resolution data can inform model-based estimates of urban groundwater budgets. Groundwater levels were measured at 154 locations in 2016 and used to estimate model parameters and uncertainty. Total water use was estimated at 1470 million litres per day (MLD). Groundwater pumping meets the majority of this use (827 MLD), followed by utility water supply (643 MLD). Total recharge was estimated at 973 MLD. Natural recharge is a much smaller portion (183 MLD) compared to anthropogenic recharge from leaking water supply and wastewater systems (791 MLD). The city experienced a net negative groundwater balance (40 MLD). Natural recharge and total water use estimates showed lower uncertainty. Spatial variation in these fluxes is described and related to secondary information. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

‘Social distance,’ and ‘social distancing’ have become the linguae francae of our world ravaged by COVID-19. Pandemic related social distancing prescriptions, however, do not operate in a vacuum. How do social distancing strategies for containing the pandemic intersect with extant social divisions? Using a unique census-scale micro-dataset from rural Karnataka (an Indian state as large as France), we meditate on this question by drawing on theoretical insights from multiple disciplines including the intellectual genealogy of ‘social distance’ as a measure of social divisions. Our rich dataset contains independent India’s first census-scale enumeration (n ? 36.5 million) and coding of elementary caste categories (?700 jatis). Our dataset is also the first to combine self-reported jati and religion information. To the best of our knowledge, we present the first systematic large-n portrait of intra-village residential segregation in rural India. Our micro-segregation analysis along jati and religion axes provides evidence for a ‘permanent cordon sanitaire.’ Our analysis also sheds light on how the pandemic intersects with internal migration trajectories in India. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Tubercular prostatitis and tuberculous orchitis are uncommon manifestations of genitourinary tuberculosis. Recto prostatic urethral fistula is also an extremely rare condition with less than 10 cases of tubercular recto-urethral fistula reported in literature. We present a case of post-tubercular recto-prostatic urethral fistula, which was diagnosed by history, clinical examination, micturating cystourethrogram, cystourethroscopy and MRI abdomen pelvis. The patient was treated by simple prostatectomy with rectal repair with omental interposition along with a diversion ileostomy, followed by a course of Anti tubercular drugs following histopathological confirmation. © 2020 The Authors

Quantile regression models are typically used for modeling non-Gaussian outcomes, and such models allow quantile-specific inference. While there exists a vast literature on conditional quantile regression (where the model parameters are estimated precisely for one prefixed quantile level), relatively less work has been reported on joint quantile regression. The challenge in joint quantile regression is to avoid quantile crossing while estimating multiple quantiles simultaneously. In this article, we propose a semi-parametric approach of handling non-Gaussian zero-inflated and incomplete longitudinal outcomes. We use a two-part model for handling the excess zeros, and propose a dynamic joint quantile regression model for the nonzero outcomes. A multinomial probit model is used for modeling the missingness. We develop a Bayesian joint estimation method where the model parameters are estimated through Markov Chain Monte Carlo. The unknown distribution of the outcome can be constructed based on the estimated quantiles. We analyze data from the health and retirement study and model the out-of-pocket medical expenditure through the proposed joint quantile regression method. Simulation studies are performed to assess the practical usefulness and efficiency of the proposed approach compared to the existing methods. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

Agricultural information delivery is the process of delivering research-based information to farmers. Currently, there is low access to agricultural information among Indian farmers, which has resulted in gaps in crop production practices. We present a review of literature about antecedents of agricultural information delivery in India, prominent methods of delivering agricultural information, current access to agricultural information, and issues in Indian agriculture due to low access to agricultural information. The review of literature is followed by an edited transcript from a panel discussion on the topic. The article highlights the complexity involved in delivering information to farmers and some recent developments in the field. © 2020

This round table article discusses the main aspects of analytics education that are necessary to provide the required knowledge and skills to excel in the field of analytics. Analytics education is an interdisciplinary education domain that seeks to develop an interactive problem-solving approach by merging the theoretical knowledge of learning with the data, and by working on real life data in order to drive innovation. The panel discussion report in the latter part of the article describes the key findings and best practices required for analytics education. It is important that an ecosystem be developed with a focus on educating students and working professionals who can demonstrate the knowledge and capability to solve real world problems in this field. We believe that analytics leaders need to continuously refine the analytics curriculum to align with the needs of the users. © 2020

Formal education is often viewed as a proxy for the quality of leaders. Recently, candidates with low education levels have been disqualified from contesting local elections in some states in India. But there is no conclusive evidence linking education to the effectiveness of leaders. Against this backdrop, we investigate whether having educated political leaders in the state legislatures in India improves education outcomes. Using comprehensive data on various outcomes such as learning levels, enrollment, school funding and infrastructure, we find that the effectiveness of educated leaders depends on the initial level of development of the state. Educated leaders yield better education outcomes for their constituents only in those states where the initial level of development is high. There is no impact of educated leaders in less-developed states or in the overall sample. Our identification strategy is based on an instrumental variable that exploits the quasi-experimental election outcomes of close elections between educated and less-educated politicians. The results are consistent throughout various robustness analyses. These findings have implications for recent policy changes mandating minimum education requirements on candidates in two states and similar proposed changes in other states. © 2020 Elsevier B.V.

In this short note, we critically examine two proposals specific to agriculture announced as part of Atmanirbhar Bharat Abhiyan and the subsequent ordinances. We argue that while the investment in infrastructure is a welcome step, market reforms proposed are inadequate to improve the prospects of smallholder farmers. An enabling ecosystem that enhances the market power of farmers must be created for smallholders to take advantage of the reform measures. © 2020 Economic and Political Weekly. All rights reserved.

This study investigates the asymmetric effect of crude oil shocks on emerging sectoral stock indices using a non-parametric causality-in-quantiles approach. This study considers the origin of the oil shock i.e. demand shock or supply shock to investigate its impact on various sectors in mean and volatility. We find that the impact of crude oil is heterogeneous across shocks (demand or supply), market states (bullish, bearish and normal) and to a limited extent across sectors. Observing a similar pattern of crude oil's impact on emerging sectors, we argue that the influence of crude oil shocks extends beyond energy-intensive sectors. © 2020 Elsevier Inc.

[No abstract available]

The accelerated expansion of markets and intense competition have motivated firms to collaborate closely with vendor firms by outsourcing critical aspects of the business, including Information Technology services. We analyze the interaction between a service provider and a client in which they collaborate to deliver services. We assume that revenue generated from the service depends on their efforts. The client determines retail price and marketing efforts while the service provider decides on quality improvement efforts in the services. We analyze the impact of revenue share proportion on the effort exerted by both firms and the impact of the capability of the service provider on the retail pricing of the service. We develop an analytical framework to characterize the actions of the service provider and the client. © 2020 Elsevier B.V.

Mobile payment services hold the potential for financial inclusion in developing economies. Low-income countries are characterized by distinctive conditions like price sensitivity, low digital penetration, high risk of failure, and competitive emerging markets, which further influence mobile payment usage. We develop a research model to identify the contextual facilitators (like price benefit, network externalities, trust, and habit) and barriers (like risk, lack of facilitating conditions, and operational constraints) driving mobile payment usage intention. We test the model using data from 298 survey respondents from India who had adopted and were currently using mobile payment services. The factors that facilitate or constrain users’ intention to continue using mobile payments are essential in understanding the technology’s sustenance and its future in enabling financial inclusion. © 2020, © 2020 Commonwealth Secretariat.

The objective of this paper is to analyse the feasibility and associated benefits of using blockchain technology for the management of corporate social responsibility (CSR) funds which have to be deployed in government-mandated areas of social sector development. The paper addresses the specific problems that exist in the current ways of managing CSR activities by comparing the current model to the proposed blockchain-based framework. The paper further goes on to show how this proposed method could bridge this gap in terms of accountability and transparency using blockchain-enabled technology, while at the same time increasing citizen involvement, i.e., pervasive participation, in the process. © 2020 Inderscience Enterprises Ltd.

We present a comprehensive multi-scale test of the diversity-deficit hypothesis that posits a negative association between diversity and development. We develop a ‘scale-flip hypothesis’ that formalises how the political salience of diversity is contingent on the level of analysis. We also contribute to the political economy of public goods literature using the largest dataset used to date–n ? 1.2 million village-year points from a two-period panel of all villages in the Indian national census data. We find evidence for ‘scale-flip’ so that there is a robust positive association between diversity and public goods at the local level. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

We consider the scheduling problem for biomanufacturing projects that involve multiple tasks and “no-wait” constraints between some of these tasks. The aim is to create schedules that ensure timely delivery of products while enabling schedule revisions to accommodate additional constraints realized during the execution of these projects. We formulate the problem as a mixed-integer linear programming model with the objective of minimizing total tardiness, and propose a dynamic scheduling approach that solves a series of modified mixed-integer linear programming models to revise and improve the schedule. We conduct numerical studies to investigate the performance of this approach and compare its performance to a traditional proactive scheduling approach. In collaboration with biomanufacturing companies, we create a scheduling tool and validate the approach with implementation in an industry setting. © 2020 Elsevier Ltd

Despite minimally invasive neck dissection (MIND) being popular technique, there is a paucity of literature emphasizing its safety and efficacy. In this meta-analysis, we compared the efficacy and safety of MIND over CND techniques in treating oral/head and neck cancer. We systematically searched PubMed, MEDLINE, Embase, from database inception to January, 2019 for the relevant studies comparing MIND and CND. Two independent reviewers performed quality check and data were extracted for primary outcomes to assess length of hospital stay, duration of surgery, intraoperative blood loss and retrieved lymph nodes. Drainage volume and duration, length of incision, satisfaction of scar and safety were the secondary outcomes. We analyzed the outcomes using standard mean differences (SMDs) and the relative risk that were pooled using random effect meta-analysis. Out of 144 studies, 17 met the final inclusion criteria. MIND technique has shown better overall efficacy with outcomes compared to CND except with duration of surgery (SMD 1.82, 95% CI 0.47–3.17). Lesser hospital stay, better nodal yield and less intra-operative blood loss was observed with MIND over CND. Duration and volume of wound drainage was comparably less in MIND with smaller length of incision. Postoperative complications were less and tolerable with MIND approach with superior cosmetic outcomes. MIND via endoscopic or robotic approach is safe and efficacious with equitable oncological outcomes in terms of lymph nodes yield compared to CND, but it requires longer surgery duration. © 2020, Association of Otolaryngologists of India.

Country-level census data are typically collected once every 10 years. However, conflicts, migration, urbanization, and natural disasters can rapidly shift local population patterns. This study demonstrates the feasibility of a "bottom-up"-method to estimate local population density in the between-census years by combining household surveys with contemporaneous geo-spatial data, including village-area and satellite imagery-based indicators. We apply this technique to the case of Sri Lanka using Poisson regression models based on variables selected using the Least Absolute Shrinkage and Selection Operator (LASSO). The model is estimated in villages sampled in the 2012/13 Household Income and Expenditure Survey, and is employed to obtain out-of-sample density estimates in the non-surveyed villages. These estimates approximate the census density accurately and are more precise than other bottom-up studies using similar geo-spatial data. While most open-source population products redistribute census population "top-down" from higher to lower spatial units using areal interpolation and dasymetric mapping techniques, these products become less accurate as the census itself ages. Our method circumvents the problem of the aging census by relying instead on more up-to-date household surveys. The collective evidence suggests that our method is cost effective in tracking local population density with greater frequency in the between-census years.

We study firm level antecedents that drive different motives of internationalization of emerging economy firms. Based on firm's resource based considerations of asset exploitation versus asset augmentation and locational advantages of host countries, we provide a framework to classify the motives of internationalization of emerging economy firms belonging to knowledge intensive industries. Motives of internationalization have been classified into three broad categories-market-seeking, opportunity-seeking, and strategic asset-seeking. We determine motives behind different modes of internationalization- A lliances, acquisitions, and greenfield ventures. Drawing upon the adaptability, amalgamation, and ambidexterity (AAA) advantages from the springboard perspective, we find that firm characteristics like R and D investments, availability of financial slack, firm's ownership structure, and family control shape up its motive of internationalization. © 2020 The International Association for Chinese Management Research.

Does feedback on success in a task increase individuals' beliefs about their chance to succeed in a subsequent, unrelated, task? Does feedback on failure have a symmetric effect? Is the distortion of beliefs, possibly due to motivated beliefs, mistakes in updating or the feeling of having a lucky day, heterogeneous across individuals, in particular according to their status in the society? Conducting an artefactual field experiment in India with participants from different castes, we show that feedback on success in a forced competition in a first task increases winners' self-confidence and competitiveness in the subsequent task. Such feedback spillovers on self-confidence are asymmetric and heterogeneous according to status and more likely for already more confident individuals. © 2020 Elsevier Inc.