Missing millions: undercounting urbanization in India

Population and Environment - The measurement and characterization of urbanization crucially depends upon defining what counts as urban. The government of India estimates that only 31% of the...     

BIG DATA AND BIG CITIES: THE PROMISES AND LIMITATIONS OF IMPROVED MEASURES OF URBAN LIFE

New, “big data” sources allow measurement of city characteristics and outcome variables at higher collection frequencies and more granular geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big urban data has the most value for the study of cities when it allows measurement of the previously opaque, or when it can be coupled with exogenous shocks to people or place. We describe a number of new urban data sources and illustrate how they can be used to improve the study and function of cities. We first show how Google Street View images can be used to predict income in New York City, suggesting that similar imagery data can be used to map wealth and poverty in previously unmeasured areas of the developing world. We then discuss how survey techniques can be improved to better measure willingness to pay for urban amenities. Finally, we explain how Internet data is being used to improve the quality of city services. (JEL R1, C8, C18)     

Improving patients’ satisfaction in a mobile hospital using Lean Six Sigma – a design-thinking intervention

Abstract

Lean Six Sigma (LSS) that offers a unique integration of Lean and Six Sigma has been accepted globally across services sectors. Previous studies that examined the application of LSS in healthcare were steered in traditional hospital settings. Apparently, there are no studies that highlight the application of LSS in mobile hospitals, an emerging trend in the healthcare sector. The aim of this article is to explore the applicability of LSS in a mobile hospital. The authors present a case study of improving patients’ satisfaction in a mobile hospital, through reducing turnaround time. Design-thinking intervention to LSS helped resolve a real-time problem using Define-Measure-Analyze-Design-Verify roadmap. This article contributes to the ‘healthcare quality” body of knowledge and is novel as it is the first of its kind of LSS application in a mobile hospital underpinning Design thinking. This study is unique as it demonstrates academia–industry collaboration in solving an organizational problem.     

Challenges and opportunities of business process outsourcing in India

We identify managerial challenges and opportunities faced by business process outsourcing (BPO) firms in India by interviewing 28 executives in lower (n = 10), middle (n = 11), and upper management (n = 7) in 15 BPO firms. Content analysis of the responses revealed human resources and organizationrelated challenges as the most critical issues. SWOT (Strengths, Weaknesses, Opportu-nities, Threats) analysis uncovered future opportunities, e.g., Knowledge Process Outsourcing. Secondary data, from four global research agencies and a national trade association, supported our findings. Organizational changes to address challenges and to exploit opportunities are explored.     

Financial insecurity and risk experiences of justice involved persons with severe mental illness

More information is needed about the financial experiences of justice-involved persons with severe mental illness. Qualitative and quantitative methods were used to examine the financial resources, financial risk experiences, and financial literacy of a random sample of 12 mental health court participants. Mental health court participants had limited financial resources, frequently ran out of money, and had engaged very few financial tools to manage their money. Justice-involved persons with severe mental illness need financial management interventions to help develop strategies for managing depleted resources and to develop the financial resources needed to live in community settings.     

Estimation and prediction for a Burr type-III distribution with progressive censoring

We consider estimation of unknown parameters and reliability characteristics of a Burr type-III distribution under progressive censoring. Predictive estimates for censored observations and the associated prediction intervals are also obtained. We derive maximum-likelihood estimators of unknown quantities using the EM algorithm and then also obtain the observed Fisher information matrix. We provide various Bayes estimators for unknown parameters under the squared error loss function. Highest posterior density and asymptotic intervals are also constructed. We evaluate performance of proposed methods using simulations. Finally, an illustrative example is presented in support of the methods discussed.     

Modeling and forecasting persistent financial durations

This article introduces the Markov-Switching Multifractal Duration (MSMD) model by adapting the MSM stochastic volatility model of Calvet and Fisher (2004) to the duration setting. Although the MSMD process is exponential β-mixing as we show in the article, it is capable of generating highly persistent autocorrelation. We study, analytically and by simulation, how this feature of durations generated by the MSMD process propagates to counts and realized volatility. We employ a quasi-maximum likelihood estimator of the MSMD parameters based on the Whittle approximation and establish its strong consistency and asymptotic normality for general MSMD specifications. We show that the Whittle estimation is a computationally simple and fast alternative to maximum likelihood. Finally, we compare the performance of the MSMD model with competing short- and long-memory duration models in an out-of-sample forecasting exercise based on price durations of three major foreign exchange futures contracts. The results of the comparison show that the MSMD and the Long Memory Stochastic Duration model perform similarly and are superior to the short-memory Autoregressive Conditional Duration models.