‘The Ethics of Managerial Subjectivity’

This paper examines ethics in organizations in relation to the subjectivity of managers. Drawing on the work of Michel Foucault we seek to theorize ethics in terms of the meaning of being a manager who is an active ethical subject. Such a manager is so in relation to the organizational structures and norms that govern the conduct of ethics. Our approach locates ethics in the relation between individual morality and organizationally prescribed principles assumed to guide personal action. In this way we see ethics as a practice that is powerfully intertwined in an individual’s freedom to make choices about what to do and who to be, and the organizational context in which those choices are situated, framed and governed.     

Mortgage Prepayment and Default Decisions: A Poisson Regression Approach

This paper uses an extensive and geographically dispersed sample of single-family fixed rate mortgages to assess the prepayment and default behavior of individual homeowners. We make use of Poisson regression to efficiently estimate the parameters of a proportional hazards model for prepayment and default decisions. Poisson regression for grouped survival data has several advantages over partial likelihood methods. First, when dealing with time-dependent covar-iates, it is considerably more efficient in terms of computations. Second, it is possible to estimate full-hazard models which include, for example, functions of time as well as multiple time scales (i.e., age of the loan and calendar time), in a much more straightforward manner than partial likelihood methods for un-grouped data. Third, Poisson regression can be used to estimate non-proportional hazards models such as additive excess risk specifications. Taken together, our data and estimation methodology allow us to obtain a better understanding of the economic factors underlying prepayment and default decisions.     

TV white spaces exploitation through a bicameral geo-location database

The exploitation of TV white spaces can meet the increasing demand for spectrum resources and create opportunities for deploying a variety of wireless services in a flexible manner. However, uncertainties from technologies, business models and regulatory policies hinder the take-off of TV white spaces exploitation. This paper proposes a bicameral (or two-chambered) geo-location database, which allows/supports both free and paid access to the TV white spaces: i.e., one chamber supports free access through opportunistic or geo-location database access; and the other chamber supports paid usage through secondary spectrum trading. Consequently, four technological scenarios for the acquisition of TV white spaces emerge, namely: sensing only, joint sensing and geo-location database access, geo-location database access only, and broker based secondary spectrum trading. An analysis of these scenarios is performed based on a theoretical framework for emerging technology evaluation while considering technological, business models and regulatory dimensions. The analyses show that free and paid access to TV white space complement each other; and that despite considerable infrastructure costs, the bicameral geo-location database is positioned to create viable TV white spaces exploitation value chains; hence have the most optimal technological, business and regulatory prospects.     

Intuition and rationality in intrapreneurship and innovation outputs: The case of health professionals in primary health care

The aim of this article is to explore the role of cognitive styles and intrapreneurship in health professionals’ innovation outputs, as well the mediated effect of intrapreneurship between cognitive styles and innovation output. This study used the survey method of data collection, through a self-administered questionnaire. Partial least square structural equation modelling method was used to analyse the result of the sample of 209 professionals of primary health care providers in Northern Portugal. Our findings reveal that cognitive style plays a significant role in intrapreneurship and innovation outputs, which are mediated by intrapreneurship. In particular, health care professionals with the rational cognitive style are likely to be more of a intrapreneur and innovative as compared to those with a intuitive cognitive style. Promoting intrapreneurship is crucial for successful innovation outputs. This study reveals that an understanding of the cognitive style of the health care professionals can help managers allocate appropriate individuals to different healthcare tasks. Our primary contribution to theory has been to highlight the importance of cognitive styles in intrapreneurship and innovation within the context of primary health care organizations.

     

Monetary,fiscal, and structural drivers of inflation in Ethiopia: new empirical evidence from time series analysis

This study empirically investigates the drivers of inflation in Ethiopia using monthly data over the period July 1998 to September 2020. It explores short-run and long-run effects of domestic and external determinants of inflation—including demand-side, supply-side, and structural factors—using the cointegration and vector error-correction methodology. Four measures of inflation are considered: cereals, food, nonfood, and all-items Consummer Price Index (CPI) inflation. A key contribution to the existing literature is the investigation of the role of the fiscal sector in modeling inflation, a topic that has been neglected in the existing studies on inflation in Ethiopia. The empirical results show that disequilibria in the monetary sector, grains sector, and food markets have long-run effects on inflation. In the short run, inflation is driven by structural factors (notably, cereal output gaps and imported inflation) as well as demand-side factors (notably, money growth and public sector borrowing). The results hold when analysis is limited to the high growth period from 2005 onward, following the end of the International Monetary Fund (IMF) program in the country. The evidence provides valuable insights in the context of ongoing macroeconomic policy reforms in Ethiopia.     

Machine learning advances for time series forecasting

In this paper, we survey the most recent advances in supervised machine learning (ML) and high-dimensional models for time-series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feedforward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of ML in economics and finance and provide an illustration with high-frequency financial data.     

A narrative exploratory study on the perspectives about physical exercise practise in a sample of Portuguese elderly

Quality & Quantity - The overall prevalence of inactivity among individuals aged 55 or older in Portugal is the highest in Europe therefore it’s important to develop effective strategies...     

“When Life Gives You Lemons”: Using Cross-Sectional Surveys to Identify Chronic Poverty in the Absence of Panel Data

At the core of poverty eradication is the need to eliminate that poverty that is persistent over time (chronic poverty). Unfortunately, traditional approaches to identifying chronic poverty require longitudinal data that is rarely available. In its absence, this paper proposes an alternative approach that only requires 1 year of cross-sectional data on monetary and non-monetary poverty. It puts forth two conjectures and contends that the combined profile of a household as both income poor and multidimensionally poor can be used as a proxy of that household being chronically income poor. To explore the viability of this approach, we use a probit model and longitudinal data for three Latin American countries to estimate households’ probabilities of remaining in income poverty based on their past income and multidimensional poverty statuses. We find empirical support for the approach that is significant, consistent across countries, and robust to various controls and periods of analysis.