Inequality and entrepreneurial thresholds

We explore the relationship between inequality and entrepreneurial activity. Drawing on cross-sectional data from a largescale survey of the economic conditions of individuals across India, we develop a number of dimensions of inequality to explore empirically how inequality interacts with entrepreneurship, operationalized as self-employment or as employing other people. We find compelling evidence that there are thresholds to becoming self-employed, and even more so to assembling the combinations of resources and personal attributes required to become an employer. Greater inequality leaves more people unable to make the transition to self-employment, leaving casual laboring as the occupation of necessity. At the same time, inequality increases the number of employers in a society, by concentrating resources - particularly land and finance - enough for significant numbers of people to be able to cross this higher threshold. Lastly, greater differentiation into social or religious groups curtails the ability to cross either entrepreneurial threshold, presumably by limiting the extent and benefits of social networks of value for entrepreneurship.     

When age does not harm innovative behavior and perceptions of competence: Testing interdepartmental collaboration as a social buffer

Can older managers overcome stereotypes relating age to low competence? We integrate the literature on age and cognitive ability with research on innovation to explore whether—and if so, when—employees' age harms performance and promotability appraisals made by their supervisors. Multisource, time‐lag data from 305 project managers indicate that the negative stereotypes can be explained through decreased innovative behavior. However, older employees are not always seen as poorer performers with less potential to be promoted due to their reduced innovative behavior. Rather, interdepartmental collaboration moderates these effects. Specifically, older employees with low interdepartmental collaboration are less innovative and receive worse performance and promotability appraisals than younger employees, but the “age handicap” vanishes when older employees collaborate with members of other departments. Organizations should foster formal or informal collaboration among units to prevent negative consequences of an aging workforce.     

Who do you care about? Scientists’ personality traits and perceived impact on beneficiaries

We investigate the roots of scientists' perceptions of the impact of their work by examining stable psychological characteristics such as personality traits. An analysis of personality traits highlights the effects of policies related to gender equality, allocation of research time and skills acquisition. It improves our understanding of the conflicts related to scientists’ perceptions of the impact of their research on beneficiaries. For example, conscientiousness increases the perceived impact on clinical beneficiaries, but reduces the perceived impact on industrial beneficiaries. Organizational scientific freedom increases the effects of personality traits on perceived impact on beneficiaries such that scientists affiliated to a university are less likely than colleagues working in other research settings to perceive the simultaneous impact of their work on both industrial and clinical beneficiaries.     

Time series copulas for heteroskedastic data

We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first‐order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co‐movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value‐at‐risk forecasts.     

The impact of purchasing strategy-structure (mis)fit on purchasing cost and innovation performance

The organizational design literature strongly supports the notion of “structure follows strategy”, and suggests that a misfit between the two has a negative effect on performance. Building on this line of argument, we examine to what extent the (mis)fit between purchasing strategy and purchasing structure impacts purchasing performance. We focus on cost and innovation purchase category strategies, and examine how the deviation from an ideal purchasing structure defined along three dimensions (centralization, formalization, and cross-functionality) impacts purchasing performance. Analysing data collected from 469 firms in ten countries, we demonstrate that a strategy-structure misfit negatively impacts purchasing performance in both cost and innovation strategies. We also find that purchasing proficiency is a mediator in this relationship between misfit and performance. Our findings aid managerial decision making by empirically validating the necessity of having the right purchasing structure for successfully executing different purchasing strategies.     

Inflation,real economic growth and unemployment expectations: an empirical analysis based on the ECB survey of professional forecasters

Expectations are at the centre of modern macroeconomic theory and policymakers. In this article, we examine the predictive ability and the consistency properties of macroeconomic expectations using data of the European Central Bank (ECB) Survey of Professional Forecasters (SPF). In particular, we provide evidence on the properties of forecasts for three key macroeconomic variables: the inflation rate, the growth rate of real gross domestic product and the unemployment rate.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

Easing the assessment of emerging technologies in technology observatories. Findings about patterns of dissemination of emerging technologies on the internet

This paper considers the Web as a big data container that can be used by Technology Observatories and administrations to track emerging issues and more specifically emerging technologies. It considers information that is available on the Internet for free from different sources, and proposes a framework that can be useful to characterise them and to detect patterns of dissemination. This framework is made up of 30 metrics obtained from different kinds of sources (general web, patents, scholars?…). Some of them are obtained directly as the number of hits retrieved by queries on a search engine, and other ones calculated by means of ratios. This paper contains the development of a complete case that utilises this framework to characterise emerging technologies included in the well-known Hype Cycle for Emerging Technologies, in this case the 2015 release¹ and to analyze patterns of dissemination of these technologies on the Internet.     

Job growth in early transition: Comparing two paths*

Small start‐up firms are the engine of job creation in early transition. We ask about differences in their growth across two different transition economies: Estonia, which experienced rapid destruction of pre‐existing firms, and the Czech Republic, which reduced the old sector gradually. We find that the majority of job growth corresponds to within‐industry reallocation. The within‐industry growth of small start‐up firms is similar in the two countries, in line with the convergence to Western industry firm‐size distributions. We also find similar patterns in the evolution of wage differentials between start‐ups and old firms and small differences in the extent of low‐wage employment in start‐ups across the two transition paths. JEL Classifications: J2, J3, J4, L1, O1, P2.