What makes people stay in or leave shrinking cities? An empirical study from Portugal

The relative attractiveness of cities as places to live determines population movements in or out of them. Understanding the appealing features of a city is fundamental to local governments, particularly for cities facing population decline. Pull and push attributes of cities can include economic aspects, the availability of amenities and psychological constructs, initiating a discussion around which factors are more relevant in explaining migration. However, a pull–push approach has been underexplored in studies of shrinking cities. In the present study, we contribute to the discussion by identifying pull and push factors in Portuguese shrinking cities. Data were collected using a face-to-face questionnaire survey of 701 residents in four shrinking cities: Oporto, Barreiro, Peso da Régua and Moura. Factor analysis and automatic linear modelling were used to analyse the data. Our results support previous findings that the economic activity of a city is the most relevant feature for retaining residents. However, other characteristics specific to each city, especially those related to heritage and natural beauty, are also shown to influence a city’s attractiveness as a place to live. The cause of population shrinkage is also found to influence residents’ assessments of the pull and push attributes of each city. Furthermore, the results show the relevance of social ties and of place attachment to inhabitants’ intention to continue living in their city of residence.

Automated quantitative analysis of MCC-IMS spectra

Ion Mobility Spectrometry coupled with Multi Capillary Columns (MCC -IMS) is a fast analytical technique working at atmospheric pressure with high sensitivity and selectivity making it suitable for the analysis of complex biological matrices. MCC-IMS analysis generates its information through a 3D spectrum with peaks, corresponding to each of the substances detected, providing quantitative and qualitative information. Sometimes peaks of different substances overlap, making the quantification of substances present in the biological matrices a difficult process. In the present work we use peaks of isoprene and acetone as a model for this problem. These two volatile organic compounds (VOCs) that when detected by MCC-IMS produce two overlapping peaks. In this work it’s proposed an algorithm to identify and quantify these two peaks. This algorithm uses image processing techniques to treat the spectra and to detect the position of the peaks, and then fits the data to a custom model in order to separate the peaks. Once the peaks are separated it calculates the contribution of each peak to the data.

Productivity gains from migration: An analysis of Cape Verdean return migrants

Are return migrants more productive than non-migrants? If so, is it a causal effect or simply self-selection? Existing literature has not reached a consensus on the role of return migration for origin countries. To answer these research questions, an empirical analysis was performed based on household data collected in Cape Verde. One of the most common identification problems in the migration literature is the presence of migrant self-selection. In order to disentangle potential selection bias, we use instrumental variable estimation using variation provided by unemployment rates in migrant destination countries, which is compared with OLS and Nearest Neighbor Matching (NNM) methods. The results using the instrumental variable approach provide evidence of labour income gains due to return migration, while OLS underestimates the coefficient of interest. This bias points towards negative self-selection of return migrants on unobserved characteristics, although the different estimates cannot be distinguished statistically. Interestingly, migration duration and occupational changes after migration do not seem to influence post-migration income. There is weak evidence that return migrants from the United States have higher income gains caused by migration than the ones who returned from Portugal.

Enterprise risk management adoption: An empirical investigation of its effects on firm performance

Enterprise Risk Management (ERM) is gaining relevance among financial and non-financial companies but its benefits still are uncertain. This paper aims at investigating the relationship between ERM adoption and firm performance based on a sample of 1130 non-financial companies belonging to the STOXX® index. A content analysis of individual accounts is performed to distinguish adopters, and a regression analysis explores the effect of ERM adoption on firm performance, proxied by Tobin’s Q. The findings suggest that there is a statistical significant positive effect of ERM adoption on firm performance, meaning that firms are benefiting from the implementation of this process.

Impact of mobile app user acquisition on managerial decisions: Evidence from empirical experiment

In this research we conducted a mixed research, using qualitative and quantitative analysis to study the relationship and impact between mobile advertisement and mobile app user acquisition and the conclusions companies can derive from it. Data was gathered from management of mobile advertisement campaigns of a portfolio of three different mobile apps. We found that a number of implications can be extracted from this intersection, namely to product development, internationalisation and management of marketing budget. We propose further research on alternative app users sources, impact of revenue on apps and exploitation of product segments: wearable technology and Internet of Things.

Genetic algorithm for shipping route estimation with long-range tracking data : automatic reconstruction of shipping routes based on the historical ship positions for maritime safety applications

Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range tracking systems currently in operations allow the storage of ship position data over many years: a valuable source of knowledge about the shipping routes between different ocean regions. The outcome of this Master project is a software prototype for the estimation of the most operated shipping route between any two geographical locations. The analysis is based on the historical ship positions acquired with long-range tracking systems. The proposed approach makes use of a Genetic Algorithm applied on a training set of relevant ship positions extracted from the long-term storage tracking database of the European Maritime Safety Agency (EMSA). The analysis of some representative shipping routes is presented and the quality of the results and their operational applications are assessed by a Maritime Safety expert.