Using bibliometrics, this study explores the land-scape of social computing sciences within the Turkish academic sphere. The analysis conducted at both the country and organizational levels reveals that collaborations in the field of social computing are not solely influenced by geographical factors. Through network analysis of keyword co-occurrences, distinct thematic patterns emerge, highlighting areas such as health-related applications, the social implications of computational approaches, the intersection of computational methods with the economy and agriculture, machine learning techniques, and the utilization of big data analytics for business applications. These findings provide evidence of the multidisciplinary nature inherent in social computing research.

Google Trends (GT) collate the volumes of search keywords over time and by geographical location. Such data could, in theory, provide insights into people’s ex ante intentions to migrate, and hence be useful for predictive analysis of future migration. Empirically, however, the predictive power of GT is sensitive, it may vary depending on geographical context, the search keywords selected for analysis, as well as Google’s market share and its users’ characteristics and search behavior, among others. Unlike most previous studies attempting to demonstrate the benefit of using GT for forecasting migration flows, this article addresses a critical but less discussed issue: when GT cannot enhance the performances of migration models. Using EUROSTAT statistics on first-time asylum applications and a set of push-pull indicators gathered from various data sources, we train three classes of gravity models that are commonly used in the migration literature, and examine how the inclusion of GT may affect models’ abilities to predict refugees’ destination choices. The results suggest that the effects of including GT are highly contingent on the complexity of different models. Specifically, GT can only improve the performance of relatively simple models, but not of those augmented by flow Fixed-Effects or by Auto-Regressive effects. These findings call for a more comprehensive analysis of the strengths and limitations of using GT, as well as other digital trace data, in the context of modeling and forecasting migration. It is our hope that this nuanced perspective can spur further innovations in the field, and ultimately bring us closer to a comprehensive modeling framework of human migration.

Migration models have evolved significantly during the last decade, most notably the so-called flow Fixed-Effects (FE) gravity models. Such models attempt to infer how human mobility may be driven by changing economy, geopolitics, and the environment among other things. They are also increasingly used for migration projections and forecasts. However, recent research shows that this class of models can neither explain, nor predict the temporal dynamics of human movement. This shortcoming is even more apparent in the context of forced migration, in which the processes and drivers tend to be heterogeneous and complex. In this article, we derived a Flow-Specific Temporal Gravity (FTG) model which, compared to the FE models, is theoretically similar (informed by the random utility framework), but empirically less restrictive. Using EUROSTAT data with climate, economic, and conflict indicators, we trained both models and compared their performances. The results suggest that the predictive power of these models is highly dependent on the length of training data. Specifically, as time-series migration data lengthens, FTG's predictions can be increasingly accurate, whereas the FE model becomes less predictive.

Population aging presents a looming challenge for sustaining intergenerational transfers from the economically productive population to dependent elderly. This chapter offers an interdisciplinary perspective on whether immigration may alleviate this economic challenge for an aging welfare state. Specifically, it demonstrates how immigration may be beneficial from a pure demographic perspective, but less so or costly from an economic perspective.

In this paper, we investigate whether integration policy improves refugees' economic performance, specifically examining the effects on refugees' income of Sweden's 2010 reform of the introduction programme (or IP). We also evaluate how the reform effects vary depending on refugees' gender and educational attainment. Our key finding shows a strong positive effect of the reform on refugees' income, immediately after the completion of the IP. More importantly, this positive effect intensifies over time, with no signs of diminishing, which implies a longer-term effect of the reform. Furthermore, the effects of the reform do not significantly vary between men and women or between the highly educated and the less-educated, suggesting that the new Swedish IP benefits refugees to the same extent, regardless of their gender and educational attainment.

Population ageing is making it increasingly difficult for countries to sustain theircurrent levels social welfare transfers from the economically active populationto the dependent elderly. To meet this challenge, the Swedish government hasimplemented various reforms since the 1990s aimed at reducing incentives totake early retirement. However, a critical question has emerged in response tothese reforms: namely, whether members of certain socially and demographicallydisadvantaged groups will, in practice, be able to work longer. This paper providesa detailed overview of retirement trends in Sweden, disaggregated by educationalattainment, health status, and country of birth. Our results show that the growthpattern in the average effective retirement age since the mid-1990s was shared byindividuals regardless of their educational level, health status, or country of birth.This shared growth pattern suggests that it is possible to extend the working livesof all groups of individuals, regardless of their socio-economic and demographiccharacteristics.

The current Swedish pension system is flexible. Workers may choose to retire, partially or fully, at any time after the age of 61, while still working fullor part-time. The system also allows retirees to temporarily stop collecting pension benefits and return to employment, but they have no right to continue working after the age of 67. Like in many other countries, the effective retirement age has been rising in Sweden since the mid-1990s and today it is the highest in the European Union (EU). In the following, we document the changes in effective retirement age by gender, education and health status. We also discuss what factors might underlie these changes. We start with an overview of the pension system, the development of health and the effective pension age for different groups, before making some reflections about challenges with regard to increasing the employment levels among elderly workers in Sweden in the future.

This paper investigates whether the Notional Defined Contribution (NDC) scheme prolongs working life. The evidence from the 1994 Swedish pension reform shows a gender and socio-economic gradient in the labor supply responses to phasing in NDC. While the reform exerted a large and significant positive effect on the average retirement age among highly educated and skilled, it had little or negative effect on those with low level of human capital. And the overall effect is more profound among older men, compared to older women. These findings imply that the aggregate impact of NDC may only be positive if the average level of older workers’ education and skills is high, whereas it may be moderate (or even adverse) if the majority of the older workers are less educated and engage in low-skill jobs. This highlights the importance of incorporating the gender and socio-economic aspects into the evaluation of how a multi-pillar pension scheme, such as NDC, may increase the average working life expectancy.