The demand side of information provision: Using multivariate time series clustering to construct multinational uncertainty proxies

[EN] Information demand in the modern world is met to a huge extent by information supply from the search engine Google. Humans use the search engine to gather information which shall help to reduce perceived personal uncertainty about a specific subject. Google Trends is providing insights into this information demand in a timely manner and for a variety of different countries. In this paper, multinational Google Trends data and unsupervised learning techniques are used to construct meaningful country clusters resembling the economic, geographic and political relationships of the considered countries. Additionally, these clusters are stable over time. Under the assumption that an increase in Google search requests reflect elevated uncertainty, the cluster information is used to construct economic and political uncertainty time series for 43 different countries. This uncertainty index Granger causes quarterly GDP growth in more countries compared to an existing multinational uncertainty index proofing its usefulness in the field of forecasting. Furthermore, the new index is available up to a daily frequency and can be applied to additional countries and regions.Schütze, F. (2022). The demand side of information provision: Using multivariate time series clustering to construct multinational uncertainty proxies. En 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. 155-163. https://doi.org/10.4995/CARMA2022.2022.1508015516

Mobility and Citizenship in the Shadow of the Euro Crisis: Explaining New Trends in Immigration and Naturalization across Europe

Austerity, slow growth, rising unemployment have had worrying effects on the countries of the European Union in the aftermath of the Euro crisis. Fears of the Eurozone’s demise, although somewhat abated for the moment, continue to bring political and economic uncertainty to citizens and governments across the continent. One relatively unexplored consequence of the Euro crisis and the uncertainty it has engendered has been the new changes in mobility and citizenship acquisition trends within the EU. During periods of economic crisis, immigrants often find themselves pushed across borders by slow growth and high unemployment at home in search of better economic opportunities that pull them abroad. During the Euro crisis, while governments have been restricting flows of third-­‐country nationals within Europe in general, many Europeans have taken advantage mobility rights within the Schengen area and have gone in search of better opportunities elsewhere in the EU. For many, the economic benefits of mobility are alluring. However, new evidence suggests that the Euro crisis is increasing the economic benefits of citizenship acquisition as well, prompting new trends in naturalization across a growing number of countries. How has the crisis affected immigration and naturalization trends across Europe in its aftermath

The usefulness of big data in creating innovations. The example of Google Trends

A huge amount of data is collected by search engines. According to estimates, the Google search engine, which is dominant on the market, receives billions of search requests daily. Of particular note is that a large part of the collected data is available through the Google Trends service. As a consequence, various types of data can be used by enterprises for their development but they often do not take advantage of this opportunity. Therefore, the purpose of this article is to prove the suitability of the big data concept for creating and implementing product innovations, using the example of Google Trends. Discovering human needs and searching for answers to them is not only the domain of entrepreneurs, therefore this study may have a fairly broad practical applications. By adopting general assumptions, i.e. ones that do not refer to specific products or industries, the author has shown that the presented path may be recreated by both entrepreneurs and creators of political programs, as well as leaders of non-governmental organizations who need to implement innovations. The results revealed the selection of specific ways of entering queries in Google Trends and certain periods of analysis which are the most useful for creating innovations. Descriptive statistics (such as median) clearly show that the results typed in Google Trends are better when taken from a user perspective and can be used to create innovations. Despite substantial differences, the results do not allow for the conclusion that these differences were statistically significant. Thus, preliminary data supports the hypothesis, but more research is needed

Can electoral popularity be predicted using socially generated big data?

Today, our more-than-ever digital lives leave significant footprints in cyberspace. Large scale collections of these socially generated footprints, often known as big data, could help us to re-investigate different aspects of our social collective behaviour in a quantitative framework. In this contribution we discuss one such possibility: the monitoring and predicting of popularity dynamics of candidates and parties through the analysis of socially generated data on the web during electoral campaigns. Such data offer considerable possibility for improving our awareness of popularity dynamics. However they also suffer from significant drawbacks in terms of representativeness and generalisability. In this paper we discuss potential ways around such problems, suggesting the nature of different political systems and contexts might lend differing levels of predictive power to certain types of data source. We offer an initial exploratory test of these ideas, focussing on two data streams, Wikipedia page views and Google search queries. On the basis of this data, we present popularity dynamics from real case examples of recent elections in three different countries.Comment: To appear in Information Technolog

Synthetic Controls: A New Approach to Evaluating Interventions

Synthetic control methods are a novel approach to comparative case study research using observational data. Though developed within political science, the methods can potentially be applied to a wide range of evaluation problems in economics, public health, social policy and other disciplines. In the traditional approach, an area in which a new or redesigned service is being implemented is compared with another ‘control’ area (in which there is no change) and statistical adjustment used to account for any differences between areas that might bias the comparison. In the new approach, a synthetic control is derived using data on past trends in all potentially comparable areas, providing a more robust basis for identifying the impact of the service change. Synthetic control methods may be a valuable addition to the range of techniques available for non-randomised evaluations of social, economic and public health interventions. To date there have been few applications in a UK context, and none in Scotland. Published evidence suggests considerable potential to apply synthetic controls to public service innovations at NHS Board, local authority or Community Planning Partnership level, and may widen the range of policy and practice changes that can usefully be evaluated

Constructing a global counterterrorist legislation database: dilemmas, procedures, and preliminary analyses

Counterterrorist legislation is one of the main ways in which countries, particularly democracies, respond to terror attacks. Yet, there is to date no comprehensive cross-national database of counterterrorist legislation. This article introduces an overarching global counterterrorist legislation database (GCLD), covering more than 1,000 laws in 219 countries and territories over the years 1850-2009. I present the dilemmas and difficulties involved in constructing a global terrorism database and explain how these difficulties were addressed when assembling the current database. The article also brings descriptive statistics and analyses of the data, focusing on the historical development of global counterterrorist legislation and on the regional distribution of this legislation. It concludes with some recommendations for future researchers who may want to use the database.Publisher PD