Small cities face greater impact from automation

The city has proven to be the most successful form of human agglomeration and provides wide employment opportunities for its dwellers. As advances in robotics and artificial intelligence revive concerns about the impact of automation on jobs, a question looms: How will automation affect employment in cities? Here, we provide a comparative picture of the impact of automation across U.S. urban areas. Small cities will undertake greater adjustments, such as worker displacement and job content substitutions. We demonstrate that large cities exhibit increased occupational and skill specialization due to increased abundance of managerial and technical professions. These occupations are not easily automatable, and, thus, reduce the potential impact of automation in large cities. Our results pass several robustness checks including potential errors in the estimation of occupational automation and sub-sampling of occupations. Our study provides the first empirical law connecting two societal forces: urban agglomeration and automation's impact on employment

Professional Gender Gaps Across US Cities

Gender imbalances in work environments have been a long-standing concern. Identifying the existence of such imbalances is key to designing policies to help overcome them. In this work, we study gender trends in employment across various dimensions in the United States. This is done by analyzing anonymous, aggregate statistics that were extracted from LinkedIn's advertising platform. The data contain the number of male and female LinkedIn users with respect to (i) location, (ii) age, (iii) industry and (iv) certain skills. We studied which of these categories correlate the most with high relative male or female presence on LinkedIn. In addition to examining the summary statistics of the LinkedIn data, we model the gender balance as a function of the different employee features using linear regression. Our results suggest that the gender gap varies across all feature types, but the differences are most profound among industries and skills. A high correlation between gender ratios of people in our LinkedIn data set and data provided by the US Bureau of Labor Statistics serves as external validation for our results.Comment: Accepted at a poster at ICWSM 2018. Please cite the ICWSM versio

Influence of artificial intelligence on public employment and its impact on politics: A systematic literature review

Goal:Public administration is constantly changing in response to new challenges, including the implementation of new technologies such as robotics and artificial intelligence (AI). This new dynamic has caught the attention of political leaders who are finding ways to restrain or regulate AI in public services, but also of scholars who are raising legitimate concerns about its impacts on public employment. In light of the above, the aim of this research is to analyze the influence of AI on public employment and the ways politics are reacting. Design / Methodology / Approach: We have performed a systematic literature review to disclose the state-of-the-art and to find new avenues for future research. Results: The results indicate that public services require four kinds of intelligence – mechanical, analytical, intuitive, and empathetic – albeit, with much less expression than in private services. Limitations of the investigation: This systematic review provides a snapshot of the influence of AI on public employment. Thus, our research does not cover the whole body of knowledge, but it presents a holistic understanding of the phenomenon. Practical implications: As private companies are typically more advanced in the implementation of AI technologies, the for-profit sector may provide significant contributions in the way states can leverage public services through the deployment of AI technologies. Originality / Value: This article highlights the need for states to create the necessary conditions to legislate and regulate key technological advances, which, in our opinion, has been done, but at a very slow pace.info:eu-repo/semantics/publishedVersio

Modeling Employment and Automation in the United States

When people change jobs, it is useful for both employers and employees to find best-fit jobs on the basis of the employees’ skillsets. We utilize the O*NET database to introduce the notion of the job distance, which allows us to measure the difference between jobs based on the skillsets required to successfully perform them. We then apply this measure to data from the Bureau of Labor Statistics (BLS) to model the job distribution in each metropolitan or rural area. Novel graph metrics are found along the way, but we ultimately address the impact of automation by combining a gravity and Markov model.Ope

Reprint of The new paradigm of economic complexity

Economic complexity offers a potentially powerful paradigm to understand key societal issues and challenges of our time. The underlying idea is that growth, development, technological change, income inequality, spatial disparities, and resilience are the visible outcomes of hidden systemic interactions. The study of economic complexity seeks to understand the structure of these interactions and how they shape various socioeconomic processes. This emerging field relies heavily on big data and machine learning techniques. This brief introduction to economic complexity has three aims. The first is to summarize key theoretical foundations and principles of economic complexity. The second is to briefly review the tools and metrics developed in the economic complexity literature that exploit information encoded in the structure of the economy to find new empirical patterns. The final aim is to highlight the insights from economic complexity to improve prediction and political decision-making. Institutions including the World Bank, the European Commission, the World Economic Forum, the OECD, and a range of national and regional organizations have begun to embrace the principles of economic complexity and its analytical framework. We discuss policy implications of this field, in particular the usefulness of building recommendation systems for major public investment decisions in a complex world

The new paradigm of economic complexity

Economic complexity offers a potentially powerful paradigm to understand key societal issues and challenges of our time. The underlying idea is that growth, development, technological change, income inequality, spatial disparities, and resilience are the visible outcomes of hidden systemic interactions. The study of economic complexity seeks to understand the structure of these interactions and how they shape various socioeconomic processes. This emerging field relies heavily on big data and machine learning techniques. This brief introduction to economic complexity has three aims. The first is to summarize key theoretical foundations and principles of economic complexity. The second is to briefly review the tools and metrics developed in the economic complexity literature that exploit information encoded in the structure of the economy to find new empirical patterns. The final aim is to highlight the insights from economic complexity to improve prediction and political decision-making. Institutions including the World Bank, the European Commission, the World Economic Forum, the OECD, and a range of national and regional organizations have begun to embrace the principles of economic complexity and its analytical framework. We discuss policy implications of this field, in particular the usefulness of building recommendation systems for major public investment decisions in a complex world.publishedVersio

Cidades de pequeno porte: uma aproximação conceitual e comparativa da região metropolitana de Londrina/PR a partir das Regiões de Influência das Cidades (REGIC)

Os debates acerca das cidades de pequeno porte têm se tornado mais presentes nos últimos anos com um maior número de publicações por parte dos especialistas da área que incentivam terceiros a notarem a relevâncias dessas no cenário geográfico brasileiro. Por mais que se produza muito sobre o tema em voga, existem muitas discussões a serem exploradas. Nesse sentido, o presente trabalho tem como objetivo abordar a discussão conceitual para se diferenciar as cidades pequenas das cidades locais com o auxílio da pesquisa do Instituto Brasileiro de Geografia e Estatística (IBGE) que consiste nas Regiões de Influência das Cidades (REGIC) e a partir disso, compreender o índice de atração da Região de Metropolitana de Londrina a partir das variáveis “compras” e “saúde”. Dessa forma, foi possível compreender que para que se considere uma cidade que tenha padrões de cidade pequena, é levado em consideração que ela possua não somente índice de atração relevante em uma só temática, mas sim, em ambas elas, tanto da área da saúde, quanto área relacionada a compras

The geography of innovation and technology news - An empirical study of the German news media

Variations in the frequency and tone of news media are the focus of a growing literature. However, to date, empirical investigations have primarily confirmed the existence of such differences at the country level. This paper extends those insights to the subnational level. We provide theoretical arguments and empirical support for systematic regional variations in the frequency and sentiments of news related to innovation and new technologies. These variations reflect regional socio-economic structures. We find that the average newspaper circulating in urban areas features more news on innovation and new technologies than media in more rural areas. Similar findings hold for locations in East Germany and to a certain degree for regions with low unemployment. The sentiments of innovation and new technology news are negatively associated to the unemployment rate, and they tend to be lower in regional newspapers than in national ones. Overall, our results suggest a strong link between the regional socioeconomic conditions and how newspapers circulating in these places report on innovation and new technologies.publishedVersio

Impatti dell'automazione sul mercato del lavoro. Prime stime per il caso italiano.

The causes of the present decline of demand in labor markets in developed countries are subject to considerable theoretical debate. More specifically, according to some authors, globalization and offshoring together with technological innovation, could lead to further negative impacts on real employment. Some studies estimate that the contribution of automation is the actual cause of job loss: in the US the introduction of robots by 2021 could lead to a cut of more than 6% of the workforce (FORRESTER 2016), and as much as 54% in Europe in the coming decades (Bowles 2014), although the greatest impact would occur in developing countries, where automation could weaken the traditional comparative advantages in terms of labor costs (UN 2016). The Italian case is particularly interesting, as the automation was introduced in large enterprises over three decades ago, determining a deep impact in terms of loss for low skilled jobs. This paper aims to provide a first quantification of the impacts on Italian labor market determined by the spread of latest technological innovations, both in terms of employment levels and social/territorial mobility, by differentiating its effects per macro-geographical breakdown of the country