Technological novelty profile and invention's future impact

We consider inventions as novel combinations of existing technological capabilities. Patent data allow us to explicitly identify such combinatorial processes in invention activities. Unconsidered in the previous research, not every new combination is novel to the same extent. Some combinations are naturally anticipated based on patent activities in the past or mere random choices, and some appear to deviate exceptionally from existing invention pathways. We calculate a relative likelihood that each pair of classification codes is put together at random, and a deviation from the empirical observation so as to assess the overall novelty (or conventionality) that the patent brings forth at each year. An invention is considered as unconventional if a pair of codes therein is unlikely to be used together given the statistics in the past. Temporal evolution of the distribution indicates that the patenting activities become more conventional with occasional cross-over combinations. Our analyses show that patents introducing novelty on top of the conventional units would receive higher citations, and hence have higher impact.Comment: 20 pages, 7 figure

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

Constructing cities, deconstructing scaling laws

Cities can be characterised and modelled through different urban measures. Consistency within these observables is crucial in order to advance towards a science of cities. Bettencourt et al have proposed that many of these urban measures can be predicted through universal scaling laws. We develop a framework to consistently define cities, using commuting to work and population density thresholds, and construct thousands of realisations of systems of cities with different boundaries for England and Wales. These serve as a laboratory for the scaling analysis of a large set of urban indicators. The analysis shows that population size alone does not provide enough information to describe or predict the state of a city as previously proposed, indicating that the expected scaling laws are not corroborated. We found that most urban indicators scale linearly with city size regardless of the definition of the urban boundaries. However, when non-linear correlations are present, the exponent fluctuates considerably.Comment: Accepted for publication, Journal of the Royal Society Interfac

A common trajectory recapitulated by urban economies

Is there a general economic pathway recapitulated by individual cities over and over? Identifying such evolution structure, if any, would inform models for the assessment, maintenance, and forecasting of urban sustainability and economic success as a quantitative baseline. This premise seems to contradict the existing body of empirical evidences for path-dependent growth shaping the unique history of individual cities. And yet, recent empirical evidences and theoretical models have amounted to the universal patterns, mostly size-dependent, thereby expressing many of urban quantities as a set of simple scaling laws. Here, we provide a mathematical framework to integrate repeated cross-sectional data, each of which freezes in time dimension, into a frame of reference for longitudinal evolution of individual cities in time. Using data of over 100 millions employment in thousand business categories between 1998 and 2013, we decompose each city's evolution into a pre-factor and relative changes to eliminate national and global effects. In this way, we show the longitudinal dynamics of individual cities recapitulate the observed cross-sectional regularity. Larger cities are not only scaled-up versions of their smaller peers but also of their past. In addition, our model shows that both specialization and diversification are attributed to the distribution of industry's scaling exponents, resulting a critical population of 1.2 million at which a city makes an industrial transition into innovative economies

Deconstructing Human Capital to Construct Hierarchical Nestedness

Modern economies generate immensely diverse complex goods and services by coordinating efforts and know-how of people in vast networks that span across the globe. This increasing complexity puts us under the pressure of acquiring an ever-increasing specialized and yet diverse skill portfolio in order to stay effective members of a complex economy. Here, we analyze the skill portfolios of workers in an effort to understand the latent structure and evolution of these portfolios. Analyzing the U.S. survey data (2003-2019) and 20 million resumes, we uncover a tree structure of vertical skill dependencies such that skills that only a few jobs need (specialized) are located at the leaves under the broadly demanded (general skills). The resulting structure exhibits an unbalanced tree shape. The unbalanced shape allows the further categorization of specialized skills: nested branching out of a deeply rooted sturdy trunk reflecting a dense web of common prerequisites, and un-nested lacking such support. Our longitudinal analyses show individuals indeed become more specialized, going down the nested paths as moving up the career ladder to enjoy higher wage premiums. The specialization, however, is most likely accompanied by demands for a higher level of general skills, and furthermore, specialization without the strengthening of general skills is deprived of wage premiums. We examine the geographic and demographic distribution of skills to explain disparities in wealth. Finally, historical changes in occupation skill requirements show these branches have become more fragmented over the decade, suggesting the increasing labor gap.Comment: 26 pages, 7 figure