Augmenting the availability of historical GDP per capita estimates through machine learning.

Can we use data on the biographies of historical figures to estimate the GDP per capita of countries and regions? Here, we introduce a machine learning method to estimate the GDP per capita of dozens of countries and hundreds of regions in Europe and North America for the past seven centuries starting from data on the places of birth, death, and occupations of hundreds of thousands of historical figures. We build an elastic net regression model to perform feature selection and generate out-of-sample estimates that explain 90% of the variance in known historical income levels. We use this model to generate GDP per capita estimates for countries, regions, and time periods for which these data are not available and externally validate our estimates by comparing them with four proxies of economic output: urbanization rates in the past 500 y, body height in the 18th century, well-being in 1850, and church building activity in the 14th and 15th century. Additionally, we show our estimates reproduce the well-known reversal of fortune between southwestern and northwestern Europe between 1300 and 1800 and find this is largely driven by countries and regions engaged in Atlantic trade. These findings validate the use of fine-grained biographical data as a method to augment historical GDP per capita estimates. We publish our estimates with CI together with all collected source data in a comprehensive dataset.

Estimating digital product trade through corporate revenue data.

Despite global efforts to harmonize international trade statistics, our understanding of digital trade and its implications remains limited. Here, we introduce a method to estimate bilateral exports and imports for dozens of sectors starting from the corporate revenue data of large digital firms. This method allows us to provide estimates for digitally ordered and delivered trade involving digital goods (e.g. video games), productized services (e.g. digital advertising), and digital intermediation fees (e.g. hotel rental), which together we call digital products. We use these estimates to study five key aspects of digital trade. We find that, compared to trade in physical goods, digital product exports are more spatially concentrated, have been growing faster, and can offset trade balance estimates, like the United States trade deficit on physical goods. We also find that countries that have decoupled economic growth from greenhouse gas emissions tend to have larger digital exports and that digital exports contribute positively to the complexity of economies. This method, dataset, and findings provide a new lens to understand the impact of international trade in digital products.

A systematic review on cross-cultural validations and psychometric solidity of the orthotics and prosthetics user survey.

Research is essential to reflect patients' satisfaction with their devices in the field of Prosthetics and Orthotics, record their performance and health-related quality of life. This requires culturally adapted questionnaires for each country. Periodic assessment of validity and test fit are essential elements for the long-term utility and effectiveness of psychometric tests. This article reviews the psychometric properties of the Orthotics and Prosthetics Users Survey (OPUS). The purpose, in addition to its adaptation to the Spanish-speaking population, involves a review/update of content, statistical analyses, and validity studies, until a larger number of studies are conducted. Study design: a Systematic review. A systematic literature search was carried out in specialized search engines: Alcorze (University of Zaragoza), MEDLINE (PubMed), and EMBASE of original articles published since 2000. Eleven items belonging to the OPUS were obtained, according to the language of the country where they were validated, and promising psychometric properties were confirmed (reflecting reliability values between 0.62 and 0.95; Cronbach's α scores between 0.73 and 0.98) with sample sizes between 10 and 321. The study concluded by stating that the OPUS was validated in different languages, reporting good psychometric robustness so far. Further deployment, refinement, and validation of this survey by country is warranted in view of its promising use.

Understanding political divisiveness using online participation data from the 2022 French and Brazilian presidential elections.

Digital technologies can augment civic participation by facilitating the expression of detailed political preferences. Yet, digital participation efforts often rely on methods optimized for elections involving a few candidates. Here we present data collected in an online experiment where participants built personalized government programmes by combining policies proposed by the candidates of the 2022 French and Brazilian presidential elections. We use this data to explore aggregates complementing those used in social choice theory, finding that a metric of divisiveness, which is uncorrelated with traditional aggregation functions, can identify polarizing proposals. These metrics provide a score for the divisiveness of each proposal that can be estimated in the absence of data on the demographic characteristics of participants and that explains the issues that divide a population. These findings suggest that divisiveness metrics can be useful complements to traditional aggregation functions in direct forms of digital participation.

Evaluation of the Effect of Binary Fly Ash-Lime Mixture on the Bearing Capacity of Natural Soils: A Comparison with Two Conventional Stabilizers Lime and Portland Cement.

This study evaluates a binary mixture of fly ash and lime as a stabilizer for natural soils. A comparative analysis was performed on the effect on the bearing capacity of silty, sandy and clayey soils after the addition of lime and ordinary Portland cement as conventional stabilizers, and a non-conventional product of a binary mixture of fly ash and Ca(OH)2 called FLM. Laboratory tests were carried out to evaluate the effect of additions on the bearing capacity of stabilized soils by unconfined compressive strength (UCS). In addition, a mineralogical analysis to validate the presence of cementitious phases due to chemical reactions with FLM was performed. The highest UCS values were found in the soils that required the highest water demand for compaction. Thus, the silty soil added with FLM reached 10 MPa after 28 days of curing, which was in agreement with the analysis of the FLM pastes, where soil moistures higher than 20% showed the best mechanical characteristics. Furthermore, a 120 m long track was built with stabilized soil to evaluate its structural behavior for 10 months. An increase of 200% in the resilient modulus of the FLM-stabilized soils was identified, and a decrease of up to 50% in the roughness index of the FLM, lime (L) and Ordinary Portland Cement (OPC)-stabilized soils compared to the soil without addition, resulting in more functional surfaces.

Reciprocity heightens academic performance in elementary school students.

Social relationships are pivotal for human beings. Yet, we still lack a complete understanding of the types and conditions of social relationships that facilitate learning among children. Here, we present the results of a study involving 855 elementary school children from 14 different public schools in Chile designed to understand their social learning strategies in classrooms. We mapped students' social relationships using a behavioral experiment-a non-anonymous social dilemma-that allows us to measure cooperation and infer reciprocal and asymmetrical relationships between peers. We implemented the experiment synchronously in each classroom using networked tablets and a friendly user interface to mitigate cognitive barriers and boost students' engagement. Using regression models, we found a positive and significant association between reciprocity and academic performance. This result holds after controlling for class attendance, sex, parents' education, social status, individual cooperative dispositions, and fixed effects per class group. Finally, using a difference-in-difference framework, we found robust evidence that reciprocity heightens academic performance by comparing two consecutive academic semesters. This effect is heterogeneous and is considerably more prominent for the top 20% students experiencing higher levels of reciprocity in their social relationships. We expect these results to inform cooperative learning interventions in elementary education.

Phosphorylation of Endothelin-Converting Enzyme-1c at Serines 18 and 20 by CK2 Promotes Aggressiveness Traits in Colorectal Cancer Cells.

Endothelin-converting enzyme-1 (ECE1) activates the endothelin-1 peptide, which upregulates pathways that are related to diverse hallmarks of cancer. ECE1 is expressed as four isoforms differing in their N-terminal domains. Protein kinase CK2 phosphorylates the N-terminus of isoform ECE1c, enhancing its stability and promoting invasiveness of colorectal cancer cells. However, the specific residues in ECE1c that are phosphorylated by CK2 and how this phosphorylation promotes invasiveness was unknown. Here we demonstrate that Ser-18 and Ser-20 are the bona fide residues phosphorylated by CK2 in ECE1c. Thus, biphospho-mimetic ECE1cDD and biphospho-resistant ECE1cAA mutants were constructed and stably expressed in different colorectal cancer cells through lentiviral transduction. Biphospho-mimetic ECE1cDD displayed the highest stability in cells, even in the presence of the specific CK2 inhibitor silmitasertib. Concordantly, ECE1cDD-expressing cells showed enhanced hallmarks of cancer, such as proliferation, migration, invasiveness, and self-renewal capacities. Conversely, cells expressing the less-stable biphospho-resistant ECE1cAA showed a reduction in these features, but also displayed an important sensitization to 5-fluorouracil, an antineoplastic agent traditionally used as therapy in colorectal cancer patients. Altogether, these findings suggest that phosphorylation of ECE1c at Ser-18 and Ser-20 by CK2 promotes aggressiveness in colorectal cancer cells. Therefore, phospho-ECE1c may constitute a novel biomarker of poor prognosis and CK2 inhibition may be envisioned as a potential therapy for colorectal cancer patients.

Complex economic activities concentrate in large cities.

Human activities, such as research, innovation and industry, concentrate disproportionately in large cities. The ten most innovative cities in the United States account for 23% of the national population, but for 48% of its patents and 33% of its gross domestic product. But why has human activity become increasingly concentrated? Here we use data on scientific papers, patents, employment and gross domestic product, for 353 metropolitan areas in the United States, to show that the spatial concentration of productive activities increases with their complexity. Complex economic activities, such as biotechnology, neurobiology and semiconductors, concentrate disproportionately in a few large cities compared to less--complex activities, such as apparel or paper manufacturing. We use multiple proxies to measure the complexity of activities, finding that complexity explains from 40% to 80% of the variance in urban concentration of occupations, industries, scientific fields and technologies. Using historical patent data, we show that the spatial concentration of cutting-edge technologies has increased since 1850, suggesting a reinforcing cycle between the increase in the complexity of activities and urbanization. These findings suggest that the growth of spatial inequality may be connected to the increasing complexity of the economy.

Humans judge faces in incomplete photographs as physically more attractive.

Attractive people are perceived to be healthier, wealthier, and more sociable. Yet, people often judge the attractiveness of others based on incomplete and inaccurate facial information. Here, we test the hypothesis that people fill in the missing information with positive inferences when judging others' facial beauty. To test this hypothesis, we conducted seven experiments where participants judged the attractiveness of human faces in complete and incomplete photographs. Our data shows that-relative to complete photographs-participants judge faces in incomplete photographs as physically more attractive. This positivity bias is replicated for different types of incompleteness; is mostly specific to aesthetic judgments; is stronger for male participants; is specific to human faces when compared to pets, flowers, and landscapes; seems to involve a holistic processing; and is stronger for atypical faces. These findings contribute to our understanding of how people perceive and make inferences about others' beauty.

The universal decay of collective memory and attention.

Collective memory and attention are sustained by two channels: oral communication (communicative memory) and the physical recording of information (cultural memory). Here, we use data on the citation of academic articles and patents, and on the online attention received by songs, movies and biographies, to describe the temporal decay of the attention received by cultural products. We show that, once we isolate the temporal dimension of the decay, the attention received by cultural products decays following a universal biexponential function. We explain this universality by proposing a mathematical model based on communicative and cultural memory, which fits the data better than previously proposed log-normal and exponential models. Our results reveal that biographies remain in our communicative memory the longest (20-30 years) and music the shortest (about 5.6 years). These findings show that the average attention received by cultural products decays following a universal biexponential function.

How the medium shapes the message: Printing and the rise of the arts and sciences.

Communication technologies, from printing to social media, affect our historical records by changing the way ideas are spread and recorded. Yet, finding statistical evidence of this fact has been challenging. Here we combine a common causal inference technique (instrumental variable estimation) with a dataset on nearly forty thousand biographies from Wikipedia (Pantheon 2.0), to study the effect of the introduction of printing in European cities on Wikipedia's digital biographical records. By using a city's distance to Mainz as an instrument for the adoption of the movable type press, we show that European cities that adopted printing earlier were more likely to become the birthplace of a famous scientist or artist during the years following the invention of printing. We bring these findings to recent communication technologies by showing that the number of radios and televisions in a country correlates with the number of globally famous performing artists and sports players born in that country, even after controlling for GDP, population, and including country and year fixed effects. These findings support the hypothesis that the introduction of communication technologies can bias historical records in the direction of the content that is best suited for each technology.

The role of industry-specific, occupation-specific, and location-specific knowledge in the growth and survival of new firms.

How do regions acquire the knowledge they need to diversify their economic activities? How does the migration of workers among firms and industries contribute to the diffusion of that knowledge? Here we measure the industry-, occupation-, and location-specific knowledge carried by workers from one establishment to the next, using a dataset summarizing the individual work history for an entire country. We study pioneer firms-firms operating in an industry that was not present in a region-because the success of pioneers is the basic unit of regional economic diversification. We find that the growth and survival of pioneers increase significantly when their first hires are workers with experience in a related industry and with work experience in the same location, but not with past experience in a related occupation. We compare these results with new firms that are not pioneers and find that industry-specific knowledge is significantly more important for pioneer than for nonpioneer firms. To address endogeneity we use Bartik instruments, which leverage national fluctuations in the demand for an activity as shocks for local labor supply. The instrumental variable estimates support the finding that industry-specific knowledge is a predictor of the survival and growth of pioneer firms. These findings expand our understanding of the micromechanisms underlying regional economic diversification.

Unpacking the polarization of workplace skills.

Economic inequality is one of the biggest challenges facing society today. Inequality has been recently exacerbated by growth in high- and low-wage occupations at the expense of middle-wage occupations, leading to a "hollowing" of the middle class. Yet, our understanding of how workplace skills drive this process is limited. Specifically, how do skill requirements distinguish high- and low-wage occupations, and does this distinction constrain the mobility of individuals and urban labor markets? Using unsupervised clustering techniques from network science, we show that skills exhibit a striking polarization into two clusters that highlight the specific social-cognitive skills and sensory-physical skills of high- and low-wage occupations, respectively. The connections between skills explain various dynamics: how workers transition between occupations, how cities acquire comparative advantage in new skills, and how individual occupations change their skill requirements. We also show that the polarized skill topology constrains the career mobility of individual workers, with low-skill workers "stuck" relying on the low-wage skill set. Together, these results provide a new explanation for the persistence of occupational polarization and inform strategies to mitigate the negative effects of automation and offshoring of employment. In addition to our analysis, we provide an online tool for the public and policy makers to explore the skill network: skillscape.mit.edu.

Optimal diversification strategies in the networks of related products and of related research areas.

Countries and cities are likely to enter economic activities that are related to those that are already present in them. Yet, while these path dependencies are universally acknowledged, we lack an understanding of the diversification strategies that can optimally balance the development of related and unrelated activities. Here, we develop algorithms to identify the activities that are optimal to target at each time step. We find that the strategies that minimize the total time needed to diversify an economy target highly connected activities during a narrow and specific time window. We compare the strategies suggested by our model with the strategies followed by countries in the diversification of their exports and research activities, finding that countries follow strategies that are close to the ones suggested by the model. These findings add to our understanding of economic diversification and also to our general understanding of diffusion in networks.

Computer vision uncovers predictors of physical urban change.

Which neighborhoods experience physical improvements? In this paper, we introduce a computer vision method to measure changes in the physical appearances of neighborhoods from time-series street-level imagery. We connect changes in the physical appearance of five US cities with economic and demographic data and find three factors that predict neighborhood improvement. First, neighborhoods that are densely populated by college-educated adults are more likely to experience physical improvements-an observation that is compatible with the economic literature linking human capital and local success. Second, neighborhoods with better initial appearances experience, on average, larger positive improvements-an observation that is consistent with "tipping" theories of urban change. Third, neighborhood improvement correlates positively with physical proximity to the central business district and to other physically attractive neighborhoods-an observation that is consistent with the "invasion" theories of urban sociology. Together, our results provide support for three classical theories of urban change and illustrate the value of using computer vision methods and street-level imagery to understand the physical dynamics of cities.

Pantheon 1.0, a manually verified dataset of globally famous biographies.

We present the Pantheon 1.0 dataset: a manually verified dataset of individuals that have transcended linguistic, temporal, and geographic boundaries. The Pantheon 1.0 dataset includes the 11,341 biographies present in more than 25 languages in Wikipedia and is enriched with: (i) manually verified demographic information (place and date of birth, gender) (ii) a taxonomy of occupations classifying each biography at three levels of aggregation and (iii) two measures of global popularity including the number of languages in which a biography is present in Wikipedia (L), and the Historical Popularity Index (HPI) a metric that combines information on L, time since birth, and page-views (2008-2013). We compare the Pantheon 1.0 dataset to data from the 2003 book, Human Accomplishments, and also to external measures of accomplishment in individual games and sports: Tennis, Swimming, Car Racing, and Chess. In all of these cases we find that measures of popularity (L and HPI) correlate highly with individual accomplishment, suggesting that measures of global popularity proxy the historical impact of individuals.

Disconnected, fragmented, or united? a trans-disciplinary review of network science.

During decades the study of networks has been divided between the efforts of social scientists and natural scientists, two groups of scholars who often do not see eye to eye. In this review I present an effort to mutually translate the work conducted by scholars from both of these academic fronts hoping to continue to unify what has become a diverging body of literature. I argue that social and natural scientists fail to see eye to eye because they have diverging academic goals. Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms. In the following pages I discuss the differences between both of these literatures by summarizing the parallel theories advanced to explain link formation and the applications used by scholars in each field to justify their approach to network science. I conclude by providing an outlook on how these literatures can be further unified.