Is urban economic performance driven by a few factors? We study a simple model for the probability that an individual in a city is employed in a given urban activity. The theory posits that three quantities drive this probability: the activity-specific complexity, individual-specific knowhow, and the city-specific collective knowhow. We use data on employment across industries and metropolitan statistical areas in the US, from 1990 to 2016, to show that these drivers can be measured and have measurable consequences over measures of urban economic performance. First, we analyze the functional form of the probability function proposed by the theory, and show its superiority when compared to competing alternatives. Second, we show that individual and collective knowhow correlate with measures of urban economic performance, suggesting the theory can provide testable implications for why some cities are more prosperous than others.
We show how increasing returns to scale in urban scaling can artificially emerge, systematically and predictably, without any sorting or positive externalities. We employ a model where individual productivities are independent and identically distributed lognormal random variables across all cities. We use extreme value theory to demonstrate analytically the paradoxical emergence of increasing returns to scale when the variance of log-productivity is larger than twice the log-size of the population size of the smallest city in a cross-sectional regression. Our contributions are to derive an analytical prediction for the artificial scaling exponent arising from this mechanism and to develop a simple statistical test to try to tell whether a given estimate is real or an artifact. Our analytical results are validated analyzing simulations and real microdata of wages across municipalities in Colombia. We show how an artificial scaling exponent emerges in the Colombian data when the sizes of random samples of workers per municipality are 1% or less of their total size.
We combine a sequence of machine-learning techniques, together called Principal Smooth-Dynamics Analysis (PriSDA), to identify patterns in the dynamics of complex systems. Here, we deploy this method on the task of automating the development of new theory of economic growth. Traditionally, economic growth is modelled with a few aggregate quantities derived from simplified theoretical models. PriSDA, by contrast, identifies important quantities. Applied to 55 years of data on countries’ exports, PriSDA finds that what most distinguishes countries’ export baskets is their diversity, with extra weight assigned to more sophisticated products. The weights are consistent with previous measures of product complexity. The second dimension of variation is proficiency in machinery relative to agriculture. PriSDA then infers the dynamics of these two quantities and of per capita income. The inferred model predicts that diversification drives growth in income, that diversified middle-income countries will grow the fastest, and that countries will converge onto intermediate levels of income and specialization. PriSDA is generalizable and may illuminate dynamics of elusive quantities such as diversity and complexity in other natural and social systems.
Knowhow in societies accumulates as it gets transmitted from group to group, and from generation to generation. However, we lack of a unified quantitative formalism that takes into account the structured process for how this accumulation occurs, and this has precluded the development of a unified view of human development in the past and in the present. Here, we summarize a paradigm to understand and model this process. The paradigm goes under the general name of the Theory of Economic Complexity (TEC). Based on it, we present a combination of analytical, numerical and empirical results that illustrate how to characterize the process of development, providing measurable quantities that can be used to predict future developments. The emphasis is the quantification of the collective knowhow an economy has accumulated, and what are the directions in which it is likely to expand. As a case study we consider data on trade, which provides consistent data on the technological diversification of 200 countries across more than 50 years. The paradigm represented by TEC should be relevant for anthropologists, sociologists, and economists interested in the role of collective knowhow as the main determinant of the success and welfare of a society.
The prevalence of many urban phenomena changes systematically with population size. We propose a theory that unifies models of economic complexity and cultural evolution to derive urban scaling. The theory accounts for the difference in scaling exponents and average prevalence across phenomena, as well as the difference in the variance within phenomena across cities of similar size. The central ideas are that a number of necessary complementary factors must be simultaneously present for a phenomenon to occur, and that the diversity of factors is logarithmically related to population size. The model reveals that phenomena that require more factors will be less prevalent, scale more superlinearly and show larger variance across cities of similar size. The theory applies to data on education, employment, innovation, disease and crime, and it entails the ability to predict the prevalence of a phenomenon across cities, given information about the prevalence in a single city.
Labor informality, associated with low productivity and lack of access to social security services, dogs developing countries around the world. Rates of labor (in)formality, however, vary widely within countries. This paper presents a new stylized fact, namely the systematic positive relationship between the rate of labor formality and the working age population in cities. We hypothesize that this phenomenon occurs through the emergence of complex economic activities: as cities become larger, labor is allocated into increasingly complex industries as firms combine complementary capabilities derived from a more diverse pool of workers. Using data from Colombia, we use a network-based model to show that the technological proximity (derived from worker transitions between industry pairs) of current industries in a city to potential new complex industries governs the growth of the formal sector in the city. The mechanism proposed has robust strong predictive power, and fares better than alternative explanations of (in)formality.
This paper presents a descriptive analysis of wage inequality in Colombia by cities and industries and attempts to evaluate the impact of the inequality of industries on inequality of cities. Using the 2010-2014 Colombian Social Security data, we calculate the gini coefficient for cities and industries and draw comparisons between their distributions. Our results show that while cities are unequal in similar ways, industries differ widely on how unequal they can be with ginis. Moreover, industrial structure plays a significant role to determine city inequality. Industrial framework proves to be a key element in this area for researches and policymakers.
Several past studies have found that media reports of suicides and homicides appear to subsequently increase the incidence of similar events in the community, apparently due to the coverage planting the seeds of ideation in at-risk individuals to commit similar acts.
Here we explore whether or not contagion is evident in more high-profile incidents, such as school shootings and mass killings (incidents with four or more people killed). We fit a contagion model to recent data sets related to such incidents in the US, with terms that take into account the fact that a school shooting or mass murder may temporarily increase the probability of a similar event in the immediate future, by assuming an exponential decay in contagiousness after an event.
We find significant evidence that mass killings involving firearms are incented by similar events in the immediate past. On average, this temporary increase in probability lasts 13 days, and each incident incites at least 0.30 new incidents (p = 0.0015). We also find significant evidence of contagion in school shootings, for which an incident is contagious for an average of 13 days, and incites an average of at least 0.22 new incidents (p = 0.0001). All p-values are assessed based on a likelihood ratio test comparing the likelihood of a contagion model to that of a null model with no contagion. On average, mass killings involving firearms occur approximately every two weeks in the US, while school shootings occur on average monthly. We find that state prevalence of firearm ownership is significantly associated with the state incidence of mass killings with firearms, school shootings, and mass shootings.
Objectives Rampant urbanisation rates across the globe demand that we improve our understanding of how infectious diseases spread in modern urban landscapes, where larger and more connected host populations enhance the thriving capacity of certain pathogens.
Methods A data-driven approach is employed to study the ability of sexually transmitted diseases (STDs) to thrive in urban areas. The conduciveness of population size of urban areas and their socioeconomic characteristics are used as predictors of disease incidence, using confirmed-case data on STDs in the USA as a case study.
Results A superlinear relation between STD incidence and urban population size is found, even after controlling for various socioeconomic aspects, suggesting that doubling the population size of a city results in an expected increase in STD incidence larger than twofold, provided that all other socioeconomic aspects remain fixed. Additionally, the percentage of African–Americans, income inequalities, education and per capita income are found to have a significant impact on the incidence of each of the three STDs studied.
Conclusions STDs disproportionately concentrate in larger cities. Hence, larger urban areas merit extra prevention and treatment efforts, especially in low-income and middle-income countries where urbanisation rates are higher.
Urban scaling analysis has introduced a new scientific paradigm to the study of cities. With it, the notions of size, heterogeneity and structure have taken a leading role. These notions are assumed to be behind the causes for why cities differ from one another, sometimes wildly. However, the mechanisms by which size, heterogeneity and structure shape the general statistical patterns that describe urban economic output are still unclear. Given the rapid rate of urbanization around the globe, we need precise and formal mathematical understandings of these matters. In this context, I perform in this dissertation probabilistic, distributional and computational explorations of (i) how the broadness, or narrowness, of the distribution of individual productivities within cities determines what and how we measure urban systemic output,(ii) how urban scaling may be expressed as a statistical statement when urban metrics display strong stochasticity,(iii) how the processes of aggregation constrain the variability of total urban output, and (iv) how the structure of urban skills diversification within cities induces a multiplicative process in the production of urban output.
Much of the socioeconomic life in the United States occurs in its urban areas. While an urban economy is defined to a large extent by its network of occupational specializations, an examination of this important network is absent from the considerable body of work on the determinants of urban economic performance. Here we develop a structure-based analysis addressing how the network of interdependencies among occupational specializations affects the ease with which urban economies can transform themselves. While most occupational specializations exhibit positive relationships between one another, many exhibit negative ones, and the balance between the two partially explains the productivity of an urban economy. The current set of occupational specializations of an urban economy and its location in the occupation space constrain its future development paths. Important tradeoffs exist between different alternatives for altering an occupational specialization pattern, both at a single occupation and an entire occupational portfolio levels.
Urban scaling relations characterizing how diverse properties of cities vary on average with their population size have recently been shown to be a general quantitative property of many urban systems around the world. However, in previous studies the statistics of urban indicators were not analyzed in detail, raising important questions about the full characterization of urban properties and how scaling relations may emerge in these larger contexts. Here, we build a self-consistent statistical framework that characterizes the joint probability distributions of urban indicators and city population sizes across an urban system. To develop this framework empirically we use one of the most granular and stochastic urban indicators available, specifically measuring homicides in cities of Brazil, Colombia and Mexico, three nations with high and fast changing rates of violent crime. We use these data to derive the conditional probability of the number of homicides per year given the population size of a city. To do this we use Bayes’ rule together with the estimated conditional probability of city size given their number of homicides and the distribution of total homicides. We then show that scaling laws emerge as expectation values of these conditional statistics. Knowledge of these distributions implies, in turn, a relationship between scaling and population size distribution exponents that can be used to predict Zipf’s exponent from urban indicator statistics. Our results also suggest how a general statistical theory of urban indicators may be constructed from the stochastic dynamics of social interaction processes in cities.
Social transportation systems are remarkable examples of Complex Systems. Understanding their patterns and their dynamics in a holistic and global way is an obligated duty if we humans want to ever be capable of managing our social systems in an efficient way, friendly with our surrounding ecosystems and with ourselves. The general purpose of this research project is to give a first and modest ground of understanding concerning transport phenomena in networks, from a complex system perspective. Topological properties of transportation networks have been the most studied ones, and the spatial ones have been often left aside. In this work it is presented and suggested a mathematical model that replicates certain characteristics of spatial transportation networks. We use the measure of betweenness centrality to approximate the traffic within the network, and we show that holes are crucial actors that affect the transport phenomena. Some important results are observed from the statistics of the betweenness centrality. General guidelines for traffic assessment and road network management are given in light of the results shed by the model.
Andres Gomez-Lievano Growth Lab, Harvard Kennedy School, Harvard University Cambridge, MA 02138