A meme is a concept introduced by Dawkins12 as an equivalent in cultural studies of a gene in biology. A meme is a cultural unit, perhaps a joke, musical tune, or behavior, that can replicate in people's minds, spreading from person to person. During the replication process, memes can mutate and compete with each other for attention, because people's consciousness has finite capacity. Meme viral spreading causes behavioral change, for the better, as when, say, the "ALS Bucket Challenge" meme caused a cascade of humanitarian donations,a and for the worse, as when researchers proved obesity7 and smoking8 are socially transmittable diseases. A better theory of meme spreading could help prevent an outbreak of bad behaviors and favor positive ones.
Complex networks are a useful tool for the understanding of complex systems. One of the emerging properties of such systems is their tendency to form hierarchies: networks can be organized in levels, with nodes in each level exerting control on the ones beneath them. In this paper, we focus on the problem of estimating how hierarchical a directed network is. We propose a structural argument: a network has a strong top-down organization if we need to delete only few edges to reduce it to a perfect hierarchy—an arborescence. In an arborescence, all edges point away from the root and there are no horizontal connections, both characteristics we desire in our idealization of what a perfect hierarchy requires. We test our arborescence score in synthetic and real-world directed networks against the current state of the art in hierarchy detection: agony, flow hierarchy and global reaching centrality. These tests highlight that our arborescence score is intuitive and we can visualize it; it is able to better distinguish between networks with and without a hierarchical structure; it agrees the most with the literature about the hierarchy of well-studied complex systems; and it is not just a score, but it provides an overall scheme of the underlying hierarchy of any directed complex network.
Estimating the trustworthiness of a set of actors when all the available information is provided by the actors themselves is a hard problem. When two actors have conflicting reports about each other, how do we establish which of the two (if any) deserves our trust? In this paper, we model this scenario as a network problem: actors are nodes in a network and their reports about each other are the edges of the network. To estimate their trustworthiness levels, we develop an iterative framework which looks at all the reports about each connected actor pair to define its trustworthiness balance. We apply this framework to a customer/supplier business network. We show that our trustworthiness score is a significant predictor of the likelihood a business will pay a fine if audited. We show that the market network is characterized by homophily: businesses tend to connect to partners with similar trustworthiness degrees. This suggests that the topology of the network influences the behavior of the actors composing it, indicating that market regulatory efforts should take into account network theory to prevent further degeneration and failures.
Are there Marshallian externalities in job search? We study how workers who lose their jobs in establishment closures in Germany cope with their loss of employment. About a fifth of these displaced workers do not return to social-security covered employment within the next three years. Among those who do get re-employed, about two-thirds leave their old industry and one-third move out of their region. However, which of these two types of mobility responses workers will choose depends on the local industry mix in ways that are suggestive of Marshallian benefits to job search. In particular, large concentrations of one’s old industry makes it easier to find new jobs: in regions where the pre-displacement industry is large, displaced workers suffer relatively small earnings losses and find new work faster. In contrast, large local industries skill-related to the pre-displacement industry increase earnings losses but also protect against long-term unemployment. Analyzed through the lens of a job-search model, the exact spatial and industrial job-switching patterns reveal that workers take these Marshallian externalities into account when deciding how to allocate search efforts among industries.
Does technology require labour mobility to diffuse? To explore this, we use German social-security data and ask how plants that pioneer an industry in a location – and for which the local labour market offers no experienced workers – assemble their workforces. These pioneers use different recruiting strategies than plants elsewhere: they hire more workers from outside their industry and from outside their region, especially when workers come from closely related industries or are highly skilled. The importance of access to experienced workers is highlighted in the diffusion of industries from western Germany to the post-reunification economy of eastern German. While manufacturing employment declined in most advanced economies, eastern German regions managed to reindustrialise. The pioneers involved in this process relied heavily on expertise from western Germany: while establishing new manufacturing industries in the East, they sourced half of their experienced workers from the West.
Governments in modern societies undertake an array of complex functions that shape politics and economics, individual and group behavior, and the natural, social, and built environment. How are governments structured to execute these diverse responsibilities? How do those structures vary, and what explains the differences? To examine these longstanding questions, we develop a technique for mapping Internet “footprint” of government with network science methods. We use this approach to describe and analyze the diversity in functional scale and structure among the 50 US state governments reflected in the webpages and links they have created online: 32.5 million webpages and 110 million hyperlinks among 47,631 agencies. We first verify that this extensive online footprint systematically reflects known characteristics: 50 hierarchically organized networks of state agencies that scale with population and are specialized around easily identifiable functions in accordance with legal mandates. We also find that the footprint reflects extensive diversity among these state functional hierarchies. We hypothesize that this variation should reflect, among other factors, state income, economic structure, ideology, and location. We find that government structures are most strongly associated with state economic structures, with location and income playing more limited roles. Voters’ recent ideological preferences about the proper roles and extent of government are not significantly associated with the scale and structure of their state governments as reflected online. We conclude that the online footprint of governments offers a broad and comprehensive window on how they are structured that can help deepen understanding of those structures.
Urban areas with larger and more connected populations offer an auspicious environment for contagion processes such as the spread of pathogens. Empirical evidence reveals a systematic increase in the rates of certain sexually transmitted diseases (STDs) with larger urban population size. However, the main drivers of these systemic infection patterns are still not well understood, and rampant urbanization rates worldwide makes it critical to advance our understanding on this front. Using confirmed-cases data for three STDs in US metropolitan areas, we investigate the scaling patterns of infectious disease incidence in urban areas. The most salient features of these patterns are that, on average, the incidence of infectious diseases that transmit with less ease– either because of a lower inherent transmissibility or due to a less suitable environment for transmission– scale more steeply with population size, are less predictable across time and more variable across cities of similar size. These features are explained, first, using a simple mathematical model of contagion, and then through the lens of a new theory of urban scaling. These frameworks help us reveal the links between the factors that determine the transmissibility of infectious diseases and the properties of their scaling patterns across cities.
The degree to which modern technologies are able to substitute for groups of job tasks has renewed fears of near-future technological unemployment. We argue that our knowledge, skills and abilities (KSA) go beyond the specific tasks we do at the job, making us potentially more adaptable to technological change than feared. The disruptiveness of new technologies depends on the relationships between the job tasks susceptible to automation and our KSA. Here we first demonstrate that KSA are general human capital features while job tasks are not, suggesting that human capital is more transferrable across occupations than what job tasks would predict. In spite of this, we document a worrying pattern where automation is not randomly distributed across the KSA space – it is concentrated among occupations that share similar KSA. As a result, workers in these occupations are making longer skill transitions when changing occupations and have higher probability of unemployment.
This article provides an empirical assessment of global scientific mobility over the past four decades, based on bibliometric data. We find (i) an increasing diversity of origin and destination countries integrated in global scientific mobility, with (ii) the centre of gravity of scientific knowledge production and migration destinations moving continuously eastwards by about 1300 km per decade, (iii) an increase in average migration distances of scientists reflecting integration of global peripheries into the global science system, (iv) significantly lower mobility frictions for internationally mobile scientists compared to non-scientist migrants, (v) with visa restrictions establishing a statistically significant barrier affecting international mobility of scientists hampering the global diffusion of scientific knowledge.
Using a large individual-level survey spanning several years and more than 150 countries, we examine the importance of social networks in influencing individuals’ intention to migrate internationally and locally. We distinguish close social networks (composed of friends and family) abroad and at the current location, and broad social networks (composed of same-country residents with intention to migrate, either internationally or locally). We find that social networks abroad are the most important driving forces of international migration intentions, with close and broad networks jointly explaining about 37% of variation in the probability intentions. Social networks are found to be more important factors driving migration intentions than work-related aspects or wealth (wealth accounts for less than 3% of the variation). In addition, we find that having stronger close social networks at home has the opposite effect by reducing the likelihood of migration intentions, both internationally and locally.
Using a large individual-level survey spanning several years and more than 150 countries, we examine the importance of social networks in influencing individuals' intention to migrate internationally and locally. We distinguish close social networks (composed of friends and family) abroad and at the current location, and broad social networks (composed of same-country residents with intention to migrate, either internationally or locally). We find that social networks abroad are the most important driving forces of international migration intentions, with close and broad networks jointly explaining about 37% of variation in the probability intentions. Social networks are found to be more important factors driving migration intentions than work-related aspects or wealth (wealth accounts for less than 3% of the variation). In addition, we nd that having stronger close social networks at home has the opposite effect by reducing the likelihood of migration intentions, both internationally and locally.
The fact that firms benefit from close proximity to other firms with which they can exchange inputs, skilled labor or know-how helps explain why many industrial clusters are so successful. Studying the evolution of coagglomeration patterns, we show that which type of agglomeration benefits firms has drastically changed over the course of a century and differs markedly across industries. Whereas, at the beginning of the twentieth century, industries tended to colocate with their value chain partners, in more recent decades the importance of this channels has declined and colocation seems to be driven more by similarities industries' skill requirements. By calculating industry-specific Marshallian agglomeration forces, we are able to show that, nowadays, skill-sharing is the most salient motive in location choices of services, whereas value chain linkages still explain much of the colocation patterns in manufacturing. Moreover, the estimated degrees to which labor and input-output linkages are reflected in an industry's coagglomeration patterns help improve predictions of city-industry employment growth.
The recent economic depression in Greece hit the population of Albanian migrants in Greece particularly hard, spurring a wave of return migration that increased the Albanian labor force by 5% in less than four years, between 2011 and 2014. We study how this return migration affected the employment chances and earnings of Albanians who never migrated. We find positive effects on the wages of low-skilled non-migrants and overall positive effects on employment. The gains partially offset the sharp drop in remittances in the observed period. An important part of the employment gains are concentrated in the agricultural sector, where most return migrants engage in self-employment and entrepreneurship. Businesses run by return migrants seem to pull Albanians from non-participation, unemployment and subsistence agriculture into commercial agriculture.
Networks are powerful instruments to study complex phenomena, but they become hard to analyze in data that contain noise. Network backbones provide a tool to extract the latent structure from noisy networks by pruning non-salient edges. We describe a new approach to extract such backbones. We assume that edge weights are drawn from a binomial distribution, and estimate the error-variance in edge weights using a Bayesian framework. Our approach uses a more realistic null model for the edge weight creation process than prior work. In particular, it simultaneously considers the propensity of nodes to send and receive connections, whereas previous approaches only considered nodes as emitters of edges. We test our model with real world networks of different types (flows, stocks, cooccurrences, directed, undirected) and show that our Noise-Corrected approach returns backbones that outperform other approaches on a number of criteria. Our approach is scalable, able to deal with networks with millions of edges.
About four years ago, at the onset of CID’s engagement in Albania, the country faced two issues that were threatening its macro-fiscal stability: a skyrocketing public debt and an insolvent, publicly-owned electricity distribution system that was plagued by theft and technical inefficiency. These two interlinked issues constrained both short-term economic growth and the ability of the country to develop new drivers of long-term growth. Over the subsequent years, the government was able to successfully respond to these constraints through a now-concluded IMF program and through a series of reforms in the electricity sector. With these constraints now relaxed, CID saw the need for a new analysis of the current and emerging constraints to growth in Albania. This analysis will guide future research and inform the government and non-government actors on emerging economic issues for prioritization.
While growth has accelerated over the last several years, to over 3% in 2016, this is not a pace that will allow for a rapid convergence of incomes and well-being in Albania with that of developed countries in Europe and elsewhere. This growth diagnostic attempts to identify the binding constraint to sustainably higher economic growth in Albania.
Recognizing that economic growth requires a number of complementary inputs, from roads to human capital to access to finance and many more, this report compares across eight potentially binding constraints using the growth diagnostic methodology to identify which constraint is most binding. This research was conducted throughout 2016, building on prior research conducted by CID and other organizations in Albania. Each constraint discussed in this report is cited by analysts within or outside the country as the biggest problem for growth in Albania. Through the growth diagnostic framework, we are able to evaluate the evidence and show that some constraints are more binding than others.
Despite serious issues in many other areas, we find that the binding constraint to stronger growth in Albania is a lack of productive knowhow. By “knowhow,” we mean the knowledge and skills needed to produce complex goods and services. Albania faces a unique knowhow constraint that is deeply rooted in its closed-off past, and the limited diversification that has taken place in the private sector can, in nearly all cases, be linked to distinct inflows of knowhow. The strongest sources of knowhow inflows into Albania have been through foreign direct investment and immigration, especially returning members of the diaspora who start new businesses or upgrade the productivity of existing businesses.
The evidence also points to particular failings in rule of law in Albania that play an important role in keeping Albania in a low-knowhow equilibrium. Weaknesses in Albania’s rule of law institutions, including frequent policy reversals and corruption in the bureaucracy and judiciary, increase the risk of investments and transaction costs of business. While it is difficult to separate perceptions from reality in this area, both perceptions of weak rule of law and actual rule of law failings appear to play critical roles in constraining more diversified investment in Albania. We find that while existing firms in Albania successfully navigate the rule of law weaknesses, and in some cases benefit from the system, potential new investors are acutely sensitive to rule of law issues.
The recent economic depression in Greece hit the population of Albanian migrants in Greece particularly hard, spurring a wave of return migration which increased the Albanian labor force by 5 percent in less than four years, between 2011 and 2014. We study how this return migration affected the employment chances and earnings of Albanians who never migrated. We find positive effects on the wages of low-skilled non-migrants and overall positive effects on employment. The gains partially offset the sharp drop in remittances in the observed period. An important part of the employment gains are concentrated in the agricultural sector, where most return migrants engage in self-employment and entrepreneurship. Businesses run by return migrants seem to pull Albanians from non-participation, unemployment and subsistence agriculture into commercial agriculture.
How important is working with people who complement one's skills? Using administrative data that record which of 491 educational tracks each worker in Sweden absolved, I quantify the educational fit among coworkers along two dimensions: coworker match and coworker substitutability. Complementary coworkers raise wages with a comparable factor as does a college degree, whereas working with close substitutes is associated with wage penalties. Moreover, this coworker fit does not only account for large portions of the urban and large-plant wage premiums, but the returns to own schooling and the urban wage premium are almost completely contingent on finding complementary coworkers.
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.
The prevalence of many urban phenomena changes systematically with population size1. We propose a theory that unifies models of economic complexity2,3 and cultural evolution4 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.
The prevalence of many urban phenomena changes systematically with population size1. We propose a theory that unifies models of economic complexity2, 3 and cultural evolution4 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.