nep-net New Economics Papers
on Network Economics
Issue of 2020‒11‒09
sixteen papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Why are connections to editorial board members of economics journals valuable? By Lorenzo Ductor; Bauke Visser
  2. Peers, Gender, and Long-Term Depression By Giulietti, Corrado; Vlassopoulos, Michael; Zenou, Yves
  3. Social Finance By Theresa Kuchler; Johannes Stroebel
  4. Close social networks among older adults: the online and offline perspectives By Beatriz Sofía Gil-Clavel; Emilio Zagheni; Valeria Bordone
  5. Explaining residential clustering of fertility By Janna Bergsvik; Sara Cools; Rannveig K. Hart
  6. Liquidity, Interbank Network Topology and Bank Capital By Aref Ardekani
  7. Off the Grid... and Back Again? The Recent Evolution of American Street Network Planning and Design By Geoff Boeing
  8. Socioeconomic Network Heterogeneity and Pandemic Policy Response By Akbarpour, Mohammad; Cook, Cody; Marzuoli, Aude; Mongey, Simon; Nagaraj, Abhishek; Saccarola, Matteo; Tebaldi, Pietro; Vasserman, Shoshana; Yang, Hanbin
  9. Economic complexity for competitiveness and innovation: A novel bottom-up strategy linking global and regional capacities By PUGLIESE Emanuele; TACCHELLA Andrea
  10. Does it Pay Off to Learn a New Skill? Revealing the Economic Benefits of Cross-Skilling By Fabian Stephany
  11. Peer Gender and Mental Health By Getik, Demid; Meier, Armando N.
  12. Technology network structure conditions the economic resilience of regions By Gergo Toth; Zoltan Elekes; Adam Whittle; Changjun Lee; Dieter F. Kogler
  13. Empirical likelihood and uniform convergence rates for dyadic kernel density estimation By Harold D. Chiang; Bing Yang Tan
  14. Interpretable Neural Networks for Panel Data Analysis in Economics By Yucheng Yang; Zhong Zheng; Weinan E
  15. Sparse Network Asymptotics for Logistic Regression By Bryan S. Graham
  16. Input-Output Networks and Misallocation By Jing Hang; Pravin Krishna; Heiwai Tang

  1. By: Lorenzo Ductor (Department of Economic Theory and Economic History, University of Granada.); Bauke Visser (Erasmus University Rotterdam and Tinbergen Institute)
    Abstract: Using novel and large-scale data, we estimate the causal effect of being connected to an editorial board member of an economics journal on a department’s or coauthor’s success in publishing in the journal. Prior studies suggests that editors are helping colleagues not themselves and that connections lead to markedly better papers. Instead, we explicitly take into account that authors and editorial board members are not two distinct sets of persons and find that of the overall 27% increase in a department’s annual publication record in a journal, 73% is thanks to the increase in the number of publications by editorial board members themselves. At the individual level, co-authors publish 7% more articles in the journal, excluding the work with the editorial board member. More editorial power, captured by the member’s role in the submission process, and long service on the editorial board lead to substantially larger increases. We analyze various mechanisms. Rather than a marked increase in quality thanks to connections, we find no such increase (nor signs of favoritism). Analysis of individual-level connections suggests that connections act as signals of a coauthor’s quality.
    Keywords: editorial boards, networks, collaboration
    JEL: A11 D71 I26 J44 O30
    Date: 2020–10–20
  2. By: Giulietti, Corrado (University of Southampton, UK); Vlassopoulos, Michael (University of Southampton, UK); Zenou, Yves (Monash University)
    Abstract: This study investigates whether exposure to peer depression in adolescence affects own depression in adulthood. We find a significant long-term depression peer effect for females but not for males in a sample of U.S. adolescents who are followed into adulthood. An increase of one standard deviation of the share of own-gender peers (schoolmates) who are depressed increases the probability of depression in adulthood by 2.6 percentage points for females (or 11.5% of mean depression). We also find that the peer effect is already present in the short term when girls are still in school and provide suggestive evidence for why it persists over time. In particular, we show that peer depression negatively affects the probability of college attendance and the likelihood of working, and leads to a reduction in income of adult females. Further analysis reveals that individuals from families with a lower socioeconomic background are more susceptible to peer influence, thereby suggesting that family can function as a buffer.
    Keywords: Peer effects; Depression; Contagion; Gender; Family background; Adolescence; Policy
    JEL: I12 Z13
    Date: 2020–10–13
  3. By: Theresa Kuchler; Johannes Stroebel
    Abstract: We review an empirical literature that studies the role of social interactions in driving economic and financial decision making. We first summarize recent work that documents an important role of social interactions in explaining household decisions in housing and mortgage markets. This evidence shows, for example, that there are large peer effects in mortgage refinancing decisions and that individuals' beliefs about the attractiveness of housing market investments are affected by the recent house price experiences of their friends. We also summarize the evidence that social interactions affect the stock market investments of both retail and professional investors as well as household financial decisions such as retirement savings, borrowing, and default. Along the way, we describe a number of easily accessible recent data sets for the study of social interactions in finance, including the "Social Connectedness Index," which measures the frequency of Facebook friendship links across geographic regions. We conclude by outlining several promising directions for further research at the intersection of household finance and "social finance."
    JEL: G0
    Date: 2020–10
  4. By: Beatriz Sofía Gil-Clavel (Max Planck Institute for Demographic Research, Rostock, Germany); Emilio Zagheni (Max Planck Institute for Demographic Research, Rostock, Germany); Valeria Bordone (Max Planck Institute for Demographic Research, Rostock, Germany)
    Abstract: Qualitative studies have found that the use of Information and Communication Technologies is related to an enhanced quality of life for older adults, as these technologies might act as a medium to access social capital regardless of distance. In order to quantitatively study the association between older people’s characteristics and the likelihood of having a network of close friends offline and online, we use data from the Survey of Health, Ageing and Retirement in Europe and from Facebook. Using a novel approach to analyze aggregated and anonymous Facebook data within a regression framework, we show that the associations between having close friends and age, sex and being a parent are the same offline and online. Migrants who use internet are less likely to have close friends offline, but migrants who are Facebook users are more likely to have close friends online, suggesting that digital relationships may compensate for the potential lack of offline close friendships among older migrants.
    Keywords: Europe, old age, social capital, social network
    JEL: J1 Z0
    Date: 2020
  5. By: Janna Bergsvik (Statistics Norway); Sara Cools; Rannveig K. Hart
    Abstract: Numerous studies have shown that fertility behavior is spatially clustered. In addition to pure context effects, two causal mechanisms could drive this pattern. First, neighbors may influence each other’s fertility behavior, and second, household fertility intentions and behavior may influence residential decisions. This study provides an empirical examination of these two potential causal mechanisms using the sex composition of the two firstborn children and twin births as instrumental variables (IVs) for having a third child. We measure effects of the third child on three separate outcomes: mothers’ propensity to move, characteristics of their final neighborhood, and the fertility of their neighbors. Residential and childbearing histories for the years 2000-2018 are drawn from Norwegian administrative registers (N ~ 167,000 women). Individual neighborhoods are defined using timevarying geo-coordinates on place of residence. We identify selective moves as one plausible causal driver of the residential clustering of fertility. The effects are relatively small, though statistically significant. This suggests that the residential clustering of fertility is also driven by factors that we effectively control for in our design – most importantly self-selection based on preferences for a family-oriented life style. Because of the difficulty to measure social interaction effects among neighbors we are reluctant to say that they do not exist, even though we do not identify them. As such, we contribute to the understanding of fertility and relocation, but also to the literature on social interaction effects in fertility by testing the relevance of yet another network, i.e. that of neighbors.
    Keywords: IV estimation; spatial fertility; k-nearest neighbors; family size; third births
    JEL: J11 J13 R20 R21 R23
    Date: 2020–10
  6. By: Aref Ardekani (UP1 - Université Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, UNILIM - Université de Limoges)
    Abstract: By applying the interbank network simulation, this paper examines whether the causal relationship between capital and liquidity is influenced by bank positions in the interbank network. While existing literature highlights the causal relationship that moves from liquidity to capital, the question of how interbank network characteristics affect this relationship remains unclear. Using a sample of commercial banks from 28 European countries, this paper suggests that banks' interconnectedness within interbank loan and deposit networks affects their decisions to set higher or lower regulatory capital rations when facing higher illiquidity. This study provides support for the need to implement minimum liquidity ratios to complement capital ratios, as stressed by the Basel Committee on Banking Regulation and Supervision. This paper also highlights the need for regulatory authorities to consider the network characteristics of banks.
    Keywords: Interbank network topology,Bank regulatory capital,Liquidity risk,Basel III
    Date: 2020–10
  7. By: Geoff Boeing
    Abstract: This morphological study identifies and measures recent nationwide trends in American street network design. Historically, orthogonal street grids provided the interconnectivity and density that researchers identify as important factors for reducing vehicular travel and emissions and increasing road safety and physical activity. During the 20th century, griddedness declined in planning practice alongside declines in urban form compactness, density, and connectivity as urbanization sprawled around automobile dependence. But less is known about comprehensive empirical trends across US neighborhoods, especially in recent years. This study uses public and open data to examine tract-level street networks across the entire US. It develops theoretical and measurement frameworks for a quality of street networks defined here as griddedness. It measures how griddedness, orientation order, straightness, 4-way intersections, and intersection density declined from 1940 through the 1990s while dead-ends and block lengths increased. However, since 2000, these trends have rebounded, shifting back toward historical design patterns. Yet, despite this rebound, when controlling for topography and built environment factors all decades post-1939 are associated with lower griddedness than pre-1940. Higher griddedness is associated with less car ownership - which itself has a well-established relationship with vehicle kilometers traveled and greenhouse gas emissions - while controlling for density, home and household size, income, jobs proximity, street network grain, and local topography. Interconnected grid-like street networks offer practitioners an important tool for curbing car dependence and emissions. Once established, street patterns determine urban spatial structure for centuries, so proactive planning is essential.
    Date: 2020–10
  8. By: Akbarpour, Mohammad (Stanford U); Cook, Cody (Stanford U); Marzuoli, Aude (Overland Park, Kansas); Mongey, Simon (U of Chicago); Nagaraj, Abhishek (U of California, Berkeley); Saccarola, Matteo (U of Chicago); Tebaldi, Pietro (U of Chicago); Vasserman, Shoshana (Stanford U); Yang, Hanbin (Harvard U)
    Abstract: We develop a heterogeneous-agents network-based model to analyze alternative policies during a pandemic outbreak, accounting for health and economic trade-offs within the same empirical framework. We leverage a variety of data sources, including data on individuals' mobility and encounters across metropolitan areas, health records, and measures of the possibility to be productively working from home. This combination of data sources allows us to build a framework in which the severity of a disease outbreak varies across locations and industries, and across individuals who differ by age, occupation, and preexisting health conditions. We use this framework to analyze the impact of different social distancing policies in the context of the COVID-19 outbreaks across US metropolitan areas. Our results highlight how outcomes vary across areas in relation to the underlying heterogeneity in population density, social network structures, population health, and employment characteristics. We find that policies by which individuals who can work from home continue to do so, or in which schools and firms alternate schedules across different groups of students and employees, can be effective in limiting the health and healthcare costs of the pandemic outbreak while also reducing employment losses.
    JEL: H12 H75 I18
    Date: 2020–06
  9. By: PUGLIESE Emanuele (European Commission - JRC); TACCHELLA Andrea (European Commission - JRC)
    Abstract: Economic Complexity is a data driven empirical approach developed to inform the study of territorial development with quantitative metrics. Techniques inspired by complex systems analysis and network theory allow measuring the intangible capabilities necessary for a country or region to be competitive, both in overall terms and in specific markets. We exemplarily analyse the case of Slovakia's industrial and innovation competitiveness by looking at the overall potential of the country and focusing on its Electronics sector.
    Keywords: Economic Complexity Economic Forecasting Structural Change Regional Innovation System
    Date: 2020–10
  10. By: Fabian Stephany
    Abstract: This work examines the economic benefits of learning a new skill from a different domain: cross-skilling. To assess this, a network of skills from the job profiles of 4,810 online freelancers is constructed. Based on this skill network, relationships between 3,525 different skills are revealed and marginal effects of learning a new skill can be calculated via workers' wages. The results indicate that the added economic value of learning a new skill strongly depends on the already existing skill bundle but that acquiring a skill from a different domain is often beneficial. As technological and social transformation is reshuffling jobs' task profiles at a fast pace, the findings of this study help to clarify skill sets required for mastering new technologies and designing individual training pathways. This can help to increase employability and reduce labour market shortages.
    Date: 2020–10
  11. By: Getik, Demid; Meier, Armando N.
    Abstract: Adolescent mental health is key for later well-being. Yet, causal evidence on environmental drivers of adolescent mental health is scant. We study how an important classroom feature---the gender composition in compulsory-school---affects mental health. We use Swedish administrative data (N=576,285) to link variation in gender composition across classrooms within cohorts to mental health. We find that a higher share of female peers in a classroom increases the incidence of mental health diagnoses, particularly among boys. The effect persists into adulthood. Peer composition is thus an important and persistent driver of mental health.
    Keywords: school, gender, peer effects, mental health
    JEL: I12 I19 I21 I31 J16 J24
    Date: 2020–10
  12. By: Gergo Toth; Zoltan Elekes; Adam Whittle; Changjun Lee; Dieter F. Kogler
    Abstract: This paper assesses the network robustness of the technological capability base of 269 European metropolitan areas against the potential elimination of some of their capabilities. By doing so it provides systematic evidence on how network robustness conditioned the economic resilience of these regions in the context of the 2008 economic crisis. The analysis concerns calls in the relevant literature for more in-depth analysis on the link between regional economic network structures and the resilience of regions to economic shocks. By adopting a network science approach that is novel to economic geographic inquiry, the objective is to stress-test the technological resilience of regions by utilizing information on the co-classification of CPC classes listed on European Patent Office patent documents. Findings from a regression analysis indicate that metropolitan regions with a more robust technological knowledge network structure exhibit higher levels of resilience with respect to changes in employment rates. This finding is robust to various random and targeted elimination strategies concerning the most frequently combined technological capabilities. Regions with high levels of employment in industry but with vulnerable technological capability base are particularly challenged by this aspect of regional economic resilience.
    Keywords: regional economic resilience, network robustness, metropolitan regions, technology space
    Date: 2020–09
  13. By: Harold D. Chiang; Bing Yang Tan
    Abstract: This paper studies the asymptotic properties of and improved inference methods for kernel density estimation (KDE) for dyadic data. We first establish novel uniform convergence rates for dyadic KDE under general assumptions. As the existing analytic variance estimator is known to behave unreliably in finite samples, we propose a modified jackknife empirical likelihood procedure for inference. The proposed test statistic is self-normalised and no variance estimator is required. In addition, it is asymptotically pivotal regardless of presence of dyadic clustering. The results are extended to cover the practically relevant case of incomplete dyadic network data. Simulations show that this jackknife empirical likelihood-based inference procedure delivers precise coverage probabilities even under modest sample sizes and with incomplete dyadic data. Finally, we illustrate the method by studying airport congestion.
    Date: 2020–10
  14. By: Yucheng Yang; Zhong Zheng; Weinan E
    Abstract: The lack of interpretability and transparency are preventing economists from using advanced tools like neural networks in their empirical work. In this paper, we propose a new class of interpretable neural network models that can achieve both high prediction accuracy and interpretability in regression problems with time series cross-sectional data. Our model can essentially be written as a simple function of a limited number of interpretable features. In particular, we incorporate a class of interpretable functions named persistent change filters as part of the neural network. We apply this model to predicting individual's monthly employment status using high-dimensional administrative data in China. We achieve an accuracy of 94.5% on the out-of-sample test set, which is comparable to the most accurate conventional machine learning methods. Furthermore, the interpretability of the model allows us to understand the mechanism that underlies the ability for predicting employment status using administrative data: an individual's employment status is closely related to whether she pays different types of insurances. Our work is a useful step towards overcoming the "black box" problem of neural networks, and provide a promising new tool for economists to study administrative and proprietary big data.
    Date: 2020–10
  15. By: Bryan S. Graham
    Abstract: Consider a bipartite network where N consumers choose to buy or not to buy M different products. This paper considers the properties of the logistic regression of the N × M array of “i-buys-j” purchase decisions, [Y ij ] 1≤i≤N,≤j≤M , onto known functions of consumer and product attributes under asymptotic sequences where (i) both N and M grow large and (ii) the average number of products purchased per consumer is finite in the limit. This latter assumption implies that the network of purchases is sparse: only a (very) small fraction of all possible purchases are actually made (concordant with many real-world settings). Under sparse network asymptotics, the first and last terms in an extended Hoeffding-type variance decomposition of the score of the logit composite log-likelihood are of equal order. In contrast, under dense network asymptotics, the last term is asymptotically negligible. Asymptotic normality of the logistic regression coefficients is shown using a martingale central limit theorem (CLT) for triangular arrays. Unlike in the dense case, the normality result derived here also holds under degeneracy of the network graphon. Relatedly, when there “happens to be” no dyadic dependence in the dataset in hand, it specializes to recently derived results on the behavior of logistic regression with rare events and iid data. Sparse network asymptotics may lead to better inference in practice since they suggest variance estimators which (i) incorporate additional sources of sampling variation and (ii) are valid under varying degrees of dyadic dependence.
    JEL: C01 C31 C33 C55
    Date: 2020–10
  16. By: Jing Hang; Pravin Krishna; Heiwai Tang
    Abstract: This paper develops a framework for studying the macroeconomic costs of resource misallocation. The framework enables the assessment of the conditions under which the existing estimates in the misallocation literature, which are largely based on a value-added production structure and ignore inter-sectoral linkages, provide an unbiased estimate of misallocation costs in relation to a more general setting, in which production of gross output relies upon input-output linkages across sectors. We show that in the absence of intermediate input distortions, the two approaches are isomorphic and will yield the same estimated aggregate productivity loss. When firm-specific intermediate input distortions are present, however, the value-added model produces biased estimates of TFP losses due to both model misspecification and incorrect inferences of firms' productivity and distortions. Using Chinese and Indian enterprise data, we find quantitatively similar TFP losses from resource misallocation for China, regardless of the model used, while for India, we infer significantly larger TFP losses under the gross output model.
    JEL: E1 E23 L16 O11 O4
    Date: 2020–10

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