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In this paper, we use tools from network theory to trace the properties of the matching function to the structure of granular connections between applicants and firms. We link seemingly disparate parts of the literature and recover existing functional forms as special cases. Our overarching message is that structure counts. For rich structures, captured by non-random networks, the matching function depends on whole sets rather than just the sizes of the two sides of the market. For less rich-random network-structures it depends on the sizes of the two sides and a few structural parameters. Structures characterized by greater asymmetries reduce the matching function's efficacy, while denser structures can have ambiguous effects on it. For the special case of the Erdös-Rényi network, we show that the way the network varies with the sizes of the two sides of the market determines if the matching function exhibits constant returns to scale, or even if it is of a specific functional form, such as CES.

We introduce loss aversion into a model of conspicuous consumption in networks. Agents allocate their income between a standard good and a status good to maximize a Cobb-Douglas utility. Agents interact over a connected network and compare their status consumption to their neighbors' average consumption. Loss aversion has a profound impact. If loss aversion is large enough relative to income heterogeneity, a continuum of Nash equilibria appears and all agents consume the same quantity of status good. Otherwise, there is a unique Nash equilibrium and richest agents earn strict status gains while poorest agents earn strict status losses.

What patterns of economic relations arise when people are altruistic rather than strategically self-interested? This paper introduces an altruism network into a simple model of choice among partners for economic activity. With concave utility, agents effectively become inequality averse towards friends and family. Rich agents preferentially choose to work with poor friends despite productivity losses. Hence, network inequality-the divergence in incomes within sets of friends and family-is key to how altruism shapes economic relations and output. Skill homophily also plays a role; preferential contracts and productivity losses decline when rich agents have poor friends with requisite skills.

We study a two-period one-to-one dynamic matching environment in which agents meet randomly and decide whether to match early or defer. Crucially, agents can match with either partner in the second period. This "recall" captures situations where, e.g., a firm and worker can conduct additional interviews before contracting. Recall has a profound impact on incentives and on aggregate outcomes. We show that the likelihood to match early is nonmonotonic in type: early matches occur between the good-but-not-best agents. The option value provided by the first-period partner provides a force against unraveling, so that deferrals occur under small participation costs.

We study how altruism networks affect the adoption of formal insurance. Agents have private CARA utilities and are embedded in a network of altruistic relationships. Incomes are subject to both a common shock and a large idiosyncratic shock. Agents can adopt formal insurance to cover the common shock. We show that ex-post altruistic transfers induce interdependence in ex-ante adoption decisions. We characterize the Nash equilibria of the insurance adoption game. We show that adoption decisions are substitutes and that the number of adopters is unique in equilibrium. The demand for formal insurance is lower with altruism than without at low prices, but higher at high prices. Remarkably, individual incentives are aligned with social welfare. We extend our analysis to CRRA utilities and to a fixed utility cost of adoption.

Additional file 1: Additional data on experimental design and individual measures.

Heckman and MaCurdy (1985) first showed that binary outcomes are compatible with linear econometric models of interactions. This key insight was unduly discarded by the literature on the econometrics of games. We consider general models of linear interactions in binary outcomes that nest linear models of peer effects in networks and linear models of entry games. We characterize when these models are well defined. Errors must have a specific discrete structure. We then analyze the models' game-theoretic microfoundations. Under complete information and linear utilities, we characterize the preference shocks under which the linear model of interactions forms a Nash equilibrium of the game. Under incomplete information and independence, we show that the linear model of interactions forms a Bayes-Nash equilibrium if and only if preference shocks are iid and uniformly distributed. We also obtain conditions for uniqueness. Finally, we propose two simple consistent estimators. We revisit the empirical analyses of teenage smoking and peer effects of Lee, Li, and Lin (2014) and of entry into airline markets of Ciliberto and Tamer (2009). Our reanalyses showcase the main interests of the linear framework and suggest that the estimations in these two studies suffer from endogeneity problems.

We provide the first analysis of the risk-sharing implications of altruism networks. Agents are embedded in a fixed network and care about each other. We study whether altruistic transfers help smooth consumption and how this depends on the shape of the network. We identify two benchmarks where altruism networks generate efficient insurance: for any shock when the network of perfect altruism is strongly connected and for any small shock when the network of transfers is weakly connected. We show that the extent of informal insurance depends on the average path length of the altruism network and that small shocks are partially insured by endogenous risk-sharing communities. We uncover complex structural effects. Under iid incomes, central agents tend to be better insured, the consumption correlation between two agents is positive and tends to decrease with network distance, and a new link can decrease or increase the consumption variance of indirect neighbors. Overall, we show that altruism in networks has a first-order impact on risk and generates specific patterns of consumption smoothing.

We first clarify the precise theoretical foundations behind the notion of diffusion centrality. This allows us to address a minor inconsistency in the model description of Banerjee et al. (2013). We then identify unnatural implicit assumptions in the model of political intermediation proposed by Cruz, Labonne & Querubfn (2017). We introduce two extensions of diffusion centrality, targeting centrality and reachability, which we believe better capture features of contexts with targeted requests. We derive general explicit formulas to compute these new measures.

– NRM2018 Introduction The pathological features of Parkinson’s Disease (PD) are mainly driven by the loss of the dopaminergic projection neurons in the substantia nigra (SN) resulting in a dopamine (DA) deficiency in the striatum [1]. We developed a pathologically relevant rodent PD model; overexpressing the mutant (A53T) human alpha-synuclein protein in the SN [2]. Our aims are triple: 1- map neuronal loss and DA deficiency over time using two different presynaptic PET tracers, 2- evaluate the respective sensitivity of each radioligand, and 3- correlate individual PET data to behavioural and histological results. Materials & Methods A total of ten rats were unilaterally injected in the SN with a viral vector (AAV2/6) overexpressing mutated (A53T) human alpha-synuclein, and were studied either at 6 weeks post-injection (6wpi, n=6, 543±36g) or 12wpi (n=4, 573±40g). PET imaging was performed using a ligand substrate for AADC, 6-[18F]fluoro-L-m-tyrosine (“FMT”, 60min acquisition, 31.3-60.8MBq; pre-treatment by IP injection of 10mg/kg benserazide 30’ before imaging [3]), or a ligand for DA transporter (DAT), [18F]-LBT999 [4] (“LBT", 90min acquisition, 40.3-63.0MBq). For behaviour, rats were subjected for 5 minutes to the cylinder test, in which contralateral and ipsilateral paw use was compared. After the in vivo studies rats were sacrificed for histological studies using tyrosine hydroxylase immunohistochemistry. From LBT and FMT PET scans, quantitative uptake images (BPnd and Ki, respectively) were calculated using Logan and Patlak graphical methods, with the cerebellum as a reference. Unilateral AAV injections allowed the contralateral striatum to serve as internal control. Paired student t-test were used to compare the contra- and ipsilateral sides in imaging studies, while an ANOVA was used to compare contralateral paw use to a control group. Results Injection of benserazide was not effective in 42% of the animals, thus Ki values could not reasonably be estimated for FMT. Additionally, Ki images showed more non-specific binding than LBT BPnd images. At 6wpi we did not observe any asymmetry in the Ki of quantifiable scans (n=5, p=0.084, Fig 1A, D), however LBT data show decreased BPnd in the ipsilateral caudate putamen (n=6, p=0.027; Fig 1B, D). At 12wpi we observed a similar pattern (Ki, n=2, p=0.336; BPnd, n=4, p=0.003; Fig 1A, B, D). These results are in concordance with the behavioural observations, showing roughly only 30% use of the contralateral forepaw at 8wpi (n=7, p=0.044) and 12wpi (n=8, p=0.045; Fig 1C). No significant correlations between PET data and behaviour were observed. Histological comparisons are still ongoing. Fig 1: Positron emission tomography and behavioural studies. PET scans were obtained from α-syn-A53T rats at 6wpi and 10/12wpi, using a tracer substrate of AADC (18F-FMTyr) (A), or a dopamine transporter ligand (DAT, 18F-LBT999) (B). (A) Neither at 6wpi (n=5) nor at 12wpi (n=2) a difference was observed in AADC metabolism (FMT). (B) In contrast the DAT tracer (LBT999) showed a significant difference at 6wpi (n=6) and 12wpi (n=4). (C) Cylinder tests at 8wpi (n=7) and 10wpi (n=8) detected motor deficits in α-syn-A53T overexpressing rats. (D) Representative PET images of both tracers. Discussion/Conclusion We created an AAV rat model of PD that shows progressive DA deficiency and neuronal loss detectable by FMT and LBT PET imaging. Our parametric data suggest that the DAT tracer is more sensitive to detect a mild PD phenotype as compared to the AADC tracer. This phenomenon has previously been described, and is possibly due to a combination of reduced nerve terminal DAT binding sites and downregulation of DAT in surviving neurons, in an attempt to increase DA availability [5]. More FMT scans will have to be done to increase numbers and compensate for ineffective benserazide blocking. Further analysis of PET data will allow correlating PET data to behavioural and histological measurements. Acknowledgements This project has been funded by the European Union Horizon 2020 Programme (H2020-MSCA-ITN-2015) under the Marie Skłodowska-Curie Innovative Training Network and Grant Agreement No. 676408. References 1. Dauer W. and Przedborski S. [2003], Neuron, 39(6): p. 889-909. 2. Cresto N. et al. [2017], Neurodegener Dis 2017;17(suppl 1):8-590 – Page 448, 2017. 3. Becker G. et al. [2017] J Neurochem, 141(4):626-635. 4. Serriere S. et al. [2014] Nucl Med Biol, 41(1): p. 106-13. 5. Arena J. and Stoessl A. [2016] Relat Disord, 22 Suppl 1: p. S47-51., This project has been funded by the European Union Horizon 2020 Programme (H2020-MSCA-ITN-2015) under the Marie Skłodowska-Curie Innovative Training Network and Grant Agreement No. 676408.

NECTAR 2018 Parkinson’s Disease (PD) is characterized by dopaminergic neuronal loss in the substantia nigra (SN) resulting in a dopamine deficiency in the striatum.1 We developed a pathologically relevant rat model of PD using viral vectors to overexpress mutant alpha-synuclein.2 Our aims were to evaluate the dopaminergic changes and neuronal loss at different time-points in this model with two different presynaptic PET tracers, and relate imaging results with behavioural and histological changes. Therefor rats were unilaterally injected in the SN with AAV2/6 viral vector coding for mutated (A53T) human alpha-synuclein, and were studied at 6 or 12 weeks post-injection (wpi). PET imaging was performed using 6-[18F]fluoro-L-m-tyrosine3 (FMT), a substrate for AADC and [18F]-LBT9994 (LBT), a radioligand for dopamine transporter (DAT). Quantitative uptake images were calculated using Logan and Patlak graphical methods, with a cerebellar reference. At 6wpi we did not observe any asymmetry in the striatum; neither with FMT (n=5;p=0.421) nor LBT (n=6;p=0.310). At 12wpi we observed an asymmetry with LBT (n=4;p=0.003), but not with FMT (n=2;p=0.336). Histological evaluation showed 28% loss of TH-positive cells in the SN at 12wpi (n=8;p=0.031), but not at 6wpi (n=6;ns). A motor deficit was observed with the cylinder test at both time-points (n=7/8;pin vivo in a rat model characterized by progressive and mild neurodegeneration of the nigro-striatal pathway. This PET tracer may be of particular interest to test neuroprotective or neuro-restorative therapeutic strategies in future studies using our PD rat model. References: 1. [10.1016/S0896-6273(03)00568-3] 2. Cresto et al. [2017], Neurodegener Dis 2017;17(suppl 1):8-590 – Page 448. 3. [10.1111/jnc.14016] 4. [10.1016/j.nucmedbio.2013.09.007] Acknowledgements: This project has been funded by the European Union Horizon 2020 Programme (H2020-MSCA-ITN-2015) under the Marie Skłodowska-Curie Innovative Training Network and Grant Agreement No.676408., This project has been funded by the European Union Horizon 2020 Programme (H2020-MSCA-ITN-2015) under the Marie Skłodowska-Curie Innovative Training Network and Grant Agreement No.676408.

– YRLS 2018 Acknowledgements This project has been funded by the European Union Horizon 2020 Programme (H2020-MSCA-ITN-2015) under the Marie Skłodowska-Curie Innovative Training Network and Grant Agreement No. 676408. Abstract Parkinson’s disease (PD) is the second most prevalent age-related neurodegenerative disorder, resulting in a dopamine (DA) deficiency in the striatum [10.1016/S0896-6273(03)00568-3]. We developed two rodent PD models overexpressing either WT or mutant (A53T) human alpha-synuclein protein (α-syn) in the substantia nigra (SN) [Cresto et al., unpublished]. Our aims are to evaluate the DA changes and neuronal loss in these models by two different presynaptic PET tracers and correlate these results with behaviour and histological data. Two groups of rats were unilateral injected in the SN with an AAV-viral vector overexpressing WT-α-syn or mutant α-syn (A53T-α-syn) and were studied using cylinder test and PET imaging at 12 weeks post injection, and subsequently sacrificed for stereological counting after tyrosine hydroxylase staining. PET imaging was performed using 6-[18F]fluoro-L-m-tyrosine [10.1111/jnc.14016](“FMT”), or [18F]-LBT999 [10.1016/j.nucmedbio.2013.09.007](“LBT"). We did not observed decreased AADC activity in ipsilateral striata with FMT for the WT-α-syn (n=3, p=0.255) nor A53T-α-syn rats (n=2, p=0.336). LBT showed decreased DA transporter (DAT) binding in the ipsilateral striata of A53T-α-syn animals (n=4, p=0.003), and a trend towards decreased DAT binding in the WT-α-syn group (n=3, p=0.051). These results are in concordance with the behavioural observations, showing roughly only 30% use of the contralateral forepaw for A53T-α-syn (n=8, p=0.045), while the WT-α-syn rats showed no significant difference (n=7, p=0.949). All counts using stereological methods and comparisons, are still ongoing. We have shown, using in vivo PET imaging and behavioural analysis, that the A53T-α-syn vector induces stronger dopaminergic pathology than the WT-α-syn vector. This model is interesting to evaluate therapeutic strategies for PD by in vivo imaging. Additionally we have defined DAT transporter imaging, as more sensitive and robust as compared to AADC substrate imaging., This project has been funded by the European Union Horizon 2020 Programme (H2020-MSCA-ITN-2015) under the Marie Skłodowska-Curie Innovative Training Network and Grant Agreement No. 676408.

International audience; The Oxford Handbook of the Economics of Networks represents the frontier of research into how and why networks they form, how they influence behavior, how they help govern outcomes in an interactive world, and how they shape collective decision making, opinion formation, and diffusion dynamics. From a methodological perspective, the contributors to this volume devote attention to theory, field experiments, laboratory experiments, and econometrics. Theoretical work in network formation, games played on networks, repeated games, and the interaction between linking and behavior is synthesized. A number of chapters are devoted to studying social process mediated by networks. Topics here include opinion formation, diffusion of information and disease, and learning. There are also chapters devoted to financial contagion and systemic risk, motivated in part by the recent financial crises. Another section discusses communities, with applications including social trust, favor exchange, and social collateral; the importance of communities for migration patterns; and the role that networks and communities play in the labor market. A prominent role of networks, from an economic perspective, is that they mediate trade. Several chapters cover bilateral trade in networks, strategic intermediation, and the role of networks in international trade. Contributions discuss as well the role of networks for organizations. On the one hand, one chapter discusses the role of networks for the performance of organizations, while two other chapters discuss managing networks of consumers and pricing in the presence of network-based spillovers. Finally, the authors discuss the internet as a network with attention to the issue of net neutrality. Contributors to this volume - Daron Acemoglu Sinan Aral Lori Beaman Francis Bloch Vincent Boucher Yann Bramoulle Emily Breza Antonio Cabrales Arun Chandrasekhar Thomas Chaney Syngjoo Choi Daniele Condorelli Wouter Dessein Marcin Dziubinski Nick Economides

OS; International audience; The Oxford Handbook of the Economics of Networks represents the frontier of research into how and why networks they form, how they influence behavior, how they help govern outcomes in an interactive world, and how they shape collective decision making, opinion formation, and diffusion dynamics. From a methodological perspective, the contributors to this volume devote attention to theory, field experiments, laboratory experiments, and econometrics. Theoretical work in network formation, games played on networks, repeated games, and the interaction between linking and behavior is synthesized. A number of chapters are devoted to studying social process mediated by networks. Topics here include opinion formation, diffusion of information and disease, and learning. There are also chapters devoted to financial contagion and systemic risk, motivated in part by the recent financial crises. Another section discusses communities, with applications including social trust, favor exchange, and social collateral; the importance of communities for migration patterns; and the role that networks and communities play in the labor market. A prominent role of networks, from an economic perspective, is that they mediate trade. Several chapters cover bilateral trade in networks, strategic intermediation, and the role of networks in international trade. Contributions discuss as well the role of networks for organizations. On the one hand, one chapter discusses the role of networks for the performance of organizations, while two other chapters discuss managing networks of consumers and pricing in the presence of network-based spillovers. Finally, the authors discuss the internet as a network with attention to the issue of net neutrality. Contributors to this volume - Daron Acemoglu Sinan Aral Lori Beaman Francis Bloch Vincent Boucher Yann Bramoulle Emily Breza Antonio Cabrales Arun Chandrasekhar Thomas Chaney Syngjoo Choi Daniele Condorelli Wouter Dessein Marcin Dziubinski Nick Economides