The Network Picture of Labor Flow

We construct a data-driven model of flows in graphs that captures the essential elements of the movement of workers between jobs in the companies (firms) of entire economic systems such as countries. The model is based on the observation that certain job transitions between firms are often repeated over time, showing persistent behavior, and suggesting the construction of static graphs to act as the scaffolding for job mobility. Individuals in the job market (the workforce) are modelled by a discrete-time random walk on graphs, where each individual at a node can possess two states: employed or unemployed, and the rates of becoming unemployed and of finding a new job are node dependent parameters. We calculate the steady state solution of the model and compare it to extensive micro-datasets for Mexico and Finland, comprised of hundreds of thousands of firms and individuals. We find that our model possesses the correct behavior for the numbers of employed and unemployed individuals in these countries down to the level of individual firms. Our framework opens the door to a new approach to the analysis of labor mobility at high resolution, with the tantalizing potential for the development of full forecasting methods in the future.Comment: 27 pages, 6 figure

Frictional Unemployment on Labor Flow Networks

We develop an alternative theory to the aggregate matching function in which workers search for jobs through a network of firms: the labor flow network. The lack of an edge between two companies indicates the impossibility of labor flows between them due to high frictions. In equilibrium, firms' hiring behavior correlates through the network, generating highly disaggregated local unemployment. Hence, aggregation depends on the topology of the network in non-trivial ways. This theory provides new micro-foundations for the Beveridge curve, wage dispersion, and the employer-size premium. We apply our model to employer-employee matched records and find that network topologies with Pareto-distributed connections cause disproportionately large changes on aggregate unemployment under high labor supply elasticity

The cause of universality in growth fluctuations

Phenomena as diverse as breeding bird populations, the size of U.S. firms, money invested in mutual funds, the GDP of individual countries and the scientific output of universities all show unusual but remarkably similar growth fluctuations. The fluctuations display characteristic features, including double exponential scaling in the body of the distribution and power law scaling of the standard deviation as a function of size. To explain this we propose a remarkably simple additive replication model: At each step each individual is replaced by a new number of individuals drawn from the same replication distribution. If the replication distribution is sufficiently heavy tailed then the growth fluctuations are Levy distributed. We analyze the data from bird populations, firms, and mutual funds and show that our predictions match the data well, in several respects: Our theory results in a much better collapse of the individual distributions onto a single curve and also correctly predicts the scaling of the standard deviation with size. To illustrate how this can emerge from a collective microscopic dynamics we propose a model based on stochastic influence dynamics over a scale-free contact network and show that it produces results similar to those observed. We also extend the model to deal with correlations between individual elements. Our main conclusion is that the universality of growth fluctuations is driven by the additivity of growth processes and the action of the generalized central limit theorem.Comment: 18 pages, 4 figures, Supporting information provided with the source files

Agent activation in a replication of the zero-intelligence trader double auction market simulation

A model of a double auction market of zerointelligence traders was replicated as an agent-based model using the same market supply and demand curves. The original results were reproduced, and these results and other behavior of the model were examined under different schemes of agent activation, both exogenous and endogenous. While the qualitative differences were typically minor, there were statistically significant differences in all the measures of all the markets in the original research and important divergence in the extended evolution of the simulation. These differences have important implications for all follow-on replications of a zero-intelligence trading model, and for the replication process in general

The Network Composition of Aggregate Unemployment

We develop an alternative framework to the aggregate matching function in which workers search for jobs through a network of firms: the labor flow network. The lack of an edge between two companies indicates the impossibility of labor flows between them due to high frictions. In equilibrium, firms' hiring behavior correlates through the network, generating highly disaggregated local unemployment. Hence, aggregation depends on the topology of the network in non-trivial ways. This theory provides new micro-foundations for the the Beveridge curve, wage dispersion, and the employer-size premium. Using employer-employee matched records, we find that the empirical topology of the network, in conjunction with the supply elasticity, may be a major contributor of aggregate unemployment

Changing How We Discount to Make Public Policy More Responsive To Citizens'Time Preferences

Laws and regulations have both benefits and costs, comparisons of which provide useful information. When such benefits and costs occur over time it is normal practice to discount them, with costs in the distant future considered less important than those occurring in the present, and distant benefits less valuable than present benefits. Conventionally, this discounting is done at a constant rate each period'so-called exponential discounting' with both cost and benefit streams being treated as if they were financial assets. However, humans are not exponential discounters. Rather, people tend to discount more than exponentially in the short run but less than exponentially in the long run. Such 'hyperbolic' discounting is empirically ubiquitous but neglected administratively. The recent "data quality act" requires that information utilized by Federal agencies "adhere to a basic standard of quality, including objectivity, utility and integrity." Here we argue that exponential discounting of many benefit streams, particularly non-pecuniary ones, fails the "data quality" test and should be abandoned in favor of empirically-observed hyperbolic discounting.

The role of B cells in primary progressive multiple sclerosis

The success of ocrelizumab in reducing confirmed disability accumulation in primary progressive multiple sclerosis (PPMS) via CD20-targeted depletion implicates B cells as causal agents in the pathogenesis of PPMS. This review explores the possible mechanisms by which B cells contribute to disease progression in PPMS, specifically exploring cytokine production, antigen presentation, and antibody synthesis. B cells may contribute to disease progression in PPMS through cytokine production, specifically GM-CSF and IL-6, which can drive naïve T-cell differentiation into pro-inflammatory Th1/Th17 cells. B cell production of the cytokine LT-α may induce follicular dendritic cell production of CXCL13 and lead indirectly to T and B cell infiltration into the CNS. In contrast, production of IL-10 by B cells likely induces an anti-inflammatory effect that may play a role in reducing neuroinflammation in PPMS. Therefore, reduced production of IL-10 may contribute to disease worsening. B cells are also capable of potent antigen presentation and may induce pro-inflammatory T-cell differentiation via cognate interactions. B cells may also contribute to disease activity via antibody synthesis, although it\u27s unlikely the benefit of ocrelizumab in PPMS occurs via antibody decrement. Finally, various B cell subsets likely promulgate pro- or anti-inflammatory effects in MS

Peroxisome proliferator-activated receptor delta limits the expansion of pathogenic Th cells during central nervous system autoimmunity.

Peroxisome proliferator-activated receptors (PPARs; PPAR-alpha, PPAR-delta, and PPAR-gamma) comprise a family of nuclear receptors that sense fatty acid levels and translate this information into altered gene transcription. Previously, it was reported that treatment of mice with a synthetic ligand activator of PPAR-delta, GW0742, ameliorates experimental autoimmune encephalomyelitis (EAE), indicating a possible role for this nuclear receptor in the control of central nervous system (CNS) autoimmune inflammation. We show that mice deficient in PPAR-delta (PPAR-delta(-/-)) develop a severe inflammatory response during EAE characterized by a striking accumulation of IFN-gamma(+)IL-17A(-) and IFN-gamma(+)IL-17A(+) CD4(+) cells in the spinal cord. The preferential expansion of these T helper subsets in the CNS of PPAR-delta(-/-) mice occurred as a result of a constellation of immune system aberrations that included higher CD4(+) cell proliferation, cytokine production, and T-bet expression and enhanced expression of IL-12 family cytokines by myeloid cells. We also show that the effect of PPAR-delta in inhibiting the production of IFN-gamma and IL-12 family cytokines is ligand dependent and is observed in both mouse and human immune cells. Collectively, these findings suggest that PPAR-delta serves as an important molecular brake for the control of autoimmune inflammation

Changes in lower body muscular performance following a season of NCAA Division I Men's Lacrosse

The tactical and technical components of training become a primary emphasis, leaving less time for targeted development of physical qualities that underpin performance during the competition phase of a training program. A deemphasis on physical preparation during the in-season training phase may make athletes more susceptible to injury and decrease performance on the field. Two weeks prior to the start and one week following the conclusion of the 16-week collegiate lacrosse season, lower body force production was assessed in eight National Collegiate Athletic Association (NCAA) Division I Men's Lacrosse athletes. Lower body force production capabilities were determined via the performance of countermovement jumps (CMJ) and drop jumps (DJ) performed on a force plate and isokinetic strength testing of the quadriceps and hamstring muscle groups across three velocities. Isokinetic strength of the hamstrings and the hamstring to quadriceps strength ratio were maintained or increased over the course of the competition phase of training. Relative peak force obtained from the CMJ and the reactive strength index from the DJ decreased significantly over the season. The maintenance of isokinetic strength and the decrease in CMJ and DJ performance may indicate the presence of neuromuscular fatigue that accumulated over the course of the season