Government spending shocks and the multiplier: New evidence from the U.S. based on natural disasters

The literature on estimating macroeconomic effects of fiscal policy requires suitable instruments to identify exogenous and unanticipated spending shocks. So far, the instrument of choice has been military build-ups. This instrument, however, largely limits the analysis to the US as few other countries have been involved in mainly extraterritorial conflicts. Moreover, the expenditure associated with military build-ups affects primarily the defense sector so that the resulting multiplier does not necessarily approximate the effects of changes to general government spending. We propose an alternative instrument: government relief expenditure in the wake of natural disasters which is more similar in its scope to general government spending. We construct a rich data set of natural disasters and the corresponding government responses at the US state level. We apply this methodology both at the state as well as national levels and show that natural disasters serve as a powerful instrument for identifying government spending shocks. Furthermore, we show that the multiplier pertaining to non-defense government spending is higher than the defense-spending multiplier estimated in the literature using military build-ups

Probing electronic excitations in molecular conduction

We identify experimental signatures in the current-voltage (I-V) characteristics of weakly contacted molecules directly arising from excitations in their many electron spectrum. The current is calculated using a multielectron master equation in the Fock space of an exact diagonalized model many-body Hamiltonian for a prototypical molecule. Using this approach, we explain several nontrivial features in frequently observed I-Vs in terms of a rich spectrum of excitations that may be hard to describe adequately with standard one-electron self-consistent field (SCF) theories.Comment: Significantly different content -- inadequacy of SCF approach described with simple model, and a whole new class of experiments showing gate modulated current steps discussed in terms of excitations in the molecular many-body spac

A Parameterized Centrality Metric for Network Analysis

A variety of metrics have been proposed to measure the relative importance of nodes in a network. One of these, alpha-centrality [Bonacich, 2001], measures the number of attenuated paths that exist between nodes. We introduce a normalized version of this metric and use it to study network structure, specifically, to rank nodes and find community structure of the network. Specifically, we extend the modularity-maximization method [Newman and Girvan, 2004] for community detection to use this metric as the measure of node connectivity. Normalized alpha-centrality is a powerful tool for network analysis, since it contains a tunable parameter that sets the length scale of interactions. By studying how rankings and discovered communities change when this parameter is varied allows us to identify locally and globally important nodes and structures. We apply the proposed method to several benchmark networks and show that it leads to better insight into network structure than alternative methods.Comment: 11 pages, submitted to Physical Review