Estimating Equilibrium Models of Sorting across Locations

With the growing recognition of the role played by geography in all sorts of economic problems, there is strong interest in measuring the size and scope of local spillovers (i.e., simple anonymous agglomeration or congestion effects, or more complicated interactions between individuals or firms of specific types). It is well-understood, however, that such spillovers cannot be distinguished from unobservable local attributes using just the observed location decisions of individuals or firms. We propose an empirical strategy for recovering estimates of spillovers in the presence of unobserved local attributes for a broadly applicable class of equilibrium sorting models. This approach relies on an instrumental variables strategy derived from the internal logic of the sorting model itself. We show practically how the strategy is implemented, provide intuition for our instrumental variables, and discuss the role of effective choice-set variation in identifying the model, and carry-out a series of Monte Carlo experiments to demonstrate the instruments' performance in small samples.Local Spillovers, Location Choice, Economic Geography, Natural Advantage, Social Interactions, Network Effects, Endogenous Sorting, Discrete Choice Models, Agglomeration, Congestion

A Note on the Equilibrium Properties of Locational Sorting Models

A central feature of many models of location choice -- whether of firms or households, within or across cities -- is the role of local interactions or spillovers, whereby the payoffs from choosing a location depend in part on the number or attributes of other individuals or firms that choose the same or nearby locations in equilibrium. The main goal of this paper is to develop the equilibrium properties of a broadly applicable and readily estimable class of sorting models that allow the location decision to depend on both fixed local attributes (including unobserved attributes) and such local interactions. In particular, we prove uniqueness in the case of congestion effects and use a series of simulations to demonstrate that a unique equilibrium is more likely to obtain (i) the smaller are any agglomeration effects, (ii) the larger are the set of choices available to the agents, (iii) the more "meaningful variation" there is in those choices, and (iv) the more heterogeneous are the agents themselves. This is encouraging for the use of our model to describe the sorting of individuals and firms over geographic space, where the number of choices is usually large and variation in exogenous fixed attributes can be important. Moreover, these results conveniently coincide with the conditions required for econometric identification of our model.Local Spillovers, Social Interactions, Economic Geography, Natural Advantage, Endogenous Sorting, Discrete Choice Models, Agglomeration, Congestion, Random Utility

Migration and Hedonic Valuation: The Case of Air Quality

Conventional hedonic techniques for estimating the value of local amenities rely on the assumption that households move freely among locations. We show that when moving is costly, the variation in housing prices and wages across locations may no longer reflect the value of differences in local amenities. We develop an alternative discrete-choice approach that models the household location decision directly, and we apply it to the case of air quality in U.S. metro areas in 1990 and 2000. Because air pollution is likely to be correlated with unobservable local characteristics such as economic activity, we instrument for air quality using the contribution of distant sources to local pollution %u2013 excluding emissions from local sources, which are most likely to be correlated with local conditions. Our model yields an estimated elasticity of willingness to pay with respect to air quality of 0.34 to 0.42. These estimates imply that the median household would pay $149 to$185 (in constant 1982-1984 dollars) for a one-unit reduction in average ambient concentrations of particulate matter. These estimates are three times greater than the marginal willingness to pay estimated by a conventional hedonic model using the same data. Our results are robust to a range of covariates, instrumenting strategies, and functional form assumptions. The findings also confirm the importance of instrumenting for local air pollution.

Nonparametric Identification and Estimation in a Generalized Roy Model

This paper considers nonparametric identification and estimation of a generalized Roy model that includes a non-pecuniary component of utility associated with each choice alternative. Previous work has found that, without parametric restrictions or the availability of covariates, all of the useful content of a cross-sectional dataset is absorbed in a restrictive specification of Roy sorting behavior that imposes independence on wage draws. While this is true, we demonstrate that it is also possible to identify (under relatively innocuous assumptions and without the use of covariates) a common non-pecuniary component of utility associated with each choice alternative. We develop nonparametric estimators corresponding to two alternative assumptions under which we prove identification, derive asymptotic properties, and illustrate small sample properties with a series of Monte Carlo experiments. We demonstrate the usefulness of one of these estimators with an empirical application. Micro data from the 2000 Census are used to calculate the returns to a college education. If high-school and college graduates face different costs of migration, this would be reflected in different degrees of Roy-sorting-induced bias in their observed wage distributions. Correcting for this bias, the observed returns to a college degree are cut in half.

A Dynamic Model of Demand for Houses and Neighborhoods

We develop a tractable model of neighborhood choice in a dynamic setting along with a computationally straightforward estimation approach. This approach uses information about neighborhood choices and the timing of moves to recover moving costs and preferences for dynamically-evolving housing and neighborhood attributes. The model and estimator are potentially applicable to the study of a wide range of dynamic phenomena in housing markets and cities. We focus here on estimating the marginal willingness to pay for non-marketed amenities – neighborhood racial composition, air pollution, and violent crime – using rich dynamic data. Consistent with the time-series properties of each amenity, we find that a static demand model understates willingness to pay to avoid pollution and crime but overstates willingness to pay to live near neighbors of one’s own race. These findings have important implications for the class of static housing demand models typically used to value urban amenities.

A Theory-Based Approach to Hedonic Price Regressions with Time-Varying Unobserved Product Attributes: The Price of Pollution

We propose a new strategy for a pervasive problem in the hedonics literature—recovering hedonic prices in the presence of time-varying correlated unobservables. Our approach relies on an assumption about homebuyer rationality, under which prior sales prices can be used to control for time-varying unobservable attributes of the house or neighborhood. Using housing transactions data from California’s Bay Area between 1990 and 2006, we apply our estimator to recover marginal willingness to pay for reductions in three of the EPA’s “criteria” air pollutants. Our findings suggest that ignoring bias from time-varying correlated unobservables considerably understates the benefits of a pollution reduction policy.

Scalable, biofunctional, ultra-stable nano- bio- composite materials containing living cells

Three-dimensional encapsulation of cells within nanostructured silica gels or matrices enables applications as diverse as biosensors, microbial fuel cells, artificial organs, and vaccines. It also allows study of individual cell behaviors. Recent progress has improved the performance and flexibility of cellular encapsulation, yet there remains a need for robust scalable processes for large format production of cell-encapsulating materials. Here, we detail two novel techniques, that enable the large-scale production of functional Nano-Bio-Composites (NBCs) containing living cells within ordered 3-D lipid/silica nanostructures: 1) thick-casting and 2) spray drying. Furthermore, we detail a third technique for material scaling in which aqueous, silicate-based gel monoliths encapsulate biofunctional yeast or bacteria. Both dry processes are demonstrated to work with multiple cell types and result in dry powders exhibiting a unique combination of properties including: highly ordered 3-D nanostructure, extended lipid fluidity, tunable macro-morphologies and aerodynamic diameters, and unexpectedly high physical strength. Nanoindentation of the encasing nanostructure revealed Young’s modulus and hardness of 13 and 1.4 GPa respectively, which was unexpected considering the low processing conditions. We hypothesized and confirmed that NBC-encapsulated cells would remain viable for extended periods of time under elevated aging conditions. We attribute this due to the high material strength as observed with nanoindentation, which would prevent cell growth and force bacteria into viable but not culturable (VBNC) states. In concordance with the VBNC state, cellular ATP levels remained elevated even over eight months confirming temperature stable, viable cells. However, their ability to undergo resuscitation and enter growth phase greatly decreased with time in the VBNC state. A quantitative method of determining resuscitation frequencies was developed and showed that, after 36 weeks in an NBC-induced VBNC state, less than 1 in 10,000 cells underwent resuscitation. We verify the VBNC phenotype in gel-encapsulated cells by studying cellular RNA expression levels. These latent behaviors are further demonstrated with an in-vivo immunological study in which mice, immunized with NBCs containing the vaccine Bacillus Calmette-Guérin, were observed to be immunized against a latent form of Tuberculosis. This finding is, in our understanding, the first demonstration of a latent disease-specific live cell immunotherapy. The NBC platform production of industrially scalable quantities of VBNC cells is of interest for research in bacterial persistence and screening of drugs targeting such cells. NBC’s may also enable long-term preservation of living cells for applications in cell-based sensing and the packaging and delivery of live-cell vaccines. Moreover, our methodology represents a novel process for preparing formulations of latent cells in-silico, which could find application in basic cellular research and for the development of a latent-specific vaccine

Rabies virus matrix protein interplay with eIF3, new insights into rabies virus pathogenesis

Viral proteins are frequently multifunctional to accommodate the high density of information encoded in viral genomes. Matrix (M) protein of negative-stranded RNA viruses such as Rhabdoviridae is one such example. Its primary function is virus assembly/budding but it is also involved in the switch from viral transcription to replication and the concomitant down regulation of host gene expression. In this study we undertook a search for potential rabies virus (RV) M protein's cellular partners. In a yeast two-hybrid screen the eIF3h subunit was identified as an M-interacting cellular factor, and the interaction was validated by co-immunoprecipitation and surface plasmon resonance assays. Upon expression in mammalian cell cultures, RV M protein was localized in early small ribosomal subunit fractions. Further, M protein added in trans inhibited in vitro translation on mRNA encompassing classical (Kozak-like) 5′-UTRs. Interestingly, translation of hepatitis C virus IRES-containing mRNA, which recruits eIF3 via a different noncanonical mechanism, was unaffected. Together, the data suggest that, as a complement to its functions in virus assembly/budding and regulation of viral transcription, RV M protein plays a role in inhibiting translation in virus-infected cells through a protein–protein interaction with the cellular translation machinery