The decision to emigrate depends both on where the potential immigrant is going and where he or she is coming from. Changes in the conditions and laws in a given country affect people differently in different countries, depending on the conditions and laws there. Yet, when collecting information about immigration, countries tend to be interested only in their own policies, and they tend to track only the total immigrant flows across their own borders. No one nation has much incentive to collect, reconcile, or share detailed information about what is happening elsewhere. This grant to the International Migration Institute at Oxford University supports the work of a team lead by Hein de Haas to compile information about the flow patterns and the policy determinants of high-skilled immigration, concentrating on relocation decisions by students and academics. The project, called DEMIG, studies the "DEterminants of International MIGration" by producing sharable datasets that are bilateral and longitudinal, i.e., that record both sending and receiving information between pairs of countries repeatedly over time. Funds from this grant will allow de Hass and his team to extend DEMIG's Migration Flow Database to include skill indicators like education and employment, and extend DEMIG's Policy Database beyond immigration laws to track factors like fellowship or research funding levels that can specifically influence student and faculty mobility decisions.

Do immigrant workers crowd out native ones? How do specific changes in U.S. immigration policy affect scientific and engineering labor markets? Why do foreign-born students and workers who stay in the U.S. decide to stay? How do these considerations vary across scientific fields? This grants supports the work of professors John Bound and Sarah Turner to build well?specified, carefully estimated, and policy-relevant models of how the supply of and demand for scientists and engineers in the U.S. adjust in a global context. Within this comprehensive framework, they will investigate and quantify given factors such as U.S. immigration policies, economic conditions in foreign countries, and U.S. market conditions for tertiary education as these interact with observed factors such as wages, unemployment rates, and flows between specialties in domestic labor markets for scientists and engineers.

Funds from this grant provide administrative and operational support for the continued publication of the Brookings Papers on Economic Activity (BPEA), one of the premier outlets for policy-relevant economic research.

Funds from this grant support a project by Benjamin Bederson, Ginger Jin, and Phillip Leslie to compile, compare, and curate restaurant health inspection datasets so that they can be freely used by consumers and researchers in many cities across the U.S. The undertaking will involve reconciling the different laws, rating systems, and ranking criteria that preponderate across differing municipal, county, and state jurisdictions, as well as combining large amounts of data collected under varying formats, standards, and protocols. When completed, Bederson, Jin, and Leslie's efforts will not only render health inspection information easier for consumers to access and interpret but also provide a robust dataset for use by economists, sociologists, and other researchers interested in the efficacy of regulation and its effect on behavior.

Epidemiologists explain and predict the spread of infectious disease using what they call probabilistic cellular automata (PCA) models. A PCA consists of nodes in a network, each of which is in a state that changes from one time period to the next depending both on the states of nearby nodes and, to some extent, on chance, too. So imagine each node represents a person, and that each person can be in one of three states: healthy, ill, or deceased. Once researchers specify a rule for how likely you are to get sick or die tomorrow given the health of those around you today, they can run the model forward in time and begin to investigate patterns. Such techniques have helped explain how, when, and why to vaccinate, to quarantine, or to take other steps for managing the outbreak of a particular disease. The same kind of model can also describe the spread of financial distress, where nodes represent banks that are connected to other banks through a network of loans or other obligations. This grant to economist Robert Mackay at the University of Warwick will fund a project to convene an international team of researchers to develop theorems, tools, and applicable techniques for constructing PCA models of how financial distress propagates through financial institutions, with the eventual goal of determining how circuit breakers, bailouts, enhanced regulation, or other interventions can mitigate systemic risk.

Datasets that contain private or proprietary information pose a vexing problem. On the one hand, we want to make these datasets available for statistical studies and other research. On the other, we want to protect the privacy of those people or firms referenced in the data. Effective solutions to the problem of how to maximize the usefulness of data while at the same time ensuring privacy have proven elusive. Sheltering datasets off in data enclaves does a good job of protecting proprietary information, but severely restricts their availability for research and inhibits the reproducibility of results. Releasing "anonymized" versions of the data-scrubbed of the private information-greatly increases its accessibility to researchers, but often results in data that is not useful or that can be combined with other public data to "reverse engineer" the removed private information. Funds from this grant support the work of Stanford University's Cynthia Dwork, who is developing methods for accessing data that both maximizes its usefulness to researchers and ensures the privacy and confidentiality of sensitive information in the data. Dwork's primary insight is the development of a precise mathematical definition she calls "differential privacy", which maintains that a data access system assures differential privacy if the outcome of any admissible analysis is essentially independent of whether or not any given individual's information is included in the dataset, and her work has already shown mathematically that several useful data release mechanisms can ensure privacy in this sense. Her work has the potential to become the basis for new ways of exploring sensitive data that could revolutionize empirical research in the social sciences.

States, localities, and countries compete aggressively to attract or retain manufacturing plants by offering companies sizable tax breaks and subsidies. This practice is generally presumed to be economical because the benefits from having these facilities-the "spillovers"-will be greater than the cost of the subsidies. When several competing firms locate in the same geographic area, an economically desirable cluster (or "agglomeration economy") is created, which has the potential to yield higher productivity for all the other firms in that cluster ("productivity spillovers"). Funds from this grant support the ongoing work of M.I.T. economists Daron Acemoglu and David Autor, who are studying the economic importance of these geographic clusters of manufacturing firms and the relationship between innovation and manufacturing within these clusters. Using data from the U.S. Census Bureau's Annual Survey of Manufacturers, Census of Manufacturers, and Economic Census, Acemoglu and Autor will investigate whether and how strongly the closing of U.S. manufacturing plants depresses the productivity of other manufacturing plants in the same geographic area.

The financial sector has traditionally played a minor role in macroeconomic models, typically acting only as a passive transmission mechanism for monetary transactions. Prior to 2008, this minor role seemed justified. Financial crises seemed to have only limited effects on the general economy, so an oversimplified portrayal of the financial sector did not seem unreasonable. After all, the Dow fell almost 23% on Black Monday, October 19, 1987, without triggering a recession. The negative effects of the turn-of-the-century dot-com bubble were mostly confined to Silicon Valley. So when the 2008 financial crisis resulted in a recession in the U.S. and elsewhere, many macroeconomists were at a loss. We are now acutely aware of the need for re-imagined macroeconomic models that take systematic financial risk into account, models that address how financial shocks can be transmitted to the wider economy and vice versa, and that help policy-makers identify and limit systematic risks. Such research has hardly begun, however, and economists are divided on how new models should be formulated. Funds from this grant will support the efforts of Lars Hansen of the University of Chicago and Andrew Lo of M.I.T to convene a working group of leading economists to explore, critique, and consolidate new approaches to modeling systemic financial risk and how the financial sector is connected to the broader economy.

The Penn World Table (PWT), a set of national accounts data which measures real GDP and relative price levels across countries and over time, is one of the most frequently cited datasets in economics. A 2009 found that, of all the cross-country empirical publications in the economic growth and development literature, nearly two thirds are based on the PWT. Version 6.1 of the PWT has more than 3,000 citations. Until now, the PWT has been a product of the Center for International Comparisons at the University of Pennsylvania, and was produced through the pioneering work of Simon Kuznets, Irving Kravis, Alan Heston, Robert Summers, and Bettina Aten. Yet this team has now either retired or moved on to other pursuits. At this point, Heston, at 77 years old, is the only one still active in preparing the PWT, and shortages of staff and funding have slowed revisions and methodological improvements to the tables. The PWT needs a new home. This grant will partially fund the transition of the Penn World Table from the University of Pennsylvania to the University of California, Davis and the University of Groningen under the care of Robert Feenstra and Marcel Timmer, respectively. Additional funds are provided to evaluate and, if appropriate, implement proposed methodological improvements to the PWT.

Researchers who study household finance study how households make investment decisions, with a particular eye on the common, costly mistakes households make when making important investment choices, such as purchasing a home or allocating savings for retirement. As such, conclusions reached by researchers in this field are highly relevant to policy and regulatory issues of consumer protection, financial literacy, and financial product design. Unfortunately, however, household finance research has tended to focus on households in a small number of developed countries such as the U.S., Sweden, and Finland, that have good, easily accessible data on household investment behavior. Though understandable-good research requires good data-the small number of such countries limits the usefulness of the research conducted, since it is unclear how lessons learned from the household financial behavior of Swedes might be applied to Brazil or India where market conditions, social norms, and national institutions differ substantially. This grant supports the work of Tarun Ramadorai of Oxford University and John Y. Campbell of Harvard University who are working to broaden and enrich the field of household finance by bringing new international comparative perspectives to the field. Over the next three years, Ramadorai and Campbell will create a new international dataset on household financial behavior sourced from both developed and developing economies. Grant funds will support the creation of this dataset, its analysis, a fellowship to encourage participation by foreign economists in the project, and several conferences and academic workshops focused on topics relevant to the field.