acknowledged. Research results and conclusions expressed are those of the authors and do not necessarily reflect the We quantify agglomeration spillovers by estimating the impact of the opening of a large new manufacturing plant on the total factor productivity (TFP) of incumbent plants in the same county. Articles in the corporate real estate journal Site Selection reveal the county where the “Million Dollar Plant ” ultimately chose to locate (the “winning county”), as well as the one or two runner-up counties (the “losing counties”). The incumbent plants in the losing counties are used as a counterfactual for the TFP of incumbent plants in winning counties in the absence of the plant opening. Incumbent plants in winning and losing counties have economically and statistically similar trends in TFP in the 7 years before the opening, which supports the validity of the identifying assumption. After the new plant opening, incumbent plants in winning counties experience a sharp relative increase in TFP. Five years after the opening, TFP of incumbent plants in winning counties is 12 % higher than TFP of incumbent plants in losing counties. Consistent with some theories of agglomeration, this effect is larger for incumbent plants that share similar labor and technology pools with the new plant. We also find evidence of a relative increase in skill-adjusted labor costs in winning counties, indicating that the

This study evaluates the impact of high-skilled immigrants on US technology formation. We use reduced-form speci…cations that exploit large changes in the H-1B visa program. Fluctuations in H-1B admissions levels signi…cantly in‡uence the rate of Indian and Chinese patenting in cities and …rms dependent upon the program. We also …nd weak crowding-in e¤ects, such that total invention is increased with higher admission levels.

This study explores the importance of tacit knowledge transfer for international technology diffusion by examining ethnic scientific communities in the US and their ties to their home countries. US ethnic research communities are quantified by applying an ethnic-name database to individual patent records. International patent citations confirm knowledge diffuses through ethnic networks, and manufacturing output in foreign countries increases with an elasticity of approximately 0.3 to stronger scientific integration with the US frontier. To address reverse-causality concerns, reduced-form specifications exploit exogenous changes in US immigration quotas. Consistent with a model of sector reallocation, output growth in less developed economies is facilitated by employment gains, while more advanced economies experience sharper increases in labor productivity. The findings suggest tacit knowledge channels partly shape the effective technology frontiers of developing economies.

We estimate the factors determining specialization of crop choice at the level of individual fields, distinguishing between the role of natural advantage (soil characteristics) and economies of density (scale economies achieved when farmers plant neighboring fields with the same crop). Using rich geographic data from North Dakota, including new data on crop choice collected by satellite, we estimate the analog of a social interactions econometric model for the planting decisions on neighboring fields. We find that planting decisions on a field are heavily dependent on the soil characteristics of the neighboring fields. Through this relationship, we back out the structural parameters of economies of density. Setting an Ellison-Glaeser dartboard level of specialization as a benchmark, we find that of the actual level of specialization achieved beyond this benchmark, approximately two-thirds can be attributed to natural advantage and one-third to density economies.

Abstract: Firms and workers are much more productive in large and dense urban environments. There is substantial evidence of such agglomeration economies based on three aproaches. First, on a clustering of production beyond what can be explained by chance or comparative advantage. Second, on spatial patterns in wages and rents. Third, on systematic variations in productivity with the urban environment. However, more needs to be learned about the causes of agglomeration economies. We have good models of agglomeration through sharing and matching, but not a deep enough understanding of learning in cities. Despite recent progress, more work is needed to distinguish empirically between alternative causes. Key words: agglomeration economies. jel classification: r30 This paper has been written for the Journal of Regional Science’s 50 th

We study the long run effects of one of the most ambitious place based economic development policies in U.S. history: the Tennessee Valley Authority (TVA). Using a rich panel dataset of counties, we conduct an evaluation of the dynamic effects of the TVA on local economies in the seventy years following the program’s inception. We find that the TVA led to short run gains in agricultural employment that were eventually reversed, while impacts on manufacturing employment continued to intensify well after the program’s subsidies had lapsed – a pattern consistent with the presence of agglomeration economies in the manufacturing sector. Economists have long cautioned that the local gains created by place based policies may be offset by losses elsewhere, yielding ambiguous effects on the U.S. as a whole. We develop a novel approach to assessing the aggregate consequences of place based policies. Our findings suggest that the TVA boosted national manufacturing productivity by roughly 0.3 % and that the dollar value of these productivity gains exceeded the program’s costs.

Why do levels of entrepreneurship differ across America’s cities? This paper presents basic facts on two measures of entrepreneurship: the self-employment rate and the number of small firms. Both of these measures are correlated with urban success, suggesting that more entrepreneurial cities are more successful. There is considerable variation in the self-employment rate across metropolitan areas, but about one-half of this heterogeneity can be explained by demographic and industrial variation. Self-employment is particularly associated with abundant, older citizens and with the presence of input suppliers. Conversely, small firm size and employment growth due to unaffiliated new establishments is associated most strongly with the presence of input suppliers and an appropriate labor force. I also find support for the Chinitz (1961) hypothesis that entrepreneurship is linked to a large number of small firms in supplying industries. Finally, there is a strong connection between area-level education and entrepreneurship. 1 Kristina Tobio provided superb research assistance and oversaw a team consisting of Yün-ke Chin-Lee, Elizabeth

This paper was prepared for the “Handbook of Economic Geography and Industry Studies ” to be published by Edward Elgar Publishing. The authors especially thank Jake Carr and Robert O’Loughlin for excellent research assistance. This paper has benefited from comments from Robert Hunt, Leonard Nakamura, and Tony Smith. We alone are responsible for any remaining errors. The views expressed here are those of the authors and do not necessarily represent the views of the Federal Reserve Bank of Philadelphia or the Federal Reserve System. This paper is available free of charge at www.philadelphiafed.org/research-and-data/publications/workingpapers/.WORKING PAPER NO. 09-16

We estimate the local spillovers from research university activity in a sample of urban counties. We use the fact that universities tend to follow a rigid endowment spending policy based on the market value of their endowments to identify the causal effect of university activity on labor income in the non-education sector. Our instrument for university expenditures is based on the interaction between each university’s lagged endowment level and the variation in stock market shocks over time. We find statistically significant spillover effects from university activity, and the magnitude of the spillover is significantly larger when local universities are more research intensive or when firms are technologically closer to universities, as measured by labor market pooling and patent citations. The findings provide a rationale for place-based university policies, so long as they focus on industry fundamentals. The results also suggest that the longer-term effects that universities have on their local economies may grow over time as the composition of local industries evolve to take advantage of the knowledge spillovers we identify.

Bryson, Keith Maskus, and Debbie Smeaton for comments and data assistance. The research in this paper was conducted while the authors were Special Sworn Status researchers of the U.S. Census Bureau at the Boston Census Research Data Center (BRDC). Support for this research from NSF grant (ITR-0427889) is gratefully acknowledged. Research results and conclusions expressed are our own and do not necessarily reflect the views of the Census Bureau or NSF. This paper has been Many industries are geographically concentrated. Many mechanisms that could account for such agglomeration have been proposed. We note that these theories make different predictions about which pairs of industries should be coagglomerated. We discuss the measurement of coagglomeration and use data from the Census Bureau’s Longitudinal Research Database from 1972 to 1997 to compute pairwise coagglomeration measurements for U.S. manufacturing industries. Industry attributes are used to construct measures of the relevance of each of Marshall’s three theories of industry agglomeration to each industry pair: (1) agglomeration saves transport costs by proximity to input suppliers or final consumers, (2) agglomeration allows for labor market pooling, and (3) agglomeration facilitates intellectual spillovers. We assess the importance of the theories via regressions of coagglomeration indices on these measures. Data on characteristics of corresponding industries in the United Kingdom are used as instruments. We find evidence to support each mechanism. Our results suggest that input-output dependencies are the most important factor, followed by labor pooling