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Who are the people in my neighborhood? The 'contextual fallacy' of measuring individual context with census geographies
February 2018
Working Paper Number:
CES-18-11
Scholars deploy census-based measures of neighborhood context throughout the social sciences and epidemiology. Decades of research confirm that variation in how individuals are aggregated into geographic units to create variables that control for social, economic or political contexts can dramatically alter analyses. While most researchers are aware of the problem, they have lacked the tools to determine its magnitude in the literature and in their own projects. By using confidential access to the complete 2010 U.S. Decennial Census, we are able to construct'for all persons in the US'individual-specific contexts, which we group according to the Census-assigned block, block group, and tract. We compare these individual-specific measures to the published statistics at each scale, and we then determine the magnitude of variation in context for an individual with respect to the published measures using a simple statistic, the standard deviation of individual context (SDIC). For three key measures (percent Black, percent Hispanic, and Entropy'a measure of ethno-racial diversity), we find that block-level Census statistics frequently do not capture the actual context of individuals within them. More problematic, we uncover systematic spatial patterns in the contextual variables at all three scales. Finally, we show that within-unit variation is greater in some parts of the country than in others. We publish county-level estimates of the SDIC statistics that enable scholars to assess whether mis-specification in context variables is likely to alter analytic findings when measured at any of the three common Census units.
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Reservation Employer Establishments: Data from the U.S. Census Longitudinal Business Database
January 2017
Working Paper Number:
CES-17-57
The presence of employers and jobs on American Indian reservations has been difficult to analyze due to limited data. We are the first to geocode confidential data on employer establishments from the U.S. Census Longitudinal Business Database to identify location on or off American Indian reservations. We identify the per capita establishment count and jobs in reservation-based employer establishments for most federally recognized reservations. Comparisons to nearby non-reservation areas in the lower 48 states across 18 industries reveal that reservations have a similar sectoral distribution of employer establishments but have significantly fewer of them in nearly all sectors, especially when the area population is below 15,000 (as it is on the vast majority of reservations and for the majority of the reservation population). By contrast, the total number of jobs provided by reservation establishments is, on average, at par with or somewhat higher than in nearby county areas but is concentrated among casino-related and government employers. An implication is that average job numbers per establishment are higher in these sectors on reservations, including those with populations below 15,000, while the remaining industries are typically sparser within reservations (in firm count and jobs per capita). Geographic and demographic factors, such as population density and per capita income, statistically account for some but not all of these differences.
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Geography in Reduced Form
January 2017
Working Paper Number:
CES-17-10
Geography models have introduced and estimated a set of competing explanations for the persistent relationships between firm and location characteristics, but cannot identify these forces. I introduce a solution method for models in arbitrary geographies that generates reduced-form predictions and tests to identify forces acting through geographic linkages. This theoretical approach creates a new strategy for spatial empirics. Using the correct observables, the model shows that geographic forces can be taken into account without being directly estimated; establishment and employment density emerge as sufficient statistics for all geographic forces. I present two applications. First, the model can be used to evaluate whether geographic linkages matter and when simplified models suffice: the mono-centric model is a good fit for business services firms but cannot capture the geography of manufactures. Second, the model generates reduced-form tests that distinguish between spillovers and firm sorting and finds evidence of sorting.
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The Impact of Immigration on the Labor Market Outcomes of Native Workers: Evidence using Longitudinal Data from the LEHD
January 2016
Working Paper Number:
CES-16-56
Empirical estimates of the effect of immigration on native workers that rely on spatial comparisons have generally found small effects, but have been subject to the criticism that out-migration by native workers dampens the observed effect by spreading it over a larger area. In contrast, studies that rely on variation in immigration across industries, occupations, or education-based skill-levels often report large negative effects, but rely primarily on repeated cross-sectional data sets which also cannot account for the adjustment of native workers over time. In this paper, we use a newly available data set, the Longitudinal Employer Household Data (LEHD), which provides quarterly earnings records, geographic location, and firm and industry identifiers for 97% of all privately employed workers in 29 states. We use this data to analyze the impact of immigration on earnings changes and the mobility response of native workers. Overall, we find that although immigration has a negative effect on the earnings and employment of native workers, and positive effects on their firm, industry, and cross-state mobility, the overall size of the effects is small.
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Disconnected Geography: A Spatial Analysis of Disconnected Youth in the United States
January 2016
Working Paper Number:
CES-16-37
Since the Great Recession, US policy and advocacy groups have sought to better understand its effect on a group of especially vulnerable young adults who are not enrolled in school or training programs and not participating in the labor market, so called 'disconnected youth.' This article distinguishes between disconnected youth and unemployed youth and examines the spatial clustering of these two groups across counties in the US. The focus is to ascertain whether there are differences in underlying contextual factors among groups of counties that are mutually exclusive and spatially disparate (non-adjacent), comprising two types of spatial clusters ' high rates of disconnected youth and high rates of unemployed youth. Using restricted, household-level census data inside the Census Research Data Center (RDC) under special permission by the US Census Bureau, we were able to define these two groups using detailed household questionnaires that are not available to researchers outside the RDC. The geospatial patterns in the two types of clusters suggest that places with high concentrations of disconnected youth are distinctly different in terms of underlying characteristics from places with high concentrations of unemployed youth. These differences include, among other things, arrests for synthetic drug production, enclaves of poor in rural areas, persistent poverty in areas, educational attainment in the populace, children in poverty, persons without health insurance, the
social capital index, and elders who receive disability benefits. This article provides some preliminary evidence regarding the social forces underlying the two types of observed geospatial clusters and discusses how they differ.
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Research Funding and Regional Economies
January 2016
Working Paper Number:
CES-16-32
Public support of research typically relies on the notion that universities are engines of economic development, and that university research is a primary driver of high wage localized economic activity. Yet the evidence supporting that notion is based on aggregate descriptive data, rather than detailed links at the level of individual transactions. Here we use new micro-data from three countries - France, Spain and the United States - to examine one mechanism whereby such economic activity is generated, namely purchases from regional businesses. We show that grant funds are more likely to be expended at businesses physically closer to universities than at those farther away. In addition, if a vendor has been a supplier to a grant once, that vendor is subsequently more likely to be a vendor on the same or related grants. Firms behave in a way that is consistent with the notion that propinquity is good for business; if a firm supplies a research grant at a university in a given year it is more likely to open an establishment near that university in subsequent years than other firms.
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Multiregional Firms and Region Switching in the US Manufacturing Sector
January 2015
Working Paper Number:
CES-15-22
This paper uses data on US manufacturing firms to study a new extensive margin, the reallocation of resources that takes place within surviving firms as they open and close establishments in different regions. To motivate the empirical analysis, I extend existing models of industry dynamics to include production-location decisions within firms. The empirical results provide support for the mechanisms emphasized by the theoretical model. In the data, only about 3 percent of firms make the same product in more than one region, but these multiregional firms are more productive on average
compared to single-region firms, and they account for about two-thirds of output. The results also show that "region-switching" is pervasive among multiregional firms, is correlated with changes in firm characteristics, and leads to a more efficient allocation of resources within firms.
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SYNTHETIC DATA FOR SMALL AREA ESTIMATION IN THE AMERICAN COMMUNITY SURVEY
April 2013
Working Paper Number:
CES-13-19
Small area estimates provide a critical source of information used to study local populations. Statistical agencies regularly collect data from small areas but are prevented from releasing detailed geographical identifiers in public-use data sets due to disclosure concerns. Alternative data dissemination methods used in practice include releasing summary/aggregate tables, suppressing detailed geographic information in public-use data sets, and accessing restricted data via Research Data Centers. This research examines an alternative method for disseminating microdata that contains more geographical details than are currently being released in public-use data files. Specifically, the method replaces the observed survey values with imputed, or synthetic, values simulated from a hierarchical Bayesian model. Confidentiality protection is enhanced because no actual values are released. The method is demonstrated using restricted data from the 2005-2009 American Community Survey. The analytic validity of the synthetic data is assessed by comparing small area estimates obtained from the synthetic data with those obtained from the observed data.
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DO LOCAL MANAGERS GIVE LABOR AN EDGE?
April 2013
Working Paper Number:
CES-13-16
Based on the psychological theory of place attachments, native local managers should be more rooted in their communities than non-locals and should act accordingly. Consistent with this, local managers are 33% less likely to lay of employees than their non-local industry peers following industry distress. Additionally, when managers are forced to lay off employees, establishments near managers' homes are less likely to experience layoffs than those located elsewhere. Locals pay for these higher employment levels by spending cash, cutting investment, and selling assets. While there is no direct evidence that labor-friendly policies of locals have a differential impact on firm performance or value, only locals with weaker incentives implement these policies, suggesting that favoritism by locals may be suboptimal. Taken together these results suggest that managerial preferences impact corporate employment decisions.
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The Location of Industrial Innovation: Does Manufacturing Matter?
March 2013
Working Paper Number:
CES-13-09
What explains the location of industrial innovation? Economists have traditionally attempted to answer this question by studying firm-external knowledge spillovers. This paper shows that firm-internal linkages between production and R&D play an equally important role. I estimate an R&D location choice model that predicts patents by a firm in a location from R&D productivity and costs. Focusing on large R&D-performing firms in the chemical industry, an average-sized plant raises the firm's R&D productivity in the metropolitan area by about 2.5 times. The elasticity of R&D productivity with respect to the firm's production workers is almost as large as the elasticity with respect to total patents in the MSA, while proximity to academic R&D has no significant effect on R&D productivity in this sample. Other manufacturing industries exhibit similar results. My results cast doubt on the frequently-held view that a country can divest itself of manufacturing and specialize in innovation alone.
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