Do labor market networks have an important spatial dimension?

We test for evidence of spatial, residence-based labor market networks. Turnover is lower for workers more connected to their neighbors generally and more connected to neighbors of the same race or ethnic group. Both results are consistent with networks producing better job matches, while the latter could also reflect preferences for working with neighbors of the same race or ethnicity. For earnings, we find a robust positive effect of the overall residence-based network measure, whereas we usually find a negative effect of the same-group measure, suggesting that the overall network measure reflects productivity-enhancing positive network effects, while the same-group measure may capture a non-wage amenity

Social Capital Determinants and Labor Market Networks

We explore the links between determinants of social capital and labor market networks at the neighborhood level. We harness rich data taken from multiple sources, including matched employer-employee data with which we measure the strength of labor market networks, data on neighborhood homogeneity that has previously been tied to social capital, and new data – not previously used in the study of social capital – on the number and location of non-profit sector establishments at the neighborhood level. We use a machine learning algorithm to identify the potential determinants of social capital that best predict neighborhood-level variation in labor market networks. We find evidence suggesting that smaller and less centralized schools, and schools with fewer poor students, foster social capital that builds local labor market networks, as does a larger Republican vote share. The presence of establishments in a number of non-profit-oriented industries are identified as predictive of strong labor market networks, likely because they either provide public goods or facilitate social contacts. These industries include, for example, churches and other religious institutions, fire and rescue services including volunteer fire departments, country clubs and golf courses, labor unions, chamber music groups, hobby clubs, and schools

Two Perspectives on Commuting: A Comparison of Home to Work Flows Across Job-Linked Survey and Administrative Files

This paper is a newer version of https://hdl.handle.net/1813/50976Commuting flows and workplace employment data have a wide constituency of users including urban and regional planners, social science and transportation researchers, and businesses. The U.S. Census Bureau releases two, national data products that give the magnitude and characteristics of home to work flows. The American Community Survey (ACS) tabulates households’ responses on employment, workplace, and commuting behavior. The Longitudinal Employer-Household Dynamics (LEHD) program tabulates administrative records on jobs in the LEHD Origin-Destination Employment Statistics (LODES). Design differences across the datasets lead to divergence in a comparable statistic: county-to-county aggregate commute flows. To understand differences in the public use data, this study compares ACS and LEHD source files, using identifying information and probabilistic matching to join person and job records. In our assessment, we compare commuting statistics for job frames linked on person, employment status, employer, and workplace and we identify person and job characteristics as well as design features of the data frames that explain aggregate differences. We find a lower rate of within-county commuting and farther commutes in LODES. We attribute these greater distances to differences in workplace reporting and to uncertainty of establishment assignments in LEHD for workers at multi-unit employers. Minor contributing factors include differences in residence location and ACS workplace edits. The results of this analysis and the data infrastructure developed will support further work to understand and enhance commuting statistics in both datasets.This work received support from NSF grant SES-1131848 (NCRN Cornell).Downloads for this item at https://digitalcommons.ilr.cornell.edu/ldi/38/ as of 9/11/2020: 35

Recalculating - How Uncertainty in Local Labor Market Definitions Affects Empirical Findings

The analysis, conclusions, and opinions expressed herein are those of the author(s) alone and do not necessarily represent the views of the U.S. Census Bureau or the Federal Deposit Insurance Corporation. All results have been reviewed to ensure that no confidential information is disclosed, and no confidential data was used in this paper. This document is released to inform interested parties of ongoing research and to encourage discussion of work in progress. Much of the work developing this paper occurred while Mark Kutzbach was an employee of the U.S. Census Bureau.This paper evaluates the use of commuting zones as a local labor market definition. We revisit Tolbert and Sizer (1996) and demonstrate the sensitivity of definitions to two features of the methodology. We show how these features impact empirical estimates using a well-known application of commuting zones. We conclude with advice to researchers using commuting zones on how to demonstrate the robustness of empirical findings to uncertainty in definitions.Vilhuber acknowledges support under NSF Grant #1131848 (NCRN).Downloads for this item at https://digitalcommons.ilr.cornell.edu/ldi/45/ as of 9/11/2020: 20

Access to Workers or Employers? An Intra-Urban Analysis of Plant Location Decisions

This analysis attributes economies of agglomeration to either labor market pooling or employer-based productivity spillovers by distinguishing the effect of access to workers, measured by place-of-residence, from the effect of access to employers. New establishment location choices serve as a measure of productivity advantages, while census tract level data on access to same-industry employment, other-industry employment, and specialized workers, as well as metropolitan area fixed effects, measure sources of agglomeration and other locational characteristics. The four industries included are selected so that each relies on a workforce with a specialized occupation that is identifiable by place-of-residence, and that productivity and cost advantages are the primary drivers of location choice. The results show that both access to specialized workers and access to same-industry employers contribute to economies of agglomeration at an intra-urban spatial scale, and that the magnitude of the worker effect is large relative to employer-based productivity spillovers.Economies of Agglomeration; Labor Market Pooling; Commuting

Motorization in developing countries: Causes, consequences, and effectiveness of policy options

This paper examines the rise in car use and decline in bus use in developing countries using a theoretical, mode choice model and numerical simulations. The empirical literature points to rising per capita income as a primary determinant of rising motor vehicle use, known as motorization. This analysis of commuter car/bus mode choice shows that in addition to rising income, other factors may drive rising car use at the urban level. First, greater income inequality increases car use if car use is still low, and reduces car use if it is already high. Second, traffic congestion hinders buses more than cars, causing positive feedback between car use and travel time that reduces bus use and contributes to its abrupt collapse. Third, policy interventions to reduce congestion in urban areas, such as tolling car use and reserving lanes for buses, increase consumer surplus by maintaining bus service as an alternate travel mode, even as incomes rise. Socially optimal reserved bus lanes may achieve most of the gains from a socially optimal toll on car use.Motorization Mode choice Congestion Urban growth Developing countries

Permian phytogeographic patterns and climate data/model comparisons

The most recent global "icehouse-hothouse" climate transition in earth history began during the Permian. Warmer polar conditions, relative to today, then persisted through the Mesozoic and into the Cenozoic. We focus here on two Permian stages, the Sakmarian (285-280 Ma) and the Wordian (267-264 Ma; also known as the Kazanian), integrating floral with lithological data to determine their climates globally. These stages postdate the Permo-Carboniferous glaciation but retain a moderately steep equator-to-pole gradient, judging by the level of floral and faunal differentiation. Floral data provide a particularly useful means of interpreting terrestrial paleoclimates, often revealing information about climate gradations between "dry" and "wet" end-member lithological indicators such as evaporites and coals. We applied multivariate statistical analyses to the Permian floral data to calibrate the nature of floral and geographical transitions as an aid to climate interpretation. We then classified Sakmarian and Wordian terrestrial environments in a series of regional biomes ("climate zones") by integrating information on leaf morphologies and phytogeography with patterns of eolian sand, evaporite, and coal distributions. The data-derived biomes are compared here with modeled biomes resulting from new Sakmarian and Wordian climate model simulations for a range of CO2 levels (one, four, and eight times the present levels), presented in our companion article. We provide a detailed grid cell comparison of the biome data and model results by geographic region, introducing a more rigorous approach to global paleoclimate studies. The simulations with four times the present CO2 levels (4 × CO2) match the observations better than the simulations with 1 × CO2, and, at least in some areas, the simulations with 8 × CO2 match slightly better than those for 4 × CO2. Overall, the 4 × CO2 and 8 × CO2 biome simulations match the data reasonably well in the equatorial and midlatitudes as well as the northern high latitudes. However, even these highest CO2 levels fail to produce the temperate climates in high southern latitudes indicated by the data. The lack of sufficient ocean heat transport into polar latitudes may be one of the factors responsible for this cold bias of the climate model. Another factor could be the treatment of land surface processes and the lack of an interactive vegetation module. We discuss strengths and limitations of the data and model approaches and indicate future research directions