Social Capital and Labor Market Networks / Brian J. Asquith, Judith K. Hellerstein, Mark J. Kutzbach, David Neumark.

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Asquith, Brian J

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w23959.Publication details: Cambridge, Mass. National Bureau of Economic Research 2017.Description: 1 online resource: illustrations (black and white)Subject(s):

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Abstract: We explore the links between 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 behavior such as voting patterns that have 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 important potential social capital measures 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 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, police departments, fire and rescue services including volunteer fire departments, country clubs, mayors' offices, chamber music groups, hobby clubs, and museums.

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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w23959 (Browse shelf(Opens below))	Not For Loan

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October 2017.

We explore the links between 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 behavior such as voting patterns that have 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 important potential social capital measures 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 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, police departments, fire and rescue services including volunteer fire departments, country clubs, mayors' offices, chamber music groups, hobby clubs, and museums.