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Human Capital Spillovers and the Geography of Intergenerational Mobility. (2015). Gallipoli, Giovanni ; Abbott, Brant.
In: 2015 Meeting Papers.
RePEc:red:sed015:319.

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Cites: 23

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Cocites: 50

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  1. Occupational Mobility: Theory and Estimation for Italy. (2021). Fiaschi, Davide ; Brunetti, Irene.
    In: Papers.
    RePEc:arx:papers:2104.01285.

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  2. Human Capital Inequality: Empirical Evidence. (2018). Gallipoli, Giovanni ; Abbott, Brant.
    In: Working Papers.
    RePEc:hka:wpaper:2018-085.

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References

References cited by this document

  1. (2014): “Unobservable skill dispersion and comparative advantage,” Journal of International Economics, 92(2), 317–329.
    Paper not yet in RePEc: Add citation now
  2. ABBOTT, B., G. GALLIPOLI, C. MEGHIR, AND G. VIOLANTE (2013): “Education policy and intergenerational transfers in equilibrium,” Discussion paper, National Bureau of Economic Research.

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  8. CHETTY, R., N. HENDREN, P. KLINE, AND E. SAEZ (2014): “Where is the Land of Opportunity ? The Geography of Intergenerational Mobility in the United States,” Discussion paper, National Bureau of Economic Research.

  9. CORAK, M. (2006): “Do poor children become poor adults? Lessons for public policy from a Cross-Country comparison of generational earnings mobility,” Dynamics of Inequality and Poverty, 13, 143–188.

  10. CUNHA, F., J. J. HECKMAN, AND S. M. SCHENNACH (2010): “Estimating the technology of cognitive and noncognitive skill formation,” Econometrica, 78(3), 883–931.

  11. DEL BOCA, D., C. FLINN, AND M. WISWALL (2013): “Household Choices and Child Development, ” Review of Economic Studies, Forthcoming, –.
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  12. Finally, we merge the mobility measures, provided by Chetty, Hendren, Kline, and Saez (2014), with the data on commuting zones. In this way we have a data set containing measures of mobility and skill complementarity for a large set (712) of US commuting zones. More specifically, for each commuting zone observation we have several mobility measures, NAICS and ISIC industry codes with corresponding employment counts and various proxies of skill complementarity based on O*NET measures. A.3 Taxes and Public Education Spending
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  13. HEATHCOTE, J., K. STORESLETTEN, AND G. L. VIOLANTE (2010): “The Macroeconomic Implications of Rising Wage Inequality in the United States,” Journal of Political Economy, 118(4), 681–722.

  14. ICHINO, A., L. KARABARBOUNIS, AND E. MORETTI (2010): “The political economy of intergenerational income mobility,” Economic Inquiry, 49(1), 47–69.

  15. JANTTI, M., B. BRATSBERG, K. RØED, O. RAAUM, R. NAYLOR, E. OSTERBACKA, A. BJORKLUND, AND T. ERIKSSON (2006): “American exceptionalism in a new light: a comparison of intergenerational earnings mobility in the Nordic countries, the United Kingdom and the United States,” IZA Discussion Paper.

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  17. LEE, S. Y. T., AND A. SESHADRI (2014): “On the Intergenerational Transmission of Economic Status,” Discussion paper, Mimeo, University of Wisconsin, Madison.
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  18. MORETTI, E. (2004): “Workers education, spillovers, and productivity: Evidence from plantlevel production functions,” The American Economic Review, 94(3), 656–690.

  19. PYCIA, M. (2012): “Stability and preference alignment in matching and coalition formation,” Econometrica, 80(1), 323–362.

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  21. SOLON, G. (2004): “A Model of Intergenerational Mobility Variation over Time and Place,” in Generational Income Mobility in North America and Europe, ed. by M. Corak, pp. 38–47. Cambridge University Press, Cambridge.

  22. STORESLETTEN, K., C. I. TELMER, AND A. YARON (2004): “Consumption and Risk Sharing Over the Life Cycle,” Journal of Monetary Economics, 51, 609–33. A Appendix A.1 Cross-Country Industry composition: STAN data In this paper, we use data provided by the OECD Statistics to approximate for the relative importance of different industries in a set of countries. The data used is from the structural analysis STAN databases for value added and total employment. The analysis consists of data from 31 categories of the industries, calculated as percentages of the total. The following table shows the specific industries that we have used.

  23. The next step is to link the ISIC codes corresponding to the corresponding NAICS industry categories using the CBP data. The NAICS codes in the Dorn’s files adopt the 1997 classification codes, while the ISIC codes are from 2002. We only have a crosswalk from 2002 NAICS codes to 2002 ISIC labels, therefore we need to perform an intermediate step to link 1997 NAICS to 2002 ISIC. More specifically, the Census Bureau provides a crosswalk from 1997 NAICS codes to 2002 NAICS codes,10 as well as a crosswalk between 2002 NAICS codes and 2002 ISIC codes. Using these files we construct a correspondence between 2-digit ISIC codes and 1997 NAICS.
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  4. The Intergenerational Correlation of Employment: Is There a Role for Work Culture?. (2019). Mayr, Lukas ; Koll, David ; Galassi, Gabriela .
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  6. The Intergenerational Correlation of Employment: Is There a Role for Work Culture?. (2019). Galassi, Gabriela ; Mayr, Lukas ; Koll, David.
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  8. Investment Opportunities and Economic Outcomes: Who Benefits From College and the Stock Market?. (2018). Neelakantan, Urvi ; Vidangos, Ivan ; Ionescu, Felicia ; Athreya, Kartik.
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  11. Human Capital in a Credit Cycle Model. (2017). Zoerner, Thomas ; Kubin, Ingrid ; Zorner, Thomas .
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  12. Human Capital Spillovers and the Geography of Intergenerational Mobility. (2017). Gallipoli, Giovanni ; Abbott, Brant.
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