Fabian Eckert

@fpeckert

Post-doc , AP to be . Urban Macroeconomist. Cities, Structural Change, and Inequality. Bavarian European.

Princeton, NJ
Vrijeme pridruživanja: travanj 2019.

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  1. Prikvačeni tweet
    28. lis 2019.

    🚨Call for Papers - please RT🚨 Clare Balboni (MIT) and I are organizing a Quantitative Spatial Economics Junior Workshop in Princeton on May 8 and 9, 2020. We invite submission of quantiative work on a wide range of spatial topics. Full call at or👇

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  2. proslijedio/la je Tweet

    Amsterdam and other global superstar cities are losing population, as housing costs soar and telecommuting becomes easier

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  3. proslijedio/la je Tweet
    17. sij

    It is great that additional researchers such as and are trying to improve local labor market data by overcoming suppressions in County Business Patterns.

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  4. proslijedio/la je Tweet
    14. sij

    A linear programming method to impute missing employment in the US County Business Patterns data. A data appendix provides the raw files, code, and imputed employment, from , Teresa C. Fort, Peter K. Schott, and Natalie J. Yang

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  5. proslijedio/la je Tweet
    14. sij

    Kudos to for the great data work & for making it so easy to access! :)

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  6. proslijedio/la je Tweet
    14. sij

    Thanks to all speakers and participants for a wonderful workshop. And thanks for the generous support of that has made this workshop possible

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  7. proslijedio/la je Tweet
    14. sij

    Very useful augmented County Business Patterns data by , , & , carefully imputing suppressed data & making industries consistent. Great public service, thank you

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  8. 14. sij

    BTW: applied for Econ PhDs recently! I knew her as a prolific Yale undergrad & have seen her become even more skilled working for Richard Hornbeck at for 2 years. She is VERY well prepared - consider making her an offer!

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  9. proslijedio/la je Tweet
    14. sij

    Come for the evidence, stay for the q-theory... In our new WP with Rodrigo and Nitya we explain why some technological transitions are particularly unequal and slow to play out. Ungated:

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  10. proslijedio/la je Tweet
    14. sij

    Check out new, publicly available data on US employment by county and year from 1977 to 2016! Cleaned files with missing values filled in and concorded to a single industry classification system. Enjoy!

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  11. proslijedio/la je Tweet
    14. sij

    Anyone who’s used the County Business Patterns knows how frustrating cell suppression can be. New resource cleverly exploits the data structure to recover some of the missing information!

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  12. 14. sij

    [All imputations in this paper are to assist researchers in conducting statistical analyses and impart no information about the underlying firms in counties or industries with suppressed data.] [N/N]

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  13. 14. sij

    The raw and imputed data sets, our code, & industry concordances are available in the data appendix to our paper at . We thank Udit Jain & Yunus Tuncbilek for outstanding research assistance, and for financial support. [5/N]

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  14. 14. sij

    On (2): We build on and ’s earlier work to provide CBP-specific industry concordances. The concordances map the different SIC & NAICS industry code vintages used in the CBP to a consistent NAICS 2012 basis. [4/N]

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  15. 14. sij

    On (1): Each CBP edition contains three files which record national, state, and county employment by industry. We develop a linear programming method that exploits adding-up constraints implicit in the hierarchical arrangement of the data to impute missing employment. [3/N]

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  16. 14. sij

    The US Census’ CBP files track employment by county+industry from 1946 to 2017. Two features limit their use in research: (1) Employment for a majority of county+industry cells is suppressed to protect confidentiality. (2) Industry classifications change every 5 years. [2/N]

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  17. 14. sij

    🚨New Data Resource/Please RT!🚨 & I augmented the County Business Patterns (CBP) data to facilitate research. CBP = most detailed public data source on the spatial industrial structure of the US. Data: . Details👇 [1/N]

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  18. proslijedio/la je Tweet
    13. sij

    My team shows that complex economic activities increasingly concentrate in large cities . Spatial inequality is a defining feature of complex economic systems. It's time to re-think policy actions for the 4th industrial revolution.

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  19. proslijedio/la je Tweet
    13. sij

    ... A single acronym can explain growing wage disparities and agglomeration effects in the US (by )

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  20. proslijedio/la je Tweet

    Superstars in the age of information: how high skill bias + declining communication costs contribute to growing inequality. ’s Doug Clement explores recent research from by cc:

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  21. proslijedio/la je Tweet
    31. pro 2019.

    Consider this the first of MANY future tweets--if you do work on inequality/mobility, please consider submitting to this conference--it should be a great group! We particularly welcome submissions by junior researchers. (No NBER affiliation needed!)

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