Alice in Dataland

This is a collaboration with Rebecca Hasdell and David Rehkopf. The post provides an updated analysis to the post COVID-19: Unemployment and Income Supports April Wave by including data available until the June wave of the current Population Survey. For more details please refer to the previous post. Statistical Exploration We are interested in analyzing the effects of COVID-19 on the employment status with a focus on the Earned Income Tax Credit (EITC).

This is a collaboration with Rebecca Hasdell and David Rehkopf. Data Sources: Census Current Population Survey (CPS) Source files available: https://github.com/alice1020/COVID-unemployment-and-income-supports Data come from the Current Population Survey, which have been download from: https://cps.ipums.org/cps/index.shtml (registration required). The codebooks for the extraction are the files: Core_2019_2020_Extr_Codebook.xml for the core set of questions for monthly samples from 01-2019 to 04-2020. Variables selected: YEAR, SERIAL, MONTH, HWTFINL, CPSID, ASECFLAG, PERNUM, WTFINL, CPSIDP, FAMSIZE, NCHILD, ELDCH, YNGCH, EMPSTAT, LABFORCE, OCC, CLASSWKR, UHRSWORKT, UHRSWORK1, UHRSWORK2, AHRSWORKT, AHRSWORK1, AHRSWORK2, MARBASECIDP, LNKFW1MWT, LNKFW1YWT, LNKFW8WT, LNKFWMIS45WT, LNKFWMIS14WT, LNKFWMIS58WT, PANLWT, EARNWT, HOURWAGE, PAIDHOUR, EARNWEEK, UHRSWORKORG, WKSWORKORG.

I know, the title sounds intriguing boring and in effect it is. When I started my PhD I did not even know what spatial statistics exactly was. It was my inspiring colleague, Dr. Giacomo Benini, who introduced me to that. My PhD is in Demography, more precisely on migration studies. So why should not I apply some spatial statistics to human migration movements? Most of the time I think I have come up with a brilliant idea someone else has already had it.