analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

The Current Population Survey Civic Engagement and Volunteering (CEV) Supplement is the most robust longitudinal survey about volunteerism and other forms of civic engagement in the United States. Produced by AmeriCorps in partnership with the U.S. Census Bureau, the CEV takes the pulse of our nation’s civic health every two years. The data on this page was collected in September 2023. The next wave of the CEV will be administered in September 2025. The CEV can generate reliable estimates at the national level, within states and the District of Columbia, and in the largest twelve Metropolitan Statistical Areas to support evidence-based decision making and efforts to understand how people make a difference in communities across the country. Click on "Export" to download and review an excerpt from the 2023 CEV Analytic Codebook that shows the variables available in the analytic CEV datasets produced by AmeriCorps. Click on "Show More" to download and review the following 2023 CEV data and resources provided as attachments: 1) 2023 CEV Dataset Fact Sheet – brief summary of technical aspects of the 2023 CEV dataset. 2) CEV FAQs – answers to frequently asked technical questions about the CEV 3) Constructs and measures in the CEV 4) 2023 CEV Analytic Data and Setup Files – analytic dataset in Stata (.dta), R (.rdata), SPSS (.sav), and Excel (.csv) formats, codebook for analytic dataset, and Stata code (.do) to convert raw dataset to analytic formatting produced by AmeriCorps. These files were updated on January 16, 2025 to correct erroneous missing values for the ssupwgt variable. 5) 2023 CEV Technical Documentation – codebook for raw dataset and full supplement documentation produced by U.S. Census Bureau 6) 2023 CEV Raw Data and Read In Files – raw dataset in Stata (.dta) format, Stata code (.do) and dictionary file (.dct) to read ASCII dataset (.dat) into Stata using layout files (.lis)

The data file provided is in .dta format. This can be easily read in STATA or in R (using read.dta function). The code provided is instead STATA code. All variables are intuitively labelled. The data is part of an original survey based upon representative samples of working individuals in 11 countries. The survey questioned individuals on a number of areas – including their knowledge and awareness of insurance-related topics, personal and vicarious past experience of income losses, personal health and well-being, financial risk tolerance, and financial literacy – in order to understand better the drivers of demand for income protection insurance. The countries part of the survey were 11 countries, including Germany, Italy, Spain, Switzerland, and the UK (Europe); Brazil, Mexico, and the USA (the Americas); and Australia, Hong Kong, and Malaysia (Asia-Pacific).

Version 3 release notes: Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. The data sets here combine all data from the years 1992-2015 into a single file. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), changed the name of some UCR offense codes (e.g. from "agg asslt" to "aggravated assault"), made all character values lower case, reordered columns. I also added state, county, and place FIPS code from the LEAIC (crosswalk) and generated incident month, weekday, and month-day variables from the incident date variable included in the original data. The zip file contains the data in the following formats and a codebook: .csv - Microsoft Excel.dta - Stata.sav - SPSS.rda - RIf you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. The data sets here combine all data from the years 1992-2015 into a single file. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), changed the name of some UCR offense codes (e.g. from "agg asslt" to "aggravated assault"), made all character values lower case, reordered columns. I also added state, county, and place FIPS code from the LEAIC (crosswalk) and generated incident month, weekday, and month-day variables from the incident date variable included in the original data. The zip file contains the data in the following formats and a codebook: .csv - Microsoft Excel.dta - Stata.sav - SPSS.rda - RIf you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.