DOI: 10.3886/e105403v6-101400
title: Jacob Kaplan's Concatenated Files: Uniform Crime Reporting (UCR) Program Data: Property Stolen and Recovered (Supplement to Return A) 1960-2019

description: <b>Version 6 release notes</b>Changes release notes description, does not change data.<b>Version 5 release notes:</b>Adds data for 2019Note that the number of months reported variable sharply changes starting in 2018. This is probably due to changes in UCR reporting of the "status" variable which is used to generate the months missing county (the code I used does not change). So pre-2018 and 2018+ years may not be comparable for this variable. <b>Version 4 release notes:</b><b></b>Adds data for 2018<br><b>Version 3 release notes:<br></b>Adds data in the following formats: Excel.Changes project name to avoid confusing this data for the ones done by NACJD.<b>Version 2 release notes:</b>Adds data for 2017.Adds a "number_of_months_reported" variable which says how many months of the year the agency reported data.Property Stolen and Recovered is a Uniform Crime Reporting (UCR) Program data set with information on the number of offenses (crimes included are murder, rape, robbery, burglary, theft/larceny, and motor vehicle theft), the value of the offense, and subcategories of the offense (e.g. for robbery it is broken down into subcategories including highway robbery, bank robbery, gas station robbery). <br><br>The majority of the data relates to theft. Theft is divided into subcategories of theft such as shoplifting, theft of bicycle, theft from building, and purse snatching. For a number of items stolen (e.g. money, jewelry and previous metals, guns), the value of property stolen and and the value for property recovered is provided. This data set is also referred to as the Supplement to Return A (Offenses Known and Reported). <br><br>All the data was received directly from the FBI as text or .DTA files. <br>There may be inaccuracies in the data, particularly in the group of columns starting with "auto." To reduce (but certainly not eliminate) data errors, I replaced the following values with NA for the group of columns beginning with "offenses" or "auto" as they are common data entry error values (e.g. are larger than the agency's population, are much larger than other crimes or months in same agency): 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99942. This cleaning was NOT done on the columns starting with "value."<br>For every numeric column I replaced negative indicator values (e.g. "j" for -1) with the negative number they are supposed to be. These negative number indicators are not included in the FBI's codebook for this data but are present in the data. I used the values in the FBI's codebook for the Offenses Known and Clearances by Arrest data.<br><br>To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. If an agency has used a different FIPS code in the past, check to make sure the FIPS code is the same as in this data.<br>
description: Smallest Geographic Unit: City
description: self-enumerated questionnaire;