The data set from Eastern State Penitentiary (ESP) in Philadelphia, Pennsylvania available from the American Philosophical Society Library contains information retrieved from the admissions ledgers for prisoners admitted to the penitentiary between April 24, 1830 and May 24, 1843. The collection description from the American Philosophical Society Library indicates that the records from which this data set are derived were recorded upon a prisoner’s admission into ESP.
There are 1,614 rows of data each documenting the admittance of 998 individual prisoners. In several instances, identical prisoner data is recorded in multiple rows. Nor does each entry row with a unique prisoner number (Column F) necessarily detail a new individual as numerous entries reflect multiple sentences served at the prison by repeat offenders (Column K). In addition to Larcombe’s annotations, recorded data includes prisoner’s name; age; ethnicity (or race), religion, or occupation (Column D); birthplace; prisoner number; admission date; location of sentencing; offense; length of sentence; sentencing location; number of convictions; and discharge notes. Column L, which is labeled “ColumnNote” contains a range of information regarding each incarcerated person, but generally includes items such as marital status, literacy, writing ability, temperateness; and language(s) spoken. Many of the records contain annotations relating to the perceived moral character of the incarcerated person in a column labeled “Description” (Column N) According to the finding aid, much of the information was recorded by the prison’s “Moral Instructor,” Thomas Larcombe. The data set indicates that prisoners at ESP were convicted of 107 different offenses (Column I) with larceny presenting by far the largest number of cases (828) and burglary (215) a distant second.
Because column D contains a range of data to include a prisoner’s ethnicity (or oftentimes race), occupation, and/or religion; the dataset offers only an incomplete view of the racial, ethnic, or socioeconomic makeup of the prison population at Eastern State Penitentiary. “Black” is the most often recorded entry in the column with 270 entries, and “Mulatto” with 107. According to the data, the most common occupation of the prisoners admitted to ESP were laborers (119 entries), shoemakers (63), and blacksmiths (35). In total, there were 247 unique values noted in this column. While many of the entries in column D note the racial/ethnic background of the individual as well as an occupation, a significant number only indicate race/ethnicity or occupation. White/caucasian does not appear to have been a common entry term. Only two prisoners were recorded as “White, however, it is doubtful that researchers could interpolate whiteness to those prisoners for whom no race/ethnicity was recorded. In addition, there are 348 rows in which no data was recorded in the column.
Another somewhat glaring omission in the data collected is the gender of the admitted prisoners. It is evident that between 1830 and 1843 ESP was solely reserved for male prisoners. In 27 rows, the term “female” was recorded in column D (EthnicityOccupationReligion). While not definitive, the presence of multiple other prisoners with given names such as Ann (6 rows), Elizabeth (11 rows), or Mary (11 rows), that a not insignificant portion of the prison population was female. It is unclear what rationale was used to determine if/when the gender information of an incarcerated person was included in the data.
Variations in the manner in which information was recorded makes sorting and organization of the data challenging. The presence of significant numbers of rows with duplicate data also presents challenges in analyzing the data and creates the potential for skewed analytics.Ultimately, this data set from Eastern State Penitentiary affords researchers a potentially significant level of information regarding the prisoner population in and around Philadelphia, Pennsylvania between 1830 and 1843 for those willing to put in the time and effort necessary to carefully extract the desired information.