Abstract

The 1033 Program involves the transfer of military equipment to law enforcement agencies across the United States. The Program allows law enforcement unparalleled access to a variety of military equipment for discretionary use. Owing to issues of data, not much is known about the impact of 1033 on relevant law enforcement strategies and outcomes; including how much equipment an agency has, how the equipment is used, and the impacts of the equipment. With an increasingly controversial socio-political landscape involving police and communities, the potential connection between 1033 equipment, police militarization and use of force, and other topics of interest is pertinent. To resolve such questions, scholars and practitioners must appropriately obtain, structure, and categorize relevant 1033 data. The current article responds to Koslicki (2023) and other field inquiries to publish an open-source 1033 database that is derived from the appropriate sources, is correctly structured, and categorizable for analyses.

Version-of-record in Police Practice and Research: An International Journal
https://doi.org/10.1080/15614263.2023.2254887

Keywords: 1033 Program, Policing, Law Enforcement, Militarization, Militarism

Introduction

Use of lethal force by police in recent years (e.g., Michael Brown, Freddie Gray, Breonna Taylor, and George Floyd) generated nationwide attention and response (Mullinix et al., 2021), prompting scholars, policymakers, and the public to question the interactions between police and civilians (Pryce & Gainey, 2022). In addition to discourse related to race and use of force, issues of police militarization—the use of equipment and tools historically reserved for military conflict abroad by domestic police departments—took center stage.

Contemporary research on police militarization began some thirty years ago (see Kraska, 1993, 1994, 1996; Kraska and Paulsen, 1996; and Kraksa and Kappeler, 1997). Studies on police militarization come from several disciplines including criminology, economics, and history, with some engaged in quantitative analyses of budgets, others examining the adoption of “militaristic” qualities among police departments, and still others exploring the linkages between foreign intervention and militarization (see Haggerty and Ericson, 1999; Lutterbeck, 2004; Hall and Coyne, 2013; Balko, 2014; and Coyne and Hall, 2018 for examples).

Statistical treatments of police militarization largely focus on the 1033 Program. Begun in 1990 and expanded as part of the war on terror, this program allows the transfer of surplus military equipment from the Department of Defense (DOD) to state and local police departments.1 Studies have analyzed the effects of the 1033 Program on crime (see Bove and Gavrilova, 2017; Gunderson et al, 2021; Lowande, 2021; Ramey and Steidley, 2018), officer and departmental behavior (Burkhardt and Baker 2018; Delehanty et al, 2017), officer and civilian safety (Carriere and Encinosa, 2017; Harris et al, 2017; Lawson, 2019), and the use of Special Weapons and Tactics (SWAT) Teams (Radil et al, 2017). Discussion surrounding the 1033 Program has increased substantially in the last few years due to the events noted above (Jenkins et al. 2021; Walby, 2021) and a national audit of police use of force (Bennell et al. 2021; Cobbina, 2019). Portions of the program were changed under President Obama in 2015 but were reversed under the Trump Administration in 2017. Later, the policy changes to the 1033 Program were reinstated by the Biden administration (in 2022).

How do transfers under the 1033 Program affect crime in areas served by “militarized” police? The results are mixed. Early studies indicated that counties that procured military equipment observed a decrease in crime (Gunderson et al., 2021). Other scholars, however, identified problems with these studies, particularly incorrect data use (e.g., missing data, inappropriate aggregation, etc. See, Gunderson et al., 2021; Lowande, 2021). Examining the properly specified data, Gunderson et. al (2021) found that the program had no effect on local crime rates. In an examination of agency specific recalled equipment, Lowande (2021) concluded that loss of military equipment also had no effect on crime rates (See also, Mummolo, 2021).

Despite the attempts at analysis, the data are far from clear. Koslicki (2023) highlights several issues with the operationalization of 1033 Program data. Analyzing 14 articles, she found 12 did not properly utilize data. For example, prior studies incorrectly treated the available data as longitudinal. The Defense Logistics Agency (DLA) releases 1033 Program data quarterly as a cross-sectional snapshot of the present inventory for participating agencies. The data reference equipment acquired in prior quarters, but only for equipment still held in the present quarter’s inventory. As such, longitudinal analysis is inappropriate. She noted additional problems related to data sources and access, data structure and validity, and data categorization and recency. Taken together, she notes these problems limit the scope of what researchers know about the effects of the 1033 Program and we should interpret findings with caution (Koslicki, 2023).

We rectify the data issues identified by Koslicki (2023) by providing an updated, corrected, open-access database of the 1033 Program. The current article follows with a detailed description of the database and its features, a discussion of the advantages of open data in research, and an appendix of replication files and data access statements for continued scholarly and practitioner use.

Koslicki (2023) Recommendations

Recommendation 1: Data Sources

Koslicki (2023: 124) highlights the importance of data selection in studying the effects of the 1033 Program. Despite attempts to track military equipment transfers to law enforcement via the 1033 Program by various media outlets, the only official outlet is the Defense Logistics Agency Law Enforcement Support Office (DLA LESO). This data, published quarterly, includes all current equipment transferred, returned, and held in an existing inventory catalog across the US and its territories. Unlike more aggregated presentations, the DLA LESO releases data at the agency-level.

In response to this recommendation, we produced a database of agency-level 1033 Program data derived from the DLA LESO. Data at the agency-level can be tested with greater specificity, as the de-centralized system of law enforcement utilized in the US results in agencies with different policies, approaches, and leadership strategies that may impact their desire to obtain military equipment, the types of equipment obtained, and how such equipment may ultimately be used. The open-source database provides 1033 Program data for 9,385 unique agencies that can be aggregated into counties or states for more macro-level analyses as necessary. The database tracks shipments to each agency over archived quarters from the fourth quarter of 2014 to the second quarter of 2023. The DLA LESO website only lists shipments and transfers for the most recent quarter, which requires past years to be ascertained using the “Wayback Machine.”2 For quarters unavailable through the Wayback Machine, related prior research was used to acquire necessary data on 1033. Table 1 below includes the quarters available in the open-access database and their source, with each dataset available in an associated open access file in the appendix for review.3

Table 1. 1033 Quarterly Data and Sources

Recommendation 2: Data Structure

The second recommendation in using 1033 Program data relates to the data’s incorrect usage in longitudinal studies (Koslicki, 2023: 124-126). DLA LESO data is cross-sectional and published quarterly. The maintained 1033 data only reflect current transfers and inventory at a given point in time for a specific quarter. The 1033 data includes all currently existing inventory and a record of when the inventory was first transferred to the agency. This often results in the mischaracterization of the database as being longitudinal. The DLA LESO data exclusively reflects the current inventory, meaning all prior transfers and equipment no longer in the existing inventory due to usage or return are no longer chronically recorded. Each quarterly dataset only provides a snapshot in time of the current transfers and equipment (LESO Public Information, 2023). Thus, any attempt to undertake a longitudinal examination of 1033 requires every quarter of the study period to be obtained and pieced together separately. To correctly operationalize the data, all available 1033 quarters were recovered using the “Wayback Machine,” prior published 1033 research, and previous Freedom of Information Act (FOIA) requests so that each shipment record in the database could be assigned to a corresponding law enforcement agency by month, quarter, and year.

We produced a longitudinally sequenced database of all available 1033 Program quarters from 2014-2023 (current year-to-date). The panel structure of the database allows for time-series explorations of potential changes in 1033 acquisitions, equipment types, and agency participation levels over time. The time component of the data is critical to track, as participating agencies both receive and turn in 1033 equipment. Following the framework set forth by Lowande (2021), the current article produced a longitudinal database of all 1033 equipment transfers using a panel structure that simultaneously accounts for unique agencies, shipments, and equipment items. The database includes a panel identifier attributable to each unique agency, a shipment identifier that delineates each shipment for a given agency, an item identifier that tracks each agency item in a shipment by count, and an equipment identifier that logs the total count of items shipped to an agency. The database preserves the longitudinal structure by accounting for when an item was shipped to an agency (original shipping date), and how long it stayed in the agency’s inventory across quarters. This level of added detail allows for subsequent analyses to better operationalize “time” relative to the specific items active in 1033 inventories.

Recommendation 3: Data Categorization

Koslicki’s (2023: 126-127) third and final recommendation for 1033 Program research is to maintain consistent categorization of key variables. Prior studies of 1033 Program equipment have used different classifications to discuss the same equipment, which may help explain differing results (Koslicki, 2023). The official DLA LESO database includes identifiers for categorization based on the level of militarization, whether the item must be returned (controlled or non-controlled), and the costs of items. However, these identifiers vary in application and are not always preserved across data produced in research.

The constructed database preserves the unique identifiers present in the DLA LESO database and provides criteria for consistently operationalizing variables for research. By including the original integrity code variables, researchers and practitioners utilizing the database can make informed (and replicable) decisions about the classification of equipment. The equipment type, quantity, and length of time items are preserved across an agency’s inventory can also be cross referenced with the National Stock Number (NSN) of each record to determine if the item is intended to be controlled or non-controlled or relatedly militarized or non-militarized. To add additional information to the scope of normal 1033 data, the database produced also includes information on shipment monetary values by equipment type and total. This allows for agency spending on equipment to be tracked longitudinally.

Data Availability

The current version of the database is publicly available on the Harvard Dataverse at: https://doi.org/10.7910/DVN/EHC3RV. It includes individual datasets for each state and each quarter, a replication script for reproducing the completed longitudinal database, and a current version of the 1033 Program database (2014 Q4 – 2023 Q2). The “README.pdf” file in the appendix includes directions for accessing the transparency statement regarding the development of the database, the steps for locating, replicating, and using the associated data, and information on other critical aspects of the 1033 Program database. In perpetuity, the materials used to produce the database and the current version of record will be updated following each release of DLA LESO 1033 Program quarterly data.5

Conclusion/Discussion

In order to draw any reliable conclusions about the 1033 Program or its impacts, it is necessary to utilize reliable, valid data. Our work offers a potential solution to the issues identified by Koslicki (2022; 2023) and offers scholars in different disciplines an updated, corrected, open-access database of the 1033 Program data.

Our open-access database rectifies three concerns raised. First, Koslicki (2023) points out that the studies she reviewed are based on a variety of source data, including media outlets and the DOD. To draw reliable conclusions about the 1033 program, it is necessary to compile information from reliable sources without gaps. We address this concern by bringing together official program data from the DLA LESO to provide a reliable source for consistent data. This approach offers scholars and practitioners a solution to the problem of varying data sources and adds nuance to the data by sequencing it at the agency-level.

Second, Koslicki (2023) highlights the need for a consistent data structure, as the studies used in her review of the literature vary in how they operationalize 1033 Program inventories. By eliminating the reliance on data that omits transferred and returned equipment, our database creates a way to more accurately assess the movement and use of military equipment associated with the 1033 Program longitudinally. This approach enhances the rigor of studies on 1033 and allows for changes in agency participation and outcomes to be examined over time. Further, this rectifies potential inaccuracies and misinterpretations of results where cross-sectional 1033 data was inappropriately treated as longitudinal.

Finally, Koslicki's (2023) third point, that the studies she reviewed vary in terms of their operationalization of key measures including the purchase, transfer, and requisition of military equipment, is addressed by the creation of this database. By using the original integrity code variables, the classification of equipment can more reliably be utilized to answer questions about the spending, movement of equipment, and impact of the 1033 Program. Further, the database allows researchers to cross reference the data with the NSNs to confirm whether the equipment utilized is militaristic. In addition to examining equipment inventories over time, the database also provides researchers the opportunity to track the quantity and spending of law enforcement agencies participating in the 1033 Program. This is an important improvement over prior sources (and categorization), as it reduces the possibility of inaccurately drawing conclusions about equipment that was used and returned, for example.

The increasing attention to the 1033 Program, and the use of military equipment and related tactics in law enforcement, more broadly, is an important area of scholarly attention. The examination of police engagement with the 1033 Program and potential resultant impacts on controversial issues like the elevated level of police militarization in communities of color (Koslicki, 2023), outcomes observed in police use of force encounters (Koslicki et al., 2021; Piza et al. 2023), and policy decisions surrounding police funding decisions (Bruce et al. 2019) are paramount. To effectively answer such questions, we offer a resource for consistently observing the relationship between police militarization and potential outcomes of interest. The data could readily be used to examine law enforcement engagement with 1033 across relevant topics ranging from agency use of force levels, agency transparency efforts, or agency community perceptions etc. The current article provides information about the database and its features, a discussion of the advantages of open data in research for consistent and reliable application, and an appendix of replication files and data access statements to produce meaningful insights regarding law enforcement use of military equipment.

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Author Bios

Nathan T. Connealy is an Assistant Professor in the Department of Criminology and Criminal Justice at the University of Tampa. His research focuses on the spatial analysis of crime patterns, the intersection of policing, crime prevention, and technology, and quantitative research designs. His scholarship has recently been published in academic journals including Criminology, Justice Quarterly, Criminology & Public Policy, and Crime & Delinquency among others.

Abigail R. Hall is an Associate Professor in the Department of Economics at the University of Tampa. Her work focuses on U.S. defense policy, including foreign and domestic outcomes. She is the coauthor of Tyranny Comes Home: The Domestic Fate of U.S. Militarism and Manufacturing Militarism: U.S. Government Propaganda in the War on Terror, both with Stanford University Press.

Chivon H. Fitch is an Associate Professor in the Department of Criminology and Criminal Justice at the University of Tampa. Her current work focuses on the experiences of race, gender and sexuality, as well as aging, among the incarcerated. Her research has been published in Deviant Behavior, Criminal Behaviour and Mental Health, Crime, Law, and Social Change, and the Journal of Family Issues.