Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Multilevel Modeling

Oxford Research Encyclopedia of Criminology and Criminal Justice, 2025

Multilevel models are statistical methods used to investigate associations between data from different levels of analysis or clusters. Given the inherent relationships between crime and its surrounding social and physical context, multilevel modeling is crucial for criminology and criminal justice research. Typically, research designs often assume observations are independent and internally homogeneous regarding the characteristics of interest. However, the social world consists of different grouping structures, where observations within the same group share more common characteristics than those in different groups. Ignoring these group-level influences can lead to incorrect analysis and interpretations. Multilevel models extend single-level regression models to account for the nested data structures. These models offer several statistical and theoretical advantages. On the one side, they avoid biased estimation of the relationships of interest. On the other side, they prevent interpretative errors, such as ecological and atomistic fallacies, and allow exploration of associations between variables measured at different levels of analysis. Various types of multilevel models exist, including: random intercept models that estimate a baseline level of the outcome of interest for each cluster of observations; random coefficient models that capture how the studied relationship varies across different groups; and fixed effect models that control for all characteristics shared by observations within the same cluster. Additionally, growth model is a specific type of multilevel model used to analyze the effects of time-related variables on an outcome measured consistently over time within the same set of observations. The choice of the most suitable multilevel model depends on statistical, methodological, and theoretical assumptions.

Recommended citation: Dugato, Marco and Tissandier, Gaspard. (2025). Multilevel Modeling; Oxford Research Encyclopedia of Criminology and Criminal Justice

Evaluating the Effect of Enhanced LED Street Lighting on Nighttime Crime

R&R at the Journal of Quantitative Criminology, 2025

This study examines the deterrent effect of enhanced street lighting on nighttime violent crime in Newark, NJ, where high-pressure sodium lights were replaced with LED fixtures between 2019 and 2021. Using a quasi-experimental design with Difference-in-Difference setting and the 2021 Gardner estimator, we assess the impact of approximately 1,500 streetlight upgrades. Results show a significant short-term reduction in nighttime and outdoor violent crime of approximately 50% in the first two quarters post-replacement. The effect is mainly driven by a decrease in aggravated assaults and robberies. However, this effect disappears after two quarters, suggesting an adaptation to the new lighting environment. Long-term analysis does not show any significant reduction in violent or property crime in the long run (two years post-replacement).

Recommended citation: Gimenez-Santana Alejandro, Tissandier Gaspard, Zlaoui Khalil, Santos Adriana, Caplan Joel, Kennedy, Leslie (2025). Evaluating the Effect of Enhanced LED Street Lighting on Nighttime Crime

PAVED With Good Intentions? An Evaluation of a French Police Predictive Policing System

R&R at the International Review of Law and Economics, 2025

From late 2017 to early 2019, one of the two french law enforcement agencies (the Gendarmerie) tested in 11 out of 101 departments a predictive policing system named PAVED. The system designed by the Gendarmerie predicts burglaries and vehicle thefts with the stated objective of better allocating patrols and thus increasing deterrence. We use month-law enforcement jurisdiction area panel data to evaluate whether the system produces the expected reduction in these thefts. Using a TWFE approach and considering several alternative counterfactuals, our results consistently indicate no detectable effect of PAVED on burglaries. With regard to vehicle theft, small variations are observed following the implementation of PAVED, but these variations are not consistent or robust across the different counterfactuals considered.

Recommended citation: Lecorps, Yann and Tissandier Gaspard. (2025). PAVED With Good Intentions? An Evaluation of a French Police Predictive Policing System

Working Papers

Resources Misallocation and Predictive Policing: A Data Generation Process

Forthcoming working paper, 2024

The use of Machine Learning and Deep Learning methods in data-driven policy making, such as predictive policing, has surged in recent years. This paper explores the connection between misallocation of police resources and the replication in prediction errors due to non-representative data. The study presents a framework where a policy maker allocates police resources to minimize crime in a jurisdiction using predictive models, and addresses two key questions: how misallocation errors in resource allocation can replicate as prediction errors and how to estimate the replication factor between these errors when no closed form expression exists. The paper demonstrates that over-policing in an area amplifies misallocation errors in predictions, creating a feedback loop. In cases involving non-trivial pre-dictive methods, the paper highlights an errors replication pipeline, allowing empirical estimation of the replication factor. The methodology is applied to predict crime levels in New York City.

Recommended citation: Tissandier Gaspard. (2024). Resources Misallocation and Predictive Policing: A Data Generation Process

The Heterogeneous Effect of Dropping Out for Higher Education Students: The French Case

Working paper on SSRN, 2024

For higher education systems with general and vocational degrees, the question of resource allocation for dropout policies is fundamental. In France, the university concentrates most of the focus and resources compared to the vocational track. I estimate the heterogeneous effect of dropping out on labor market outcomes (employment rate and wages), conditional on the student’s degree, to evaluate the validity of this allocation. I use a causal Random Forest methodology to account for the heterogeneous cohort composition of these degrees, with the distance to the closest higher education institution as an instrument for dropout. Using 2SLS leads to underestimating the overall effect of dropping out by 9 percentage points for the employment rate and by 4 percentage points for the average wage. Vocational degree dropouts are more penalized than university dropouts on their average wage but not on their time in employment. Finally, using a multidimensional categorization of students can be beneficial for creating a targeted dropout policy.

Recommended citation: Tissandier Gaspard. (2024). The Heterogeneous Effect of Dropping Out for Higher Education Students: The French Case

Gaspard - Postdoctoral associate