Synthetic Control Methods with Micro- And Meso-Level Data
The R package microsynth (Robbins & Davenport, 2025) was developed for implementation of the synthetic control methodology for comparative case studies involving disaggregated (i.e., micro- or meso-level) data. The methodology implemented within microsynth is designed to assess the efficacy of a treatment or intervention within a well-defined geographic region that is itself a composite of several smaller regions (where data are available at the more granular level for comparison regions as well). The effect of the intervention on one or more time-varying outcomes is evaluated by determining a synthetic control region that resembles the treatment region across pre-intervention values of the outcome(s) and time-invariant covariates and that is a weighted composite of many untreated comparison regions. The microsynth procedure includes functionality that enables its user to (1) calculate weights for synthetic control, (2) tabulate results for statistical inferences, and (3) create time series plots of outcomes for treatment and synthetic control.
The methodology employed in microsynth was originally presented in an article published in the Journal of the American Statistical Association (Robbins et al., 2017). The algorithm was recently updated to incorporate several improvements, including more robust omnibus testing (Neil et al., 2025).
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References
Manuals
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microsynth: Synthetic Control Methods with Micro- And Meso-Level Data
M. Robbins and S. Davenport
Apr 2025
R package version 2.0.51
Journal Articles
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A framework for synthetic control methods with high dimensional, micro-level data: Evaluating a neighborhood-specific crime intervention
M. W. Robbins, J. Saunders, and B. Kilmer
Journal of the American Statistical Association, Apr 2017
The synthetic control method is an increasingly popular tool for analysis of program efficacy. Here, it is applied to a neighborhood-specific crime intervention in Roanoke, VA, and several novel contributions are made to the synthetic control toolkit. We examine high-dimensional data at a granular level (the treated area has several cases, a large number of untreated comparison cases, and multiple outcome measures). Calibration is used to develop weights that exactly match the synthetic control to the treated region across several outcomes and time periods. Further, we illustrate the importance of adjusting the estimated effect of treatment for the design effect implicit within the weights. A permutation procedure is proposed wherein countless placebo areas can be constructed, enabling estimation of p-values under a robust set of assumptions. An omnibus statistic is introduced that is used to jointly test for the presence of an intervention effect across multiple outcomes and post-intervention time periods. Analyses indicate that the Roanoke crime intervention did decrease crime levels, but the estimated effect of the intervention is not as statistically significant as it would have been had less rigorous approaches been used. Supplementary materials for this article are available online.
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The impact of drug possession decriminalization on arrests: A race-specific synthetic control analysis of Oregon’s Measure 110
R. Neil, B. Ghosh Dastidar, B. Kilmer, M. W. Robbins, and K. Warren
Journal of Quantitative Criminology, Apr 2025
Objectives
Racial disparities in arrests are a major concern, particularly when it comes to drug enforcement. In 2021, Oregon decriminalized the possession of controlled substances as part of Measure 110 (M-110), an unprecedented policy change in the United States. We estimate how M-110 affected five types of arrests, overall and by race.
Methods
National Incident-Based Reporting System data covering 3,642 police agencies from 43 states for 2018–2023 are combined with 2020 Census data. We extend a synthetic control methodology developed for micro-level data to test whether policy effects differ across groups and whether policies affect disparities, using permutation inference to quantify uncertainty.
Results
M-110 reduced drug possession arrest rates in Oregon for the overall population (67.8%) and for the three racial groups we focus on: Black (75.6%), Hispanic (77.5%); and White (66.2%), with the reduction being statistically significantly larger for Hispanic and Black than White individuals. M-110 reduced disorder arrest rates by 30.9% for Black individuals, which is statistically significantly different from zero and the White estimate. Black-White rate differences in drug possession arrests fell by 79.5% and in disorder arrests by 41.7%. In general, M-110 did not affect arrest rates for violent, property, or drug trafficking offenses.
Conclusions
M-110 reduced drug possession arrests while reducing Black-White rate differences. M-110 led to a decrease in disorder arrests for Black individuals, suggesting police did not substitute one arrest type for another for this population. Our method offers a new approach for examining heterogeneous policy effects and how policies affect disparities.