scholarly journals Joint culpability: The effects of medical marijuana laws on crime

2020 ◽  
Author(s):  
Yu-Wei Chu ◽  
W Townsend
Keyword(s):  
Regression Analysis ◽  
Fixed Effects ◽  
Control Method ◽  
National Level ◽  
Medical Marijuana ◽  
Property Crime ◽  
Causal Effects ◽  
Synthetic Control ◽  
Synthetic Control Method ◽  
Medical Marijuana Laws

© 2018 Elsevier B.V. Most U.S. states have passed medical marijuana laws. In this paper, we study the effects of these laws on violent and property crime. We first estimate models that control for city fixed effects and flexible city-specific time trends. To supplement this regression analysis, we use the synthetic control method which can relax the parallel trend assumption and better account for heterogeneous policy effects. Both the regression analysis and the synthetic control method suggest no causal effects of medical marijuana laws on violent or property crime at the national level. We also find no strong effects within individual states, except for in California where the medical marijuana law reduced both violent and property crime by 20%.

scholarly journals Joint culpability: The effects of medical marijuana laws on crime

2020 ◽  
Author(s):  
Yu-Wei Chu ◽  
W Townsend
Keyword(s):  
Regression Analysis ◽  
Fixed Effects ◽  
Control Method ◽  
National Level ◽  
Medical Marijuana ◽  
Property Crime ◽  
Causal Effects ◽  
Synthetic Control ◽  
Synthetic Control Method ◽  
Medical Marijuana Laws

© 2018 Elsevier B.V. Most U.S. states have passed medical marijuana laws. In this paper, we study the effects of these laws on violent and property crime. We first estimate models that control for city fixed effects and flexible city-specific time trends. To supplement this regression analysis, we use the synthetic control method which can relax the parallel trend assumption and better account for heterogeneous policy effects. Both the regression analysis and the synthetic control method suggest no causal effects of medical marijuana laws on violent or property crime at the national level. We also find no strong effects within individual states, except for in California where the medical marijuana law reduced both violent and property crime by 20%.

Democratization and Economic Output in Sub-Saharan Africa

2016 ◽  
Vol 7 (1) ◽  
pp. 63-84 ◽  
Author(s):  
Daniel de Kadt ◽  
Stephen B. Wittels
Keyword(s):  
Control Method ◽  
National Level ◽  
Economic Effects ◽  
Sub Saharan Africa ◽  
Third Wave ◽  
Synthetic Control ◽  
Synthetic Control Method ◽  
Economic Output ◽  
Sub Saharan ◽  
Positive Effect

Does democratization increase economic output? Answers to this question are inconsistent partly due to the challenges of examining the causal forces behind political and economic phenomena that occur at the national level. We employ a new empirical approach, the synthetic control method, to study the economic effects of democratization in Sub-Saharan Africa over the period 1975–2008. This method yields case-specific causal estimates, which show that political reform associated with the “third wave” of democracy had highly heterogeneous, yet often substantively important effects in Africa. In some countries democratization adversely affected economic output while in others it exerted an analogous positive effect.

scholarly journals Decoupling synthetic control methods to ensure stability, accuracy and meaningfulness

SERIEs ◽  
2021 ◽  
Author(s):  
Daniel Albalate ◽  
Germà Bel ◽  
Ferran A. Mazaira-Font
Keyword(s):  
Optimization Problem ◽  
Control Method ◽  
Economic Effect ◽  
Causal Effects ◽  
Synthetic Control ◽  
Control Methods ◽  
Synthetic Control Method ◽  
Synthetic Control Methods ◽  
Quasi Experimental ◽  
Accuracy And Stability

AbstractThe synthetic control method (SCM) is widely used to evaluate causal effects under quasi-experimental designs. However, SCM suffers from weaknesses that compromise its accuracy, stability and meaningfulness, due to the nested optimization problem of covariate relevance and counterfactual weights. We propose a decoupling of both problems. We evaluate the economic effect of government formation deadlock in Spain-2016 and find that SCM method overestimates the effect by 0.23 pp. Furthermore, we replicate two studies and compare results from standard and decoupled SCM. Decoupled SCM offers higher accuracy and stability, while ensuring the economic meaningfulness of covariates used in building the counterfactual.

scholarly journals Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models

2017 ◽  
Vol 25 (1) ◽  
pp. 57-76 ◽  
Author(s):  
Yiqing Xu
Keyword(s):  
Causal Inference ◽  
Fixed Effects ◽  
Control Method ◽  
The United States ◽  
Control Group ◽  
Synthetic Control ◽  
Synthetic Control Method ◽  
Varying Coefficients ◽  
Fixed Effects Models ◽  
Interactive Fixed Effects

Difference-in-differences (DID) is commonly used for causal inference in time-series cross-sectional data. It requires the assumption that the average outcomes of treated and control units would have followed parallel paths in the absence of treatment. In this paper, we propose a method that not only relaxes this often-violated assumption, but also unifies the synthetic control method (Abadie, Diamond, and Hainmueller 2010) with linear fixed effects models under a simple framework, of which DID is a special case. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method has several advantages. First, it allows the treatment to be correlated with unobserved unit and time heterogeneities under reasonable modeling assumptions. Second, it generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. Third, with a built-in cross-validation procedure, it avoids specification searches and thus is easy to implement. An empirical example of Election Day Registration and voter turnout in the United States is provided.

Applying Synthetic Control Method to Estimate the Impact of the Lava Jato Operation on the Worldwide Governance Indicators in Brazil

2021 ◽  
pp. 2631309X2110178
Author(s):  
Eduardo Carvalho Nepomuceno Alencar ◽  
Bryant Jackson-Green
Keyword(s):  
Rule Of Law ◽  
Control Method ◽  
Synthetic Control ◽  
Synthetic Control Method ◽  
The Rule Of Law ◽  
Governance Indicators ◽  
Regulatory Quality ◽  
Legal Judgments ◽  
The Impact ◽  
Control Of Corruption

In 2014, the most prominent anti-corruption investigation in Latin America called Lava Jato, exposed a Brazilian corruption scheme with reverberations in 61 countries, resulting in legal judgments for nearly 5 billion USD in reimbursements thus far. This article applies the synthetic control method on data from 135 countries (2002–2018) to test the hypothesis that Lava Jato impacts the Worldwide Governance Indicators in Brazil. The findings reveal that Lava Jato negatively affects control of corruption, the rule of law, and regulatory quality. There are signs of possible improvement in at least the corruption and the rule of law measures. This paper brings value to the criminological body of literature, notably lacking in the Global South.

scholarly journals When is a Match Sufficient? A Score-based Balance Metric for the Synthetic Control Method

2020 ◽  
Vol 8 (1) ◽  
pp. 209-228
Author(s):  
Layla Parast ◽  
Priscillia Hunt ◽  
Beth Ann Griffin ◽  
David Powell
Keyword(s):  
Los Angeles ◽  
Control Method ◽  
Control Unit ◽  
Treated Group ◽  
Control Group ◽  
Synthetic Control ◽  
Donor Pool ◽  
Synthetic Control Method ◽  
The Mean ◽  
The Impact

AbstractIn some applications, researchers using the synthetic control method (SCM) to evaluate the effect of a policy may struggle to determine whether they have identified a “good match” between the control group and treated group. In this paper, we demonstrate the utility of the mean and maximum Absolute Standardized Mean Difference (ASMD) as a test of balance between a synthetic control unit and treated unit, and provide guidance on what constitutes a poor fit when using a synthetic control. We explore and compare other potential metrics using a simulation study. We provide an application of our proposed balance metric to the 2013 Los Angeles (LA) Firearm Study [9]. Using Uniform Crime Report data, we apply the SCM to obtain a counterfactual for the LA firearm-related crime rate based on a weighted combination of control units in a donor pool of cities. We use this counterfactual to estimate the effect of the LA Firearm Study intervention and explore the impact of changing the donor pool and pre-intervention duration period on resulting matches and estimated effects. We demonstrate how decision-making about the quality of a synthetic control can be improved by using ASMD. The mean and max ASMD clearly differentiate between poor matches and good matches. Researchers need better guidance on what is a meaningful imbalance between synthetic control and treated groups. In addition to the use of gap plots, the proposed balance metric can provide an objective way of determining fit.

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