The economic consequences of Hugo Chavez: A synthetic control analysis

2016 ◽  
Vol 125 ◽  
pp. 1-21 ◽  
Author(s):  
Kevin Grier ◽  
Norman Maynard
2018 ◽  
Vol 30 (5) ◽  
pp. 592-608 ◽  
Author(s):  
Cruz A. Echevarría ◽  
Javier García-Enríquez

2016 ◽  
Vol 22 (3) ◽  
pp. 213-246 ◽  
Author(s):  
Jose Roberto Balmori de la Miyar

AbstractMilitary crackdowns often disrupt economic development by exacerbating violence. This paper examines the case of the Mexican Drug War, employing synthetic control methods. To prove causality, I use variation on statewide military operations, as well as the rollout of the war. Findings indicate a decrease in GDP per capita equal to 0.5%, in states with military operations. Determinants by which the Mexican Drug War hampered economic development include a proportional reduction in consumption per capita, and a decline in productive investment of at least 0.3%, driven by a drop of 3.2% in commercial credit granted to businesses.


2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S591-S591
Author(s):  
Sindiso Nyathi ◽  
Hannah Karpel ◽  
Kristin L Sainani ◽  
Yvonne Maldonado ◽  
Peter J Hotez ◽  
...  

Abstract Background Vaccine hesitancy in low vaccine coverage “hot spots” has led to recent outbreaks of vaccine-preventable diseases across the United States. State policies to improve vaccine coverage by restricting non-medical (personal belief) exemptions are heavily debated and their effectiveness is unclear due to limited rigorous policy analysis. In 2016, a California policy (SB 277) eliminated non-medical exemptions from kindergarten requirements. To address the ongoing debate on such policies, we performed a quasi-experimental, controlled analysis of the policy’s impact on vaccine and exemption outcomes. Methods We used state vaccine coverage and exemption data (2011–2017) from the CDC and health data from public sources. We prespecified a primary outcome of MMR coverage (%) and secondary outcomes of medical and non-medical exemptions (%). We included covariates related to socioeconomic and health measures (e.g., insurance, well child visits) and pre-2016 mean coverage. Using the synthetic control method, with 2016 as the treatment year and a 2-year post-policy period, we constructed a “control” California, from a weighted sum of states. We used permutation testing to repeat the process for each of the other states and their unique synthetic control, to determine whether there was a meaningful difference in California (i.e., a change in California’s coverage relative to its control in the top 5th percentile of states). We tested the model’s sensitivity to various analytical assumptions. Results Of 43 control states, synthetic California was predominantly comprised of Idaho, Mississippi, and Arkansas, and had a good pre-policy match on outcomes. MMR coverage in California increased by 3.2% relative to synthetic California in the post period (Top 1 of 44 states, Figure 1). Medical exemptions increased by 0.4%, while non-medical exemptions decreased by 2.2% in the post-period (Top 1 of 43 states). The model was robust to changes in covariates and control states. Conclusion The policy resulted in a meaningful increase in MMR coverage and reduction in non-medical exemptions. We measured a modest increase in medical exemptions, but this was offset by the larger reduction in non-medical exemptions. State policies removing non-medical exemptions can be effective in increasing vaccination coverage. Disclosures All authors: No reported disclosures.


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