Austerity Regimes at the Local Level: Examining Fiscal Effects of Austerity Commissioners Using a Time-Series Synthetic Control Method

2019 ◽  
Vol 43 (2) ◽  
pp. 414-432
Author(s):  
Steffen Zabler
Keyword(s):  
Time Series ◽  
Control Method ◽  
Local Level ◽  
Synthetic Control ◽  
Synthetic Control Method

Do Local Immigrant-Welcoming Efforts Increase Immigration? The Detroit Experience

2021 ◽  
pp. 107808742110252
Author(s):  
Xi Huang
Keyword(s):  
United States ◽  
Control Method ◽  
Local Level ◽  
Statistical Significance ◽  
Local Population ◽  
The United States ◽  
Synthetic Control ◽  
Synthetic Control Method ◽  
Skilled Immigrants ◽  
The Past

Immigration policymaking has been active at the local level in the United States over the past few decades. This study examines whether the economic development-oriented immigrant-welcoming efforts that started in 2010 in Detroit have increased the local immigrant population. It uses the synthetic control method to construct a comparison region that resembles Detroit in the preintervention periods to serve as a counterfactual. Empirical results reveal a statistically significant increase in the immigrant share of the population in the metropolitan area during the postintervention period of 2011–2014. The increase is robust to various sets of specifications and placebo tests. The share of high-skilled immigrants in the local population also increased during this time, albeit with a weak statistical significance. These findings point to the potential of immigrant-welcoming programs in attracting and retaining immigrants and immigrant talent.

scholarly journals Synthetic control methodology as a tool for evaluating population-level health interventions

2018 ◽  
Vol 72 (8) ◽  
pp. 673-678 ◽  
Author(s):  
Janet Bouttell ◽  
Peter Craig ◽  
James Lewsey ◽  
Mark Robinson ◽  
Frank Popham
Keyword(s):  
Public Health ◽  
Time Series ◽  
Time Series Analysis ◽  
Observational Data ◽  
Control Method ◽  
Health Interventions ◽  
Synthetic Control ◽  
Public Health Interventions ◽  
Synthetic Control Method ◽  
Control Units

BackgroundMany public health interventions cannot be evaluated using randomised controlled trials so they rely on the assessment of observational data. Techniques for evaluating public health interventions using observational data include interrupted time series analysis, panel data regression-based approaches, regression discontinuity and instrumental variable approaches. The inclusion of a counterfactual improves causal inference for approaches based on time series analysis, but the selection of a suitable counterfactual or control area can be problematic. The synthetic control method builds a counterfactual using a weighted combination of potential control units.MethodsWe explain the synthetic control method, summarise its use in health research to date, set out its advantages, assumptions and limitations and describe its implementation through a case study of life expectancy following German reunification.ResultsAdvantages of the synthetic control method are that it offers an approach suitable when there is a small number of treated units and control units and it does not rely on parallel preimplementation trends like difference in difference methods. The credibility of the result relies on achieving a good preimplementation fit for the outcome of interest between treated unit and synthetic control. If a good preimplementation fit is established over an extended period of time, a discrepancy in the outcome variable following the intervention can be interpreted as an intervention effect. It is critical that the synthetic control is built from a pool of potential controls that are similar to the treated unit. There is currently no consensus on what constitutes a ‘good fit’ or how to judge similarity. Traditional statistical inference is not appropriate with this approach, although alternatives are available. From our review, we noted that the synthetic control method has been underused in public health.ConclusionsSynthetic control methods are a valuable addition to the range of approaches for evaluating public health interventions when randomisation is impractical. They deserve to be more widely applied, ideally in combination with other methods so that the dependence of findings on particular assumptions can be assessed.

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.

scholarly journals Campaign Finance Regulations and Public Policy

2021 ◽  
pp. 1-8
Author(s):  
MARTIN GILENS ◽  
SHAWN PATTERSON ◽  
PAVIELLE HAINES
Keyword(s):  
Government Policy ◽  
Policy Making ◽  
Campaign Finance ◽  
Control Method ◽  
Synthetic Control ◽  
Synthetic Control Method ◽  
Citizens United ◽  
Independent Expenditures ◽  
American Elections ◽  
The U.S

Abstract Despite a century of efforts to constrain money in American elections, there is little consensus on whether campaign finance regulations make any appreciable difference. Here we take advantage of a change in the campaign finance regulations of half of the U.S. states mandated by the Supreme Court’s Citizens United decision. This exogenously imposed change in the regulation of independent expenditures provides an advance over the identification strategies used in most previous studies. Using a generalized synthetic control method, we find that after Citizens United, states that had previously banned independent corporate expenditures (and thus were “treated” by the decision) adopted more “corporate-friendly” policies on issues with broad effects on corporations’ welfare; we find no evidence of shifts on policies with little or no effect on corporate welfare. We conclude that even relatively narrow changes in campaign finance regulations can have a substantively meaningful influence on government policy making.

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