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.

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 Assessment of the impact of the employment roadmap on the unemployment rate of the Republic of Kazakhstan: synthetic control method

2021 ◽  
pp. 121-130
Author(s):  
T. S. Sokira ◽  
Z. T. Myshbayeva
Keyword(s):  
Unemployment Rate ◽  
Control Method ◽  
Action Plan ◽  
Control Group ◽  
Synthetic Control ◽  
Donor Pool ◽  
Synthetic Control Method ◽  
The Republic ◽  
The Impact ◽  
Weighted Combination

The purpose of the research is to assess the impact of the action plan of the Employment Roadmap on the unemployment rate in Kazakhstan.Methodology. Synthetic Control Method was used in this paper. The method, which compares one or more units exposed to the event and determines what would have happened if the unit had not been treated. In other words, this method creates a weighted combination of control states to create a single «synthetic» control group, in order to approach the counterfactual unit in Kazakhstan in the absence of a plan or Roadmap.The originality / value of the research based on the analysis, panel data from Kazakhstan and 13 donor pool countries for the period 2000-2019 were taken for modeling.Findings: As a result of the study, it was revealed that the unemployment rate would have been 2% higher in 2019 if Kazakhstan had not adopted an action plan in the form of an Employment Roadmap in 2009.

scholarly journals The effect of reopening policy on COVID-19 related cases and deaths

2020 ◽  
Author(s):  
Qiyao Zhou
Keyword(s):  
United States ◽  
Test Data ◽  
Control Method ◽  
State Level ◽  
The United States ◽  
The State ◽  
Necessary Condition ◽  
Synthetic Control ◽  
Synthetic Control Method

AbstractBy May 29, 2020, all 50 states in the United States had reopened their economies to some extent after the coronavirus lockdown. Although there are many debates about whether states reopened their economies too early, no study has examined this effect quantitatively. This paper takes advantage of the daily cases, deaths, and test data at the state level, and uses the synthetic control method to address this question. I find that reopening the economy caused an additional 2000 deaths in the 6 states (Alabama, Colorado, Georgia, Mississippi, Tennessee, and Texas) that reopened before May 1st by three weeks after reopening. It also increased daily confirmed cases by 40%, 52%, and 53% after the first, second, and third week of reopening, respectively. Moreover, contrary to scientists’ prescription that expanding tests is a necessary condition for reopening, these states witnessed a decline in daily tests by 17%, 47%, and 31% after the first, second, and third week of reopening, respectively.

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%.

scholarly journals Stay Home Save Lives: A Machine Learning Approach to Causal Inference to Evaluate Impact of Social Distancing in the US

2020 ◽  
Author(s):  
Syed Muhammad Ishraque Osman ◽  
Nazmus Sakib
Keyword(s):  
Machine Learning ◽  
Fixed Effects ◽  
Control Method ◽  
Control Group ◽  
Synthetic Control ◽  
Us States ◽  
Social Distancing ◽  
Machine Learning Approach ◽  
The Us ◽  
The Impact

Abstract Although there are few studies done to provide estimations of the impact of COVID-19 pandemic, however, there is a need for an actual policy evaluation of the already implemented social distancing measures. In the US context in specific, this is especially instrumental because nearly a dozen US states are considering the reopening of the economy following anti social distancing protests. Using a machine learning based Generalized Synthetic Control Method, considering the US states that adopted early social distancing approaches as the treatment group and the states that adopted social distancing much later as the control group and controlling for state and time fixed effects (to cancel out the selection bias and endogeneity), this paper finds that social distancing is associated with lower COVID-19 infection growth rate (by 192%) when compared to the no policy intervention counterfactual.

Do nonproliferation agreements constrain?

2021 ◽  
pp. 002234332097135
Author(s):  
Bradley C Smith ◽  
William Spaniel
Keyword(s):  
United States ◽  
South Korea ◽  
Control Method ◽  
Theoretical Work ◽  
The United States ◽  
Synthetic Control ◽  
Synthetic Control Method ◽  
Proficiency Levels ◽  
Technological Constraint ◽  
Do So

One way nuclear agreements might keep signatories from proliferating is by constraining nuclear capacity. Theoretical work on nonproliferation often points to such constraints as an important driver of nonproliferation success. Some have argued that, absent sufficient constraint, states with the desire and capability to proliferate will do so. Faced with more costly routes to a weapon, states subject to technological constraint may abide by the terms of the deal. This perspective poses an important empirical question: do nonproliferation agreements result in significant technological constraint in practice? This article evaluates the empirical prevalence of constraints arising from nonproliferation deals. Doing so requires (1) providing an appropriate measure of nuclear proficiency and (2) developing an estimate of the counterfactual, no-agreement capacity of states that received such agreements. This study addresses both of these points. First, new data are gathered to estimate proficiency, improving upon existing measures in the literature. Second, the generalized synthetic control method is applied to estimate counterfactual proficiency levels for the recipients of agreements. With this approach, the constraining effects of deals the United States implemented with Japan, South Korea, and Taiwan and the Declaration of Iguaçu between Brazil and Argentina are evaluated. The findings indicate that the constraining effect of these nonproliferation agreements is minimal.

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 Essays in policy evaluation and labor economics

2021 ◽  
Author(s):  
◽  
Zhiyang You
Keyword(s):  
Control Method ◽  
Control Group ◽  
Synthetic Control ◽  
Cross Sectional ◽  
Synthetic Control Method ◽  
Assimilation Effect ◽  
The Law ◽  
The Difference ◽  
Cohort Quality ◽  
The U.S

This dissertation contains three chapters. The first chapter evaluates the effect of a gun control act in California. State legislators in the U.S. are striving to curb gun violence. A common approach is to extend the existing firearms ban list. This paper examines the effect of legislation restricting sales of selected firearms in California using the synthetic control method. This case study method forms a synthetic unit using a linear combination of other states in the U.S. as the control group. The results show substantial increases in firearm sales in California from the point of passage until the law becomes effective. After the surge ends when the law becomes effective, the sale of firearms is only moderately affected thereafter. This paper also creates robustness checks to confirm that the synthetic control method is working properly with low firearm density in California, which calls into question some of the assumptions underlying the synthetic control method. The Difference-in-Difference regression reaches the same conclusion. The second chapter focuses on immigrant assimilation in the U.S. Assimilation is the process in which immigrants improve earnings as they become more adapted to the host country society. Cross-sectional studies show that immigrants have lower earnings upon arrival and faster earnings growth compared to natives. Longitudinal studies conclude that estimates based on cross-sectional data are positively biased due to decreasing cohort quality and negatively selected outmigration. I reproduce such estimates with recent U.S. data. The estimates would appear to show "bias," as inclusion of cohort fixed effects alter estimates. However, in contrast to expectations based on the current literature, decreasing cohort quality and outmigration do not explain the difference. Next, I apply a non-parametric method to make the wage distributions visually comparable across cohorts and time. I find that the linear specification of assimilation is misleading. Finally, I revisit the classic model with a quadratic assimilation term and expand it to explore the assimilation process's heterogeneity. I find that the "bias" disappears with a quadratic assimilation effect. The assimilation effect is sensitive to age at arrival and country of origin. The third chapter considers an unexplained puzzle in one of the most widely used public datasets in the U.S. The American Community Survey (ACS) replaced the Decennial Census as the primary data source for identifying immigrants' socioeconomic characteristics. This paper focuses on cohort analysis, in which a cohort combines immigrants arriving in a given year from surveys in multiple years. Tracking the sizes of cohorts from 2006 to 2019 using the ACS, we observe an abnormal increase in cohort size in the 10th and 20th years since arrival. Two hypotheses are tested, population estimate structural break and the renewal of green card. Neither appears to explain the puzzle.

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