Оценка влияния экономических санкций на российскую экономику с применением метода синтетической контрольной группы

With the rapid growth of omnichannel retailing, digitally native retailers are increasingly opening physical stores. A critical issue for many digitally native retailers is to estimate the causal effect of a new store opening on their online sales. To assess the causal effect, a randomized control field experiment is infeasible, so quasi-experiments offer the best hope. Often, due to the non-availability of a readily matched control group, the use of synthetic control (SC) groups to estimate the causal effect is becoming popular. A crucial identifying assumption for the SC method is the parallel trends assumption, which states that the treatment unit would have followed a path parallel to the synthetic control group unit in the absence of treatment. However, this assumption may not hold in real data, in particular, in the omnichannel context. If this assumption is violated, current methods may yield incorrect and misleading estimates of causal effects. Unfortunately, no formal test of this assumption exists. We propose a new two-step synthetic control (TSSC) method that comprises a new test for the parallel trends assumption in the first step, and the application of an appropriate synthetic control method in the second step. Thus, our approach unifies the synthetic control and the modified synthetic control (MSC) methods. We examine the finite sample performance of our testing procedure using simulation. We apply this method to estimate the cross-channel effect of a digitally native retailer opening a physical showroom on its sales at two locations: Columbus, OH and Austin, TX. We demonstrate the value of our TSSC method by revealing that the cross-channel effect from the TSSC method for Columbus is positive and significant, contrary to the incorrect and misleading result from the traditional SC method that shows a negative and significant effect.

Estimating treatment (or policy or intervention) effects on a single individual or unit has become increasingly important in health and biomedical sciences. One method to estimate these effects is the synthetic control method, which constructs a synthetic control, a weighted average of control units that best matches the treated unit's pre-treatment outcomes and other relevant covariates. The intervention's impact is then estimated by comparing the post-intervention outcomes of the treated unit and its synthetic control, which serves as a proxy for the counterfactual outcome had the treated unit not experienced the intervention. The augmented synthetic control method, a recent adaptation of the synthetic control method, relaxes some of the synthetic control method's assumptions for broader applicability. While synthetic controls have been used in a variety of fields, their use in public health and biomedical research is more recent, and newer methods such as the augmented synthetic control method are underutilized. This paper briefly describes the synthetic control method and its application, explains the augmented synthetic control method and its differences from the synthetic control method, and estimates the effects of an antimalarial initiative in Mozambique using both the synthetic control method and the augmented synthetic control method to highlight the advantages of using the augmented synthetic control method to analyze the impact of interventions implemented in a single region.

Synthetic control methods for n-of-1 and parallel-group trials in Alzheimer's disease: A proof-of-concept study using the I-CONECT.

This paper examines the synthetic control method in contrast to commonly used difference‐in‐differences (DiD) estimation, in the context of a re‐evaluation of a pay‐for‐performance (P4P) initiative, the Advancing Quality scheme. The synthetic control method aims to estimate treatment effects by constructing a weighted combination of control units, which represents what the treated group would have experienced in the absence of receiving the treatment. While DiD estimation assumes that the effects of unobserved confounders are constant over time, the synthetic control method allows for these effects to change over time, by re‐weighting the control group so that it has similar pre‐intervention characteristics to the treated group.We extend the synthetic control approach to a setting of evaluation of a health policy where there are multiple treated units. We re‐analyse a recent study evaluating the effects of a hospital P4P scheme on risk‐adjusted hospital mortality. In contrast to the original DiD analysis, the synthetic control method reports that, for the incentivised conditions, the P4P scheme did not significantly reduce mortality and that there is a statistically significant increase in mortality for non‐incentivised conditions. This result was robust to alternative specifications of the synthetic control method. © 2015 The Authors. Health Economics published by John Wiley & Sons Ltd.

In recent years, a widespread consensus has emerged about the necessity of establishing bridges between quantitative and qualitative approaches to empirical research in political science. In this article, we discuss the use of the synthetic control method as a way to bridge the quantitative/qualitative divide in comparative politics. The synthetic control method provides a systematic way to choose comparison units in comparative case studies. This systematization opens the door to precise quantitative inference in small-sample comparative studies, without precluding the application of qualitative approaches. Borrowing the expression from Sidney Tarrow, the synthetic control method allows researchers to put “qualitative flesh on quantitative bones.” We illustrate the main ideas behind the synthetic control method by estimating the economic impact of the 1990 German reunification on West Germany.

In recent years a widespread consensus has emerged about the necessity of establishing bridges between quantitative and approaches to empirical research in political science. In this article, we discuss the use of the synthetic control method as a way to bridge the quantitative/qualitative divide in comparative politics. The synthetic control method provides a systematic way to choose comparison units in comparative case studies. This systematization opens the door to precise quantitative inference in small-sample comparative studies, without precluding the application of approaches. Borrowing the expression from Sidney Tarrow, the synthetic control method allows researchers to put qualitative flesh on quantitative bones.'' We illustrate the main ideas behind the synthetic control method by estimating the economic impact of the 1990 German reunification on West Germany.

This study estimates the impact of economic sanctions on oil exports and economic growth in the case study of Iran. By creating a synthetic control group method that reproduces the oil exports and economic growth before economic sanctions are imposed in the case of Iran, we compare the oil exports as well as the economic growth of the Synthetic and the actual for each period. Using the synthetic control method, we fill a major gap in the sanctioned literature in the petrostate economies case study. Our study finds that both oil exports and the economic growth of Iran would have been lower had it not been exposed to economic sanctions. This research is embedded in the comparative and international landscape linked to the relations of international influences with the domestic economy. The findings explain that economic sanctions are a leading factor in the variations in oil exports and economic growth, which can be reflected in the oil curse. We claim that our empirical investigation can contribute to policy formulation in the domestic and foreign arena by sanctioned countries. Overall, the findings confirm that the imposition of sanctions on a petrostate economy like (Iran) can be operated as another channel of the resource curse from international and foreign policy perspectives.

BACKGROUND:Critics of free trade agreements have argued that they threaten public health, as they eliminate barriers to trade in potentially harmful products, such as sugar. Here we analyze the North American Free Trade Agreement (NAFTA), testing the hypothesis that lowering tariffs on food and beverage syrups that contain high-fructose corn syrup (HFCS) increased its use in foods consumed in Canada.METHODS:We used supply data from the Food and Agriculture Organization of the United Nations to assess changes in supply of caloric sweeteners including HFCS after NAFTA. We estimate the impact of NAFTA on supply of HFCS in Canada using an innovative, quasi-experimental methodology — synthetic control methods — that creates a control group with which to compare Canada’s outcomes. Additional robustness tests were performed for sample, control groups and model specification.RESULTS:Tariff reductions in NAFTA coincided with a 41.6 (95% confidence interval 25.1 to 58.2) kilocalorie per capita daily increase in the supply of caloric sweeteners including HFCS. This change was not observed in the control groups, including Australia and the United Kingdom, as well as a composite control of 16 countries. Results were robust to placebo tests and additional sensitivity analyses.INTERPRETATION:NAFTA was strongly associated with a marked rise in HFCS supply and likely consumption in Canada. Our study provides evidence that even a seemingly modest change to product tariffs in free trade agreements can substantially alter population-wide dietary behaviour and exposure to risk factors.

My dissertation is a comprehensive economic history study to the public health impacts of milk pasteurization in the United States. It has four major focuses which are included into four chapters. Chapter I is a case study to the public health impact of Chicago’s pasteurization ordinance. This study sets up the causal relationship between milk pasteurization and health outcomes. Chapter II extends the new econometric tool, the synthetic control methods, from a single unit to multiple treated units. This chapter also measures the impacts of pasteurization ordinances in a group of cities. Chapter III is written from an econometric perspective. It concerns how the synthetic control method can be transformed into a linear regression based model, which has more potential for empirical policy evaluations. Chapter 4 takes an alternative view to milk pasteurization. It discusses how the extent of pasteurization could make difference to public health. It also compares estimations of regular least square model and robust panel data model. Using Chicago’s 1916 pasteurization ordinance as a comparative case study, the first chapter focuses on how to measure the health impacts of food safety interventions. Empirical evidence suggests there was a clear causality relation between milk pasteurization and variations in the health outcomes of interest in Chicago. Thus, I applied the non-parametric synthetic control approach to capture causal health effects of this ordinance. The results suggest that the effect of this policy intervention was more pronounced in Chicago than in its 20 comparison cities, so I conclude that Chicago’s 1916 pasteurization ordinance had positive health effects. The second chapter examines causal health effect of mandatory city pasteurization ordinances in the United States. I apply the synthetic control methods to multiple treated units (MTSCM). Results indicate noticeable health benefits are observed in some cities but not all. For inferences, non-parametric rank-sum tests are preferred because of non-normal outcomes in the control group. This study also suggests regression based Difference-in-Difference (DD) models lead to different results than SCM, since SCM reveals more information like unit-varying and time-varying treatment effect. The third chapter aims to provide a robustness test for major conclusions obtained from prior chapters, e.g. the effect of Chicago’s 1916 milk pasteurization ordinances. Using the synthetic control methods (SCM), I found a significant treatment effect. To verify SCM results, I use a linear regression based cross-sectional time series model (CTM) to re-estimate this intervention. CTM results confirm major findings in my

Does China’s carbon emission trading scheme reduce CO[formula omitted] emissions? Comprehensive evaluation from synthetic control method using lasso

We apply the synthetic control method to a case study of the Dutch State Treasury Agency’s funding policy. We study empirically what effect a change in funding strategy by the Dutch treasury had on market conditions. First, our results suggest that introducing more uncertainty to the funding policy, by means of a target range for capital market issuances, does not lead to a higher risk premium on Dutch government debt. Second, our paper shows that the synthetic control method, or the related constrained regression, is more suitable than a difference-in-differences method for this particular case study. The synthetic control method and constrained regression only include control units that are similar to the Netherlands. The difference-in-differences estimator includes all control countries, even ones that are not similar to the Netherlands. The difference-in-differences estimates incorrectly suggest that introducing more uncertainty in the funding policy leads to a higher risk premium. This shows that synthetic control and constrained regression are in some cases more suitable than a standard difference-in-differences. Furthermore, the synthetic control and constrained regression results hold when time and unit placebo tests are applied, whereas difference-in-differences results are not robust for this case study.

Synthetic control method has been used in comparative case studies, in which the existence of a counter-factual unit with high level of similarities and comparability is crucial. On the other hand, many studies have been done and many methods have been offered in order to overcome the potential shortages of a traditional regression framework in such case studies. In this paper we compare the synthetic control method with a dynamic panel data regression framework. First, we show that the synthetic control method provides an unbiased estimator if the underlying model of the outcome variable of interest is a dynamic panel data model. Second, we compare the prediction power of these two methods. To apply the idea we use the recent sanctions on Iran as the suitable case of a policy intervention and a comparative case study.

Large firms dominate R&D investment in most countries and receive the majority of public R&D funding. Due to methodological difficulties, however, evaluation of the effect of government-sponsored R&D programmes mainly focuses on small- and medium-sized enterprises. The scarcity of large firms and their heterogeneity hampers the ability to find proper counter-factuals for very large companies and makes it difficult to use proper inference methods to measure the impact of a specific policy. In order to address these methodological issues, we propose using the synthetic control method, initially developed by Abadie et al. (2010) to evaluate programmes on a regional scale. We apply this method to evaluate the impact of a new French science-industry transfer initiative and compare the results with the random trend model and more standard counterfactual approaches. Based on data covering a long pre-treatment period (1998–2011) and ongoing treatment period (2012–2015), we reveal a convergence between the results obtained with the synthetic control method and the random trend model, and demonstrate that traditional counterfactual evaluation methods are not appropriate for large firms. Moreover, the synthetic control method has the advantage of providing an individual assessment of the policy impact on each firm. In the specific case of the French science-industry transfer initiative, it reveals that the impact on private R&D is highly heterogenous both on RD inputs and cooperation behaviors. Beyond this specific transfer policy, this study suggests that the synthetic control method opens new research perspectives in policy impact evaluation at the firm level.

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...

Since their introduction by Abadie and Gardeazabal, Synthetic Control (SC) methods have quickly become one of the leading methods for estimating causal effects in observational studies in settings with panel data. Formal discussions often motivate SC methods by the assumption that the potential outcomes were generated by a factor model. Here we study SC methods from a design-based perspective, assuming a model for the selection of the treated unit(s) and period(s). We show that the standard SC estimator is generally biased under random assignment. We propose a Modified Unbiased Synthetic Control (MUSC) estimator that guarantees unbiasedness under random assignment and derive its exact, randomization-based, finite-sample variance. We also propose an unbiased estimator for this variance. We document in settings with real data that under random assignment, SC-type estimators can have root mean-squared errors that are substantially lower than that of other common estimators. We show that such an improvement is weakly guaranteed if the treated period is similar to the other periods, for example, if the treated period was randomly selected. While our results only directly apply in settings where treatment is assigned randomly, we believe that they can complement model-based approaches even for observational studies.